Binarybardakshat/SVLM-ACL-DATASET · Datasets at Hugging Face

lin-etal-2023-linear

Linear Classifier: An Often-Forgotten Baseline for Text Classification

https://aclanthology.org/2023.acl-short.160

Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points.

# Linear Classifier: An Often-Forgotten Baseline For Text Classification Yu-Chen Lin1,2,3, Si-An Chen1, Jie-Jyun Liu1, and Chih-Jen Lin1,3 1National Taiwan University 2ASUS Intelligent Cloud Services 3Mohamed bin Zayed University of Artificial Intelligence {b06504025,d09922007,d11922012,cjlin}@csie.ntu.edu.tw ## Abstract Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may only sometimes get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points. ## 1 Introduction Text classification is an essential topic in natural language processing (NLP). Like the situations in most NLP tasks, nowadays, large-scale pre-trained language models (PLMs) such as BERT (Devlin et al., 2019) have become popular solutions for text classification. Therefore, we have seen that many practitioners directly run pre-trained language models with a fixed number of epochs on their text data. Unfortunately, this way may only sometimes lead to satisfactory results. In this opinion paper, through an intriguing illustration, we argue that for text classification, a simple baseline like linear classifiers on bag-of-words features should be used along with the advanced models for the following reasons. - Training linear classifiers such as linear SVM (Boser et al., 1992) or logistic regression on bag-of-words features is simple and efficient. This approach may give competitive performance to advanced models for some problems. While various settings of bag-of-words features such as bi-gram or tri-gram can be considered, we advocate that simple uni-gram TF-IDF features trained by linear classifiers can be a useful baseline to start with for text classification. - Advanced architectures such as BERT may only achieve the best results if properly used. Linear methods can help us check if advanced methods' results are reasonable. In the deep-learning era, the younger generation often thinks that linear classifiers should never be considered. Further, they may be unaware of some variants of linear methods that are particularly useful for text classification (see Section 3.1). Therefore, the paper serves as a reminder of this oftenforgotten technique. For our illustration, we re-investigate an existing work (Chalkidis et al., 2022) that evaluates both linear SVM and pre-trained language models, but the authors pay more attention to the latter. The linear method is somewhat ignored even though the performance is competitive on some problems. We carefully design experiments to compare the two types of methods. Our results fully demonstrate the usefulness of applying linear methods as simple baselines. Some recent works (e.g., Yu et al., 2022; Gomes et al., 2021) have shown the usefulness of linear classifiers in the deep-learning era. However, they either consider sophisticated applications or investigate advanced settings in which linear methods are only one component. In contrast, in this paper, we consider the basic scenario of text classification. A more related work (Wahba et al., 2023) has demonstrated the effectiveness of linear classifiers over PLMs on some problems. However, our investigation on linear methods is more comprehensive. The discussion also reminds us the trade-off between performance gain and the cost including running time, model size, etc. Simple methods are useful to benchmark and justify the usage of advanced methods. 1876 | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS # | | | | | | | | |-------------------|------------------|-------------|-------------|-------------|--------------|----------------|--------|------|--------|------|------|--------|-----| | µ-F1 | T | µ-F1 | T | µ-F1 | T | µ-F1 | T | µ-F1 | T | µ-F1 | T | params | | | TF-IDF+SVM | 64.5 | N/A | 74.6 | N/A | 78.2 | N/A | 71.3 | N/A | 87.2 | N/A | 95.4 | N/A | N/A | | BERT | 71.2 3h 42m 79.7 | 3h 9m | 68.3 | 1h 24m 71.4 | 3h 36m | 87.6 | 6h 9m | 95.6 | N/A | 110M | | | | | RoBERTa | 69.2 | 4h 11m 77.3 | 3h 43m 71.6 | 2h 46m 71.9 | 3h 36m | 87.9 | 6h 22m | 95.2 | N/A | 125M | | | | | DeBERTa | 70.0 | 7h 43m 78.8 | 6h 48m 71.1 | 3h 42m 72.1 | 5h 34m | 88.2 | 9h 29m | 95.5 | N/A | 139M | | | | | Longformer | 69.9 | 6h 47m 79.4 | 7h 31m 72.9 | 6h 27m 71.6 | 11h 10m 88.2 | 15h 47m 95.5 | N/A | 149M | | | | | | | BigBird | 70.0 | 8h 41m 78.8 | 8h 17m 72.8 | 5h 51m 71.5 | 3h 57m | 87.8 | 8h 13m | 95.7 | N/A | 127M | | | | | Legal-BERT | 70.0 | 3h 52m 80.4 | 3h 2m | 76.4 | 2h 2m | 72.1 | 3h 22m | 88.2 | 5h 23m | 96.0 | N/A | 110M | | | CaseLaw-BERT 69.8 | 3h 2m | 78.8 | 2h 57m 76.6 | 2h 34m 70.7 | 3h 40m | 88.3 | 6h 8m | 96.0 | N/A | 110M | | | | Table 1: Micro-F1 scores (µ-F1), training time (T) and number of parameters presented in Chalkidis et al. (2022). In each Micro-F1 column, the best result is bold-faced. "N/A" means not available in their work. For example, the authors did not report the training time and the number of parameters of linear SVMs. This paper is organized as follows. In Section 2 we take a case study to point out the needs of considering linear methods as a baseline for text classification. We describe the linear and BERT-based methods used for investigation in Section 3. The experimental results and main findings are in Section 4, while Section 5 provides some discussion. Additional details are in Appendix. Programs used for experiments are available at https://github.com/JamesLYC88/ text_classification_baseline_code. ## 2 Text Classification These Days: Some Issues In Applying Training Methods Large PLMs have shown dramatic progress on various NLP tasks. In the practical use, people often directly fine-tune PLMs such as BERT on their data for a few epochs. However, for text classification, we show that this way may not always get satisfactory results. Some simple baselines should be considered to know if the obtained PLM model is satisfactory. We illustrate this point by considering the work on legal document classification by Chalkidis et al. (2022), which evaluates the following sets. - Multi-class classification: SCOTUS, LEDGAR; for this type of sets, each text is associated with a single label. - Multi-label classification: ECtHR (A), ECtHR (B), EUR-LEX, UNFAIR-ToS; for this type of sets, each text is associated with multiple (or zero) labels. - Multiple choice QA: CaseHOLD. We focus on text classification in this work, so CaseHOLD is not considered. For each problem, training and test sets are available.1 The study in Chalkidis et al. (2022) comprehensively evaluates both BERT-based PLMs and linear SVMs. They use Micro-F1 and Macro-F1 to measure the test performance.2In Table 1, we present their Micro-F1 results and running time of each model. ## 2.1 Linear Models Worth More Investigation The investigation in Chalkidis et al. (2022) focuses on BERT and its variants, even though from Table 1, the performance of BERT-based methods may not differ much. While they did not pay much attention to linear SVM, by a closer look at the results, we get intriguing observations: - Linear SVM is competitive to BERT-based PLMs on four of the six data sets. For SCOTUS, linear SVM even outperforms others with a clear gap. - Surprisingly, given linear SVM's decent performance, its training time was not shown in Chalkidis et al. (2022), nor was the number of parameters; see the "N/A" entries in Table 1. With the observations, we argue that the results of linear models are worth more investigation. ## 3 Settings For Investigation To better understand the performance of linear models and BERT-based PLMs, we simulate how people work on a new data set by training these methods. We consider a text classification package LibMultiLabel3 because it supports both types of train- ## 3.1 Linear Methods For Text Classification To use a linear method, LibMultiLabel first generates uni-gram TF-IDF features (Luhn, 1958; Jones, 1972) according to texts in the training set, and the obtained factors are used to get TF-IDF for the test set. It then provides three classic methods that adopt binary linear SVM and logistic regression for multi-class and multi-label scenarios.4 Here we consider linear SVM as the binary classifier behind these methods. - One-vs-rest: This method learns a binary linear SVM for each label, so data with/without this label are positive/negative, respectively. Let fℓ(x) be the decision value of the ℓ-th label, where x is the feature vector. For multi-class classification, yˆ = argmaxℓ fℓ(x) is predicted as the single associated label of x. For multi-label classification, all labels ℓ with positive fℓ(x) are considered to be associated with x. This method is also what "TF-IDF+SVM" in Chalkidis et al. (2022) did, though our TF-IDF feature generation is simpler than theirs by considering only uni-gram.5 - Thresholding (Yang, 2001; Lewis et al., 2004; Fan and Lin, 2007): This method extends one-vsrest by modifying the decision value for optimizing Macro-F1. That is, we change the decision value to fℓ(x) + ∆ℓ, where ∆ℓis a threshold decided by cross validation. - Cost-sensitive (Parambath et al., 2014): For each binary problem, this method re-weights the losses on positive data. We decide the reweighting factor by cross validation to optimize Micro-F1 or Macro-F1. These methods basically need no further hyperparameter tuning, so we can directly run them. The last two methods are extensions of one-vs-rest to address the imbalance of each binary problem (i.e., few positives and many negatives). The design relies on the fact that the binary problems are independent, so such approaches cannot be easily applied to deep learning, which considers all labels together in a single network. ## 3.2 Bert-Based Methods For Text Classification LibMultiLabel also provides BERT-based methods, which involve several hyper-parameters, such as the learning rate. While practitioners may directly choose hyper-parameters, to seriously compare with linear methods, we run BERT by conducting hyper-parameter selection. More details are in Appendix F. ## 4 Experimental Results And Analysis In Table 2, we follow Chalkidis et al. (2022) to report Micro-F1 and Macro-F1 on the test set. The training time is in Table 3. ## 4.1 Linear Methods Are Good Baselines In Table 2, our one-vs-rest results are slightly worse than the linear SVM results in Chalkidis et al. (2022), which also applies the one-vs-rest strategy. As mentioned in Section 3.1, the difference is mainly due to our use of simple uni-gram TF-IDF features. Anyway, our one-vs-rest is still competitive to BERT results in Chalkidis et al. (2022) on the last four problems. More importantly, the two extensions of one-vsrest (i.e., thresholding and cost-sensitive) improve almost all situations. For data sets ECtHR (A) and ECtHR (B), where originally one-vs-rest is significantly lower than BERT results in Chalkidis et al. (2022), the gap reduced considerably. For the training time in Table 3, though the two extensions take more time than the basic one-vsrest strategy, all the linear methods are still hundreds of times faster than BERT. Further, linear methods were run on a CPU (Intel Xeon E5-2690), while for BERT we need a GPU (Nvidia V100). The model sizes listed in Table 4 also show that linear SVM requires a much smaller model than BERT, where details of our calculation are in Appendix D. The results demonstrate that linear methods are useful baselines. They are extremely simple and efficient, but may yield competitive test performance. ## 4.2 Linear Methods Can Help To See If Advanced Methods Are Properly Used Surprisingly, our running of LibMultiLabel's BERT leads to worse test performance than linear methods on almost all data sets. More surprisingly, a comparison between the BERT results by LibMultiLabel and those in Chalkidis et al. (2022) shows | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | | | | | | | |-------------------------|-------------|-------------|----------|-----------|----------|--------------|------|------|------|------|------|------| | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | | | Linear one-vs-rest | 64.0 | 53.1 | 72.8 | 63.9 | 78.1 | 68.9 | 72.0 | 55.4 | 86.4 | 80.0 | 94.9 | 75.1 | | thresholding | 68.6 | 64.9 | 76.1 | 68.7 | 78.9 | 71.5 | 74.7 | 62.7 | 86.2 | 79.9 | 95.1 | 79.9 | | cost-sensitive | 67.4 | 60.5 | 75.5 | 67.3 | 78.3 | 71.5 | 73.4 | 60.5 | 86.2 | 80.1 | 95.3 | 77.9 | | Chalkidis et al. (2022) | 64.5 | 51.7 | 74.6 | 65.1 | 78.2 | 69.5 | 71.3 | 51.4 | 87.2 | 82.4 | 95.4 | 78.8 | | BERT Ours | 61.9 | 55.6 | 69.8 | 60.5 | 67.1 | 55.9 | 70.8 | 55.3 | 87.0 | 80.7 | 95.4 | 80.3 | | Chalkidis et al. (2022) | 71.2 | 63.6 | 79.7 | 73.4 | 68.3 | 58.3 | 71.4 | 57.2 | 87.6 | 81.8 | 95.6 | 81.3 | Table 2: Micro-F1 (µ-F1) and Macro-F1 scores (m-F1) for our investigation on two types of approaches: linear SVM and BERT. For each type, we show results achieved by LibMultiLabel and scores reported in Chalkidis et al. (2022). In each column, the best result is bold-faced. | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | |-------------------------|-------------|-------------|----------|-----------|----------|--------------| | Linear one-vs-rest | 28s | 29s | 1m 11s | 4m 2s | 28s | 2s | | thresholding | 59s | 1m 0s | 2m 11s | 28m 8s | 3m 26s | 3s | | cost-sensitive | 1m 38s | 1m 43s | 3m 28s | 50m 36s | 4m 45s | 4s | | Chalkidis et al. (2022) | N/A | N/A | N/A | N/A | N/A | N/A | | BERT Ours | 5h 8m | 5h 51m | 3h 21m | 38h 14m | 43h 48m | 4h 5m | | Chalkidis et al. (2022) | 3h 42m | 3h 9m | 1h 24m | 3h 36m | 6h 9m | N/A | Table 3: Training time for our multiple settings on linear SVM and BERT. We show results from running LibMultiLabel and values reported in Chalkidis et al. (2022). Note that Chalkidis et al. (2022) use fixed parameters for BERT, while for our BERT, we use 4 GPUs to conduct the hyper-parameter search and report the total time used. | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | |---------------|-------------|-------------|----------|-----------|----------|--------------| | Linear | 924K | 924K | 2M | 15M | 2M | 50K | | BERT variants | 110M ∼ 149M | | | | | | Table 4: A comparison between the model size of linear methods and BERT variants. Note that all three linear methods in LibMultiLabel have the same model size. For BERT variants, we borrow the calculation in Table 1 by Chalkidis et al. (2022). More details are in Appendix D. that the former is much worse on data sets ECtHR (A) and ECtHR (B). Interestingly, from Section 4.1, only for these two sets the BERT results in Chalkidis et al. (2022) are much better than linear methods. Thus, our direct run of BERT in LibMultiLabel is a total failure. The training time is much longer than linear methods, but the resulting model is worse. It is essential to check the discrepancy between the two BERT results. We find that Chalkidis et al. (2022) use some sophisticated settings to run BERT for the first three sets (i.e., ECtHR (A), ECtHR (B), and SCOTUS). They split every document into 64 segments, each of which has no more than 128 tokens, and apply BERT on each segment. Then, they collect the intermediate results as inputs to an upper-level transformer. After repeating the same process via LibMultiLabel, we can reproduce the results in Chalkidis et al. (2022); see details in Appendices E, F, and G. We learned that they considered the more sophisticated setting of running BERT because by default, BERT considers only the first 512 tokens. Thus, for long documents, the training process may miss some important information. However, in practice, users may forget to check the document length and are not aware of the need to apply suitable settings. The above experiments demonstrate that BERT can achieve superior results if properly used, but sometimes, a direct run lead to poor outcomes. Linear methods can serve as efficient and robust baselines to confirm the proper use of an advanced approach. ## 5 Discussion And Conclusions In our experiments, we encounter an issue of whether to incorporate the validation set for training the final model, which is used for predicting the test set. For linear methods, we follow the common practice to include the validation set for obtaining the final model. However, for BERT or some other deep learning models, the validation set is often used only for selecting the best epoch and/or the best hyper-parameters. To fully use the available data, we have investigated how to incorporate the validation set for BERT. Experimental results and more details are in Appendix H. For some text sets evaluated in this work, we have seen that simple linear methods give competitive performance. The reason might be that each document in these sets is not short.6 Then TF-IDF features are sufficiently informative so that linear methods work well. Across all NLP areas, an important issue now is when to use PLMs and when not. We demonstrate that when PLMs may not perform significantly better, traditional methods are much simpler and require fewer resources. However, having a simple quantitative measurement to pre-determine when to use which remains a challenging future research problem. In summary, the study reminds us of the importance of employing simple baselines in NLP applications. ## Limitations In this work, we do not propose any new methods because, as an opinion paper, we focus on raising the problems and making vivid demonstrations to readers. The experiments are limited to linear SVM and BERT on data sets in the benchmark LexGLUE. We hope that, within the page limit, our experiments sufficiently convey the points to readers. ## Ethics Statement We ensure that our work complies with the ACL Ethics Policy. ## Acknowledgements This work was supported by NSTC of Taiwan grant 110-2221-E-002-115-MY3 and ASUS Intelligent Cloud Services. The authors thank Ming-Wei Chang and reviewers for constructive comments. ## References Bernhard E. Boser, Isabelle Guyon, and Vladimir Vapnik. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152. ACM Press. 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In Proceedings of the 24th ACM International Conference on Research and Development in Information Retrieval, pages 137–145, New Orleans, US. ACM Press, New York, US. Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, and Inderjit S. Dhillon. 2022. PECOS: Prediction for enormous and correlated output spaces. Journal of Machine Learning Research, 23(98):1–32. ## A Issue About Data Without Labels For multi-label problems considered in Chalkidis et al. (2022), instances that are not associated with any labels, called unlabeled instances as follows, account for a considerable portion in some data sets: ECtHR (A) (11.3%), ECtHR (B) (1.6%) and UNFAIR-ToS (89.0%). In the training process, Chalkidis et al. (2022) keep the unlabeled | Parameter | LibMultiLabel | Chalkidis et al. (2022) | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|---------------------------| | Data pre-processing (TF-IDF feature generation) stop_words None english ngram_range (1, 1) (1, 3) min_df 1 5 max_features None [10000, 20000, 40000] Model loss squared_hinge ['hinge', 'squared_hinge'] solving primal/dual primal dual C 1.0 [0.1, 1.0, 10.0] | | | training instances without any modification. Thus, in, for example, the one-vs-rest setting described in Section 3.1, an unlabeled instance is on the negative side in every binary problem. However, in evaluating the validation and test sets, they introduce an additional class to indicate the unlabeled data. Specifically, an unlabeled instance is associated with this "unlabeled" class, but not others. Chalkidis et al. (2022) consider this way to more seriously evaluate the model predictability on unlabeled instances. However, this setting is not a standard practice in multi-label classification, nor is it supported by LibMultiLabel. Thus we modify the scripts in LibMultiLabel to have the same evaluation setting as Chalkidis et al. (2022). ## B Additional Details Of Linear Methods The binary linear SVM is in the following form. $$\operatorname*{min}_{\mathbf{w}}{\frac{1}{2}}\mathbf{w}^{\top}\mathbf{w}+C\sum_{i}\xi(y_{i}\mathbf{w}^{\top}\mathbf{x_{i}}),\qquad(1)$$ where (xi, yi) are data-label pairs in the data set, yi = ±1, w is the parameters of the linear model, and ξ(·) is the loss function. The decision value function is f(x) = w⊤x. For one-vs-rest, please see descriptions in Section 3.1. We follow the default setting in LibMultiLabel by using C = 1. For more details about thresholding and cost-sensitive, please refer to the explanations in Lin et al. (2022). ## C Differences Between Our Implementation Of Linear Methods And Chalkidis Et Al. **(2022)** We summarize the implementation differences between LibMultiLabel and Chalkidis et al. (2022) in ## Table 5. For the data-preprocessing part, both use scikitlearn for TF-IDF feature generations. The meanings of each parameter are listed as follows. stop_words: Specify the list of stop words to be removed. For example, Chalkidis et al. (2022) set stop_words to "english," so tokens that include in the "english" list are filtered. ngram_range: Specify the range of n-grams to be extracted. For example, LibMultiLabel only uses uni-gram, while Chalkidis et al. (2022) set ngram_range to (1, 3), so uni-gram, bi-gram, and tri-gram are extracted into the vocabulary list for a richer representation of the document. min_df: The parameter is used for removing infrequent tokens. Chalkidis et al. (2022) remove tokens that appear in less than five documents, while LibMultiLabel does not remove any tokens. max_features: The parameter decides the number of features to use by term frequency. For example, Chalkidis et al. (2022) consider the top 10,000, 20,000, and 40,000 frequent terms as the search space of the parameter. For more detailed explanations, please refer to the TfidfVectorizer function in scikit-learn. The binary classification problem in (1) is referred to as the primal form. The optimization problem can be transferred to the dual form and the optimal solutions of the two forms lead to the same decision function. Thus we can choose to solve the primal or the dual problem; see Table 5. For the model training, they both use the solver provided by LIBLINEAR (Fan et al., 2008). | Property | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | |------------|-------------|-------------|----------|-----------|----------|--------------| | # labels | 10 | 10 | 13 | 100 | 100 | 8 | | W | 1,662.08 | 1,662.08 | 6,859.87 | 1,203.92 | 112.98 | 32.70 | | # features | 92,402 | 92,402 | 126,406 | 147,465 | 19,997 | 6,291 | Table 6: Data statistics for LexGLUE, the benchmark considered in Chalkidis et al. (2022). W means the average \# words per instance of the whole set. The \# features indicates the \# TF-IDF features used by linear methods. | LibMultiLabel reproduced | Chalkidis et al. (2022) | | | | | |----------------------------|---------------------------|-------------|----------|----------|----------| | default | tuned | SCOTUS | other | | | | LEDGAR | problems | | | | | | maximum #epochs | 15 | 15 | 20 | 15 | 20 | | weight_decay | 0.001 | 0 | 0 | 0 | 0 | | patience | 5 | 5 | 5 | 5 | 3 | | val_metric | Micro-F1 | Micro-F1 | Micro-F1 | Micro-F1 | Micro-F1 | | early_stopping_metric | Micro-F1 | Micro-F1 | loss | Micro-F1 | loss | | learning_rate | 5e-5 | See Table 8 | 3e-5 | 3e-5 | 3e-5 | | dropout | 0.1 | 0.1 | 0.1 | 0.1 | | | Parameter | | | | | | Table 7: Parameter differences of BERT between LibMultiLabel and Chalkidis et al. (2022). For the meaning of each parameter, please refer to the software LibMultiLabel. | Parameter | ECtHR (A) ECtHR (B) SCOTUS EUR-LEX LEDGAR UNFAIR-ToS | | | | | | |----------------------|--------------------------------------------------------|--------------------|------|------|------|------| | max_seq_length space | [128, 512] | | | | | | | selected | 512 | 512 | 512 | 512 | 512 | 512 | | learning_rate | space | [2e-5, 3e-5, 5e-5] | | | | | | selected | 2e-5 | 3e-5 | 2e-5 | 5e-5 | 2e-5 | 3e-5 | | dropout | space | [0.1, 0.2] | | | | | | selected | 0.1 | 0.2 | 0.1 | 0.1 | 0.2 | 0.1 | Table 8: Hyper-parameter search space and the selected values of LibMultiLabel's tuned setting. ## D Additional Details About Model Size We calculate the model size of linear SVM by multiplying the number of TF-IDF features by the number of labels; see details in Table 6. For BERT, we directly copy the number of parameters from Chalkidis et al. (2022). ## E Additional Details About Bert Design In Chalkidis Et Al. **(2022)** E.1 Standard Bert For Classification The setting considers the original implementation in Devlin et al. (2019). They truncate the documents to have at most 512 tokens. We then take a pre-trained BERT appended with an additional linear layer for fine-tuning. ## E.2 Document Lengths In Table 6, we present the document length for each data set in LexGLUE, the benchmark considered in Chalkidis et al. (2022). For ECtHR (A), ECtHR (B), SCOTUS, and EUR-LEX, the document lengths all exceed 512, the length limitation of BERT. Note that the numbers are underestimated because BERT uses a sub-word tokenizer that further tokenizes some words into sub-words. ## E.3 Hierarchical Bert Chalkidis et al. (2022) design a variant of the standard BERT for ECtHR (A), ECtHR (B), and SCOTUS to deal with long document lengths. The detailed steps are as follows. - Each document is split into 64 segments, where each segment contains at most 128 tokens. - Each segment is then fed into BERT. - The [CLS] tokens generated from each segment Method ECtHR (A) ECtHR (B) SCOTUS EUR-LEX LEDGAR UNFAIR-ToS µ-F1 m-F1 µ-F1 m-F1 µ-F1 m-F1 µ-F1 m-F1 µ-F1 m-F1 µ-F1 m-F1 BERT in LibMultiLabel default 60.5 53.4 68.9 60.8 66.3 54.8 70.8 55.3 85.2 77.9 95.2 78.2 tuned 61.9 55.6 69.8 60.5 67.1 55.9 70.8 55.3 87.0 80.7 95.4 80.3 reproduced 70.2 63.7 78.8 73.1 70.8 62.6 71.6 56.1 88.1 82.6 95.3 80.6 BERT in Chalkidis et al. (2022) paper 71.2 63.6 79.7 73.4 68.3 58.3 71.4 57.2 87.6 81.8 95.6 81.3 reproduced 70.8 64.8 78.7 72.5 70.9 61.9 71.7 57.9 87.7 82.1 95.6 80.3 Table 9: Micro-F1 (µ-F1) and Macro-F1 scores (m-F1) for our investigation on BERT. Table 10: Training time for our multiple settings on BERT. The average time of running five seeds is reported. are collected and fed into an upper-level transformer encoder. - Max pooling is applied to the output of the transformer encoder. - The pooled results are then fed into a linear layer for the final prediction. ## F Differences Between The Two Bert Implementations We summarize the implementation differences of BERT between LibMultiLabel and Chalkidis et al. (2022) in Table 7. Here we also try to reproduce results in Chalkidis et al. (2022) by using LibMultiLabel. For LibMultiLabel, we explain our choices of hyper-parameters as follows. default: This method references the parameters chosen in an example configuration7from LibMultiLabel. tuned: This method performs a parameter search and is marked as "our BERT" in the main paper; see Table 8 for the search space and the chosen values. reproduced: This method aims to reproduce the BERT results from Chalkidis et al. (2022) using LibMultiLabel. We begin with imposing the same 7https://github.com/ASUS-AICS/LibMultiLabel/ blob/master/example_config/EUR-Lex-57k/bert.yml | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | |----------------------------------------------------|-------------|-------------|----------|-----------|----------|--------------| | BERT in LibMultiLabel default 59m 48s | 1h 2m | 39m 49s | 6h 38m | 8h 44m | 47m 48s | | | tuned | 5h 8m | 5h 51m | 3h 21m | 38h 14m | 43h 48m | 4h 5m | | reproduced | 10h 27m | 9h 41m | 9h 26m | 6h 37m | 5h 49m | 15m 9s | | BERT in Chalkidis et al. (2022) paper 3h 42m 3h 9m | 1h 24m | 3h 36m | 6h 9m | N/A | | | | reproduced | 7h 56m | 6h 59m | 7h 5m | 4h 30m | 5h 11m | 7m 3s | weight_decay, learning_rate, and dropout values as Chalkidis et al. (2022) and also the same validation metric. However, for other parameters, which may less affect the results, we use the same values as **default** and **tuned**; see Table 7. Except SCOTUS and LEDGAR, we were able to generate similar results to those in Chalkidis et al. (2022). To fully reproduce the results on SCOTUS and LEDGAR, we try to follow every setting did in Chalkidis et al. (2022). Specifically, we replace the PyTorch trainer originally used in LibMultiLabel with the Hugging Face trainer adopted in Chalkidis et al. (2022) and align some of the parameters with the ones used in Chalkidis et al. (2022); see a column in Table 7 for these two sets. LibMultiLabel supports standard BERT discussed in Appendix E.1. For the "default" and "tuned" settings, we directly run standard BERT. For the "reproduced" method, we follow Chalkidis et al. (2022) to use hierarchical BERT explained in Appendix E.3 for ECtHR (A), ECtHR (B), and SCOTUS and use standard BERT for other data sets. ## G Detailed Bert Results In Tables 9 and 10, we respectively present the test performance and the training time. For settings of running LibMultiLabel, see Appendix F. For BERT | Method | ECtHR (A) | ECtHR (B) | SCOTUS | EUR-LEX | LEDGAR | UNFAIR-ToS | | | | | | | |----------------------------------------------------------------|-------------|-------------|----------|-----------|----------|--------------|------|------|------|------|------|------| | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | µ-F1 | m-F1 | | | BERT in LibMultiLabel default 60.5 53.4 68.9 | 60.8 | 66.3 | 54.8 | 70.8 | 55.3 | 85.2 | 77.9 | 95.2 | 78.2 | | | | | tuned | 61.9 | 55.6 | 69.8 | 60.5 | 67.1 | 55.9 | 70.8 | 55.3 | 87.0 | 80.7 | 95.4 | 80.3 | | BERT in LibMultiLabel (re-trained) default 63.0 56.1 69.6 62.8 | 69.5 | 58.8 | 75.6 | 59.2 | 85.3 | 78.4 | 94.0 | 65.4 | | | | | | tuned | 62.4 | 55.9 | 70.3 | 62.3 | 71.4 | 61.9 | 75.6 | 59.2 | 87.2 | 81.5 | 95.2 | 79.8 | Table 11: A performance comparison between the setting without and with re-training. in Chalkidis et al. (2022), we present the following two results. paper: Results in the paper by Chalkidis et al. (2022) are directly copied. reproduced: Results from our running of their scripts.8 For ECtHR (A), ECtHR (B), and SCOTUS, because there exist some issues when running the fp16 setting in our environment, we run the code of Chalkidis et al. (2022) by using fp32 instead. This change causes the time difference between the "paper" and "reproduced" settings in Table 10. Except numbers borrowed from Chalkidis et al. (2022), we run five seeds for all BERT experiments and report the mean test performance over all seeds. Chalkidis et al. (2022) also run five seeds, but their test scores are based on the top three seeds with the best Macro-F1 on validation data. For the "tuned" setting, because the version of LibMultiLabel that we used does not store the checkpoint after hyper-parameter search, we must conduct the training again using the best hyperparameters. Thus, the total time includes hyperparameter search and the additional training.9 In Appendix I, we give an additional case study to assess the performance of the hierarchical BERT when documents are long. ## H Issue Of Using Training, Validation, And Test Sets For each problem in LexGLUE, training, validation, and test sets are available. In our experiments, of course the test set is independent from the training process. However, some issues occur in the use of the training and validation sets. For linear methods, in contrast to deep learning methods, they do not need a validation set for the termination of the optimization process or for selecting the iteration that yields the best model. Further, they may internally conduct crossvalidation to select hyper-parameters (e.g., thresholds in the thresholding method). Therefore, we combine training and validation subsets as the new training set used by the linear methods. This is the standard setting in traditional supervised learning. For BERT training, the validation set is used for selecting the best epoch and/or the best hyperparameters. We follow the common practice to deploy the model achieving the best validation performance for prediction. However, in linear methods, the model used for prediction, regardless of whether internal cross-validation is needed, is always obtained by training on all available data (i.e., the combination of training and validation sets). Therefore, for BERT we may also want to incorporate the validation set for the final model training. We refer to such a setting as the re-training process. Unfortunately, an obstacle is that the optimization process cannot rely on a validation set for terminating the process or selecting the best model in all iterations. Following Goodfellow et al. (2016), we consider the following setting to train the combined set. 1. Record the number of training steps that leads to the best validation Micro-F1 as e∗. 2. Re-train the final model using the combination of training and validation sets for e∗epochs. BERT results without/with re-training are shown in Table 11. In general, the re-training process improves the performance, especially for the data sets SCOTUS and EUR-LEX. However, results are slightly worse in both the default and tuned settings for the data set UNFAIR-ToS. Thus the outcome of re-training may be data-dependent. A comparison between linear methods and BERT with re-training shows that conclusions made earlier remain the same. Because re-training | Property | Value | |------------------------|----------------| | # training instances | 10,182 | | # validation instances | 1,132 | | # test instances | 7,532 | | # classes | 20 | | W | 283.66 | | Wmax | 11,821 | | T | 552.82 | | Tmax | 138,679 | | # documents | 4,927 (26.14%) | | exceeding 512 tokens | | Table 12: Data statistics for 20 Newsgroups. We conduct a 90/10 split to obtain the validation data. W/T means the average \# words/tokens per instance of the whole set, and Wmax/Tmax means the maximum \# words/tokens of the whole set. | Method | µ-F1 | m-F1 | |--------------------|--------|--------| | Linear one-vs-rest | 85.3 | 84.6 | | thresholding | 85.3 | 84.6 | | cost-sensitive | 85.2 | 84.5 | | BERT default | 84.0 | 83.3 | | tuned | 85.6 | 84.9 | | hierarchical | 84.9 | 84.2 | is not conducted in Chalkidis et al. (2022), in the main paper we report the results without retraining. ## I A Case Study Of Bert On 20 Newsgroups Wahba et al. (2023) applied BERT for training the data set 20 Newsgroups (Lang, 1995) but did not check the document length. To assess the importance of the document length, we downloaded the 20 Newsgroups set from scikit-learn10 with default parameters. Further, we checked the document length from the word and token levels where the tokens are obtained by the "bert-base-uncased" tokenizer. The data statistics are presented in Table 12. We found that the 20 Newsgroups data set includes a considerable number of documents that exceed 512 tokens. This may be an issue because BERT can only process up to 512 tokens without further design; see Appendix E for more details. To investigate this problem, we conducted experiments using both linear classifiers and BERT. Results are in Table 13. The observations are summarized as follows. - The results of linear classifiers do not improve by using thresholding and cost-sensitive techniques to handle class imbalance. The reason is that the data set has a small number of labels and a more balanced class distribution. In addition, linear methods are still competitive with BERT. - The tuned setting of BERT has the best Micro-F1 among all the methods. Thus, for running BERT on this set, parameter selection seems to be important. Interestingly, when we considered the document length using the hierarchical methods in Appendix E.3, the performance was not better than the tuned setting. In conclusion, linear methods are still a simple and efficient solution to this problem. For BERT, we showed that using the hierarchical setting to handle long document length may not always lead to the best performance. The result of applying hierarchical BERT may be data-dependent. Thus a general setting for handling long documents still need to be investigated. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitations. ✗ A2. Did you discuss any potential risks of your work? Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Sections Abstract and 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3. ✓ B1. Did you cite the creators of artifacts you used? Section 3. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank. ✗ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section 4. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix F. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix G. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix C. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ruoss-etal-2023-randomized

Randomized Positional Encodings Boost Length Generalization of Transformers

https://aclanthology.org/2023.acl-short.161

Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly simple tasks such as duplicating a string. Moreover, simply training on longer sequences is inefficient due to the quadratic computation complexity of the global attention mechanism. In this work, we demonstrate that this failure mode is linked to positional encodings being out-of-distribution for longer sequences (even for relative encodings) and introduce a novel family of positional encodings that can overcome this problem. Concretely, our randomized positional encoding scheme simulates the positions of longer sequences and randomly selects an ordered subset to fit the sequence{'}s length. Our large-scale empirical evaluation of 6000 models across 15 algorithmic reasoning tasks shows that our method allows Transformers to generalize to sequences of unseen length (increasing test accuracy by 12.0{\%} on average).

# Randomized Positional Encodings Boost Length Generalization Of Transformers Anian Ruoss∗1 Grégoire Delétang∗1 Tim Genewein1 **Jordi Grau-Moya**1 Róbert Csordás†2 Mehdi Bennani1 Shane Legg1 **Joel Veness**1 ## Abstract ![0_Image_0.Png](0_Image_0.Png) Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly simple tasks such as duplicating a string. Moreover, simply training on longer sequences is inefficient due to the quadratic computation complexity of the global attention mechanism. In this work, we demonstrate that this failure mode is linked to positional encodings being out-of-distribution for longer sequences (even for relative encodings) and introduce a novel family of positional encodings that can overcome this problem. Concretely, our randomized positional encoding scheme simulates the positions of longer sequences and randomly selects an ordered subset to fit the sequence's length. Our large-scale empirical evaluation of 6000 models across 15 algorithmic reasoning tasks shows that our method allows Transformers to generalize to sequences of unseen length (increasing test accuracy by 12.0% on average). ## 1 Introduction Transformers are emerging as the new workhorse of machine learning as they underpin many recent breakthroughs, including sequence-to-sequence modeling (Vaswani et al., 2017), image recognition (Dosovitskiy et al., 2021), and multi-task learning (Reed et al., 2022). However, recent work (Delétang et al., 2023) demonstrated that Transformers fail to generalize to longer sequences on seemingly simple tasks such as binary addition. Thus, while certain problems can be solved without length generalization, algorithmic reasoning generally requires this ability, similar to many real-world settings such as online or continual learning. While the Transformer's attention mechanism can recognize complex relationships amongst to- *Equal contribution. 1DeepMind. 2The Swiss AI Lab, IDSIA, USI & SUPSI. †Work performed while the author was at DeepMind. Correspondence to {anianr, gdelt}@deepmind.com. Figure 1: **Test-time evaluation with longer inputs.** The standard positional encoding vector has values larger than those observed during training. Our approach avoids this problem by assigning a random (ordered) positional encoding vector using the full range of possible test positions to each training example. kens in the input sequence, it is limited by its lack of positional awareness. Thus, the input sequence is generally augmented with *positional encodings* to inject position information into the computation. However, current approaches only consider positions up to the maximum training sequence length N, and thus all the positions N + 1*, . . . , M* for test sequences of length up to M will appear out-ofdistribution during evaluation (top of Fig. 1). This work We introduce a novel family of *randomized positional encodings*, which significantly improves Transformers' length generalization capabilities on algorithmic reasoning tasks. Our approach is compatible with any existing positional encoding scheme and augments the existing methods by subsampling an ordered set of positions from a much larger range of positions than those observed during training or evaluation (i.e., up 1889 to L ≫ M; bottom of Fig. 1). Thus, over the course of training, the Transformer will learn to handle very large positional encodings and, therefore no longer encounter out-of-distribution inputs during evaluation. Importantly, our method leaves in-domain generalization performance unaffected and is also significantly more efficient than the naive approach of simply training the Transformer on longer sequences. Our main contributions are: - A novel family of positional encoding schemes that significantly improves the length generalization capabilities of Transformers, while leaving their in-domain generalization performance unaffected. - A large-scale empirical evaluation on a wide range of algorithmic reasoning tasks showing the superiority of our method over prior work (an increase of the test accuracy by 12.0% on average and up to 43.5% on certain tasks). - An open-source implementation of our method, available at https://github. com/deepmind/randomized_ positional_encodings. ## 2 Related Work Our work is most closely related to the growing line of research on Transformers' positional encodings. The first approaches simply added a transformation of the tokens' positions, e.g., scaled sinusoids (Vaswani et al., 2017) or learned embeddings (Gehring et al., 2017), to the embeddings of the input sequence. Dai et al. (2019) subsequently showed that computing the attention (at every layer) using the relative distances between the key and query vectors improves the modeling of long-term (inter-context) dependencies. Similarly, Su et al. (2021) proposed to inject position information by rotating the key-query products according to their relative distances. Finally, Press et al. (2022) improved the length generalization on natural language processing tasks by adding a constant bias to each key-query attention score (proportional to their distance). However, as our experiments in Section 4 will show, these approaches fail at length generalization on algorithmic reasoning tasks, which is precisely the goal of our work. A concurrent work developed randomized learned positional encodings (Li and McClelland, 2022), which are a special case of our family of randomized positional encodings. We also note that the necessity of feature and position randomization for length generalization has been discussed in the context of graph neural networks, which subsume Transformers (Ibarz et al., 2022; Sato et al., 2021). Finally, Liu et al. (2020b) proposed to model the position information as a continuous dynamical system in an effort to handle sequences longer than those seen during training time. Our work is also related to the research area on improving the systematic (length) generalization capabilities of Transformers (Ontañón et al., 2022), which includes approaches investigating embedding scaling or early stopping (Csordás et al., 2021), adaptive computation time (Dehghani et al., 2019), geometric attention with directional positional encodings and gating (Csordás et al., 2022), and hierarchical reinforcement learning (Liu et al., 2020a). Such length generalization studies are often conducted in the context of formal language theory, and we evaluate our method on the recent benchmark by Delétang et al. (2023), which unifies a large body of work on Transformers' capability to recognize formal languages (Ackerman and Cybenko, 2020; Bhattamishra et al., 2020; Ebrahimi et al., 2020; Hahn, 2020; Hao et al., 2022; Merrill, 2019; Merrill and Sabharwal, 2022). ## 3 Randomized Positional Encodings Unlike RNNs (Elman, 1990), which are unrolled over tokens one step at a time, Transformers process large chunks of the input sequence in parallel via global attention (Vaswani et al., 2017). As a result, Transformers do not need to "remember" previous tokens, but they do have to break the permutation-invariance of the attention mechanism. To that end, the embeddings of the input sequence are generally augmented with positional encodings. For example, the vanilla Transformer adds the following positional encodings to the embedded input sequence before passing it to the attention layers: $$\mathrm{PE}(\mathrm{pos},2i)=\sin\left({\frac{\mathrm{pos}}{10000^{\frac{2i}{d_{\mathrm{model}}}}}}\right),\quad(1)$$ $$\mathrm{PE}(\mathrm{pos},2i+1)=\cos\left({\frac{\mathrm{pos}}{10000^{\frac{2i}{d_{\mathrm{model}}}}}}\right),\quad(2)$$ where pos is the token's position in the sequence, dmodel ∈ N is the dimension of the input embedding, and i ∈ {1, 2*, . . . , d*model/2}. While positional encodings generally succeed at inducing the required positional information for sequences of fixed length, they are one of the main failure modes preventing length generalization. Concretely, for a Transformer with standard positional encodings trained on a curriculum of sequences of maximum length N, test sequences of length *M > N* will shift the distribution of the resultant positional encodings away from those seen in training, with the shift getting increasingly large as M grows. To address this, we propose a randomized encoding scheme, which relies only on order information, and can be expected to generalize up to sequences of length M, where *N < M* ≤ L, with a configurable hyperparameter L. Randomized positional encodings We assume that each training step will perform a step of loss minimization on a batch of data of fixed size. Let U(S) denote the discrete uniform distribution over set S, and let Pk := {S ⊆ {1, . . . , L*} | |*S| = k}. For each training step, we first sample a random length n ∼ U({1*, . . . , N*}) (following Delétang et al., 2023) and then a random set of indices I ∼ U(Pn). We then sort I in ascending order, such that I = {i1*, . . . , i*n} for i1 < i2 < · · · < in, noting that I is sampled without replacement. Finally, we compute our randomized positional encoding for token 1 ≤ j ≤ N as RPE(j, ·) := PE(ij , ·). At test time, when processing a sequence of length M > N, we use the same procedure but for all token positions 1 ≤ j ≤ M. The intuition behind our method is to preserve the known good properties of relative encoding but in a way that is independent of the maximum training length N and thus allows generalization to longer sequences at test time. When applying our randomized positional encoding scheme, we subsample the extended positions only once per batch and not individually for every sequence. For the sin / cos (Vaswani et al., 2017), learned (Gehring et al., 2017), and RoPE encodings (Su et al., 2021), we apply our method as described above, i.e., we directly replace the original token positions with their sampled counterpart. For the relative encoding (Dai et al., 2019), we compute the relative distances between the sampled positions instead of the original positions. Finally, for ALiBi (Press et al., 2022), we sample the bias values from the set of extended positions. As a consequence, our tokens' positional encodings are no longer directly related to their exact position (the encodings even change during training as they are resampled at every step). However, since we maintain the order of the encodings, the Transformer can still learn to extract the relevant positional information from the subsampled encodings. Indeed, we validate the necessity of ordering the sampled positions in our ablation study in Appendix B.1. Thus, the success of our encoding scheme offers an interesting insight into the inductive biases of the Transformer architecture. As we will show in Section 4, our randomized encodings trained only on lengths up to N perform the same on sequences of length M as prior approaches trained on lengths up to M. Therefore, our method demonstrates that Transformers can be efficiently trained on short sequences as long as (i) the longer sequences share the same structure and (ii) the longer positions are observed during training. Moreover, as the running time of global attention is O(ℓ 2) for sequence length ℓ, our encoding scheme is significantly faster than directly training a model on long sequences. Furthermore, we also note that our randomized positional encoding scheme significantly boosts length generalization while leaving the in-domain generalization performance largely unaffected (see Fig. 4). The main limitation of our approach is that the maximum test sequence length M has to be known in advance to choose L ≫ M. However, our method is compatible with a wide range of values for L (see Appendix B.1), and we note that this is a much weaker assumption than that required for the naive approach of simply training on longer sequences. However, note that if L is chosen to be much larger than N or M, it is theoretically unlikely for the model to encounter enough unique indices during training, likely leading to poor performance (both in- and out-of-distribution). ## 4 Experimental Evaluation Problem setup We closely follow the experiment setup of Delétang et al. (2023) and evaluate our method on a wide range of algorithmic reasoning tasks such as modular arithmetic, reversing/duplicating a string, binary addition/multiplication, and bucket sort. The tasks are derived from formal language recognition and thus grouped according to the Chomsky hierarchy (Chomsky, 1956), which partitions languages into regular (R), context-free, context-sensitive (CS), and recursively enumerable. Regular tasks can be solved by a finite-state automaton (FSA), deterministic context-free (DCF) tasks can be solved by an FSA with access to a deterministic stack, and | Randomized (Ours) | | | | | | | | | | | | | |-----------------------------|--------------------|-------------------------|-------|------|---------|--------------------|-------|-------|----------|-------|------|------| | Level | Task | None sin / cos Relative | ALiBi | RoPE | Learned | sin / cos Relative | ALiBi | RoPE | Learned⋆ | | | | | EVEN PAIRS | 50.4 | 50.9 | 96.4 | 67.3 | 51.0 | 50.7 | 100.0 | 100.0 | 81.5 | 100.0 | 97.5 | | | MODULAR ARITHMETIC (SIMPLE) | 20.1 | 20.5 | 21.8 | 24.2 | 21.6 | 20.2 | 25.7 | 28.1 | 21.2 | 25.5 | 21.1 | | | R | PARITY CHECK† | 51.9 | 50.5 | 51.8 | 51.7 | 51.3 | 50.3 | 52.6 | 52.2 | 50.3 | 52.3 | 52.6 | | CYCLE NAVIGATION† | 61.9 | 26.3 | 23.0 | 37.6 | 23.6 | 24.2 | 59.0 | 58.8 | 29.8 | 73.6 | 49.7 | | | STACK MANIPULATION | 50.3 | 50.1 | 53.6 | 57.5 | 51.2 | 49.2 | 72.8 | 77.9 | 70.6 | 68.2 | 69.1 | | | REVERSE STRING | 52.8 | 50.6 | 58.3 | 62.3 | 51.9 | 50.7 | 75.6 | 95.1 | 77.1 | 69.9 | 52.9 | | | DCF | MODULAR ARITHMETIC | 31.0 | 28.3 | 30.3 | 32.5 | 25.1 | 25.1 | 33.8 | 34.9 | 31.3 | 32.7 | 31.9 | | SOLVE EQUATION | 20.1 | 21.0 | 23.0 | 25.7 | 23.1 | 20.4 | 24.5 | 28.1 | 22.0 | 24.5 | 22.1 | | | DUPLICATE STRING | 52.8 | 50.7 | 51.7 | 51.3 | 50.9 | 50.8 | 72.4 | 75.1 | 68.9 | 68.9 | 53.0 | | | MISSING DUPLICATE | 52.5 | 51.3 | 54.0 | 54.3 | 56.5 | 51.0 | 52.5 | 100.0 | 79.7 | 88.7 | 52.7 | | | ODDS FIRST | 52.8 | 51.6 | 52.7 | 51.4 | 51.3 | 50.6 | 65.9 | 69.3 | 64.7 | 65.6 | 52.7 | | | BINARY ADDITION | 50.1 | 49.8 | 54.3 | 51.4 | 50.4 | 49.8 | 64.4 | 64.5 | 56.2 | 60.2 | 61.7 | | | BINARY MULTIPLICATION | 49.9 | 50.1 | 52.2 | 51.0 | 50.2 | 49.6 | 52.1 | 50.1 | 50.5 | 51.7 | 51.9 | | | COMPUTE SQRT | 50.2 | 50.1 | 52.4 | 50.9 | 50.5 | 50.2 | 52.5 | 53.3 | 51.2 | 52.3 | 52.0 | | | BUCKET SORT† | 23.7 | 30.1 | 91.9 | 38.8 | 30.6 | 25.9 | 100.0 | 100.0 | 99.6 | 99.6 | 99.5 | | | CS | | | | | | | | | | | | | CS tasks can be solved by an FSA with access to a bounded tape. Note that the relation to the Chomsky hierarchy is largely irrelevant for our work and only included for completeness. We evaluate our method on Delétang et al. (2023)'s benchmark as it is currently out of reach for Transformers and clearly demonstrates their failure to generalize on algorithmic reasoning tasks. We refer interested readers to the original paper for more details. We consider the encoder-only model of the original seq-to-seq Transformer (Vaswani et al., 2017), as used in popular pre-trained language models such as BERT (Devlin et al., 2019) or Gopher (Rae et al., 2021). Thus, for tasks that require a multitoken output sequence y (e.g., duplicating a string), we pad the input sequence with |y| empty tokens and compute the entire Transformer output from the padded sequence (i.e., we do not use autoregressive sampling). We train the model on sequences of length sampled uniformly from U(1, N), with N = 40, and evaluate it on sequences of length {N + 1*, . . . , M*}, with M = 500. We set the maximum position L = 2048 (and visualize the impact of other values on the performance in Appendix B.1). We report the accuracy averaged over all unseen sequence lengths, i.e., N + 1*, . . . , M*, for the best-performing model out of 10 different parameter initialization seeds and three learning rates 1 × 10−4, 3 × 10−4, 5 × 10−4. We use the same hyperparameters as Delétang et al. (2023) and provide the full experiment setup in Appendix A. We make our code publicly available at https://github.com/deepmind/ randomized_positional_encodings. Comparison to prior work We compare our method to a wide range of positional encodings: none, sin / cos (Vaswani et al., 2017), relative (Dai et al., 2019), ALiBi (Press et al., 2022), RoPE (Su et al., 2021), learned (Gehring et al., 2017), and label-based (Li and McClelland, 2022). Note that the label encodings proposed by Li and McClelland (2022) are equivalent to randomized learned positional encodings and thus subsumed by our method. We instantiate our randomized positional encoding scheme with all the above encodings and show the average test accuracy in Table 1 (with performance curves over test lengths in Appendix B.2). We observe that our randomized versions significantly increase the test accuracy across most tasks (by 12.0% on average and up to 43.5%). In particular, the randomized relative encoding solves tasks that were previously out of reach for prior work (e.g., REVERSE STRING or MISSING DUPLICATE). Efficiency comparison We now show that our method allows us to train a model on short sequences and obtain a test accuracy above 90%, roughly 35.4 times faster than the naive approach of training a model on longer sequences. To that end, we train the randomized relative encodings on sequences up to length 40 and the classical relative positional encoding (Dai et al., 2019) on sequences ![4_image_0.png](4_image_0.png) up to length 500 and show the test accuracy (averaged over lengths 41 to 500) in Fig. 2 over training time (in seconds). Our model obtains a strong test accuracy significantly faster due to the quadratic cost (in terms of sequence length) of global attention, which means that our model trains at 168.4 steps per second compared to 22.1 steps per second for the naive approach (on a NVIDIA V100 GPU). ## 5 Conclusion We introduced a novel family of positional encodings that significantly improves the length generalization capabilities of Transformers. Our positional encodings are based on the insight that conventional positional encodings will be out-ofdistribution when increasing the sequence length. Thus, to overcome this issue, we randomly sample our encodings from a wider range than the lengths seen at test time while keeping the order. Our largescale empirical evaluation demonstrates that our method significantly outperforms prior work in terms of length generalization while offering superior computational performance over the naive approach of training the model on longer sequences. ## Limitations While our work shows promising results in improving the generalization capabilities of Transformers to sequences of arbitrary length, some limitations must be considered. First, our evaluation is confined to synthetic algorithmic reasoning tasks, which may not fully capture the complexity and diversity of natural language. We focused on synthetic datasets since they showed clear and somewhat surprising limitations of Transformer architectures (Delétang et al., 2023). However, the generalizability of our approach to other tasks and domains remains an open question, and additional research, such as evaluation on SCAN (Lake and Baroni, 2018), CFQ (Keysers et al., 2020), COGS (Kim and Linzen, 2020), or the Long Range Arena (Tay et al., 2021), is necessary to understand its potential in real-world applications. Second, our approach introduces a new hyperparameter - the maximum sequence position L. Although our experiments in Appendix B.1 show that our method's performance is largely unaffected by the precise value of L, practitioners may still have to tune the parameter depending on their specific problem domains. Third, we only isolate and ameliorate one failure mode of Transformer length generalization on synthetic datasets. 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Roformer: Enhanced transformer with rotary position embedding. *arXiv:2104.09864*. Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler. 2021. Long range arena : A benchmark for efficient transformers. In *9th International Conference on Learning* Representations. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In *Advances in Neural Information Processing Systems 30*. ## A Experimental Details We use the experiment suite proposed by Delétang et al. (2023), which consists of 15 algorithmic reasoning tasks and is publicly available at https://github.com/deepmind/ neural_networks_chomsky_hierarchy under the Apache 2.0 License. The tasks do not consist of fixed-size datasets but define training and testing distributions from which one can sample continuously. We train the models for 2 000 000 steps with a batch size of 128, which corresponds to 256 000 000 (potentially non-unique) training examples. At test time, we evaluate a single batch of size 500 for every sequence length in {41*, . . . ,* 500}, which corresponds to 230 000 testing examples. We use the Adam optimizer (Kingma and Ba, 2015) with gradient clipping and sweep over three learning rates: 1 × 10−4, 3 × 10−4, and 5 × 10−4. Furthermore, for each task and positional encoding, we use 10 different parameter initialization random seeds. We consider the encoder-only Transformer architecture (Vaswani et al., 2017), with 5 blocks of 8 heads each and dmodel = 64, which corresponds to 249 026 parameters (270 146 in the case of relative and randomized relative positional encodings). We run every task-encodinghyperparameter triplet on a single NVIDIA V100 GPU from our internal cluster. As a result, we used 15 (tasks) · 13 (positional encodings) · 3 (learning rates)· 10 (seeds) = 5850 GPU-units for the results in Tables 1, 4 and 5 and Fig. 4. For the results in Fig. 2, we used an additional 2 (positional encodings) · 3 (learning rates) · 10 (seeds) = 60 GPU-units. Finally, for Fig. 3, we used 4 (maximum positions)·3 (learning rates)· 10 (seeds) = 120 GPU-units, yielding a grand total of 6030 GPU-units. We report all running times in Table 2 and observe that our method induces a negligible computational overhead. ## B Additional Results B.1 Ablation Study In this section, we conduct an ablation study over the two main components of our method: (i) the maximum sampling position L, and (ii) the sorting of the subsampled positions. We train the randomized relative positional encoding for a wide range of different maximum positions L: 1024, 2048, 4096, and 8192. Figure 3 ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) shows that the test accuracy (averaged over all unseen sequence lengths) is largely unaffected by the value of L on the REVERSE STRING and MISSING DUPLICATE tasks. As a consequence, a practitioner wanting to apply our method will not have to carry out extensive tuning of this parameter (as long as it is larger than the maximum evaluation sequence length M, but not unreasonably large). Next, we investigate the performance of our randomized sin / cos positional encoding with and without sorting of the subsampled positions. Note that this experiment is meant as a "sanity-check" since we do not expect the Transformer to perform well without order information. Table 3 shows the test accuracy (averaged over all unseen sequence lengths) for the two versions of our method. We observe that sorting the positions is crucial, as it increases the test accuracy by 15.7% on average Table 2: Mean and standard deviation of the running times (in hours) for all the positional encodings and tasks. Level Task None sin / cos **Relative ALiBi RoPE Learned** sin / cos **Relative ALiBi RoPE Learned**⋆ R PARITY CHECK† 0.86 ± 0.17 0.87 ± 0.17 1.63 ± 0.28 0.87 ± 0.17 1.41 ± 0.24 0.90 ± 0.18 0.92 ± 0.18 1.75 ± 0.29 0.94 ± 0.19 1.66 ± 0.31 1.12 ± 0.23 REVERSE STRING 1.17 ± 0.21 1.18 ± 0.22 2.61 ± 0.39 1.17 ± 0.22 2.01 ± 0.35 1.23 ± 0.23 1.24 ± 0.23 2.75 ± 0.41 1.27 ± 0.24 2.42 ± 0.43 1.62 ± 0.32 CYCLE NAVIGATION† 0.86 ± 0.17 0.87 ± 0.17 1.62 ± 0.27 0.86 ± 0.17 1.41 ± 0.25 0.91 ± 0.18 0.92 ± 0.18 1.75 ± 0.29 0.94 ± 0.19 1.66 ± 0.31 1.12 ± 0.22 EVEN PAIRS 0.86 ± 0.17 0.87 ± 0.17 1.63 ± 0.27 0.86 ± 0.17 1.41 ± 0.24 0.91 ± 0.18 0.92 ± 0.18 1.75 ± 0.29 0.95 ± 0.19 1.65 ± 0.31 1.12 ± 0.22 DCF STACK MANIPULATION 8.09 ± 0.97 8.00 ± 0.82 9.50 ± 0.89 8.07 ± 0.94 8.87 ± 0.84 8.46 ± 0.84 8.47 ± 0.88 10.04 ± 0.96 8.55 ± 0.90 10.61 ± 1.58 9.58 ± 1.12 MODULAR ARITHMETIC 5.48 ± 0.63 5.55 ± 0.67 6.32 ± 0.81 5.50 ± 0.65 6.07 ± 0.69 5.69 ± 0.65 5.66 ± 0.64 6.56 ± 0.70 5.69 ± 0.65 6.41 ± 0.84 5.92 ± 0.80 BINARY MULTIPLICATION 1.83 ± 0.33 1.83 ± 0.30 2.86 ± 0.43 1.84 ± 0.31 2.32 ± 0.39 2.24 ± 0.35 2.23 ± 0.35 3.13 ± 0.43 2.24 ± 0.35 3.21 ± 0.51 2.88 ± 0.46 BINARY ADDITION 1.83 ± 0.32 1.82 ± 0.31 2.89 ± 0.42 1.81 ± 0.32 2.34 ± 0.39 2.22 ± 0.35 2.22 ± 0.35 3.17 ± 0.44 2.24 ± 0.35 3.29 ± 0.62 2.90 ± 0.49 | R DCF CS | |------------| BINARY ADDITION 1.83 ± 0.32 1.82 ± 0.31 2.89 ± 0.42 1.81 ± 0.32 2.34 ± 0.39 2.22 ± 0.35 2.22 ± 0.35 3.17 ± 0.44 2.24 ± 0.35 3.29 ± 0.62 2.90 ± 0.49 COMPUTE SQRT 1.39 ± 0.24 1.40 ± 0.25 2.20 ± 0.34 1.40 ± 0.25 1.86 ± 0.30 1.73 ± 0.29 1.72 ± 0.29 2.43 ± 0.37 1.74 ± 0.30 2.53 ± 0.41 2.23 ± 0.38 SOLVE EQUATION 5.60 ± 0.65 5.60 ± 0.67 6.41 ± 0.68 5.63 ± 0.66 6.14 ± 0.68 5.74 ± 0.65 5.78 ± 0.66 6.69 ± 0.76 5.83 ± 0.69 6.50 ± 0.80 6.01 ± 0.84 DUPLICATE STRING 1.58 ± 0.28 1.59 ± 0.28 4.10 ± 0.54 1.58 ± 0.27 2.71 ± 0.40 1.64 ± 0.28 1.65 ± 0.29 4.24 ± 0.54 1.67 ± 0.29 3.18 ± 0.49 2.05 ± 0.38 MODULAR ARITHMETIC (SIMPLE) 0.99 ± 0.19 1.00 ± 0.19 1.74 ± 0.29 0.99 ± 0.19 1.51 ± 0.26 1.03 ± 0.20 1.05 ± 0.20 1.87 ± 0.31 1.06 ± 0.21 1.74 ± 0.31 1.23 ± 0.23 MISSING DUPLICATE 0.88 ± 0.17 0.90 ± 0.18 1.64 ± 0.27 0.88 ± 0.17 1.43 ± 0.26 0.93 ± 0.19 0.94 ± 0.19 1.78 ± 0.30 0.97 ± 0.19 1.66 ± 0.30 1.15 ± 0.23 ODDS FIRST 1.17 ± 0.22 1.19 ± 0.22 2.61 ± 0.38 1.17 ± 0.22 2.00 ± 0.31 1.23 ± 0.23 1.24 ± 0.23 2.74 ± 0.40 1.26 ± 0.23 2.40 ± 0.39 1.59 ± 0.29 BUCKET SORT† 1.17 ± 0.23 1.18 ± 0.22 2.61 ± 0.43 1.16 ± 0.22 2.01 ± 0.34 1.22 ± 0.23 1.24 ± 0.23 2.74 ± 0.40 1.25 ± 0.23 2.40 ± 0.41 1.60 ± 0.30 | Randomized (Ours) | |---------------------| and up to 76.3% on certain tasks. In fact, without sorting, our approach fails to beat the (baseline) random accuracy on all but the CYCLE NAVIGATION task, which is permutation-invariant (i.e., it can be solved without positional information). This confirms our intuition that the Transformer only needs to know the relative order of the positional encodings (and not their exact values), but that it fails to solve tasks when presented with positional encodings whose order does not correspond to the tokens' positions. ## B.2 Comparison To Prior Work In Section 4, we compared our method to a wide range of positional encodings: none, sin / cos (Vaswani et al., 2017), relative (Dai et al., 2019), ALiBi (Press et al., 2022), RoPE (Su et al., 2021), learned (Gehring et al., 2017), and label- | Randomized sin / cos | | | | |-----------------------------|----------------|-------------|------------| | Level | Task | w/o Sorting | w/ Sorting | | EVEN PAIRS | 50.4 | 100.0 | | | MODULAR ARITHMETIC (SIMPLE) | 20.0 | 25.7 | | | R | PARITY CHECK† | 52.2 | 52.6 | | CYCLE NAVIGATION† | 59.3 | 59.0 | | | STACK MANIPULATION | 50.4 | 72.8 | | | DCF | REVERSE STRING | 52.8 | 75.6 | | MODULAR ARITHMETIC | 31.0 | 33.8 | | | SOLVE EQUATION | 20.2 | 24.5 | | | DUPLICATE STRING | 52.8 | 72.4 | | | MISSING DUPLICATE | 53.1 | 52.5 | | | ODDS FIRST | 52.8 | 65.9 | | | BINARY ADDITION | 50.0 | 64.4 | | | BINARY MULTIPLICATION | 49.9 | 52.1 | | | COMPUTE SQRT | 50.2 | 52.5 | | | BUCKET SORT† | 23.7 | 100.0 | | | CS | | | | based (Li and McClelland, 2022). Here, we provide additional results for these experiments, as well as a comparison to the geometric attention and directional encodings of Csordás et al. (2022). We recall that Table 1 showed the test accuracy maximized over the 10 parameter initialization seeds and the three different learning rates. We reported the maximum following the experiment setup in Delétang et al. (2023), which investigates whether an architecture is capable of solving a task at all (and not on average). However, we also report the means and standard deviations (over the random seeds) in Table 4 for the best-performing learning rate. We observe that our randomized positional encoding also significantly outperform their original counterparts on average. We visualize the test accuracy per sequence length in Fig. 4. We highlight the case of learned positional encodings, which fail to beat the random accuracy baseline (cf. Tables 1 and 4). This is because the columns of the embedding matrix corresponding to the positions that are larger than the maximum training length N are not learned during training and are thus entirely random. In contrast, our randomized version of the learned encodings considers all possible embedding columns during training and thus achieves non-trivial to strong length generalization on most tasks. Finally, we also compare our method to a variant of the Neural Data Router (NDR) (Csordás et al., 2022), which was developed to improve the systematic generalization capabilities of Transformers. We only consider the most related aspects of the NDR architecture, i.e., the geometric attention and the directional encoding (we do not use gating or shared layers). Table 5 compares the test accuracy of geometric attention and directional encodings | R DCF CS | |------------| Level Task None sin / cos **Relative ALiBi RoPE Learned** sin / cos **Relative ALiBi RoPE Learned**⋆ | Randomized (Ours) | |---------------------| R EVEN PAIRS 50.1 ± 0.1 50.4 ± 0.2 67.6 ± 15.3 59.8 ± 3.2 50.4 ± 0.3 50.4 ± 0.2 99.7 ± 0.3 99.6 ± 0.6 71.4 ± 5.6 **100.0** ± 0.0 96.2 ± 0.7 MODULAR ARITHMETIC (SIMPLE) 20.0 ± 0.0 20.2 ± 0.2 20.7 ± 0.5 23.2 ± 0.9 20.8 ± 0.5 20.1 ± 0.1 24.2 ± 1.4 **24.9** ± 1.7 20.8 ± 0.3 23.5 ± 1.6 20.2 ± 0.4 PARITY CHECK† 50.4 ± 0.8 50.3 ± 0.2 50.4 ± 0.6 50.5 ± 0.6 50.4 ± 0.4 50.0 ± 0.1 51.1 ± 1.3 **51.4** ± 0.5 50.0 ± 0.2 50.4 ± 1.0 50.6 ± 0.9 CYCLE NAVIGATION† 33.9 ± 10.5 23.8 ± 1.4 21.7 ± 0.8 31.1 ± 3.8 22.3 ± 0.9 21.0 ± 1.2 30.3 ± 10.7 45.9 ± 9.9 26.3 ± 2.4 **52.9** ± 15.3 31.9 ± 8.2 DCF STACK MANIPULATION 50.2 ± 0.1 47.3 ± 1.9 50.1 ± 3.3 51.0 ± 8.0 49.6 ± 3.0 44.9 ± 3.7 69.2 ± 3.2 **71.7** ± 4.7 69.5 ± 1.1 66.0 ± 2.0 66.1 ± 2.5 REVERSE STRING 52.7 ± 0.1 50.4 ± 0.1 54.2 ± 1.5 56.3 ± 2.6 51.2 ± 0.3 50.4 ± 0.2 72.9 ± 1.6 **77.1** ± 6.6 75.1 ± 1.3 67.7 ± 1.1 52.7 ± 0.2 MODULAR ARITHMETIC 31.0 ± 0.1 24.3 ± 2.2 26.1 ± 2.0 28.1 ± 3.4 24.0 ± 2.4 22.3 ± 1.5 29.6 ± 4.6 28.8 ± 5.5 29.3 ± 1.6 28.6 ± 3.9 **30.3** ± 2.6 SOLVE EQUATION 20.1 ± 0.0 20.9 ± 0.2 21.9 ± 0.7 23.6 ± 1.9 21.9 ± 0.6 20.2 ± 0.2 23.6 ± 0.5 **25.4** ± 1.8 21.1 ± 0.7 22.3 ± 1.6 21.1 ± 0.7 DUPLICATE STRING 52.7 ± 0.1 50.4 ± 0.2 51.0 ± 0.4 51.0 ± 0.2 50.4 ± 0.2 50.4 ± 0.2 69.0 ± 2.9 **73.1** ± 1.5 67.9 ± 1.4 67.1 ± 2.0 52.8 ± 0.1 MISSING DUPLICATE 51.4 ± 1.0 50.1 ± 0.6 51.1 ± 1.1 53.5 ± 0.4 53.9 ± 1.6 50.1 ± 0.4 50.4 ± 1.5 **91.4** ± 9.8 75.2 ± 3.4 73.2 ± 1.2 51.2 ± 1.4 ODDS FIRST 52.7 ± 0.1 51.3 ± 0.2 51.5 ± 0.5 51.1 ± 0.2 50.8 ± 0.2 50.5 ± 0.1 62.5 ± 2.0 **65.9** ± 1.6 62.2 ± 1.4 62.9 ± 1.3 52.7 ± 0.1 BINARY ADDITION 49.4 ± 0.3 47.3 ± 3.8 51.7 ± 1.3 48.5 ± 3.6 47.8 ± 5.4 48.9 ± 0.8 61.2 ± 1.7 **62.0** ± 1.1 54.3 ± 1.5 57.4 ± 1.2 59.9 ± 1.3 BINARY MULTIPLICATION 49.8 ± 0.0 48.8 ± 1.0 50.2 ± 3.5 49.9 ± 2.3 49.6 ± 0.6 48.7 ± 1.7 **51.8** ± 0.2 39.1 ± 7.1 49.2 ± 1.2 45.7 ± 6.6 51.6 ± 0.2 COMPUTE SQRT 50.2 ± 0.0 50.1 ± 0.0 51.5 ± 0.4 50.5 ± 0.2 50.3 ± 0.1 50.1 ± 0.1 51.9 ± 0.5 **52.4** ± 0.6 51.1 ± 0.1 51.8 ± 0.3 51.0 ± 0.8 BUCKET SORT† 23.7 ± 0.0 25.6 ± 2.6 83.4 ± 6.6 29.3 ± 6.7 23.6 ± 3.8 20.7 ± 2.9 99.3 ± 0.4 **99.4** ± 0.3 98.8 ± 0.7 99.3 ± 0.3 98.9 ± 0.4 Table 5: Accuracy (in %) averaged over all test lengths for geometric attention with directional encoding. Max Avg ± SD Level Task Table 1 Geometric Table 4 **Geometric** R EVEN PAIRS **100.0 100.0** 100.0 ± 0.0 94.5 ± 8.8 MODULAR ARITHMETIC (SIMPLE) 28.1 **43.6** 24.9 ± 1.7 27.2 ± 8.2 PARITY CHECK† **52.6** 52.4 51.4 ± 0.5 51.6 ± 0.6 CYCLE NAVIGATION† **73.6** 41.3 52.9 ± 15.3 32.9 ± 4.7 DCF STACK MANIPULATION **77.9** 58.3 71.7 ± 4.7 55.6 ± 2.3 REVERSE STRING **95.1** 65.2 77.1 ± 6.6 59.3 ± 3.2 MODULAR ARITHMETIC 34.9 **36.5** 30.3 ± 2.6 32.8 ± 2.8 SOLVE EQUATION 28.1 **31.7** 25.4 ± 1.8 28.5 ± 2.0 DUPLICATE STRING **75.1** 58.6 73.1 ± 1.5 54.9 ± 1.6 MISSING DUPLICATE **100.0** 64.4 91.4 ± 9.8 60.3 ± 2.3 ODDS FIRST **69.3** 64.2 65.9 ± 1.6 58.1 ± 2.6 BINARY ADDITION **64.5** 54.9 62.0 ± 1.1 53.5 ± 1.5 BINARY MULTIPLICATION 50.1 **53.6** 51.8 ± 0.2 52.1 ± 2.5 COMPUTE SQRT 53.3 **54.1** 52.4 ± 0.6 52.3 ± 0.9 BUCKET SORT† **100.0** 78.3 99.5 ± 0.3 57.7 ± 11.4 with the best results from Table 1 (for the maximum) and Table 4 (for the mean). We observe that our randomized positional encodings outperform the geometric attention overall (with a 9.7% higher maximum test accuracy on average) but not on all tasks. In particular, geometric attention performs substantially better on MODULAR ARITHMETIC (SIMPLE), which has an inherent locality bias, i.e., numbers closer to the operation symbols are generally more relevant, which can be captured by "radiating outwards" as geometric attention does. | R DCF CS | |------------| ## B.3 Analysis Analyzing the activations As illustrated in Fig. 1, the main intuition behind our randomized encodings is that they do not lead to outof-distribution activations when evaluating on sequences longer than the maximal training length. We confirm this intuition in our analysis in Fig. 5, which shows a 2D projection of activations onto the first two principal components when evaluating on sequences of length 40 (i.e., the maximum training length N, shown in blue) and length 150 (i.e., the generalization regime, shown in orange), using the same transformation. While the activations of our randomized relative encoding strongly overlap for the training and the generalization regimes in all layers, the standard relative encoding leads to outof-distribution activations for sequence length 150 in layers 3 and 4. We obtained qualitatively similar results for the sin / cos and learned encodings. To compute the results in Fig. 5, we generated 30 sequences of length 40 and 150 respectively, on the REVERSE STRING task and passed them through a well-trained model with either relative or randomized relative encodings. For each layer shown, we fitted a (non-whitened) 2D PCA on the activations obtained from sequence length 40 and projected all activations from sequence length 150 into two dimensions using the same transformations (yielding 30 × 40 and 30 × 150 activationdatapoints per layer). The random relative encoding (our method) attains an average accuracy of 1.0 and 0.994 on the 30 sequences of length 40 and 150, respectively. The standard relative encoding (the baseline) attains an average accuracy of 1.0 on sequence-length 40 and 0.596 on length 150, indicating the model's failure to generalize well under the standard relative encoding. Analyzing the attention matrices We also analyze the attention matrices learned with the relative positional encoding and our corresponding random- ![10_image_0.png](10_image_0.png) ![11_image_0.png](11_image_0.png) ![11_image_1.png](11_image_1.png) (b) Randomized relative positional encoding (ours). ized version on the REVERSE STRING task. To that end, we follow Csordás et al. (2022) and visualize the maximum over the 8 attention matrices (one per head) for each of the 5 layers in Fig. 6. We compare the attention matrices for sequences of length 40 (i.e., the maximum training length) and 150 (i.e., significantly longer than the maximum training length). For length 40, both encodings produce a noticeable X pattern, which corresponds to the reversal of the string. However, for length 150, the pattern only remains visible for our randomized encodings while it breaks down for the original version, indicating the failure to generalize. ![12_image_0.png](12_image_0.png) (a) Relative (baseline) with a sequence of length 40 (in-distribution). ![12_image_1.png](12_image_1.png) (b) Relative (baseline) with a sequence of length 150 (out-of-distribution). ![12_image_2.png](12_image_2.png) (c) Randomized relative (our method) with a sequence of length 40 (in-distribution). ![12_image_3.png](12_image_3.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations section A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 And Appendix A ✓ B1. Did you cite the creators of artifacts you used? Section 4 and Appendix A ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix A ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 4 and Appendix A B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4 and Appendix A ## C ✓ **Did You Run Computational Experiments?** Section 4 And Appendix B ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4 and Appendix A The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4 and Appendices A and B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix B C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

kamigaito-etal-2023-table

Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models

https://aclanthology.org/2023.acl-short.162

In this paper, we propose a table and image generation task to verify how the knowledge about entities acquired from natural language is retained in Vision {\&} Language (V {\&} L) models. This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity. In both tasks, the model must know the entities used to perform the generation properly. We created the Wikipedia Table and Image Generation (WikiTIG) dataset from about 200,000 infoboxes in English Wikipedia articles to perform the proposed tasks. We evaluated the performance on the tasks with respect to the above research question using the V {\&} L model OFA, which has achieved state-of-the-art results in multiple tasks. Experimental results show that OFA forgets part of its entity knowledge by pre-training as a complement to improve the performance of image related tasks.

# Table And Image Generation For Investigating Knowledge Of Entities In Pre-Trained Vision And Language Models Hidetaka Kamigaito†, Katsuhiko Hayashi‡**, Taro Watanabe**† †Nara Institute of Science and Technology ‡Hokkaido University {kamigaito.h, taro}@is.naist.jp [email protected] ## Abstract In this paper, we propose a table and image generation task to verify how the knowledge about entities acquired from natural language is retained in Vision & Language (V&L) models. This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity. In both tasks, the model must know the entities used to perform the generation properly. We created the Wikipedia Table and Image Generation (WikiTIG) dataset from about 200,000 infoboxes in English Wikipedia articles to perform the proposed tasks. We evaluated the performance on the tasks with respect to the above research question using the V&L model OFA (Wang et al., 2022), which has achieved state-of-the-art results in multiple tasks. Experimental results show that OFA forgets part of its entity knowledge by pre-training as a complement to improve the performance of image related tasks. ## 1 Introduction Vision & Language (V&L), which is the fusion of vision and language tasks, has achieved great success in tasks such as caption generation from images (Xu et al., 2015) and image generation from texts (Reed et al., 2016). This progress has been driven by pre-trained V&L models that are trained on large-scale V&L datasets (Du et al., 2022). To generate appropriate captions and images for input, pre-trained V&L models need to have prior knowledge of the features of the objects they are generating (Cao et al., 2020; Yun et al., 2021). These models retain knowledge about entities in particular by inheriting parameters from pre-trained language models used in natural language processing to indirectly utilize data resources such as Wikipedia. In this way, V&L models (Lu et al., 2019; Su et al., 2020; Li et al., 2020; Cho et al., 2021; Wang ![0_image_0.png](0_image_0.png) et al., 2022; Saharia et al., 2022) map the inherited textual knowledge into visual representations through additional training on V&L datasets. This learning process raises a number of questions, such as whether the knowledge about entities acquired from natural language is adequately retained in the pre-trained V&L model, or whether it is enhanced by combining it with image features. These are important in understanding the limits of what can be generated by the pre-trained V&L model. To answer these questions, we propose a task of generating tables and images of infoboxes in English Wikipedia. Figure 1 shows an example of the target infobox, in which either tables or images are generated by the proposed task. In both cases, the model must know the entities to generate them properly. We collected about 200,000 infoboxes to construct the Wikipedia Table and Image Generation (WikiTIG) dataset necessary to perform the pro-1https://en.wikipedia.org/wiki/Fish_and_chips ![1_image_0.png](1_image_0.png) posed task. In addition, we used OFA (Wang et al., 2022), a pre-trained V&L model that has achieved state-of-the-art performance in various V&L tasks. Our evaluation of the table generation revealed that part of the knowledge in the V&L model acquired from natural language is lost when the V&L model is pre-trained. We also found that additional knowledge for entities was acquired by supplementing image information, which was not possible solely from textual data. In image generation, we found that OFA can generate more accurate images by using the knowledge expressed in the table. We also found that the models trained only on natural language can infer table knowledge, which increases the diversity of generated images. Our code and dataset will be released at https://github.com/kamigaito/WikiTIG. ## 2 Vision & Language Models Many pre-trained V&L models have achieved stateof-the-art performance on various tasks by inheriting the weights of the conventional pre-trained models for natural language and images (Lu et al., 2019; Su et al., 2020; Li et al., 2020; Cho et al., 2021; Wang et al., 2022; Saharia et al., 2022) before learning V&L datasets. Our study examines how the knowledge represented in the pre-trained model for natural language is transformed through such a learning process. We select OFA, which has achieved state-of-the-art performance in multiple V&L tasks, as our target model. Figure 2 shows the network structure of OFA and its relation to each dataset2. OFA uses VQGAN (Esser et al., 2020) on the decoder to transform images into discrete sequences so that the same Transformer (Vaswani et al., 2017) is used for image and natural language generation. Because OFA inherits | Task | Input | Output | |------------------|-----------------------|----------| | Table Generation | Title, Image | Table | | Image Generation | Title, Caption, Table | Image | Table 1: Outline of each task. See Figure 1 for the parts of the infobox to which each term refers. Alternative names | Fish supper / Fish 'n' chips <> Course | Main dish <> Place of origin | England <> Region or state | Northwestern Europe <> Serving temperature | Hot <> Main ingredients | Battered and fried fish with deep-fried chips Figure 3: This example is a linearized version of the table in Figure 1. parameters from BART (Lewis et al., 2020), which shares a similar Transformer structure, OFA should include knowledge acquired from natural language such as Wikipedia articles. Unlike the decoder, the encoder handles images directly; thus, OFA uses the output of ResNet (He et al., 2016) to embed images in addition to the embedding layer inherited from BART. ## 3 Table And Image Generation In this section, we describe two tasks for verifying knowledge behavior in the V&L model: table generation and image generation. Both tasks are based on infoboxes in Wikipedia articles, which correspond to summary information of the Wikipedia articles comprising tables and images3. Thus, it is suitable for verifying the knowledge about entities in Wikipedia kept in the pre-trained V&L model. In the following subsections, we explain the details of each task. ## 3.1 Table Generation In the table generation task, the target V&L model generates a table from a title and/or image of the infobox. To do this, the model generates linearized tables, similarly to table generation by descriptions (Wu et al., 2022b). In our setting, we linearize tables as shown in Figure 3 using the column separator "|" and the row separator "<>" to reuse pretrained token embeddings. The separator symbols are accompanied by spaces before and after for use in BPE tokenization. We investigate the target model by directly generating such linearized text. We use the following settings for the investigation. 2Appendix A describes the data for the pre-training. 3https://en.wikipedia.org/wiki/Help:Infobox Generation from titles We investigate the knowledge about entities held by V&L models by comparing tables generated from titles by pre-trained V&L models and by pre-trained models trained only on natural language. Generation from title and images We generate tables from titles with images and compare the results with those generated from only titles. This enables us to investigate the new knowledge in pretrained V&L models transferred from images. Metrics For comparison, we use the following evaluation metrics to measure how close the generated tables are to the actual ones. - **ROUGE**: Since the linearized tables are text data and the infobox plays the role of summarizing the article, we use ROUGE (Lin, 2004), the most widely used evaluation method for automatic summarization. In our evaluation with ROUGE, we convert the column separator "|" and the row separator "<>" to spaces so that the sequence of strings is not restricted to rows and columns. - **Table-F**1: To evaluate the tables with respect to their structure, we divide the cells by their types and then evaluate the matches with the reference table in terms of the F1 measure for each case and average them. When calculating the matches, we apply clipping used in ROUGE to prevent the score from increasing due to the repetition of the same cell in the output4. We treat cells of each type separately5as follows: - **Group**: The infobox sometimes divides the table into groups, with the first row of each group serving as a header for the group name. The prediction performance for the group names is important for verifying what aspects of knowledge the model has about the entities. Since these rows consist of a single column, we target rows consisting of a single column in this type of cell. - **Header**: The head of each row in the table consisting of more than one column is usually the header of a subsequent cell in the same row. Therefore, the prediction performance for headers is important for the same reason as for group names. - **Value**: The second cells in each row of a table with two columns have values corresponding 4Appendix B.1 shows the details of this calculation. 5Appendix C shows an example of the cell types. | Task | Total | Train | Valid | Test | |------------------|---------|---------|---------|--------| | Table Generation | 204,460 | 184,124 | 10,081 | 10,255 | | Image Generation | 86,654 | 78,012 | 4,261 | 4,381 | Table 2: The data size for each task in the WikiTIG dataset. to the headers. Therefore, the prediction performance of the values is important for knowing whether the model has detailed knowledge about the entity. To examine the correspondence between headers and their values, we treat a header and its corresponding value as a pair. - **Corpus-F**1: Because the above Table-F1 computes each case individually, it is difficult to evaluate how much diverse knowledge the model outputs. To solve this problem, we share cells across all instances and compute F1 values in a batch. Similarly to Table-F1, we apply clipping to the score calculation6and treat cell types Group, Header, and Value separately as defined in Table-F1. ## 3.2 Image Generation In the image generation task, the model receives a title, caption, and table to generate the corresponding image: ## Generation From A Title And Caption By Using the minimum input required to generate images, we investigate the difficulty of generating them compared to other datasets. Generation from a title, caption, and table We investigate the impact of knowledge about entities on image generation by generating images from input, including tables, and compare the results to the setting without tables. Metrics We use the following three widely used measures for evaluating image generation. - **CLIP:** The relevance of the input text to the generated images inferred by the pre-trained V&L model CLIP (Radford et al., 2021). - **Inception Score (IS)**: How easily a model can distinguish the differences between each image and the variety of generated images (Salimans et al., 2016). It is inferred by the pre-trained image classification model Inception-v3 (Szegedy et al., 2016). - **Frechet Inception Distance (FID)**: How close the generated image is to the reference image, es-6Appendix B.2 shows the details of this calculation. | Model | Input | ROUGE ↑ | Table-F1 ↑ | Corpus-F1 ↑ | | | | | | | |---------|---------|-----------|--------------|---------------|----------|----------|---------|----------|----------|----------| | 1 | 2 | L | Header | Group | Value | Header | Group | Value | | | | BART | Title | 28.8±0.2 | 14.0±0.1 | 26.6±0.1 | 38.9±0.1 | 24.3±0.1 | 4.9±0.0 | 62.9±0.3 | 35.5±0.0 | 11.7±0.0 | | OFA | Title | 28.1±0.2 | 13.4±0.1 | 25.7±0.2 | 34.7±0.4 | 22.8±0.2 | 4.3±0.1 | 57.8±0.7 | 33.3±0.2 | 10.7±0.2 | | OFA | Image | 28.0±0.1 | 11.5±0.0 | 25.8±0.1 | 41.9±0.1 | 21.2±0.1 | 2.7±0.0 | 57.4±0.2 | 26.6±0.2 | 6.8±0.0 | | OFA | Both | 31.3±0.1 | 14.2±0.1 | 28.7±0.1 | 43.5±0.1 | 23.2±0.1 | 3.7±0.0 | 59.2±0.2 | 28.6±0.1 | 8.2±0.1 | timated by Inception-v3 like IS. A lower FID is more ideal. ## 4 Dataset Creation We created the Wikipedia Table and Image Generation (WikiTIG) dataset by extracting infoboxes from the HTML dump data of the English Wikipedia8. To ensure consistency in the format of infoboxes, we limited the extraction target to those containing a title in the first row and an image in the second row, as shown in Figure 1. In order to use only entities with sufficient information, we targeted entities for which the table was not empty. In addition, to ensure reliable correspondence, only rows one column wide, which often describe groups, and rows two columns wide, which often consist of a header and its value, were targeted for extraction. The target images are limited to those in jpeg, png, and gif formats. Since some captions do not include a title, we used a hyphen to join the title at the beginning of the caption in such cases. Table 2 shows the size of each dataset. The dataset size diverges between two tasks because some infoboxes do not include captions9. ## 5 Evaluation & Analysis 5.1 Table Generation Settings We chose OFA (Wang et al., 2022), a pre-trained V&L model, and BART (Lewis et al., 2020), pre-trained only in natural language, as models for comparison. For both models, we used the base settings with the hyperparameters reported in Wang et al. (2022). We performed the training three times with different seeds and reported their average scores with their standard deviations10. Results Table 3 shows the results for each setting in the table generation11. When only the title is used as input, the result of BART is more accurate than that of OFA, indicating that part of the knowledge acquired from natural language is lost due to additional learning in the V&L model. The use of image information improves Table-F1 for headers, indicating that images reinforce the knowledge of what kind of features an entity has. In contrast, F1 for cell values did not improve, indicating that information obtained from images does not complement detailed knowledge, such as the values corresponding to each header obtained from natural language. The results of BART in Corpus-F1 also suggest that BART contains more diverse knowledge internally than in other settings. This result reinforces that the V&L model forgot part of the knowledge from natural language through additional learning, and images could not fully complement them. ## 5.2 Image Generation Settings Similarly to the table generation, we chose OFA for the comparison. We additionally join the reference tables (Gold) and those generated by models in §5.1 (OFA, BART) as the input in order to investigate the impact of the ability to infer table knowledge. We also used the base settings with the hyperparameters reported in Wang et al. (2022). We also performed the training three times with different seeds and reported their average scores with their standard deviations12. Results Table 4 shows the results for each setting in the image generation13. Since the CLIP value 10See Appendix E.1 for the detailed settings. 11Appendix F.1 shows the generated images. 12See Appendix E.2 for the detailed settings. 13Appendix F.2 shows the generated images. | Input | CLIP ↑ | IS ↑ | FID ↓ | |-----------------|----------|----------|----------| | Title & Caption | 28.7±0.0 | 10.5±0.1 | 31.1±0.2 | | +Table (Gold) | 29.4±0.0 | 11.3±0.2 | 28.5±0.3 | | +Table (BART) | 28.1±0.0 | 10.6±0.2 | 32.4±0.3 | | +Table (OFA) | 28.0±0.1 | 10.6±0.2 | 33.1±0.4 | in OFA is close to the result (Wang et al., 2022) in MS COCO (Chen et al., 2015) for image generation, the use of our created dataset is reasonable for training models. In addition, the input of Table (Gold) improves all metrics, indicating that the model produces higher quality images when provided with complementary knowledge about the entities. This result also indicates that OFA does not retain sufficient knowledge of the entities in English Wikipedia. In addition, we did not observe any performance improvement in CLIP and FID when fed with automatically generated tables from BART and OFA. However, tables generated by BART improves IS with the lower performance degradation of FID than that by OFA, indicating that automatically generated tables can improve the diversity of the output images and accurate tables are more important for improving performance in image generation. ## 6 Related Work Following the advancements in V&L models (Du et al., 2022), there have been various studies that investigate V&L models. Cao et al. (2020) conducted a comprehensive analysis of V&L models including the difference between model structures. Through their analysis, they revealed the importance of text information in V&L tasks over image information. Several studies focused on the performance differences between V&L models and text-only models. Yun et al. (2021) investigated the improvement of linguistic representations by pre-training V&L models on PhysicalQA (PIQA) (Bisk et al., 2020) and the probing framework of (Tenney et al., 2019). They concluded that the benefit of pretrained V&L models for text-only tasks is marginal. Iki and Aizawa (2021); Hagström and Johansson (2022) compared the performance of V&L models and text-only models on the text-only benchmark, GLUE (Wang et al., 2018) and determined that the text-only model achieved higher scores than the V&L models. However, even though various kinds of V&L models (Lu et al., 2019; Su et al., 2020; Li et al., 2020; Cho et al., 2021; Wang et al., 2022; Saharia et al., 2022) inherit language-related knowledge from pre-trained language-only models, how the knowledge is inherited has yet to be investigated. Our work clarifies this by using our created dataset, Wikipedia Table and Image Generation (WikiTIG). ## 7 Conclusion This paper investigates how knowledge about entities are preserved in a pre-trained V&L model which is originally transferred from a pre-trained natural language model. We analyzed a pre-trained V&L model by creating the Wikipedia Table and Image Generation (WikiTIG) dataset for generating images and tables of the infoboxes in Wikipedia. WikiTIG consists of 200,000 infoboxes and their corresponding images from English Wikipedia. Experimental results on a pre-trained V&L model OFA (Wang et al., 2022) showed that the model forgot part of the knowledge about entities during pre-training, and the image information did not fully compensate for the forgotten knowledge. ## Limitations Regarding the Wikipedia articles used for creating our dataset Wikipedia Table and Image Generation (WikiTIG), some infoboxes may not follow the defined format and rules. This is because various users can freely edit infoboxes. Moreover, the HTML dump data published by English Wikipedia is not based on recent information. In image generation, due to the standard settings recommended by Zhang et al. (2021); Ramesh et al. (2021); Wang et al. (2022); Wu et al. (2022a), our image generation task requires generating a cropped fixed-size square image instead of the original aspect ratio. In addition, a table in an infobox may contain cells unrelated to image generation, and thus it may be redundant for image generation. ## Ethical Considerations In this study, we created our dataset from English Wikipedia. The editors of English Wikipedia remove unnecessarily offensive content and compile them into an encyclopedia (https://en.wikipedia.org/wiki/ Wikipedia:Offensive_material). However, as stated on the official pages (https: //en.wikipedia.org/wiki/Wikipedia: Neutral_point_of_view\#Bias_in_sources, https://en.wikipedia.org/wiki/Wikipedia: Reliable_sources\#Biased_or_opinionated_ sources), the current English Wikipedia permits the use of biased information sources. Thus, there is a possibility that our created dataset also inherits the original biases of English Wikipedia. ## Acknowledgments This work was supported by JSPS KAKENHI Grant Numbers JP21K17801, JP23H03458. ## References Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020. 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In *Proceedings* of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 833–842. ## A Details Of The Datasets For Pre-Training Ofa OFA pre-training uses various datasets for pretraining tasks in language, vision, and vision & language modalities, as shown in Table 5. Note that 1.53% of Pile (Gao et al., 2021) listed in Table 5 contains information from English Wikipedia. Therefore, we can understand that although OFA's pre-training focuses on V&L tasks, it is also designed to prevent the knowledge acquired from natural language data from forgetting. ## B Details Of The Metric Calculation B.1 Table-F1 Let e be an element of a target cell type. Here, we define a function M atchr,g(e) that calculates the exact match of elements in reference and generated tables as follows: $$\begin{array}{c}{{M a t c h_{r,g}(e)}}\\ {{=M i n(C o u n t_{r}(e),C o u n t_{g}(e)),}}\end{array}$$ where Countr(e), *Count*g(e) are functions that return frequencies of e in a generated table g and a reference table r, respectively. Note that *M in* is a function that returns the minimum value from the given one. By using M atchr,g(e), we calculate T able-F1 as follows: $$P(g,r)=\frac{\sum_{e\in g}Match_{r,g}(e)}{\sum_{e^{\prime}\in g}Count_{g}(e^{\prime})},\tag{2}$$ $$R(g,r)=\frac{\sum_{e\in r}Match_{r,g}(e)}{\sum_{e^{\prime}\in g}Count_{r}(e^{\prime})},$$ (3) $$F_{1}(g,r)=\frac{2P(g,r)R(g,r)}{P(g,r)+R(g,r)},$$ (4) $$Table\text{-}F_{1}=\frac{1}{|D|}\sum_{(g,r)\in(G,R)}F_{1}(g,r),\tag{5}$$ where $|D|$ denotes a number of tables, $G$ denotes all generated tables, and R denotes all reference tables. ## B.2 Corpus-F1 Instead of M atchr,g(e), we define M atchR,G(e) as follows: $$M a t c h_{R,G}(e)$$ $$\begin{array}{c}{{M a t c h_{R,G}(e)}}\\ {{=M i n(C o u n t_{R}(e),C o u n t_{G}(e)),}}\end{array}$$ where CountR(e), *Count*R(e) are functions that return frequencies of e in all generated tables G and all reference tables R, respectively. By using M atchR,G(e), we calculate *Corpus*-F1 as follows: $$\begin{array}{l}{{P(G,R)=\frac{\sum_{e\in G}M a t c h_{R,G}(e)}{\sum_{e^{\prime}\in G}C o u n t_{G}(e^{\prime})},}}\\ {{R(G,R)=\frac{\sum_{e\in R}M a t c h_{R,G}(e)}{\sum_{e^{\prime}\in R}C o u n t_{R}(e^{\prime})},}}\\ {{C o r p u s\ldot{\ldot{\l}}=\frac{2P(G,R)R(G,R)}{P(G,R)+R(G,R)}.}}\end{array}$$ (7) $$\begin{array}{l}\small\text{(8)}\end{array}$$ = $$\begin{array}{l}\small\text{(9)}\end{array}$$ . , (7) , (8) . (9) $$(1)$$ C Groups/Headers/Values in an infobox ![7_image_0.png](7_image_0.png) (2) $$\begin{array}{l}\small\text{(3)}\end{array}$$ = (4) $$\begin{array}{l}\small\text{(4)}\end{array}$$ . $$({\boldsymbol{S}})$$ Figure 4 shows an example infobox that includes multiple groups. In this example, we can see two groups named with "Highest point" and "Naming". The headers "Elevation", "Prominence", "Isolation", "Listing", and "Cordinates" are grouped into "Highest point". The headers "Etymology", "Native name", and "English translation" are grouped into "Naming". The headers have corresponding values 14https://en.wikipedia.org/wiki/Mount_Everest | Modality | Task | Dataset | |-----------------------------------------------|-------------------------------------|---------------------------------| | Vision & Language | Image Captioning | CC12M, CC3M, SBU, COCO, VG-Cap | | Image-Text Matching Visual Question Answering | VQAv2, VG-QA, GQA | | | Visual Grounding | RefCOCO, RefCOCO+, RefCOCOg, VG-Cap | | | Grounded Captioning | | | | Vision | Detection | OpenImages, Object365, VG, COCO | | Image Infilling | OpenImages, YFCC100M, ImageNet-21K | | | Language | Masked Language Modeling | Pile | Header 12,804 12,071 3,373 3,401 Group 201,937 183,728 13,252 13,444 Value 772,392 705,556 54,292 55,162 Appearance Frequency Type Total Train Valid Test Header 1,535,791 1,383,138 75,870 76,783 Group 518,125 466,337 25,745 26,043 Value 1,535,791 1,383,138 75,870 76,783 | Type Frequency | | | | | |----------------------|-------|-------|-------|------| | Type | Total | Train | Valid | Test | | Appearance Frequency | | | | | | Type | Total | Train | Valid | Test | such as the value "Holy Mother" to the header "English translation". In the evaluation, we treat values as pairs with including their corresponding headers like ("English translation", "Holy Mother") for the last row of the infobox in Figure 4. ## D Details Of Our Created Dataset Wikipedia HTML dump data contains Wikipedia articles in HTML format, so we extracted infoboxes by using BeautifulSoup15. Since the infoboxes contain links to the references of the main article in the form of [\#number], we removed them. We filtered out table rows that have more than two columns. In table generation, if the short side of the input image exceeded 480px, we reduced the short side to 480px while maintaining the aspect ratio. In image generation, we changed the short side of the original image to 256px while maintaining the aspect ratio and then cropped the center of the image with a 256px square. To measure the performance of both small and large models in the future, we also created additional datasets for the table generation with the 15https://www.crummy.com/software/ BeautifulSoup/bs4/doc/ Type frequencies of values for each header Split Mean Std. Max Min All 60.3 548.4 18,518 1 Train 58.5 516.5 17,050 1 Valid 16.1 79.1 1,506 1 Test 16.2 80.3 1,557 1 Appearance frequencies of values for each header Split Mean Std. Max Min All 119.9 1244.0 48,150 1 Train 114.6 1153.2 43,350 1 Valid 22.5 118.7 2,391 1 Test 22.6 119.4 2,409 1 Split Mean Std. Max Min ![8_image_0.png](8_image_0.png) short side of the image up to 256px and 384px, respectively. Similarly, we also created a dataset for image generation with both sides of the image set to 128px. For the sake of future expansion and to avoid data confusion, we divided the collected data into test data if the remainder of the SHA256 value of the title divided by 20 is 0, development data if the remainder is 1, and training data otherwise. Please see Table 2 for the size of the dataset. Table 6 shows the frequencies of each type of cells used for F1 in §3.1. This result indicates | All | 17.6 | 7.6 | 149 | 1 | |-------|--------|-------|-------|-----| | Train | 17.6 | 7.6 | 149 | 1 | | Valid | 17.6 | 7.6 | 99 | 1 | | Test | 17.5 | 7.6 | 68 | 1 | | Title | Image | BART | OFA (Title & Image) | Reference | |-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------|-------------| | Elevation | 1,859 m (3,927 ft) <> Location | England <> Range | Lake District <> Prominence | c. 1 m <> Parent peak | Low Pike <> Topo map | OS Landranger 89, 90, Explorer OL4 <> OS grid reference | NN93722 <> Listing | Marilyn, Hewitt, Nuttall | Elevation | 1,000 m (1,000 ft) <> Location | South England <> Coordinates | 45°49'0"N, 7°10'4"W <> Range | South East England <> > Range | south east england <> Topo map | CDT | Elevation | 508 m (1,667 ft) <> Range | Lake District, Eastern Fells <> Prominence | 28 m <> Parent peak | Dove Crag <> Topo map | OS Landranger 90 OS Explorer 7 <> OS grid reference | NY373077 <> Listing | Wainwright | | | | Low Pike | Conservation status <> Least Concern <> Scientific classification <> Kingdom: Animalia Phylum: Chordata Class: Aves Order: Passeriformes Family: Emberizidae Genus: Emberiza Species: E. ferruginus <> Domain: | Animalia | Conservation status <> Least Concern <> Scientific classification <> Kingdom: Animalia Phylum: Chordata Class: Aves Order: Passeriformes Family: Pterodactylidae Genus: Ferruginous Species: F. cinereus <> kingdom: | Animalia | Conservation status <> Least Concern <> Scientific classification <> Kingdom: Animalia Phylum: Chordata Class: Aves Order: Strigiformes Family: Strigidae Genus: Glaucidium Species: G. brasilianum <> Kingdom: | Animalia <> Phylum: | Chordata <> Class: | Aves <> Order: | Strigiformes <> Family: | Strigidae <> Genus: | Glaucidium <> Species: | G. brasilianum <> Binomial name <> Glaucidium brasilianum (Gmelin, 1788) | | | Ferruginous Pygmy-owl | Scientific classification <> Kingdom: Plantae Division: Magnoliophyta Class: Liliopsida Order: Asparagales Family: Orchidaceae Subfamily: Higher Epidendroideae Genus: Achlys L. | Scientific classification <> Kingdom: Plantae Division: Magnoliophyta Class: Liliopsida Order: Asterales Family: Asteraceae Genus: Achlys Species: C. lilius <> kingdom: | Plantae <> Division: | Magnoliopsida <> Class: | Liliaceae <> Order: | Astrales <> Family: | Asteraceae | Scientific classification <> Kingdom: Plantae Division: Magnoliophyta Class: Magnoliopsida Order: Ranunculales Family: Berberidaceae Genus: Achlys DC. <> Kingdom: | Plantae <> Division: | Magnoliophyta <> Class: | Magnoliopsida <> Order: | Ranunculales <> Family: | Berberidaceae <> Genus: | Achlys DC. <> Species <> 2 or 3 - see text | | | Achlys (plant) | Developer(s) | Capcom <> Publisher(s): | Capcom (Japan) <> Platform(s: | PlayStation 2 <> Release date | JP November 15, 2002 NA November 20, 2002 <> Genre(s)/ | Adventure game <> Mode(es) | Single player, multiplayer <> Media | DVD-ROM <> Input methods | DualShock 2 Giant's Castle | Elevation | 1,922 metres (1,923 ft) <> Location | New York, United States <> Coordinates | 41°44'00"N, 73°48'50"W <> Range | North York, New York <> Prominence | 2,944 metres (2,924 ft) | Elevation | 3,315 metres (10,877 feet) <> Location | KwaZuluNatal, South Africa <> Range | Drakensberg <> Coordinates | 29°20'S, 29°29'E <> Easiest route | scramble | | | Giant's Castle | | | | | that all types of cells have large number of type frequencies. Table 7 shows the statistics of frequencies for values in each header. Note that in Table 7, we do not take into account groups for the calculation different from the F1 in §3.1. From the table, we can understand that frequencies of values for each header have large variances. Table 8 shows the statistics for the number of cells for each table. This result indicates that tables in infoboxes have the various number of cells. Taking into account these results, we can understand that predicting cells based only on a label classification setting is difficult due to the various and diverse characteristics of the infobox tables. To strictly comply with the license, we will only release text data to the public in the dataset release. For images, we will provide their URLs and preprocessing scripts for reproducing our dataset. ## E Details Of Experimental Settings For both tasks, we modified the publicly available implementation16 by the authors of OFA. Since the released OFA uses the number of words after splitting by spaces for determining the maximum token length, we modified the OFA to use subwords to specify the maximum token length in the same way as BART. We set the maximum length for input and output in table and image generation to 1024 subwords. In addition, from the perspective of investigating the characteristics of the model and dataset, we used only maximum likelihood estimation for training and did not perform reinforcement learning. We ran training of each model three times with different seeds 0, 1, and 2. ## E.1 Table Generation To avoid an unfair comparison of BART and OFA due to different implementations, we transferred BART's weight parameters17 to OFA and ran BART on OFA. We used the hyperparameters in the summarization of OFA for generation from titles. We also used the hyperparameters in captioning of OFA for generation from images. For a fair comparison, we used the captioning settings for all inferences. When the input includes titles, we used the prompt *What is the infobox of " {ENTITY_NAME} "?*. When the input only includes images, we used the prompt *What is the infobox of the* image?. We performed the text-only experiments with four RTX 3090s in one day and the imageincluded experiments with four RTX A6000s in one day. ## E.2 Image Generation Basically, we inherited the hyperparameters used in OFA, but due to learning time, we set the beam size to 1 when generating images in the development data after each epoch in training. We used beam size 24 for testing, the same as in the original setting. We used the prompt What is the complete image? Caption: {CAPTION} to generate images. When using tables, we combined the input with the delimiter <> at the end of the original input. We performed each experiment with four RTX A6000s in two days. ## F Generated Examples F.1 Tables Table 9 shows the generated tables in the test data. In the first row regarding "Low Pike", BART generated a table for the mountain, whereas OFA generated a table for a city in the United Kingdom. This result is along with the result of the automatic evaluation that BART's prediction performance of values is better than other methods. However, even BART did not specify the detailed location of the mountain. This result indicates the difficulty of storing large amounts of geographic information in a pre-trained model. In the second row regarding "Ferruginous Pygmy-owl", BART wrongly recognized it as a bunting ("Emberizidae"), at least a bird, and OFA wrongly recognized it as a pterosaur ("Pterodactylidae"). Thus, this is a case that the forgotten knowl17https://dl.fbaipublicfiles.com/fairseq/ models/bart.base.tar.gz (MIT License). edge about the entity was not completed with the image. In the third row regarding "Achlys (plant)", both models recognized it as a plant ("Plantae"), and OFA precisely predicted its division as "Magnoliopsida" by the image. However, both models could not predict further details. This result indicates the difficulty of identifying plants with diverse species. In the fourth row regarding "Giant's Castle", BART wrongly recognized it as a video game by its misleading name, even though OFA at least recognized it as a building in New York. The result is a case that the image supports the table generation by completing the knowledge about the entity. However, this support is not enough to generate precise information. ## F.2 Images Table 10 shows the generated images in the test data. In the first row, regarding "Upper Lake (Bhopal)", we can see both settings generated images along with the caption. Since such landscape photographs do not require the depiction of details, it is clear that images can be generated without detailed knowledge. In the second row regarding "May Lake", only w/ Tab. generated a lake with the mountain corresponding to the information in the table that shows the lake is at a high place. This result indicates that the table information can support generating images based on correct knowledge. In the third row regarding "Littoral Rockthrush", we can see that both w/ Tab. and w/o Tab. struggled to generate bird images. However, even in this difficult situation, w/ Tab. generated a more precise image than w/o Tab. by using the table information. This result is along with our automatic evaluation results that table information can improve image generation performances. In the fourth row regarding "Gießen (region)", we can understand from this result that using a table alone is insufficient to generate precise images of geographic information. We can see interesting results in the fifth row regarding "Giant's Castle", which is a mountain. Both w/o Tab. and w/ Tab. wrongly generated large castles due to the misleading name "Giant's Castle". Furthermore, w/ Tab. generated a large castle that looks like a mountain based on the knowledge of 3,315 meters in the table. This result indicates a limit to disambiguation based solely on the table. ![11_image_0.png](11_image_0.png) Table 10: Generated images. w/ Tab denotes the setting with tables, w/o Tab denotes the setting without tables, and Ref. denotes the reference images. Title: Upper Lake (Bhopal) Caption: Upper Lake (Bhopal) - Sunset Table: Location | Madhya Pradesh, Bhopal <> Primary inflows | Kolans River <> Catchment area | 361 km² <> Basin countries | India <> Surface area | 31 km² Title: May Lake Caption: May Lake - View from the trail up Mt. Hoffman. Table: Location | Yosemite National Park, California <> Coordinates | 37°50'50"N, 119°29'37"WCoordinates: 37°50'50"N, 119°29'37"W <> Basin countries | United States <> Surface elevation | 9,270 ft (2,830 m) Title: Littoral Rock-thrush Caption: Littoral Rockthrush, M. imerinus Table: Conservation status <> Least Concern <> Scientific classification <> Kingdom: Animalia Phylum: Chordata Class: Aves Order: Passeriformes Family: Muscicapidae Genus: Monticola Species: M. imerinus <> Kingdom: | Animalia <> Phylum: | Chordata <> Class: | Aves <> Order: | Passeriformes <> Family: | Muscicapidae <> Genus: | Monticola <> Species: | M. imerinus <> Binomial name <> Monticola imerinus (Hartlaub, 1860, St Augustine Bay, southeast Madagascar) Title: Gießen (region) Caption: Map of Hesse highlighting the Regierungsbezirk of Gießen Table: State | Hesse <> District seat | Gießen <> Area | 5,381.14 km² <> Population | 1,061,444 (30 Sep. 2005) <> Pop. density | 197 /km² <> Web page | www.rp-giessen.de Title: Giant's Castle Caption: Panorama at Giant's Castle Table: Elevation | 3,315 metres (10,877 feet) <> Location | KwaZulu-Natal, South Africa <> Range | Drakensberg <> Coordinates | 29°20'S, 29°29'E <> Easiest route | scramble

lou-tu-2023-improving

Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints

https://aclanthology.org/2023.acl-short.163

Neural QCFG is a grammar-based sequence-to-sequence model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suffers from expensive inference. In this paper, we study two low-rank variants of Neural QCFG for faster inference with different trade-offs between efficiency and expressiveness. Furthermore, utilizing the symbolic interface provided by the grammar, we introduce two soft constraints over tree hierarchy and source coverage. We experiment with various datasets and find that our models outperform vanilla Neural QCFG in most settings.

# Improving Grammar-Based Sequence-To-Sequence Modeling With Decomposition And Constraints ## Chao Lou, Kewei Tu∗ School of Information Science and Technology, ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging {louchao,tukw}@shanghaitech.edu.cn ## Abstract Neural QCFG is a grammar-based sequence-tosequence (seq2seq) model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suffers from expensive inference. In this paper, we study two low-rank variants of Neural QCFG for faster inference with different trade-offs between efficiency and expressiveness. Furthermore, utilizing the symbolic interface provided by the grammar, we introduce two soft constraints over tree hierarchy and source coverage. We experiment with various datasets and find that our models outperform vanilla Neural QCFG in most settings. ## 1 Introduction Standard neural seq2seq models are versatile and broadly applicable due to its approach of factoring the output distribution into distributions over the next words based on previously generated words and the input (Sutskever et al., 2014; Gehring et al., 2017; Devlin et al., 2019). Despite showing promise in approximating complex output distributions, these models often fail when it comes to diagnostic tasks involving compositional generalization (Lake and Baroni, 2018; Bahdanau et al., 2019; Loula et al., 2018), possibly attributed to a lack of inductive biases for the hierarchical structures of sequences (e.g., syntactic structures), leading to models overfitting to surface clues. In contrast to neural seq2seq models, traditional grammar-based models incorporate strong inductive biases to hierarchical structures but suffer from low coverage and the hardness of scaling up (Wong and Mooney, 2006; Bos, 2008). To benefit from both of these approaches, blending traditional methods and neural networks has been studied (Herzig and Berant, 2021; Shaw et al., 2021; Wang et al., 2021, 2022). In particular, Kim (2021) proposes ∗Corresponding Author Neural QCFG for seq2seq learning with a quasisynchronous context-free grammar (QCFG) (Smith and Eisner, 2006) that is parameterized by neural networks. The symbolic nature of Neural QCFG makes it interpretable and easy to impose constraints for stronger inductive bias, which leads to improvements in empirical experiments. However, all these advantages come at the cost of high time complexity and memory requirement, meaning that the model and data size is restricted, which leads to a decrease in text generation performance and limited application scenarios. In this work, we first study low-rank variants of Neural QCFG for faster inference and lower memory footprint based on tensor rank decomposition (Rabanser et al., 2017), which is inspired by recent work on low-rank structured models (Cohen et al., 2013; Chiu et al., 2021; Yang et al., 2021, 2022). These variants allow us to use more symbols in Neural QCFG, which has been shown to be beneficial for structured latent variable models (Buhai et al., 2020; Chiu and Rush, 2020; Yang et al., 2021, 2022). Specifically, we study two low-rank variants with different trade-off between computation cost and ranges of allowed constraints: the efficient model (E model), following the decomposition method in TN-PCFG (Yang et al., 2021), and the expressive model (P model), newly introduced in this paper. Furthermore, we propose two new constraints for Neural QCFG, including a soft version of the tree hierarchy constraint used by vanilla Neural QCFG, and a coverage constraint which biases models in favour of translating all source tree nodes1. We conduct experiments on three datasets and our models outperform vanilla Neural QCFG in most settings. Our code is available at https://github.com/LouChao98/seq2seq_with_qcfg. ## 2 Preliminary: Neural Qcfg Let s1, s2 be the source and target sequences, and t1, t2 be the corresponding constituency parse trees (i.e., sets of labeled spans). Following previous work (Smith and Eisner, 2006; Kim, 2021), we consider QCFG in Chomsky normal form (CNF; Chomsky, 1959) with restricted alignments, which can be denoted as a tuple G[t1] = (S, N ,P, Σ, R[t1], θ), where S is the start symbol, N /P/Σ are the sets of nonterminals/preterminals/terminals respectively, R[t1] is the set of grammar rules in three forms: $$\begin{array}{r l}{{S\to A[\alpha_{i}]}}&{{\mathrm{where}\ A\in{\mathcal{N}},\ \alpha_{i}\in t_{1},}}\\ {{A[\alpha_{i}]\to B[\alpha_{j}]C[\alpha_{k}]\ \mathrm{where}}}\\ {{\qquad A\in{\mathcal{N}},\ B,C\in{\mathcal{N}}\cup{\mathcal{P}},\ \alpha_{i},\alpha_{j},\alpha_{k}\in t_{1},}}\\ {{D[\alpha_{i}]\to w}}&{{\mathrm{where}\ A\in{\mathcal{P}},\ \alpha_{i}\in t_{1},\ w\in\Sigma,}}\end{array}$$ and θ parameterizes rule probablities pθ(r) for each r ∈ R[t1]. Recently, Kim (2021) proposes Neural QCFG for seq2seq learning. He uses a source-side parser to model p(t1|s1) and a QCFG to model p(t2|t1). The log marginal likelihood of the target sequence s2 is defined as follows: $$\begin{array}{r l}{{}}&{{}\log p_{\theta,\phi}(s_{2}|s_{1})}\\ {{}}&{{}={}}&{{}\log\sum_{t_{1}\in{\mathcal{T}}(s_{1})}p_{\theta}(s_{2}|t_{1})p_{\phi}(t_{1}|s_{1})}\\ {{}}&{{}={}}&{{}\log\sum_{t_{1}\in{\mathcal{T}}(s_{1})}\sum_{t_{2}\in{\mathcal{T}}(s_{2})}p_{\theta}(t_{2}|t_{1})p_{\phi}(t_{1}|s_{1}),}\end{array}$$ where T (·) denotes the set of possible parse trees for a sequence and *θ, ϕ* are parameters. Due to the difficulty of marginalizing out t1 and t2 simultaneously, Kim (2021) resorts to maximizing the lower bound on the log marginal likelihood, $$\log p_{\theta,\phi}(s_{2}|s_{1})\geq\mathbb{E}_{t_{1}\sim p_{\phi}(t_{1}|s_{1})}\left[\log p_{\theta}(s_{2}|t_{1})\right].$$ ## 3 Low-Rank Models Marginalizing t2 in Neural QCFG has a high time complexity of O(|N |(|N | + |P|) 2S 3T 3) where S/T are the source/target sequence lengths. In particular, the number of rules in QCFG contributes to a significant proportion, O(|N |(|N |+|P|) 2S 3), of the complexity. Below, we try to reduce this complexity by rule decompositions in two ways. ![1_image_0.png](1_image_0.png) ## 3.1 Efficient Model (E Model) Let R be a new set of symbols. The E model decomposes binary rules rb into three parts: A[αi] → R, R → B[αj ] and R → C[αk] (Fig. 1a), where R ∈ R such that $$\begin{array}{l}{{p(A[\alpha_{i}]\to B[\alpha_{j}]C[\alpha_{k}])=\sum_{R}p(A[\alpha_{i}]\to R)}}\\ {{\qquad\qquad\times p(R\to B[\alpha_{j}])\times p(R\to C[\alpha_{k}]).}}\end{array}$$ In this way, |N |(|N | + |P|) 2S 3 binary rules are reduced to only GE := (3*|N |* + 2|P|)|R|S decomposed rules, resulting in a time complexity of O(GET 3) 2for marginalizing t2. Further, the complexity can be improved to O(|R|T 3 + |R|2T 2) using rank-space dynamic programming in Yang et al. (2022) 3. However, constraints that simultaneously involve αi, αj , αk (such as the tree hierarchy constraint in vanilla Neural QCFG and those to be discussed in Sec. 4.1) can no longer be imposed because of two reasons. First, the three nodes are in separate rules and enforcing such constraints would break the separation and consequently undo the reduction of time complexity. Second, the rank-space dynamic programming algorithm prevents us from getting the posterior distribution p(αi, αj , αk|t1, s2), which is necessary for many methods of learning with constraints (e.g., Chang et al., 2008; Mann and McCallum, 2007; Ganchev et al., 2010) to work. ## 3.2 Expressive Model (P Model) In the P model, we reserve the relation among αi, αj , αk and avoid their separation, $$\begin{array}{c}{{p(A[\alpha_{i}]\to B[\alpha_{j}]C[\alpha_{k}])=}}\\ {{\sum_{R}p(A[\alpha_{i}]\to R)\times p(R,\alpha_{i}\to\alpha_{j},\alpha_{k})\times}}\\ {{p(R,\alpha_{j}\to B)\times p(R,\alpha_{k}\to C),}}\end{array}$$ as illustrated in Fig. 1b. The P model is still faster than vanilla Neural QCFG because there are only GP := |R|S 3 + (3*|N |* + 2|P|)|R|S decomposed rules, which is lower than vanilla Neural QCFG but higher than the E model. However, unlike the E model, the P model cannot benefit from rank-space dynamic programming4and has a complexity of O(|R|S 2T 3+((2|N |+|P|)|R|S+|R|S 3)T 2) for marginalizing t2 5. Rule R, αi → αj , αk is an interface for designing constraints involving αi, αj , αk. For example, by setting p(R, α1 → α2, α3) = 0 for all R ∈ R and certain αi, αj , αk, we can prohibit the generation A[α1] → B[α2]C[α3] in the original QCFG. With this interface, the P model can impose all constraints used by vanilla Neural QCFG as well as more advanced constraints introduced next section. ## 4 Constraints 4.1 Soft Tree Hierarchy Constraint Denote the distance between two tree nodes6 as d(αi, αj ) and define d(αi, αj ) = ∞ if αj is not a descendant of αi. Then, the distance of a binary rule is defined as d(r) = max(d(αi, αj ), d(αi, αk)). Neural QCFG is equipped with two hard hierarchy constraints. For A[αi] → B[αj ]C[αk], αj , αk are forced to be either descendants of αi (i.e., d(r) < ∞), or more strictly, distinct direct children of αi (i.e., d(r) = 1). However, we believe the former constraint is too loose and the latter one is too tight. Instead, we propose a soft constraint based on distances: rules with smaller d(r) are considered more plausible. Specifically, 4Below is an intuitive explanation. Assume there is only one nonterminal symbol. Then we can remove *A, B, C* because they are constants. The decomposition can be simplified to αi → *R, Rα*i → αjαk, which is equivalent to αi → αjαk, an undecomposed binary rule. The concept "rank-space" is undefined in an undecomposed PCFG. 5It is better than O(GP T 3) because we can cache some intermediate steps, as demonstrated in Cohen et al. (2013); Yang et al. (2021). Details can be found in Appx. A. 6The distance between two tree nodes is the number of edges in the shortest path from one node to another. we encode the constraint into a reward function of rules, ζ(d(r)), such that ζ(1) > ζ(2) *> . . .* and ζ(a)ζ(b) > ζ(c)ζ(d) for a + b = c + d and max(*a, b*) < max(*c, d*). A natural choice of the reward function is ζ(d(r)) := d(r)e−d(r). We optimize the expected rewards with a maximum entropy regularizer (Williams and Peng, 1991; Mnih et al., 2016), formulated as follows: $$\log\sum_{t_{2}\in{\mathcal{T}}(s_{2})}p_{\theta}(t_{2}|t_{1})\zeta(t_{2})+\tau\mathbb{H}\left(p_{\theta}(t_{2}|t_{1},s_{2})\right),$$ - $\prod_{n\in\mathbb{N}}\zeta(d(n))^{\sf T}$, $p_{\lambda}(t_2|t_1)$ where ζ(t2) = Qr∈t2 ζ(d(r))7, pθ(t2|t1, s2) = pθ(t2|t1)/Pt∈T (s2) pθ(t|t1), H represents entropy, and τ is a positive scalar. ## 4.2 Coverage Constraint Our experiments on vanilla neural QCFG show that inferred alignments could be heavily imbalanced: some source tree nodes are aligned with multiple target tree nodes, while others are never aligned. This motivates us to limit the number of alignments per source tree node with an upper bound8, u. Because the total number of alignments is fixed to |t2|, this would distribute alignments from popular source tree nodes to unpopular ones, leading to more balanced source coverage of alignments. We impose this constraint via optimizing the posterior regularization likelihood (Ganchev et al., 2010), Et1 (log pθ(s2|t1) + γ minq∈Q KL(q(t2)||pθ(t2|t1, s2))), where KL is the Kullback-Leibler divergence (KL), γ is a positive scalar and Q is the constraint set {q(t2)|Eq(t)ϕ(t) ≤ ξ}, i.e., expectation of feature vector ϕ over any distribution in Q is bounded by constant vector ξ. We define the target tree feature vector ϕ(t2) ∈ N|t1|such that ϕi(t2) represents the count of source tree node αi being aligned by nodes in t2 and ξ = u1. Ganchev et al. (2010) provide an efficient algorithm for finding the optimum q, which we briefly review in Appx. C. After finding q, the KL term of two tree distributions, q and pθ, can be efficiently computed using the Torch-Struct library (Rush, 2020). | Approach | Simple | Jump | A. Right | Length | Approach | nil | +H1 | +H2 | +S | +C | |--------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|--------|------------|----------|-------------------------------------|-------|-------|-------|------|------| | vNQ1 | 96.9 | 96.8 | 98.7 | 95.7 | | | | | | | | EModel | 9.01 | - | 1.2 | - | | | | | | | | PModel | 95.27 | 97.08 | 97.63 | 91.72 | Active to passive (ATP) vNQ1 − 66.2 | − | − | − | | | | vNQ2 | 71.42 | 71.56 | − | 71.62 | 73.86 | | | | | | | EModel | 73.48 | × | × | × | 74.25 | | | | | | | PModel | 75.06 | 69.88 | − | 73.11 | 75.44 | | | | | | | Adjective Emphasise (AEM) vNQ1 − 31.6 − | − | − | | | | | | | | | | vNQ2 | 28.82 | 31.52 | − | 36.77 | 30.81 | | | | | | | EModel | 28.33 | × | × | × | 28.67 | | | | | | | PModel | 31.81 | 29.14 | − | 35.91 | 30.12 | | | | | | | Verb Emphasise (VEM) vNQ1 − 31.9 | − | − | − | | | | | | | | | vNQ2 | 26.09 | 29.64 | − | 30.50 | 28.50 | | | | | | | EModel | 25.21 | × | × | × | 24.67 | | | | | | | PModel | 27.43 | 24.77 | − | 26.81 | 30.66 | | | | | | | En-Fr machine translation vNQ1 − − 26.8 | − | − | | | | | | | | | | vNQ2 | 28.63 | − | 29.10 | 30.45 | 31.87 | | | | | | | EModel | 28.93 | × | × | × | 29.33 | | | | | | | PModel | 29.27 | − | 29.76 | 30.51 | 29.69 | | | | | | | Table 1: Accuracy on the SCAN datasets. | vNQ1 is | | | | | | | | | | | vanilla Neural QCFG from Kim (2021). | vNQ1 and | | | | | | | | | | | PModel use the hard constraint d(r) < ∞. | vNQ² | | | | | | | | | | | 75 74 73 72 71 | E model |R| = 50 E model |R| = 100 E model |R| = 300 P model |R| = 50 P model |R| = 100 P model |R| = 300 | | | | | | | | | | | 8 10 12 14 16 18 32 64 128 | | | | | | | | | | | | Figure 2: BLEU-4 scores on the ATP task. No constraint | | | | | | | | | | | ## 5 Experiments We conduct experiments on the three datasets used in Kim (2021). Details can be found in Appx. D.1. ## 5.1 Scan We first evaluate our models on four splits of the SCAN dataset (Lake and Baroni, 2018). We report accuracy in Tab. 1. The P model equipped with constraints can achieve almost perfect performance similar to vanilla Neural QCFG, while the E model fails due to a lack of constraints. ## 5.2 Style Transfer And En-Fr Translation Next, we evaluate the models on the three hard transfer tasks from the StylePTB dataset (Lyu et al., 2021) and a small-scale En-Fr machine translation dataset (Lake and Baroni, 2018). Tab. 2 shows results of the models with different constraints9. Low-rank models generally achieve comparable or better performance and consume much less memory10. We can also find that the soft tree hierarchy constraint outperforms hard constraints and is very helpful when it comes to extremely small data (i.e., AEM and VEM). The coverage constraint also improves performance in most cases. ## 5.3 Analysis We study how the number of nonterminals affects performance. On our computer11, we can use at most 18/64/128 nonterminals in vanilla Neural QCFG/the P model/the E model, showing that our low-rank models are more memory-friendly than vanilla Neural QCFG. We report results in Fig. 2. There is an overall trend of improved performance with more nonterminals (with some notable exceptions). When the numbers of nonterminals are 10We report speed and memory usage briefly in Sec 5.4 and in detail in Appx. D.3. 11One NVIDIA TITIAN RTX with 24 GB memory. ![4_image_0.png](4_image_0.png) ![4_image_1.png](4_image_1.png) the same, the P model outperforms vanilla Neural QCFG consistently, showing its superior parameter efficiency. In contrast, the E model is defeated by vanilla QCFG and the P model in many cases, showing the potential harm of separating αi, αj , αk. ## 5.4 Speed Comparison We benchmark speed and memory usage using synthetic datasets with different sequence lengths. Fig. 3 and 4 illustrate the results. Compared to the standard Neural QCFG, the E model and P model are significantly faster and have a lower memory footprint. This enables them to model longer sequences effectively. For data construction and more results, please refer to Appx. D.3. ## 6 Conclusion We have presented two low-rank variants of Neural QCFG based on decomposition for efficiency and two new constraints over tree hierarchy and source coverage. Experiments on three datasets validate the effectiveness and efficiency of our proposed models and constraints. ## 7 Limitations First, unlike decoders in neural seq2seq models, which can attend to any previously generated tokens, QCFGs have a strong context-free independence assumption during generation. With this assumption, Neural QCFG cannot model some complex distributions. A potential solution is to use stronger grammars, such as RNNG (Dyer et al., 2016) and Transformer Grammars (TG; Sartran et al., 2022). Second, we assume that both the grammars used by the source-side parser and QCFG are in CNF. Although it is convenient for discussion and implementation, CNF does not suit for modeling the structure of practical sequences. In semantic representations (e.g., Abstract Meaning Representation (Banarescu et al., 2013)), a predicate could have more than two arguments. Ideally, we should represent n-ary predicates with n-ary rules. However, for grammars in CNF, n − 1 unnatural binary rules are required to represent n-ary predicates. In natural language, we will face semantically meaningless spans due to CNF, which is discussed in Sec 4.2. Third, although using decomposition improves the speed and the memory requirement, our lowrank models still cost much more computation resources than neural seq2seq models for two main reasons. (1) A large amount of nonterminal symbols increase the memory cost significantly. (2) Because finding the most probable string t2 from pθ(t2|t1) is NP-hard (Sima'an, 1996; Lyngsø and Pedersen, 2002), we follow Kim (2021) to use a decoding strategy with heavy sampling. For real data, we may need to sample hundreds or thousands of sequences and then rank them, which can be much slower than the decoding of neural seq2seq models. ## Acknowledgments We thank the anonymous reviewers for their constructive comments. 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Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1556– 1566, Beijing, China. Association for Computational Linguistics. Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016. Modeling coverage for neural machine translation. In *Proceedings of the 54th Annual Meeting of the Association for Computational* Linguistics (Volume 1: Long Papers), pages 76–85, Berlin, Germany. Association for Computational Linguistics. Bailin Wang, Mirella Lapata, and Ivan Titov. 2021. Structured reordering for modeling latent alignments in sequence transduction. In Thirty-Fifth Conference on Neural Information Processing Systems. Bailin Wang, Ivan Titov, Jacob Andreas, and Yoon Kim. 2022. 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PCFGs can do better: Inducing probabilistic contextfree grammars with many symbols. In *Proceedings* of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1487–1498, Online. Association for Computational Linguistics. Xiaodan Zhu, Parinaz Sobihani, and Hongyu Guo. 2015. Long short-term memory over recursive structures. In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of *Proceedings of* Machine Learning Research, pages 1604–1612, Lille, France. PMLR. ## A Time Complexity Of P Model Let βij , βjk ∈ R*|N |×|*t1| be two cells in the chart of the dynamic programming. βij (*x, y*) denotes indexing into the matrix. Denote A[α1] → B[α2]C[α3] as rb. The state transition equation is $$\beta_{i k}(A,\alpha_{1})=\sum_{\begin{array}{c}{{j,B,C}}\\ {{\alpha_{2},\alpha_{3}}}\end{array}}p(r_{b})\beta_{i j}(B,\alpha_{2})\beta_{j k}(C,\alpha_{3}).$$ Let's define following terms: $$\begin{array}{c}{{\tilde{\beta}_{i j}(R,\alpha_{2})=\sum_{B}p(R,\alpha_{2}\to B)\beta_{i j}(B,\alpha_{2})}}\\ {{\tilde{\beta}_{j k}(R,\alpha_{3})=\sum_{C}p(R,\alpha_{3}\to C)\beta_{i j}(C,\alpha_{3})}}\\ {{\hat{p}=p(A[\alpha_{1}]\to R)p(R,\alpha_{1}\to\alpha_{2},\alpha_{3})}}\end{array}$$ Then the state transition equation can be reformulated as: $$\beta_{i k}(A,\alpha_{1})=\sum_{R,\alpha_{2},\alpha_{3}}\hat{p}\underbrace{\sum_{j}\hat{\beta}_{i j}(R,\alpha_{2})\hat{\beta}_{j k}(R,\alpha_{3})}_{\hat{\beta}_{i k}},$$ where βˆij ∈ R|R|×|t1|×|t1|. We can compute β˜ij in O((|N | + |P|)|R|S) and cache it for composing βˆij . Then βˆik can be computed in O(|R|S 2T). Finally, we can compute βik in O(|R|S 3 +*|N ||R|*S) by sum out α2, α3 first: $$\begin{array}{l}{{\beta_{i k}(A,\alpha_{1})=}}\\ {{\sum_{R}p(A[\alpha_{1}]\to R)\sum_{\alpha_{2},\alpha_{3}}p(R,\alpha_{1}\to\alpha_{2},\alpha_{3})\hat{\beta}_{i k}}}\end{array}$$ So, summing terms of all the above steps and counting the iteration over *i, k*, we will get O(|R|S 2T 3 + ((2|N | + |P|)|R|S + |R|S 3)T 2). ## B Neural Parameterization We mainly follow (Kim, 2021) to parameterize the new decomposed rules. First, we add embeddings of terms on the same side together. For example, we do two additions elhs = eR + eαi and erhs = eαj+eαk for R, αi → αj , αk, where ex denotes the embedding of x. Note that we use the same feedforward layer f as (Kim, 2021) to obtain ex from some feature hx. i.e. ex = f(hx). Then, we compute the inner products of embeddings obtained in the previous step as unnormalized scores. For example, p(R, αi → αj , αk) ∝ exp(e⊤ lhserhs). ## C Posterior Regularization The problem minq∈Q KL(q(t2)||p(t2|t1, s2)) has the optimal solution $$q^{*}=\frac{1}{Z(\lambda^{*})}p(t_{2}|t_{1},s_{2})\exp\{-\lambda^{*}\phi(t_{2})\},$$ where $$Z(\lambda^{*})=\sum_{t_{2}}p(t_{2}|s_{1},t_{1})\exp\{-\lambda^{*}\phi(t_{2})\}$$ and $\lambda^{*}$ is the solution of the dual problem: max λ≥0 −b · λ − log Z(λ) We can reuse the inside algorithm to compute Z(λ∗) efficiently because our ϕ(t) can be factored as p(t2|t1, s2): $$\begin{array}{c}{{p(t_{2}|t_{1},s_{2})=\prod_{r\in t_{2}}p_{\theta}(r)}}\\ {{\phi(t)=\sum_{r\in t_{2}}\phi(r,t_{1}),}}\end{array}$$ where ϕ(*r, t*1) = 1 if t1 is in the left-hand side of r and ϕ(*r, t*1) = 0 otherwise. Then, the solution q∗ can be written as $$q^{*}(t_{2})\propto\prod_{r\in t_{2}}p_{\theta}(r)\exp\{-\lambda\phi(r,t_{1})\}.$$ Recall that we define ϕ(t) to be the counts of source nodes being aligned by nodes in t. We can factor ϕ(t) in terms of r because each target tree non-leaf node invokes exactly one rule and only occurs on the left-hand side of that rule. So, the sum over r is equivalent to the sum over target tree nodes. ## D Experiments D.1 Experimental Details We implement vNQ2, the E model, and the P model using our own codebase. We inherit almost all hyperparameters of Kim (2021) and a basic constraint: the target tree leaves/non-leaf nodes can only be aligned to source tree leaves/non-leaf nodes, and especially, the target tree root can only be aligned to the source tree root. One major difference is that, in our experiments, we do not use early-stopping and run fixed optimization steps, which are much more than the value set in Kim (2021) (i.e., 15). It is because in preliminary experiments12, we found that the task metric (e.g., BLEU) almost always get improved consistently with the process of training, while the lowest perplexity occurs typically at an early stage (which is the criteria of early-stopping in Kim (2021)), and computing task metric is very expensive for Neural QCFGs. We report metrics on test sets averaged over three runs on all datasets except for SCAN. As mentioned in the code of Kim (2021), we need to run several times to achieve good performance on SCAN. Therefore, we report the maximum accuracy in twenty runs. SCAN (Lake and Baroni, 2018) is a diagnostic dataset containing translations from English commands to machine actions. We conduct experiments on four splits: We evaluate our models on four splits of the SCAN (Lake and Baroni, 2018) dataset: simple, add primitive (jump), *add template (around right)* and *length*. The latter three splits are designed for evaluating compositional generalization. Following (Kim, 2021), we set |N | = 10, |P| = 1. StylePTB (Lyu et al., 2021) is a text style tranfer dataset built based on Penn Treebank (PTB; Marcus et al., 1993). Following Kim (2021), we conduct experiments on three hard transfer tasks: textitactive to passive (2808 examples), *adjective* emphasis (696 examples) and *verb emphasis* (1201 examples). According to Tab. 2, we set *|N |* = |P| = 32, |R| = 100 for the E model and set |N | = |P| = 64, |R| = 100 for the P model. En-Fr MT (Lake and Baroni, 2018) is a smallscale machine translation dataset. We use the same split as Kim (2021). The size of training/validate/test set is 6073/631/583. We set |N | = |P| = 32, |R| = 100 for the E model and |N | = |P| = 32, |R| = 196 for the P model. 12We run 100 epochs and evaluate task metrics on validation sets every 5 epochs. ## D.2 Tune Hyperparameter We tune hyperparameters according to metrics on validation sets, either manually or with the Bayesian Optimization and Hyperband (BOHB) search algorithm (Falkner et al., 2018) built in the wandb library. First, we tune |N |, |P|, |R| and the learning rate of parameters for parameterizing QCFG. We freeze hyperparameters related to the source-side parser, the contextual encoder (i.e., LSTM), and the TreeLSTM (Tai et al., 2015; Zhu et al., 2015). For the ATP task from StylePTB, we run the grid search to plot Fig. 2 and choose the best hyperparameters. For other tasks, we run about 20 trials according to BOHB for each manually set search range. Typically, the size of a search range is 256 (four choices for each tunable hyperparameter). Next, we tune the strength of the coverage constraint for all models by running with γ = 0.5, 1, 2. ## D.3 Speed And Memory Usage Comparison Tab. 3 shows the time and memory usage on synthetic datasets. Each synthetic dataset contains 1000 pairs of random sequences with the same length sampled from a vocabulary with size 5000, i.e., {(s1, s2)1, . . .(s1, s2)1000}, s1, s2 ∈ Σ v, |Σ| = 5000 where v is the length. We set |N | = |P| = 8 for vanilla Neural QCFG and |N | = *|N |* = 50, |R| = 200 for others. We train models on a computer with an NVIDIA GeForce RTX3090. Note that we disable the copy mechanism in Kim (2021) because of its complicated effects on memory usage, such that the results differ from Fig. 2 (in which models enable the copy mechanism). | v | Approach | Constraint | Batch size | Time (s) | GPU Memory (GB) | |-------------|------------|--------------|--------------|------------|-------------------| | nil | 8 | 25.6 | 1.42 | | | | +H1 | 8 | 25.5 | 1.43 | | | | +H2 | 8 | 113.8 | 7.67 | | | | +S | 8 | 60.5 | 2.46 | | | | +C | 8 | 132.7 | 3.08 | | | | vNQ2 EModel | nil | 8 | 20.1 | 1.59 | | | +C | 8 | 40.4 | 1.59 | | | | 10 | nil | 8 | 30.7 | 3.78 | | | +H1 | 8 | 31.3 | 3.79 | | | | +H2 | 8 | 64.0 | 6.41 | | | | +S | 8 | 45.8 | 4.08 | | | | +C | 8 | 73.9 | 4.02 | | | | PModel | nil | 8 | 341.2 | 14.49 | | | +H1 | 8 | 342.4 | 14.60 | | | | +H2 | 1 | ≈16539.4 | 14.13 | | | | +S | 2 | ≈1734.4 | 8.93 | | | | +C | 2 | ≈4657.1 | 12.24 | | | | EModel | nil | 8 | 40.0 | 4.58 | | | +C | 4 | 173.4 | 14.48 | | | | vNQ2 | | | | | | | 20 | nil | 8 | 111.3 | 8.25 | | | +H1 | 8 | 110.8 | 8.29 | | | | +H2 | 4 | 452.3 | 9.83 | | | | +S | 8 | 269.8 | 18.76 | | | | +C | 4 | 643.5 | 18.20 | | | | PModel vNQ2 | 1 | × | × | | | | EModel | nil | 8 | 82.5 | 14.95 | | | +C | 8 | 177.0 | 14.95 | | | | nil | 4 | ≈2102.7 | 16.78 | | | | +H1 | 4 | ≈2097.6 | 16.96 | | | | +H2 | 1 | × | × | | | | +S | 2 | ≈2729.3 | 10.63 | | | | +C | 1 | × | × | | | | 40 | PModel | | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7 ✗ A2. Did you discuss any potential risks of your work? We cannot see any potential risk. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract; 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 5 ✓ B1. Did you cite the creators of artifacts you used? 5 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? All of them have been well-known and publicly available for a long time. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 5 ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? They have been well-studied. We follow previous work to conduct experiments on them. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appx. D.1 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appx. D.1 ## C ✓ **Did You Run Computational Experiments?** 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 5; D The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? D ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? D.1 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 5 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ai-etal-2023-tecs

{T}e{CS}: A Dataset and Benchmark for Tense Consistency of Machine Translation

https://aclanthology.org/2023.acl-short.164

Tense inconsistency frequently occurs in machine translation. However, there are few criteria to assess the model{'}s mastery of tense prediction from a linguistic perspective. In this paper, we present a parallel tense test set, containing French-English 552 utterances. We also introduce a corresponding benchmark, tense prediction accuracy. With the tense test set and the benchmark, researchers are able to measure the tense consistency performance of machine translation systems for the first time.

## Tecs: A Dataset And Benchmark For Tense Consistency Of Machine Translation Yiming Ai, Zhiwei He, Kai Yu, And Rui Wang∗ Shanghai Jiao Tong University {Aiyiming, Zwhe.Cs, Kai.Yu, Wangrui12}@Sjtu.Edu.Cn Abstract Tense inconsistency frequently occurs in machine translation. However, there are few criteria to assess the model's mastery of tense prediction from a linguistic perspective. In this paper, we present a parallel tense test set, containing French-English 552 utterances1. We also introduce a corresponding benchmark, tense prediction accuracy. With the tense test set and the benchmark, researchers are able to measure the tense consistency performance of machine translation systems for the first time. ## 1 Introduction Translation tools are often found in a variety of social situations to enable cross-linguistic communication. Tenses are used to express time relative to the moment of speaking. Human translators frequently pay close attention to tense correspondence (Gagne and Wilton-Godberfforde, 2020). Similarly, machine translation (MT) systems are supposed to maintain temporal consistency between the original text and the predicted text to avoid misunderstandings by users. However, accurately keeping the tense consistency is undoubtedly difficult. Taking French-English (one of the most classic language pairs for MT) as an example in Table 1, the original text is in *plus-que-parfait de l'indicatif* of French, corresponding to the *past perfect* tense in English, while the English prediction provided by Google Translator is in the *past simple* tense. In fact, this is not an isolated case. You can also find several examples in Appendix B. Besides. the translation mechanics may not the only reason leading to tense inconsistency. The corpora matter as well. For example, we have extracted 20,000 pairs English-French parellel sentences from the widely used dataset Europarl (Koehn, 2005), and ∗Corresponding author 1The following updates will be shown at: https://github.com/rutilel/ TeCS-A-Dataset-and-Benchmark-for-Tense-Consistency. | Sentence | Tense | |--------------------------------------------------------------------------------------------------|--------------| | FR: Mais on les avait votés lors de la dernière période de session. | Plus-queparfait | | EN: But we voted on them | Past simple | | during the last part-session. Correction: But we had voted on them during the last part-session. | Past perfect | Table 1: An example of tense corrspondence in machine translation we have observed all groups of parallel utterances where the original French texts are in the *plus-queparfait de l'indicatif* tense, examining the tenses of their English counterparts. As a sentence may include several tenses, there are 195 occurences of plus-que-parfait tense in total. Among them, only 35.28% English sentences are in the correct past perfect tense, as shown in Table 2. Although, compared to other tense correspondences, the pair of plus-que-parfait and *past-perfect* is prone to error in datasets and there are only 0.94% of sentences in Europarl are in plus-que-parfait, we cannot easily ignore this issue. Like Europarl, tense correspondences are generally credible but unreasonable for certain tenses in several common datasets. | Tense of Counterpart | Proportion | |------------------------|--------------| | Past perfect (correct) | 35.28% | | Past simple | 54.46% | | Present perfect | 8.21% | | Present | 2.05% | Table 2: Preliminary statistics of translation tense In addition to the train set, the difficulty of remaining tense consistency also stems from the lack of metrics on measuring the model's mastery of tense information. The research of Marie et al. French Tenses English Tense Format **Example** Imparfait, Passé composé, Passé simple, Passé récent Past simple / progressive *Past* That was the third point. Présent, Future proche Present simple / progressive *Present* The world **is changing**. Future simple, Future proche Future simple / progressive *Future* I **will communicate** it to the Council. Plus-que-parfait Past perfect *PasPerfect* His participation **had been notified**. Passé composé Present perfect *Preperfect* This phenomenon **has become** a major threat. Future antérieur Future perfect *Futperfect* We **will have finished** it at that time. Subjonctif, Conditionnel including Modal verbs *Modal* We **should be** less rigid. (2021) shows that 98.8% of *ACL papers2in the field of MT from 2010 to 2020 used BLEU (Papineni et al., 2002) scores to evaluate their models. However, the reliability of BLEU has been questioned in the era of neural machine translation (NMT) as its variants only assess surface linguistic features (Shterionov et al., 2018), and many studies have shown that BLEU has difficulty in portraying the degree of semantic information mastered by the model, i.e. its score does not necessarily improve when more semantic information is mastered (Mathur et al., 2020; He et al., 2023), not to mention specific tense information. We have also applied BLEU to measure various baselines on our tense test set in Section 4, and the results explicitly support the above statement. In addition, reviewing the evaluation criteria related to MT tasks over the past ten years, we are surprised to find that there are no criteria to assess the model's mastery of tense prediction from a linguistic perspective. Therefore, our paper is devoted to the study of NMT based on semantic understanding in terms of tense. We construct a tense parallel corpus test set consisting of 552 pairs of tense-rich, error-prone parallel utterances for NMT systems, and then propose a new task for evaluating the effectiveness of model translations from the perspective of tense consistency. This paper makes three contributions: (1) the presentation of the construction of the tense test set, including its tense labels; (2) the proposal of a feasible and reproducible benchmark for measuring the tense consistency performance of NMT systems; and (3) the various experiments for different baselines with the above test set and corresponding benchmark. ## 2 Annotation Rules And Tools As the first work of the MT tense study, we choose English-French, one of the most classic language pairs of MT, to construct the dataset3. TENSE, the dominant topic of our research, is a combination of tense and aspect. In the modern grammar system of English, "a tense system is a system associated with the verb where the basic contrasts in meaning have to do with the location in time of the situation, or the part of it under consideration" (Huddleston et al., 2021). The modern grammatical system divides tense into present and preterit based on the inflections added to the end of verbs, and the aspect into perfective and progressive on the state where an action is (Kamp, 1991). While this tense classification system is too crude for daily life, we therefore apply the following classification methods. On the one hand, we classify the tenses according to the macro-temporal interval of the action into three major time intervals, namely present, past and future tenses; on the other hand, we classify the tenses according to the state of the action into general, progressive and perfect aspects. Hence, 9 kinds of tenses are born through combining the three tenses and the three aspects. French and English belong to the same IndoEuropean language family and share many similarities in various respects. The main difference is that in French there is another grammatical point called mode, part of which is like the *aspect* in English. In terms of tenses, we will generally discuss the tenses in the indicative mode of French and will describe the others later in this section. In the following, if there is no mode qualifier before a tense, it is by default in the indicative mode. Careful identification and comparison of the subdivided tenses in the three main tense intervals, English and French, reveals a very similar usage of the tenses, as sum3Please refer to the Limitations for more details. marised in Table 3. As there is no progressive tense in French, we do not distinguish the progressive tense in English, but rather merge the progressive tense into its corresponding base tense, e.g. the present perfect progressive tense into the category of the present perfect tense. When discussing tenses from a semantic point of view, the modes also need to be taken into account. The grammatical correlations between French and English modes are quite complicated. Considering the corresponding grammatical expressions of 2 modes strongly related to tense, *conditionnel* and subjonctif, in French rely on the usage of modal verbs, we introduce *modal verbs* to simplify the distinguishment of the modes. Based on these grammatical rules, we merge the nine common tenses in English into seven categories that correspond reasonably and rigorously to French, namely the 6 tense categories of past/present/future + simple/perfect and statements containing *modal* verbs that correspond to the French *subjonctif* and *conditionnel* tenses. We construct an automatic annotation method based on the spaCy package (Honnibal et al., 2020). First, we label the grammatical components of each word in the sentence based on the spaCy package, and then we define and compare the grammatical structures of the verb phrases with the structures of each tense classification to derive the sentence tense labels. During this process, to simplify the annotation process and better correspond with French futur proche tense, we classify the expression 'be going to do', grammatically in Future tense, into the Present tense, just like expressions 'be about to do' and '*be + verb progressive*', whose stucture are in *Present* tense but the real meaning is about the close future. Also, a sentence may have several tense structures, in this case, the tense label consists several tenses. For example, the label of the sentence 'So it is in that spirit that we have made this change.' is '*Present+PrePerfect*'. ## 3 Corpus Design And Characteristics 3.1 Corpus Design We choose the tense-rich Europarl, namely EuroparlPV, processed by Loáiciga et al. (2014) as the source corpus, for it contains all the sentences with predicate verb structures in the original Europarl dataset (Koehn, 2005). First, we cleaned the source corpus, including deleting sentences without counterparts, English sentences in the French | Classfication | Times | Proportion | |-----------------|---------|--------------| | Past | 101 | 12.95% | | Present | 444 | 56.92% | | Future | 56 | 7.18% | | Past perfect | 22 | 2.82% | | Present perfect | 43 | 5.52% | | Future perfect | 10 | 1.28% | | Modal | 104 | 13.33% | part and vice versa. After this, we obtain 201,374 tense-rich parallel French-English sentence pairs, namely EuroparlTR. We randomly divided them into a training set, a validation set and a test set in the ratio of 8:1:1, and trained a transformer baseline based on this using fairseq (Ott et al., 2019) with a BLEU value of 33.41. Then we compared a total of 20,000 parallel sentences' triples (original Europarl French text, original Europarl English text, transformer English prediction). In the construction process, with the code mentioned in Section 2, we first automatically annotated the original English text and English prediction in the 20,000 pairs of parallel utterances, given the corresponding tense labels. Then, we filtered 6,779 parallel French-English sentence triples with different tense labels for English originals and predictions. On the basis of the automatic selection, we manually screened out the representative parallel French-English sentence pairs with a certain degree of translation difficulty and a complex grammatical structure. We also corrected the reference translations that did not justify the tense or semantics. It is worth noting that the author has a level of English and French that meets the C1 standard of The Common European Framework of Reference for Languages (CEFR), representing the ability to express herself effectively and flexibly in English and French in social, academic and work situations. A total of 570 parallel pairs of statements were selected at this stage. Following this, two other reviewers at CEFR C1 level, reviewed the tense test set for semantic and tense correspondence, and the tense labels marked by the automatic annotation code. The tense test set was further refined. The final test set contains 552 parallel French-English sentence pairs. You can see more details in Appendix D. | System | Tense set | Europarl testset | WMT15 testset | Tense | | | | |-----------------------------|-------------|--------------------|-----------------|---------|-------|--------|--------| | Accuracy | | | | | | | | | BLEU | COMET | BLEU | COMET | BLEU | COMET | | | | Transformer (tense-rich) | 47.71 | 0.631 | 27.38 | 0.269 | 14.17 | -0.429 | 66.30% | | Transformer (tense-poor) | 43.24 | 0.588 | 27.28 | 0.264 | 14.68 | -0.444 | 58.33% | | LSTM (tense-rich) | 44.21 | 0.558 | 25.53 | 0.126 | 12.04 | -0.590 | 67.75% | | LSTM (tense-poor) | 41.92 | 0.483 | 26.17 | 0.147 | 12.27 | -0.598 | 58.70% | | CNN (tense-rich) | 47.10 | 0.567 | 26.83 | 0.147 | 15.30 | -0.512 | 68.48% | | CNN (tense-poor) | 43.23 | 0.502 | 26.95 | 0.144 | 14.96 | -0.525 | 57.97% | | Bi-Transformer (tense-rich) | 47.10 | 0.632 | 28.17 | 0.295 | 14.72 | -0.392 | 64.13% | | Bi-Transformer (tense-poor) | 43.87 | 0.578 | 28.30 | 0.298 | 14.39 | -0.428 | 55.25% | | Bing Translator | 61.72 | 0.895 | - | - | - | - | 77.36% | | DeepL Translator | 59.50 | 0.904 | - | - | - | - | 79.02% | | Google Translator | 57.00 | 0.878 | - | - | - | - | 81.70% | Table 5: Experimental results of various baselines and common business translators ## 3.2 Corpus Characteristics In the following paragraphs, we describe the statistical features of our corpus and the elimination of gender coordination influence. Tense distribution. The corpus consists of 780 tense structures in 552 sentences, and the distribution of tense classifications is shown in Table 4. In the test set, sentences in present tense are the most, corresponding the situation of the reality: we use present tense most frequently and future perfect sense least frequently. Elimination of gender effect. Unlike English, gender coordination exists in French. For example, the French sentences 'Nous nous sommes donc *abstenus*.' and 'Nous nous sommes donc *abstenues*.' both correspond to the English '*We therefore abstained.*'. That is, the MT system's ability to learn gender coordination affects its ability to recognize tense structures, which in consequence affects the maintenance of tense consistency between original French text and predicted English sentence. Therefore, to better measure the tense-predicting capability of different MT systems, rather than their ability to recognize pronominal gender, we controlled for the gender variable by defaulting all pronouns, which do not indicate explicitly their genders, as masculine. These pronouns consists of 167 je (I), 114 *nous* (we, us) and 28 *vous* (you). ## 4 Experimental Results To measure the tense consistency performance of different systems, we introduce a benchmark called tense (prediction) accuracy, as shown in Eq. (1). $$\mathrm{Accuracy}={\frac{N_{c}}{N_{t}}},$$ $$(1)$$ , (1) where Nc is the number of predicted utterances with the same tense as its reference and Ntis the total number of utterances in the tense set. To verify the validity of our tense corpus, the following approach was adopted: To begin with, 100, 000 parallel utterance pairs from the EuroparlTR (containing 201, 374 pairs) mentioned in Section 3.1 were extracted as the tense-rich train set, and 100, 000 parallel utterance pairs from the Europarl corpus (Koehn, 2005) were extracted as the tense-poor train set. There were no overlapping utterances between the latter and the former. We performed the same preprocessing procedure, including data cleaning, tokenization and BPE coding. We then trained four pairs of French-English NMT systems with different architectures based on fairseq (Ott et al., 2019), where two systems in each pair differed only in the train set. After this, we summarized the scores evaluated by SacreBLEU (Post, 2018) and COMET (Rei et al., 2020) and tense prediction accuracies of the eight systems on different test sets. We have applied three types of test sets: our tense set, the Europarl test set and the WMT15 test set. The Europarl test set contains 3,000 parallel utterance pairs drawn from the Europarl corpus, the exact same field of train set, while the WMT15 is a test set for the WMT15 (Bojar et al., 2015), deriving from data in the different field of train set. Besides, we also apply our approach to mesure the tense consistency performance of several business translators, including Bing Translator, DeepL Translator and Google Translator. The results are listed in Table 5: 1) The BLEU and COMET scores based on the Europarl set and the WMT15 set are quite similar for each system pair, which indicates that the translation capabilities of the two systems are similar in the general evaluation dimension. This suggests that by relying solely on the difference in BLEU scores on traditional test sets, we are unable to measure the tense prediction ability of the systems. 2) However, there are large differences in our tense set. The tense consistency performance of systems trained on the tense-rich train set was significantly better than that of systems trained on the tense-poor train set. This indicates that our tense set can capture the tense consistency performance. 3) Further investigation of the BLEU or COMET) scores and tense prediction accuracy for each system reveals their positive correlation for the same architecture, but not across architectures. To measure the tense consistency performance across different architectures, we should focus more on tense accuracy rather than BLEU scores only. ## 5 Conclusion We presented the French-English parallel tense test set and introduced the corresponding benchmark tense prediction accuracy, providing a brand-new approach to measure the tense consistency performance of machine translation systems. This test set firstly focuses on the tense prediction ability, posing a new dimension to improve the MT quality. In the future, we will endeavour to generalize the test set to other languages. Considering there are statements like "the use of tense A in language X is equivalent or similar to the use of tense B in English" in grammar books of other languages(Durrell et al., 2015), even across language families(Gadalla, 2017) and human translators also apply such rules(Santos, 2016), we are confident in taking this forward. ## Limitations In this work, we focus on creating the EnglishFrench tense corpus. These two languages are among the most frequently and widely used languages in the world. In addition, they have several similarities in tenses, which are pretty helpful for research on tense consistency through machine translation. Thanks to the distinctive tense structures, the study of these two languages makes it possible to examine many common tense issues, but there are also some tense issues in other languages that are not covered by this study. For example, the implicit tense expressions in Chinese are difficult to correspond to the explicit tense expressions in English (Jun, 2020). Hence, our next step will be to extend the tense test set to other language families and even cross-language families to further study tense consistency. Also, as for future work, we will optimize both the tense annotation method and the tense prediction accuracy calculation. Besides, we did not propose a new method to improve the tense prediction accuracy. To be further, we will endeavour to improve the existing machine translation systems according to tense consistency. ## Acknowledgements Yiming, Zhiwei, and Rui are with MT-Lab, Department of Computer Science and Engineering, School of Electronic Information and Electrical Engineering, and also with the MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200204, China. Rui is supported by the General Program of National Natural Science Foundation of China (6217020129), Shanghai Pujiang Program (21PJ1406800), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), Beijing Academy of Artificial Intelligence (BAAI) (No. 4), CCF-Baidu Open Fund (No. CCF-BAIDU OF2022018, and the Alibaba-AIR Program (22088682). We also thank the computational resource from the SJTU student innovation center. ## Ethics Statement Our tense test set is based on the widely used public corpus Europarl in the field of machine translation. In creating this test set, we only corrected tense and description errors of some English references and did not change the original semantics, so there are no ethical issues arising. ## References Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, and Marco Turchi. 2015. Findings of the 2015 workshop on statistical machine translation. In Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 1–46, Lisbon, Portugal. Association for Computational Linguistics. Martin Durrell, Katrin Kohl, Gudrun Loftus, and Claudia Kaiser. 2015. *Essential German Grammar*. Routledge. Hassan Abdel-Shafik Hassan Gadalla. 2017. *Translating tenses in Arabic-English and English-Arabic* contexts. Cambridge Scholars Publishing. Christophe Gagne and Emilia Wilton-Godberfforde. 2020. *English-French Translation: A Practical Manual*. Routledge. Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Yujiu Yang, Rui Wang, Zhaopeng Tu, Shuming Shi, and Xing Wang. 2023. Exploring humanlike translation strategy with large language models. arXiv preprint arXiv:2305.04118. Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020. spaCy: Industrialstrength Natural Language Processing in Python. Rodney Huddleston, Geoffrey K Pullum, and Brett Reynolds. 2021. A student's introduction to English grammar. Cambridge University Press. Guo Jun. 2020. Translation principles of tense problem in machine translation in process of chinese-english translation. *Solid State Technology*, 63(4):5678– 5687. Matt Post. 2018. A call for clarity in reporting BLEU scores. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186– 191, Brussels, Belgium. Association for Computational Linguistics. Hans Kamp. 1991. Tense and aspect in english and french. *Edinburgh: DYANA*. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Machine Translation Summit X: Papers, pages 79–86, Phuket, Thailand. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondˇrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pages 177–180, Prague, Czech Republic. Association for Computational Linguistics. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In *Proceedings of the 54th Annual* Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715–1725, Berlin, Germany. Association for Computational Linguistics. Sharid Loáiciga, Thomas Meyer, and Andrei PopescuBelis. 2014. English-French verb phrase alignment in Europarl for tense translation modeling. In *Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)*, pages 674–681, Reykjavik, Iceland. European Language Resources Association (ELRA). Dimitar Shterionov, Riccardo Superbo, Pat Nagle, Laura Casanellas, Tony O'dowd, and Andy Way. 2018. Human versus automatic quality evaluation of NMT and PBSMT. *Machine Translation*, 32(3):217–235. Benjamin Marie, Atsushi Fujita, and Raphael Rubino. 2021. Scientific credibility of machine translation research: A meta-evaluation of 769 papers. In Proceedings of the 59th Annual Meeting of the Association for - Google Translator: https://translate. google.com/ as of December of 2022. ## A Online Translation Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7297–7306, Online. Association for Computational Linguistics. Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2020. Tangled up in BLEU: Reevaluating the evaluation of automatic machine translation evaluation metrics. In *Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*, pages 4984–4997, Online. Association for Computational Linguistics. Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In *Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)*, pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In *Proceedings of the* 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020. COMET: A neural framework for MT evaluation. In *Proceedings of the 2020 Conference* on Empirical Methods in Natural Language Processing (EMNLP), pages 2685–2702, Online. Association for Computational Linguistics. Diana Santos. 2016. *Translation-based corpus studies:* Contrasting English and Portuguese tense and aspect systems. Brill. - Bing Translator: https://www.bing.com/ translator as of December of 2022. - Determine whether the corresponding tense label of the English translation is correct according to the natural language understanding. - DeepL Translator: https://www.deepl. com/translator as of December of 2022. ## B Examples Of Translators' Errors Table 6 shows several translating errors of common business translators. The display form is a group of five sentences: original French sentence, corresponding English reference, Bing translation, DeepL translation, and Google translation. ## C **Examples Of Baseline Prediction Errors** And Corresponding Annotations Table 7 shows several examples of predictions and corresponding annotations of baselines in Section 4. Each group consists ten sentences, which are original French sentence, corresponding English reference, Transformer(tense-rich) prediction, Transformer(tense-poor) prediction, LSTM(tenserich) prediction, LSTM(tense-poor) prediction, CNN(tense-rich) prediction, CNN(tense-poor) prediction, Bi-Transformer(tense-rich) prediction and Bi-Transformer(tense-poor) prediction. ## D Additional Notes On Human Review D.1 Recruitment Of Human Reviewers We recruited reviewers from students majoring in French. Taking Diplôme Approfondi de Langue Française(DALF) C1 French exam results, International English Language Testing System(IELTS) exam results, and their GPA in French courses into account, we recruited 2 reviewers in the same country of the authors' at last. ## D.2 Instructions Given To Reviewers We offer the annotation rules in Section 2, and require the reviewers to accomplish the following tasks: - Determine whether the tense of the English translation is accurate and reasonable. If not, give an English translation that you consider reasonable. - Determine whether the meaning of the English translation is correct. If not, give an English translation that you consider reasonable. ## E Experimental Setup E.1 Model Table 8 provides the number of parameters, training budget, and hyperparameters of each model. All experiments were performed on a single V100 GPU and the hyperparameters are by default. We report the result of a single run for each experiment. ## E.2 Data Table 9 shows the data statistics we used in this paper. ## E.3 Packages Table 10 shows the packages we used for preprocessing, model training, evaluation and tense labeling. | Sentence | Tense | |-------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------| | Origin: On avait fait des comparaisons. Reference: We had made comparisons. | Past perfect | | Bing: Comparisons were made. | Past simple | | DeepL: Comparisons were made. | Past simple | | Google: We made comparisons. | Past simple | | Origin: Qui avait cru qu 'il serait facile de réunir l' Europe ? Reference: Who had thought that it would be easy to reunite Europe? | Past perfect+Modal | | Bing: Who thought it would be easy to bring Europe together? | Past simple+Modal | | DeepL: Who thought it would be easy to reunite Europe? | Past simple+Modal | | Google: Who thought it would be easy to reunite Europe? | Past simple+Modal | | Origin: Je pensais avoir été assez clair. Reference: I thought I had been quite clear. | Past simple+Past perfect | | Bing: I thought I was pretty clear. | Past simple+Past simple | | DeepL: I thought I had made myself clear. | Past simple+Past perfect | | Google: I thought I was clear enough. | Past simple+Past simple | | Origin: Un versement similaire avait eu lieu l 'année précédente. Reference: A similar payment had taken place in the previous year. | Past perfect | | Bing: A similar payment had taken place the previous year. | Past perfect | | DeepL: A similar payment was made the previous year. | Past simple | | Google: A similar payment had taken place the previous year. | Past perfect | | Origin: C 'est pour cela que la voie avait été tracée à Helsinki. Reference: That's why the way had been paved in Helsinki. | Present simple+Past perfect | | Bing: That is why the path was paved out in Helsinki. | Present simple+Past simple | | DeepL: That is why the way was paved in Helsinki. | Present simple+Past simple | | Google: This is why the way had been traced in Helsinki. | Present simple+Past perfect | | Origin: Je citerai pour exemple le vote à la majorité qualifiée. Reference: I will cite qualified majority voting as an example. | Future simple | | Bing: One example is qualified majority voting. | Present simple | | DeepL: An example is qualified majority voting. | Present simple | | Google: I will cite as an example qualified majority voting. | Future simple | | Origin: Nous espérons tous qu 'elle finira. Reference: We all hope that it will come to an end. | Present simple+Future simple | | Bing: We all hope that it will end. | Present simple+Future simple | | DeepL: We all hope it will end. | Present simple+Future simple | | Google: We all hope it ends. | Present simple+Present simple | | Origin: Que se passera-t-il si une nouvelle crise survient l 'année prochaine ? Reference: What will happen if a new crisis occurs next year? | Future simple+Present simple | | Bing: What will happen if a new crisis occurs next year? | Future simple+Present simple | | DeepL: What happens if there is another crisis next year? | Present simple+Present simple | | Google: What will happen if a new crisis occurs next year? | Future simple+Present simple | | Origin: Nous en avons terminé avec les explications de vote. Reference: We have finished with the explanations of vote. | Present perfect | | Bing: That concludes the explanations of vote. | Present simple | | DeepL: That concludes the explanations of vote. | Present simple | | Google: We have finished with the explanations of vote. | Present perfect | | Origin: Le fait est que le génie Internet est sorti de sa bouteille. Reference: The fact is that Internet genius has gone out of its bottle. | Present simple+Present perfect | | Bing: The fact is that the Internet genie is out of the bottle. | Present simple+Present simple | | DeepL: The fact is that the Internet genie is out of the bottle. | Present simple+Present simple | | Google: The thing is, the internet genius is out of the bottle. | Present simple+Present simple | | Origin: Je voulais simplement le mentionner puisqu 'on a cité certains pays. Reference: I just wanted to mention that because some countries have been mentioned. | Past simple+Present perfect | | Bing: I just wanted to mention this because some countries have been mentioned. | Past simple+Present perfect | | DeepL: I just wanted to mention it because some countries were mentioned. | Past simple+Past simple | | Google: I simply wanted to mention it since certain countries have been mentioned. | Past simple+Present perfect | | Origin: La dynamique de croissance et de création d 'emplois est évacuée. Reference: The dynamic of growth and job creation has run its course. | Present perfect | | Bing: The momentum for growth and job creation has been removed. | Present perfect | | DeepL: The dynamics of growth and job creation are evacuated. | Present simple | | Google: The dynamic of growth and job creation is evacuated. | Present simple | | Table 6: French-English utterances and corresponding translations by Bing, DeepL, Google translators. The words | | Table 6: French-English utterances and corresponding translations by Bing, DeepL, Google translators. The words in orange indicate the translated verbs. The tenses in blue indicate the wrong predictions. | Sentence | Tense | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------| | Origin: J 'avais considéré que Mme Lulling était une Luxembourgeoise. Reference: I had assumedthat Mrs Lulling was a Luxembourgoise. | PasPerfect+Past | | Transformer1: I believed that Mrs Lulling was a Luxembourgois. | Past+Past | | Transformer2: I considered that Mrs Lulling was a daughter. | Past+Past | | LSTM1: I thought that Mrs Lulling was a Luxembourgoof. | Past+Past | | LSTM2: I considered that Mrs Lulling was a stranglehold. | Past+Past | | CNN1: I considered that Mrs Lulling was a Luxembourgo. | Past+Past | | CNN2: In my view, Mrs Lulling was a Luxembourger. | Past+Past | | Bi-Transformer1: I thought that Mrs Lulling was a Luxembourgois. | Past+Past | | Bi-Transformer2: I thought that Mrs Lulling was a sort of Greens. | Past+Past | | Origin: Mais on les avait votés lors de la dernière période de session. Reference: However, they had been voted on at the last part-session. | PasPerfect | | Transformer1: But we voted for them at the last part-session. | Past | | Transformer2: But we voted for them at the last part-session. | Past | | LSTM1: However, we had voted in favour of the last part-session. | PasPerfect | | LSTM2: However, we had voted in the last part-session. | PasPerfect | | CNN1: But we voted in the last part-session. | Past | | CNN2: However, we voted in the last part-session. | Past | | Bi-Transformer1: But we were voting on them at the last part-session. | Past | | Bi-Transformer2: We, though, voted on them at the last part-session. | Past | | Origin: Il avait été averti par l 'association des employeurs irlandais. Reference: He had been alerted by the Irish employers' association. | PasPerfect | | Transformer1: He was told it by the Irish employers' association. | Past | | Transformer2: The Irish employers' association had warned. | PasPerfect | | LSTM1: He was told it by the Irish employers' association. | Past | | LSTM2: It was warned by the association of the Irish employers. | Past | | CNN1: He was told by the Irish employers' association. | Past | | CNN2: It was warned by the association of the Irish employers. | Past | | Bi-Transformer1: He was told it by the Irish employers' association. | Past | | Bi-Transformer2: The Irish employers' association had been notified by the Irish employers' | PasPerfect | | association. Origin: Je suis très curieux de voir ce que nous allons faire. Reference: I am very curious to see what we are going to do. | Present | | Transformer1: I am very curious to see what we are going to do. | Present | | Transformer2: I am very curious about what we are going to do. | Present | | LSTM1: I am very curious to see what we will do. | Present+Future | | LSTM2: I am very keen to see what we are going to do. | Present | | CNN1: I am very curious to see what we are going to do. | Present | | CNN2: I am very curious to see what we are going to do. | Present | | Bi-Transformer1: I am very curious to see what we are going to do. | Present | | Bi-Transformer2: I am very interested to see what we are going to do. | Present | | Origin: Nous espérons maintenant qu 'il va agir de façon énergique. Reference: We now hope that he is going to act decisively. | Present | | Transformer1: We now hope that it will act decisively. | Present+Future | | Transformer2:Let us now hope that it will act energetically. | Present+Future | | LSTM1: We now hope that it will act vigorously. | Present+Future | | LSTM2: Let us hope now that it will act energetically. | Present+Future | | CNN1: We now hope that it is going to act energetically. | Present | | CNN2: Let us hope that it is going to act vigorously. | Present | | Bi-Transformer1: We now hope that it will act vigorously. | Present+Future | | Bi-Transformer2: Let us now hope that this will take a strong stand. | Present+Future | | Origin: D'ici là, je suis sûr que nous serons passés à au moins 27 États membres. Reference: By then, I am sure we will have enlarged to at least 27 Member States. | Present+FutPerfect | | Transformer1: That is why I am sure that we will be left to at least 27 Member States. | Present+Future | | Transformer2: In this connection, I am sure we will have had at least 27 Member States. | Present+FutPerfect | | LSTM1: I am sure that we will be at least 27 Member States. | Present+Future | | LSTM2: That is why I am sure we will be at least 27 Member States. | Present+Future | | CNN1: I am sure that we will be at least 27 Member States. | Present+Future | | CNN2: That is why I am sure we will be able to pass on at least 27 Member States. | Present+Future | | Bi-Transformer1: I am sure that we will be doing so at least 27 Member States. | Present+Future | | Bi-Transformer2: I am sure that we will have at least 27 Member States. | Present+Future | | Table 7: French-English utterances and corresponding predictions by baselines mentioned in Section 4. The words | | Table 7: French-English utterances and corresponding predictions by baselines mentioned in Section 4. The words in orange indicate the translated verbs. The tenses in blue indicate the wrong predictions. | Model | # Param. | GPU Hours | Hyperparam. | | |----------------|------------|-------------|---------------|-----| | learning rate | dropout | | | | | Transformer | 83M | 0.9h | 5e-4 | 0.3 | | LSTM | 58M | 0.8h | 1e-3 | 0.2 | | CNN | 30M | 0.7h | 0.25 | 0.2 | | Bi-Transformer | 83M | 1.7h | 5e-4 | 0.3 | Table 8: The number of parameters, training budget (in GPU hours), and hyperparameters of each model. | Split | Name | # Sent. | Domain | |--------------------------------------|----------------------------------------|-----------|----------| | Train | Train set from EuroparlTR (tense-rich) | 97K | Politics | | Train set from Europarl (tense-poor) | 97K | Politics | | | Tense set | 552 | Politics | | | Test | Europarl test set | 2950 | Politics | | WMT15 test set | 3003 | News | | | Valid | Valid set from EuroparlTR (tense-rich) | 717 | Politics | Table 9: Data statistics. Training data has been filtered to avoid data leakage. | Usage | Package | License | |---------------------------------------------|-------------------------------------|------------| | Preprocessing | mosesdecoder (Koehn et al., 2007) 1 | LGPL-2.1 | | 2 | MIT | | | subword-nmt (Sennrich et al., 2016) 3 | MIT | | | Model training | fairseq (Ott et al., 2019) | | | Evaluation | SacreBLEU (Post, 2018) 4 | Apache 2.0 | | COMET (Rei et al., 2020) 5 | Apache 2.0 | | | Tense labeling | spaCy (Honnibal et al., 2020) 6 | MIT | | 1 https://github.com/moses-smt/mosesdecoder | | | 1 https://github.com/moses-smt/mosesdecoder 2 https://github.com/rsennrich/subword-nmt 3 https://github.com/facebookresearch/fairseq 4 https://github.com/mjpost/sacrebleu (nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.3.1) 5 https://github.com/Unbabel/COMET (wmt20-comet-da) 6 https://github.com/explosion/spaCy Table 10: Packages we used for preprocessing, model training, evaluation and tense labeling. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitations ✓ A2. Did you discuss any potential risks of your work? Section Limitations ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 1,2,3,4 ✓ B1. Did you cite the creators of artifacts you used? Section 1,2,3,4 and Appendix E ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 2,3,4 and Appendix E ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section Ethics Statement ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section Ethics Statement ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3 and Appendix D, E ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3,4 and Appendix E ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix E The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix E ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 and Appendix E ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 2,4 and Appendix E D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 3.1 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix D ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Appendix D ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Appendix D and Section Ethics statement ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section Ethics statement ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Appendix D

rogers-etal-2023-report

Program Chairs{'} Report on Peer Review at ACL 2023

https://aclanthology.org/2023.acl-long.report

We present a summary of the efforts to improve conference peer review that were implemented at ACL{'}23. This includes work with the goal of improving review quality, clearer workflow and decision support for the area chairs, as well as our efforts to improve paper-reviewer matching for various kinds of non- mainstream NLP work, and improve the overall incentives for all participants of the peer review process. We present analysis of the factors affecting peer review, identify the most problematic issues that the authors complained about, and provide suggestions for the future chairs. We hope that publishing such reports would (a) improve transparency in decision-making, (b) help the people new to the field to understand how the *ACL conferences work, (c) provide useful data for the future chairs and workshop organizers, and also academic work on peer review, and (d) provide useful context for the final program, as a source of information for meta-research on the structure and trajectory of the field of NLP.

# Program Chairs' Report On Peer Review At Acl 2023 Anna Rogers} Marzena Karpinska~ Jordan Boyd-Graber **Naoaki Okazaki**| }IT University of Copenhagen ~University of Massachusetts Amherst University of Maryland |Tokyo Institute of Technology [email protected] [email protected] [email protected] [email protected] ## Abstract We present a summary of the efforts to improve conference peer review that were implemented at ACL'23. This includes work with the goal of improving review quality, clearer workflow and decision support for the area chairs, as well as our efforts to improve paper-reviewer matching for various kinds of nonmainstream NLP work, and improve the overall incentives for all participants of the peer review process. We present analysis of the factors affecting peer review, identify the most problematic issues that the authors complained about, and provide suggestions for the future chairs. We hope that publishing such reports would (a) improve transparency in decision-making, (b) help the people new to the field to understand how the *ACL conferences work, (c) provide useful data for the future chairs and workshop organizers, and also academic work on peer review, and (d) provide useful context for the final program, as a source of information for meta-research on the structure and trajectory of the field of NLP. ## 1 Introduction With the continued growth of our field and the rising number of conference submissions, peer review draws more and more attention from the community—as an application area (Hua et al., 2019; Anjum et al., 2019; Stelmakh et al., 2019, inter alia), in meta-research (Rogers and Augenstein, 2020; Church, 2020, inter alia), in initiatives to organize and release peer review data (Kang et al., 2018; Jecmen et al., 2022; Dycke et al., 2022, inter alia), and, of course, in the regular heated social media discussions during submission deadlines, review release dates, and acceptance notifications. It is unlikely that peer review will ever be perfect - it remains 'the least bad system' we have for ensuring the quality of scientific publications (Smith, 2010). Still, with each iteration we should learn a little more about what works better for organizing peer review at such scale, and in a community so diverse in expertise and experience. As a step in that direction, ACL'23 makes its peer review report public and an official part of the conference proceedings, complementing the introduction and other administrative materials. The goal is to increase the visibility of the results of the conference process, as well as any incidental findings from conference organizations and the lessons learned the hard way that may be useful to the future chairs and workshop organizers. Such publications also provide extra incentives for the future program chairs to invest more effort in the analysis of their process, and they provide a useful background to the composition of the final program that may be useful for meta-science research (since they essentially document the selection process for that program). Last but not least, such publications will improve the transparency of the *ACL conference process, which may be useful to the researchers who are new to the field. We present the core statistics per track (§2), analysis of resubmissions (§3) and core demographics (§4), our efforts for improving peer review quality (§5), improving decision support for the chairs (§6), out analysis of various factors contributing to review scores and final decisions (§7), ethics review and best paper selection (§8), and our efforts towards improving incentives for the authors, reviewers and chairs (§9). We conclude with overall recommendations for future conference organizers (§10). The materials we developed will be available at a dedicated repository1. The results presented here are based on the analysis of internal data of ACL'23, as well as exit surveys that we sent to the chairs, authors and reviewers. We received responses from 25 senior area chairs (SACs) 1https://github.com/acl-org/acl-2023-materials | Direct submissions | ARR submissions | | | | | | |--------------------------------------------------------|-------------------|----------|----------------|----------|--------|--------| | Track | Submitted Main | Findings | Submitted Main | Findings | | | | Computational Social Science and Cultural Analytics | 113 | 22.12 | 19.47 | 10 | 90.00 | 10.00 | | Dialogue and Interactive Systems | 269 | 24.54 | 15.24 | 19 | 21.05 | 42.11 | | Discourse and Pragmatics | 52 | 21.15 | 34.62 | 1 | 100.00 | 0.00 | | Ethics and NLP | 54 | 22.22 | 31.48 | 7 | 42.86 | 42.86 | | Generation | 175 | 25.71 | 20.57 | 6 | 66.67 | 16.67 | | Information Extraction | 279 | 25.45 | 16.13 | 33 | 24.24 | 36.36 | | Information Retrieval and Text Mining | 94 | 14.89 | 21.28 | 9 | 44.44 | 0.00 | | Interpretability and Analysis of Models for NLP | 189 | 24.34 | 28.04 | 20 | 35.00 | 55.00 | | Language Grounding to Vision, Robotics, and Beyond | 147 | 24.49 | 21.77 | 5 | 40.00 | 40.00 | | Large Language Models | 252 | 28.17 | 21.03 | 10 | 50.00 | 30.00 | | Linguistic Diversity | 18 | 27.78 | 22.22 | 1 | 0.00 | 100.00 | | Linguistic Theories, Cog. Modeling & Psycholinguistics | 38 | 23.68 | 23.68 | 8 | 50.00 | 37.50 | | Machine Learning for NLP | 313 | 21.09 | 23.32 | 37 | 56.76 | 2.70 | | Machine Translation | 198 | 25.25 | 18.18 | 7 | 0.00 | 57.14 | | Multilingualism and Cross-Lingual NLP | 85 | 20.00 | 30.59 | 12 | 25.00 | 16.67 | | NLP Applications | 354 | 22.88 | 19.77 | 25 | 52.00 | 8.00 | | Phonology, Morphology, and Word Segmentation | 21 | 28.57 | 19.05 | 0 | | | | Question Answering | 197 | 18.78 | 18.78 | 22 | 45.45 | 18.18 | | Resources and Evaluation | 213 | 28.17 | 19.72 | 23 | 56.52 | 0.00 | | Semantics: Lexical | 54 | 25.93 | 25.93 | 3 | 66.67 | 33.33 | | Semantics: Sentence-level Semantics | 81 | 27.16 | 11.11 | 9 | 22.22 | 22.22 | | Sentiment Analysis, Stylistic Analysis, Arg. Mining | 107 | 17.76 | 30.84 | 10 | 30.00 | 0.00 | | Speech and Multimodality | 72 | 27.78 | 36.11 | 7 | 57.14 | 14.29 | | Summarization | 139 | 23.02 | 21.58 | 12 | 33.33 | 8.33 | | Syntax: Tagging, Chunking, and Parsing | 69 | 23.19 | 21.74 | 5 | 20.00 | 20.00 | | Theme: Reality Check | 110 | 26.36 | 30.91 | 1 | 100.00 | 0.00 | | Total | 4559 | 20.73 | 18.36 | 305 | 42.30 | 20.98 | Table 1: Number of submissions and acceptance rates per track for direct and ARR submissions to ACL'23. (35.7% response rate), 134 area chairs (ACs) (30.5% response rate), 510 reviewers (11.4% response rate), and 556 authors (4.07% response rate of all authors2). ## 2 Tracks And Acceptance Statistics ACL'23 had 26 tracks, most of which have also been offered at other recent NLP conferences. At the suggestion of EMNLP 2022 chairs, we kept their separation of "*Large Language Models*"3 track from "*Machine Learning for NLP*" track. At community requests we added the following tracks: "*Linguistic* Diversity" and "*Multilingualism and Cross-lingual NLP*". Each track had at least two Senior Area Chairs (SACs), who then recruited area chairs (ACs) for that track. The full list of senior chairs per track is available at the conference website.4 Internally, in the START system there were also two special tracks: "*Ethics review*" track (which handled the reviews of papers that were flagged for ethical issues), and "*Conflicts of interest*" (COI) track, which handled the papers with which the SACs of the relevant tracks had a COI. ACL'23 implemented a hybrid process, in which it was possible to submit papers either directly to the START system (to be reviewed through ACL'23 internal peer review process to be described in this report), or commit it through ACL ROlling Review (ARR) with reviews already performed at ARR. Most submissions to ACL'23 were direct submissions (4559), and 305 more came through ACL Rolling Review (ARR). Table 1 shows acceptance for each type of submission and in each track. (a) Submissions vs resubmissions (b) Prior venues of resubmissions (c) The fate of resubmitted papers ![2_image_0.png](2_image_0.png) ACL Rolling Review (ARR). Table 1 shows that in most tracks, ARR submissions had a much higher acceptance rate, sometimes twice higher. This is to be expected because ARR submissions self-select for high scores and positive reviews before committing to ACL. Since in the hybrid process ARR submissions and direct submissions directly compete for acceptance, a question arises to what extent this is a fair competition. We asked that question to our SACs. 58.3% believe that this process is fair enough, 12.5% - that it is unfair to the direct submissions, and 29.6%—that it is unfair to the ARR submissions. Of 17 SACs who believed that this situation is unfair in some way, 23.5% suggested that they should have separate acceptance rate, 41.2%—that they should have a separate process and acceptance criteria, and 47.1%—that there should be some other solution (many comments pointing to the confusion, the apples-to-oranges comparisons of reviews performed with different evaluation, the less-than-ideal import of openreview data into START (browsing attachments takes more time). Many expressed a preference for a non-hybrid process. As program chairs, our biggest challenge with ARR was that by design it provides reviews and metareviews, but the acceptance decisions are then made by our SACs—who generally do not provide extra feedback to either direct submissions or ARR submissions (nor can they be expected to: some tracks had over 300 papers per 3 SACs). For direct submissions, nobody expects SAC-level feedback. But to ARR authors, who likely self-selected for high scores and positive reviews, to be rejected without explanation is more frustrating, and we received a lot of angry emails demanding extra feedback (even though neither we nor ARR promised that). It seems that by design, a process where there are acceptance quotas, and decisions are fully decoupled from feedback, will necessarily leave the majority of authors rejected without explanation—and hence disappointed and unsure what they could do to improve their work (and we agree that this would indeed be frustrating to the authors). The above factors could transform into a bigger problem in the future. We only had 305 ARR submissions, but if a majority of our submissions came with high scores and positive reviews—this just would not be a useful signal anymore. The acceptance odds of direct submissions would decrease (as compared to a process where everyone starts at the same stage of peer review). The SAC-ing would become harder (since selecting among high-quality papers is less easy than among papers of varying quality), and the authors would be disappointed because many would be rejected with high scores and no idea what they could do differently. ## 3 Resubmissions Among the 4559 direct submissions to ACL'23, 754 indicated that they were resubmissions (see fig. 1a). The biggest "donors" were EACL5 (296), EMNLP (258), ICLR (103), AAAI (52), and ACL Rolling Review6 (39). Although the selectivity of top-tier conferences means that the majority of papers are ![3_image_0.png](3_image_0.png) * All information is self-reported, not independently verified, and does not correspond to any specific definition of affiliation, gender, or country (e.g., some authors from Edinburgh may elect to list their country as "Scotland" rather than "UK".) rejected, the bulk of the ACL'23 submissions are new, which means that at this point **the burden of** re-reviewing is relatively low. It is possible that this is due to the wider acceptance of Findings as a publication channel, as more *ACL conferences continue to offer this option. Moreover, ACL'23 authors had the option to submit previous reviews as an attachment, but only 243 submissions used this option, which suggests that most resubmitters preferred to have a completely new set of reviewers. ARR allows that option within ARR, but the ARR submissions themselves did not have a high rate of revise-and-resubmit (only 8/305), as shown in fig. 1b. Intuitively, one could expect that resubmissions have a higher chance of acceptance, since these are the papers that have received feedback and had a chance to revise. But fig. 1c suggests otherwise. See more analysis in §7.3. ## 4 Authors And Reviewers At Acl'23 We received a record 4864 submissions (4559 direct, 305 from ARR) from the total of 13,658 authors, reviewed by 4490 reviewers. This section reviews our recruitment process and the three demographic variables (country, affiliation type, and gender) to which we had access in the global START profiles of all participants of ACL peer review process. Reviewer recruitment. We initially sent review invitations to the reviewer list which we had received from the organizers of previous conferences. We also required the authors of all submissions to nominate at least one experienced reviewer, whom we also sent invitations. As we elicited reviewer data, we found that for a quarter of our reviewers7 **there is no reliable** Semantic Scholar publication history data that can be used for paper-reviewer matching. For conferences that fully rely on automated paper–reviewer matching based on publication history, this factor obviously sets a bound on their possible performance. Often the author pages exist because Semantic Scholar automatically created them, but the authors did not claim them and did not clean them up, which 7Out of the reviewers who filled in our sign-up forms, only 75.4% confirmed that their Semantic Scholar profile is accurate and can actually be used to estimate their areas of interest and expertise. In addition to that, 8.9% reviewers listed in START did not specify their Semantic Scholar IDs in their profiles. may result in the addition of publications by namesake authors (e.g. the automatically created profile for "Anna Rogers" originally had contributions from at least three researchers with that name.) This is particularly worrying because at this point many venues have used this information for paper-reviewer matching, and urged the NLP community to maintain their Semantic Scholar profiles. We also specifically reminded about this, but still a quarter of our sign-up pool stated that their publication history is not accurate. In addition to this problem, matching based on publication history has the issue with establishing expertise of different authors on on multi-author publications. Hence, we developed an alternative matching approach described in §5.2. Affiliation types. Figure 2a presents the overall distribution of the affiliations of our authors and reviewers (as stated in START profiles). The biggest group of authors, reviewers, and chairs are academic faculty. The second biggest group (by absolute numbers) in all three categories is industry, which is relevant to the recent concerns about the influence of industry on academic NLP research (Abdalla et al., 2023). Furthermore, students form at least 26% of reviewer pool (Ph.D. 22.7%, M.Sc. 3.3%). This was also our experience as area chairs at other recent conferences, and it highlights the need to **continue the** reviewer training efforts. Gender distribution. Based on the information in softconf profile, about 20% of ACL peer review participants in all roles did not answer the question about their gender (Figure 2b). For a part of this population this is likely a deliberate choice, but judging by how many other fields in the START profiles were not accurately filled in or updated, in many cases this likely signals simply the lack of desire to fill in forms, especially for the new authors who had to register in START last minute in order to make a submission. Considering only those profiles that responded to this question, we see a heavy imbalance for "male", in agreement with the reports on under-representation of women in Computer Science (Jaccheri et al., 2020; Pantic and Clarke-Midura, 2019), where a lot of NLP research is currently happening. This underscores the need to **continue the Diversity and Inclusion efforts**. Top contributing countries. The analysis of the countries of all authors and reviewers suggests that the balance between reviewing and submitting papers is considerably off for many locations, and particularly China.8 We believe that this is at least partly due to the fact that our recruitment efforts started with the pool of the previous conferences. That pool needs to be deliberately expanded by **more active and** targeted reviewer recruitment efforts among Chinese institutions. Church (2020) estimates that at 20% acceptance rate the authors of published papers "owe" the community at least 15 reviews per each publication (3 for their own paper, and 4x3 for the papers that didn't get in). While some dis-balance between the author and reviewer list is to be expected (e.g., since many junior authors are not yet qualified to review, and many senior authors perform other organization roles)—we clearly need to decrease it in order to decrease the reviewer load. Our default quota was six papers9 per reviewer, in line with most recent conferences. This is a significant workload, and it can hardly be expected to improve the quality of reviews. Moreover, the more reviewers are in the pool, the smaller the trade-off between optimizing for best matches or smaller workload per reviewer. ## 5 Efforts Towards Improving Review Quality This section describes the following steps that ACL'23 proposed and implemented within its peer review process to improve review quality: review tutorials (§5.1), Area-Contribution-Language paper-reviewer matching (§5.2), flagging of review issues by the authors (§5.3). The efforts to improve the overall incentives are decribed in §9.2 and §9.3. ## 5.1 Reviewer Training As part of reviewer training, we prepared the following public materials (as a revision of an earlier tutorial10, developed by Anna Rogers and Isabelle Augenstein for ARR): - ACL'23 Peer Review Process: the general tutorial about review process for novice reviewers, that covers the basic structure of *ACL peer review process, author response, and discussion period, as well as tips for planning the time, reporting conflicts of interest and assessing whether to ask for reassignment. These materials were optional for experienced reviewers, and could be used across different *ACL venues as is. - ACL'23 Peer Review Policies: the tutorial explaining our review form and responsible NLP checklist (§9.1), as well as our peer review policy: specific, professional reviews with scores supported by the text. Our list of reviewer heuristics such as "reject if not SOTA" currently contains 14 heuristics (continued from the original eight heuristics pioneered at EMNLP 2020 (Cohn et al., 2020)). We asked even experienced reviewers to read this tutorial. The future chairs could reuse parts of this tutorial, with necessary updates to the review form description and review policies. Feedback. The exit survey indicates that the reviewers found the materials clear (43% respondents rated them as at 4 out of 4 and 40.5% - as 3 out of 4 on 4-point scale). One avenue of improvement suggested in many free comments was adding examples of good reviews. We also asked the reviewers about their preferences for alternative formats, and the self-paced text-based tutorial was the majority choice (62.5% vs 13% preferring video tutorials and 9.6% preferring interactive tutorial with quizzes). But 13.4% respondents said that they would probably never be able to spend time on reviewer training, no matter what format it is offered in. This suggests that reviewer training, while valuable, will not help in all cases, and could perhaps be interpreted as an upper bound on the effect of any reviewer training. ## 5.2 Acl Paper-Reviewer Matching: Area-Contribution-Language One of the peer review issues that authors (and chairs) often complain about is "meh" reviews: the reviewer does not really find any significant problems with methodology or execution of the paper, but the overall recommendation is middling. This could be a symptom of paper-reviewer mismatch: the reviewer just is not sufficiently interested in the overall topic or approach, and hence no matter how good the paper is, it would not elicit much enthusiasm. In a recent survey (Thorn Jakobsen and Rogers, 2022) of authors, reviewers and ACs about their prior experience at NLP venues, many reviewers stated that "the area match was right, but... the subject of the paper was not interesting to me (e.g. I would prefer another NLP task, model, or data)" (54%), or *the paper was not asking a research question that would be interesting* for me" (45%). At the same time, over 27% of the author respondents in that survey reported that they had experience of reviews where the reviewer was not interested in the subject of the paper. Most recent *ACL conferences and ARR work with some version of an automated paper-reviewer matching system that computes affinity scores between the abstract and title of the submission and the candidate reviewer, based on their publication history. Interestingly, the same survey by Thorn Jakobsen and Rogers (2022) found that both authors, reviewers, and ACs generally considered these scores to be the least important factor for paper-reviewer matching. Besides the limitations of the current systems, one factor here is probably the noise in the reviewer publication history data (only 75% of our reviewers indicated that their Semantic Scholar profiles were accurate enough to use for review assignments, see §4). Then there is also the inherent difficulty with establishing level of expertise on a particular topic in multi-author papers. A traditional alternative to affinity scores, that also addresses the issue with reviewer interest, is bidding: the reviewers explicitly say which papers they would be interested in. But this process is rather laborious: for a big track, a reviewer would need to indicate their interest for hundreds of papers. It also opens up the possibility of collusion rings (Littman, 2021). In our experience, many reviewers do not even respond to bidding calls on time, which once again leads to some part of assignments being essentially random. 10https://aclrollingreview.org/reviewertutorial | Match by area | Match by contribution | Match by language | Review count | Review % | |-----------------|-------------------------|---------------------|----------------|------------| | 3 | 3 | English | 8996 | 71.36 | | n/a* | n/a | n/a | 1052 | 8.35 | | 7 | 3 | English | 691 | 5.48 | | 3 | 7 | English | 558 | 4.43 | | 3 | 3 | 3 | 476 | 3.78 | | 3 | 3 | 7 | 345 | 2.74 | | 7 | 3 | 3 | 164 | 1.3 | | 7 | 7 | English | 142 | 1.13 | | 7 | 7 | 3 | 52 | 0.41 | | 3 | 7 | 3 | 50 | 0.40 | Thus, we experimented with a new workflow that we dub **ACL (Area-Contribution-Language) paperreviewer-matching**. It is a keywords-based matching process that explicitly targets three dimensions of submissions: track sub-areas (topical match), contribution types (match by focus/methodology), and target language (for submissions not focusing on English). To the extent possible, the paper-reviewer matching aimed to provide matches across all these dimensions. This approach further enabled us to provide the ACs with explanations for the specific matches (see §6.3). Track sub-areas. Each track at ACL 2023 had an associated set of keywords describing its potential sub-areas. The goal was to describe the biggest expected sub-areas, and hopefully provide the authors with a better idea of the kind of work that the track was inviting. The full list of our keywords is publicly available in our blog post.11 Our keywords were provided by the SACs of all tracks independently, but the future chairs may wish to take a more top-down approach to editing this list, and to ask their SACs to check that the list still describes the sub-areas for which the most submissions are expected, and the individual keywords are sufficiently clear for the authors. Language(s). Due to the "default" status of English (Bender, 2019), submissions targeting other languages may be perceived as "niche" by reviewers. Additionally, the lack of expertise in a language may make it harder for reviewers to spot potential issues. Hence, for papers on languages other than English, we endeavoured to also maximize reviewer matches along this dimension. Contribution types. The contribution types cross-cut tracks, and we hope they would help to decrease the amount of cases where the reviewer just fundamentally does not recognize a certain type of work (Bawden, 2019) and hence scores it down, or has unreasonable expectations (e.g. experimental results in a position paper). For example, the category of compute/data-efficiency creates a de-facto equivalent of efficiency track spread across all tracks. Our contribution types are based on COLING 2018 classification (Bender and Derczynski, 2018), which we extended as follows: (1) NLP engineering experiment (most papers proposing methods to improve state-of-the-art), (2) approaches for low-compute settings, efficiency, (3) approaches for low-resource settings, (4) data resources, (5) data analysis (6) model analysis & interpretability, (7) reproduction studies, (8) position papers, (9) surveys, (10) theory, (11) publicly available software and pre-trained models. Implementation. To collect the information for this kind of matching, we asked the authors at submission time to specify their preferred track (up to two), the best-matching keywords in that track (multiple selection possible, or "other" option with free text entry), the best matching contribution type(s) and target language(s). Correspondingly, at reviewer recruitment stage we asked the reviewers to fill in a form specifying their preferences for the tracks, keywords, contribution types, and the language(s) the work on which they could review. The matching itself was based on Integer Linear Programming, aiming to maximize matches across the three keyword types (with more types of matching being more valuable than 11https://2023.aclweb.org/blog/reviewer-assignment/ e.g. more matches only by area). As a fallback, we also retrieved Semantic Scholar profile data for the reviewers and computed the similarity between submission abstracts to the abstracts in the publication history of candidate reviewers, but this factor was given the lowest priority in the assignment strategy. The Area-Contribution-Language matches, as well as the most similar paper of the reviewer, then also became the basis for the rationales for the match (see §6.3). The SACs were given the opportunity to selectively check and adjust the matches as described in §6.2 (although few of them did), and the ACs and SACs were able to see the rationales for the matches when considering the reviews. From the analysis of the final 12606 reviews in START, 1052 (8.3%) did not have the match information (due to manual reviewer reassignment by the chairs, most likely emergency reviewers). Of the remaining 93.7% reviews made by our criteria, only 1.13% reviews with automated assignment were assigned based on the similarity scores from publication history, after exhausting the possible keywords-based matches in the reviewer pool. 82.9% reviews had at least one match by the type of area, 84.97% - by contribution type. Importantly for DEI efforts and development of NLP for languages other than English, we had 1167 reviews for submissions that specified at least one target language other than English - and we were able to provide a reviewer matching by (at least one) language in 63.58% such reviews. Feedback. When asked to rate on 4-point scale how well the paper-reviewer matching worked for them, 85.5% ACL'23 reviewers rated it positively (35.7% at 4/4, 49.8% at 3/4). When asked for the kinds of mismatch, if any, 28.4% pointed at the topic, 13.7% at the methods, 10.4% at the type of contribution, 4.5% at languages, and 5.7% at other kinds of mismatch. We conclude that Area-Contribution-Language assignments are overall a promising direction that can contribute to DEI efforts in the field and diversity of its contributions (see also §7). The matches could be further refined by (a) revising the area keywords12, and (b) more targeted reviewer recruitment to include speakers of various languages. One of our SACs suggested providing a glossary together with the list of keywords. We also recommend investing effort into a dedicated interface for checking reviewer assignments that would enable ACs to help with reviewer assignment checks while seeing the up-to-date reviewer availability information, and highlighting the possible problems with the current assignments (such as imperfect matches, rare types of contributions or languages that may need extra attention, insufficient pool for a area or a contribution that turns out to be more popular this year). ## 5.3 Review Issue Flagging Even with all the above efforts, we anticipated that there would still be problematic and mismatched reviews. Given that the only people with the incentive to read the reviewer guidelines and enforce them are the authors, we developed a way for them to flag reviews for specific issues, which the ACs could be given specific instructions about, and be able to address more systematically. Unfortunately, the START system does not have an editor for the author response form or meta-review form. Hence we had to provide the authors and ACs with the list of possible issues, and ask them to specify their type and rationale in plain text form, as shown in Figure 3. As could be expected, even with a template there were many format errors. We recommend that the future conferences use a form with a multi-selector, per each reviewer. The authors actively used this feature at ACL'23, flagging 12.9% of all reviews. This is reassuring: judging by the intensity of online discussions of peer review at each review release day, *most* reviews are bad). The frequency of various reported issues is shown in Table 3. The biggest reported problem is the heuristics such as "not novel", "not surprising", "too simple", and "not SOTA". Particularly concerning are the rude/unprofessional reviews: even though there are only 1.69%, they have the most potential to impact the mental health of the authors, and we should strive for that number to be 0. The author-reported issues should be interpreted as a lower bound on the number of review issues, because of 100 papers were reviewed but withdrew before the final decisions. It is possible that they did because they (a) agreed with the criticism and wished to revise the paper, or (b) that they disagreed but did not see a chance to persuade the reviewers. Assuming the latter, and that all their reviews were problematic, this would raise the upper bound of problematic reviews to 15.3%. But it is unlikely that all 12In particular, our Language Grounding SACs indicated that their keywords should be revised and clarified. ![8_image_0.png](8_image_0.png) withdrawn papers were of the (b) type, and the comments from ACs also suggest that many issues were not fully justified. When asked to rate the utility of this system at ACL'23 on 4-point scale, with 4 being the Feedback. highest score, 42.1% of the authors in our exit survey rated it at 4/4, and 40.3% - at 3/4. We interpret it as overwhelming support, and recommend that this feature is maintained in the future conferences. However, the qualitative analysis of the authors' comments suggests that in some cases the ACs did not respond to the flagged issues properly, which entails the need for further training and monitoring by the SACs. Our follow-up analysis suggests that ACs reported addressing the author-flagged issues in at least 30.59% submissions (judging by their using a similar template to Figure 3 in the "confidential notes to chairs" in the meta-review. This should be interpreted as a lower bound: since the interface was very clunky, it is possible that some ACs did consider the flagged issues, but did not report their actions. But, clearly, many issues were not properly addressed, and there is much room for improvement and further training of ACs. Still, given that this is the first implementation of this system, this is a promising approach and it should improve in the future. ## Reviewer Discussion 5.4 Similarly to most of the recent *ACL conferences, we implemented the author response period: a week during which the authors have the opportunity to read the reviews and send their response. The goal of this process is improving the quality of the reviews, and we supplemented that goal with the above new option for the authors to flag specific types of review issues (§5.3). The authors could (but didn't have to) provide a response and flag review issues; this was done for 88.3% of reviewed submissions. In 57.3% review forms the reviewers indicated that they read the response (it is possible that more did read the response but did not fill in the form). Those comments were seen by the ACs, not the reviewers. The ACs had the option to initiate reviewer discussions for the cases where they saw significant disagreements, quality issues, or misunderstandings. Each paper had an associated "forum" on START, where the reviewers could communicate in an | Type of issue | Number of reviews | % of reviews | |--------------------------------------------------------------------------------------|---------------------|----------------| | A: The review is not specific enough | 272 | 2.16 | | B: Review heuristics such as "not novel", "not surprising", "too simple", "not SOTA" | 678 | 5.38 | | C: The scores do not match the review text | 448 | 3.55 | | D: The review is rude/unprofessional | 213 | 1.69 | | E: The review does not evince expertise | 542 | 4.3 | | F: The review does not match the paper type | 98 | 0.78 | | G: The review does not match the type of contribution | 152 | 1.21 | | H: The review is missing or too short | 205 | 1.63 | | I: The review was late | 12 | 0.1 | | J: Other | 162 | 1.29 | Table 3: Review issue statistics anonymized fashion (as R1, R2, R3). The ACs were provided with instructions and suggested starter message template. In total, out of 4559 direct submissions to ACL, 4069 had received reviews, and for 2901 out of those the ACs initiated discussions. In total, ACL review process generated 8553 messages (3879 by the ACs). However, only 2107 discussions (72.63%) had at least one response from at least one reviewer. Somewhat consistently, the discussions were overall initiated by 77.4% of all ACs. We conclude that both AC and reviewer involvement have room for improvement. We reviewed one case of a strong paper that ended up being rejected. The AC could have been persuaded by a "champion" reviewer, and there was one such expert in the set who was surprised by the final outcome—but they did not engage in the forum discussion. We followed up with the reviewer, and they explained that since their review was already positive, they did not feel that they needed to be "on the case" anymore. We cannot establish how common this misconception is, but we would urge all reviewers to always read all reviews and author response, and when certain of the merit of a paper—to try to make sure that the AC is convinced. ## 6 Improving Decision Support For The Chairs In addition to the efforts for improving the quality of peer review (§5), we implemented the following steps for facilitating the decision support by ACs and SACs: revised SAC and AC guidelines (§6.1), guidance for assignment checking (§6.2), match rationales (§6.3), *Soundness/Excitement* scores (§6.4). ## 6.1 Updated Sac And Ac Guidelines We updated the SAC/AC guidelines that we received from the program chairs of ACL'21 in following ways. We reformatted it to Markdown to utilize the ecosystem of GitHub (e.g., version control, asynchronous collaboration among PCs, automated deployment). The guides were built by Sphinx13 with MyST extension14, which enables to use Markdown and variables (making it easy to keep the consistency of dates and external URLs between SAC and AC guides and for the future chairs to adapt to their timeline). We also adjusted the existing instructions and created new instructions to incorporate everything we developed, from the new reviewer guidelines to guidelines for making recommendations. We shared the guides before the review process so that SACs and ACs can be prepared for the tasks and workloads. Feedback. 83.3% SACS and 90.3% ACs rated the clarity of instructions at 3/4 or 4/4. Some of the free-text comments indicated a preference for shorter guidelines, but since the process is complex, and the guidelines need to serve both new and experienced chairs, there are limits to how much they can be shortened. ## 6.2 Support For Checking Assignments L As mentioned above, the usual workflow in large conferences is that the assignments are made automatically based on affinity scores between candidate reviewers' publication history and submissions. Usually, the automated assignments are then shown to the ACs and SACs to check manually, but this is very difficult in practice: SACs cannot process such a large volume on their own, so they need to rely on ACs. But ACs, at least on START, do not have access to the list of possible reviewers together with their current number of assignments and all their COIs, which means that even if they spot an error—it is difficult for them to identify and recommend an available alternative. Providing the up-to-date quota and COI information on all reviewers in track to the ACs is not possible in the current START platform. There are also no detailed guidelines for this step, which means that even if ACs had the reviewer information, everybody would be suggesting alternatives based on different criteria. In our experience as SACs in previous conferences, although the automated assignments are not perfect, very few ACs actually report the problems or propose alternatives. To see whether this was widespread, we asked our SACs in the exit about whether, in their experience, the ACs asked to check the automated assignments usually recommend many changes. Only 9 of our respondents previously served as SACs in this set-up, but most of them (6/9) concurred with our experience, reporting that ACs adjust very few assignments. When asked why the ACs do not recommend more changes, 33.3% SACs stated that there are no adjustments because the ACs don't really check, 29.9%—that it happens because the automated assignments are already good enough, 29.2%—because of the difficulty with sharing up-to-date reviewer availability information with them, and 20.8%—that there are no better candidates even if the ACs check. 37.5% indicated that there are also other issues contributing to the ACs not recommending more changes. We interpret these results as pointing to the fundamental issue of systematically sharing up-to-date reviewer availability information together with their preferences, experience, and profile information, in a way that would make it easy for the ACs to perform such checks and recommend alternatives. Given that the above factors make it unrealistic to adjust assignments with help of ACs, and that the volume of assignments to check was too large for SACs, we experimented with an alternative approach: since we had the "explanations" for the matches and also the quantitative information about different types of contributions, languages and area keywords, this information would make it possible for SACs to identify the types of submissions most in need of extra checks, and to focus on those. This way the workload would remain manageable, and the SACs would be able to do that while having full access to the latest reviewer availability data. To assist in this process, we developed Jupyter notebooks with quantitative analysis per track (identifying which keywords, types of contributions and languages were rare and could need extra attention)—as well as reviewer lookup functionality by preferred keywords, languages or types of contribution (or any combination thereof). This solution was better than nothing, but admittedly clunky and could be much improved. Feedback. 66.7% of SACs stated that they believed selective checking to be overall sufficient given sufficiently strict reviewer pool criteria (although in our specific case not all reviewers in our pool were up to all SAC's standards). Caveat: we encountered difficulty with uploading the final automated assignments due to dynamic computation of conflicts-of-interest in START. Because of that, several hundred automated assignments had to be redone manually at the last minute. For the conferences based on START, we strongly recommend that this computation is frozen after the main part of reviewers and chairs are added to the tracks. ## 6.3 Paper-Reviewer Match Rationales Given the information for the paper-reviewer matches that we had collected (§5.2), we were able to provide the ACs with a list of rationales for each match (except for those reviewers who were added manually by the chairs, and for whom we did not have this information.) A sample "explanation" for a match is shown in Figure 4a. The idea was to provide the AC with not only the general information about the reviewer, but also what are their interests that match this submission. Importantly, we highlighted the cases where the author-stated type of contribution or language was not among the reviewer's stated (a) Example of paper-reviewer match rationales. The most similar paper titles directly link to the ![11_image_0.png](11_image_0.png) ![11_image_1.png](11_image_1.png) interests, which would ideally provide the AC with grounds to check potential bias against certain kinds of work. Feedback. This feature received overwhelming support from the chairs: 87.5% SACs and 73.9% ACs rated its utility at 3 or 4 out of 4 (Figure 4b). Among the suggestions for the future improvement, the SACs suggested indicating whether the reviewer was an emergency reviewer, and how late the review was, as well as some elements of reviewer history (e.g. whether they were late for other conferences). The numerical similarity scores were less useful than the titles of the most similar papers. While predominantly the ACs were very positive about easily accessible links to reviwer profiles (Figure 4b), some ACs raised fair concerns about the effect of this feature on reviewer deanonymization: the reviewers are already visible to ACs since they need this information for chasing late reviews, but providing links to reviewer profiles increases the saliency of the reviewers' identities, and hence may by itself increase bias against, for instance, student reviewers. ## 6.4 Soundness/Excitement **Scores** While most of the experimental aspects of the ACL 2023 process was focused on matching reviewers to papers more effectively, a larger change visible to authors and reviewers was the introduction of two new scores on the review form to replace the *Overall Recommendation* that was previously the centerpiece of *CL review forms. We asked reviewers for two scores: *Soundness* and *Excitement*. 15 Our goal was that any sound paper would be accepted to some ACL affiliated venue (i.e., Findings), but that the "main conference" distinction (limited by space) would be focused on the most exciting papers. Our hope was that *Soundness*, as a more specific rubric with more objective criteria, would be less noisy than a single *Overall Recommendation* score, which would help reduce the randomness of decisions. The AC guidelines had explicit instructions for how these scores should map to their recommended status. One more factor motivating our proposal was that the *Soundness/Excitement* distinction could help with the author-reviewer communication during the author response. When a reviewer points out issues with 15See our definitions and rubrics for the review form and extra explanation here. Soundness, the authors generally have a fair chance to clear any misunderstandings or issues with review quality, and the chairs are interested in this kind of discussion. The *Excitement*, however, is subjective, and the authors do not have a fair chance to convince reviewers that their general views or research agenda are wrong. The *Soundness/Excitement* distinction helps to focus the response on the *Soundness* issues, and hence have a more productive discussion. Feedback. Judging by the exit surveys, this change was overall well received: over 80% of the chairs, reviewers and authors either expressed support or did not object to this change. 38.1% authors, 35.1% reviewers and 29.9% ACs indicated that while the idea was good, it could be better executed. Among the named issues was the clarity of communication about what these scores meant, the difference in granularity (our scale for *Excitement* had 9 points, and *Soundness* only 5), and the wording could be adjusted to remove the semblance to *Overall recommendation* score. We made these recommendations to the program chairs of EMNLP 2023, who decided to keep this system. From the communication with the authors who expressed dislike for this system, our impression is that one of the factors here is the mistaken impression that the final decisions are overall based on scores, and the papers with similar scores should be guaranteed the same outcome—whereas in reality the chairs know that scores can be noisy and miscalibrated, and hence the final decisions are made on case-by-case basis, with the full view of the reviews and meta-review, and also taking into account the acceptance quotas and their editorial priorities.16 The *Soundness/Excitement* scores were rather intended to make it harder for the chairs to just sort by the scores. ## 7 What Factors Contribute To Acl Peer Review Outcome? Here we present the results of statistical analysis of ACL'23 data, with the goal of explicating what factors contributed to the final decisions and to the quality of individual reviews. We hope that this process both improves the transparency around chair decision-making, and highlights the potential biases and points of improvement for future conferences. For the new authors, we should explain the general process for the acceptance decisions at ACL'23. First, the reviewers contribute their reviews. At the author response the authors see the reviews and have an opportunity to respond: a process mostly intended to clarify any misunderstandings (we disallowed submitting new results). Then the ACs initiate the reviewer discussion, with the goal to clarify misunderstandings and improve the quality of the reviews. Based on the final reviews and their own expertise, they write the meta-reviews and make recommendations for acceptance (Main track or Findings) or rejection. They are not concerned with the acceptance quotas. Their recommendations and meta-reviews (as well as reviews and author response if necessary) are then considered by the SACs, who have the constraint of the target acceptance quota (which we set at about 22% for the main track and 35% for Findings). Their decisions are based on three main factors: meta-reviews, quotas, and editorial priorities (with case-by-case consideration as needed). If they run out of their quota, they may additionally rank more papers by priority that may be accepted to main/track Findings if there is space (e.g., because some tracks did not use their quota fully). The final step is that the program chairs confirm the SAC decisions, and try to fit in as many papers of the ranked "maybes" as possible. In our case, that resulted in accepting more Findings papers than we originally planned based on prior conferences. ## 7.1 Review Scores: Overall Distribution We start by exploring the overall distribution of the new *Excitement* and *Soundness* scores (described in §6.4) and how they mapped to the three possible decision outcomes (Rejection, acceptance to the Main track, or Findings). Both *Excitement* and *Soundness* are ordinal variables, and we use the mean as a rough estimate of the central tendency. Figure 5a shows that for both scores the means are higher for main track than for Findings, and for Findings they are higher than for rejections. For *Excitement* this is fully in line with our instructions to the chairs. For the main track, this suggests that higher (above 3) *Soundness* scores ![13_image_0.png](13_image_0.png) | Findings Coeff | Main Coeff | Findings SE | Main SE | | |-------------------------|--------------|---------------|-----------|------| | (Intercept) | -1.48 | 3.77 | 0.79 | 1.43 | | Soundness Mean | 0.71 | 0.76 | 0.22 | 0.37 | | Excitement Mean | 0.61 | 0.03 | 0.23 | 0.42 | | AC Recommendation (L) | 2.66 | 4.50 | 0.50 | 0.94 | | AC Recommendation (Q) | -1.16 | -0.05 | 0.43 | 0.81 | | AC Recommendation (C) | -0.04 | 0.10 | 0.31 | 0.58 | | AC Recommendation (^4) | 0.04 | -0.27 | 0.19 | 0.37 | | SAC Recommendation (L) | 5.84 | 28.26 | 0.47 | 0.71 | | SAC Recommendation (Q) | -1.06 | 13.59 | 0.34 | 0.77 | | SAC Recommendation (C) | 1.18 | 7.82 | 0.60 | 0.82 | | SAC Recommendation (^4) | 1.52 | 4.48 | 0.64 | 0.74 | also played a role in main vs Findings decisions, although the difference is less than between Findings and rejection. The overall score distribution is shown in Figure 5b. ## 7.2 Factors Impacting The Final Acceptance Decisions 7.2.1 Reviewer Scores And Chair Recommendations To establish the odds of a paper being accepted into Findings or the Main track vs it being Rejected, based only on reviewer and chair recommendations, we fit a multinomial log-linear model with multinom() function from the NNET package in R (Venables and Ripley, 2002).18 The dependent variable (DV) is the *Outcome* coded as a three-layer categorical variable (Main track, Findings, or Reject) with Reject being set as the reference level. The independent variables (IVs) are *AC Recommendation* (ordinal), SAC Recommendation (ordinal), mean *Soundness* score (interval), and mean *Excitement* score (interval).19 The analysis is performed on the papers submitted directly to the conference as the ARR submissions were reviewed through a different process and had different scores. The model coefficients are shown in Table 4. The model is a good fit for the data with McFadden's pseudo-R2 of 0.777 (McFadden, 1973).20 | LR Chisq | Df | Pr(>Chisq) | | | |--------------------|---------|--------------|--------|-----| | Soundness Mean | 10.88 | 2 | 0.0043 | ** | | Excitement Mean | 9.67 | 2 | 0.0080 | ** | | AC Recommendation | 209.71 | 8 | 0.0000 | *** | | SAC Recommendation | 1438.12 | 8 | 0.0000 | *** | Table 5: Type III Analysis of Deviance for Multinomial Logistic Regression in Table 4. 17 To obtain the significance values for each IV (Table 5), we use the ANOVA() function in R on the fitted model (Type III Anova). As expected, all four IVs are significant (p<0.05) but at different levels. The *SAC Recommendation* (2(8) = 1438.12, p< 0.001) 21 and *AC Recommendation* (2(8) = 209.71, p< 0.001) significantly predict the *Outcome* with the *SAC Recommendation* appearing to be a better predictor (as expected, since *AC recommendation* are made without regards to the acceptance quotas). The mean *Soundness* score (2(2) = 10.88, p= 0.0043) and mean *Excitement* score (2(2) = 9.67, p = 0.0080) are also significant at p<.05. To establish the exact contributions of mean *Soundness* and *Excitement* scores to acceptance decisions for the Main track and Findings, we can look at Table 4 again. Note that since it is a multinomial regression model, the coefficients indicate an increase in log odds rather than directly interpretable odds (for which the coefficients need to be exponentiated). The "Findings Coeff" and "Main Coeff" correspond to the log-odds of being accepted into the Findings and Main track as opposed to being rejected. Soundness. In the case of the mean *Soundness* score the coefficient is positive for both Findings (0.71) and the Main track (0.76). This means that for one unit increase in the mean *Soundness* score the log-odds of being accepted as opposed to being rejected increase by 0.71 for Findings and 0.76 for the Main track. By taking the exponential of these values, we see that for one unit increase in the mean *Soundness* score the odds to be accepted increase 2.03 times for Findings and 2.14 times for the Main track. Excitement. Similarly, both coefficients are positive for the mean *Excitement* score for both Findings (0.61) and the Main track (0.03). This means that for one unit increase in the mean *Excitement* score the log-odds of being accepted vs rejected increase by 0.61 for Findings and 0.03 for the Main track. By taking the exponential of these values we see that for one unit increase in the mean *Excitement* score the odds of being accepted increase 1.84 times for Findings and 1.03 times for the Main track. While the values are still positive, this increase is much lower22 than for the mean *Soundness* scores, especially for the Main track. The overall distribution of these scores per acceptance status is shown in Figure 5b. AC Recommendations. Since *AC Recommendation* is an ordinal variable, it is coded using polynomial contrast, so the L indicates linear effect, Q a quadratic effect, C a cubic effect, and so on. Here we look mostly at the linear effect since it has a direct (linear) effect on the outcome. We see that both coefficients are positive, indicating that with an increase of one unit, the log-odds of being accepted vs being rejected increase by 2.66 units for Findings and 4.50 units for the Main track. By taking the exponential of these values we see that one unit increase in *AC Recommendation* corresponds to a 14.30-fold increase in the odds of being accepted into Findings (vs being rejected) and 90.02-fold increase in the odds of being accepted into the Main track (vs being rejected). SAC Recommendations. *SAC Recommendation* is also an ordinal variable, hence we see the same types of coefficients. However, the magnitude of the SAC's decision appears to be much greater with a greater effect on the final outcome. With one unit increase in *SAC Recommendation* the log-odds of being accepted vs being rejected increase by 5.84 units for Findings, and 28.26 units for the Main track. | LR Chisq | Df | Pr(>Chisq) | | | |----------------------------|-------|--------------|--------|-----| | Paper Type | 12.47 | 2 | 0.0020 | ** | | Review Issues | 43.61 | 2 | 0.0000 | *** | | Preprinted | 47.96 | 2 | 0.0000 | *** | | Previous Submissions | 4.38 | 2 | 0.1120 | | | Languages Number | 0.57 | 2 | 0.7528 | | | Languages not only English | 3.53 | 2 | 0.1711 | | | Contribution: Efficiency | 1.18 | 2 | 0.5540 | | | Contribution: Resource | 4.34 | 2 | 0.1139 | | | Contribution: Reproduction | 16.59 | 2 | 0.0002 | *** | | Contribution: Theory | 7.70 | 2 | 0.0213 | * | | Contribution: Software | 19.62 | 2 | 0.0001 | *** | Converting these values to their exponentials, we see that one unit increase in *SAC Recommendation* corresponds to a 343.78-fold increase in the odds of being accepted into the Findings (vs being rejected) and a massive increase of 1.88 ⇥ 1012 for the odds of acceptance into the Main track (vs being rejected). The model hence shows that the SAC recommendation is a much stronger predictor than the AC recommendation, which helps to explain why it is possible for a paper to be rejected even with a positive meta-review. AC recommendations are made without regards to the acceptance quotas, and SACs necessarily have to override them in many cases. ## 7.3 The Impact Of Other Submission Properties There are many properties of submissions that could systematically make a difference to their final outcome. In this section we investigate the possible effect of the type of contribution, the target languages, whether the reviews were problematic (as reported by the authors), and whether the paper was available as a preprint. To establish the importance of these factors, we fit another multinom() model, similarly to what we did in Table 4, and obtain the significance levels for each variable using Type III Anova. While the ordinal model would potentially better preserve the natural order of the final outcome (rejection being the worst and acceptance to the main track being the best outcome), the fitted model violated the assumptions of the ordinal model. Since this model does not include strong predictors such as reviewer scores and chair recommendations, the fit of this model is relatively poor23 compared to the model in Table 4, which has a McFadden's pseudo-R2 of approximately 0.80 (indicating a substantial improvement over the null model). In contrast, this model has a McFadden's pseudo-R2 of approximately 0.01, suggesting that it barely improves upon the null model. Nevertheless, this model can still be used to establish the individual contributions of the submission-level properties, which likely interact in complex ways in the scores and recommendations. Statistically significant factors are also not necessarily strong predictors by themselves. The results of this experiment are shown in Table 6. According to this analysis, the following factors have a statistically significant impact on submission outcome: low-quality reviews, preprinting, short/long paper type, and three types of contributions (software, reproduction, and theory). To also assess the relative importance of our predictors in forecasting the final outcome, we employed a Random Forest algorithm (Liaw and Wiener, 2002). The results are shown in Figure 6. The most crucial predictor was *Review Issues* (i.e., author complaints about reviews25) with a Mean Decrease Gini value of 46.09. This suggests that this predictor played the most significant role in reducing the Gini impurity, and therefore, in improving the precision of our model. The second factor with the biggest Mean Decrease Gini is *Preprinting* (22.84). This analysis does not state the absolute importance of any factor (e.g., that 23Its 3-class accuracy is 52%, vs 90% for the model shown in Table 4. This is the accuracy of the model on the withheld test set when the model is fitted with 70% of the data. The accuracy of the model on all data is about 1% higher. 24Signif. codes: 'p < 0.001' '***', 'p < 0.01' '**', 'p < 0.05' '*', 'p < 0.1' '.', 'p > 0.1' ' '. 25The number of author complaints likely reflects (at least) two factors: the reviews that were truly problematic, and simply negative reviews since the authors are more likely to complain about those. In the latter case the leading cause for rejection is the negative review. ![16_image_0.png](16_image_0.png) Contribution type % submissions Match Mismatch Match-Mismatch Efficiency 9.62 50.27 46.56 3.71 NLP engineering experiment 61.5 46.66 47.33 -0.67 Software and pre-trained models 12.14 56.75 45.56 **11.19** Data resources 19 49.25 46.37 2.88 Data analysis 10.48 48.14 46.78 1.36 Reproduction studies 2.08 66.25 46.51 **19.74** Approaches for low-resource settings 18.22 49.79 46.28 3.51 Surveys 1.64 44.44 46.96 -2.52 Interpretability 25.29 51.8 45.27 6.52 Theory 3.8 56.85 46.53 **10.32** Position papers 2.57 53.54 46.74 6.8 Preprinting increases the chances of acceptance by X%), and we are not claiming that these effects are independently large—but they do appear to be statistically significant. We will discuss these factors further: short/long papers in §7.3.1, contribution types in §7.3.2, review issues in §7.5.5, preprints in §7.5.7. ## 7.3.1 Short/Long Papers Short papers have had significantly lower acceptance rates at most recent *ACL conferences. To mitigate that, we highlighted the problem in the reviewer instructions, had a separate *Soundness* formulation for short papers, and asked the SACs to consider the short and long papers separately, with their own target acceptance quotas. Despite all that, the significant effect of paper type (Table 6) is obvious: the long papers had 23.50% acceptance rate to main track vs 16.53% for short, and for Findings, the rate was respectively 41.89% vs 35.58%. The core reason seems to be that the source reviewer scores are systematically lower, despite all calls to not expect 120% thoroughness of short papers. ## 7.3.2 Types Of Contribution We were pleasantly surprised to find a significant positive effect for the contributions of theory, reproductions, and pre-trained models and software (Table 6). The two latter types are in line with the findings by (Magnusson et al., 2023) who report that reproducibility efforts are rewarded. This effect is also visible from simply considering the differences in acceptance rates for papers with and without these contribution types, shown in Table 7. In fact, the "average" acceptance rate of 46.92% is the closest to the most "mainstream" type of contribution (NLP engineering experiment, 61.5% submissions) - and all other contribution types except surveys have the acceptance rate at least slightly higher than that. | Submissions subset | Contribution type | % submissions | Match | Mismatch | Match-Mismatch | |-------------------------------------------|---------------------|-----------------|---------|------------|------------------| | Resources & Evaluation | Resource | 5.48 | 48.39 | 48.21 | 0.18 | | All tracks without Resources & Evaluation | Resource | 94.52 | 49.48 | 46.34 | 3.14 | | Interpretability and Analysis of Models | Interpretability | 4.89 | 52.69 | 57.14 | -4.45 | | All tracks without Interpretability | Interpretability | 95.11 | 51.61 | 45.18 | 6.43 | Table 8: Acceptance rate among direct submissions inside and outside tracks that targeted a resources and interpretability contributions, with (*Match*) and without (*Mismatch*) given contribution types. The average acceptance rate in this pool is 46.92%. Table 9: Inter-reviewer agreement on soundness and excitement scores, measured as raw % agreement (%) and Krippendorff's alpha (↵) with 95% confidence interval [CI].26 We consider only direct submissions to ACL'23 that were fully reviewed, and for which the final decisions were made: 3847 in total, 1805 "accept" (to either Main track of Findings), and 2042 "reject". | Accepted papers only | Rejected papers only | All papers | | | | | | |------------------------|------------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------| | % | ↵[CI] | % | ↵[CI] | % | ↵[CI] | | | | Ordinal | Soundness | 20.72 | 0.093[0.047,0.137] | 17.68 | 0.116[0.076,0.156] | 19.10 | 0.318[0.294,0.340] | | Excitement | 12.68 | 0.120[0.075,0.169] | 10.65 | 0.134[0.094,0.173] | 23.23 | 0.311[0.287,0.334] | | | Categorical | Soundness | 77.28 | 0.032[0.052,0.112] | 37.39 | 0.092[0.064,0.119] | 53.80 | 0.221[0.194,0.248] | | Excitement | 37.11 | 0.087[0.055,0.120] | 49.60 | 0.074[0.039,0.114] | 43.74 | 0.233[0.212,0.255] | | The *lack* of a visible disadvantage in acceptance rates for non-mainstream types of contributions is a very positive finding. Consider the case of efficiency-oriented papers: they did not have a dedicated track, but their acceptance rate was not lower (and even a bit higher) than for the average in the pool (where the majority of engineering-oriented submissions focuses on performance). In effect, *every* track was an efficiency track, allowing both access to the area expertise and reviewers with interest in this type of contribution. We cannot establish to what extent this is due to Area-Contribution-Language matching or an overall increased interest in the need for efficient NLP solutions. But as long as such contributions are in the minority, we would recommend ensuring the matches by this criterion. A complication for our analysis arises for two contribution types that also had large associated tracks: resources and interpretability. In this case, it is possible that the lack of difference in acceptance rate is due to the extra effort of ensuring the reviewers with matching interests through the track mechanism. To check for that, we compare the acceptance rates for these types of contributions inside and outside of the dedicated tracks (Table 8). We find that in all cases the match between tracks and contribution types yields a 3-6% increase above the average acceptance rate of 46.92%. An interesting case is interpretability and model analysis, which has a 4.45% higher acceptance rate *outside* of its dedicated track (probably indicating an appreciation for papers that perform analysis in addition to some other type of contribution). ## 7.4 How Much Do Acl Reviewers Agree? The issues with consistency of peer review were recently highlighted in the ML community by the two NeurIPS experiments (Price, 2014; Cortes and Lawrence, 2021; Beygelzimer et al., 2021). By treating peer review as an annotation problem (Rogers and Augenstein, 2020), we can apply the existing methodology for analyzing inter-annotator agreement (IAA). We consider three reviewers (annotators) per paper, discarding the rare cases of 4 reviews (from emergency assignments). We compute Krippendoff's ↵ (Krippendorff, 2011) on the *Soundness* and *Excitement* scores (Table 9). We treat these scores as ordinal data. We also experiment with mapping both scores to binary "positive/negative" categories (3–5 > "sound" for *Soundness* and 3.5–5 > "exciting" for *Excitement*, since the borderline scores were 2 for Soundness was 2 and 3 for *Excitement*). ![18_image_0.png](18_image_0.png) Consistent with the general perception of inconsistency in peer review, ↵ shows a level of IAA that seems far too low (the rule of thumb is that "substantial" agreement is in the range of 0.6-0.8 (Artstein and Poesio, 2008; Paun et al., 2022)). However, **the raw agreement for the accepted papers (in the** categorical view, i.e. as sound/unsound, exciting/unexciting) is almost twice higher for *Soundness* than for *Excitement*. We interpret this as an indication that although the scores are still noisy, it helps to ask more specific questions with more objective criteria. The much lower raw agreement on the Excitement is also in line with our point that this is overall a less relevant direction for the author response and reviewer discussion. Arguably we do not even want a high agreement on *Excitement*: everybody interested in the same thing could indicate that the field is ossifying and stagnating. As a sanity check, we also analyzed IAA for the raw reviewer scores of EMNLP 2022 and EACL 2023. Both of these conferences used a single "overall recommendation" score, formulated differently for short and long papers. In EMNLP 2022, for 3092 observations for 3 reviewers (discarding R4 data), with scores treated as ordinal data, we got ↵ 0.316 for the short papers, 0.31 for long, and 0.318 for the whole distribution - which is almost exactly the same as our ↵ for both our scores (in the ordinal case). In EACL 2023, for 1121 subjects for 3 reviewers we got ↵ 0.317 for the short papers, 0.34 for long, and 0.348 for the whole distribution. A related question is "what kind of disagreements do we actually have?" Figure 7a shows the distribution of individual score values for all papers in a given acceptance status, which suggests that even papers accepted to the main conference had some very negative reviews. Figure 7b breaks down the scores into "positive" (*Soundness* >= 3, *Excitement* >= 3.5) and "negative", and considers the combinations of three reviews as "all positive" (+ + +), "all negative" (- - -), "2 positive, 1 negative" (+ + -) and "2 negative, 1 positive" (- - +). We can see that despite disagreements on the exact scores, the papers accepted to the main track have a high ratio of "positive" review combinations for *Soundness* (88%, only 11% papers with one negative *Soundness* score). But for *Excitement* our SACs accepted to the main track 39% papers with one negative *Excitement* score, and 37% papers with a single "champion" reviewer. For Findings, they even accepted 37% papers which only 1 reviewer was excited about. Figure 7c shows the total number of submissions with various combinations of positive and negative *Soundness* and *Excitement* scores, and Figure 7d shows the same categories, but with the number of accepted papers with that score combination. Our data indicates that despite noisy scores and high disagreement, the mechanism of ACs and SACs does "rescue" many papers with one negative review, and at least the raw agreement does improve for the more specific *Soundness* score. Judging by the community feedback (§5), in this first implementation there was a lot of confusion about what the scores meant, and we expect that in future iterations the agreement could improve further. ## 7.5 Analysing Reviews And Review Scores In this section, we take a step back from the final acceptance decisions and look only at the individual reviews and their scores, rather than the final outcome of the submission. ## 7.5.1 Do The Area-Contribution-Language Matches Impact Reviewer Scores? To answer this question, Figure 8 shows the distributions of the individual reviewer scores for *Soundness*, Excitement, reviewer *Confidence*, and *Reproducibility* for all cases where the reviews were or weren't matched by the area, contribution type, or language. The biggest visible impact is in reviewer *Confidence*, where the contributions are not matched by area: the ratio of reviews with high scores (4+) is decreased by about 14%. A worrying observation is that there is a 5% *increase* in high *Confidence* scores for the submissions where the reviewer is not matched by language and could be expected to feel less rather than more confident. We also observe an 11% increase in *Soundness* ratings 3+ from reviewers matched by language vs those mismatched, and 7% in *Reproducibility*. ## 7.5.2 Do The Area-Contribution-Language Matches Impact The Reviewer Activity? To establish whether Area-Contribution-Language matching had any effect on reviewer activity, we counted the reviewers as "active" if they had at least one forum message or more than one review edit. The distributions of active/inactive reviewers that are/aren't well-matched to submissions by Area-ContributionLanguage criteria are shown in Figure 9. At a glance, there are a lot more matched & active reviewers, but since generally a lot more reviewers were matched than mismatched (see Table 2), we would generally expect that to be the case even by chance. To establish whether there are any statistically significant effects, we first fit a generalized linear model (GLM) using the glm() function in R. 27 The dependent variable was binary (the activity of the reviewer). The predictors were a contribution match (binary variable), a studied language match (threelayer categorical variable),28 and an area match (binary variable), all of which were treated as categorical variables (at least one matching keyword of the correct type). The link function was logit, corresponding to a binomial distribution of the response variable (logistic regression). ![20_image_0.png](20_image_0.png) The results of the GLM (see Table 10) suggest that contribution match is a significant predictor of the reviewer's activity ( = 0.16, SE = 0.08, z = 1.97, p = 0.048). Since the estimates relate to log-odds we consider the exponential of the reported value (1.178) which suggests that the odds of the reviewer being active when the contribution type is well-matched are 1.178 times higher than when the contribution does not match the reviewer's expertise. The remaining variables, that is language match and area match, are not significant predictors in this model (p > 0.05).30 Finally, we considered the language match as a binary variable, excluding English language papers. We conduct a Chi-square test (2) to examine the association between the language match (excluding English) and reviewer activity Table 11. The test reveals no significant association between the language match and reviewer activity (2(1)=0.73432, p = 0.3915). The chi-square test was performed using Pearson's Chi-squared test with Yates' continuity correction with the chisq.test() function in R. We conclude that of the Area-Contribution-Language matching rubrics, only the contribution type contributes to improvement in reviewer activity. Although the effect is modest (1.178 times increase in likelihood of reviewer activity), given that reviewer activity post-submission is very important, and its level needs to be improved (§5.4), we would urge the future chairs to consider this criterion in the assignments. It also provides a quick and interpretable way to consider the variety of the types of work 30McFadden's pseudo-R2 of the model is 0.0008231973, which is very low. This suggests that our model does not explain much of the variability in the data. However, it is important to note that in the context of generalized linear models, the interpretation of pseudo-R2 is not as straightforward as it is in ordinary least squares regression. The pseudo-R2 is not necessarily a measure of the proportion of variance explained by the model in the data. Instead, it is a measure of the likelihood improvement per observation relative to the null model. Despite the low pseudo-R2, our model could still provide valuable insights into the relationships between the independent variables (match type) and the reviewer's activity. ![21_image_0.png](21_image_0.png) Table 10: Generalized linear model (GLM) estimates for predicting reviewer activity using match categories. Each row represents a different predictor.29 | Estimate | Std. Error | z value | Pr(>|z|) | | | |---------------------------|--------------|-----------|------------|--------|-----| | (Intercept) | 1.1511 | 0.1012 | 11.38 | 0.0000 | *** | | Match Contribution (True) | 0.1638 | 0.0830 | 1.97 | 0.0484 | * | | Match Language (False) | -0.1076 | 0.1142 | -0.94 | 0.3461 | | | Match Language (True) | 0.0114 | 0.0921 | 0.12 | 0.9015 | | | Match Area (True) | -0.1151 | 0.0786 | -1.46 | 0.1432 | | Table 11: Results of Pearson's Chi-squared test with Yates' continuity correction for the effect of language match (excluding English) on the reviewer's activity | Test | Chisq | df | p-value | |-------------------------------------------|---------|------|-----------| | Pearson's Chi-squared (Yates' correction) | 0.73432 | 1 | 0.3915 | that are being submitted, and to provide extra attention to the assignments for the non-mainstream kinds of work. ## 7.5.3 Do Reviewer Confidence Scores Reflect Their Experience? START profiles contain self-reported reviewer experience labels ("never", "first time", "3 or fewer events", "4 events and more". We explored the relationship between this data and reviewer *Confidence* scores but found no strong effect. We do observe a small (about 4%) increase in the volume of 4+ *Confidence* scores for the most experienced reviewers, and it's significant according to the ordinal logistic regression model31. But the effect is quite small, and judging by this data we don't recommend relying on confidence as a proxy for reviewer experience. Moreover, we observe no relation between this reviewer experience data and the number of review issues reported by the authors. This is a rather depressing finding from the perspective of reviewer training, and we hope that it is rather due to START profiles not being updated by the reviewers. ## 7.5.4 Do The Reviewer Scores Correlate With Length Of The Reviews? The ACL review form had the following text input fields: summary, reasons to accept, reasons to reject, questions to the authors, missing references, suggestions&typos, and confidential notes to the chairs. We roughly estimated the length of these inputs by splitting on the whitespace, and computed Spearmans correlation (Spearman, 1987) between these variables and reviewer scores for Soundness, *Excitement*, 31We fit model in R using the polr() function from the MASS package (Venables and Ripley, 2002) with reviewer's confidence as an ordinal DV and experience as a three-layer categorical IV. We compare this model to an intercept-only model using the Anova() function. While the difference between these models is significant, McFadden's *pseudo-*R2 is extremely low (4.247533 ⇥ 104). lxi ![22_image_0.png](22_image_0.png) Confidence, and *Reproducibility*. The results are shown in Figure 10. As could be expected, we observe a significant negative correlation (-0.35-0.36) between the length of *Reasons to Reject* and both *Soundness* and *Excitement* scores, and the opposite trend for the Reasons to Accept (0.28-0.37). Interestingly, the length of *Reasons to Accept* also correlates positively with the Reproducibility score, indicating that the community appreciates this factor (0.15). *Confidence* has a similar correlation with the length of missing references. Finally, there is a high correlation between the length of "questions to the authors" and "suggestions", indicating that the reviewers who engage with the submission deeply use both of these fields. The highest positive correlation is between our *Soundness* and *Excitement* scores32 (0.68), which is in line with the intuition that unsound work would probably not be found exciting either. ## 7.5.5 What Factors Are Associated With Review Issues? As discussed in §5.3, we introduced a mechanism for the authors to flag specific types of issues with reviews, and we received such flags for 12.9% of the reviews. Figure 11 shows the ratio of reviews with complaints (True) and without (False). For both *Soundness* and *Excitement* there is a clear trend towards more complaints with lower scores, but there are also complaints for high scores (e.g., 43.1% of reviews which the authors complained about had *Soundness* 4). This makes more sense if we consider the figure Figure 11d, which shows that 95% complaints are made about reviews where at least one of the scores is 3 or less. This suggests that reported review issues are associated with negative reviews, even for *Excitement* (although we tried to make it clear that this score is subjective and does not need arguing). To explore other possible factors that could make the reviews more likely to be reported we fit a GLM model using the glm() function in R. The dependent variable is the presence or absence of reported issues (binary variable), and the predictors are the *Excitement* score (ordinal), *Soundness* score (ordinal), Confidence score (ordinal), *Reproducibility* score (ordinal), length of *Reasons to Reject* (interval), length of *Reasons to Accept* (interval), the *Contribution Match* (binary), *Area Match* (binary), *Language Match* (three-layer factor), *Reviewer's Experience* (three-layer factor), and *Reviewer's Activity* (binary). The link ![23_image_0.png](23_image_0.png) function was logit, corresponding to a binomial distribution of the response variable (logistic regression).33 The coefficients of the fitted model are presented in Table 12. We further employ the type III Anova using the ANOVA() function in R in order to obtain significance levels for each factor which are presented in Table 13. While McFadden's pseudo-R2 of the fitted model is only 0.067, several variables of this model are significant predictors of the review issues. The most significant factors are Soundness, *Excitement*, and the length of *Reasons to Accept*. All of these variables have a negative relationship with the reviewer issues, perhaps unsurprisingly, with higher scores the review is less likely to be reported. Similarly, longer text in the *Reason to Accept* field leads to less chance of the review being reported. Counter-intuitively, the positive coefficient associated with the reviewer being active suggests that when the reviewer is active (i.e. with at least one review revision or a forum message) the log-odds of the review issue increase by about 0.32, all else being equal. That is, the more active reviewers (putting in more effort) are actually receiving *more* complaints. Other significant factors are *Language Match* and the reviewer's confidence; both associated with negative coefficients. This suggests that when the reviewer is familiar with the non-English language investigated in the study, the log-odds of a review issue decrease by approximately 0.26 (i.e., the review is 1.29 times less likely to be flagged for issues). Similarly, the negative coefficient of the reviewer's | Estimate | Std. Error | z value | Pr(> |z|) | | | |---------------------------|--------------|-----------|-------------|----------|-----| | (Intercept) | 0.5999 | 0.2570 | 2.334 | 0.0196 | * | | Soundness | -0.3816 | 0.0479 | -7.967 | 1.63e-15 | *** | | Excitement | -0.4584 | 0.0549 | -8.349 | < 2e-16 | *** | | Confidence | -0.0855 | 0.0393 | -2.176 | 0.0295 | * | | Reproducibility | 0.0609 | 0.0335 | 1.816 | 0.0693 | . | | Reasons to Reject | 0.0004 | 0.0002 | 1.508 | 0.1315 | | | Reasons to Accept | -0.0052 | 0.0011 | -4.748 | 2.06e-06 | *** | | Match Contribution (True) | 0.0763 | 0.1148 | 0.664 | 0.5066 | | | Match Area (True) | -0.1352 | 0.1030 | -1.313 | 0.1892 | | | Match Language (False) | 0.0270 | 0.1476 | 0.183 | 0.8550 | | | Match Language (True) | -0.2639 | 0.1275 | -2.070 | 0.0384 | * | | Experience (Experienced) | -0.0744 | 0.0684 | -1.087 | 0.2769 | | | Experience (Zero) | -0.0274 | 0.1164 | -0.235 | 0.8143 | | | Reviewer Active (True) | 0.3172 | 0.0737 | 4.303 | 1.69e-05 | *** | | LR Chisq | Df | Pr(>Chisq) | | | |--------------------|-------|--------------|--------|-----| | Soundness | 64.65 | 1 | 0.0000 | *** | | Excitement | 70.45 | 1 | 0.0000 | *** | | Confidence | 4.71 | 1 | 0.0300 | * | | Reproducibility | 3.31 | 1 | 0.0688 | . | | Reasons to Reject | 2.23 | 1 | 0.1353 | | | Reasons to Accept | 24.17 | 1 | 0.0000 | *** | | Match Contribution | 0.45 | 1 | 0.5035 | | | Match Area | 1.69 | 1 | 0.1940 | | | Match Language | 4.61 | 2 | 0.0998 | . | | Experience | 1.24 | 2 | 0.5386 | | | Reviewer Active | 19.35 | 1 | 0.0000 | *** | Confidence suggests that with an increased *Confidence* score the likelihood of the review to be reported decreases though by a small margin. ## 7.5.6 Do We Have Bad Actors? To explore the possibility that many reported review issues are due to individual unprofessional reviewers, let us consider the fact that 1,620 reviews with reported issues were authored by 1311 reviewers, i.e. about a third of our total pool. But most of these reviewers had more than three reviews, and 1060 of them were only reported once. Of the remaining reviewers, 201 were flagged twice, and 50 reviewers had more than 3 complaints. We conclude that while there are indeed some unprofessional reviewers, and conferences need to systematically share such information and develop a system to address this problem, there are few such cases (6.2% if we consider all reviewers with more than 2 flags, and 1.2% with more than 3 flags). An interesting takeaway from Figure 11c is that the reviews that are problematic according to the authors, do not have lower confidence scores, so these are unlikely to be the new reviewers or the reviewers unfamiliar with the area. According to folk wisdom, the bad reviewer is usually Reviewer2 (sometimes Reviewer3). We clear their good name: at ACL'23, the most issues were reported for Reviewer1, as shown in Figure 12. ## 7.5.7 Can The Reviewers Tell Who The Authors Are? In 567/12606 (4.5%) reviews the reviewers indicated that they have seen the paper, either by seeing a preprint (533) or by other means (34). Additionally, 513 (4.1%) reviewers indicated that they had a good guess of the author identity based on the paper content. 11460 (90.9%) ACL'23 reviews were reported as fully anonymous. The community "recall" on the preprinted submissions is as follows: we had 628 submissions (13.8% ![25_image_0.png](25_image_0.png) of all direct submissions) for which the authors had disclosed preprints. The reviewers identified 306 (49%) of them. Hence, we estimate that although in our sample the number of "guesstimates" based on content is about the same as the number of preprinted papers, if the current 1-month embargo period was to be lifted, and the volume of preprints were to increase - the latter would also increase, while the volume of "guessed" authorship cases should stay the same (at about 4-5%). Interestingly, our reviewers reported another 102 submissions, for which preprints were not disclosed by the authors. We recommend that the future chairs investigate at earlier stages whether such cases are due to false memories of similar preprints, or preprint policy violations. ## 7.5.8 Do Preprints Affect The Peer Review Process? Having established that reviewers do have a high recall for preprints (§7.5.7), we investigate the possible connection between the reviewer's awareness of the author identity on their Soundness, *Excitement*, and Confidence scores by fitting Cumulative Link Mixed Effect models with the Laplace approximation using the clmm() function for the ordinal package in R (Christensen, 2022). The response variable is the given score and the predictor is the *Anonymity* answer (fixed effects). We also employ random intercepts for the paper (SubmissionID) and reviewer (ReviewerID) to account for this variability (random effects).36 Soundness. The results of the model fitted for the effect of *Anonymity* on the *Soundness* scores are present in Table 14. The *Anonymity* has five possible values: (1) the reviewer does not know the authors (reference level), (2) the reviewer may know the authors, (3) the reviewer knows the authors via means other than online posting, (4) the reviewer knows the authors via online posting prior to the anonymity period, and (5) the reviewer knows the authors via online posting post to the anonymity period. Estimates for different answers to the anonymity question presented in Table 14 suggest that the reviewers were 1.59 times more likely to assign higher *Soundness* scores when they thought they may know the authors, and 1.75 times more likely to assign higher *Soundness* scores when they have seen the preprint online.37 Excitement. The results of the model fitted for the effect of Anonymity on *Excitement* are present in Table 15. Estimates for different answers to the anonymity question presented in Table 15 suggest that the reviewers were 1.49 times more likely to assign higher *Excitement* scores when they thought they may know the authors, and 1.73 times more likely to assign higher *Excitement* scores when they have seen the preprint online. Confidence. The results of the model fitted for the effect of *Anonymity* on reviewer's *Confidence* are present in Table 16. Estimates for different answers to the anonymity question presented in the table suggest that the reviewers were 1.29 times more likely to report higher *Confidence* scores when they | Estimate | Std. Error | z-value | Pr(>|z|) | | | |------------------------------------------|--------------|-----------|------------|----------|-----| | Random effects: SubmissionID (Intercept) | 2.2427 | 1.4976 | | | | | ReviewerID (Intercept) | 0.7806 | 0.8835 | | | | | Fixed effects: Anonymity (2) | 0.46037 | 0.11744 | 3.920 | 8.85e-05 | *** | | Anonymity (3) | 0.02567 | 0.41291 | 0.062 | 0.9500 | | | Anonymity (4) | 0.55947 | 0.13081 | 4.277 | 1.90e-05 | *** | | Anonymity (5) | 0.36749 | 0.27565 | 1.333 | 0.1820 | | Table 14: Cumulative Link Mixed Model Results for the effect of *Anonymity* on the *Soundness* scores. The reference level is Anonymity (1) (i.e., not knowing the authors). | Estimate | Std. Error | z-value | Pr(>|z|) | | | |------------------------------------------|--------------|-----------|------------|----------|-----| | Random effects: SubmissionID (Intercept) | 1.6675 | 1.2913 | | | | | ReviewerID (Intercept) | 0.5163 | 0.7185 | | | | | Fixed effects: Anonymity (2) | 0.39828 | 0.10629 | 3.747 | 0.000179 | *** | | Anonymity (3) | 0.13179 | 0.37724 | 0.349 | 0.726816 | | | Anonymity (4) | 0.54498 | 0.11816 | 4.612 | 3.98e-06 | *** | | Anonymity (5) | 0.08329 | 0.24708 | 0.337 | 0.736049 | | thought they may know the authors, and 1.80 times more likely to assign higher *Confidence* scores when they saw the preprinted online. We thus conclude that submissions with preprints, as well as submissions where the reviewers believe they could guess the authors, systematically receive higher ratings for both *Soundness* and *Excitement*, as well as higher *Confidence* scores. We further note that preprinted papers are disproportionately recommended for consideration for best paper awards (and without such a recommendation from at least one reviewer the submissions are not considered by the best paper committee). In total, only 1.6% papers received any reviewer nominations at all, and for 30% of those papers, the authors had disclosed preprints. While our data shows the pattern of higher scores, acceptance chances, and best paper nominations for preprinted submissions, the causal mechanism remains a question: is it because such papers are inherently higher quality, or because of the benefits of community feedback they may receive, or because of the well-documented reviewer biases towards towards famous names and institutions (Peters and Ceci, 1982; Tomkins et al., 2017, among many others)? Since these possibilities necessitate different actions on the part of the chairs who strive for higher-quality program, the causal question needs to be answered for informed policy decisions. Since we observe an increase in likelihood of higher scores both for real preprints and for submissions where the reviewers only thought that they might know the authors (although the effect is smaller in that case), we can conclude that the social factor is definitely present—but more research is needed to establish its exact contribution. But the fact that we only had 13.8% preprints suggests that the current 1-month embargo policy is effective in at least reducing the volume of the problem. ## 8 Special Review Processes 8.1 Ethics Review Following the practice started at NAACL 2021, we formed an Ethics Committee (EC) dedicated to ethical issues. The review process was based on work in prior conferences and further developed by ARR and recommendations from the ACL ethics committee. Initially there were 235 technical reviews flagging 218 papers for ethics concerns, and the SACs narrowed down the list based on the guidelines developed by the ethics chairs) to 75 papers, 6 of which did not make it to the ethics review (either withdrawn or cleared). | Estimate | Std. Error | z-value | Pr(>|z|) | | | |------------------------------------------|--------------|-----------|------------|----------|-----| | Random effects: SubmissionID (Intercept) | 0.416 | 0.645 | | | | | ReviewerID (Intercept) | 3.413 | 1.847 | | | | | Fixed effects: Anonymity (2) | 0.2576 | 0.1227 | 2.099 | 0.0358 | * | | Anonymity (3) | 0.4210 | 0.4194 | 1.004 | 0.3155 | | | Anonymity (4) | 0.5874 | 0.1342 | 4.376 | 1.21e-05 | *** | | Anonymity (5) | 0.3413 | 0.2864 | 1.192 | 0.2334 | | Table 16: Cumulative Link Mixed Model Results for the effect of *Anonymity* on the *Confidence* scores. The reference level is Anonymity (1) (i.e., not knowing the authors). 20 papers under ethics review were labeled accept as-is, 43 received conditional accepts, and 6 were recommend for rejection. Of those recommended for rejection, 1 was accepted nonetheless, 1 was rejected as a result, and 4 were rejected on technical grounds. Of the conditionally accepted ones, 26 were rejected on technical grounds, and 1 was withdrawn. 16 passed the technical review and were conditionally accepted, meaning the ethics issues had to be addressed in the camera-ready version, to be verified by the SAC (based on EC guidance) prior to final acceptance. The authors of all conditionally accepted papers submitted the camera-ready version and a short response that explained how they had made the changes requested. The SAC double-checked these revised submissions and responses, and confirmed that the ethical concerns had been addressed. As a result, all conditionally accepted papers were accepted to the main conference or Findings. ## 8.2 Best Paper Selection ACL'23 implemented the new ACL award policy, aiming to expand the pool of work that is recognized as outstanding. In total, only 73 papers, i.e. 1.6% of all direct38 submissions were nominated by the reviewers or ACs for consideration for awards. These papers were assessed by the Best Paper Award Committee, and with their help we selected 4 best papers, 4 special awards (social impact, resource, reproduction, theme paper), and 39 outstanding papers. The best and outstanding papers will be announced in a dedicated plenary session for Best Paper Awards on July 10 2023. We encountered several issues with implementing the best paper policy as described in the wiki. With 73 nominated papers, to keep it down to 10 papers per judge and have 2 reviews per paper, we had to recruit 15 judges. At this scale, the workload is compatible with organizing a separate track: recruitment, paper assignments, chasing late reviews - only this time recruiting exclusively very senior and busy people, and it is very important to upheld diversity considerations (which we weren't able to do full justice). For the future, we recommend that a separate chair role is created for managing this process, similar in scope to the role of the ethics review chairs. Furthermore, since the diversity considerations in the committee selection entail incompatible time zones, we found it impractical to require the judges to meet and jointly decide on the cases where they disagree (as recommended in the policy). Hence, after the judges cast their votes39, the PCs made the final decisions on the basis of their recommendations (in particular, in the cases where one judge recommended outstanding paper and the other recommended not considering it further), we upheld the objections to flaws in the papers, shallowness of analysis, and ethical issues, which left us with 39 papers (a little short of the 1-1.5% total submissions policy target for the outstanding papers). Finally, the ACL award policy described an Area Chair Award: the award that the SACs of a given track can give to one paper in their track, fully on their own authority. This was part of the guidelines for the final SAC recommendations, but we did not require them to be made at the same time. We sent out reminders after that, but received such nominations from only 12/26 tracks (with the theme 38This is only for the direct submissions to ACL. Due to the difficulty of seeing ARR nominations in START, we did not notice the 2 nominations out of 305 ARR submissions until it was too late. 39We found the agreement on the best paper committee votes to also be not very high: only 24/73 nominated papers received a unanimous vote to either consider for (any) award or not consider further. track nomination transformed into the special Theme paper award). We recommend batching these recommendations with the final SAC recommendations as a single task. ## 9 Improving The Incentives 9.1 Improving Reporting Incentives For The Authors: Responsible Nlp Checklist Following the effort started by NAACL 2022 and continuted at ACL Rolling Review (Carpuat et al., 2021), we used the Responsible NLP Checklist as a way to ensure that all submissions conform to a certain minimum standard of reporting on their reproducibility efforts, data collection principles, and consideration of broader impacts. However, at NAACL 2022 and ACL Rolling Review, these checklists are only used internally during peer review. To improve the transparency of NLP research and create a stronger incentive to invest effort in this work, we made the Responsible NLP Checklists an official part of all published papers. The authors filled out the checklist information in a special form, and we later used that form to generate pdf versions of the checklist, which was appended to every paper pdf for the ACL Anthology. This change was announced in our Call for Papers, and we additionally communicated it to the authors. The authors had the opportunity to update the checklist form during the preparation of the camera-ready version of their papers. One modification to the checklist was introducing a mandatory question about AI writing assistance. This was motivated by the introduction of OpenAI's ChatGPT (OpenAI, 2022), the precedent of AIassisted scientific paper writing of Meta's Galactica (Taylor et al., 2022), and, more importantly, a massive wave of promotion for AI "writing assistants" shortly before our direct submission deadline. We did not aim to completely ban AI-assisted writing (which does have legitimate use cases such as assistance to non-native English speakers), but to improve transparency: just like with the other ethics-related questions in the checklists, our posted policy required authors to explicitly state what they did. Our question and policy were subsequently adopted by ACL Rolling Review. Magnusson et al. (2023) have reported that the higher rate of "yes" responses to the Reproducibility checklist at 4 NLP conferences. Given that our checklist includes reproducibility questions, and reproducibility positively correlates with both *Soundness* and *Excitement*, we would expect the Responsible NLP checklist to perform the same role. The reviewers themselves were predominantly positive about it: 66.99% rated it as "somewhat useful", 18.13% as "very useful", and only 14.35% - as "not useful". Table 17 shows the ratios of submissions answering 'yes' to the questions of the checklist, and the acceptance rates for the submissions that answered 'yes' vs those that didn't. For most questions of the checklist, there is a small increase in acceptance rate for submissions that answer 'yes'. The most significant increases are for reporting limitations (so we recommend that the conferences keep mandating this section), reporting hyperparameters and computation budget (in line with the high correlation between reproducibility ratings and reviewer scores §7.5), citing relevant work, contributing scientific artifacts such as models and software (in line with our finding of a significant effect for this contribution type discussed in §7.3). An interesting case is the "catch question" A3 (does your abstract accurately summarize your work?). It drew some criticism as "meaningless bureaucracy", since all submissions should respond "yes" to it. It was actually intended to see that the responders were not just clicking through the checklist. Most authors did respond 'yes', but those 2.24% that didn't saw a -25.4 decrease in acceptance rate. We interpret this as suggesting that the sloppiness in filling out the checklist correlates with sloppiness elsewhere in the work. Finally, our new question about the use of writing assistants is the only one where the response 'Yes' is associated with a *decrease* in acceptance rate, although not very large. ## 9.2 Improving Incentives For Reviewers: Reviewer Awards Arguably the biggest source of issues with peer review quality is the lack of incentives to invest more work in invisible service labor. One direction is *reputational* awards, eg via creating reviewer profiles, as in Publons. Another is *material* awards, such as monetary prizes similar to the best paper awards. Yet Checklist question % submissions Yes Not Yes* Yes-Not_yes | A1 (limitations) | 46.92 | 47.62 | 17.05 | 30.57 | |--------------------------|---------|---------|---------|---------| | A2 (risks) | 56.23 | 49.28 | 43.88 | 5.4 | | A3 (catch question) | 97.76 | 47.49 | 22.09 | 25.4 | | A4 (AI-assisted writing) | 7.3 | 41.28 | 47.36 | -6.08 | | B (artifacts) | 72.45 | 50.09 | 38.58 | 11.51 | | B1 (cite) | 71.02 | 49.96 | 39.46 | 10.5 | | B2 (license) | 37.8 | 52.48 | 43.54 | 8.94 | | B3 (intended use) | 45.28 | 49.48 | 44.8 | 4.68 | | B4 (PII) | 22.02 | 49 | 46.33 | 2.67 | | B5 (documentation) | 48.95 | 50.93 | 43.08 | 7.85 | | B6 (statistics) | 70.47 | 49.76 | 40.14 | 9.62 | | C (computation) | 92.31 | 47.76 | 36.82 | 10.94 | | C1 (parameters) | 78.58 | 48.96 | 39.44 | 9.52 | | C2 (hyperparams) | 85.5 | 48.49 | 37.63 | 10.86 | | C3 (stats) | 81.02 | 48.19 | 41.51 | 6.68 | | C4 (packages) | 76.01 | 47.16 | 46.15 | 1.01 | | D (humans) | 28.98 | 52.11 | 44.8 | 7.31 | | D1 (instructions) | 20.95 | 53.85 | 45.08 | 8.77 | | D2 (payment) | 21.19 | 53.5 | 45.15 | 8.35 | | D3 (consent) | 17.31 | 51.2 | 46.02 | 5.18 | | D4 (IRB) | 9.62 | 53.24 | 46.25 | 6.99 | | D5 (demographics) | 14.61 | 54.27 | 45.66 | 8.61 | another is *punitive* incentives, such as penalizing the late reviewers by delaying the reviews for their own submissions (Hauser and Fehr, 2007), or even blocking them from reviewing at future conferences. All of these approaches are not without issues. Punitive incentives generally shift the focus to not getting penalized, rather than delivering high-quality reviews. Material awards may introduce the wrong incentives (Squazzoni et al., 2013), and, depending on the institution and the country, the prize may be taxed or not even make it to the recipient. Conference fee waivers also may also reward the reviewer's institution rather than the reviewer, since the institutions usually bear the registration costs. While a survey found that reviewers generally prefer reputational awards over material (Warne, 2016), their value also depends on whether the reviewer's institution rewards such work. We proposed to the ACL exec (and received their approval for) an initiative to match the new ACL best paper award policy with recognizing about 1-1.5% of outstanding reviewers and chairs. This combines reputational and material incentives. Instead of monetary prizes, we proposed awarding vouchers for virtual attendance of any *ACL (ACL, NAACL, EACL, AACL, EMNLP) conference of the awardee's choice, to be used within a year of the award date. Since many institutions do not support the attendance of conferences without accepted papers (or even with papers accepted to workshops and Findings), we hope that this measure will increase the overall number of conferences that the awardees can attend. We asked the area chairs to nominate the reviewers in their pool who provided extra helpful reviews, high-quality emergency reviews, "champion" reviews, reviewers who were particularly active in the discussion phase, or demonstrated exceptional open-mindedness or expertise. We received 51 such nominations. We also asked the Senior Area chairs to nominate exceptional area chairs, receiving 13 nominations. Finally, we as the program chairs also nominated the (3) SACs of the track who were the most on-time, provided the most helpful feedback, and followed our instructions the most closely. Excluding the duplicates, this resulted in 67 total nominations. All awards will be announced on the conference website40. Since the total number of nominations was within our target number of awards (1-1.5% of total reviewers and chairs), we were able to award all 66 nominations (out of 4998) without creating a selection committee. In the future, we recommend that an extra volunteer role is created for managing the selection of awardees and managing the awards. Caveats: despite our calls to nominate reviewers and chairs, relatively few ACs and SACs did that: only 7/70 SACs and 28/438 ACs. We recommend that the AC/SAC guidelines are expanded with a section about these awards, and that ACs are asked to start keeping track of potential outstanding reviewers at the (a) review quality check stage, (b) discussion stage, rather than only during meta-reviews (as we did). The SACs could be asked to start keeping track of outstanding ACs at the (a) assignment checks, if that is the process used by the venue, (b) meta-reviews, (c) nominating on the basis of quantitative analysis of the activity in the discussion forum and the number of author-reported review issues that the AC addressed. ## 9.3 Improving Incentives For Chairs: Peer Review Reports Our final proposal for improving the incentives for peer review work was to increase its visibility by placing the program chair reports and any findings from their analysis of the internal conference data as an official part of the proceedings for the respective conference. This report is aiming to create a precedent for that. In the past, there have been two options for publishing such work: standalone research papers that undergo their own peer review, and miscellaneous blog posts and reports published in ACL wiki. But the former is not appropriate for reporting on incidental findings (since most of the program chairs work is not executed as a research project targeting a specific research question). The latter is unfortunately too difficult to discover, especially for the people outside of our field or new organizers who may not know which blog posts and wikis to search. This initiative aims to improve the transparency of the overall process, and lets the younger members of the community have more insight into how the *ACL conferences work. Moreover, given the increasing attention to peer review in NLP community (Gao et al., 2019; Caragea et al., 2019) and more broadly in ML conferences (Price, 2014; Stelmakh, 2020; Beygelzimer et al., 2021), it would be useful to make the incidental findings from the conferences more easily discoverable, incl. to the researchers in the ML community and other fields. The main difficulty for the program chairs and the publication chairs with implementing this proposal is that the full report needs to be prepared before the conference, when there is a lot of other work. To implement this, the set of volunteer roles would need to be expanded (see section 10). We also recommend that to the extent possible, the future chairs start documenting their workflow for the report early on (perhaps during the main review cycle). ## 10 Recommendations Improving logistics. There are several sources of papers to the ACL main conference that the program chairs have no control over: TACL, CL, Industry Track Papers, SRW papers. This means that the PCs need to ingest four different sources of information with potentially little means of interacting with the relevant authors (in contrast to direct submissions). ARR is in a liminal space between direct submissions and these other papers. The timing and format of how the papers enter ACL should be standardized. Desk rejections. Desk reject requirements should be clearly stated in the call for papers or in the ACL Paper formatting guidelines. The guidelines omit rules or lack clear thresholds for rejection. For example, there is no minimum separation between captions and tables/figures nor between section titles and the text above and below. Nor are there minimum text sizes for text within tables or figures. Adding clear rules would make the first pass reviewing more efficient and fair. ACL also needs to communicate more clearly about the role of the aclpubcheck script: it's a necessary but not sufficient check. Many authors assume that if they pass the aclpubcheck script, then they have followed all formatting guidelines. Soundness/Excitement **scores.** With predominantly positive feedback in the exit survey (§6.4), and evidence of significant improvement in raw agreement (§7.4), we believe this experiment was successful and should be continued. The formulation of the scores and the review form should be improved, and care should be taken to reduce the overall complexity of the form. Review issue flagging. This feature received overwhelming support from the authors, and should be continued and standardized (i.e., cleanly incorporated into author response form)—especially since it is likely to improve after several iterations, when everybody is more familiar with it and the reviewer guidelines. More AC training is needed to address the flagged issues. Continued reviewer policy publications. 12.9% of all ACL'23 reviews were flagged by the authors for various issues, with the most frequent problem being reviewer heuristics such as "too simple" and "not SOTA". It is reassuring to know that the ratio of bad reviews is already not very high, but of course we should strive to further decrease it. The reviewer guidelines, in combination with the review issue flagging mechanism, serve a double purpose: even if the reviewers do not read them, the authors will (since they have the incentive to call out problematic reviews), and then the area chairs also will (to handle the author-flagged issues). Hence, eventually, these policies will become widely known across the community, and enforced by it. We urge the future chairs to continue publishing their reviewer policy or simply re-use ours, and explicitly point to it in review, author response, and meta-review forms. Reviewer assignment check support. There is currently no convenient interface for the ACs to look up the assigned reviewers and browse the alternatives with up-to-date availability information. Its lack is a major hurdle for the chairs, and it may cause either delays in the process or skipping the checks. Reviewer match explanations. Our area chairs were very positive about this feature. For venues not using an interpretable assignment algorithm such as our keywords-based process, at the very least, the reviewer profiles and relevant papers should be provided directly with the review, without any extra search. Post-acceptance decision litigation. Having increased the acceptance rate for Findings, we were surprised to still receive a large volume of emails from the authors who, considering their scores and meta-review, argued that either their paper should have been accepted to the main track, or that it shouldn't have been rejected. It appears that some subcommunities share their scores with each other, under the mistaken impression that if one paper with certain scores was accepted, others with similar scores should be too. We had no capacity for anything beyond checking for clerical errors. The peer review process is by no means perfect, and there was certainly some noise in the decisions—but it is also certain that many authors who disagree with their decisions would try to argue their case if given the chance. If such litigation is not an announced an official part of the conference process—doing so for the select few would not be fair to all the other authors who also disagree with their decisions. We recommend that the future chairs either build this into their process and dedicate time and resources to it, or pre-announce that decisions are final and will not be reconsidered, beyond the cases of clerical errors. Area-Contribution-Language matching. The results of our experiment with exactly matching the reviewers with submissions by these areas allowed us to establish that it is possible to ensure a fair acceptance rate for most "non-mainstream" contribution types, and for the 63.8% of the submissions that had target languages other than English, we were able to provide a reviewer competent in that language. These results are by no means perfect, and it is important that the future venues improve on them, perhaps with other methods. But Area-Contribution-Language matching could be considered a fair baseline for the future conferences, when considering the success rates for different types of submissions and languages. All that is needed from the chairs is to include in submission forms the checkboxes for different types of contributions, and input fields for the target languages other than English. At the very minimum, the chairs would then be able to analyze the acceptance rates of different types of submissions, and compare it with ours (Table 7). One step further would be to also solicit this information from the reviewers, and estimate the quality of automated matches by the explicit keyword matches (see Table 2). One more practical takeaway for future work is that if we used a solution relying purely on publication history from Semantic Scholar—25% of our matches would have been made on unreliable information. For embeddings-based solutions to work better, we would first need to provide them with better data, and this will take a bigger Semantic Scholar cleaning campaign than what we were able to elicit. Reconsidering the acceptance rate for Findings. The initial iterations of Findings starting with EMNLP 2020 had the Findings acceptance rate at about 35%. This is the target rate we gave to our SACs, and then we tried to accommodate as many of their ranked preferences as we could. Although we had over 40% rate with Findings, still, in many SAC comments we saw that they were overriding acceptance recommendations of ACs only to meet the quotas. While the quota for the Main track will stay at 20-25% for venue ranking reasons, we do not see why Findings could not be further extended to have room for most sufficiently sound work. About 60% of our direct submissions had at least two positive (above-borderline) reviews for *Soundness* and at least one for *Excitement*. Assuming some noise in the negative reviews for *Soundness*, it would be only reasonable to expect that at least 45%-50% submissions are Findings-worthy. Of course, the track SACs would not *have* to accept that many (the ratio of high-quality papers may vary between tracks and years), but when they do not see good reasons to reject - they should not be constrained by the Findings quota. This step would presumably also further decrease the burden of re-reviewing for resubmissions. We also recommend developing a standard process for Findings authors to apply for presentation at topically matching workshops, and for at least virtual poster presentation slots at the main conference. Further research on the effect of preprinting on peer review. We find that the preprinted papers have consistently higher ratings (for both Soundness, *Excitement*, and reviewer confidence), get more recommendations for awards, and a higher acceptance rate. There are several possible underlying causes (from reviewer biases to higher initial paper quality and benefits of community feedback), which likely all contribute to this effect. Since these factors necessitate different actions if they were the major contributor to the observed effect, for informed policy decisions it is necessary to establish how they intermix. We observe however that although the present 1-month embargo policy does not solve this problem, it is effective at mitigating it, since we only had 13.8% such papers. Consistently working to improve peer review concistency. Our analysis shows that the inconsistency in numerical reviewer score ratings is remarkably consistent across *ACL conferences (at about ↵ 0.3 across EMNLP'22, EACL'23, and ACL'23). Among the likely culprits are miscalibrated scales, different interpretations of scales, at least some reviewers not even reading the guidelines, and reviewer biases. That said, we do see almost twice the raw agreement for our *Soundness* score (that is supposed to be more objective) over *Excitement* (more subjective), when the scores are mapped to the sound/unsound vs exciting/unexciting categorical variables. This suggests that asking more concrete questions does help (as long as the reviewer form does not become too complicated), and we can continue improving peer review on the basis of the general NLP methodology for iterating on guidelines and measuring agreement. Ethics review. The innovation of the ethics review is useful and necessary, but it should be explicitly built into the timeline. We particularly struggled with the conditional accepts. Responsible NLP Checklist. With predominantly positive reviewer feedback and evidence of improved acceptance rates for submissions that follow the best reporting practices, we believe that this is an important instrument for creating the right incentives for better science. We also recommend continuing to make it public, to strengthen these incentives. AI-assisted reviews. We did not expect this happen so soon, but already at ACL'23 some chairs reached out to us with questions about reviews that they suspected to be at least partly generated. The reviewer guidelines will need to be updated with respect to that as well, including how sending papers to cloud-based language models may violate confidentiality. Review policy updates. The rise of popular commercial systems such as ChatGPT that are claimed to be general-purpose, made an unfortunate match with our field's tendency to expect the popular systems in all papers as universal baselines. We did not consider this at ACL'23, since ChatGPT fell out of scope of 3-month policy for considering contemporaneous work, but we did already have at least one precedent of a reviewer asking for a comparison with ChatGPT. We recommend that future chairs develop a clear policy in the reviewer guidelines about requests for comparisons with "closed" systems, to avoid numerous issues with evaluation methodology and benchmark data contamination (Rogers, 2023). Expanding the set of volunteer roles. Our experience suggests that PC-ing a conference of ACL'23 size is a job that can no longer be realistically done by 3 volunteers. Early on, we introduced a *visa* support team41 to start early with issuing the letters of invitation for Canada. We also had crucial help from two *PC assistants*: Youmi Ma, an administrative assistant who handled much of the conference email, and Marzena Karpinska, who helped with analysis of peer review data in this report. In the future, we recommend that a dedicated role of a *peer review chair* is created, whose responsibility will be to supplement PC report with analysis of the data of the respective conference and comparing it with any records from previous conferences (so as to establish the effect of any new policies), and to coordinate the peer review awards selection and logistics (see §9.2). The growing volume of nominations for best papers requires a *best paper chair*, handling in effect the organization of a separate track and review process. Finally, we could have used a lot of help in the conference schedule: ideally there would be a dedicated schedule chair, ideally serving at several conferences so as to reduce friction and reuse the skill set as much as possible, as well as incorporate feedback from several events. Given that ACL had papers from SRW, Industry, ARR, TACL, CL, Findings, and the Main Conference, it's not necessarily feasible that the main track PCs can effectively coordinate scheduling all of these papers. Another option would be for each conference to have **two sets of PC chairs, one remaining from the** previous year and one new. This would lighten the workload and ensure a smoother process (since people do not learn how to do everything from scratch each time). The first-year PCs would do the bulk of the work after the paper notifications are sent, and the second-year PCs would concentrate on the review process, analysis and the report. The first-year PCs would observe that and have better knowledge for designing the review process (CFP, SAC nominations, review criteria, etc).The second-year PCs would observe the COI requirements. ## 11 Acknowledgements ACL'23 was the result of an incredible effort of 70 SACs, 438 ACs, 4490 reviewers, and 13,658 authors. We also thank our 2 ethics chairs and their 21 reviewers, as well as 15 judges on the best paper committee. We thank the ARR team, and particularly Jonathan K. Kummerfeld, Thamar Solorio and Mausam, for their help with integrating ARR submissions and analyzing them. We had a chance to learn from the past chairs Smaranda Muresan, Preslav Nakov and Aline Villavicencio (ACL 2022), Yoav Goldberg, Zornitsa Kozareva, Yue Zhang (EMNLP 2022), and Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur (NAACL 2021). We also thank EMNLP 2022 and EACL 2023 (Isabelle Augenstein, Andreas Vlachos) for sharing their score distribution data for our analysis. Our work is built on many iterations of previous *ACL conferences, including the AC and SAC guidelines developed at ACL 2021, and peer review tutorials developed by Anna Rogers and Isabelle Augenstein for ACL Rolling Review. Our paper-reviewer matching relied on Semantic Scholar data, kindly provided by Kyle Lo (AI2). The Semantic Scholar team also provided extra support to numerous authors working to clean up their profiles. Emma Strubell, Ian Magnusson, and Jesse Dodge helped us to prepare publishable versions of Responsible NLP checklist. We were only able to devote that much effort to peer review and its analysis thanks to the help of our brilliant assistants Youmi Ma and Marzena Karpinska. Richard Gerber (START) responded to numerous issues and implemented several changes at our request, including the possibility to include "explanations" for the paper-reviewer matching. We deeply thank the ACL Executive (especially Iryna Gurevych, Tim Baldwin, David Yarowsky, Yusuke Miyao, and Emily M. Bender) for their support of many of our crazy ideas, including the reviewer awards and the publication of this report. Last but not least, we thank our publication chairs and ACL Anthology team, in particular, Ryan Cotterell and Matt Post - for their infinite patience with this last-minute publication. ## References Mohamed Abdalla, Jan Philip Wahle, Terry Ruas, Aurélie Névéol, Fanny Ducel, Saif M. Mohammad, and Karën Fort. 2023. The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research. Omer Anjum, Hongyu Gong, Suma Bhat, Wen-Mei Hwu, and JinJun Xiong. 2019. PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 518–528, Hong Kong, China. Association for Computational Linguistics. Ron Artstein and Massimo Poesio. 2008. Survey article: Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4):555–596. Rachel Bawden. 2019. 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liu-etal-2023-one

One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems

https://aclanthology.org/2023.acl-long.1

User simulators are agents designed to imitate human users; recent advances have found that Task-oriented Dialogue (ToD) systems optimized toward a user simulator could better satisfy the need of human users. However, this might result in a sub-optimal ToD system if it is tailored to only one \textit{ad hoc} user simulator, since human users can behave differently. In this paper, we propose a framework called MUST to optimize ToD systems via leveraging Multiple User SimulaTors. The main challenges of implementing MUST fall in 1) how to adaptively determine which user simulator to interact with the ToD system at each optimization step, since the ToD system might be over-fitted to some specific user simulators, and simultaneously under-fitted to some others; 2) how to avoid catastrophic forgetting of the adaption for a simulator that is not selected for several consecutive optimization steps.To tackle these challenges, we formulate MUST as a Multi-armed bandits (MAB) problem and provide a method called MUST$_{\mathrm{adaptive}}$ that balances \textit{i}) the \textit{boosting adaption} for adaptive interactions between different user simulators and the ToD system and\textit{ii}) the \textit{uniform adaption} to avoid the catastrophic forgetting issue.With both automatic evaluations and human evaluations, our experimental results on MultiWOZ show that the dialogue system trained by MUST achieves a better performance than those trained by a single user simulator. It also has a better generalization ability when testing with unseen user simulators.

# One Cannot Stand For Everyone**! Leveraging Multiple User Simulators** To Train Task-Oriented Dialogue Systems Yajiao LIU1,2, Xin Jiang3, Yichun Yin3, Yasheng Wang3**, Fei Mi**3 Qun Liu3, Xiang Wan2**, Benyou Wang**1,2 1The Chinese University of Hong Kong, Shenzhen 2Shenzhen Research Institute of Big Data 3Huawei Noah's Ark Lab [email protected] ## Abstract User simulators are agents designed to imitate human users; recent advances have found that Task-oriented Dialogue (ToD) systems optimized toward a user simulator could better satisfy the need of human users. However, this might result in a sub-optimal ToD system if it is tailored to only one *ad hoc* user simulator, since human users can behave differently. In this paper, we propose a framework called MUST 1to optimize ToD systems via leveraging Multiple User SimulaTors. The main challenges of implementing the MUST are 1) how to adaptively determine which user simulator to interact with the ToD system at each optimization step, since the ToD system might be over-fitted to some specific user simulators, and simultaneously underfitted to some others; 2) how to avoid catastrophic forgetting of the adaption for a simulator that is not selected for several consecutive optimization steps. To tackle these challenges, we formulate MUST as a Multi-armed bandits (MAB) problem and provide a method called MUSTadaptive that balances i) the *boosting adaption* for adaptive interactions between different user simulators and the ToD system and ii) the *uniform adaption* to avoid the catastrophic forgetting issue. With both automatic evaluations and human evaluations, our experimental results on MultiWOZ show that the dialogue system trained by MUST achieves a better performance than those trained by a single user simulator. It also has a better generalization ability when testing with unseen user simulators. ## 1 Introduction Task-oriented dialogue systems aim to help users accomplish their various tasks (e.g., restaurant reservations) through natural language conversations. Training task-oriented dialogue systems in 1The code is available at https://github.com/ kiseliu/must. supervised learning approaches often requires a large amount of expert-labeled dialogues, however collecting these dialogues is usually expensive and time-consuming. Moreover, even with a large amount of dialogue data, some dialogue states may not be explored sufficiently for dialogue systems 2 (Li et al., 2016b). To this end, many researchers try to build user simulators to mimic human users for generating reasonable and natural conversations. By using a user simulator and sampling user goals, we can train the dialogue system from scratch with reinforcement learning (RL) algorithms. Previous works tend to design better user simulator models (Schatzmann et al., 2007; Asri et al., 2016; Gur et al., 2018; Kreyssig et al., 2018; Lin et al., 2021). Especially, Shi et al. (2019) builds various user simulators and analyzes the behavior of each user simulator in the popular restaurant search task from MultiWOZ (Budzianowski et al., 2018). In real scenarios, dialogue systems need to face various types of users. A single *ad hoc* user simulator can only represent one or a group of users, while other users might be under-represented. Instead of choosing the best-performing one from many dialogue systems trained by different single user simulators, we believe that it is worth trying to train a dialogue system by leveraging all user simulators simultaneously. In this paper, we propose a framework called MUST to utilize Multiple User SimulaTors simultaneously to obtain a better system agent. There exist several simple ways to implement the MUST framework, including a merging strategy, a continual reinforcement learning (CRL) strategy, and a uniform adaption strategy, namely MUSTmerging, MUSTCRL, and MUSTuniform respectively (See §3.2). However, none of them could effectively tackle the challenges: 1) how to efficiently leverage multiple user simulators to train the dialogue 2We use the dialogue systems to refer to the task-oriented dialogue systems for simplicity in this paper. system since the system might be easily over-fitted to some specific user simulators and simultaneously under-fitted to some others, and 2) it should avoid a catastrophic forgetting issue. To tackle them effectively, we first formulate the problem as a Multi-armed bandits (MAB) problem (Auer et al., 2002); similar to the exploitation vs exploration trade-off, specifying multiple user simulators should trade off a boosting adaption (tackling challenge 1) and a uniform adaption (tackling challenge 2), see §4.1 for more details. Then we implement a new method called MUSTadaptive to utilize an adaptively-updated distribution among all user simulators to sample them when training the dialogue system in the RL training. Our contributions are three-fold: (1) To the best of our knowledge, our proposed MUST is the first developed work to improve the dialogue system by using multiple user simulators simultaneously; (2) We design several ways to implement the MUST. Especially, we formulate MUST as a Multi-armed bandits (MAB) problem, based on which we provide a novel method MUSTadaptive; and (3) The results show that dialogue systems trained with MUST consistently outperform those trained with a single user simulator through automatic and human evaluations, showing its potential for robustness to the diversity of user simulators. Importantly, it significantly improves the performance of the dialogue system tested on out-of-domain evaluation. Moreover, our results show that our method MUSTadaptive can efficiently leverage multiple user simulators to train the dialogue system in terms of convergence speed. ## 2 Background 2 Dialogue system. Task-oriented dialogue systems aim to help users accomplish various tasks such as restaurant reservations through natural language conversations. Researchers usually divide the task-oriented dialogue systems into four modules (Wen et al., 2017; Ham et al., 2020; Peng et al., 2021): Natural Language Understanding (NLU) (Liu and Lane, 2016) that first comprehends user's intents and extracts the slots-values pairs, Dialog State Tracker (DST) (Williams et al., 2013) that tracks the values of slots, Dialog Policy Learning (POL) (Peng et al., 2017, 2018) that decides the dialog actions, and Natural Language Generation (NLG) (Wen et al., 2015; Peng et al., 2020) that translates the dialog actions into a natural-language form. The DST module and the POL module usually are collectively referred to as the dialogue manager (DM) (Chen et al., 2017). These different modules can be trained independently or jointly in an end-to-end manner (Wen et al., 2017; Liu and Lane, 2018; Ham et al., 2020; Peng et al., 2021). User simulator. The user simulator is also an agent but plays a user role. Different from dialogue systems, the user agent has a goal describing a target entity (e.g., a restaurant at a specific location) and should express its goal completely in an organized way by interacting with the system agent (Takanobu et al., 2020). Therefore, besides the modules of NLU, DM, and NLG like dialogue systems, the user agent should have another module called Goal Generator (Kreyssig et al., 2018), which is responsible for generating the user's goal. Building a user simulator could usually use an agenda-based approach (Schatzmann et al., 2007; Schatzmann and Young, 2009) designing handcrafted rules to mimic user behaviors or a model-based approach such as neural networks (Asri et al., 2016; Kreyssig et al., 2018; Gur et al., 2018) learned on a corpus of dialogues. Training dialogue systems with a user simulator. To start a dialogue, a user agent will have an initial goal from its Goal Generator and then expresses its goal in natural languages. However, users' goals are invisible to the system agent. Then the system agent tends to gradually understand the users' utterances, query the database to find entities, and provide useful information to accomplish users' task. When the database result returned by the system agent is empty, the user agent should learn to compromise and change its goal with the help of Goal Generator. When the dialogue ends, the user simulator will reward the system agent according to if it accomplishes the task. Then we could use the reward to update the system agent with RL algorithms (Tseng et al., 2021). ## 3 Must: A Framework To Leverage Multiple User Simulators 3.1 Motivations To Use Multiple Simulators User simulators behave differently. Shi et al. (2019) implement six user simulators (AgenT, AgenR, AgenG, RNNT, RNNR, RNN 3) with both 3Here we rename the user simulators of SLT, SLR, and SLE in Shi et al. (2019) as RNNT, RNNR, RNN for emphasizing the model structure of their DM modules. (a) Success rates of different systems. (b) Dialog act distributions of different user simulators. ![2_image_0.png](2_image_0.png) ![2_image_1.png](2_image_1.png) agenda-based methods and neural networks-based methods on the popular restaurant search task from MultiWOZ (Budzianowski et al., 2018). From their experiments, we observed that the dialogue systems trained by different user simulators vary in their performances (i.e., the success rates tested by the same user simulators). For example, when interacting with the user simulator of AgenT, the success rates of the system agents trained by Agenda-based user simulators (i.e., AgenT, AgenR, AgenG) are much higher than those of the system agents trained by RNN-based user simulators (i.e., RNNT, RNNR, RNN), see Fig. 1(a). The reason might be that these user simulators (i.e., with either handcrafted rules or data-driven learning in their DM modules) have different user dialog act distributions 4(see Fig. 1(b)) which determine the dialogue state space explored by the dialogue system. One cannot stand for everyone. Users might behave differently, one could design different user simulators with specific user dialog act distributions, see Shi et al. (2019). A single user simulator learned on a task-oriented dialogue corpus can just represent one or a group of users, while the dialogue system needs to accomplish tasks from various human users in real scenarios. We argue that it is beneficial to utilize all different user simulators to train the dialogue system. By leveraging multiple user simulators that have different user dialog act distributions, the dialogue systems can explore a larger dialogue state space, which might improve the ability of the learned dialogue system. ## 3.2 Some Preliminary Proposals For Must We propose a framework called MUST, the core idea of which is to train a better dialogue system by leveraging Multiple User SimulaTors simultaneously. There are several simple ways to implement our MUST, including a *merging* strategy (MUSTmerging), a *Continual Reinforcement Learning* strategy (MUSTCRL), and a *uniform adaption* strategy (MUSTuniform). (I) MUSTmerging first samples some dialogues from each user simulator and the corresponding dialogue system trained by this simulator. Then it combines the collected dialogues to train a new user simulator for ensembling different user dialog act distributions. Finally, it uses this new user simulator to train the dialogue system with RL. (II) MUSTCRL5treats each user simulator as an independent RL environment. It moves the trained system agent to another one (i.e., let the system agent interact with another user simulator) if the system has converged in the current environment. (III) MUSTuniform allows the system agent have chances to interact with all user simulators simultaneously. Different from MUSTCRL, MUSTuniform puts all user simulators in a single RL environment and adopts the simplest way to specify different user simulators to train the dialogue system, which is to pick a user simulator among all user simulators with a uniform distribution for each iteration in the RL training. | dynamic | avoiding forgetting | efficiency | | |--------------|-----------------------|--------------|----| | adaption | catastrophic | | | | MUSTmerging | × | × | × | | MUSTCRL | × | × | × | | MUSTuniform | × | ✓ | × | | MUSTadaptive | ✓ | ✓ | ✓ | ## Challenges To Leverage Multiple User Simulators. It is difficult to adaptively adjust weights of user simulators during training in MUSTmerging. Since the proportions of dialogues from each user simulator are fixed in MUSTmerging, user simulators might be well-adapted and others might not. The MUSTCRL strategy has a problem of catastrophic forgetting (Khetarpal et al., 2020) and would be sensitive to the order of different user agents interacting with the dialogue system, which might result in obtaining a sub-optimal dialogue system. As Shi et al. (2019) shows, the system agents trained by different user simulators have different convergence speeds and converged performances. Namely, the system agent might be easily fitted to some user simulators but might be hardly fitted to others. A uniform distribution for the simulator selection under MUSTuniform will result in inefficient training, since it would be unnecessary to assign the many training costs for easily-adapted user simulators. Overall, the challenging problems under MUST are 1) how to efficiently leverage multiple user simulators to train the system agent, and 2) avoiding the catastrophic forgetting issue. ## 4 Must As A Mab Problem To tackle the challenges in MUST, we first formulate MUST as a Multi-armed bandit (MAB) problem, see §4.1. In §4.2, we propose a method called MUSTadaptive to use an adaptively-updated distribution to replace the uniform distribution under the MUSTuniform for accelerating the MUST training. We briefly compare these different implementations of MUST in Tab. 1. ## 4.1 Formulating Must As A Mab Problem Adaptively specifying user simulators to train dialogue systems reminds us of a similar concept in machine learning, called *boosting* (Zhou, 2012). From a *boosting* point of view, one should increase the weights of weakly-performing data examples and decrease the weights for well-performing ones. In MUST, we accordingly assume that it should reduce the interactions between the dialogue system and those user simulators that the system has performed well; and meanwhile increase the interactions between the system and other user simulators that the system performs poorly. We refer to this strategy as *boosting adaption*. Meanwhile, we should also give some chances to all user simulators to relieve the catastrophic forgetting issue. We refer to this as *uniform adaption*. Such a trade-off between *boosting adaption* and uniform adaption is similar to the *the exploitation* vs exploration trade-off existing in the Multi-armed bandit (MAB) problem (Auer et al., 2002). Here, we interpret MUST as a MAB problem. We treat each user simulator as an arm. Suppose there are K arms (simulators), and each arm i has a fixed but unknown reward distribution Ri with an expectation µi. At each time step t = 1, 2*, ..., T*, one must choose one of these K arms. We denote the arm pulled at time step t as it ∈ {1*, ..., K*}. After pulling an arm, it receives a reward xit drawn from the arm's underlying reward distribution. The decision maker's objective is to maximize the cumulative expected reward over the time horizon $$\sum_{t=1}^{T}\mathbb{E}[x_{it}]=\sum_{t=1}^{T}\mu_{it}.\tag{1}$$ In MUST, the reward received in each arm In MUST, the reward received in each armpulling step refers to the possible performance gain of the dialogue system after it interacts with a selected user simulator. A significant *difference* between the standard MAB problem and MUST is that the reward expectation of a user simulator (arm) in MUST is not static; it changes over time. For example, by consecutively interacting with the same user simulator, the performance gain (reward) of the system will decay since the system might be in saturation or overfitting to this simulator. Moreover, the performance gain of the system after interacting with a simulator might increase if the simulator has not been selected for a period. To deal with this *difference*, we should tailor the solution of MAB to the MUST framework. ## 4.2 Training With Mustadaptive To solve this MAB problem in MUST, we implement a method called MUSTadaptive with a two-phase procedure, as presented in Algorithm 1. MUSTadaptive specifies user simulators in a 4 Algorithm 1: Implement MUSTadaptive with the *modified* UCB1 algorithm Input: K fixed User simulators U = {U1, U2, *· · ·*UK} and the values of hyperparameters Twarmup*, T, e, d, τ* ; 1 **Initialization**: randomly initialize System agent S; 2 **Initialization**: initialize the simulator sampling distribution p as a uniform distribution. 3 (1) **Warm-up phase:** 4 for t = 0, ..., T*warmup* − 1 do 5 sample a simulator Uj in U w.r.t. the distribution p; 6 synthesize a new dialogue using the system agent S and the sampled Uj ; 7 use the reward obtained for the dialogue to update S with a RL algorithm; 8 (2) **Adaptive phase:** 9 for t = 0*, ..., T* − 1 do 10 if t%e == 0 **then** 11 for j = 1*, ..., K* do 12 evaluate the performance i.e. the success rate x¯j of the agent S by letting it interact d times with the simulator Uj ; 13 update p based on these success rates {x¯1*, ...,* x¯K} (see Eq. 2, Eq. 3, and Eq. 4); 14 **else** 15 sample a simulator Uj in U w.r.t. the distribution p; 16 synthesizing a new dialogue using the system agent S and the sampled Uj ; 17 use the reward obtained for the dialogue to update S with a RL algorithm; Output: The learned dialogue system S. uniform distribution, similar to the UCB1 6algorithm, to train the dialogue system S in the first Twarmup steps (i.e., in the *warm-up phase*). After that, the *adaptive phase* will balance the boosting adaption and the uniform adaption by introducing an adaptively-updated distribution p, which is used to specify different user simulators to train the system S in later RL training. To accelerate the RL training, intuitively, p is expected to assign lower weights to user simulators with which S *already* performs well and *higher weights to those user* simulators with which S *performs poorly*. (1) Warm-up phase : in the first Twarmup dialogues, we use a uniform distribution to sample all user simulators to train the system agent S (lines 4-7). This phase is mainly used to warm up the dialogue system S. (2) Adaptive phase : the distribution p used to sample all user simulators will be adaptively updated. We call it as the **adaptive phase**. When this phase begins (i.e., t = 0), we will first evaluate the performance (i.e., the success rate x¯j , j ∈ {1, · · · , K}) of the dialogue system S trained after the **warm-up phase**. The success rate x¯j is obtained by letting S interact d times with the simulator Uj (e.g., j ∈ {1*, ..., K*}) and calculating the 6There exists an algorithm called UCB1 (Upper Confidence Bound 1 ) (Auer et al., 2002) that could solve the MAB problem. It first pulls each arm once in the first K steps, then will play the arm that could maximize the sum of two terms: it = arg maxi x¯i + q2 ln t Ti,t from t = K + 1 to T. success rates. Inspired by UCB1 (Auer et al., 2002), we design a calibrated **performance expectation** xˆj of the system agent S interacting with each user simulator Uj taking exploration into consideration beyond pure exploitation: **Pure exploration:** $$\hat{x}_{j}=\underbrace{\bar{x}_{j}}_{\text{exploitation}}+\underbrace{\sqrt{\frac{2\ln t}{T_{j,t}}}}_{\text{exploitation}},j\in\{1,...,K\};\tag{2}$$ where $\bar{x}_{j}$ is the success rate of the system agent $S$. $$\mathbf{\Sigma}^{0}$$ tested with user simulator Uj , and Tj,t is the number of times user simulator Uj has been selected with so far. Then we normalize xˆj into $$z_{j}=1/\left({\hat{x}}_{j}-\tau\operatorname*{min}(\{{\bar{x}}_{1},\cdots,{\bar{x}}_{K}\})\right),$$ $\uparrow$). Eq. 3 penalizes the user simulators with which the dialogue system already performs well in the expectation term. Where the hyperparameter τ is the smooth factor for distribution p = {p1, *· · ·* , pK} - the larger τ is, the sharper p is. Each probability pj in p is calculated as $$\mathbf{p}_{j}=\frac{\tilde{z}_{j}}{\sum_{i=1}^{K}z_{i}}.\tag{4}$$ In the following $T-1$ dialogues, we will specify In the following T − 1 dialogues, we will specify all user simulators to train the system agent S with this distribution p (lines 15-18). We will also evaluate the RL model S for every e episodes (line 10-12) and update the distribution p with the new K success rates (line 13). Difference with the original UCB1. The main differences between our modified UCB1 algorithm and the original UCB1 algorithm are twofold. First, we tailor the original UCB1 into our scenario by using Eq. 3 to penalize the user simulators with which the dialogue system has performed well. Secondly, we adopt a sampling schema based on a well-designed distribution (see Eq. 4) instead of taking the arm with the highest expectation. This is to increase the diversity and flexibility of arm selection. ## 5 Experiments To verify the effectiveness of MUST, we benchmark the system agents trained either with a single user simulator or multiple user simulators (including MUSTmerging, MUSTuniform, and MUSTadaptive). See MUSTCRL in the App. C. ## 5.1 Experimental Setup Available user simulators. There are six user simulators provided by Shi et al. (2019), which are Agenda-Template (**AgenT**), Agenda-Retrieval (**AgenR**), Agenda-Generation (**AgenG**), RNNTemplate (**RNNT**), RNN-Retrieval (**RNNR**), RNNEnd2End (RNN) trained with different dialog planning and generation methods. The NLU modules of all six user simulators are using the RNN model. The DM modules of AgenT, **AgenR**, and **AgenG** are rule-based methods. For the NLG module, these three simulators are using the template, retrieval, and generation methods respectively. The DM modules of **RNNT**, and **RNNR** are using Sequicity (Lei et al., 2018) as their backbones which is an RNN-based seq2seq model with copy mechanism. The NLG modules of these two simulators are using the template and retrieval methods respectively. The user simulator of RNN uses Sequicity as its backbone in an end-to-end manner. Baselines. The baselines are the dialogue systems trained by each user simulator, including SysAgenT, Sys-AgenR, Sys-AgenG, Sys-RNNT, **SysRNNR**, and **Sys-RNN**. For a fair comparison, all system agents (including the systems trained by our MUST) have the same architecture described in Shi et al. (2019). See details in App. B.1. ## Multiwoz Restaurant Domain Dataset. The original task in MultiWOZ (Budzianowski et al., 2018) is to model the system response. Shi et al. (2019) annotate the user intents and the user-side dialog acts in the restaurant domain of MultiWOZ to build user simulators, which has a total of 1,310 dialogues. Moreover, we randomly simulate 2,000 dialogues from each rule-based simulator (i.e., AgenT, AgenR, AgenG) and their corresponding system agents respectively, and processe these dialogues to have the same annotation format as the MultiWOZ restaurant domain dataset. We denote this dataset as **Simulated Agenda Dataset**, which has a total of 6,000 dialogues. Evaluation Measures. A straightforward metric to evaluate dialogue systems is **the success rate** tested by each user simulator. We calculate the success rate between a user simulator and a system agent by sampling 200 dialogues. We exclude some user simulators in training MUST and test the systems with them as **out-of-domain evaluation**. According to the previous study Gunasekara et al. (2020), there usually is a gap between automatic evaluations and human evaluations of dialogue systems. Therefore, we ask humans to converse with dialogue systems. Each dialogue system has conversed with 5 different users; each user has 10 dialogues. In total, we collect 50 dialogues for each dialogue system to calculate its success rate. See more details in App. B.5. ## 5.2 Implementations 5.2.1 Two New User Simulators We believe Pre-trained Language Models (PLMs) might improve the capacity of user simulators since they have recently shown remarkable success in building task-oriented dialogue systems (Ham et al., 2020; Peng et al., 2021; Hosseini-Asl et al., 2020). Here we implement another two user simulators using GPT (Radford et al., 2018, 2019). Building a user simulator using GPT is similar to building a ToD system with GPT. See more details in App. G. GPT Simulator. It is first fine-tuned on the *simulated agenda dataset* and then fine-tuned on the MultiWOZ restaurant domain dataset by leveraging GPT. This user simulator will be used to help implementing MUST. GPTIL **Simulator.** To implement the MUSTmerging strategy, similar to Imitation Learning (IL), we first train a new user simulator with dialogue sessions collected from different user simulators and their corresponding dialogue systems. We also learn this new user simulator based on GPT model and denote it as GPTIL. GPTIL is first fine-tuned on the simulated agenda dataset. Then we sample 1,400 dialogues from the ![6_image_0.png](6_image_0.png) base success rates. [2] ↓ (↑) indicates by what percentages the success rate has decreased (increased) compared with the base success rate by interacting with the same user simulator. simulated agenda dataset and merge them with 1,310 *MultiWOZ restaurant domain dialogues* to continue fine-tuning GPTIL. ## 5.2.2 Dialogue Systems Sys-GPT is trained with the *single* user simulator GPT. **Sys-MUST**merging is trained with GPTIL. Sys-MUSTuniform is trained by the user simulators of AgenT, AgenR, RNNT, and GPT with a uniform sampling distribution. For training **SysMUST**adaptive 7, the distribution p will be adaptively updated using our modified UCB1 algorithm. We also train the Sys-MUSTuniform and Sys-MUSTadaptive by using different subsets of the user simulators for ablation studies in App. D. ## 5.3 Experimental Results Automatic Evaluation. As seen in Tab. 2, SysMUSTuniform and Sys-MUSTadaptive outperform the dialogue systems (Sys-AgenT, Sys-AgenR, SysRNNT, and Sys-GPT) trained by a single user simulator in the overall performance, demonstrating the superiority of leveraging multiple user simulators. Especially, **Sys-MUST**adaptive has a 1.2 absolute value improvement (92.9 vs. 91.7) averagely over the previous SOTA system Sys-AgenR. Observing that Sys-MUSTmerging is not as competitive as Sys-MUSTuniform and Sys-MUSTadaptive, this comparison shows that the merging strategy cannot effectively leverage multiple user simulators. In **in-domain evaluation**, the performances of systems (Sys-AgenT, Sys-AgenR, Sys-RNNT, and Sys-GPT) trained by a single user simulator drop a lot when testing with a different simulator. It requires us to delicately select a suitable user simula7See implementations of dialogue systems in App. B.2 and policy gradient algorithm in App. B.3. | Dialogue Systems | human evaluation | |----------------------|--------------------| | Sys-AgenT | 76.0 | | Sys-AgenR | 84.0 | | single Sys-RNNT | 34.0 | | Sys-GPT | 58.0 | | Sys-MUSTmerging | 90.0 | | MUST Sys-MUSTuniform | 92.0 | | Sys-MUSTadaptive | 92.0 | tor for obtaining a good dialogue system. However, users might be multi-facet or even unknown, making the selection even more difficult. Therefore, it is essential to leverage multiple user simulators when training dialogue systems. At least, the performance gap of dialogue systems trained with our MUST becomes smaller than without MUST, see the percentages labeled in green and red colors. In **out-of-domain evaluation** where the user simulators used for testing the systems are unseen by our MUST, Sys-MUSTuniform and SysMUSTadaptive achieve at most 2.4 absolute value improvement over Sys-AgenR. This evidences that MUST has a better generalization ability for interacting with unseen user simulators. Moreover, the dialogue systems (Sys-MUSTmerging, Sys-MUSTuniform, and Sys-MUSTadaptive) trained with the proposed MUST approaches have lower standard deviations, which indicates that they are more robust to the diversity of user simulators. Human Evaluation. In Tab. 3, the human evaluation results show that our Sys-MUSTuniform and Sys-MUSTadaptive largely outperform the other dialogue systems when interacting with real users. The consistency between automatic evaluations and human evaluations evidences the effectiveness of our proposed MUST. ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) ## 5.4 Analysis And Discussions Convergences of MUSTuniform and MUSTadaptive. In Fig. 2, we show the learning curves of Sys-MUSTuniform and Sys-MUSTadaptive in 100,000 steps; the first 40,000 steps are in the **warm-up phase** for Sys-MUSTadaptive. From Fig. 2(a), we can see that training the dialogue system with AgenT, AgenR, RNNT, and GPT by MUSTadaptive converges faster than by MUSTuniform . We do ablation studies on our *modified* UCB1 algorithm to help understanding the designed distribution p, see details in App. E. We further plot the performances of the dialogue system tested by each user simulator in the RL training in Fig. 2(b)-2(e). Visualization on MUSTadaptive. Let us define the adaptation difficulty of a user simulator using how many steps it must take to train the dialogue system with this user simulator until it converges. The adaptation difficulty of all user simulators could be ranked like AgenR > AgenT > GPT > RNNT according to Fig. 2(b)-2(e). To check whether MUSTadaptive tends to sample harder-to-adapt user simulators more times in the **adaptive phase**, as assumed in §4.2, we visualize the sampling proportions of all user simulators in Fig. 3(a). We could observe that AgenR was sampled with 45.1% (the biggest proportion) and it is indeed the hardest user simulator that can be adapted by the system; RNNT has the smallest sampling proportion and it is the easiest user simulator that can be adapted by the system. The consistency between the adaptation difficulty and sampling proportions for these four user simulators evidences our assumption in §4.2. Fig. 3(b) visualizes the variations of the sampling distributions of user simulators. Interestingly, it shows that AgenR and AgenT are *competitive* with the GPT simulator; while RNNT and GPT are *cooperative* with each other. This might be because both RNNT and GPT simulators are learned from the dialogue corpus and might share some similar behaviors. ## 6 Conclusion In this paper, we propose a framework named MUST to improve dialogue systems by using multiple user simulators simultaneously. We discuss several simple methods to implement MUST, which is either inflexible or inefficient. Therefore, we formulate MUST as a Multi-armed bandits (MAB) problem, based on which we propose a novel implementation called MUSTadaptive. The experimental results on the restaurant search task from MultiWOZ demonstrate that MUST can largely improve the system agent upon baselines, especially when tested with unseen user simulators. Moreover, MUSTadaptive is more efficient than other implementations. ## Limitation The main limitation of this work is that we only conduct our experiments on the restaurant domain of the MultiWOZ since we can only find multiple user simulators from Shi et al. (2019) and they build these simulators only on the restaurant search task. In future work, we plan to apply our proposed methods to multi-domain scenarios. ## Ethics Statement There are no ethics-related issues in this paper. The data and other related resources in this work are open-source and commonly-used by many existing work. ## Acknowledgements Part of this work was done when the first author worked at Huawei Noah's Ark Lab. Besides, this work is supported by the Chinese Key-Area Research and Development Program of Guangdong Province (2020B0101350001), the Shenzhen Science and Technology Program (JCYJ20220818103001002), the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen, Shenzhen Key Research Project (C10120230151) and Shenzhen Doctoral Startup Funding (RCBS20221008093330065). We would like to thank Zichao Li, Chen Zhang, and Dong Yang for their helpful discussions. Moreover, we thank anonymous reviewers for their valuable suggestions. ## References Layla El Asri, Jing He, and Kaheer Suleman. 2016. A sequence-to-sequence model for user simulation in spoken dialogue systems. Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. *Machine Learning*, 47(2–3):235–256. 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CRC press. ## A Multi-Armed Bandit Problem Reinforcement learning policies face the exploitation versus exploration trade-off, which can be described as the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. This exploitation vs exploration dilemma has been widely studied as a Multi-armed bandit (MAB) problem. In the MAB problem, there are K arms, and each arm j has a fixed but unknown reward distribution Rj with an expectation µj . At each time step t = 1, 2*, ..., T*, the decision maker must choose one of these K arms. We denote the arm pulled at time step t as jt ∈ {1*, ..., K*}. After pulling an arm, it will receive a reward Xjt which is a realization drawn from the arm's underlying reward distribution. The decision masker's objective is to maximize the cumulative expected reward over the time horizon PT t=1 E[Xjt] = PT t=1 µjt . ## B More Details About Training Dialogue Systems B.1 The Architectures Of User Simulators And Dialogue Systems The basic modules of user simulators and dialogue systems are detailed in Tab. 4. ## B.2 The Implementations Of The Dialogue Systems The NLU modules of all system agents are a 2-layer bidirectional-GRU with 200 hidden units. The NLG modules of them are using the template-based method. The DM modules of them are a simple MLP. The input of the DM module is a state representation, which consists of the traditional dialog state and word count vector of the current utterance same as Shi et al. (2019). We mainly use the policy gradient method to train the DM modules of dialogue systems from scratch. ## B.3 The Details Of Running Policy Gradient Algorithm For training the DM modules of dialogue systems with the policy gradient method, we also apply the ϵ-greedy exploration strategy. We let ϵ be 0.5 in the beginning, and it will decrease to 0 linearly within the RL training. The dialogue ends either when the user simulators say "goodbye" or when the number of turns of the dialogue exceeds 10. The reward will be given +1 for task success, -1 for task failure, and -0.1 for each additional turn to encourage the RL-based policy module to finish the task fast. Also, a discounted factor of 0.9 is applied to all the experiences. ## B.4 The Parameters Of Training Sys-Mustadaptive The hyperparameters used to train the SysMUSTadaptive are listed in the Tab. 5. Since some user simulators used for implementing our MUST framework are based on the GPT model, we train Sys-MUSTadaptive on a V100 GPU and it will cost around 15 hours with the default hyperparameters above. ## B.5 Human Evaluation On Dialogue Systems We find 5 volunteers to conduct the human evaluations on dialogue systems. They all have good English skills and are unpaid. Before the experiments, we introduced task-oriented dialogue systems and user simulators to them and tell them how to judge if the generated dialogue is successful. Then we prepare 50 user goals from **MultiWOZ Restaurant Domain Dataset**: 20 of them are simple, and 30 of them are a little bit complex. We specify 10 user goals for each volunteer and let the volunteer converse with all dialogue systems for each same user goal. In total, we collect 50 dialogues for each | Agent Types | Agents | NLU | DM | NLG | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------|-------|----------|-------| | AgenT (Shi et al., 2019) RNN† Agenda | Template | | | | | AgenR (Shi et al., 2019) RNN† Agenda | Retrieval | | | | | AgenG (Shi et al., 2019) RNN† Agenda RNN† (Generation) RNNT (Shi et al., 2019) RNN† Template RNNR (Shi et al., 2019) RNN† Retrieval RNN (Shi et al., 2019) RNN† (NLU + NLG) GPT (ours) Transformer† (NLU + DM + NLG) GPTIL (ours) Transformer† (NLU + DM + NLG) | | | | | | Dialogue Systems All | RNN† | RNN† | Template | | | User Simulators | | | | | | Hyperparameter | Value | |------------------|---------| | T | 100,000 | | T0 | 40,000 | | e | 2,000 | | d | 200 | | τ | 0.75 | dialogue system to calculate its success rate. The criteria to judge whether a task-oriented dialogue is successful are based on two aspects: 1) the system agent correctly understands the user's goal (i.e., the predicted dialogue state tracking result is correct); and 2) the system agent provides all information (i.e., all slot values or a booking reference number) that the user requests. For human evaluations, we follow these standard criteria. Besides, we also see if the system act generated by the system agent is matched to the user act for each turn in the dialogue. There have seven user acts, which are 'inform type", "inform type change", "ask info", "anything else", "make reservation", "make reservation change time", and "goodbye". There have nine system acts, which are "ask type", "present result", "nomatch result", "no other", "ask reservation info", "provide info", "booking success", "booking fail" and "goodbye". The relationships between user acts and system acts are shown in Tab. 6. ## C Implement Must With The Mustcrl Strategy Without losing any generality, we consider two representative sequential orders: 1) AgenT, AgenR, RNNT, GPT; and 2) AgenR, GPT, AgenT, RNNT. For case 1, the first two user simulators are Agendabased user simulators; the last two user simulators are Neural networks-based user simulators. For case 2, we interleave these two types of user simulators. When the system trained by a user simulator converges, we let it continue to interact with another user simulator following the order. As seen in Tab. 7, in case 1, the system agent achieves the best performance (i.e., 92.4 in terms of the average success rate) after training with AgenT and AgenR sequentially. However, its overall performance degrades to 83.0 after training with RNNT; especially, its performance decreases by 36.0% when testing with AgenR (93.0 → 59.5). Moreover, after continuing to learn from GPT, the performance of the system agent becomes worse for AgenT (95.0 → 75.5) and AgenR (59.5 → 47.5). This indicates the catastrophic forgetting issue heavily happened when the system agent starts learning from AgenR. We also could observe a similar phenomenon from case 2. These results can confirm that implementing our proposed MUST with MUSTCRL strategy indeed has the catastrophic forgetting issue. ## D Sensitivity On Different Subsets Of User Simulators We also train the Sys-MUSTuniform and SysMUSTadaptive by using different groups of user simulators for ablation studies: 1) five user simulators of AgenT, AgenR, RNNT, RNNR, and GPT; and 2) three user simulators including AgenT, RNNT, and GPT. Superiority of MUST. From Tab. 8 and Tab. 9, we can observe that Sys-MUSTuniform and SysMUSTadaptive largely outperform the dialogue systems trained by single user simulators. Especially, they gain an improvement of 4 absolute points (85.4 vs. 81.4) when trained with three user simulators of AgenT, RNNT, and GPT. In summary, MUST inform type ask type, present result, nomatch result inform type change ask type, present result, nomatch result anything else present result, no other make reservation ask reservation info, booking success, booking fail make reservation change time ask reservation info, booking success, booking fail ask info provide info goodbye goodbye | User act | System act | |------------|--------------| | goodbye | goodbye | Dialogue Systems **User simulators** AgenT AgenR RNNT GPT Avg. Case 1 trained by *AgenT* 97.5 54.0 98.5 78.0 82.0 trained by *AgenT, AgenR* sequentially 97.0 ↓ 0.5% 93.0 97.0 82.5 **92.4** trained by *AgenT, AgenR, RNNT* sequentially 95.0↓ 2.6% 59.5↓ 36.0% 97.0 80.5 83.0 trained by *AgenT, AgenR, RNNT, GPT* sequentially 75.5 ↓22.6% 47.5↓ 48.9% 96.0↓ 1.0% 82.0 75.3 Case 2 trained by *AgenR* 96.0 90.0 98.5 82.5 **91.8** trained by *AgenR, GPT* sequentially 97.5 88.0↓2.2% 97.0 81.5 91.0 trained by *AgenR, GPT, AgenT* sequentially 96.5 78.5↓12.8% 97.0 80.0↓1.8% 88.0 trained by *AgenR, GPT, AgenT, RNNT* sequentially 97.5↑1.0% 65.5↓27.2% 95.0 78.5↓3.7% 84.1 could consistently improve the performance of the systems when using different numbers of user simulators. The ablation studies on different subsets of user simulators can demonstrate the robustness of MUST. Out-of-domain evaluation. When testing our MUST with unseen user simulators, SysMUSTuniform and Sys-MUSTadaptive can also largely outperform the dialogue systems trained by a single user simulator. As seen in Tab. 8, Sys-MUSTadaptive achieves a 2.7 absolute value improvement (92.5 vs 89.8) over Sys-AgenR. Sys-MUSTuniform and Sys-MUSTadaptive even improve at least 5.7 points (80.0 vs 74.3) over Sys-GPT (as shown in Tab. 9). These experimental results on different subsets of user simulators demonstrate that our MUST has a better generalization ability for interacting with unseen user simulators and is insensitive to the user simulator selection. Comparison between MUSTuniform and MUSTadaptive. Fig. 4 shows the learning curves of Sys-MUSTuniform and Sys-MUSTadaptive on different subsets of user simulators. The first 40,000 steps are in the **warm-up phase** for SysMUSTadaptive. We could conclude that training the dialogue system by MUSTadaptive consistently converges faster than by MUSTuniform, at least in the scenarios when using three, four, or five user simulators to implement MUST (see Fig. 4(a), Fig. 2(a), and Fig. 4(b), respectively). From Tab. 8 where MUST is trained with five user simulators, we could observe that Sys-MUSTadaptive outperforms SysMUSTuniform with 0.5 absolute point. The performance gain becomes smaller when MUST is trained with three user simulators (see Tab. 9). This probably shows that Sys-MUSTadaptive would be more beneficial when there exist more user simulators. ## E Ablation Study For The Modified Ucb1 Algorithm E.1 Necessity Of The Exploration Term Our *modified* UCB1 algorithm provides a distribution for guiding how to sample different user simulators to accelerate the entire MUST training. The exploration term in the proposed MUSTadaptive exists mainly for uniform adaption (see the detailed explanation in Sec. 4.1). The original UCB1 algorithm (Auer et al., 2002) can tell us how to pull arms in bandits to maximize the cumulative expected reward. It is well-known that it cannot explore effectively without the exploration (UCB) term; consequently, it might not find the optimal action and lead to relatively poor performance. It is difficult to theoretically prove the usefulness of the exploration term in our scenario (like in the original UCB1 algorithm), which we leave as future work. However, we alternatively conduct some ablation Dialogue Systems In-domain evaluation Out-of-domain evaluation All AgenT AgenR RNNT RNNR GPT AgenG RNN Avg.↑ Std.↓ Avg.↑ Std.↓ ![13_image_0.png](13_image_0.png) Sys-AgenT 97.5 54.0 ↓40.0% 98.5 ↓0.5% 92.5↓1.0% 78.0↓4.9% 72.5 77.0 74.8 2.3 81.4 14.8 Sys-AgenR 96.0 ↓1.5% 90.0 98.5↓0.5% 97.5↑4.3% 80.5↓1.8% 97.5 82.0 89.8 7.8 91.7 7.1 Sys-RNNT 30.5 ↓68.7% 23.0 ↓74.4% 99.0 97.5↑4.3% 75.5↓7.9% 35.5 84.0 59.8 24.3 63.6 30.5 Sys-RNNR 30.0 ↓68.7% 23.0 ↓74.4% 96.5↓2.5% 93.5 68.5↓16.5% 30.0 70.5 50.3 20.3 58.9 28.8 Sys-GPT 60.5 ↓37.9% 51.5 ↓42.8% 97.0 ↓2.0% 94.0↑0.5% 82.0 59.5 92.0 75.8 16.3 76.6 17.6 Sys-MUSTuniform 97.5 ↑0.0% 87.0 ↓3.3% 97.0↓2.0% 97.5↑4.3% 82.0↑0.0% 96.5 87.0 91.8 4.8 92.1 6.0 Sys-MUSTadaptive 97.0 ↓0.5% 89.0 ↓1.1% 97.0↓2.0% 97.5↑4.3% 82.5↑0.6% 97.5 87.5 **92.5** 5.0 **92.6 5.7** | Dialogue Systems | In-domain evaluation | Out-of-domain evaluation | All | | | | | | | | | | |----------------------|------------------------|----------------------------|----------------------------------------------|-----------|------|------|------|------|------|------|------|------| | AgenT | RNNT | GPT | AgenR AgenG RNNR RNN Avg.↑ Std.↓ Avg.↑ Std.↓ | | | | | | | | | | | Sys-AgenT | 97.5 | 98.5↓0.5% | 78.0 ↓0.5% | 54.0 | 72.5 | 92.5 | 77.0 | 74.0 | 13.7 | 81.4 | 14.8 | | | single | Sys-RNNT | 30.5 ↓68.7% | 99.0 | 75.5↓7.9% | 23.0 | 35.5 | 97.5 | 84.0 | 60.0 | 31.4 | 63.6 | 30.5 | | Sys-GPT | 60.5 ↓37.9% 97.0 ↓2.0% | 82.0 | 51.5 | 59.5 | 94.0 | 92.0 | 74.3 | 19.0 | 76.6 | 17.6 | | | | MUST Sys-MUSTuniform | 97.5↑0.0% | 96.0↓3.0% | 82.5↑0.6% | 55.0 | 82.0 | 97.5 | 87.0 | 80.3 | 15.7 | 85.4 | 13.9 | | | Sys-MUSTadaptive | 97.5↑0.0% | 97.5↓1.5% | 82.5↑0.6% | 55.5 | 80.5 | 97.0 | 87.0 | 80.0 | 15.3 | 85.4 | 13.9 | | studies to evidence the necessity of the exploration term. MUSTadaptive**w/t exploration.** If we omit the exploration term in our *modified* UCB1 algorithm, the simplest way to calculate the distribution p is to make the sample probability w.r.t a user simulator solely depend on the inversion of the system's performance. See the row called 'w/t exploration' in Tab. 10 for comparisons. In this situation, the obtained distribution p might be sharp due to the lack of the exploration term, which would be harmful for uniform adaption to some extent. As Fig. 5(a) shows, MUSTadaptive performs worse and converges slower when omitting the exploration term, compared with when our *modified* UCB1 algorithm has the exploration term. This could demonstrate both the importance of uniform adaption and the usefulness of the exploration term. ## E.2 Ablation Study On The Designed Distribution Rationale of exploitation vs exploration tradeoff. Similar to the exploitation vs exploration trade-off, the distribution p under the MUSTadaptive should trade off the boosting adaption and the uniform adaption when specifying multiple user simulators. Considering the boosting adaption, we make a *exploitation assumption* stated as follows: p is expected to *assign lower* weights to user simulators with which the system agent S *already performs well* and *higher weights* to those user simulators with which S *performs* ![13_image_1.png](13_image_1.png) ![13_image_2.png](13_image_2.png) ![13_image_3.png](13_image_3.png) poorly. Therefore, the sampling ratios for different user simulators should be inversely proportional to the system's performance on each user simulator. Rationale of the modified UCB1 algorithm. The modified UCB1 algorithm for implementing MUSTadaptive is defined as $$\begin{array}{l l}{{\hat{x}_{j}=\underbrace{\bar{x}_{j}}_{\mathrm{exploitation}}+\underbrace{\sqrt{\frac{2\ln t}{T_{j,t}}}},j\in\{1,...,K\};}}\\ {{z_{j}=1/\left(\hat{x}_{j}-\tau\operatorname*{min}(\{\bar{x}_{1},\cdots,\bar{x}_{K}\})\right),}}\\ {{\mathbf{\mathit{p}}_{i}=\frac{z_{j}}{\sum_{j=1}^{K}z_{j}}.}}\end{array}\tag{5}$$ MUSTadaptive in Eq. 5 (which is the same as Eq. 2, Eq. 3, and Eq. 4) consists of three steps: exploitation-exploration term construction, postprocessing (re-scaling operation and the inversion operation), and the probability normalization, corresponding to each line in Eq. 5. Besides this way, we could have the following three variants that shuffle the order of these three key operations (i.e., the exploitation-exploration term construction, re-scaling operation, and the inversion operation). We name these variants as as MUSTadaptiveI, MUSTadaptive-II, and MUSTadaptive-III. MUSTadaptive-I. For the exploitation assumption, we make the exploitation term inversely proportional to the system's performance x¯j on each user simulator Uj , which is denoted as ![14_image_0.png](14_image_0.png) ![14_image_2.png](14_image_2.png) MUSTadaptive-I. From Tab. 10, we can obverse that the difference between MUSTadaptive-I and MUSTadaptive is that MUSTadaptive-I take the inversion of x¯ before the exploitation-exploration term construction while MUSTadaptive take the inversion operation after the exploitation-exploration term construction. Since each x¯j , j ∈ {1, · · · , K} is smaller than 1, 1 x¯j will be larger than 1. Therefore, the term of 1 x¯j and the exploration term of q2 ln t Tj,t (smaller than 1) are not with the same magnitude, which will lead to a consequence that the exploitation term becomes dominant while the exploration term is negligible. We have discussed a similar issue of ignoring the exploration term in Sec. E.1. Therefore, we adopt MUSTadaptive in default if not specified rather than MUSTadaptive-I since the latter might suffer from the different magnitudes of the exploitation term and the exploration term. ![14_image_1.png](14_image_1.png) ![14_image_3.png](14_image_3.png) MUSTadaptive**-II and MUST**adaptive**-III.** Compared to MUSTadaptive, MUSTadaptive-II moves the inversion operation to the front of the constructed exploitation-exploration term. Likewise, MUSTadaptive-III moves the re-scaling and the inversion operations to the front of the constructed exploitation-exploration term. MUSTadaptive-II and MUSTadaptive-III are used to check the order sensitivity about the exploitation-exploration term construction, re-scaling operation, and the inversion of x¯j , j ∈ {1, · · · , K}. Results for ablation study on the variants. Experimental results of these different variants are shown in Fig. 5(b). The convergence speed of MUSTadaptive-I is much slower compared to others, which demonstrates that the exploration term is useful once more. The convergence speeds of MUSTadaptive-II and MUSTadaptive-III is comparative to MUSTadaptive. This probably shows that | variants | exploitation-exploration term | post-processing | distribution | | |-------------------------------------|---------------------------------|--------------------------------------|------------------------------------------|----| | q2 ln t | | | | | | MUSTadaptive | xˆj = x¯j + | Tj,t | zj = | 1 | | (xˆj−τ min({x¯1,··· ,x¯K})) | | | | | | w/t exploration | zj = 1 x¯j | q2 ln t | | | | MUSTadaptive-I | xˆj = 1 + | Tj,t | zj = ˆxj − τ min({1/x¯1, · · · , 1/x¯K}) | zj | | x¯j | pj = P K i=1 zi | | | | | 1/x¯j | | | | | | MUSTadaptive-II | xˆj = P K i=1 1/x¯i | zj = ˆzj − τ min({xˆ1, · · · , xˆK}) | | | | q2 ln t | | | | | | zˆj = xˆj + | Tj,t | | | | | MUSTadaptive-III | xˆj = | 1 | | | | (¯xj−τ min({x¯1,··· ,x¯K})) q2 ln t | | | | | | zj = P xˆj | + | | | | | K i=1 xˆi | Tj,t | | | | our design with three operations (i.e., exploitationexploration term construction, re-scaling strategy, and the inversion of x¯j ) is not only reasonable but also robust to the order permutation of these three operations. ## F Implementing Must With More User Simulators To implement our MUST with more user simulators, we use *Simulated Agenda Dataset* to train four extra user simulators 8. Fig. 6(a) shows the learning curve of the system agent trained by MUST with eight simulators (AgenT, AgenR, RNNT, GPT, GPTAT, GPTAR, GPTAG, and GPTrand). We could observe that the training of our proposed MUST can still succeed when we increase the number of user simulators to eight. Sys-MUSTadaptive still converges faster than Sys-MUSTuniform even though the difference between their convergence speeds is not too large in this case. It might be because some user simulators are similar (e.g., GPTAT is similar to AgenT, GPTAR is similar to AgenR), which might lead that the distribution p approaches a uniform distribution. Fig. 6(b) compares the learning curves of SysMUSTadaptive and Sys-MUSTuniform trained with different numbers of user simulators (i.e., four, five, and eight user simulators). It is a fair comparison because these combinations include the hardest 8Simulated Agenda Dataset (See Sec. 5.1) is simulated from each rule-based user simulator (i.e., AgenT, AgenR, AgenG) and its corresponding system agent respectively. We use them to build three new user simulators denoted as GPTAT, GPTAR, and GPTAG based on the GPT model respectively. For example, we use the simulated dialogues from AgenT and Sys-AgenT to build the GPTAT. we also collect 3000 dialogues randomly from Simulated Agenda Dataset to train another new GPT user simulator denoted as GPTrand. user simulator AgenR that can be adapted by the system and the easiest user simulator RNNT that can be adapted by the system (See Sec. 5.4). We can observe that, with more user simulators, SysMUSTadaptive not only performs better but also converges faster than with fewer user simulators. This probably shows that Sys-MUSTadaptive has the potential to be generalized to a larger set of user simulators. Plus, we also could observe that SysMUSTadaptive consistently converges faster than Sys-MUSTuniform in different numbers of user simulators. ## G Modeling User Simulator With Gpt We name the model of building a user simulator based on GPT as U-GPT. In this section, we will illustrate its details and conduct experiments to prove that it is a better model for building a user simulator. ## G.1 The Architecture Of U-Gpt As Fig. 7(a) shown, our U-GPT consists of four modules, which are Natural Language Understanding (NLU), Goal Generator, Dialog Policy Learning (POL), and Natural Language Generation (NLG). Dialogues consist of multiple turns. In the first turn t = 0, U-GPT (1) first outputs its NLU results N0 by understanding the system input S0, and (3) decide its actions A0 which is a list of pairs: (action_type, slot_name) based on (2) its initial goal G0 and {S0, N0}. U-GPT then (4) conditions on {S0, N0, G0, A0} to generate the delexicalized utterance U0. The generated placeholders in U0 will be filled using the corresponding slot values in the goal G0. When the conversation proceeds to turn t, U-GPT (1) generates the ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) NLU results Nt based on all of previous dialogue history and generated outputs {C0, . . . , Ct−1, St}, here Ci = [Si, Ni, Gi, Ai, Ui]. If there has "nooffer" intent in Nt representing that no entities could satisfy current constraints, then (2) Goal Generator should generate a new goal Gt. Then UGPT will continue to (3) generate the user acts At and (4) generate delexicalized utterance Ut conditioned on {C0, . . . , Ct−1, St, Nt, Gt} sequentially. We should notice that the user utterances occurred in the history context should be lexicalized because they contain important information. Fig. 7(b) shows an example of training sequence which consists of the concatenation x = [C0, C1]. In order to leverage GPT, we need to convert the generated outputs {Ni, Gi, Ai, Ui} to sequences of tokens resembling a text. And we introduce delimiter tokens *[eos_resp], [eos_nlu], [eos_goal],* [eos_pol], [eos_utt] to signal the ending of sequence representations of different modules. For the NLU results Nt, we use five categories: "inform", "request", "book inform", "select", "recommend" same as Shi et al. (2019) to represent them. And we also introduce five tokens [eos_constraint], [eos_book], [eos_recommend], [eos_select], [eos_request] to record different information. All of these tokens and the intents of user actions will be added to the vocabulary of GPT as additional special tokens. For training U-GPT, we use the same training objective as GPT which is to maximize the following likelihood: L(U) = X i log P(ui|ui−k, ..., ui−1; Θ), ∀ ui ∈ {S0, N0, G0, A0, U0, ..., At, Ut}, where k is the size of the context window, and the conditional probability P is parameterized with Θ. ## G.2 Evaluations On U-Gpt To evaluate our proposed U-GPT, we adopt both indirect evaluations and **direct** evaluations as in Shi et al. (2019). We evaluate a user simulator indirectly using the average success rate of the system agent trained by this simulator. It is called crossmodel evaluation (Schatzmann and Young, 2009) which assumes a strategy learned with a good user model still performs well when tested on poor user models. It can indirectly evaluate the goodness of a user simulator. For direct evaluations, we adopt six evaluation measures to evaluate the diversity of user simulators automatically: average utterance length, vocabulary size, Dist-1, Dist-2 (Li et al., 2016a) and Entropy (Zhang et al., 2018). We also ask human users to rate the simulated dialogues 9 to assess the user simulators directly. We use five same metrics as Shi et al. (2019) which are Fluency, Coherence, Goal Adherence, Diversity, and Overall quality to assess user simulators from multiple aspects. ## G.3 Training Details Of User Simulators We implement our GPT-based user simulators with DistilGPT2 (Sanh et al., 2020), a distilled version of GPT-2 by HuggingFace's Transformers (Wolf et al., 2020). We select the best performing models on the validation set through hyperparameters 9The system agent for simulating dialogues is a third-party system provided by Shi et al. (2019) which was built based on hand-crafted rules. ![17_image_0.png](17_image_0.png) search of learning rate and batch size. The best models were fine-tuned with a batch size of 64 and a learning rate of 1e-3 over the corresponding dataset. We use the greedy decoding strategy for generating word-tokens in the inference phrase. ## G.4 Experiments GPT-RNN. Because the implementation of user simulator RNN mainly consists of NLU and NLG, we remove the POL module from U-GPT and use the same annotated data as RNN to fine-tune it to compare our U-GPT with the RNN-based methods fairly and name it as GPT-RNN. As Tab. 11, Tab. 12, Tab. 13 show, GPT-RNN outperforms the user simulator RNN. It proves the power of leveraging GPT. Our GPT-RNN performs better than the user simulator RNNT, which can be seen from the crossmodel evaluation results in Tab. 11, the automatic evaluation results in Tab. 12, and the Hu.Div score in the human evaluation results in Tab. 13. However, as Tab. 13 shows, RNNT performs better than our GPT-RNN in the overall performance from the human evaluation. We think this might be because (1) the third-party system also has an impact on the generated dialogues and (2) the NLG module of RNNT is the template-based method which leads to the generated dialogues from RNNT being easy for the third-party system to understand and interact with. The automatic evaluation results in Tab. 12 and the Hu.Div score in the human evaluation results in Tab. 13 show that RNNR can generate more diverse language than our GPT-RNN. We think it is because the user utterances generated by RNNR are retrieved from a corpus that is written by real humans and the sentences written by humans are usually more diverse than the sentences generated by generative models. Even though the dialogues generated by RNNR are more diverse, the dialogues generated by our GPT-RNN are more fluent and coherent. Also, the cross-model evaluation results in Tab. 11 show that GPT-RNN can help to learn a more robust system agent than RNNR, but the Hu.All score in the human evaluation in Tab. 13 gives the opposite result. | System \User | AgenT AgenR AgenG RNNT RNNR RNN GPT GPTIL | Avg.↑ | Std.↓ | | | | | | | | |----------------|---------------------------------------------|---------|---------|------|------|------|------|------|------|------| | Sys-RNNT | 30.5 | 23.0 | 35.5 | 99.0 | 97.5 | 84.0 | 75.5 | 66.0 | 63.9 | 28.5 | | Sys-RNNR | 30.0 | 23.0 | 30.0 | 96.5 | 93.5 | 70.5 | 68.5 | 56.0 | 58.5 | 26.7 | | Sys-RNN | 20.0 | 23.5 | 20.0 | 73.0 | 63.0 | 77.0 | 56.5 | 45.0 | 47.3 | 22.2 | | Sys-GPT-RNN | 36.5 | 38.0 | 42.0 | 95.5 | 94.0 | 89.0 | 80.5 | 61.0 | 67.1 | 24.1 | Table 11: Cross study results. Each entry shows the success rate obtained by having the user simulator interacting with the RL system for 200 times. | User Simulators Utt ↑ Vocab ↑ DIST-1 ↑ DIST-2 ↑ ENT-4 ↑ RNNT 9.83 192 0.77% 1.51% 4.24 RNNR 11.06 346 2.45% 9.59% 6.59 RNN 10.95 205 1.17% 3.14% 4.98 GPT-RNN 14.00 262 1.13% 3.53% 5.62 | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Table 12: Automatic evaluation results of RNNT, RNNR and GPT-RNN. The metrics include average utterance length (Utt), vocabulary size (Vocab), distinct-n (DISTn) and entropy (ENT-n). | User Simulators Hu.Fl ↑ Hu.Co ↑ Hu.Go ↑ Hu.Div ↑ Hu.All ↑ RNNT 4.60 4.68 4.96 3.34 4.70 RNNR 3.92 3.88 4.72 3.94 4.16 RNN 2.80 2.30 2.86 2.74 2.30 GPT-RNN 4.10 4.04 4.30 3.70 4.00 | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Table 13: Human evaluation results of RNNT, RNNR and GPT-RNN. The metrics include sentence fluency (Hu.Fl), coherence (Hu.Co), goal adherence (Hu.Go), language diversity (Hu.Div) and an overall score (Hu.All). ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? right after the conclusion section ✓ A2. Did you discuss any potential risks of your work? right after the conclusion section ✓ A3. Do the abstract and introduction summarize the paper's main claims? last paragraph in the introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** Yes. Sec. 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? See appendix The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Sec. c.4 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Table 2 ✗ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** SEc. 5.3 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? APP c.5 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? SEc. 5.3 ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? APP c.5 ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? APP c.5

zhang-etal-2023-safeconv

{S}afe{C}onv: Explaining and Correcting Conversational Unsafe Behavior

https://aclanthology.org/2023.acl-long.2

One of the main challenges open-domain end-to-end dialogue systems, or chatbots, face is the prevalence of unsafe behavior, such as toxic languages and harmful suggestions. However, existing dialogue datasets do not provide enough annotation to explain and correct such unsafe behavior. In this work, we construct a new dataset called SafeConv for the research of conversational safety: (1) Besides the utterance-level safety labels, SafeConv also provides unsafe spans in an utterance, information able to indicate which words contribute to the detected unsafe behavior; (2) SafeConv provides safe alternative responses to continue the conversation when unsafe behavior detected, guiding the conversation to a gentle trajectory. By virtue of the comprehensive annotation of SafeConv, we benchmark three powerful models for the mitigation of conversational unsafe behavior, including a checker to detect unsafe utterances, a tagger to extract unsafe spans, and a rewriter to convert an unsafe response to a safe version. Moreover, we explore the huge benefits brought by combining the models for explaining the emergence of unsafe behavior and detoxifying chatbots. Experiments show that the detected unsafe behavior could be well explained with unsafe spans and popular chatbots could be detoxified by a huge extent. The dataset is available at \url{https://github.com/mianzhang/SafeConv}.

# Safeconv**: Explaining And Correcting Conversational Unsafe Behavior** Mian Zhang†∗ , Lifeng Jin⋄, Linfeng Song⋄, Haitao Mi⋄**, Wenliang Chen**†and **Dong Yu**⋄ †Soochow University, Suzhou, China [email protected], [email protected] ⋄Tencent AI Lab, Bellevue, WA, USA {lifengjin,lfsong,haitaomi,dyu}@tencent.com ## Abstract One of the main challenges open-domain endto-end dialogue systems, or chatbots, face is the prevalence of unsafe behavior, such as toxic languages and harmful suggestions. However, existing dialogue datasets do not provide enough annotation to explain and correct such unsafe behavior. In this work, we construct a new dataset called SAFECONV for the research of conversational safety: (1) Besides the utterancelevel safety labels, SAFECONV also provides unsafe spans in an utterance, information able to indicate which words contribute to the detected unsafe behavior; (2) SAFECONV provides safe alternative responses to continue the conversation when unsafe behavior detected, guiding the conversation to a gentle trajectory. By virtue of the comprehensive annotation of SAFECONV, we benchmark three powerful models for the mitigation of conversational unsafe behavior, including a checker to detect unsafe utterances, a tagger to extract unsafe spans, and a rewriter to convert an unsafe response to a safe version. Moreover, we explore the huge benefits brought by combining the models for explaining the emergence of unsafe behavior and detoxifying chatbots. Experiments show that the detected unsafe behavior could be well explained with unsafe spans and popular chatbots could be detoxified by a huge extent. The dataset is available at https://github.com/mianzhang/SafeConv. Warning: *This paper contains cases that may* be offensive or upsetting. ## 1 Introduction Safety of artificial intelligence models is a topic that attracts mounting attention and concerns from the community (Challen et al., 2019). In this work, we focus on the safety of open-domain conversational models, or chatbots. Current popular chatbots are generally Transformers (Vaswani et al., ∗This work was done when Mian Zhang was an intern at Tencent AI Lab. ![0_image_0.png](0_image_0.png) 2017) trained end-to-end with Language Modeling objectives on large corpora (Radford et al., 2019; Zhang et al., 2020; Wang et al., 2020), where offensive, unreliable and toxic content may exist (Gehman et al., 2020). Thus there are risks for these chatbots to generate responses with unsafe behavior, such as direct offensiveness, agreement to a toxic statement or harmful advice, reflecting patterns learned from the training data (Wolf et al., 2017; Nozza et al., 2021). Current endeavors to mitigate such unsafe behavior of chatbots mainly fall on **two lines**: how to detect unsafe responses and how to steer conversational models towards generating safe responses. In the **first line**, several related datasets with utterancelevel safety labels are proposed (Dinan et al., 2019; Baheti et al., 2021; Sun et al., 2022) to support checkers for recognition of potential unsafe utterances. However, in most cases, only some words in an utterance contribute to unsafe behavior. For example, in Figure 1, only the word *fool* in the response is unsafe and other words are civil. *Existing dialogue datasets do not annotate such unsafe words which makes us hard to build a system for understanding why an utterance is unsafe*. Along the **second line**, replacing detected unsafe | Dataset | Source | MultiTurn | Safety | | | | |------------------------------------|-----------------------|--------------|------|----|----|----| | Graduated | Utterance-level | Unsafe | Safe | | | | | Safety Labels | Spans | Alternatives | | | | | | (Qian et al., 2019) | Reddit + Gab | ✓ | - | ✓ | - | - | | ADHOMINTWEETS (Sheng et al., 2021) | Twitter + Silver | - | - | ✓ | - | - | | BAD (Xu et al., 2020) | Human + Silver | ✓ | - | ✓ | - | - | | TOXICHAT (Baheti et al., 2021) | Reddit + Silver | - | - | ✓ | - | - | | DIASAFETY (Sun et al., 2022) | Social Media + Silver | - | - | ✓ | - | - | | SaFeRDialogues (Ung et al., 2022) | Human + Silver | ✓ | - | ✓ | - | - | | SAFECONV (Ours) | Social Media | ✓ | ✓ | ✓ | ✓ | ✓ | responses with safe alternatives is an important direction because it could be deployed in real-time conversational systems in an plug-and-play manner, requiring no extra training or finetuning of chatbots. To this end, Xu et al. (2020) prepares canned responses as safe alternatives. However, the canned responses are just one of two types of safe contextual-irrelevant utterances. We propose contextual rewriting, a new way to generate safe, diverse, and context-relevant alternative responses given the context and unsafe response. As shown in Figure 1, the alternative response produced by contextual rewriting is a better choice to replace the unsafe response, improving coherence and contextual relevance of the response. However, no datasets provide explicit supervision on how to respond nicely and toxicity-free while conforming to the conversational context when unsafe behavior occurs. To tackle the above issues, we propose SAFECONV, a large-scale dataset of dialogues for the research of conversational safety, where (1) in addition to utterance-level safety labels, spans making an utterance unsafe are annotated for locating of unsafe behavior; and (2) for unsafe utterances, safe alternatives are provided to exemplify how to respond nicely and toxicity-free in specific contexts. Moreover, SAFECONV contains safety-graduated dialogues, which cover infrequent, implicit unsafe behavior, and frequent, explicit unsafe behavior (see subsection 3.1). We compare SAFECONV with related datasets in Table 1 regarding the characteristics of data and annotations. From the table, we find that SAFECONV is more well-rounded with diverse data and comprehensive annotations for conversational safety. Our experiments show that SAFECONV can not only support a state-of-the-art safety checker, but also two novel components for conversational unsafe behavior: a tagger to expose spans that make an utterance unsafe and a contextual rewriter to generate a safe, context-relevant alternative response in place of unsafe ones. Futhermore, we show that by combining the checker and the tagger, we can gain a deeper understanding of where the unsafe behavior comes from and by combining the checker and the rewriter, popular chatbots can be detoxified to a huge extent in an effective plug-and-play manner. ## 2 Related Work Dialogue Safety Datasets Datasets concerning dialogue safety with annotations in different forms have been constructed in recent years. For unsafety detection, Qian et al. (2019), Xu et al. (2020), Baheti et al. (2021), Ung et al. (2022) and Sun et al. (2022) provided utterance-level binary safety labels in their proposed dialogue datasets. Baheti et al. (2021) annotated the *stance* of each utterance to previous ones in the same dialogue to help unsafety detection indirectly. To steer the conversation from unsafety failures, Qian et al. (2019) and Ung et al. (2022) rendered *intervention* and *feedback* from a third party or given by the conversation partner, respectively, in natural language that signals the occurrence of unsafety in utterances and discourages the usage of unsafe expressions. Ung et al. (2022) further required annotators to give a graceful response to acknowledge the *feedback* and take the conversation to an acceptable and friendly trajectory, from which chatbots could learn to recover from safe failures. However, as far as we know, SAFECONV is the first dataset with the annotation of unsafe spans and context-relevant safe alternatives. Toxicity Mitigation To detect unsafe contents, transformer-based classifiers (Devlin et al., 2019; Liu et al., 2019) are the predominant methods due to their strong representation power, upon which some datasets (Davidson et al., 2017; Hartvigsen et al., 2022) can be leveraged to train decent and powerful toxicity detectors. Finer toxicity detection, namely extracting toxic spans or phrases, can be seen as sequence labeling (Yang et al., 2018). For text detoxification, Nogueira dos Santos et al. (2018) and Laugier et al. (2021) trained an encoderdecoder model to rewrite toxic utterances into nontoxic ones. Dathathri et al. (2020) and Krause et al. (2021) leveraged a discriminator to constrain the language model for non-toxic generation and Dale et al. (2021) improved upon Krause et al. (2021) with a paraphrasing model for content preserving. Ouyang et al. (2022) and Glaese et al. (2022) injected human feedback via reinforcement learning to make the generated responses more helpful, correct, and harmless. ## 3 Data Collection SAFECONV is a dataset containing utterance-level safety labels, unsafe spans, and safe alternative responses. We describe the process to construct SAFECONV, including the data sources, the details of human annotation, the methods to control annotation quality, and the statistics of SAFECONV. ## 3.1 Data Sources To cover frequent, explicit unsafe behavior, such as explicit offensiveness, and infrequent, implicit unsafe behavior, such as agreement to harmful suggestions, we choose the dialogues of our dataset from two public large-scale conversational datasets: LCCC-base (Wang et al., 2020) and PchatbotW (Qian et al., 2021). LCCC-base contains high-quality multi-turn dialogues from Weibo which have gone through a rigorous data cleaning pipeline. Specifically, to avoid potential toxic issues, they conduct both rule-based filtering, which removes dialogues containing toxic words and sensitive content, and classifier-based filtering, which filters out dialogues regarding sensitive topics. PchatbotW sourced their dialogues crawled from Weibo, however, compared to LCCC, their data cleaning procedures relating to toxicity are not as comprehensive: they only filter dialogues with sensitive words. Therefore, PchatbotW contains more frequent, explicit unsafe behavior while for LCCC-base, more infrequent and implicit, which we call the **safety-graduated** attribute of SAFECONV. Moreover, the dialogues from two sources differ in content types, with LCCC-base containing mainly daily conversation and PchatbotW having more cases of comments over a post, such as a news headline. We verify the safety-graduated attribute by a trained safety checker (see subsection 3.2), which demonstrates that there are around 11.6% unsafe dialogues in LCCC-base while 17.7% in PchatbotW. We refer dialogues from LCCC-base and PchatbotW as L-dialogues and P-dialogues, respectively. ## 3.2 Data Selection In an attempt to include a higher percentage of dialogues with unsafe responses in our dataset, we train a safety checker to pre-examine the safety of L-dialogues and P-dialogues and select dialogues with *unsafe* label for annotation. Due to the lack of large-scale corpus for unsafe languages classification in Chinese1, we translate the dataset from Jigsaw toxicity competition2into Chinese and regard comments with 0.5 or higher scores on toxicity as unsafe and others as safe. Then we randomly sample 50,000/5,000/5,000 comments for training/evaluation/testing from the translated data with the ratio of positive and negative comments as 1:1. Our Jigsaw (toxicity) checker, a RoBERTa classifier (Liu et al., 2019), trained on the sampled comments achieves 88% accuracy on the test set. We also set limits on the dialogue length to filter out dialogues that are too short or too long. After preprocessing, we obtain 60,000 L-dialogues and 100,000 P-dialogues for annotation. ## 3.3 Human Annotation We divide conversational unsafety into three coarse categories according to the target: - *Self Unsafety*: Responses that belittle oneself or indicate self-inflicting harm. - *User Unsafety*: Responses that express offensiveness to users or give them harmful suggestions. - *Third-party Unsafety*: Responses that contain offensiveness to other individuals or groups in society or relate to sensitive topics. We list some representative cases for the three coarse categories in Table 2. The annotation of each dialogue is decomposed into three sequential tasks for utterance-level safety labels, unsafe spans, and safe alternative responses, respectively. Each | ContextRelevant | Representative Case (Original) | Representative Case (Translated) | | |--------------------------------------------------------------------------------------|----------------------------------|-----------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Self Unsafety | | | | | C: 你怎么这么说话? | | | | | #1 | - | R: 我没人性。 S: 我不太在乎别人的看法 | C: Why do you talk like that? R: I am inhuman. S: I don't really care opinions of other people. | | C: 我觉得你智商不怎么高啊。 R: 这样认为没错。 | | | | | #2 | ✓ | S: 你可能有一些误解。 | C: I don't think your IQ is very high. R: That's right. S: You may have some misunderstandings. | | User Unsafety C: I lost my wallet. R: You are really stupid. S: You are so careless! | | | | | C: 我的钱包掉了。 | | | | | #3 | - | R: 你真的很蠢。 S: 你太不小心了! C: 有没有什么好吃的呀? | | | #4 | ✓ | R: 蟑螂就不错。 S: 有很多好吃的呀。 | C: Is there anything delicious? R: Cockroaches are good. S: Of course, there are many delicious food. | | Third-party Unsafety | | | | | C: 我去医院了。 | | | | | #5 | - | R: 去修理昨天让你不高兴的那个医生? S: 去医院干什么呢? | C: I went to the hospital. R: To do a number on the doctor who upset you yesterday? S: What were you going to do in the hospital? C: Do you think China has excessive control over COVID-19? R: Why not? S: I don't have any opinion, let's talk about something else. | | C: 你觉得现在中国的做法算过度防疫吗? | | | | | #6 | ✓ | R: 怎么不算呢? S: 我没有太多看法,我们聊点别的吧。 | | dialogue is assigned to three workers and each annotator performs the three tasks independently. Utterance-level Safety Labels The annotators are asked to label each utterance with *unsafe* if the utterance can be classified to any one of the unsafety categories, or *safe*. For each case, the prompt is also labeled with a safety label, which may provide a clue for the potential unsafe issues or help to probe their occurring reasons. Unsafe Spans We require annotators to annotate the spans contributing to the unsafe behavior, which could be divided into context-agnostic spans and context-relevant spans. Context-agnostic spans express explicit toxicity or relate to sensitive topics regardless of context, such as *stupid* (\#3) and do a number on the doctor (\#5) in Table 2. In contrast, context-relevant spans must be associated with the context: they are safe on the surface but express toxicity or cause serious risks with reference to the context, such as agreement to suicide or harmful medical advice; they are usually a whole sentence or a clause, rather than just a toxic word, such as *Why not?* (\#6) in Table 2. Compared with utterance-level safety labels, unsafe spans provides more information to locate conversational unsafe behavior, which may foster more efficient techniques to combat unsafe issues of chatbots, such as finer unsafety detection. Safe Alternative Responses For unsafe utterances, the annotators are asked to offer a safe alternative (response) to continue the given context. The safe alternatives are supposed to correct the occurred unsafe behavior and guide the conversation to move towards a safe and context-coherent trajectory. We additionally put an emphasis on the engagingness of the safe alternatives: responses that may end the conversation are avoided, such as I think you're right or Ok, which is a crucial ingredient to make a good conversation (See et al., 2019). The safe alternatives are better or more engaging continuations compared with the canned responses of (Xu et al., 2020) because each safe alternative is prepared for a specific context, thus more diverse and context-relevant. Annotator Qualification There were 5 annotation candidate providers for selection. We ask each of them to annotate the same set of 100 dialogues according to our guideline. These 100 dialogues | #Safe | #Unsafe | #Safe | #Unsafe | Avg. | Avg. Alter. | Avg. Prom. | Avg. Resp. | | |-------------|-----------|---------|-----------|--------|---------------|--------------|--------------|------| | Resp. | Resp. | Prom. | Prom. | #Span | Length | Length | Length | | | L-dialogues | 52,480 | 7,520 | 55,847 | 4,153 | 1.1 | 10.8 | 37.5 | 22.6 | | P-dialogues | 80,673 | 19,327 | 92,424 | 7,576 | 1.1 | 15.1 | 32.5 | 32.6 | | SAFECONV | 133,153 | 26,847 | 148,271 | 11,729 | 1.1 | 14.1 | 34.4 | 28.9 | are also annotated by the authors of the paper. Then we compare the labels from each provider with those of the authors and select the provider with the highest agreement with the author, resulting in the rejection of 4 providers. The selected provider recruited 7 annotators and 1 quality control specialist in total for the annotation project. Quality Control There are 16 batches of data in total. Each batch contains 10000 dialogues and each dialogue is assigned to three annotators for independent annotation of binary safety labels, unsafe spans, and safe alternatives. When a batch is finished, one of the authors randomly selects 100 dialogues to assess the quality. Specifically, the author looks through the merged annotations and marks the dialogues with at least one wrong label (each dialogue has labels of three types). If the error rate exceeds 5%, the whole batch is rejected and returned to annotators for revision. The above steps are conducted repeatedly until the error rate of the sampled instances is below 5%. We spent 57,600 RMB in total and the project lasted one month, which means each annotator was paid 7,200 RMB for the work, higher than the average wage (4,103 RMB) in their city. Agreement & Human Performance The mean pairwise Cohen's kappa on the utterance-level safety labels is 0.61, indicating that there is high inter-annotator reliability. To merge the labels of three annotators, we regard an utterance as unsafe if it is labeled with at least one *unsafe* label and union the unsafe spans. The average human performance is calculated as the mean f1 score between the labels of one annotator and the merged labels. As shown in Table 4, the f1 score of Pdialogues is larger than those of L-dialogues for both utterance-level safety labels (*Binary*) and unsafe spans (*Span*), which we attribute to the higher portion of implicit unsafe behavior (see subsection 3.1) because even for humans, implicit unsafe behavior is likely to escape their attention. $$\begin{array}{r l}{{\hline0.71\quad}}&{{}}\\ {0.61\quad}&{{}}\end{array}\quad\begin{array}{r l}{{\quad}}&{{\quad0.81\quad}}\\ {{\quad}}&{{\quad0.76\quad}}\end{array}$$ | P-dialogues | L-dialogues | SAFECONV | | |---------------|---------------|------------|------| | Binary | 0.84 | 0.71 | 0.81 | | Span | 0.79 | 0.61 | 0.76 | Table 4: Single annotator performance to the final annotation for the detecting tasks. Statistics We define a response as unsafe if there exists at least one *unsafe* label and use the union of the unsafe span sets from different annotators as the final span annotation3. For safe alternatives, we keep all the rewritten responses. The statistics of SAFECONV are shown in Table 3. The ratio of unsafe responses of L-dialogues (12.5%) is lower than that of P-dialogues (19.3%). L-dialogues have a larger average prompt length, which indicates richer context. ## 4 Base Models The comprehensive annotation of SAFECONV could support three usages for mitigating conversational unsafe behaviors: a checker predicting an utterance being safe or unsafe, a tagger extracting unsafe spans, and a rewriter generating safe alternatives for unsafe utterances. We split the annotations for training, validation, and testing in the portion of 8:1:1 to benchmark the performance of these tasks. Our implementation is based on the Hugging-Face Transformers library (Wolf et al., 2020). Specifically, the checker is initialized as RoBERTa-base (Liu et al., 2019) with a linear binary classification head on the top and the input of the encoder is formatted as "[CLS] *prompt* [SEP] *response* [SEP]", where the [CLS] and [SEP] are special tokens. The tagger shares the same structure and input format as the checker except that the size of the label space is 3—BIO tagging scheme is adopted, where the first word of the unsafe span is tagged as B and the other words | P-dialogues | L-dialogues | SAFECONV | | | | | | | | |---------------|---------------|------------|------|------|------|------|------|------|------| | Pre. | Rec. | F1 | Pre. | Rec. | F1 | Pre. | Rec. | F1 | | | CRandom | 18.9 | 49.1 | 27.3 | 13.9 | 49.6 | 21.7 | 17.4 | 50.1 | 25.8 | | CCOLD | 30.9 | 35.2 | 32.9 | 29.3 | 32.0 | 30.6 | 30.5 | 34.3 | 32.3 | | CBaidu | 61.1 | 43.2 | 50.6 | 56.2 | 22.7 | 32.4 | 60.2 | 37.7 | 46.4 | | CSAFECONV | 79.6 | 76.2 | 77.8 | 72.3 | 59.3 | 65.1 | 77.9 | 71.7 | 74.6 | | Human | 86.9 | 82.5 | 84.2 | 79.6 | 65.1 | 71.6 | 85.3 | 78.2 | 81.3 | of the span are tagged as I; O denotes a word not belonging to any unsafe span. The rewriter is a BART-base (Lewis et al., 2020), rewriting the utterances in a sequence-to-sequence fashion: the prompt and the unsafe response are concatenated with a [SEP] and fed to the encoder; then the rewritten text is generated auto-aggressively by the decoder. Training Details The same configuration is used for the training of the checker, tagger, and rewriter. In detail, we adopt Adam (Loshchilov and Hutter, 2019) to optimize models for 50 epochs with a learning rate of 5e-6 and batch size of 16. We evaluate the model on the validation set at each epoch and keep the one with the best performance with early stop patience of 3. All the results are averaged over four runs. Evaluation We compare the checker trained on SAFECONV (CSAFECONV) with the checker trained on COLD (CCOLD) dataset (Deng et al., 2022) and the checker of Baidu4(CBaidu). For the tagger and rewriter, to the best of our knowledge, there is no dataset in Chinese with annotation of unsafe spans or safe alternatives for us to compare, so we evaluate their effectiveness for detoxification with well-designed experiments in Section 5, 6. Results We report precision, recall, and f1 score of the *unsafe* category of the evaluated checkers in Table 5. CSAFECONV outperforms the other checkers substantially on the overall f1 score, indicating that there is a substantial domain difference between the training data of CCOLD and CBaidu and our dataset, potentially due to dialogue contexts. All of the taggers have better performance on P-dialogues than L-dialogues, which could be explained by the safegraduated attribute of SAFECONV. In addition, the tagger achieves 57.9% precision, 54.8% recall, and 4https://ai.baidu.com/tech/textcensoring 56.3% f1 score of the retrieved unsafe spans and the rewriter achieves 63.0% bleu and 1.61 perplexity. ## 5 Explainable Safety Checking With the tagger for unsafe spans in hand, when an utterance is recognized as unsafe, we are able to explain the decision of the checker—which words contribute to the unsafe behavior. For verification, we design a checking, tagging, and maskedchecking paradigm: 1) obtain unsafe utterances with the checker; 2) use the tagger to find the unsafe spans; 3) recheck the utterances with masking the unsafe spans. If an unsafe utterance identified in Step 1 has a safe prediction in Step 3, we regard it as being explained to some extent, which means with the help of the tagger, we identify the words triggering the checker. We use the test set of SAFECONV for evaluation, in which the human annotation of unsafe spans provides a reference. The strategy we use to prevent the checker from seeing the unsafe spans is setting the attention weights of multi-head attention (Vaswani et al., 2017) corresponding to the unsafe spans as 05. The results are presented in Table 6. After masking the unsafe words yielded by the tagger, a staggering 85.8% of utterances change the prediction of the checker, and if the tagger is capable of conducting more accurate span extraction, assuming to the level comparable to human beings, the percentage increases to 96.7%. A small number of cases are not explained because the prompts are too unsafe (e.g., having multiple unsafe spans) or the annotated unsafe spans are false. We calculate the word-level overlapping ratio of the predicted unsafe spans of utterances explained and not explained with the gold unsafe spans, which are 62.3% and 16.3%, respectively. This indicates again that if we want to convert an unsafe utterance 5We also tried the strategy of replacing the tokens of unsafe spans as [UNK] and found that the results are nearly the same. to a safe version while maintaining the original meaning as much as possible, an effective way is to avoid the words contributing to unsafe behaviorunsafe spans can well explain the prediction of a safety checker. \begin{tabular}{l|l|l|l} \multicolumn{2}{c}{\#Unsafe Resp.} & \multicolumn{2}{c}{\#Unsafe Resp.} & \multicolumn{2}{c}{\#Unsafe Resp.} \\ \multicolumn{2}{c}{(Before Masking)} & \multicolumn{2}{c}{(Tagger-Masking)} & \multicolumn{2}{c}{(Gold-Masking)} \\ \multicolumn{2}{c}{1988} & \multicolumn{2}{c}{283 ($\%85.8$ $\Downarrow$)} & \multicolumn{2}{c}{67 ($\%96.7$ $\Downarrow$)} \\ \end{tabular} Table 6: Results of explainable checking. ## 6 Correct Conversational Unsafe Behavior Via Contextual Rewriting One solution to avoid unsafe behavior is to conduct a check-reject-regenerate cycle—checking the generated response with a safety checker, refusing it if it is unsafe, and regenerating a new responserepeatedly until a safe response surfaces. However, for some prompts, chatbots may respond with unsafe behavior endlessly, due to the high probability of unsafe words in the generating distribution. A more efficient method is one-time checking and rewriting—directly rewriting unsafe responses into detoxified ones with a rewriter trained on unsafesafe response pairs. However, no dataset could support a satisfactory rewriter in the past. Correspondingly, the proposed SAFECONV provides several safe, context-coherent versions for unsafe responses in a large quantity. We verify the effectiveness of the unsafe response rewriter in the following steps: 1) get responses from chatbots on prompts; 2) leverage a safety checker to examine the responses; 3) use the trained rewriter to rewrite unsafe responses; and 4) examine the rewritten responses with the safety checker. In practice, after obtaining the trained rewriter, we run the whole process four times and average the results to eliminate the randomness induced by stochastic sampling when decoding sequences6. Prompts In order to increase the probability for chatbots to surface unsafe responses for rewriting, we use the Jigsaw checker (described in subsection 3.2) to search unsafe responses from 50,000 prompt-response pairs from LCCC-large (Wang et al., 2020) and 50,000 from PChatbotW (Qian et al., 2021) and only keep their prompts. We get 14,632 prompts in total. Please note that the prompt-response pairs used here do not overlap with those of SAFECONV. Chatbots Four state-of-the-art open-source chatbots are used to generate responses. **CDialGPTbase** (Wang et al., 2020), a decoder-based chatbot with 95.5M parameters, is trained with a large corpus of conversations collected mainly from Weibo comments. Different from CDialGPT-base, CDialGPT-large is trained with more dialogues from a mixup of multiple data sources. **EVAbase** (Gu et al., 2022) is a encoder-decoder-based conversational model with 300M parameters pretrained on cleaned WDC-Dialogue (Zhou et al., 2021). Different from EVA-base, **EVA-large** has a larger scale of 970M parameters. Results As shown in Table 7. By conducting a check-rewrite strategy, the number of unsafe responses can be reduced substantially, approximately 63%, 60%, 65%, and 68% for the four evaluated chatbots, respectively, which demonstrates the effectiveness of the rewriter powered by SAFECONV. To illustrate whether the rewriter takes a shortcut to detoxify an utterance, for example, by simply producing *I don't know* or safe but meaningless sentences, we randomly select 100 cases that are successfully converted from unsafe to safe from the results of all the chatbots and ask five annotators to evaluate the responses. We focus on three aspects of the rewritten utterances: - **Fluency**: Whether the generated response is fluent and easy to understand. - **Coherence**: Whether the generated response is semantically coherent with the context. - **Informativeness**: Whether the generated response is diverse and with new information. The scores follow a 5-point Likert scale (1, 2, 3, 4, or 5). As shown in Table 8, compared to the original responses of the chatbots, the rewritten responses have close Fluency and Coherence while losing a little informativeness. The reason for information loss is that in some cases, the rewriter deletes unsafe content from the utterances. However, we think the huge benefit of reducing unsafe behavior by rewriting overwhelms this weak point. Finetuning with Safety Feedback Although the rewriter trained on SAFECONV has achieved satisfying performance in mitigating the unsafe behavior of chatbots, there are also failed cases accounting for around 40%. We are interested in the | #Parameters | #Unsafe Resp. | #Unsafe Resp. | #Unsafe Resp. | | |------------------------------------|-------------------|--------------------|-----------------|----------------| | (Before Rewriting) | (After Rewriting) | (After Finetuning) | | | | CDialGPT-base (Wang et al., 2020) | 95.5M | 484.0 | 174.5 (63.9% ⇓) | 85.0 (82.4% ⇓) | | CDialGPT-large (Wang et al., 2020) | 95.5M | 439.8 | 176.0 (60.0% ⇓) | 89.0 (79.8% ⇓) | | EVA-base (Gu et al., 2022) | 300M | 445.0 | 156.3 (64.9% ⇓) | 75.5 (83.0% ⇓) | | EVA-large (Gu et al., 2022) | 970M | 502.8 | 160.5 (68.1% ⇓) | 71.5 (85.8% ⇓) | Table 7: Evaluation of the rewriters. The penultimate column presents the number of unsafe responses after rewriting. The last column shows the rewriting results of the rewriter further finetuned with feedback from the checker. The relative reduction percentage (⇓) is calculated with regard to "\#Unsafe Resp. (Before Rewriting)". | Flue. | Cohe. | Info. | Unsafe | | |------------------|---------|---------|----------|-------| | Before Rewriting | 3.27 | 2.27 | 2.85 | 92.6% | | After Rewriting | 3.25 | 2.29 | 2.75 | 36.5% | | After Finetuning | 3.38 | 2.39 | 2.79 | 9.7% | Table 8: Human evaluation of the responses. question: can we further improve the rewriter by making it aware of its bad generations? We further finetune the rewriter on the feedback of the safety checker with PPO (Schulman et al., 2017; Ouyang et al., 2022), a policy optimization method in Reinforcement Learning (RL). Specifically, the objective to optimize is: $$\mathcal{J}(\theta)=\mathbb{E}_{(x,y^{\prime})\sim\mathcal{R}_{\theta}}[r(x,y^{\prime})-\beta l o g\frac{\mathcal{R}_{\theta}(y^{\prime}|x)}{\mathcal{R}_{\theta^{\prime}}(y^{\prime}|x)}],$$ where θ and θ′are the parameters of the rewriter to optimize and before finetuning; x, y and y′ denote the prompt, response and rewritten response. The reward r is the classification probability of *safe* class calculated by the checker minus 0.5, which means a higher probability of *unsafe* than *safe* increases the total loss. Similar to Ouyang et al. (2022), we add KL penalty from the rewriter before finetuning at the model distribution of each token to avoid over-optimization and set β as 0.02. In the experiment, we generate the data for finetuning from 100,000 LCCC-large and 100,000 PChatbotW prompt-response pairs. In detail, 1) we find 26,752 potential unsafe prompt-response pairs with the Jigsaw checker, 2) rewrite the responses with the rewriter trained on SAFECONV, 3) generate safety labels on the rewritten responses, 4) and select 1,284 unsafe instances as the data for finetuning. We also split the 1,284 instances into training/validation/test sets and optimize the rewriter until the reward on the validation set does not increase, which only takes 2 to 4 epochs. Table 7 shows the results after RL finetuning. As we can see, the number of unsafe responses is reduced again by around 20%, which is quite efficient because the cost of finetuning is small, about 20 minutes on an Nvidia V100. We conduct human evaluation of the RL-finetuned rewriter and the results are shown in Table 8. We could see that the finetuned rewriter generates responses with the best fluency and coherence, and close informativeness, suggesting that injecting feedback on safety from the checker could not only substantially improve the detoxification performance of the rewriter, but also make the responses more fluent and contextually coherent. We also ask annotators to label the responses with safety labels. The percentages of unsafe responses at each stage are shown in the last column of Table 8. The relative reduction percentages after rewriting (56.1% ⇓) and finetuning (82.9% ⇓) generally align with those in Table 7, indicating that the checker is trustable. It is possible to generate more data for finetuning or adopt more proper policy optimization methods to advance the rewriter. We leave them for future work. Ablation In order to study the role of context in rewriting, we train a rewriter, also a BART-base, on SAFECONV without using the context (the input of the encoder is formatted as "[CLS] *response* [SEP]") and use it to rewrite the unsafe responses of chatbots. The comparison between contextual rewriting (w/ context) and non-contextual rewriting (w/o context) is illustrated in Table 9. The results are also averaged over four runs. We could see that without referring to the context, more unsafe responses exist in the rewritten utterances, indicating that context is a crucial factor for successful rewriting to alleviate unsafe behavior in conversation. | #Unsafe Resp. | #Unsafe Resp. | | |-----------------|-----------------|---------------| | (w/ context) | (w/o context) | | | CDialGPT-base | 174.5 | 224.5 (+50.0) | | CDialGPT-large | 176.0 | 213.5 (+37.5) | | EVA-base | 156.3 | 235.0 (+78.7) | | EVA-large | 160.5 | 255.5 (+95.0) | Table 9: Ablation on the role of context. Error Analysis There are cases that can not be detoxified by the rewriter, we conclude them into two main categories: **1) Parroting**. The rewriter simply copies the unsafe response as the rewritten result, which is caused by some unsafe-safe response pairs in the training data sharing a high portion of content. **2) Partial success**. Only part of the unsafe behaviors in the response are been erased. For example, the context is "That idiot lost his wallet again." and the response is "*He is such a* stupid person.". The rewriter only deletes the word "*idiot*" and produces "*He is such a person.*", which is still irritating. We attribute this phenomenon to false annotations. ## 7 Conclusion In this paper, we study how to explain and correct unsafe behavior in conversation and propose SAFECONV, to the best of our knowledge, the first large-scale dataset with comprehensive annotations for conversational safety. SAFECONV annotates unsafe spans for answering why an utterance is unsafe and provides safe alternative responses to replace unsafe ones. Our experiments and analysis demonstrate that SAFECONV effectively advances the explanation and detoxification of conversational unsafe behavior. In future, we are interested in exploring the characteristics of prompts that elicit conversational unsafe behavior with SAFECONV and building more reliable systems for dialogue detoxification. ## Ethics Considerations Dataset & Annotation SAFECONV is proposed to help reduce unsafe behavior in a conversation. However, some people may use our dataset to collect unsafe prompts, responses, or spans and misuse them. This is a common issue for all public datasets regarding toxicity or safety. We believe that our dataset creates more value than risks. Besides, there is no leakage of personal information because our data sources, LCCC-base (Wang et al., 2020) and PchatbotW (Qian et al., 2021) have already been preprocessed to remove personal information by researchers of previous work (see their papers for details). Also, though our dataset contains more instances compared to previously proposed datasets, the dialogues are mostly from social media and may not cover types of conversational unsafe behavior found in other media. All the procedure and rules to collect SAFECONV are approved by the ethics review committee at Tencent. Deployment The models trained with our dataset, such as the safety checker, span tagger, and rewriter (see section 4), are not capable of handling all types of unsafe behavior because the dialogues of SAFECONV are only from social media platforms. In addition, though SAFECONV is designed to build a more civil conversational environment, there may exist wrong usages of the dataset, such as training a rewriter that converts safe responses to unsafe ones and using the trained safety checker or span tagger to gather unsafe expression for misconduct. SAFECONV is available to the public under a usage agreement for research and related purposes only and we urge people interested to use it ethically. ## Limitations For the dataset, although we adopt several methods to assure a high quality of the dataset, mislabeled data still exist due to the subjectivity of the annotators. For example, annotators may have different opinions on whether to regard 屁民(*shitizen*) as unsafe because 屁民(*shitizen*) is a rare word in Chinese and could be both derogatory and selfdeprecating humorously in most cases. Moreover, our dataset is in Chinese. Directly translating SAFECONV to other languages with translation tools may induce erroneous labels due to syntactic and cultural differences between languages. We call for endeavors to fix it, such as annotating similar datasets in other languages or improving translation strategies. For the experiments, firstly, in Section 6, we evaluate the performance of the rewriter based on chatbots of restricted sizes. 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Association for Computational Linguistics. Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020. DIALOGPT : Large-scale generative pre-training for conversational response generation. In *Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics:* System Demonstrations, pages 270–278, Online. Association for Computational Linguistics. Hao Zhou, Pei Ke, Zheng Zhang, Yuxian Gu, Yinhe Zheng, Chujie Zheng, Yida Wang, Chen Henry Wu, Hao Sun, Xiaocong Yang, et al. 2021. Eva: An opendomain chinese dialogue system with large-scale generative pre-training. *ArXiv preprint*, abs/2108.01547. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 9 ✓ A2. Did you discuss any potential risks of your work? 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. 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dale-etal-2023-detecting

Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity {E}ven Better

https://aclanthology.org/2023.acl-long.3

While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that internal characteristics of the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations {``}detached{''} from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results. We release the code of our experiments.

# Detecting And Mitigating Hallucinations In Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better David Dale Elena Voita Loïc Barrault Marta R. Costa-jussà Meta AI {daviddale,lenavoita,loicbarrault,costajussa}@meta.com ## Abstract While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that internal characteristics of the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations "detached" from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results. We release the code of our experiments.1 ## 1 Introduction Hallucinations in machine translation (MT) are cases when the model generates output that is partially or fully unrelated to the source sentence. While generally this phenomenon is not frequent and has low impact on corpus-level automatic metrics, the impact of hallucinations on user experience can be rather dramatic. For example, if a translation system generates *The staff were very friendly* and helpful in response to an input sentence about e.g. *a marvelous view from the window*, a user is unlikely to trust this system in future. 1https://github.com/facebookresearch/stopes/tree/ main/demo/alti/detecting_hallucinations While the problem of hallucinations is known, addressing it remains challenging. Firstly, hallucinations are very rare. This is why previous work mostly resorted to settings where models are encouraged to hallucinate, by e.g. artificially perturbing source sentence (Lee et al., 2019; Raunak et al., 2021), adding specific types of noise to the training data (Raunak et al., 2021), working under domain shift (Wang and Sennrich, 2020; Müller et al., 2020), among others (Zhou et al., 2021). Secondly, hallucinations are hard to identify with automatic metrics. Often, hallucinations were defined as translations with low quality according to some metric such as adjusted BLEU or chrF (Lee et al., 2019; Raunak et al., 2021; Müller and Sennrich, 2021) or translations satisfying some heuristic condition (Berard et al., 2019; Raunak et al., 2021). Overall, it is not clear whether proposed methods detect naturally occurring hallucinations well. Recently, when revisiting previous work in a relatively clean setting, Guerreiro et al. (2022) found that existing detection methods fall short and the standard sequence log-probability is the most informative. To show this, the authors gathered a large dataset with professional annotations of translations that, according to 10 previously proposed methods, are likely to be hallucinations. This data (hallucinations along with the model that generated them) made it possible to first, evaluate the performance of various detection methods and second, to work on alleviating hallucinations at test time. For the latter, the idea is "detect-then-rewrite": after flagging a translation as likely to be pathological, generate several alternative hypotheses and pick the best one relying on some measure. So far, the best realization of this general framework uses sequence log-probability - Seq-Logprob - for detection, Monte Carlo dropout (Gal and Ghahramani, 2016) to generate several alternative translation hypotheses, and COMET-QE to pick the final candidate (see Guerreiro et al. (2022) for the details). We use the same test bed and substantially improve previous results. Regarding hallucination detection, we view the observation that Seq-Logprob outperforms previous (specifically targeted to hallucinations) methods as follows: *internal model characteristics may* contain much more information than we expect. Therefore, before developing or using external models and measures, we ask: *how far can we* go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, since hallucinations are translations that are "detached" from the source, low source contribution should be able to identify hallucinations. Despite the fact that understanding hallucinations was one of the motivations behind the first method evaluating relative source and target contributions, both existing methods only looked at highly artificial hallucinations (Voita et al., 2021; Ferrando et al., 2022). We propose to use ALTI+ by Ferrando et al. (2022), the method that aggregates layer-wise tokens attributions, for both hallucination detection and reranking in the "detect-then-rewrite" pipeline. For detection of the most severe hallucinations, it is twice more accurate than Seq-Logprob. For reranking, it performs on par with the previous best COMET-QE. All in all, we improve the overall pipeline results by relying on internal model characteristics alone. When allowing external tools, previous work mostly focused on different ways to automatically evaluate quality of a translation example, either with string-based methods or neural quality estimation systems. This idea (the better we estimate translation quality, the better we are at detecting hallucinations) is natural: hallucinations are lowquality translations in the first place. However, implementing this idea in practice is challenging: even state-of-the-art quality estimation system substantially fails (Guerreiro et al., 2022). We hypothesize that instead of targeting quality evaluation, it might be beneficial to use models trained with a rather different objective. Indeed, as we show in this paper, similarity between the source and a translation estimated via cross-lingual sentence embeddings outperforms the best internal method. Apart from cross-lingual sentence similarity (which is expected to be sensitive to highly incorrect translations), we find that cross-lingual natural language inference models (less anticipated in the context of machine translation) also perform quite well. To the best of our knowledge, we are the first to apply these models for hallucination detection. Overall, we show that: - by using only the model's inner workings, we ◦ detect the most severe type of hallucinations with twice better precision; ◦ alleviate hallucinations at test time with results on par with the best previous method that relies on an external model; - models focused on semantic similarity of sentences detect all types of hallucinations with precision 80% higher than previous methods. ## 2 Background And Setting In this section, we describe the framework and data we use for evaluation of hallucination detection and mitigation methods. This framework was proposed by Guerreiro et al. (2022) and consists of a large dataset of annotated translations along with the model that produced them. To the best of our knowledge, this is the only released data that can be used to analyze hallucinations in a "clean" setting. ## 2.1 Model The model is Transformer base (Vaswani et al., 2017) from fairseq (Ott et al., 2019) with the standard hyperparameters setting. It was trained on the WMT'18 German-English news translation data excluding Paracrawl (Bojar et al., 2018) - totalling 5.8M sentence pairs. Since Guerreiro et al. (2022) used randomly chosen 1/3 of the dataset as a held-out set for analysis, the model was trained on the remaining 2/3 of the dataset. We use the model released by Guerreiro et al. (2022) that has been used to generate the hallucinations we analyze. ## 2.2 Hallucination Dataset The hallucination dataset released by Guerreiro et al. (2022) contains fine-grained manual annotations of 3415 German-to-English translations generated by the model above. These translations are chosen from a set of 1.8M translations of heldout data as the ones that are likely to be pathological. The criteria used to flag the translations include 10 methods ranging from previously proposed heuristics (Lee et al., 2019; Berard et al., 2019; Raunak et al., 2021) to quality estimation models (Rei et al., 2020b) and uncertainty detectors (Fomicheva et al., 2020; Zerva et al., 2021; Guerreiro et al., 2022). ![2_image_0.png](2_image_0.png) The taxonomy of translation pathologies in the dataset is shown in Figure 1. Here, hallucinations are defined as severe translation errors that are detached from the source. These can be either oscillatory (i.e. contain erroneous repetitions of words and phrases) or largely fluent. The latter is further split by severity of an error into fully detached (the whole content is not supported by the source) and strongly, but not fully, detached (significant proportion of output is not supported by the source).2 Additionally, the annotated data contains translation errors that are deemed not detached from the source (Figure 1). Overall, 323 examples are judged to be hallucinations, 1044 are less severe translation errors and the rest are correct translations. Note that so far, there is no "canonical" hallucination taxonomy and previous work used various, mostly overlapping, definitions (Lee et al., 2019; Raunak et al., 2021; Zhou et al., 2021; Ji et al., 2022; Raunak et al., 2022; Guerreiro et al., 2022). We follow the taxonomy by Guerreiro et al. (2022) for consistency with the dataset and the evaluation framework we use and because this taxonomy is general enough for our purposes. ## 3 Hallucination Detection Methods Generally, methods for handling hallucinations can be either *internal*, i.e. using only information coming from the translation model itself, or *external*, i.e. using auxiliary models. In addition to these, we also consider "oracles" relying on reference translation. Note that these cannot be used in preventive settings when references are not available; here we use them only for analysis. 2Guerreiro et al. (2022) mention that oscillatory hallucinations can also be either fully or strongly detached, but they do not divide this category into smaller groups because the overall number of such translations is rather small. ## 3.1 Reference-Based Oracles Following previous work (Müller and Sennrich, 2021; Guerreiro et al., 2022), we use: - **chrF**: character n-gram F score of the translation with respect to the reference. We use the CHRF++ version that also takes into account word unigrams and bigrams (Popovic´, 2017); - **COMET**: a neural quality estimation metric by Rei et al. (2020a) which was shown to be the state-of-the-art reference-based method (Kocmi et al., 2021). ## 3.2 Internal Measures Baseline: Seq-Logprob. This is the standard length-normalized sequence log-probability. Compared to previously introduced methods specifically targeting hallucinations, this simple metric performs the best (Guerreiro et al., 2022). We use ALTI: percentage of source contribution. We compute the percentage of source impact on the generated translation using the recently introduced ALTI+ (Ferrando et al., 2022). At a high level, it decomposes each transformer block into a sum of functions of individual tokens and views an output representation as a summation of transformed input vectors. Then it evaluates contribution of these vectors to the resulting sum. Among other things, ALTI+ (as well as an earlier Layerwise Relevance Propagation (LRP) -based method by Voita et al. (2021)) was used to show that for artificially created hallucinations, source influence is much lower than for "healthy" translations. Our work is the first to test this intuition in a real setting where hallucinations are generated naturally.3 Formally, for a model and its generated translation, we compute the total source contribution as the sum of contributions of all source tokens. We do it for each target token individually and then average across target tokens. The scores are computed by the same model that produced the translations (Section 2.1). ## 3.3 External Models Baseline: COMET-QE. For a reference-free model, we use the state-of-the-art COMETQE (Rei et al., 2020b) for its superior performance 3Note that of the two methods that can evaluate relative source and target contributions we choose ALTI+ by Ferrando et al. (2022) over LRP-based method by Voita et al. (2021) because the latter is more computationally expensive. compared to other quality estimators (Mathur et al., 2020; Freitag et al., 2021; Kocmi et al., 2021). We use: sentence similarity. Overall, we consider three measures based on pretrained models that evaluate semantic similarity of two sentences: - **LASER**: cosine similarity of source and translation sentence embeddings from LASER2. LASER2 (Heffernan et al., 2022) improves LASER (Artetxe and Schwenk, 2019) by replacing LSTM encoder with a Transformer and using teacher-student training; - **LaBSE**: cosine similarity of source and translation sentence embeddings from LaBSE (Feng et al., 2022). LaBSE is a dual-encoder approach based on pretrained transformers and fine-tuned for translation ranking with an additive margin softmax loss; - **XNLI**: product of the entailment probabilities of source to translation and translation to source. We compute entailment scores with RoBERTa (Conneau et al., 2020) finetuned on a combination of NLI data in 15 languages (Conneau et al., 2018).4 ## 4 Detection Experiments 4.1 Main Results Overall results are shown in Table 1. We report ROC AUC and precision at 90% recall.5In addition to overall results, we also report metrics for fully detached hallucinations separately. First, let us look at internal methods. While for all hallucinations ALTI performs comparably to Seq-Logprob, for fully detached hallucinations it has twice better precision. Since ALTI averages the source contributions over all generated tokens, it is more effective at detecting the most severe hallucinations rather than the ones where only part of the tokens are detached. Note also that for fully detached hallucinations, internal ALTI performs almost on par with the best external methods. Among external methods, LaBSE and XNLI substantially outperform previous best detector: for | All hall. | Fully detached | | | | |-------------|------------------|-------|------|-------| | Metric | AUC | P@R90 | AUC | P@R90 | | ChrF | 75.4 | 14.4 | 89.6 | 16.6 | | COMET | 83.4 | 19.2 | 87.7 | 12.6 | | Seq-Logprob | 83.0 | 13.9 | 93.5 | 31.0 | | ALTI | 84.9 | 12.5 | 98.7 | 67.4 | | COMET-QE | 70.2 | 14.2 | 66.1 | 6.0 | | LASER | 79.4 | 14.4 | 91.2 | 20.8 | | LaBSE | 91.7 | 25.9 | 98.5 | 70.3 | | XNLI | 90.9 | 24.1 | 98.7 | 60.4 | Table 1: Hallucination detection quality. Metrics: ROC AUC (↑) and P@R90 (↑). Methods: oracle, internal, external. Changes in scores are highlighted compared to Seq-Logprob. both all and fully detached hallucinations, their precision at 90% recall is roughly twice better than that of Seq-Logprob. While such a good performance might be expected for LaBSE that evaluates crosslingual sentence similarity (in a way, this might be seen as a measure of translation quality), results for XNLI are rather surprising: to the best of our knowledge, models optimized for XNLI have not been used in the context of machine translation. Note also the large difference between LaBSE and LASER: while the former shows big improvements compared to Seq-Lobprob, the latter noticeably lags behind. This is not surprising when looking at training objectives of the underlying models. LaBSE is trained on a translation ranking task and thus explicitly encourages ordering translations by severity of an error; for LASER, this is not the case. To further understand differences between detectors, we look at the distributions of the detection scores in Section 4.2 and the detected pathology types in Section 4.3. ## 4.2 Analysing Distributions Of The Scores For each of the methods, Figure 2 shows distributions of the scores for fully detached hallucinations, strongly detached hallucinations, less severe errors and correct translations. Internal methods: partial hallucinations are bimodal. ALTI and Seq-Logprob show similar behavior: errors are distributed similarly to correct translations, and the scores for partial (strongly detached) hallucinations have bimodal distribution. At a high level, for the model, some partial hallucinations "look" more like full hallucinations, and some - like errors. This can motivate future work: ![4_image_0.png](4_image_0.png) it would be interesting to understand whether it depends on detachment or on more simple patterns such as e.g. the proportion of hallucinated tokens. COMETs: blind to error severity. COMET and COMET-QE scores6 do not separate hallucinations from less severe errors. This agrees with previous work noting that since quality estimation models are mostly trained on data that lacks negative examples, COMETs may be inadequate at evaluating poor translations in general (Takahashi et al., 2021; Sudoh et al., 2021) and hallucinations in particular (Guerreiro et al., 2022). What is also expected, is that compared to reference-free COMET-QE, the overlap between the scores for correct and incorrect translations is much lower for reference-based COMET. ChrF behaves similarly to COMET. LaBSE: ranks hallucination severity best. LaBSE is the only detector with a clear order between full, partial hallucinations, and non-hallucinations. Once again, this is expected because only LaBSE is trained for ranking. Interestingly, for LASER, modes for the three distributions are also ordered; unfortunately, the distributions themselves overlap significantly which makes it not suitable as a detector. Both LaBSE and LASER ignore most of the non-hallucinated translation errors. 6The targets for COMET and COMET-QE models were calibrated with z-score transformation, so their outputs, while being unbounded, typically fall between -1 and 1. However, the dataset from Guerreiro et al. (2022) consists of translations preselected with flags of potential pathologies, so even for correct translations the scores are often highly negative. XNLI: no middle ground. Finally, XNLI distributions are very peaky and concentrated around 0 and 1. This is expected: XNLI's decision is always binary. While this provides good separation between fully detached hallucinations and correct translations, it is hard to estimate error severity. ## 4.3 Detected Pathology Types Now we come to fine-grained categories and look at detected pathology types. For each method, we flag a translation as "detected" if it belongs to a fraction (e.g. 10%) of the hallucination dataset corresponding to the lowest scores.7 Then we look at - the distribution of pathology types contained among detected examples (Figure 3); - recall for different translation types with respect to the whole dataset (Figure 4). The three best methods are similar. Figure 3 shows that ALTI, LaBSE and XNLI select similar pathology types. For them, flagged examples consist mostly of fully detached and strongly detached hallucinations, along with other errors. LASER is an outlier. Instead of focusing on pathological translations, LASER behaves differently and flags correct translations more. This explains its poor detection performance mentioned above. 7Note that we take such a large percentage because in the hallucination dataset we use, about 10% of translations are hallucinations and about 30% more are errors. ![5_image_0.png](5_image_0.png) XNLI flags undergenerations. Figure 4 shows that XNLI (and, to a lesser extent, LaBSE) flags a large proportion of undertranslations. This makes sense: these criteria are symmetric, and if we swap the source and the undergenerated translation, the longer source can be seen as a hallucination. Fully detached are the easiest to detect. As expected, fully detached hallucinations are the easiest to detect: all methods detect them entirely when taking 20% of the hallucination dataset (Figure 4), and they are the most frequent among the examples flagged by the best performing methods (Figure 3). This agrees with Guerreiro et al. (2022) that oscillatory and strongly detached hallucinations are more difficult to detect, and shows that improvements with our methods mostly come from these types. ## 5 Mitigating Hallucinations At Test Time Finally, let us come to the second part of the "detectthen-rewrite" pipeline: for a flagged translation, generate several alternative hypotheses and rerank them (Guerreiro et al., 2022) 8. This general framework has two degrees of freedom: (i) generation of hypotheses, (ii) reranking approach. We show that - for generating hypotheses, simply applying MC dropout (as done in Guerreiro et al. (2022)) outperforms more involved methods such as diverse beam search (Section 5.2); - for reranking, we can match COMET-QE with ![5_image_1.png](5_image_1.png) ## 5.1 Evaluation Methodology In this section, we explain the setup for the experiments with automatic evaluation in Sections 5.2 and 5.3. The setup for manual annotation is explained later in Section 5.3.2. Metrics. In our experiments, we use several metrics. First, we use quality evaluation metrics commonly used by the community, i.e. COMET (Rei et al., 2020b) and BLEU. Additionally, we use the two best metrics for hallucination detection: LaBSE and XNLI. We show some of the metrics in the main text and the rest in the appendix. Data. First, we analyze the impact of our method on translations of different quality levels. For this, we randomly sample 150 sentences from each of the following groups of the hallucination dataset (Section 2.2): fully detached hallucinations, strongly detached hallucinations, all other translation pathologies, and correct translations (to make sure that our mitigation does not accidentaly ruin them). We apply all versions of the hallucination mitigation algorithm to these 600 sentences. Note that in a practical application, we would apply the mitigation techniques only to the translations labeled by a detection algorithm as potential hallucination. We simulate this later in Section 5.3.2 when performing manual annotation. ## 5.2 Generation Strategies To generate alternative hypotheses, Guerreiro et al. (2022) use Monte Carlo dropout (Gal and Ghahramani, 2016). This means they leave standard beam search inference intact and achieve variability in translations via activating model dropout at inference. A natural question is whether using other ![6_image_0.png](6_image_0.png) generation strategies can give better results. For example, if we use e.g. beam search specifically designed to produce diverse translations, can we get better hypotheses? To test this, we use the following methods: - DEFAULT: standard decoding without reranking, i.e. beam search with size 5, where we pick only the top 1 candidate; - BEAM SEARCH: beam search with size n; - sampling from the predicted distribution: ◦ SAMPLING: from the whole distribution; ◦ SAMPLING P=80: from the top p = 80% of the distribution, i.e. nucleus sampling (Holtzman et al., 2020); - diverse beam search: ◦ DBS_N: method by Vijayakumar et al. (2016) with beam widths s = 1, 3, 10; ◦ D_DEC_R: diverse decoding with diversity rates r = 1, 3, 10 (Li et al., 2016); - Monte Carlo dropout: ◦ MC GREEDY: n iterations of greedy search with dropout; ◦ MC BEAM: the method used in Guerreiro et al. (2022), i.e. n iterations of beam search with dropout, each with size 10. Unless stated otherwise, n = 10 in all experiments. ## 5.2.1 The Impact Of Generation Strategy The results are shown in Figure 5. To disentangle the effect of generation strategy from the subsequent reranker performance, we show the results for all combinations. As rerankers, we considered COMET-QE used in Guerreiro et al. (2022) and the methods proposed in Section 3. We see that the MC BEAM method clearly outperforms all the other. This is interesting for two reasons. First, MC dropout is easy to use: one has to apply standard inference with dropout on without other changes to the implementation. Next, differently from modifying decoding strategies, here variability in hypotheses comes from model predictive uncertainty (Gal and Ghahramani, 2016; Zerva et al., 2021; Guerreiro et al., 2022). This is one more evidence that understanding model inner characteristics can be beneficial in various settings. Based on these results, in what follows we generate hypotheses with beam search with MC dropout. ## 5.2.2 The Impact Of Number Of Hypotheses We also check whether generating more than 10 hypotheses can improve the overall results. Figure 6 shows the final COMET scores depending on the number of hypotheses. We see that the scores increase with more hypotheses and do not saturate at 10. This implies that in cases when the quality of a translation is much more important than its computational cost, one can potentially improve the quality by generating more candidate hypotheses. ## 5.3 Reranking Approaches Apart from detecting hallucinations, the methods we propose can be applied as rerankers in the "detect-than-rewrite" pipeline. ## 5.3.1 Automatic Evaluation Figure 5 shows that, regardless of the generation method, LaBSE is the best reranker and it performs notably better than the strong COMET-QE baseline. Apart from the average results, Table 2 also shows COMET scores for each pathology type. We can see that reranking with any method is better than no reranking for all groups of original translations. Compared to the COMET-QE baseline, LABSE improves the scores for hallucinations and correct translations, but drops quality for other pathologies. The only internal method ALTI performs better than COMET-QE for fully detached hallucinations, but is inferior when looking at other translations: it | Pathologies | Cor. | Avg. | | | | |-------------------|--------|--------|-------|------|-------| | Reranker | F. | S. | O. | | | | No reranking | -1.23 | -0.97 | -0.59 | 0.27 | -0.63 | | Baseline COMET-QE | -0.21 | -0.13 | -0.14 | 0.35 | -0.03 | | Ours ALTI | -0.17 | -0.24 | -0.39 | 0.25 | -0.14 | | LASER | -0.11 | -0.23 | -0.35 | 0.27 | -0.11 | | LaBSE | -0.07 | -0.12 | -0.26 | 0.39 | -0.01 | | XNLI | -0.12 | -0.18 | -0.28 | 0.30 | -0.07 | is very sensitive to the most severe pathology, but is not capable to rank relatively good translations. Note that for former pathologies, the average COMET scores are negative even after mitigation. As we saw in Figure 2, this may be normal even for correct translations, and may reflect the fact that, while being technically correct, they are far from being perfect. ## 5.3.2 Human Evaluation Data. To confirm the results of automatic evaluation, we perform a human evaluation. With each method, we translate the same 200 source sentences. They are randomly sampled from the hallucination dataset with the distribution of pathologies roughly mimicking outputs of the best detectors (Figure 3). Overall, for 55% of the sentences their original translations are labeled as hallucinations, 25% as errors and 20% as correct translations.9 We compare the original translations and three reranking methods: the baseline COMET-QE used in Guerreiro et al. (2022), the best overall reranker LaBSE, and the only internal method ALTI. Annotation. For each of the 200 source sentence, we deduplicate and shuffle the four translations to mitigate annotator bias. The 602 resulting sentence pairs are labeled by 3 annotators into three categories: Correct, Error, and Hallucination. We aggregate the labels by majority vote; in case of ties (20 out of the 602 sentence pairs after deduplication) we pessimistically assume a hallucination. 9We select these sentences randomly rather than using proposed detection methods because the latter would affect the results of evaluating these methods as rerankers. ![7_image_0.png](7_image_0.png) We evaluate the statistical significance of the pairwise differences in the proportions of correct and hallucinated translations using two-sided Student test for two related samples with 5% confidence level. We provide more details on the annotation guidelines and inter-annotation agreement in Appendix C. Results. Human evaluation results are shown in Figure 7. All reranking methods reduce hallucinatory rate by a factor of 2.5 to 3. Interestingly, when looking at hallucinations, internal ALTI performs on par with COMET-QE: the differences between these two methods are not statistically significant. COMET-QE, however, has less errors. This is expected as it was trained to distinguish correct translations from errors. Coming to LaBSE, we find that it produces slightly less hallucinations than other reranking methods and more correct translations than ALTI; these differences are significant at 5% confidence level. Overall, by using sentence similarity from LaBSE, we improve both on hallucinations detection and mitigation at test time. Surprisingly, LaBSE and ALTI outperform COMET-QE with a large margin for hallucination detection, but not for hypotheses reranking. As we explain in Section 4.2, quality estimation models are mostly trained on data that lacks negative examples. Therefore, COMETs may be inadequate at evaluating poor translations in general and hallucinations in particular (Takahashi et al., 2021; Sudoh et al., 2021; Guerreiro et al., 2022). For reranking, the goal is the opposite: finding the best translations (as opposed to the worst), which is closer to the COMET training objective. Note that since COMET-QE is the state-of-theart quality estimator, it is a very strong baseline for the reranking stage where the goal is to find a better translation. The fact that we can match its hallucinatory rate reduction by analyzing model inner workings has value from different perspectives. For research, it can motivate future work on model understanding; for practitioners, it means that hallucination mitigation is not limited to language pairs where external models such as COMET-QE exist: model understanding might be enough. ## 6 Conclusions We start by asking how far we can go at detecting and mitigating hallucinations if we use nothing but the translation model itself. Turns out, we can improve the results of the overall "detect-then-rewrite" pipeline by evaluating the percentage of source contribution to a generated translation: translations with low source contribution are likely to be "detached" from the source, i.e. hallucinations. For detecting the most severe type of hallucinations, this method improves previous results twice; for mitigating hallucinations at test time, it matches the hallucination reduction rate of the previous best external method. We believe this can motivate future research on model analysis. When allowing external models, we expand the methods for handling hallucinations from models specialized for quality estimation to a broader set of objectives, e.g. sentence similarity from cross-lingual embeddings. Apart from showing that LaBSE improves previous results significantly, we also find that models so far overlooked in the context of machine translation (e.g. natural language inference) can be beneficial. We hope future work will build on this idea. ## 7 Limitations Our analysis and conclusions have been based only on a single translation direction (German to English), a single dataset, and a single transformerbased model. The generalization to other languages, data and models is yet to be verified. Even in this setup, we have seen that some of the proposed methods are very good at detecting fully detached hallucinations. However, none of them were able to well separate strongly detached hallucinations (when only a part of the generated translation is unrelated to the source) from correct translations. Perhaps, such partial hallucinations should be detected on the level of individual tokens instead of the whole sentence. One of the metrics that we propose, average ALTI source contribution, has an advantage of not requiring any external models except the translation model itself. However, the two best detection metrics (based on LaBSE and on XNLI model) require additional encoders trained on the source and target languages, which limits their applicability for lower-resourced languages or in the settings with limited computational resources. Being an internal method is an advantage of ALTI, but it is also a limitation: this method is suitable only for transformer-based translation models. In principle, it can be adapted to other neural architectures, but not to non-neural approaches, such as statistical machine translation. ## 8 Ethical Statement We do not foresee any considerable risks associated with our work. In principle, our framework for hallucination mitigation could be intentionally reversed to produce lower-quality translations. But there are easier ways to produce a bad translation, such as just sampling the output text randomly, so we do not think that our work poses any additional risks. This work is based on the open source dataset and model released by Guerreiro et al. (2022) and thus inherits all their potential biases. We will make our code publicly available to ensure reproducibility of our experiments. ## References Mikel Artetxe and Holger Schwenk. 2019. 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IST-unbabel 2021 submission for the quality estimation shared task. In *Proceedings of the Sixth Conference on Machine Translation*, pages 961–972, Online. Association for Computational Linguistics. Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad. 2021. Detecting hallucinated content in conditional neural sequence generation. In *Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*, pages 1393–1404, Online. Association for Computational Linguistics. ## A Implementation And Computing All our experiments were carried out on a single server with one NVIDIA Quadro GP100 GPU. The total computation time for generating and scoring translations was less than 24 hours. To compute BLEU and ChrF++, we use the SacreBLEU package10 with the default parameters. For COMET and COMET-QE, we use the COMET package11 with the wmt20-comet-da and wmt20-comet-qe-da-v2 models, respectively. The translation hypotheses, Seq-Logprob, and LASER are computed using the Fairseq framework12. To compute ALTI+, we adapt the code13 by Ferrando et al. (2022). For the inference of LaBSE and the XNLI model, we use the transformers package14. ## B Mitigating Hallucinations At Test Time Table 3 shows XNLI scores after reranking MC dropout hypotheses by various methods. Note that since here XNLI was used both to rerank and well as evaluate quality, in the experiment XNLI can be viewed as an oracle. ## C Manual Evaluation | Pathologies | Correct | Avg. | | | | |-------------------|-----------|--------|----|----|----| | Reranker | F. | S. | O. | | | | No reranking | 2 | 30 | 80 | 93 | 51 | | Baseline COMET-QE | 59 | 69 | 85 | 93 | 77 | | Ours ALTI | 64 | 73 | 92 | 91 | 80 | | LASER | 72 | 73 | 92 | 92 | 82 | | LaBSE | 74 | 80 | 92 | 94 | 85 | | XNLI (oracle) | 75 | 83 | 98 | 97 | 88 | Table 3: Average XNLI scores after reranking MC dropout hypotheses by various methods. Pathologies: fully detached hallucinations (F.), strongly detached hallucinations (S.), other pathologies (O.). number of annotators and inter-annotation agreement. Third, we report the results of statistical sigificance tests for comparing all the methods. Guidelines Annotators were provided with the guidelines shown in Table 4. For the reporting purposes, "Partial hallucination" was grouped together with "Full hallucination", and "Undertranslation" with "Other". Inter-annotation agreement We evaluated interannotation agreement by Fleiss' Kappa. For the three annotators and the three aggregated labels, it equals 0.57 on the 602 sentence pairs that were labeled (with the 5 original labels, it is 0.55). This may be interpreted as moderate agreement. The differences The Tables 5 and 6 compare proportions of correct and hallucinated translations for each of the manually evaluated methods. The Pvalues are computed with paired two-sided Student test (scipy.stats.ttest_rel). Each row of the data consists of the German source sentence, its reference English translation (it is not always accurate!), and 1 to 4 machine translation outputs. The machine translation outputs are presented in a random order, to exclude the possibility of bias toward any specific method. For each of the machine translations, you need to assign one of the following labels: - OK: An acceptable translation; it conveys the main meaning correctly and does not introduce extra meaning. Some details still may differ, and minor errors are acceptable. - Partial hallucination: a part of the translation is unrelated to the source, or is related very indirectly, such as via a common topic. - Full hallucination: most or all of the translation is unrelated to the source, or is related very indirectly. - Undertranslation: there is no hallucinations, but a significant part of the source is not translated at all. - Other: there are no hallucinations or undertranlsations, but there are other translation errors that make the translation unacceptable. Table 4: Human annotations Guidelines | Method 1 | Method 2 | Rate 1 | Rate 2 | P-value | |------------|------------|----------|----------|-----------| | LABSE | COMET-QE | 0.56 | 0.54 | 0.53 | | LABSE | ALTI | 0.56 | 0.49 | 0.02 | | LABSE | Default | 0.56 | 0.20 | 0.00 | | COMET-QE | ALTI | 0.54 | 0.49 | 0.12 | | COMET-QE | Default | 0.54 | 0.20 | 0.00 | | ALTI | Default | 0.49 | 0.20 | 0.00 | Table 5: Comparison between manually annotated rates of correct translation. | Method 1 | Method 2 | Rate 1 | Rate 2 | P-value | |------------|------------|----------|----------|-----------| | LABSE | COMET-QE | 0.16 | 0.22 | 0.01 | | LABSE | ALTI | 0.16 | 0.22 | 0.01 | | LABSE | Default | 0.16 | 0.53 | 0.00 | | COMET-QE | ALTI | 0.22 | 0.22 | 1.00 | | COMET-QE | Default | 0.22 | 0.53 | 0.00 | | ALTI | Default | 0.22 | 0.53 | 0.00 | Table 6: Comparison between manually annotated rates of hallucinated translation. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 (after conclusions) ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Yes in the abstract and first section (1) ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** We used a translation model and a dataset described in section 2 ✓ B1. Did you cite the creators of artifacts you used? Yes, in section 2 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No, the license is included in the reference to the authors ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Yes (for the existing artifacts), in section 1 and 2 ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No personal information that we are aware of ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? it was not provided in the original paper ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Sections 4 and 5 ## C ✓ **Did You Run Computational Experiments?** Sections 4 And 5 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We did not train any models. The infrastructure is reported in Appendix A. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Sections 4 and 5. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? For the manual annotations, we compute statistical significance of all the differences in the averages in the Appendix C. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix A ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 5 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? appendix C ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? The annotators were members of our team and did the job within their normal working hours. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. We used an existing published dataset. ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? We did not collect any data, except of annotating an already existing dataset ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

cheng-etal-2023-explainable

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

https://aclanthology.org/2023.acl-long.4

Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users{'} preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4{\%} improvement in prediction and 15.8{\%} improvement in explanation for TripAdvisor).

# Explainable Recommendation With Personalized Review Retrieval And Aspect Learning Hao Cheng1, Shuo Wang1, Wensheng Lu1**, Wei Zhang**1 Mingyang Zhou1, Kezhong Lu1, Hao Liao**1, 2, 3**∗ 1College of Computer Science and Software Engineering, Shenzhen University, China 2WeBank Institute of Financial Technology, Shenzhen University, China 3Ping An Bank Co., Ltd. {2110276103, 2110276109, 2210273060, 2210275010}@email.szu.edu.cn {zmy, kzlu, haoliao}@szu.edu.cn ## Abstract Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users' preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-theart baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor). ## 1 Introduction Recent years have witnessed a growing interest in the development of explainable recommendation models (Ai et al., 2018; Chen et al., 2021). In general, there are three different kinds of frameworks for explainable recommendation models, which are post-hoc (Peake and Wang, 2018), embedded (Chen et al., 2018) and multi-task learning methods(Chen et al., 2019b). Post-hoc methods generate explanations for a pre-trained model after the fact, leading to limited diversity in explanations. ∗ Corresponding author Embedded methods, on the other hand, demonstrate efficacy in acquiring general features from samples and mapping data to a high-dimensional vector space. However, since embedded methods rely on historical interactions or features to learn representations, they may struggle to provide accurate recommendations for users or items with insufficient data. In addition to the two frameworks mentioned above, there has been a utilization of multi-task learning frameworks in explainable recommendation systems, where the latent representation shared between user and item embeddings is employed (Chen et al., 2019b; Ai et al., 2018). These frameworks often employ the Transformer (Vaswani et al., 2017; Li et al., 2021b), a powerful text encoder and decoder structure widely used for textual processing tasks. While efficient for prediction tasks, they encounter challenges in generation tasks due to limited review content, leading to a significant decline in performance. Furthermore, these previous transformer-based frameworks do not incorporate personalized information and treat heterogeneous textual data indiscriminately. To address these issues, we make adaptations to the existing multi-task learning framework by incorporating two main components: retrieval enhancement, which alleviates the problem of data scarcity, and aspect enhancement, which facilitates the generation of specific and relevant explanations. Real-world datasets usually contain redundant reviews generated by similar users, making the selected reviews uninformative and meaningless, which is illustrated in Figure 1. To address this issue, a model-agnostic retrieval enhancement method has been employed to identify and select the most relevant reviews. Retrieval is typically implemented using established techniques, such as TF-IDF (Term Frequency-Inverse Document Frequency) or BM25 (Best Match 25) (Lewis et al., 2020), which efficiently match keywords with an ![1_image_0.png](1_image_0.png) inverted index and represent the question and context using high-dimensional sparse vectors. This approach facilitates the generation of sufficient specific text, thereby attaining enhanced textual quality for the user. Generally, Wikipedia is utilized as a retrieval corpus for the purpose of aiding statement verification (Karpukhin et al., 2020; Yamada et al., 2021). Here, we adopt a novel approach wherein the training set of each dataset is utilized as the retrieval corpus. By integrating this component into our framework, we are able to generate sentences with more specific and relevant details. Consequently, this enhancement facilitates the generation of explanations that are more accurate, comprehensive, and informative at a finer granularity. Moreover, users rarely share a common preference (Papineni et al., 2002). Therefore, aspects (Zhang et al., 2014), extracted from corresponding reviews, can be utilized to assist in the modeling of user representation. The incorporation of aspect enhancement has resulted in not only improved prediction accuracy, but also more personalized and user-specific text during the text generation process. By incorporating retrieval enhancement and aspect enhancement into our model, we adjust the transformer architecture to meet our needs, achieving better performance in both prediction and generation tasks. The main contributions of our framework are as follows: - In response to the problem of insufficient historical reviews for users and items in explainable recommendation systems, we propose a retrieval enhancement technique to supplement the available information with knowledge bases obtained from a corpus. To the best of our knowledge, this study represents the first application of retrievalenhanced techniques to review-based explainable recommendations. - We propose a novel approach wherein different aspects are selected for individual users when interacting with different items, and are subsequently utilized to facilitate the modeling of user representation, thereby leading to the generation of more personalized explanations. - Experimental results on real-world datasets demonstrate the effectiveness of our proposed approach, achieving superior performance compared to state-of-the-art baselines2. ## 2 Related Work 2.1 Explainable Recommendation With Generation Explainable recommendation systems (Zhang et al., 2020) have been extensively studied using two primary methodologies: machine learning and human-computer interaction. The former (Gedikli et al., 2014; Chen and Wang, 2017) investigates how humans perceive different styles of explanations, whereas the latter generates explanations through the application of explainable recommendation algorithms, which is more relevant to our research. Numerous approaches exist for explaining recommendations, including the use of definition templates (Li et al., 2021a), image visualization (Chen et al., 2019a), knowledge graphs (Xian et al., 2019), and rule justifications (Shi et al., 2020). Among these methods, natural language explanations (Chen et al., 2019b; Li et al., 2021b) are gaining popularity due to their user accessibility, advancements in natural language processing techniques, and the availability of vast amounts of text data on recommendation platforms. Several studies have employed Recurrent Neural Network (RNN) networks (Li et al., 2017), coupled with Long Short-Term Memory (LSTM) (Graves and Graves, 2012), for generating explanatory texts, while others have utilized co-attention and Gated 2https://github.com/lileipisces/PETER Recurrent Unit (GRU) (Cho et al., 2014) in conjunction with Convolutional Attentional Memory Networks (CAML) (Chen et al., 2019b) for text generation. More recently, transformer-based networks have seen increased utilization for score prediction and interpretation generation. (Li et al., 2021b) ## 2.2 Pre-Trained Models The pre-trained model has gained significant traction in the field of NLP recently. These models, such as (Devlin et al., 2019; Reimers and Gurevych, 2019) are trained on large-scale opendomain datasets utilizing self-supervised learning tasks, which enables them to encode common language knowledge. The ability to fine-tune these models with a small amount of labeled data has further increased their utility for NLP tasks (Qiu et al., 2020; Ren et al., 2021). For example, a pre-trained model is Sentence-BERT (Reimers and Gurevych, 2019), which utilizes a multi-layer bidirectional transformer encoder and incorporates Masked Language Model and Next Sentence Prediction to capture word and sentence-level representations. Another example is UniLM (Dong et al., 2019), which builds upon the architecture of BERT and has achieved outstanding performance in a variety of NLP tasks including unidirectional, bidirectional, and sequence-to-sequence prediction. Furthermore, research has demonstrated that pre-trained models possess the capability to capture hierarchysensitive and syntactic dependencies (Qiu et al., 2020), which is highly beneficial for downstream NLP tasks. The utilization of pre-trained models has proven to be a powerful approach in NLP field, with the potential to further improve performance on a wide range of tasks. ## 2.3 Retrieval Enhancement Retrieval-enhanced text generation has recently received increased attention due to its capacity to enhance model performance in a variety of natural language processing (NLP) tasks (Ren et al., 2021; Qiu et al., 2020). For instance, in open-domain question answering, retrieval-enhanced text generation models can generate the most up-to-date answers by incorporating the latest information during the generation process (Li and Gaussier, 2021; Li et al., 2020a). This is not possible for traditional text generation models, which store knowledge through large parameters, and the stored information is immutable. Retrieval-based methods also have an advantage in scalability, as they require fewer additional parameters compared to traditional text generation models (Ren et al., 2021). Moreover, by utilizing relevant information retrieved from external sources as the initial generation condition (Ren et al., 2021), retrieval-enhanced text generation can generate more diverse and accurate text compared to text generation without any external information. ## 3 Problem Statement Our task is to develop a model that can accurately predict ratings for a specific product and provide a reasonable explanation for the corresponding prediction. The model's input is composed of various elements, namely the user ID, item ID, aspects, reviews, and retrieval sentences, whereas the resulting output of the model encompasses both a prediction and its explanation. We offer a detailed description of our models' input and output data in this section. ## Input Data - **Heterogeneous information**: The variables included in the framework encompass user ID u, item ID v, aspects A, retrieval sentences S and review R. Aspects A are captured in the form of a vector representing user's attention, denoted as (Au,1, . . . , Au,n), where Au,j represents the j-th aspect extracted from the reviews provided by user u. As an illustration, the review The screen of this phone is too small encompasses the aspect *(screen, small)*. Regarding users, we extract the most important sentence Su,j from the set (Su,1, ..., Su,n). Similar operations are performed for items, where Sv,j is employed. Ultimately, the user's review for the item Ru,v is fed into the training process to enhance the ability to generate sentences. ## Output Data - **Prediction and explaination**: Given a user u and an item v, we can obtain a rating prediction rˆu,v, representing user u's preference towards item v and a generated explanatory text L = (l1, l2*, . . . , l*T ), providing a rationale for the prediction outcome. In this context, li denotes the i-th word within the explanation text, while T represents the maximum length of the generated text. ## 4 Methodology 4.1 Overview Of Model Here we present a brief overview of ERRA model. As shown in Figure 2, our model mainly consists of three components, each corresponding to a subprocess of the information processing model: - **Retrieval Enhancement** aims to retrieve external knowledge from the training sets. - **Aspect Enhancement** aims to identify the most important aspects that users are concerned about in their reviews. - **Joint Enhancement Transformers** is responsible for the integration of the retrieved sentences and aspects with a transformer structure for simultaneously performing the prediction and explanation tasks. Next, we will provide an in-depth description of each component and how they are integrated into a unified framework. ## 4.2 Retrieval Enhancement A major challenge in generating rich and accurate explanations for users is the lack of sufficient review data. However, this problem can be alleviated via retrieval-enhanced technology, which introduces external semantic information. ## 4.2.1 Retrieval Encode The retrieval corpus is constructed using the training set. To obtain fine-grained information, lengthy reviews are divided into individual sentences with varied semantics. Using these sentences as searching unit allows the model to generate more fine-grained text. Sentence-BERT (Reimers and Gurevych, 2019) is utilized to encode each sentence in the corpus, which introduces no additional model parameters. We did not use other LLMs (Large Language Models) for retrieval encoding because it is optimized for dense retrieval and efficient for extensive experiments. Sentence-BERT is considerably faster than BERT-large or RoBERTa when encoding large-scale sentences and possesses an enhanced capacity for capturing semantic meaning, making it particularly well-suited for the retrieval task. The encoded corpus is saved as an embedding file, denoted as C. During the retrieval process, the most relevant information is directly searched from the saved vector C, which greatly improves the efficiency of retrieval. ## 4.2.2 Retrieval Method We adopt a searching model commonly used in the field of question answering (QA) and utilize cosine similarity for querying as a simple and efficient retrieval method. Here, we use the average of the review embedding Uavg of each user as the query. This representation is in the same semantic space and also captures user preferential information to a certain extent. The average embedding Uavg of all the reviews for a user is used as a query to retrieve the most similar n sentences (Su,1, ..., Su,n) in the previous corpus C. Our approach incorporates the Approximate Nearest Neighbor (ANN) search technique, with an instantiation based on the Faiss3library to improve retrieval speed through index creation. This optimization substantially decreases the total retrieval search duration. Then, in our implementation, we set n as 3 and stitch these sentences together to form a final sentence. Sentence-BERT is then used to encode this final sentence to obtain a vector Su,v, which represents the user for the item retrieval. Similarly, Sv,u is used for items to retrieve users. ## 4.3 Aspect Enhancement Users' preferences are often reflected in their reviews. To better represent users, we need to select the most important aspects of their reviews. Specifically, we first extract aspects from each user and item review using extraction tools. The extracted aspects from user reviews represent the style of the users in their reviews, while the extracted aspects from item reviews represent the most important features of the item. We aim to identify the most important aspects that users are concerned about in their reviews. It is worth noting that users' interests may vary in different situations. For example, when choosing a hotel, a user may care more about the environment. Whereas, price is a key factor to consider when buying a mobile phone. To address this, we use the average vector Avi,avg, vi ∈ V , representing all aspects under the item reviews, as the query. This vector is encoded using SentenceBERT. For each user, we construct a local corpus of their aspects collection (Aui,1, ..., Aui,l), ui ∈ U and use cosine similarity as the measurement indicator. We search for the top-n aspects from the local corpus by Avi*,avg*. These retrieved aspects represent the top-n aspects that the user is concerned about this item. 3https://github.com/facebookresearch/faiss ![4_image_0.png](4_image_0.png) 4.4 Joint Enhancement Transformers In our proposed model, we adopt the transformer structure in the prediction and explanation tasks. The transformer consists of multiple identical layers with each layer comprising two sub-layers: the multi-head self-attention and the position-wise feed feedback network. Previous research has made various modifications to the transformer architecture (Li et al., 2021b; Geng et al., 2022). Here we integrate the retrieved aspects with the encoding of multiple sentences in various ways. The retrieved sentences SU,j , SV,j are encoded uniformly as the input hidden vector suv , svu and are introduced into the first layer of the transformer. Below, we use one layer as an example to introduce our calculation steps. $$\mathbf{A}_{i,h}=\mathrm{softmax}\left(\frac{\mathbf{Q}_{i,h}\mathbf{K}_{i,h}^{\top}}{\sqrt{d}}\right)\mathbf{V}_{i,h}$$ $$\mathbf{Q}_{i,h}=\mathbf{S}_{i-1}\mathbf{W}_{i,h}^{Q},\mathbf{K}_{i,h}=\mathbf{S}_{i-1}\mathbf{W}_{i,h}^{K},$$ $$\mathbf{V}_{i,h}=\mathbf{S}_{i}\mathbf{W}_{i,h}^{V}$$ $$\mathbf{V}_{i,h}=\mathbf{S}_{i}\mathbf{W}_{i,h}^{V}$$ $$\quad(1)$$ Vi,h (1) i,h, (2) $\text{m}\left|S\right|\times d$ : the $\text{\hspace{0.17em}}$ is the d . where Si−1 ∈ R|S|×dis the i-th layer's output, WQ i,h,WK i,h,WV i,h ∈ R d× dH are projection matrices, d denotes the dimension of embeddings and is set to 384. |S| denotes the length of the input sequence. Subsequently, we incorporate aspect information into the model. As aspects are closely related to both users and items, we modify the internal mask structure of the model and combine the user's aspects and ID information through a selfattention mechanism. Not only does this strategy account for the uniqueness of the ID when modeling users, but also increase the personalization of the user's interactions with the item. Specifically, the same user may have different points of attention when interacting with different items. As illustrated in Figure 2, we make the information of the first four positions attend to each other, because the first two positions encode the unique user identifier, while the third and fourth positions encapsulate the personalized aspects of the user's preferences. The underlying rationale for selecting these positions is to facilitate the attention mechanism in capturing the interactions between users and products, ultimately enhancing the model's accuracy. At this point, our final input is as follows: [Uid, Vid, Au1 , Au2 , suv, svu, t1, . . . , t|tlen|]. After including the location [P1, P2, P3*, . . . , P*|s|], where |s| is the length of the input, the final input becomes [H1, H2, H3*, . . . , H*|s|]. For the two different information of ID and aspects, we use them jointly to represent the user and item. We use the self-attention mechanism to combine these two different semantic information, however, we found that it causes the final ID embedding matrix to be very close to the word embedding matrix, resulting in the loss of unique ID information and high duplication in generated sentences. To address this problem, we adopt the strategy from previous research (Geng et al., 2022) that only uses an ID to generate texts, and compares the generated text with the real text to compute the loss Lc. To a certain extent, this method preserves the unique ID information in the process of combining aspects, thereby reducing the problem of repetitive sentences. $$\mathcal{L}_{c}=\sum_{(u,v)\in\mathcal{T}}\frac{1}{|t_{len}|}\sum_{t=1}^{|t_{len}|}-\log H_{v}^{g_{ti}}\tag{4}$$ where $\mathcal{T}$ denotes the training set. $g_{ti}$ denotes that only use the hidden vector of the position Hv to generate the i-th word, i ∈ 1, 2..., tlen. ## 4.5 Rating Prediction We utilized the two positions of the final layer (denoted as Hv) as the input. To combine the information of the ID and the hidden vector Hv, we employed a multi-layer perceptron (MLP) to map the input into a scalar. The loss function used in this model is the Root Mean Square Error (RMSE) function. $$\mathbf{r}_{u,v}=\mathrm{ReLU}\left([H_{v},u_{i d},v_{i d}]\mathbf{W}_{l,1}\right)\mathbf{W}_{l,2}$$ $$\mathcal{L}_{r}=\frac{1}{|\mathcal{T}|}\sum_{(u,v)\in\mathcal{T}}\left(r_{u,v}-\hat{r}_{u,v}\right)^{2}\tag{6}$$ where where $\mathbf{W}_{1}\in\mathbb{R}^{3d\times d},\mathbf{W}_{2}\in\mathbb{R}^{d\times1}$ are weight parameters, ru,v is the ground-truth rating. ## 4.6 Explanation Generation We adopt an auto-regressive methodology for word generation, whereby words are produced sequentially to form a coherent interpretation text. Specifically, we employ a greedy decoding strategy, wherein the model selects the word with the highest likelihood to sample at each time step. The model predicts the subsequent hidden vector based on the previously generated one, thereby ensuring the preservation of context throughout the entire generation process. $$\mathbf{e}_{t}=\mathrm{softmax}\left(\mathbf{W}^{v}\mathbf{H}_{L,t}+\mathbf{b}^{v}\right)$$ $$\pi_{\mathbb{T}}|{\mathcal{V}}|\times d$$ v) (7) where Wv ∈ R*|V|×*dand b v ∈ R|V| are weight parameters. The vector et represents the probability distribution over the vocabulary V. ## 4.6.1 Aspect Discriminator To increase the probability that the selected aspects appear in explanation generation. We use the previous method (Chen et al., 2019b) and adapt it to our task. We represent τ as the aspects that interest this user, τ ∈ R|V|. If the generated word at time t is an aspect, then τa is 1. Otherwise, it is 0. The loss function is as follows: $${\mathcal{L}}_{a}={\frac{1}{|{\mathcal{T}}|}}\sum_{(u,v)\in{\mathcal{T}}}{\frac{1}{|t_{l e n}|}}\sum_{t=1}^{|t_{l e n}|}(-\tau_{a}\log e_{t,a})\quad{\mathrm{(8)}}$$ ## 4.6.2 Text Generation We propose a mask mechanism that allows for the efficient integration of ID, aspects, and retrieved sentence information into the hidden vector of the Beginning of Sentence (BOS) position. At each time step, the word hidden vector is transformed into a vocabulary probability through a matrix, and | Datasets | Yelp | Amazon | TripAdvisor | |-------------------|-----------|-----------|---------------| | Number of users | 27,147 | 157,212 | 9,765 | | Number of items | 20,266 | 48,186 | 6,280 | | Number of reviews | 1,293,247 | 1,128,437 | 320,023 | | Records per user | 47.64 | 7.18 | 32.77 | | Records per item | 63.81 | 23.41 | 50.96 | the word with the highest probability is selected via the Greedy algorithm. The generation process terminates when the predicted word is the End of Sentence (EOS) marker. To ensure that the generated text adheres to a specific length, we employ a padding and truncation strategy. When the number of generated words falls short of the set length, we fill in the remaining positions with a padding token (PAD). Conversely, when the number of generated words exceeds the set length, we truncate the later words. Here we use the Negative log-likelihood loss as a generated text Lg. This loss function ensures the similarity between the generated words and the ground truth ones. $${\mathcal{L}}_{g}={\frac{1}{|{\mathcal{T}}|}}\sum_{(u,v)\in{\mathcal{T}}}{\frac{1}{|t_{l e n}|}}\sum_{t=1}^{|t_{l e n}|}-\log e_{6+t}^{g_{t}}\quad(9)$$ $$(7)$$ where T denotes the training set. gt denotes the utilization of the 6+t position hidden vector to generate the t-th word, t ∈ 1, 2..., tlen. 6 represents the initial first six positions vector information before the BOS, and t represents the current moment. ## 4.7 Multi-Task Learning We aggregate losses to form the final objective function of our multi-task learning framework. The objective function is defined as: $${\mathcal{L}}=p l{\mathcal{L}}_{r}+\lambda_{c}{\mathcal{L}}_{c}+g l{\mathcal{L}}_{g}+a l{\mathcal{L}}_{a}+\lambda_{l}\|\Theta\|_{2}^{2}\tag{10}$$ where Lg represents the loss function of text generation and Lc is the loss function for context prediction, with pl and gl as their weights, respectively. La is the loss function for aspect discriminator and al is its weights. Θ contains all the neural parameters. ## 5 Experiments 5.1 Datasets We performed experiments on three datasets, namely Amazon (cell phones), Yelp (restaurants), and TripAdvisor (hotels) (Li et al., 2020b). We | PMF | 1.097 | 0.883 | 1.235 | 0.913 | 0.870 | 0.704 | |-------|---------|---------|---------|---------|---------|---------| | SVD++ | 1.022 | 0.793 | 1.196 | 0.871 | 0.811 | 0.623 | | NARRE | 1.028 | 0.791 | 1.176 | 0.865 | 0.796 | 0.612 | | DAML | 1.014 | 0.784 | 1.173 | 0.858 | 0.793 | 0.617 | | NRT | 1.016 | 0.796 | 1.188 | 0.853 | 0.797 | 0.611 | | CAML | 1.026 | 0.798 | 1.191 | 0.878 | 0.818 | 0.622 | | PETER | 1.017 | 0.793 | 1.181 | 0.863 | 0.814 | 0.635 | | ERRA | 1.008 | 0.781 | 1.158 | 0.832 | 0.787 | 0.603 | filtered out users with fewer than 5 comments and re-divided the dataset into three sub-datasets in the ratio of 8:1:1. The details of the datasets are shown in Table 1. We use an aspects extraction tool (Zhang et al., 2014) to extract the aspects in each review and correspond it to the respective review. ## 5.2 Evaluation Metrics For rating prediction, in order to evaluate the recommendation performance, we employ two commonly used indicators: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), which measure the deviation between the predicted ratings r and the ground truth ratings r∗. For generated text, we adopt a variety of indicators that consider the quality of the generated text from different levels. **BLEU** (Papineni et al., 2002), **ROUGE** (Lin, 2004) and **BERTscore** (Reimers and Gurevych, 2019) are commonly used metrics in natural language generation tasks. BLEU-N (N=1,4) mainly counts on the N-grams. R2-P, R2-R, R2-F, RL-P, RL-R and RL-F denote Precision, Recall and F1 of ROUGE-2 and ROUGE-L. BERT-S represents similarity scores using contextual embeddings to calculate. They are employed to objectively evaluate the similarity between the generated text and the targeted content. ## 5.3 Baseline Methods 5.3.1 Prediction The performance in terms of accuracy of rating prediction is compared with two types of methods: Machine Learning and Deep Learning: - Deep learning models: **NARRE** (Chen et al., 2018) is a popular type of neural network for textbased tasks. **PETER** (Li et al., 2021b) and NRT (Li et al., 2017) are deep learning models that use review text for prediction and explanation at the same time. - Factorization methods: PMF (Salakhutdinov and Mnih, 2007) is a matrix factorization method that uses latent factors to represent users and **SVD++** (Koren, 2008) leverages a user's interacted items to enhance the latent factors. ## 5.3.2 Explainability To evaluate the performance of explainability, we compare against three explanation methods, namely CAML (Chen et al., 2019b) and ReXPlug (Hada et al., 2021) and NRT and PETER. - **ReXPlug** uses GPT-2 to generate texts and is capable of rating prediction. - **CAML** uses users' historical reviews to represent users and uses co-attention mechanisms to pick the most relevant reviews and concepts and combine these concepts to generate text. - NRT is an advanced deep learning method for explanation tasks. As a generative method, NRT mainly generates explanations based on predicted ratings and the distribution of words in tips. - **PETER** is a powerful model improved by a transformer. This model effectively integrates the ID in the transformer and combines this ID information as the starting vector to generate text. ## 5.4 Reproducibility We conduct experiments by randomly splitting the dataset into a training set (80%), validation set (10%), and test set (10%). The baselines are tuned by following the corresponding papers to ensure the best results. The embedded vector dimension is 384 and the value yielded superior performance after conducting a grid search within the range of [128, 256, 384, 512, 768, 1024]. The maximum length of the generated sentence is set to 15-20. The weight of the rating prediction (pl) is set to 0.2, and the weight of the λc and al is set to either 0.8 or 0.05. For the explanation task, the parameter gl is adjusted to 1.0 and is initialized using the Xavier method (Glorot and Bengio, 2010). The models are optimized using the Adam optimizer with a learning rate of 10−1and L2 regularization of 10−4. When the model reaches the minimum loss in a certain epoch, the learning rate will be changed at that time and multiplied by 0.25. When the total loss of continuous three epochs has not decreased, the training process will terminate. More implementation details can be found on github4. 4https://github.com/Complex-data/ERRA | Table 3: Results of explanation | | | | | | | | | | |-----------------------------------|---------|-----------|-------|-------------|--------|-------|-------|-------|-------| | Datasets | Metrics | Baselines | Ours | Improvement | | | | | | | NRT | CAML | ReXPlug | PETER | ERRA-A | ERRA-R | ERRA | | | | | BLEU1 | 13.37 | 11.19 | 10.8 | 13.78 | 14.07 | 13.28 | 14.38 | 4.17% | | | BLEU4 | 1.44 | 1.12 | 1.29 | 1.68 | 1.76 | 1.64 | 1.88 | 10.6% | | | R2-P | 2.06 | 1.48 | 2.17 | 2.21 | 2.67 | 2.37 | 2.71 | 14.8% | | | R2-R | 2.08 | 1.23 | 1.12 | 2.02 | 2.86 | 2.33 | 2.93 | 17.6% | | | R2-F | 1.97 | 1.24 | 1.22 | 1.97 | 2.34 | 2.18 | 2.57 | 21.2% | | | RL-P | 12.52 | 9.32 | 9.20 | 12.62 | 15.85 | 13.49 | 16.13 | 19.7% | | | RL-R | 12.20 | 10.11 | 10.58 | 12.06 | 14.11 | 12.67 | 14.41 | 16.3% | | | RL-F | 10.77 | 8.11 | 8.73 | 11.07 | 12.49 | 11.97 | 13.87 | 18.1% | | | BERT-S | 75.4 | 74.9 | 75.3 | 76.2 | 78.1 | 77.3 | 79.8 | 4.5% | | | Amazon | BLEU1 | 10.5 | 9.91 | 8.59 | 10.29 | 10.62 | 10.59 | 10.71 | 3.92% | | BLEU4 | 0.67 | 0.56 | 0.57 | 0.69 | 0.71 | 0.71 | 0.73 | 5.43% | | | R2-P | 1.95 | 1.78 | 1.49 | 1.91 | 1.95 | 1.90 | 2.03 | 5.91% | | | R2-R | 1.29 | 1.05 | 1.07 | 1.31 | 1.34 | 1.29 | 1.36 | 3.6% | | | R2-F | 1.35 | 1.25 | 1.11 | 1.43 | 1.46 | 1.41 | 1.48 | 2.36% | | | RL-P | 15.88 | 14.25 | 13.32 | 16.07 | 16.45 | 15.95 | 16.60 | 3.19% | | | RL-R | 10.72 | 14.26 | 9.56 | 10.14 | 10.83 | 10.21 | 11.23 | 9.7% | | | RL-F | 9.53 | 9.16 | 8.70 | 10.26 | 10.62 | 10.14 | 10.82 | 5.1% | | | BERT-S | 83.6 | 83.2 | 82.2 | 83.3 | 84.7 | 83.1 | 85.2 | 2.2% | | | Yelp | BLEU1 | 15.78 | 14.43 | 12.64 | 15.33 | 15.93 | 15.43 | 16.13 | 5.9% | | BLEU4 | 0.85 | 0.86 | 0.71 | 0.89 | 1.02 | 0.95 | 1.06 | 15.8% | | | R2-P | 1.98 | 1.49 | 1.61 | 1.92 | 2.03 | 1.97 | 2.09 | 8.1% | | | R2-R | 1.92 | 1.91 | 1.49 | 2.01 | 2.1 | 1.98 | 2.15 | 9.7% | | | R2-F | 1.9 | 1.92 | 1.61 | 1.94 | 2.02 | 1.99 | 2.05 | 5.3% | | | RL-P | 14.85 | 13.36 | 11.38 | 13.54 | 15.3 | 14.84 | 15.40 | 8.6% | | | RL-R | 14.03 | 12.38 | 10.22 | 14.75 | 14.93 | 14.77 | 15.02 | 1.81% | | | RL-F | 12.25 | 12.39 | 9.97 | 12.61 | 13.08 | 12.79 | 13.17 | 4.50% | | | BERT-S | 82.7 | 84.8 | 83.2 | 86.4 | 87.6 | 86.9 | 88.1 | 1.96% | | | TripAdvisor | | | | | | | | | | ## 5.5 Explainability Study Explainability results: Table 3 shows that our proposed ERRA method consistently outperforms the baselines in terms of BLEU and ROUGE on different datasets. For instance, take BLEU as an example, our method demonstrates the largest improvement on the TripAdvisor dataset. It is likely due to the smaller size of the dataset and the relatively short length of the reviews, which allows for additional information from the retrieved sentences and aspects to supplement the generated sentences, leading to an enhancement in their richness and accuracy. In contrast, the increase in BLEU on the Yelp dataset is relatively small. It is due to the large size of the Yelp dataset, which allows the model to be trained on a vast amount of data. The GPT (Brown et al., 2020) series also prove this case, large amounts of data can train the model well, resulting in our retrieval not having as obvious an improvement compared to other datasets. Similarly, when compared with NRT and PE- TER, our model consistently outperforms them in all metrics. Whether it is in terms of the fluency of the sentence, the richness of the text, or the consistency with the real label, our model has achieved excellent results. Case study: We take three cases generated from three datasets by NRT, PETER, and ERRA method as examples. Table 4 shows that ERRA model can predict keywords, which are both closer to the original text and match the consumers' opinions, generating better explanations compared to the baseline. While the baseline model always generates statements and explanations that are not specific and detailed enough, our model can generate personalized, targeted text, such as *the battery doesn't last* long in Case 2 and *excellent! The food here is very* delicious! in Case 3. This either is the same as or similar to the ground truth. Human evaluation: We also evaluate the model's usefulness in generated sentences via the fluency evaluation experiment, which is done by human judgment. We randomly selected 1000 samples and invited 10 annotators to assign scores. Five ![8_image_1.png](8_image_1.png) points mean very satisfied, and 1 point means very bad. Table 5 reports the human evaluation results. Kappa (Li et al., 2019) is an indicator for measuring classification accuracy. Results demonstrate that our model outperforms the other three methods on fluency and Kappa metrics. ## 5.6 Accuracy Of Prediction The evaluation result of prediction accuracy is shown in Table 2. As we can see, it shows that our method consistently outperforms baseline methods including PMF, NRT, and PETER in RMSE and MSE for all datasets. We mainly compare the performance of our model with the PETER model, which is a state-of-the-art method. Our model demonstrates a significant improvement over the baseline methods on the TripAdvisor dataset. We attribute this improvement to the way we model users. By taking aspects into consideration, our model is capable of accurately modeling users. And this in turn can generate more accurate predictions. As shown in Table 2, ERRA's predictive indicator is the best result on each dataset. ## 5.7 Ablation Analysis In order to investigate the contribution of individual modules in our proposed model, we performed ablation studies by removing the retrieval enhancement and aspect module denoted as "ERRA-R" and "ERRA-A", From Figure 3(a), we can see that the retrieval module plays a crucial role in enhancing the performance of the explanation generation task. Specifically, for the Amazon and TripAdvisor datasets, the difference between "ERRA-R" and ERRA is the largest for explanation generation, while showing mediocrity in the prediction task. Additionally, we also evaluated the impact of ![8_image_0.png](8_image_0.png) ![8_image_2.png](8_image_2.png) Table 5: Results of the fluency evaluation. Measures NRT CAML ReXPlug ERRA Fluency 2.73 2.92 3.11 **3.45** Kappa (0.67) (0.63) (0.74) (**0.79**) the aspect enhancement module on performance. Without this key module, discernible degradation can be observed in both the prediction and explanation tasks, which is shown in Figure 3(b). This can be attributed to the diverse attention points of individual users. The aspects can more accurately represent the user's preference, thus making the prediction more accurate and the generated text more personalized. ## 6 Conclusion In this paper, we propose a novel model, called ERRA, that integrates personalized aspect selection and retrieval enhancement for prediction and explanation tasks. To address the issue of incorrect embedding induced by data sparsity, we incorporate personalized aspect information and rich review knowledge corpus into our model. Experimental results demonstrate that our approach is highly effective compared with state-of-the-art baselines on both the accuracy of recommendations and the quality of corresponding explanations. ## 7 Limitation Despite the promising results obtained in our model, there are still several areas for improvement. Firstly, when dealing with a large corpus, the online retrieval function becomes challenging as it requires a significant amount of computational resources and time. Additionally, creating a vectorized corpus dynamically every time becomes difficult. Secondly, the process of collecting a large number of reviews from users raises privacy concerns. The collection of data, especially from private and non-public sources, may pose difficulties. ## 8 Acknowledgments The authors thank all the anonymous reviewers for their valuable comments and constructive feedback. The authors acknowledge financial support from the National Natural Science Foundation of China (Grant Nos. 62276171 and 62072311), Shenzhen Fundamental Research-General Project (Grant Nos. JCYJ20190808162601658, 20220811155803001, 20210324094402008 and 20200814105901001), CCF-Baidu Open Fund (Grant No. OF2022028), and Swiftlet Fund Fintech funding. Hao Liao is the corresponding author. ## References Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, 11(9):137. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Amanda, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, pages 1583–1592. 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In The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 83–92. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7 A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 A4. Have you used AI writing assistants when working on this paper? Not applicable. Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 5 ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** 5.5 D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. 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liu-etal-2023-binary

Binary and Ternary Natural Language Generation

https://aclanthology.org/2023.acl-long.5

Ternary and binary neural networks enable multiplication-free computation and promise multiple orders of magnitude efficiency gains over full-precision networks if implemented on specialized hardware. However, since both the parameter and the output space are highly discretized, such networks have proven very difficult to optimize. The difficulties are compounded for the class of transformer text generation models due to the sensitivity of the attention operation to quantization and the noise-compounding effects of autoregressive decoding in the high-cardinality output space. We approach the problem with a mix of statistics-based quantization for the weights and elastic quantization of the activations and demonstrate the first ternary and binary transformer models on the downstream tasks of summarization and machine translation. Our ternary BART base achieves an R1 score of 41 on the CNN/DailyMail benchmark, which is merely 3.9 points behind the full model while being 16x more efficient. Our binary model, while less accurate, achieves a highly non-trivial score of 35.6. For machine translation, we achieved BLEU scores of 21.7 and 17.6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26.8. We also compare our approach in the 8-bit activation setting, where our ternary and even binary weight models can match or outperform the best existing 8-bit weight models in the literature. Our code and models are available at: \url{https://github.com/facebookresearch/Ternary_Binary_Transformer}.

# Binary And Ternary Natural Language Generation Zechun Liu∗ Reality Labs, Meta Inc. [email protected] Barlas Oguz ˘∗ Meta AI [email protected] Aasish Pappu Meta AI [email protected] Yangyang Shi Reality Labs, Meta Inc. [email protected] ## Abstract Ternary and binary neural networks enable multiplication-free computation and promise multiple orders of magnitude efficiency gains over full-precision networks if implemented on specialized hardware. However, since both the parameter and the output space are highly discretized, such networks have proven very difficult to optimize. The difficulties are compounded for the class of transformer text generation models due to the sensitivity of the attention operation to quantization and the noise-compounding effects of autoregressive decoding in the high-cardinality output space. We approach the problem with a mix of statistics-based quantization for the weights and elastic quantization of the activations and demonstrate the first ternary and binary transformer models on the downstream tasks of summarization and machine translation. Our ternary BART base achieves an R1 score of 41 on the CNN/DailyMail benchmark, which is merely 3.9 points behind the full model while being 16x more efficient. Our binary model, while less accurate, achieves a highly nontrivial score of 35.6. For machine translation, we achieved BLEU scores of 21.7 and 17.6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26.8. We also compare our approach in the 8-bit activation setting, where our ternary and even binary weight models can match or outperform the best existing 8-bit weight models in the literature. Our code and models are available at: https://github.com/facebookresearch/ Ternary_Binary_Transformer. ## 1 Introduction Generative pre-trained transformers (Brown et al., 2020; Lewis et al., 2020; Radford et al., 2018) have emerged as powerful and generic tools, driving breakthroughs not only in language understanding but the field of AI in general. These models owe ∗Equal contribution ## Raghuraman Krishnamoorthi Reality Labs, Meta Inc. [email protected] their success mainly to their seemingly infinite ability to scale to ever-larger data and model sizes. Unfortunately, such scaling comes at the cost of large computational requirements, putting extensively large generative transformers out of reach of all but the most resource-rich institutions. Even moderately sized pre-trained transformers have limited applications due to their size and computational cost. Making generative transformers more efficient is imperative for widening their use to more devices and practical applications. In this work, we explore making generative pretrained transformers more efficient via the quantization of their weights and activations. Quantizing the weights of a neural network is useful for compression and allows the model to be stored more efficiently. However, compression alone does not reduce computation costs since the network's activations need to be computed in full precision. Quantizing both weights and activations allows computation to be performed with lower precision, potentially leading to significant efficiency gains depending on the quantization level and hardware implementation. Quantizing neural networks have a long history, and multiple works have attempted to quantize pre-trained transformers at various quantization levels (Shen et al., 2020; Zhang et al., 2020; Liu et al., 2022; Qin et al., 2021). Most of this work focuses on encoder-only models (mainly BERT) for sentence and token classification tasks. Quantizing text generation models has generally been regarded as a more difficult task (Behnke et al., 2021; Tao et al., 2022) due to the large output vocabulary and sequential decoding. Recent work has tackled this problem, though only for mild quantization levels (down to 8-bit activations) and with mixed success. In contrast, we are interested in very low-bit quantization, down to ternary and even binary weights and activations. In order to achieve this, we combine and unify best practices for weight and activation quantization and present a frame65 work that uses gradient-matching quantization for weights and elastic quantization for activations. We apply our method to natural language generation tasks and, for the first time, demonstrate low-bit generative transformers of competitive accuracy. Our ternary (weight and activation) model lags a full-precision BART (Lewis et al., 2020) model by only 4 points in ROUGE on the XSUM summarization dataset. In contrast, our model with ternary weights and 8-bit activations comes within 1 point and even outperforms comparable state-of-the-art models with 8-bit weights. We also demonstrate a fully binary (weights and activations) model. While not as competitive, it is able to achieve a highly non-trivial ROUGE-1 score of 31.7. Our results also extend to machine translation models. On the WMT16 En-Ro benchmark, we quantize an mBART model to extend the ternaryweight 8-bit activation SoTA by 1.2 points while demonstrating fully ternary and fully binary translation models for the first time. We summarize our contributions as follows: - We propose a novel combination of statisticsbased weight quantization with learning-based activation quantization, which enables stably training transformer encoder-decoder models to converge in the fully ternary/binary settings, which was not previously possible. - We significantly improve the state-of-the-art text generation models in the 8-bit activation and ternary/binary weight settings while setting the first non-trivial baselines for the fully ternary and fully binary settings. ## 2 Method In this section, we first introduce the previous practices in binarization and ternarization. Then, we introduce a unified statistic-based weight binarization / ternarization method that can alleviate the gradient mismatch issue and enhance the quantized weights entropy. Lastly, we analyze the difference between weight quantization and activation quantization and propose an elastic ternarization method for activations. We abbreviate our method as TBT, short for "Ternary / Binary Transformer". ## 2.1 Preliminary 2.1.1 Ternarization Ternary neural networks, where real values are quantized to three levels, are first introduced in (Li et al., 2016). Thus, these values can be represented in 2 bits, leading to a 16× reduction in size and computation. Moreover, the computations can be calculated multiplication-free, leading to even further computation gains on suitable hardware. The recent work integrates the ternarization algorithm in natural language models for quantizing the weights and activations in classification tasks (Zhang et al., 2020) and ternarizing the weight (8bit activations are used) in generative models (Li et al., 2022; Tao et al., 2022). The general formula (Li et al., 2016) for ternarization is as follows: XiT = −αT , if XiR < −∆ 0, if − ∆ ⩽ XiR ⩽ ∆ +αT , if XiR > ∆ ∆ = 0.7 · ||XR||l1 nXR αT = Pi XiR · 1|XiR|>∆ Pi 1|XiR|>∆ (1) $$\begin{array}{l}\small\end{array}$$ (2) $$\begin{array}{l}\small\end{array}$$ (3) . Here XT denotes the ternary weights/activations, and XR represents their real-valued counterparts. nXR denotes the total number of elements in the tensor. ∆ is the ternary threshold, and αT is the scaling factor that minimizes l2-loss between XT and XR. 2.1.2 Binarization The neural network binarization denotes representing the weights and/or activation with bi-level values. It is first proposed in BNN (Courbariaux et al., 2016) and has evolved in the follow-up works (Rastegari et al., 2016; Liu et al., 2018). Rastegari et al. (2016) formulates binarization as: $$\mathbf{X_{B}^{i}}=\alpha_{\mathbf{B}}\cdot\operatorname{Sign}(\mathbf{X_{R}^{i}})=\begin{cases}-\alpha_{\mathbf{B}},\text{if}\mathbf{X_{R}^{i}}<0\\ +\alpha_{\mathbf{B}},\text{if}\mathbf{X_{R}^{i}}\geq0\end{cases}\tag{4}$$ $$\alpha_{\mathbf{B}}=\frac{||\mathbf{X_{R}}||_{l1}}{n_{\mathbf{X_{R}}}}\tag{5}$$ Here XB can represent binary weights or binary activations. αB denotes the scaling-factor that minimize the l2 loss between XR and αB·Sign(XR). The acceleration and compression effect of ternary/binary neural networks is significant. By representing the weights and activations with {−1, 0, 1}, the network enjoys ∼16× memory saving compared to its 32-bit floating-point counterpart. When further binarize the weights and activations to only 1-bit (i.e., {−1, 1}), up to 32× 66 model-size reduction and 58× speedup on CPUs have been achieved (Rastegari et al., 2016), where the matrix multiplication operations are replaced with light-weighted bitwise XNOR operations. Despite its appealing characteristics, naively binarizing or ternarizing the transformer model for natural language generation results in several accuracy drops or even a total failure in training. It has been observed that the attention layers of the transformer network are difficult to quantize to low bits. Also, the auto-regressive decoding tends to accumulate errors due to quantization. Given the nature of generative language networks that require highprecision output, quantizing both the activations and weights in these models to extreme bit values is non-trivial and has not been explored before. ## 2.2 **Stats-Based Max-Entropy Isometric Weight** Quantization We propose a statistics-based method for weight binarization/ternarization. Particularly, this novel quantization method considers maximizing the entropy of the quantized weights and reducing the gradient mismatch in the backward pass. Previous works (Courbariaux et al., 2016; Bai et al., 2021b; Zhang et al., 2020) are mainly focused on minimizing the l2 loss between the quantized weights and the real-valued weights to find the optimal quantization scheme, $$\alpha^{*}=\arg\operatorname*{min}||\alpha{\hat{\mathbf{W}}}_{\mathbf{Q}}-\mathbf{W_{R}}||_{l2}\qquad(6)$$ where Wˆ Q denotes binary/ternary weights and α∗ denotes the optimal scaling factor calculated. Despite the broad application and great success of the classic quantization scheme, we found that merely minimizing the l2 loss neglects several critical but intractable issues in ultra-low-bit weight quantization: (1) The information entropy of the quantized weights is not considered. Eq. 1 and Eq. 4 calculate the quantized weights to minimize the distance to the real-valued weights, which could lead to imbalanced quantized weight distribution and harm the quantized weights representation capacity. (2) The quantization function Eq. 1 and Eq. 4 are not isometric, meaning that it does not consider the magnitude consistency between the quantized weights and real-valued weights, while we find that magnitude consistency contributes significantly to accurate gradient estimation. Considering the above two limitations in previous solutions, we are motivated to design a novel quantization function that enhances information entropy and reduces gradient mismatch. To boost the weights representation capability, in information theory, more information is preserved when the quantized weights contain higher entropy: $$\max_{p_{i}}\ {\cal H}=-p_{i}\log(p_{i}),s.t.\sum_{i=1}^{N}p_{i}=1\tag{7}$$ with pi denoting the proportion of real-valued weights being quantized to i th quantization level in total N levels. Eq. 7 can be easily solved with a Lagrange multiplier, and the optimal p∗ i = 1 N , i ∈ {1, 2*, . . . , N*}, suggesting the best quantization scheme to preserve maximum information entropy is to distribute the real-valued weights in all quantization levels as evenly as possible. For reducing the gradient mismatch, as suggested by the previous binarization work (Liu et al., 2020b), the magnitude difference between the quantized weight and the real-valued weight will greatly influence the gradient scale and a mismatch in magnitude will be amplified in back-propagation and cause gradient vanishing or explosion during training. Thus it is important to ensure the magnitude of real-valued weights and quantized weights are consistent. Combining two requirements discussed above, we proposed max-entropy isometric weight quantization. In ternarization, it is formulated as $$\mathbf{W_{T}^{i}}=\alpha_{\mathbf{T}}\lfloor\mathrm{Clip}({\frac{\mathbf{W_{R}^{i}}-\mu_{\mathbf{T}}}{\alpha_{\mathbf{T}}}},-1,1)\rfloor\tag{8}$$ where $\,\mu_{\mathbf{T}}={\overline{\mathbf{W_{R}}}}$, $$\alpha_{\mathbf{T}}={\frac{4}{3}}\cdot{\frac{||\mathbf{W_{R}}-\mu_{\mathbf{T}}||_{l1}}{n_{\mathbf{W_{R}}}}}$$ Where WT and WR refer to the ternary weights and real-valued weights, respectively. The rounding function ⌊·⌉ and Clip(·) function quantize weights to {−1, 0, 1}. µT is the mean of realvalued weights and nWR denotes the number of weights in the weight matrix. Scaling factor α is calculated from the weight statistics and follows the entropy rule to scale the real-valued weight WR to be evenly distributed in quantization levels. In the ternary case, the weights are quantized to {−αT , 0, αT}. When the real-valued weights are initialized as uniformly and symmetrically distributed (He et al., 2015; Glorot and Bengio, 2010), the scaling factor αT will distribute WiR αT to [−1.5, 1.5], such that the output ternary weights 67 ![3_image_0.png](3_image_0.png) will have near uniform distribution in three ternary levels. Meanwhile, Eq. 8 is an isometric mapping where the real-valued weights are scaled by 1 αT to near [-1, 1] and time αT to scale back after quantization. In this way, the magnitude is preserved. Correspondingly, in the binary case we have, $$\mathbf{W_{B}^{i}}=\alpha_{\mathbf{B}}\cdot\operatorname{Sign}({\frac{\mathbf{W_{R}^{i}}-\mu_{\mathbf{B}}}{\alpha_{\mathbf{B}}}})$$ where $\mu_{\mathbf{B}}={\overline{{\mathbf{W_{R}}}}}$, $$\alpha_{\mathbf{B}}={\frac{||\mathbf{W_{R}}-\mu_{\mathbf{B}}||_{l1}}{n_{\mathbf{W_{R}}}}}$$ $\eqref{eq:walpha}$. Here WB denotes the binary weights, where substracting the average µB makes the realvalued weight zero-centered before binarization and thus encourages an even distribution in binarized weights. Then the scaling factor αB matches the magnitude between real-valued and binary weights. Particularly, in Eq. 9, WiB = αB· Sign(WiR−µB αB) = αB· Sign(WiR − µB ), we explicitly include the αB in the denominator to keep the binarization function isometric and the gradients *w.r.t.* weights can be calculated straight- $$\frac{\partial\mathbf{W_{B}^{i}}}{\partial\mathbf{W_{R}^{i}}}\stackrel{S T E}{\approx}\mathbf{1}_{|\frac{\mathbf{w_{B}^{i}}-\mu_{B}}{\alpha_{B}}|<1}\quad\quad\quad(10)$$ STE is abbreviated for straight-through estimator (Bengio et al., 2013), which replaces the nondifferentiable Sign function with Clip function in the backward pass. We show that the proposed maxentropy isometric weight quantization improves the accuracy of weight binarization / ternarization by 6.0 / 11.53 RougeL scores on the CNN/DailyMail benchmark, respectively. More details can be found in Sec. 3.2. ## 2.3 Learning-Based Activation Quantization In contrast to neural network weights that are stored on the disk, activations are calculated on-the-fly. The distribution of activations in a particular layer depends on the network weights as well as the corresponding input sequence, and thus varies from batch to batch. In order to have the quantization function better capture the underlying activation distribution, we propose learning-based activation quantization. Inspired by BiT (Liu et al., 2022), we divide the activation layers into two categories: the activation layers with non-negative values (XR ∈ R+), *i.e.*, Softmax/ReLU layer outputs and the rest of the layers with both positive and negative activations (XR ∈ R). We binarize / ternarize the first activation category (XR ∈R+) to {0, α} / {0*, α,* 2α}, and symmetrically quantize the later activation category (XR ∈ R) to {−*α, α*} and {−α, 0, α} in binary and ternary cases respectively. In this way, the activation distribution matches the original fullprecision activations and thus reduces the quantization error. Further, we learn to scale the real-valued activations to better fit quantization thresholds, and this learnable scaling factor can be updated endto-end with the gradients from the network loss to better account for overall network optimization. In the ternary case, we propose the elastic ternarization function formulated as, $$\mathbf{X}_{\mathbf{T}}^{i}=\alpha_{\mathbf{T}}\mathbf{\hat{X}}_{\mathbf{T}}^{i}$$ $$=\begin{cases}\alpha_{\mathbf{T}}\lfloor\text{Clip}(\frac{\mathbf{X}_{\mathbf{R}}^{i}}{\alpha_{\mathbf{T}}},0,2)\rfloor,\text{if}\mathbf{X}_{\mathbf{R}}\!\in\!\mathbb{R}_{+}\\ \alpha_{\mathbf{T}}\lfloor\text{Clip}(\frac{\mathbf{X}_{\mathbf{R}}^{i}}{\alpha_{\mathbf{T}}},-1,1)\rfloor,\text{if}\mathbf{X}_{\mathbf{R}}\!\in\!\mathbb{R}\end{cases}\tag{11}$$ where XR and XT denote real-valued and ternary activations, respectively. To keep the formula concise, we set X′R = XR − XR, denoting the zeromean real-valued activations. αT is the scaling factor. Different from the weight quantization, the scaling factor in Eq. 11 is learned with the gradient update. We follow the practice in (Zhou et al., 2016; Esser et al., 2019) to calculate the gradients with straight-through estimation (STE) bypassing the non-differentiable rounding function: ∂XiT ∂αT ST E ≈ Xˆ iT − XiR αT ·10⩽XiR⩽2αT , if XR ∈R+ (12) Xˆ iT − X′iR αT ·1|X′iR|⩽αT , if XR ∈R The learnable scaling factor can dynamically adapt to different activation distributions and improve the ternarization accuracy. In the binary case, it is formulated as. $$\mathbf{X_{B}^{i}}=\alpha_{\mathbf{B}}\mathbf{\hat{X}_{B}^{i}}$$ $$=\begin{cases}\alpha_{\mathbf{B}}\left|\mathrm{Clip}(\frac{\mathbf{X_{B}^{i}}}{\alpha_{\mathbf{B}}},0,1)\right|,\text{if}\mathbf{X_{R}}\!\in\!\mathbb{R}_{+}\\ \alpha_{\mathbf{B}}\cdot\mathrm{Sign}(\frac{\mathbf{X_{B}^{i i}}}{\alpha_{\mathbf{B}}}),\qquad\text{if}\mathbf{X_{R}}\!\in\!\mathbb{R}\end{cases}\tag{13}$$ Here $\mathbf{X_{B}}$ denotes the binary activations. Correspondingly, the gradients *w.r.t.* the scaling factor α can be easily calculated as $$\begin{array}{l}{{\frac{\partial\mathbf{X_{B}^{i}}}{\partial\alpha_{_{B}}}\stackrel{S T E}{\approx}}}\\ {{\left\{\begin{array}{l l}{{\hat{\mathbf{X}}_{\mathbf{B}}^{i}-\frac{\mathbf{X_{B}^{i}}}{\alpha_{_{B}}}\cdot\mathbf{1}_{0\leqslant\mathbf{X_{R}^{i}\leqslant\alpha_{B}},\mathrm{~if~}\mathbf{X_{R}\in\mathbb{R}_{+}}}}\\ {{\mathrm{Sign}(\mathbf{X_{R}^{i}}),}}\end{array}\right.}}\end{array}\right.\tag{14}$$ We demonstrate that with the learning-based activation quantization method and statistics-based weight quantization scheme, the proposed TBT for the first time is able to quantize the BART model for natural language generation tasks to ternary and even binary weights and activations, and achieve reasonable accuracy on summarization and translation benchmarks. ## 3 Experiments In this section, we evaluate the effectiveness of our low-bit quantization scheme for natural language generative model on text summarization benchmarks: CNN/DailyMail (Nallapati et al., 2016) and XSUM (Narayan et al., 2018). We additionally experiment on the machine translation task with mBART on WMT16 English-Romanian (En-Ro) dataset (Bojar et al., 2016a). ## 3.1 Experimental Settings We follow recent work (Li et al., 2022) in training the quantized network with initialization and knowledge distillation from a full-precision pretrained model. Specifically, we use the BARTbase (Lewis et al., 2019) as our full-precision baseline for summarization tasks and mBARTlarge (Liu et al., 2020a) for the translation task. We train the quantized models for 20 epochs on 8 GPUs with a batch size of 128 and a learning rate of 2.5e-4 for 8-bit activation models and 5e-4 for binary and ternary activation models. ## 3.2 Summarization For the summarization task, we adopt the following benchmarks: The XSUM dataset (Narayan et al., **2018)** consists of 226k documents sampled from the online news website of BBC, together with short, one sentence summaries. Since the summaries are very short, abstractive methods tend to do better on this dataset. | to the BART model and report the results, denoted with ∗ . We use the rouge-{1,2,L} as evaluation metrics. XSUM CNN/DailyMail Method #Bits(E-W-A) Size (MB) FLOPs R1 R2 RL R1 R2 RL BART 32-32-32 532.0 1× 43.84 20.79 35.71 44.90 22.25 42.09 QuantBart (Tao et al., 2022) 8 - 8 - 8 138.1 - 40.25 17.78 32.70 - – - DQ-BART (Li et al., 2022) 8 - 8 - 8 138.1 - 42.51 19.61 34.61 44.66 21.92 41.86 Ternary Baseline (TWN) (Li et al., 2016) 2 - 2 - 8 39.6 0.25× 39.99 17.13 31.99 42.99 20.05 40.18 QuantBart (Tao et al., 2022) 2 - 2 - 8 39.6 0.25× 39.15 16.72 31.72 - – - DQ-BART (Li et al., 2022) 2 - 2 - 8 39.6 0.25× 40.06 17.34 32.46 42.94 20.07 40.13 TBT 2 - 2 - 8 39.6 0.25× 42.40 19.54 34.51 43.46 20.52 40.58 Baseline (TWN) (Li et al., 2016) 2 - 2 - 2 39.6 0.0625× 12.80 1.21 11.4 12.92 0.32 12.42 TernaryBert∗ (Zhang et al., 2020) 2 - 2 - 2 39.6 0.0625× 14.03 2.23 11.79 10.95 0.52 8.56 TBT 2 - 2 - 2 39.6 0.0625× 36.21 14.38 29.07 41.03 18.18 38.30 Binary Baseline (BWN) (Courbariaux et al., 2016) 1 - 1 - 8 23.2 0.125× 1.90 0.01 1.78 2.78 0.08 2.48 BinaryBert∗ (Bai et al., 2021b) 1 - 1 - 8 23.2 0.125× 39.76 17.05 31.99 40.66 18.52 28.36 BlockPruning (Lagunas et al., 2021) - 23 - – - – 41.4 18.7 38.4 TBT 1 - 1 - 8 23.2 0.125× 40.96 18.37 33.30 42.66 19.72 39.80 Baseline (BWN) (Courbariaux et al., 2016) 1 - 1 - 1 23.2 0.0156× 1.90 0.01 1.78 2.78 0.08 2.48 BinaryBert∗ (Bai et al., 2021b) 1 - 1 - 1 23.2 0.0156× 8.13 0.12 7.69 9.80 0.15 8.62 BiBert∗ (Qin et al., 2021) 1 - 1 - 1 23.2 0.0156× 7.58 0.06 7.54 14.22 0.13 10.06 TBT 1 - 1 - 1 23.2 0.0156× 31.68 11.19 25.29 35.56 11.71 33.23 | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| CNN/DailyMail (Nallapati et al., **2016)** is another news summarization benchmark, with longer documents (~30 sentences) and longer, multisentence summaries. The dataset contains close to 300k document-summary pairs. We use BART-base model (Lewis et al., 2019), which is an English-only encoder-decoder transformer with 140 million parameters. We compare using the standard ROUGE-{1,2,l} metrics for this task. For the ternary weights and 8-bit activations setting, we compare with two state-of-the-art methods QuantBart (Tao et al., 2022) and DQ-BART (Li et al., 2022). For the fully ternary setting, and the binary quantization experiments, there is no prior art. Therefore we provide a naive quantization baseline, using popular implementations from previous work (Li et al., 2016; Courbariaux et al., 2016), and adapt the binary and ternary methods proposed for the BERT models (Bai et al., 2021b; Qin et al., 2021; Zhang et al., 2020) to BART. Our main results are summarized in Table 1. In the ternary weights and 8-bit activations setting, TBT improves previous SoTA by up to **2.3 points** in ROUGE score on XSUM, and up to **0.5 points** on CNN/DailyMail. Both improvements are significant. Further quantizing weights to *binary*, while keeping activations at 8-bit, we are still able to achieve a ROUGE-L score of 33.3 on XSUM, which is 0.8 points higher than the previous *ternary* SoTA (DQBART), and comparable on CNN/DailyMail. This is the first demonstration of a binary-weight generative transformer model of competitive accuracy to our knowledge. Additionally, TBT binary weight BART model achieves **1.2 points** higher ROUGE score on CNN compared with the SoTA pruning method with the same compressed model size. Moving on to ternary and binary activations, there is no prior art, and previous implementations fail to produce meaningful results. Our method, on the other hand, achieves ROUGE-L scores of 29.1 and 38.3 on XSUM and CNN/DailyMail in the fully ternary setting, which are 6.6 and 3.8 points behind the full-precision baseline respectively. Our fully binary (weights and activations) model has a wider gap at 10.4 and 8.9 points, however still manages to produce highly non-trivial output at ROUGE-L scores of 25.3 and 33.2 points for XSUM and CNN/DailyMail. ## 3.3 Machine Translation We also evaluate our model on machine translation. We adopt the En-Ro benchmark from the Table 3: Ablation study on the effects of the proposed learning-based activation quantization method and stats-based weight quantization method on XSUM and CNN/DailyMail benchmark. XSUM Method **#Bits**(E-W-A) R1 R2 RL 1 Baseline (TWN) 2 - 2 - 2 12.80 1.21 11.4 2 + Activation(learning-based) 2 - 2 - 2 15.05 1.38 12.13 3 + Weight(stats-based) 2 - 2 - 2 13.79 0.87 12.74 4 + Both 2 - 2 - 2 **36.21 14.38 29.07** 5 Baseline (BWN) 1 - 1 - 1 1.90 0.01 1.78 6 + Activation(learning-based) 1 - 1 - 1 1.90 0.01 1.78 7 + Weight(stats-based) 1 - 1 - 1 10.96 0.29 10.00 8 + Both 1 - 1 - 1 **31.68 11.19 25.29** CNN/DailyMail R1 R2 RL 9 Baseline (TWN) 2 - 2 - 2 12.92 0.32 12.42 10 + Activation(learning-based) 2 - 2 - 2 13.34 0.99 12.58 11 + Weight(stats-based) 2 - 2 - 2 19.34 0.42 18.42 12 + Both 2 - 2 - 2 **41.03 18.18 38.30** 13 Baseline (BWN) 1 - 1 - 1 2.78 0.08 2.48 14 + Activation(learning-based) 1 - 1 - 1 2.78 0.08 2.48 15 + Weight(stats-based) 1 - 1 - 1 15.05 0.35 14.01 16 + Both 1 - 1 - 1 **35.56 11.71 33.23** | mBART-large model for translation on WMT16 En-Ro. Method #Bits(E-W-A) Size (GB) BLEU mBART (Liu et al., 2020a) 32-32-32 2.44 26.82 DQ-BART (Li et al., 2022) 8 - 8 - 8 0.61 25.91 DQ-BART (Li et al., 2022) 2 - 2 - 8 0.31 23.48 TBT 2 - 2 - 8 0.31 24.63 TBT 2 - 2 - 2 0.31 21.70 TBT 1 - 1 - 8 0.16 24.30 TBT 1 - 1 - 1 0.16 17.59 | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| WMT'16 shared task (Bojar et al., 2016b) to be compatible with previous work. Our base model is an mBART-large model (Liu et al., 2020a), a 680 million parameter multi-lingual encoder-decoder transformer pre-trained on 25 languages. Table 2 shows our results. In the ternary weight setting with 8-bit activations, we improve the previous SoTA by 1.2 points, achieving 24.63 BLEU. Remarkably our binary weight model also outperforms the previous ternary weight SoTA by almost a full point. It scores 24.3 BLEU - only 1.5 points behind a full mBART model while being 16× smaller. In the fully ternary and binary settings, where previous methods failed to converge, TBT models are able to reach practical levels of performance, with ternary TBT mBART achieving 21.7 BLEU, and TBT binary mBART at 17.59. ## 3.4 Ablations As stated earlier, our main proposed modeling improvement is a combination of two methods: Table 4: Generated average sequence length comparison between baseline method and our method. Method **#Bits**(E-W-A) **XSUM CNN/DailyMail** BART-base 32-32-32 30.73 99.89 Baseline 2 - 2 - 8 28.53 93.63 TBT 2 - 2 - 8 32.04 95.78 Baseline 2 - 2 - 2 48.41 14.88 TBT 2 - 2 - 2 30.71 88.38 Baseline 1 - 1 - 8 62.0 128.0 TBT 1 - 1 - 8 31.57 97.08 Baseline 1 - 1 - 1 62.0 128.0 TBT 1 - 1 - 1 29.81 67.51 statistics-based quantization for the weights, and learning-based quantization for the activations. We ablate the contribution of these methods and present the results in Table 3. The results clearly show that while each method can give moderate gains by itself over the baseline, these improvements are not sufficient by themselves to produce meaningful results. None of the ablated models can achieve an R2 score above 1.5. It's only the *combination* of the two, which together stabilize the training and result in good convergence for fully ternary and binary models. ## 3.5 Sequence Length Analysis In language generation tasks, the error compounding issue in the recursive decoder generation process will largely amplify the quantization error or even lead to divergent results, and thus is an harsh factor to test the robustness of a quantization method. The average generated sequence length indicates whether the quantized model can overcome the compounding error and generate reasonable length of text. In Table 4 we compare the generated sequence length between the proposed method and the baseline method (*i.e.*, TWN (Li et al., 2016) for ternary, BWN (Courbariaux et al., 2016) for binary). Our method successfully produces summarizations with comparable length as the full-precision model on XSUM benchmark, even when both weights and activations are binarized. Compared to XSUM dataset, for which the document are summarized to only one sentence, CNN/DailyMail is more challenging because it allows longer summary. We can clearly see that, the text generate with our 8-bit activation models can maintain near the similar average length as the full-precision BART model, while the binary and ternary activation models deviate moderately. In contrast, the baseline method is only able to derive ![7_image_0.png](7_image_0.png) reasonable summarization with 2-bit weight 8-bit activations and fails at lower bit-width, showing the difficult natural of the language generation tasks. ## 3.6 Visualization To further understand the effectiveness of the proposed method, we visualize weight and activation histograms in the BART model ternarized with the baseline method and the proposed method in Fig. 2. Both the baseline method and our method use per-row weight ternarization, and thus a tensor tensor will have \#row of scaling factors. As we can see in Fig. 2 (b) and (g), the proposed method allows the weights to be more evenly distributed in three ternarization levels, which can allow higher information entropy in quantized weights, as discussed in Sec. 2.2. Additionally, we calculate the quantized weight distribution entropy (i.e., Eq. 7) in 96 fully-connected layers in the BART-base model and found that the proposed TBT method achieves consistently higher entropy in quantized weights than the baseline method in all the layers. Further, an interesting phenomenon we can see in Fig. 2 (a) (e) is that ternary weights in a baseline model are very close to the Gaussian distribution, in contrast, weights ternarized with TBT are capturing a more sophisticated distribution. This phenomenon implies that the proposed method helps the weights learn more informative patterns and thus better satisfy the high demand for language generation tasks. For activation quantization, it is evident that the attention layer and the SoftMax output only contain the positive activations (XR ∈ R+). If simply ternarized to {−α, 0, α}, the ternary activations will waste one representative level (Fig. 2(d)) and therefore lead to lower accuracy. Instead, the proposed method uses a two-set ternarization method that ternarizes the non-negative activation layer (XR ∈ R+) to {0*, α,* 2α}, and learns the scaling factor α to better fit the underlying real-valued distribution. This ternarization method greatly reduces information loss and enhances the final accuracy. ## 4 Related Work Quantization has long been studied to make neural networks more efficient (see (Hubara et al., 2017) for a survey). Due to the popularity of BERT, numerous works have studied quantization for transformer models, starting with 8-bit quantization (Zafrir et al., 2019; Fan et al., 2020), and progressing to 4-bit (Shen et al., 2020; Zadeh et al., 2020), ternary (Zhang et al., 2020) and binary Bai et al. (2021b); Qin et al. (2021); Liu et al. (2022). All of these works have focused on the encoderonly setting. In the generative setting, Prato et al. (2019); Behnke et al. (2021) demonstrate quantized models for machine translation, and Fan et al. (2020); Bai et al. (2021a) for language modeling, though only for moderate quantization levels (4-8 bits). Most recently, Tao et al. (2022) and Li et al. (2022) pushed weight quantization down to 2 bits (with 8-bit activation quantization) and evaluated on language modeling and summarization. However, our method outperforms these works substantially, while also demonstrating accurate generative transformers with both weights and activations quantized to 2-bit and even 1-bit for the first time. ## 5 Conclusion We have demonstrated high accuracy ternary and binary natural language generation models based on a pre-trained transformer encoder-decoder backbone. Quantizing both the weights and the activations of the network allow these models to run on special-purpose hardware using binary and ternary arithmetic, which doesn't require multiplication modules. Therefore our results promise multiple orders of magnitude gains in efficiency while running these models, and can drastically expand the use cases of such models beyond just high end gpu servers. We are especially excited about the implications of our results for larger text generation models such as GPT-3 (Brown et al., 2020). These models have both demonstrated impressive capabilities, while also presenting enormous scaling and computational challenges. Low-bit quantization is a promising approach to mitigate some of these issues. Whether our approach will scale to these models is an open problem and an exciting future research direction. ## 6 Limitations We conduct experiments on public datasets of finite sentence length, while generalizability to extremely long sequences or even streaming data has not been verified. Furthermore, the generalizability of the proposed quantization method to other tasks, including computer vision or speech recognition, remains to be tested. In addition, binarization and ternarization require bit-packing to have actual memory savings and dedicated hardware support for real-time acceleration, which is more of a hardware implementation aspect and not studied in this paper. ## 7 Ethics Statement We affirm that we contribute to society, avoid harm, and are honest and trustworthy. We respect previous work and appropriately cite the methods and datasets we are using. All data we use is public and no private data is involved. There is some potential risk if the translation technique is maliciously used by a third party and thus we are committed to maintaining the compression techniques we have developed and the general summarization/machine translation techniques used correctly without incurring any form of discrimination. ## References Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, and Michael R Lyu. 2021a. Towards efficient posttraining quantization of pre-trained language models. arXiv preprint arXiv:2109.15082. Haoli Bai, Wei Zhang, Lu Hou, Lifeng Shang, Jin Jin, Xin Jiang, Qun Liu, Michael R Lyu, and Irwin King. 2021b. Binarybert: Pushing the limit of bert quantization. In *ACL/IJCNLP (1)*. 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bebensee-lee-2023-span

Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking

https://aclanthology.org/2023.acl-long.6

In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in schema, while discriminative approaches separately encode history and schema and fail to account for inter-slot and intent-slot dependencies. We introduce SPLAT, a novel architecture which achieves better generalization and efficiency than prior approaches by constraining outputs to a limited prediction space. At the same time, our model allows for rich attention among descriptions and history while keeping computation costs constrained by incorporating linear-time attention. We demonstrate the effectiveness of our model on the Schema-Guided Dialogue (SGD) and MultiWOZ datasets. Our approach significantly improves upon existing models achieving 85.3 JGA on the SGD dataset. Further, we show increased robustness on the SGD-X benchmark: our model outperforms the more than 30x larger D3ST-XXL model by 5.0 points.

# Span-Selective Linear Attention Transformers For Effective And Robust Schema-Guided Dialogue State Tracking Björn Bebensee Haejun Lee Samsung Research {b.bebensee,haejun82.lee}@samsung.com ## Abstract In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in schema, while discriminative approaches separately encode history and schema and fail to account for inter-slot and intent-slot dependencies. We introduce SPLAT, a novel architecture which achieves better generalization and efficiency than prior approaches by constraining outputs to a limited prediction space. At the same time, our model allows for rich attention among descriptions and history while keeping computation costs constrained by incorporating linear-time attention. We demonstrate the effectiveness of our model on the Schema-Guided Dialogue (SGD) and MultiWOZ datasets. Our approach significantly improves upon existing models achieving 85.3 JGA on the SGD dataset. Further, we show increased robustness on the SGD-X benchmark: our model outperforms the more than 30× larger D3ST-XXL model by 5.0 points. ## 1 Introduction Dialogue State Tracking (DST) refers to the task of estimating and tracking the dialogue state consisting of the user's current intent and set of slotvalue pairs throughout the dialogue (Williams et al., 2013). Traditional approaches to DST assume a fixed ontology and learn a classifier for each slot (Chao and Lane, 2019). However, in real-world applications services can be added or removed requiring the model to be re-trained each time the ontology changes. Recently more flexible schemaguided approaches which take as input natural language descriptions of all available intents and slots and thus can be applied zero-shot to new services have been gaining popularity (Rastogi et al., 2020; Feng et al., 2021; Zhao et al., 2022; Gupta et al., 2022). Figure 1: Span selection for schema-guided dialogue in practice. [SLOT] encodes the semantics of the natural language description of "to_location" and is matched with the span representation of "Long Beach, CA". Similarly [UTT] encodes the semantics of the current utterance and is matched with the target [INTENT] encoding. Discriminative DST models are based on machine reading comprehension (MRC) methods, meaning they extract and fill in non-categorical slot values directly from the user utterances (Chao and Lane, 2019; Ruan et al., 2020; Zhang et al., 2021). We use the terms discriminative and extractive interchangeably when referring to these methods. Generative DST models leverage seq2seq language models which conditioned on the dialog history and a prompt learn to sequentially generate the appropriate slot values. Prior generative methods do not generalize well to variations in schema (Lee et al., 2021, 2022; Zhao et al., 2022) whereas discriminative methods separately encode history and schema and fail to account for inter-slot and intent-slot dependencies. In this work we introduce the SPan-Selective Linear Attention Transformer, short SPLAT, a novel architecture designed to achieve better generalization, robustness and efficiency in DST than existing approaches. SPLAT is fully extractive and, unlike prior generative approaches, constrains the output space to only those values contained in the input sequence. Figure 1 shows an example 78 ![1_image_0.png](1_image_0.png) ![1_image_1.png](1_image_1.png) of the key idea behind our approach. We jointly encode the natural language schema and full dialogue history allowing for a more expressive contextualization. Spans in the input are represented by aggregating semantics of each individual span into a single representation vector. Then we take a contrastive query-based pointer network approach (Vinyals et al., 2015) to match special query tokens to the target slot value's learned span representation in a single pass. Our main contributions are as follows: - We propose novel span-selective prediction layers for DST which provide better generalization and efficiency by limiting the prediction space and inferring all predictions in parallel. We achieve state-of-the-art performance on the SGD-X benchmark outperforming the 30× larger D3ST by 5.0 points. - We adopt a Linear Attention Transformer which allows more expressive contextualization of the dialogue schema and dialogue history with constrained prediction time. We show our model already outperforms other models with similar parameter budgets even without other modules we propose in Table 1 and 5. - We pre-train SPLAT for better span representations with a recurrent span selection objective yielding significant further span prediction performance gains of up to 1.5 points. ## 2 Approach 2.1 Task Formulation For a given dialog of T turns let U describe the set of utterances in the dialog history U = {u1*, . . . , u*T }. Each ui can represent either a user or a system utterance. The system is providing some service to the user defined by a service schema S. The service schema consists of a set of intents I = {i1*, . . . , i*K} and their intent descriptions Dintent = {d intent 1 , . . . , dintent K } as well as a set of slots S = {s1*, . . . , s*L} and their slot descriptions Dslot = {d slot 1 , . . . , dslot L}. In practice we prepend each ui with the speaker name (user or system) and a special utterance query token [UTT] which will serve as the encoding of the system-user utterance pair. Each d slot iconsists of the slot name, a natural language description of the semantics of the slot and for categorical values an enumeration of all possible values this slot can assume. We also append a special slot query embedding token [SLOT] which serves as the slot encoding. Some slot values are shared across all slots and their representation can be modeled jointly. Unless denoted otherwise these shared target values T are special tokens [NONE] and [DONTCARE] which correspond to the "none" and "dontcare" slot values in SGD and MultiWOZ. ## 2.2 Joint Encoding With Linear Attention Linear Attention Transformers. In order to better capture the semantics of the input and to allow for a longer context as well as all the relevant schema descriptions to be encoded jointly we use a Transformer (Vaswani et al., 2017) with linear-time attention. Instead of computing the full attention matrix as the original Transformer does, its linear attention variants compute either an approximation of it (Choromanski et al., 2021) or only compute full attention for a fixed context window of size w around the current token and additional nglobal global tokens, thus lowering the complexity of the attention computation from O(n 2) for a sequence of length n to O(w + nglobal) (Beltagy et al., 2020; Zaheer et al., 2020). We focus on the windowed variant and incorporate it to DST. We denote the Linear Attention Transformer with selective global attention parametrized by θ with input sequence I and its subset of global input tokens *G ⊆ I*, i.e. inputs corresponding to tokens at positions that are attended using the global attention mechanism, as LAT(I; G; θ). While we choose the Longformer (Beltagy et al., 2020) for our implementation, in practice any variants with windowed and global attention can be used instead. Joint encoding. The full input sequence of length N is given as the concatenation of its components. We define the set of globally-attended tokens as the union of sets of tokens corresponding to the intent descriptions Dintent, the slot descriptions Dslot, and the shared target values T. Then, the joint encoding of N hidden states is obtained as the output of the last Transformer layer as $\mathcal{I}=[\text{CLS}]\ U\ [\text{SEP}]\ T\ D^{\text{intent}}\ D^{\text{slot}}\ [\text{SEP}]$ $\mathcal{G}=T\cup D^{\text{intent}}\cup D^{\text{slot}}$ $E=\text{LAT}(\mathcal{I};\mathcal{G};\theta)$. (1) ## 2.3 Intent Classification Let x [UTT] idenote the representation of the encoded [UTT] token corresponding to the i-th turn. Given the encoded sequence E, we obtain the final utterance representations by feeding x [UTT] iinto the utterance encoder. Similarly for each intent I = {i1*, . . . , i*t} and its respective [INTENT] token, we obtain final intent representations using the intent encoder: $$\begin{array}{r l}{\mathbf{h}_{i}^{[\mathrm{{UTI}}]}=\mathrm{LN}(\mathrm{FFN}(\mathbf{x}_{i}^{[\mathrm{{UTI}}]}))}\\ {\mathbf{h}_{j}^{[\mathrm{{INENTI}}]}=\mathrm{LN}(\mathrm{FFN}(\mathbf{x}_{j}^{[\mathrm{{INENTI}}]}))}\end{array}\quad(2)$$ Here LN refers to a LayerNorm and FFN to a feedforward network. We maximize the dot product similarity between each utterance representation and the ground truth active intent's representation via cross-entropy: $$\begin{array}{c}{{\mathrm{score}_{i\to j}=\sin({\bf h}_{i}^{\left[\mathrm{UTT}\right]},{\bf h}_{j}^{\left[\mathrm{INTT}\right]})}}\\ {{\mathcal{L}_{\mathrm{intent}}=-\frac{1}{T}\sum_{i=1}^{T}\log\frac{\exp(\mathrm{score}_{i\to j})}{\sum_{k=1}^{K}\exp(\mathrm{score}_{i\to k})}\cdot1_{\mathrm{GT}}}}\end{array}$$ where K is the number of intents and 1GT is an indicator function which equals 1 if and only if j is the ground truth matching i. ## 2.4 Span Pointer Module We introduce a novel Span Pointer Module which computes span representations via a span encoder and extracts slot values by matching slot queries via a similarity-based span pointing mechanism (Vinyals et al., 2015). First, for any given span of token representations xi*, . . . ,* xj in the joint encoding E we obtain the span representation h SPAN ij by concatenating the span's first and last token representation and feeding them into a 2-layer feed-forward span encoder (Joshi et al., 2020): $$\mathbf{y}_{i j}=[\mathbf{x}_{i};\mathbf{x}_{j}]$$ $\mathbf{h}_{i j}^{\mathrm{SPAN}}=\mathrm{LN}(\mathrm{FFN}_{\mathrm{GeLU}}(\mathbf{y}_{ij}))\times\mathbf{n}_{\mathrm{layers}}$ (4) Similarly, for each slot token representation x [SLOT] in E we compute a slot query representation h [SLOT] with a 2-layer feed-forward slot encoder: $$\mathbf{h}^{\left[\text{SLOT}\right]}=\text{LN}(\text{FFN}_{\text{GelU}}(\mathbf{x}^{\left[\text{SLOT}\right]}))\times\text{n}_{\text{layers}}\tag{5}$$ $$\mathrm{(1)}$$ Given slots S = {s1*, . . . , s*L} and corresponding slot query representations h [SLOT] 1 , . . . , h [SLOT] L we score candidate target spans by dot product similarity of the slot queries with their span representations. That is, for each slot query q with ground truth target span xi*, . . . , x*j we maximize sim(h [SLOT] q, h SPAN ij ) by cross-entropy. The loss function is given by $$\begin{array}{c}{{\mathrm{score}_{q\to i j}=\mathrm{sim}({\bf h}_{q}^{\left[\mathrm{SLOT}\right]},{\bf h}_{i j}^{\mathrm{SPAN}})}}\\ {{\mathcal{L}_{\mathrm{slot}}=-\frac{1}{L}\sum_{q=1}^{L}\log\frac{\exp(\mathrm{score}_{q\to i j})}{\sum_{k=1}^{K}\exp(\mathrm{score}_{q\to k})}\cdot1_{\mathrm{GT}}}}\end{array}$$ where L is the number of slots and K is the number of spans. sim(h [SLOT] q, h SPAN ij ) denotes the 80 similarity between the q-th slot query representation and the span representation of its ground truth slot value. It is computationally too expensive to compute span representations for all possible spans. In practice however the length of slot values rarely exceeds some Lans. Thus, we limit the maximum span length to Lans and do not compute scores for spans longer than this threshold. This gives us a total number of N · Lans candidate spans. Joint optimization. We optimize the intent and slot losses jointly via the following objective: $${\mathcal{L}}={\frac{{\mathcal{L}}_{\mathrm{slot}}+{\mathcal{L}}_{\mathrm{invent}}}{2}}$$ ## 2.5 **Pre-Training Via Recurrent Span Selection** Since the span pointer module relies on span embedding similarity for slot classification we believe it is crucial to learn good and robust span representations. In order to improve span representations for down-stream applications to DST we pre-train SPLAT in a self-supervised manner using a modified recurrent span selection objective (Ram et al., 2021). Given an input text I let R = {R1*, . . . ,* Ra} be the clusters of identical spans that occur more than once. Following Ram et al. (2021) we randomly select a subset *M ⊆ R* of J recurring spans such that the number of their occurrences sums up to a maximum of 30 occurrences. Then, for each selected cluster of recurring spans Mj we randomly replace all but one occurrence with the query token [SLOT]. The slot query tokens act as the queries while the respective unmasked span occurrences act as the targets. Unlike the original recurrent span selection objective we do not use separate start and end pointers for the target spans but instead use our Span Pointer Module to learn a single representation for each target span. We pre-train SPLAT to maximize the dot product similarity between the query token and the unmasked target span representation. The loss for the j-th cluster of identical masked spans is given by Equation (6) and the total loss is given as the sum of losses of over all clusters. Effectively each sentence containing a masked occurrence of the span acts as the span description while the target span acts as the span value. This can be seen as analogous to slot descriptions and slot values in DST. ## 3 Experimental Setup We describe our experimental setup including datasets used for pre-training and evaluation, implementation details, baselines and evaluation metrics in detail below. ## 3.1 Benchmark Datasets We conduct experiments on the Schema-Guided Dialogue (SGD) (Rastogi et al., 2020), SGD-X (Lee et al., 2022) and MultiWOZ 2.2 (Zang et al., 2020) datasets. $$\left(7\right)$$ Schema-Guided Dialogue. Unlike other taskoriented dialogue datasets which assume a single, fixed ontology at training and test time the SGD dataset includes new and unseen slots and services in the test set. This allows us to not only measure DST performance but also zero-shot generalization to unseen services. The dataset includes natural language descriptions for all intents and slots in its schema. We follow the standard evaluation setting and data split suggested by the authors. SGD-X. The SGD-X benchmark is an extension of the SGD dataset which provides five additional schema variants of different linguistic styles which increasingly diverge in style from the original schema with v1 being most similar and v5 least similar. We can evaluate our model's robustness to variations in schema descriptions by training our model on SGD and comparing evaluation results using the different included schema variants. MultiWOZ. The MultiWOZ dataset is set of human-human dialogues collected in the Wizardof-OZ setup. Unlike in SGD the ontology is fixed and there are no unseen services at test time. There are multiple updated versions of the original MultiWOZ dataset (Budzianowski et al., 2018): MultiWOZ 2.1 (Eric et al., 2020) and MultiWOZ 2.2 (Zang et al., 2020) fix annotation errors of previous versions, MultiWOZ 2.3 (Han et al., 2021) is based on version 2.1 and adds co-reference annotations, MultiWOZ 2.4 (Ye et al., 2022) is also based on version 2.1 and includes test set corrections. However, MultiWOZ 2.2 is the only version of the dataset which includes a fully defined schema matching the ontology. We therefore choose the MultiWOZ 2.2 dataset for our experiments. We follow the standard evaluation setting and data split. ## 3.2 Evaluation Metrics In line with prior work (Rastogi et al., 2020) we evaluate our approach according to the following two metrics. Intent Accuracy: For intent detection the intent accuracy describes the fraction of turns for which the active intent has been correctly inferred. Joint Goal Accuracy (JGA): For slot prediction JGA describes the fraction of turns for which all slot values have been predicted correctly. Following the evaluation setting from each dataset we use a fuzzy matching score for slot values in SGD and exact match in MultiWOZ. ## 3.3 Implementation Details We base our implementation on the Longformer code included in the HuggingFace Transformers library (Wolf et al., 2020) and continue training from the base model (110M parameters) and large model (340M parameters) checkpoints. We keep the default Longformer hyperparameters in place, in particular we keep the attention window size set to 512. The maximum sequence length is 4096. During pre-training we train the base model for a total of 850k training steps and the large model for 800k training steps. During fine-tuning we train all models for a single run of 10 epochs and choose the model with the highest joint goal accuracy on the development set. We use the Adam optimizer (Kingma and Ba, 2014) with a maximum learning rate of 10−5 which is warmed up for the first 10% of steps and subsequently decays linearly. We set the batch size to 32 for base models and to 16 for large models. We pre-train SPLAT on English Wikipedia. Specifically we use the KILT Wikipedia snapshot1from 2019 (Petroni et al., 2021) as provided by the HuggingFace Datasets library (Lhoest et al., 2021). For both SGD and MultiWOZ we set the shared target values T as the [NONE] and [DONTCARE] tokens and include a special intent with the name "NONE" for each service which is used as the target intent when no other intent is active. We set the maximum answer length Lans to 30 tokens. All experiments are conducted on a machine with eight A100 80GB GPUs. A single training run takes around 12 hours for the base model and 1.5 days for the large model. 1https://huggingface.co/datasets/kilt_ wikipedia ## 4 Evaluation We evaluate the effectiveness of our model through a series of experiments designed to answer the following questions: 1) How effective is the proposed model architecture at DST in general? 2) Does the model generalize well to unseen services? 3) Is the model robust to changes in schema such as different slot names and descriptions? 4) Which parts of the model contribute most to its performance? ## 4.1 Baselines We compare our model to various discriminative and generative baseline approaches. Note that not all of them are directly comparable due to differences in their experimental setups. Extractive baselines. SGD baseline (Rastogi et al., 2020) is a simple extractive BERT-based model which encodes the schema and last utterance separately and uses the embeddings in downstream classifiers to predict relative slot updates for the current turn. SGP-DST (Ruan et al., 2020) and DSDST (Zhang et al., 2020) are similar but jointly encode utterance and slot schema. Multi-Task BERT (Kapelonis et al., 2022) is also similar but uses system action annotations which include annotations of slots offered or requested by the system (e.g. "[ACTION] Offer [SLOT] location [VALUE] Fremont"). paDST (Ma et al., 2019) combines an extractive component for non-categorical slots with a classifier that uses 83 hand-crafted features (including system action annotations) for categorical slots. Additionally it augments training data via back-translation achieving strong results but making a direct comparison difficult. LUNA (Wang et al., 2022) separately encodes dialogue history, slots and slot values and learns to first predict the correct utterance to condition the slot value prediction on. Generative baselines. Seq2Seq-DU (Feng et al., 2021) first separately encodes utterance and schema and then conditions the decoder on the cross-attended utterance and schema embeddings. The decoder generates a state representation consisting of pointers to schema elements and utterance tokens. AG-DST (Tian et al., 2021) takes as input the previous state and the current turn and learns to generate the new state in a first pass and correcting mistakes in a second generation pass. AG-DST does not condition generation on the schema and slot semantics are learned implic- | Model | Pretrained Model | Single-Pass | Intent | JGA | |-----------------------------------------------------------------|-------------------------|---------------|----------|-------| | With system action annotations MT-BERT (Kapelonis et al., 2022) | BERT-base (110M) | ✗ | 94.7 | 82.7 | | paDST (Ma et al., 2019) | XLNet-large (340M) | ✗ | 94.8 | 86.5 | | No additional data SGD baseline (Rastogi et al., 2020) | BERT-base (110M) | ✗ | 90.6 | 25.4 | | MT-BERT (Kapelonis et al., 2022) | BERT-base (110M) | ✗ | - | 71.9 | | DaP (ind) (Lee et al., 2021) | T5-base (220M) | ✗ | 90.2 | 71.8 | | SGP-DST (Ruan et al., 2020) | T5-base (220M) | ✗ | 91.8 | 72.2 | | D3ST (Base) (Zhao et al., 2022) | T5-base (220M) | ✓ | 97.2 | 72.9 | | D3ST (Large) (Zhao et al., 2022) | T5-large (770M) | ✓ | 97.1 | 80.0 | | D3ST (XXL) (Zhao et al., 2022) | T5-XXL (11B) | ✓ | 98.8 | 86.4 | | SPLAT (Base) | Longformer-base (110M) | ✓ | 96.7 | 80.1 | | SPLAT (Large) | Longformer-large (340M) | ✓ | 97.6 | 85.3 | | Table 1: Results on the SGD test set. | | | | | | Model | Pretrained Model | Single-Pass | Intent | JGA | | DS-DST† (Zhang et al., 2020) | BERT-base (110M) | ✗ | - | 51.7 | | Seq2Seq-DU (Feng et al., 2021) | BERT-base (110M) | ✓ | 90.9 | 54.4 | | LUNA (Wang et al., 2022) | BERT-base (110M) | ✗ | - | 56.1 | | AG-DST (Tian et al., 2021) | GPT-2 (117M) | ✗ ‡ | - | 56.1 | | AG-DST (Tian et al., 2021) | PLATO-2 (310M) | ✗ ‡ | - | 57.3 | | DaP (seq) (Lee et al., 2021) | T5-base (220M) | ✓ | - | 51.2 | | DaP (ind) (Lee et al., 2021) | T5-base (220M) | ✗ | - | 57.5 | | D3ST (Base) (Zhao et al., 2022) | T5-base (220M) | ✓ | - | 56.1 | | D3ST (Large) (Zhao et al., 2022) | T5-large (770M) | ✓ | - | 54.2 | | D3ST (XXL) (Zhao et al., 2022) | T5-XXL (11B) | ✓ | - | 58.7 | | SPLAT (Base) | Longformer-base (110M) | ✓ | 91.4 | 56.6 | | SPLAT (Large) | Longformer-large (340M) | ✓ | 91.5 | 57.4 | Table 2: Results on the MultiWOZ 2.2 test set. Results denoted by † were reported in the original MultiWOZ 2.2 paper (Zang et al., 2020). ‡: AG-DST uses a fixed two-pass generation procedure. itly so it is unclear how well AG-DST transfers to new services. DaP (Lee et al., 2021) comes in two variants which we denote as DaP (seq) and DaP (ind). DaP (ind) takes as input the entire dialogue history and an individual slot description and decodes the inferred slot value directly but requires one inference pass for each slot in the schema. DaP (seq) instead takes as input the dialogue history and the sequence of all slot descriptions and decodes all inferred slot values in a single pass. D3ST (Zhao et al., 2022) takes a similar approach and decodes the entire dialogue state including the active intent in a single pass. Categorical slot values are predicted via an index-picking mechanism. ## 4.2 Main Results Schema-Guided Dialogue. Table 1 shows results on the SGD test set. We report results for intent accuracy and JGA. We find that our model significantly outperforms models of comparable size in terms of JGA. In particular our 110M parameter SPLAT base model outperforms the 220M model D3ST base model by 7.2 JGA points and even achieves comparable performance to the much larger D3ST large model. Going from SPLAT base to SPLAT large we observe a significant performance improvement. In particular SPLAT large outperforms the D3ST large model by 5.3 JGA and nearly achieves comparable performance to the | Model | Params. | Orig. | Avg. (v1–v5) | Avg. ∆ | Max ∆ | |----------------------------------|-----------|--------------|----------------|----------|---------| | DaP (ind) (Lee et al., 2021) | 220M | 71.8 | 64.0 | -7.8 | - | | SGP-DST (Ruan et al., 2020) | 220M | 72.2 / 60.5∗ | 49.9∗ | -10.6 | - | | D3ST (Large) (Zhao et al., 2022) | 770M | 80.0 | 75.3 | -4.7 | -10.9 | | D3ST (XXL) (Zhao et al., 2022) | 11B | 86.4 | 77.8 | -8.6 | -17.5 | | SPLAT (Base) | 110M | 80.1 | 76.0 | -4.1 | -7.8 | | SPLAT (Large) | 340M | 85.3 | 82.8 | -2.5 | -5.3 | Table 3: Joint goal accuracy on the five different SGD-X schema variants. Results denoted by ∗are based on a reimplementation in the SGD-X paper which could not reproduce the original results. Model Params. Seen Unseen Overall SGP-DST1 220M 88.0 67.0 72.2 D3ST (Base)2 220M 92.5 66.4 72.9 D3ST (Large)2 770M 93.8 75.4 80.0 D3ST (XXL)2 11B **95.8 83.3 86.4** SPLAT (Base) 110M 94.5 75.2 80.1 SPLAT (Large) 340M 94.6 82.2 85.3 Table 4: Joint goal accuracy on the SGD test set on seen and unseen services. Baseline results are reported by 1Ruan et al. (2020) and 2Zhao et al. (2022) respectively. more than 30× larger D3ST XXL model. We note that although paDST achieves the best performance of all baseline models in terms of JGA, it is not directly comparable because it is trained with hand-crafted features and additional back-translation data for training which has been shown to significantly improve robustness and generalization to unseen descriptions in schema-guided DST (Lee et al., 2022). Similarly, although MultiTask BERT achieves good performance this can mostly be attributed to the use of system action annotation as Kapelonis et al. (2022) themselves demonstrate. Without system action annotations its performance drops to 71.9 JGA. In terms of intent accuracy SPLAT base slightly underperforms D3ST base and D3ST large by 0.5 and 0.4 JGA while SPLAT large achieves better performance and slightly improves upon the D3ST large performance. Overall, SPLAT achieves strong performance on SGD. MultiWOZ. Table 2 shows results on the MultiWOZ 2.2 test set. As the majority of papers does not report intent accuracy on MultiWOZ 2.2 we focus our analysis on JGA. We find that SPLAT base outperforms most similarly-sized models including D3ST base and large and that SPLAT large performs better than all models aside from the more than 30× larger D3ST XXL. The notable exceptions to this are AG-DST and DaP (ind). AGDST large achieves performance that is similar to SPLAT large using a generative approach but it performs two decoding passes, employs a negative sampling strategy to focus on more difficult examples and is trained for a fixed schema. DaP (ind) also achieves similar performance but needs one inference pass for every slot at every turn of the dialogue. This is much slower and simply not realistic in real-world scenarios with a large number of available services and slots. The sequential variant DaP (seq) which instead outputs the full state in a single pass performs much worse. Comparison. While DaP (ind) shows strong performance that matches SPLAT on MultiWOZ, SPLAT fares much better than DaP (ind) on the SGD dataset. This can be seen to be indicative of a stronger generalization ability as MultiWOZ uses the same schema at training and test time whereas SGD includes new, unseen services at test time and thus requires the model to generalize and understand the natural language schema descriptions. ## 4.3 Robustness DST models which take natural language descriptions of intents and slots as input naturally may be sensitive to changes in these descriptions. In order to evaluate the robustness of our model to such linguistic variations we perform experiments on the SGD-X benchmark. The SGD-X benchmark comes with five crowd-sourced schema variants v1 to v5 which increasingly diverge in style from the original schema. We train SPLAT on SGD and evaluate it on the test set using all five different schema variants. As shown in Table 3, our model is considerably more robust to linguistic variations than all of the | SGD | MultiWOZ | | | | | |--------------------|------------|--------|------|--------|------| | Model | Params. | Intent | JGA | Intent | JGA | | Longformer (extr.) | 110M | 95.9 | 78.5 | 91.4 | 55.5 | | + SPM | 110M | 97.0 | 79.0 | 91.4 | 56.1 | | + SPM + RSS-PT | 110M | 96.7 | 80.1 | 91.4 | 56.6 | | Longformer (extr.) | 340M | 97.5 | 83.5 | 91.4 | 56.3 | | + SPM | 340M | 98.2 | 83.8 | 91.4 | 57.8 | | + SPM + RSS-PT | 340M | 97.6 | 85.3 | 91.5 | 57.4 | baseline models. On average SPLAT base loses around 4.1 points and SPLAT large loses around 2.5 points joint goal accuracy when compared to the results on the original schema. When considering the mean performance across all unseen schema variants SPLAT large significantly outperforms the more than 30× larger D3ST XXL by 5.0 points. These observations also hold for the base model: the 110M parameter SPLAT base even outperforms the 11B parameter D3ST XXL on the least similar schema variant v5 further highlighting the superior robustness of our model. ## 4.4 Generalization To Unseen Domains In real-world scenarios virtual assistants cover a wide range of services that can change over time as new services get added or removed requiring dialogue models to be re-trained. One of our goals is to improve generalization to unseen services thus minimizing the need for expensive data collection and frequent re-training. As the MultiWOZ dataset does not include any new and unseen services in its test set our analysis primarily focuses on the SGD dataset. Table 4 shows results on SGD with a separate evaluation for dialogues in seen and unseen domains. We find that SPLAT achieves better generalization and improves upon the baselines with a particularly large margin on unseen domains where SPLAT base outperforms D3ST base by 8.8 points and SPLAT base outperforms D3ST large by 6.8 points. ## 4.5 Ablation Study We conduct an ablation study to identify the contribution of the different components to model performance. Results can be seen in Table 5. We compare a variant of our model which does not use span representations (referred to as "Longformer (extractive)") but instead has two pointers [SLOT] and [/SLOT] which are used to select the start and end of the answer span. We find that using the Span Pointer Module to directly select the span improves performance across both model sizes and datasets. Furthermore, we find pre-training our model for better span representations via the recurrent span selection task to be crucial giving further significant performance gains for all sizes and datasets except the 340M parameter model on the MultiWOZ dataset where JGA slightly deteriorates. Across both model sizes gains from RSS pre-training are larger on the SGD dataset. We hypothesize that this may be attributed to better span representations learned through RSS pre-training which in turn generalize better to unseen domains. ## 5 Related Work Extractive DST. Following the traditional extractive setting Chao and Lane (2019) propose a machine reading comprehension (MRC) approach which decodes slot values turn-by-turn using a different learned classifier for each slot. As a classifier has to be learned for each new slot this approach cannot easily be transferred to new slots. Schema-guided approaches address this by explicitly conditioning predictions on a variable schema which describes intents and slots in natural language (Rastogi et al., 2020). Both Ruan et al. (2020) and Zhang et al. (2021) introduce schema-guided models but predict slots independently from one another requiring multiple encoder passes for each turn and failing to model intent-slot and inter-slot dependencies. Ma et al. (2019) use MRC for non-categorical and handcrafted features for categorical slots. Generative DST. In an attempt to address the lack of ability to generalize to new domains and ontologies, Wu et al. (2019) propose incorporating a generative component into DST. Based on the dialog history and a domain-slot pair a state generator decodes a value for each slot. However as each slot is decoded independently the approach cannot model slot interdependencies. Feng et al. (2021) instead generate the entire state as a single sequence of pointers to the dialogue history and input schema but separately encode history and schema. Zhao et al. (2021) model DST fully as a text-to-text problem and directly generate the entire current state as a string. Lin et al. (2021) transfer a language model fine-tuned for seq2seq question answering to DST zero-shot using the dialog history as context and simply asking the model for the slot values. By also including a natural language schema in the input, Zhao et al. (2022) show that full joint modeling and rich attention between history and schema lead to better results in DST. Furthermore, they demonstrate the flexibility of this fully language driven paradigm by leveraging strong pre-trained language models for cross-domain zero-shot transfer to unseen domains. Gupta et al. (2022) show the effectiveness of using demonstrations of slots being used in practice instead of a natural language descriptions in the prompt. ## 6 Conclusion In this work we introduced SPLAT, a novel architecture for schema-guided dialogue state tracking which learns to infer slots by learning to select target spans based on natural language descriptions of slot semantics, and further showed how to pretrain SPLAT via a recurrent span selection objective for better span representations and a stronger slot prediction performance. We find that our proposed architecture yields significant improvements over existing models and achieving 85.3 JGA on the SGD dataset and 57.4 JGA on the MultiWOZ dataset. In schema-guided DST the ability to generalize to new schemas and robustness to changes in schema descriptions is of particular interest. We demonstrated that our model is much more robust to such changes in experiments on the SGD-X benchmark where SPLAT outperforms the more than 30× larger D3ST-XXL model by 5.0 points. ## Limitations One trade-off of limiting the prediction space using an extractive pointer module is that it does not support prediction of multiple slot values which is necessary for some dialogues in the MultiWOZ 2.3 and 2.4 datasets. To keep the architecture simple we do not consider cases in which slots take multiple values in this work, but we can effectively adapt our model for this setting by introducing sequential query tokens for each slot. Another limitation is that the span representation requires a computation of O(N · Lans) complexity where N and Lans represent the length of context and answer span, respectively. For very long answers this might occur significant computational costs compared to existing span prediction approaches which have O(N) complexity. However, this can be alleviated by adding a simple sampling and filtering step during training and prediction. We plan to further study and address these limitations in future work. ## Ethics Statement We introduced a novel model architecture for schema-guided dialogue state tracking which leverages a natural language schema and a span pointer module to achieve higher accuracy in dialogue state tracking. All experiments were conducted on publicly available datasets which are commonly used in research on dialogue systems. ## References Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. *arXiv* preprint arXiv:2004.05150. Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gašic. 2018. ´ MultiWOZ - a largescale multi-domain Wizard-of-Oz dataset for taskoriented dialogue modelling. 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Association for Computational Linguistics. Jianguo Zhang, Kazuma Hashimoto, Chien-Sheng Wu, Yao Wang, Philip Yu, Richard Socher, and Caiming Xiong. 2020. Find or classify? dual strategy for slot-value predictions on multi-domain dialog state tracking. In *Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics*, pages 154–167, Barcelona, Spain (Online). Association for Computational Linguistics. Yang Zhang, Vahid Noroozi, Evelina Bakhturina, and Boris Ginsburg. 2021. Sgd-qa: Fast schema-guided dialogue state tracking for unseen services. arXiv preprint arXiv:2105.08049. Jeffrey Zhao, Raghav Gupta, Yuan Cao, Dian Yu, Mingqiu Wang, Harrison Lee, Abhinav Rastogi, Izhak Shafran, and Yonghui Wu. 2022. Descriptiondriven task-oriented dialog modeling. *arXiv preprint* arXiv:2201.08904. Jeffrey Zhao, Mahdis Mahdieh, Ye Zhang, Yuan Cao, and Yonghui Wu. 2021. Effective sequence-tosequence dialogue state tracking. In *Proceedings* of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7486–7493, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. ## A Appendix | Symbol | Definition | |-------------|-----------------------------------------------| | LAT | Linear Attention Transformer | | I | Input sequence | | G | Global inputs | | M | Set of masked recurring span clusters | | R | Set of all recurring span clusters | | Dintent | Intent descriptions | | Dslot | Intent descriptions | | E | Joint encoding obtained from LAT | | I | Intents | | S | Slots | | T | Shared target tokens | | U | Utterances | | h [INTENT] | Intent embedding | | [SLOT] | Slot embedding | | h [UTT] | Utterance embedding | | h h SPAN ij | Span embedding from position i to j | | xi | Token representation at position i | | θ | Model parameters Table 6: Glossary of symbols | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? We discussed the limitations of our work in the unnumbered limitations section. ✗ A2. Did you discuss any potential risks of your work? We only used publically available datasets that are commonly used in research on dialogue systems. We believe there are no significant risks associated with our work. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Discussed In Section 3.1 And 3.3 ✓ B1. Did you cite the creators of artifacts you used? Section 3.1 and 3.3 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We only used publically available data and adhere to the creator's license terms. The SGD dataset is freely available under the CC-BY-SA 4.0 and the MultiWOZ dataset is freely available under the MIT license. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We only used publically available data and adhere to the creator's license terms and their intended use. The SGD dataset is freely available under the CC-BY-SA 4.0 and the MultiWOZ dataset is freely available under the MIT license. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We only used publically available data that is commonly used in dialogue systems research and which does not uniquely identify people and which does not contain any personal data or offensive content. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? We did not create artifacts. Documentation of the artifacts used is provided in section 3.1 and 3.3. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3.1 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Section 3 And Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 3.3 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3.3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 3.3 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3.3 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

li-zhao-2023-em

{EM} Pre-training for Multi-party Dialogue Response Generation

https://aclanthology.org/2023.acl-long.7

Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multi-party dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method. The official implementation of this paper is available at \url{https://github.com/EricLee8/MPDRG}.

## Em Pre-Training For Multi-Party Dialogue Response Generation Yiyang Li1,2and **Hai Zhao**1,2,∗ 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University 2 Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University [email protected], [email protected] ## Abstract Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which twoparty dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multi-party dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method. The official implementation of this paper is available at https://github.com/EricLee8/MPDRG. ## 1 Introduction Inspired by the tremendous success in pre-training large language models (PLMs) in general domains (Devlin et al., 2019; Clark et al., 2020; Radford et al., 2018), efforts have been made to train PLMs for dialogue response generation (Zhang et al., 2020; Bao et al., 2020; Chen et al., 2022). However, they constrain the dialogues to be either two-party, or sequential structured (i.e. each utterance replies directly to its previous utterance). Different from them, a multi-party dialogue can involve multiple interlocutors, where each interlocutor can reply to ∗ Corresponding author. This paper was partially supported by Key Projects of National Natural Science Foundation of China (U1836222 and 61733011). any preceding utterances, making the response relations of the dialogue tree-structured and much more complicated (Zhang et al., 2018; Le et al., 2019; Shi and Huang, 2019; Wang et al., 2020). Besides, the speaker and addressee of a response utterance should be specified before it is generated in multi-party scenario, making the annotated data for multi-party dialogue response generation (MPDRG) less available. Figure 1 illustrates an example of MPDRG task taken from the Ubuntu IRC benchmark (Hu et al., 2019). The upper part shows the tree-structured addressee relations of the dialogue, where the arrows point from addressees to speakers, and different colors represent different interlocutors. The middle part displays the content of the dialogue history, where U7 is the response to be generated. The addressee (U6) and the speaker (\#4) of it are given, and the content of this response is the target of our model. The lower part gives the human response, which is also called the ground truth reference. Previous works on MPDRG fine-tune generative PLMs on small multi-party dialogue datasets with explicit addressee annotations. They utilize the response annotations to form a tree-structured response graph, then encode the dialogue history using either homogeneous or heterogeneous Graph Neural Networks (GNNs) (Hu et al., 2019; Gu et al., 2022). Nevertheless, none of them make attempts to pre-train a response generation model for multiparty dialogues due to the lack of large-scale corpora with annotated addressee labels. To solve the aforementioned problem of data scarcity, we propose an EM approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Specifically, we treat the addressee of each utterance in the dialogue history as a discrete latent variable z. During the E-steps, given the current dialogue history ct and the the response utterance rt, we 92 model the distribution of the current addressee zt as p(zt|ct, rt; θ), where θ is the current model parameters. During the M-steps, we sample (ct, rt, zt) triplets from distribution p(zt|ct, rt; θ) and optimize the generative model p(rt|ct, zt; θ) on these samples. With the iteration number increasing, the accuracy of latent variable prediction and the quality of generated responses will grow together. It is worth noting that during these iterations, annotated addressee labels are not required, which makes it possible to leverage the huge amount of multi-party dialogue corpora without addressee labels. We provide theoretical analyses to prove the feasibility of our EM method, and conduct experiments on the Ubuntu IRC benchmark, which is used in previous works (Hu et al., 2019; Gu et al., 2022). The contributions of our work can be summarized as the following three folds: - To the best of our knowledge, we are the first to study the pre-training of multi-party dialogue response generation, which is much more challenging and complicated than two-party dialogues. - We put forward an EM approach to alleviate the scarcity of multi-party dialogue data with addressee labels, making it possible to pre-train a model with huge amount of unlabeled corpora. - We provide theoretical analyses to prove the feasibility of our EM pre-training method, and experimental results on the Ubuntu IRC benchmark show our pre-trained model achieves state-of-theart performance compared with previous works. ## 2 Related Works 2.1 Pre-Training For Response Generation In recent years, researchers have gradually drawn their attention from retrieval-based dialogue systems to generation-based ones. Thanks to the huge amount of two-party dialogue corpora, various PLMs for two-party dialogue response generation have been proposed. Zhang et al. (2020) propose DialoGPT, which utilizes the sequential response chains in the Reddit Corpus to pre-train an auto-regressive response generation model based on the architecture of GPT (Radford et al., 2018). Different from their work, which focuses on sequential dialogue history, our work aims to solve the case where the agent can respond to any previous utterance in a tree-structured dialogue history. Bao et al. (2020) propose PLATO, which models the conversational intents as K discrete latent ![1_image_0.png](1_image_0.png) variables, then utilizes response selection, bag-ofwords prediction, and language modeling objectives to train the model. DialogVED (Chen et al., 2022) further extends the discrete latent variables to continuous ones, and models them with a multivariable Gaussian distribution. It utilizes KL divergence reduction to optimize the parameters of the latent distribution and applies masked language modeling, response generation, and bag-of-words prediction to train the whole model. PLATO and DialogVED focus on two-party conversations, and the conversational intents they put forward have no corresponding concepts of actual entities (e.g., intent to argue, intent to end a conversation, and so on). Distinct from their works, we lay emphasis on multi-party dialogues, and the latent variables of our method have actual meanings: variable zt = j indicates that the addressee of the response at the tth turn is the jth utterance. ## 2.2 Multi-Party Dialog Response Generation Several previous works have studied the MPDRG task. Hu et al. (2019) extract a subset of the Ubuntu Dialogue Corpus (Lowe et al., 2015) with explicit addressee labels to construct the Ubuntu IRC benchmark, where they propose a Graph Structured Neural Network (GSN) for dialogue modeling. Specifically, they first treat each utterance ![2_image_0.png](2_image_0.png) of a dialogue as a node, and the addressee relations as edges to construct a dialogue graph, then make use of GNNs to encode the dialogue history. Finally, they adopt a Gated Recurrent Unit (GRU) with cross attention as the decoder to generate responses. Gu et al. (2022) put forward HeterMPC, which models the dialogue history as a heterogeneous graph. In detail, they first design six types of edges: reply and replied-by, address and addressed-by, speak and spoken-by, among two kinds of nodes: interlocutor nodes and utterance nodes, and then encode the dialogue history using Transformers (Vaswani et al., 2017) together with heterogeneous GNNs. Finally, they utilize a Transformer Decoder to generate responses. Instead of fine-tuning models on a small dataset with annotated addressee labels as these existing work did, our work focuses on the utilization of large unlabeled corpora to pre-train a response generation model for multi-party dialogues. ## 3 Methodology To design a model for multi-party dialogue response generation and make it compatible with the EM training algorithm, there are two important things to consider: how to model p(rt|ct, zt; θ) in the maximization step, and how to compute p(zt|ct, rt; θ) in the expectation step. In this section, we will first address these two problems, then mathematically derive the feasibility of our EM pre-training algorithm. ## 3.1 Task Formulation Given an input sequence of the dialogue history and the speaker of the response at time step t, X = {S1: U1[SEP]S2: U2[SEP] *. . .* St-1: Ut-1[SEP]St:}, together with the addressee of the response zt = j, our goal is to train a model that can generate an response Y = Ut. Here each Siis the name of the speaker at time step i, which is represented as *Speaker \#*Silike those in Figure 1. Ui = {wi1, wi2*, . . . , w*ini} is the content of the ith utterance with ni words. zt = j represents that St speaks to Sj, who utters Uj, and [SEP] is a special token that indicates the end of a dialogue turn. ## 3.2 Addressee Modeling In this section, we answer the first question: how to model p(rt|ct, zt; θ), or in other words, how to incorporate the addressee information zt = j into the process of generating a response rt. We design a straightforward method that adds addressee embeddings to the positional encodings and word embeddings, before they are further encoded by a PLM. The left part of Figure 2 illustrates this method, where we use an embedding look-up table with 2 entries to indicate whether a word belongs to the addressee utterance or not. Specifically, if a word is in the addressee utterance, it will get its addressee embedding from entry 1, otherwise from entry 0. Since addressee modeling is not the key contribution of this work, we just adopt the most straightforward and effective way. In our experiments, we use BART (Lewis et al., 2020) as the backbone PLM, following previous works (Gu et al., 2022). Due to the page limit, the proverbial architecture of Transformer and BART are omitted here. ## 3.3 Latent Variable Prediction In this section, we answer the second question: how to compute p(zt|ct, rt; θ) in the expectation step, or in other words, how to predict the distribution of the unlabeled addressee zt, given the current dialogue context ct, response rt, under parameters θ. The solution to this question is essentially the most important part of our method since it delicately solves the problem of data scarcity in MPDRG. Let's consider what humans will do to participate in a multi-party conversation. First, we will read the dialogue history ct, then choose an addressee ztto reply. Once ct and zt are determined, we will utter a response according to the content of the whole dialogue and the addressee utterance. The right part of Figure 2 gives the Bayesian Network of the above process, where the joint distribution of (ct, zt, rt) can be factorized as: $$p(c,z,r)=p(c)\cdot p(z|c)\cdot p(r|c,z)$$ Here we omit the subscript t and model parameters θ for simplicity. Given Eq. (1), p(z|*c, r*; θ) can be derived as: $$\begin{split}p(z|c,r)&=\frac{p(c,z,r)}{p(c,r)}\\ &=\frac{p(c)\cdot p(z|c)\cdot p(r|c,z)}{p(c)\cdot p(r|c)}\\ &=\frac{p(z|c)\cdot p(r|c,z)}{p(r|c)}\end{split}\tag{2}$$ We assume that the probability of choosing any previous utterance as the addressee is the same given the current dialogue history, which means p(z|c) obeys a uniform distribution. Meanwhile, the denominator p(r|c) is independent of z, leaving only the term p(r|*c, z*). Now, we can induce that: $$p(z|c,r)\propto p(r|c,z)$$ p(z|c, r) ∝ p(r|*c, z*) (3) Therefore, for each $z^{i},i=1,2,\ldots,t-1$, we have: $$p(z^{i}|c,r)=\frac{p(r|c,z^{i})}{\sum_{j=1}^{t-1}p(r|c,z^{j})}\tag{4}$$ In practice, we can use the generative model p(rt|ct, zt; θ) to compute the probability distribution of p(zt|ct, rt; θ) by Eq. (4). ## 3.4 Expectation-Maximization Process Figure 3 illustrates the overview of our EM training process. During the E-steps, we compute the probability distribution of the latent variable (the addressee z). During the M-steps, we sample (*c, r, z*) triplets from this distribution and optimize the generative model by standard training algorithms. The Expectation Step is to compute the conditional distribution of the latent variable zt, given the observed data (ct, rt) and the current model ![3_image_0.png](3_image_0.png) parameters θ, where Eq. (4) gives a reasonable approximation of this value. Specifically, for a sample (ct, rt), with the model parameters θ fixed, we first calculate the un-normalized probability of each of the ith (*i < t*) utterance being the addressee: p(rt|ct, zi t; θ) using Eq. (3), then normalize them to get the conditional distribution of zt using Eq. (4). Once P(zt|ct, rt; θ) is obtained, we sample (ct, rt, zt) triplets from this distribution, which is further used in the maximization step. The Maximization Step is analogical to the normal training process. Given the sampled {(c k t, rk t, zk t)} N k=1 triplets, where N is the total number of samples, our goal is to minimize the auto-regressive language modeling loss: k=1 Xnk LG = − X N i=1 log p w k i| w k <i, ck t, zk t; θ (5) $$({\mathfrak{I}})$$ where w k i is the ith word in the response of the kth sample: r k t = {w k i} ni i=1, and niis the length of this response. Compared with the vanilla EM algorithm, there are several differences in our implementations. First of all, we do not use the initial model to generate the training data for the first round of the maximization step. Instead, we utilize the discourse parser provided by Shi and Huang (2019) to predict the addressee of each utterance in the unlabeled corpus to get a coarse initial training dataset. The reason for this initialization method is that the initialization of training data (or model parameters) is vital to the EM method, which helps it converge to a better point. Second, rather than sampling zt from its conditional distribution, we adopt a hard EM approach which takes the value z i t with highest probability as the predicted label, where i = arg max i p(z i t|ct, rt; θ). This hard EM approach is proved as more effective to boost the performance (Min et al., 2019). Finally, to ensure the quality of the generated training data in the maximization step, we set a hyper-parameter α ∈ [0, 1] to control the proportion of training data that is actually used. Specifically, we first rank the prediction confidence of each z k t according to the value of p(z k t|c k t, rk t; θ), then pick the top α×N samples with the highest confidence scores. In our experiments, α is dynamically set to ensure the addressee prediction accuracy of the selected samples is over 80% in an annotated validation set. ## 3.5 Proof Of Feasibility In a multi-party dialogue corpus without annotated addressee labels, a usual solution to train a response generation model is to maximize the marginal loglikelihood (or incomplete log-likelihood) over all possible addressees: $$\ell(c,r;\theta)=\log\mathrm{p}(\mathrm{r}|c;\theta)=\log\sum_{\mathrm{i}}\mathrm{p}(\mathrm{r},\mathrm{z_{i}}|c;\theta)\tag{6}$$ However, this objective is hard to optimize since the distribution of z is hard to obtain. Here, we define an expected complete log-likelihood where our estimation of p(zt|ct, rt; θ) can come to rescue: $$\begin{array}{c}{{\hat{\ell}(c,r;\theta)=q(z_{i})\sum_{i}\log\mathrm{p}(\mathrm{r},\mathrm{z_{i}}|\mathrm{c};\theta)}}\\ {{q(z)=p(z_{t}|c_{t},r_{t};\theta)}}\end{array}$$ $$\mathbf{\Pi}(7)$$ Our new objective now becomes maximizing the expected complete log-likelihood. The relation between ℓ and ˆℓ can be derived as follows: $$\ell(c,r;\theta)=\log\sum_{i}\mathrm{p}(r,z_{i}|c;\theta)$$ $$=\log\sum_{i}\mathrm{q}(z_{i})\cdot\frac{\mathrm{p}(r,z_{i}|c;\theta)}{\mathrm{q}(z_{i})}$$ $$\geq\sum_{i}q(z_{i})\cdot\log\frac{\mathrm{p}(r,z_{i}|c;\theta)}{\mathrm{q}(z_{i})}\tag{8}$$ $$=\sum_{i}q(z_{i})\cdot\log\mathrm{p}(r,z_{i}|c;\theta)$$ $$-\sum_{i}q(z_{i})\cdot\log\mathrm{q}(z_{i})$$ $$=\hat{\ell}(c,r;\theta)+\mathcal{H}_{q(z)}$$ where the third line is derived from the *Jensen* Inequality, and Hq(z)is the entropy of the distribution of z. Since Hq(z) ≥ 0, we can derive that ˆℓ(c, r; θ) ≤ ℓ(*c, r*; θ), which means ˆℓ is the lower bound of ℓ. By maximizing the lower bound ˆℓ, we can indirectly maximize ℓ, which is originally hard to optimize. Another important observation is hat ˆℓ = ℓ if and only if q(z) = p(zt|ct, rt; θ), which is exactly what we calculate during the E-steps in Eq. (7). Though the derivation of the posterior distribution of z is not exact since we assume uniform prior in Eq. (2), it is still much closer to the real distribution compared to random q(z). It is worth noting that the global optimal point is not guaranteed to be reached by this algorithm, and it depends heavily on the initialization of model parameters or the training data for the first round of the maximization step. This explains the reason why we utilize a discourse parser to get a coarse initial training dataset instead of using the expectation step at the first iteration in Section 3.4. ## 4 Experiments In this section, we first introduce the datasets to pre-train and evaluate our model, then present the experimental results and comparisons with previous methods. ## 4.1 Datasets And Experimental Setups For pre-training, we adopt the second version of Ubuntu Dialogue Corpus (Lowe et al., 2015), which contains no annotated addressee labels. The original dataset contains 1M dialogues for training, and 0.5M dialogues for validation and testing, respectively. Dialogues that contain less than 4 turns, or have overlap with the dataset for the downstream task (the Ubuntu IRC benchmark, Hu et al. 2019), are excluded from the pre-training data. After filtering, we eventually get a pre-training dataset that contains 764,373 dialogues. For fine-tuning, we follow previous works (Hu et al., 2019; Gu et al., 2022) to adopt the Ubuntu IRC benchmark, which is constructed by extracting all utterances with response addressees indicated by the "@" symbol in the Ubuntu Dialogue Corpus. In total, this dataset consists of 311,725 dialogues for training, and 5,000 dialogues for validation and testing, respectively. It is worth noting that this dataset contains addressee labels for every single utterance in the dialogue history, which are utilized by previous methods, yet not by ours. For both pre-training and fine-tuning, BART (Lewis et al., 2020) is used as the backbone model. Before pre-training, we initialize the pre-trained weights from BART-base. During the process of | Model | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | ROUGE-L | |---------------------------------|----------|----------|----------|----------|----------|-----------| | GPT-2 (Radford et al., 2018) | 10.37 | 3.60 | 1.66 | 0.93 | 4.01 | 9.53 | | GSN (Hu et al., 2019) | 10.23 | 3.57 | 1.70 | 0.97 | 4.10 | 9.91 | | HeterMPCBART (Gu et al., 2022) | 12.26 | 4.80 | 2.42 | 1.49 | 4.94 | 11.20 | | BART (Lewis et al., 2020) | 11.25 | 4.02 | 1.78 | 0.95 | 4.46 | 9.90 | | Pre-training Only (PO) | 11.78 | 4.67 | 2.38 | 1.41 | 4.98 | 11.19 | | Fine-tuning Only (FO) | 11.47 | 5.11 | 2.98 | 2.11 | 5.23 | 11.31 | | Pre-training + Fine-tuning (PF) | 12.31 | 5.39 | 3.34 | 2.45 | 5.52 | 11.71 | | FO + Reply-Chain | 9.11 | 3.52 | 1.99 | 1.35 | 4.32 | 9.36 | | PO w/o EM | 10.03 | 3.90 | 2.03 | 1.18 | 4.56 | 9.66 | | PF w/o EM | 11.39 | 5.04 | 3.02 | 2.15 | 5.27 | 11.20 | | Denoising + Fine-tuning | 11.49 | 5.08 | 3.02 | 2.13 | 5.25 | 11.28 | pre-training, we evaluate our model on the validation set of the Ubuntu IRC benchmark, and the best checkpoint is saved for the fine-tuning process. ## 4.2 Baseline Models And Evaluation Metrics Table 1 shows the results of our method and previous models, where GPT-2, GSN, and HeterMPC (Radford et al., 2018; Hu et al., 2019; Gu et al., 2022) are introduced in section 2.1 and 2.2, respectively. BART is a sequence-to-sequence model with encoder-decoder Transformer architecture and is trained using denoising objectives. Following Hu et al. (2019), we also adopt BLEU-1 to BLEU-4, METEOR, and ROUGE-L as the automatic evaluation metrics, which can be calculated using the pycocoevalcap package. Besides automatic evaluation, human evaluation is also conducted and will be introduced in Section 4.4. ## 4.3 Automatic Evaluation Results Let's firstly focus on the upper and middle part of Table 1, where we present the results of previous models and our methods. Three settings of our method based on BART are experimented with: pre-training only (PO), fine-tuning only (FO), and pre-training-fine-tuning (PF). Results of PO are obtained by directly using the pre-trained model to generate the response for each dialogue. FO means the checkpoint of BART is directly finetuned on the Ubuntu IRC benchmark without pretraining. PF follows a pre-training-fine-tuning paradigm, where the best checkpoint of the pretraining process is further fine-tuned on the downstream dataset. Three observations can be seen from the table. First of all, solely pre-training with our proposed EM method with unlabeled corpus is already | Model | Score | Kappa | Best (%) | |------------------|---------|---------|------------| | Human References | 2.20 | 0.56 | 28.00 | | BART | 1.68 | 0.45 | 8.00 | | HeterMPCBART | 1.88 | 0.48 | 8.00 | | Ours (PF) | 1.92 | 0.47 | 28.00 | able to achieve comparable results with the previous state-of-the-art (SOTA) models. It is surprising since the pre-training requires no annotated addressee labels, while previous models not merely utilize the addressee information of the response utterance, but also make use of the addressee labels of the dialogue history to form a response graph. Second, fine-tuning our model on the downstream dataset with the ground truth addressee labels yields better results compared with pre-training only. Since it uses the ground truth addressee labels of responses, the results of it can be regarded as an upper bound of what the EM training can achieve. Besides, FO outperforms the previous SOTA model by large margins with even simpler architecture and fewer annotations (without addressee labels in the dialogue history), demonstrating the effectiveness of our proposed addressee embeddings. Finally, by further fine-tuning the pre-trained checkpoint with the ground truth addressee labels, we achieve the best performance on all metrics, which shows the transferability of our pre-trained model. ## 4.4 Human Evaluation Results For human evaluation, we recruit a team with 8 members who have at least a Bachelor's degree in Computer Science and are familiar with Ubuntu and Linux. We randomly sample 100 examples from the testing set, then ask the team members to score each prediction and select the best one. The quality scores are considered in terms of three independent aspects: 1) relevance, 2) fluency and 3) informativeness. They are scored from 0-3 and the average values were reported. The evaluation results are shown in Table 2, where our model (Pre-training + Fine-tuning) constantly outperforms vanilla BART and the previous SOTA model HeterMPCBART. We also report the Fleiss's Kappa to indicate the agreement between annotators. Besides, the ratio of our predictions being the best response is the same as that of human responses, demonstrating the high quality of the generated responses of our model. ## 5 Analysis In order to get more insights into the proposed EM pre-training method, we dive deeper into it by conducting extensive analyses. ## 5.1 Ablation Study We conduct ablation studies to investigate the contribution of our different designs, whose results are tabulated in the lower part of Table 1. Firstly, let's focus on the first line of the lower part. To study whether other utterances that are not in the reply chain of the current addressee can help to generate a better response, we extract the reply train by traversing from the current leave utterance (the response) up to the root node (the first utterance), then train a model by inputting this chain only. We see a large performance drop on all metrics in this setting, demonstrating the significance of the side information provided by the whole context. Second, let's pay attention to the second and third lines of the lower part. In order to study the effect of the EM pre-training process, which is the key contribution of our work, we remove this process and pre-train a model using only the addressee labels obtained from the discourse parser (i.e. the initial training data used in the first iteration of our EM approach). A sharp performance drop is observed compared with PO and PF with our proposed EM pre-training strategy, demonstrating the significance of our design. Without the iterative EM procedure, the noisy addressee labels obtained from the discourse parser can cause error propaga- ![6_image_0.png](6_image_0.png) tion, which makes the model learn noisy features to predict a response, and hurts the performance. Finally, aiming at investigating whether the performance gains come from seeing more in-domain data in the pre-training process, we use the same pre-training data to train another model with the denoising objectives proposed in BART (Lewis et al., 2020), then also fine-tune it on the Ubuntu IRC benchmark. The last line of the lower part presents the results, where we observe nearly the same performance compared with FO. This observation indicates that simply performing domain adaptation using the general pre-training objectives is insufficient to benefit the MPDRG task. ## 5.2 Response Generation Vs. Addressee Prediction In Section 3.3, we prove that p(z|c, r) ∝ p(r|*c, z*). To verify the correctness of this equation and also to investigate the training process of our EM strategy, we draw the line chart of the BLEU-4 score and addressee prediction accuracy of the top-30% confidence samples on the validation set with the increasing of pre-training iterations. The addressees are predicted using Eq. (4), where we take the z i with the highest conditional probability as the predicted addressee. Figure 4 illustrates the trending of the BLEU-4 score and addressee prediction accuracy. On the one hand, we see that the trending of both metrics is consistent, which means with a more powerful response generation model comes a higher addressee prediction accuracy. This observation verifies the correctness of Eq. (3). On the other hand, with the increasing of iterations, both metrics grow mutually, then reach their tops at around the 6th iteration, demonstrating the effectiveness of the EM process. ![7_image_0.png](7_image_0.png) ## 5.3 Case Studies To understand the effect of our method intuitively, we sample two cases from the testing set and present them in this section. Figure 5 illustrates an example whose addressee relations and dialogue history are shown in Figure 1. This conversation is about how to run the *compiz* or *beryl* in a *comp* with 256MB RAM. *Speaker* \#2 points that *it's the graphic card that is important*, but *Speaker \#4* seems unsatisfied by saying that didn't tell me much. After that, *Speaker \#5* suggests using the *rdesktop* and *Speaker \#4* replies him/her. Our model is able to capture the key information *rdesktop* and *terminal* in the addressee utterance U6, and generate a proper response Well, how do I install rdesktop from the terminal, which is very close to the human answer and even better with more information *from the terminal*. On the contrary, the baseline model (BART) fails to capture the addressee information and just replies with a safe response *I tried but it didn't work*. This case shows the great significance of modeling the addressee information, and also demonstrates the effectiveness of our model design. Figure 6 presents another example sampled from the testing set, where we investigate how different addressee labels affect the generated responses. In the figure, different colors represent different utterances in the *Dialogue History* part, and different responses generated by giving the corresponding utterances as addressees in the *Generated Responses* part. This conversation is about discussing the file system in Ubuntu that can share on a network with windows machines. When the addressee is given as U1, our model suggests using *samba*, which is a solution to the question of U1. Responses to U2 and U3 are like safe responses, but they make sense in their contexts: the former expresses its confusion about a confusing utterance (U2), and the latter expresses its gratitude to the suggestion in ![7_image_1.png](7_image_1.png) U3. Response to U4 states his/her understanding towards U4, and questions if his/her understanding is right. Response to U5 acknowledges the solution gentoo in U5 by saying *using gentoo on my computer too*. In general, this case demonstrates the ability of our model to generate diverse responses according to the specified addressees and contexts of the dialogue history. ## 5.4 Response Parser: A Byproduct For Free Another contribution of our EM pre-training is that a response parser can be freely obtained. This byproduct comes from Eq. (4), where given a response generation model with addressee modeling, we can predict the addressee for each utterance in the dialogue. Previous literature has studied and proved that explicitly modeling the structural information is beneficial to understanding specific structured data. (Li et al., 2020, 2022a,b). In this context, the response parser can be used to infer the discourse structures, which contributes to boosting the performance of some multi-party dialogue comprehension tasks like response selection and question answering. (Jia et al., 2020; Li and Zhao, 2021; Ma et al., 2022) ## 6 Conclusion Most multi-party dialogue corpora are not annotated with addressee labels, making them unable to support the pre-training of response generation models. To solve this problem, we design a simple yet effective way to model the addressee of a response as a latent variable and propose an EM pre-training approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Mathematical derivation, experimental results on the Ubuntu IRC benchmark, and extensive analyses have justified the theoretical feasibility and actual effectiveness of our method. ## Limitations First, Due to the lack of datasets to evaluate the MPDRG task, we perform our experiments only on the Ubuntu IRC benchmark and pre-train our model only on the domain of Ubuntu chats. However, the potential of our approach goes far beyond that since it is applicable to any open-domain multi-party dialogue dataset. In the future work, we will consider applying our method in more open-domain conversational datasets, such as the transcripts of TV series or movies. Additionally, the pre-training process solely relies on the addressee information of individual turns, disregarding the reply-to relations within the dialogue history. This oversight prevents the model from benefiting from valuable contextual cues necessary for a comprehensive understanding of the multi-party dialogue. In our future work, we will explore the integration of discourse-level reply-to relations into the pre-training process to further enrich the capabilities of the model. ## References Siqi Bao, Huang He, Fan Wang, Hua Wu, and Haifeng Wang. 2020. PLATO: Pre-trained dialogue generation model with discrete latent variable. 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In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 270–278, Online. Association for Computational Linguistics. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The last Section. A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3. ✓ B1. Did you cite the creators of artifacts you used? Section 4. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? They are publicly available and can be found on github. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section 4. ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? They can be found on our code. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4. ✓ C3. 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ghosh-etal-2023-aclm

{ACLM}: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex {NER}

https://aclanthology.org/2023.acl-long.8

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generation, to address the data scarcity problem in low-resource complex NER. ACLM alleviates the context-entity mismatch issue, a problem existing NER data augmentation techniques suffer from and often generates incoherent augmentations by placing complex named entities in the wrong context. ACLM builds on BART and is optimized on a novel text reconstruction or denoising task - we use selective masking (aided by attention maps) to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entities. Compared with other data augmentation strategies, ACLM can generate more diverse and coherent augmentations preserving the true word sense of complex entities in the sentence. We demonstrate the effectiveness of ACLM both qualitatively and quantitatively on monolingual, cross-lingual, and multilingual complex NER across various low-resource settings. ACLM outperforms all our neural baselines by a significant margin (1{\%}-36{\%}). In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In practice, ACLM generates more effective and factual augmentations for these domains than prior methods.

# Aclm: A Selective-Denoising Based Generative Data Augmentation Approach For Low-Resource Complex Ner Sreyan Ghosh♠∗ Utkarsh Tyagi♠∗ Manan Suri♣**Sonal Kumar**♠ S Ramaneswaran♥ **Dinesh Manocha**♠ ♠University of Maryland, College Park, USA, ♣NSUT Delhi, India, ♥NVIDIA, Bangalore, India {sreyang, utkarsht, sonalkum, dmanocha}@umd.edu [email protected], [email protected] ## Abstract Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM (Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach, based on conditional generation, to address the data scarcity problem in lowresource complex NER. ACLM alleviates the context-entity mismatch issue, a problem existing NER data augmentation techniques suffer from and often generates incoherent augmentations by placing complex named entities in the wrong context. ACLM builds on BART and is optimized on a novel text reconstruction or denoising task - we use *selective masking* (aided by attention maps) to retain the named entities and certain *keywords* in the input sentence that provide contextually relevant additional knowledge or hints about the named entities. Compared with other data augmentation strategies, ACLM can generate more diverse and coherent augmentations preserving the true word sense of complex entities in the sentence. We demonstrate the effectiveness of ACLM both qualitatively and quantitatively on monolingual, crosslingual, and multilingual complex NER across various low-resource settings. ACLM outperforms all our neural baselines by a significant margin (1%-36%). In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In practice, ACLM generates more effective and factual augmentations for these domains than prior methods.1 ## 1 Introduction Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect various types of named entities (NEs) from text. Recently, there has been 1Code: https://github.com/Sreyan88/ACLM ∗These authors contributed equally to this work. considerable progress in NER using neural learning methods that achieve state-of-the-art (SOTA) performance (Wang et al., 2021; Zhou and Chen, 2021) on well-known benchmark datasets, including CoNLL 2003 (Tjong Kim Sang and De Meulder, 2003) and OntoNotes (Schwartz et al., 2012). However, these datasets are designed to evaluate the performance on detecting "relatively easy" NEs like *proper names* (e.g., people such as "Barack Obama," locations such as "New York," or organizations such as "IBM") in well-formed, contextrich text that comes from news articles (Augenstein et al., 2017). On the other hand, complex NER benchmarks like MultiCoNER (Malmasi et al., 2022) present several contemporary challenges in NER, including short low-context texts with emerging and semantically ambiguous complex entities (e.g., movie names in online comments) that reduce the performance of SOTA methods previously evaluated only on the existing NER benchmark datasets. Our experiments reveal that the performance of the current SOTA NER method (Zhou and Chen, 2021) (previously evaluated only on the CoNLL 2003 dataset) drops by 23% when evaluated on MultiCoNER and 31.8% when evaluated on a low-resource setting with just 500 training samples (more details in Table 8). Thus, we emphasize that research on building systems that can effectively detect complex NEs in the text is currently understudied in the field of NLP. In the past, researchers have made several attempts at building supervised approaches to detect complex and compositional noun phrase entities in sentences (Doddington et al., 2004; Biggio et al., 2010; Magnolini et al., 2019). However, the scarcity of annotated training data for building effective systems has always been a challenge. Data augmentation has been shown to be an effective solution for low-resource NER (Ding et al., 2020; Liu et al., 2021; Zhou et al., 2022). In practice, though these systems perform well and generate 104 coherent augmentations on common NER benchmark datasets with easy proper noun NEs, they fail to be effective for complex NER, often generating incoherent augmentations. We first argue that certain types of complex NEs follow specific linguistic patterns and appear only in specific contexts (examples in Appendix 4), and augmentations that do not follow these patterns impede a NER model from learning such patterns effectively. This sometimes also leads to augmentations with context-entity mismatch, further hurting the learning process. For e.g., unlike proper names, substituting complex NEs from other sentences in the corpus or replacing them with synonyms (Dai and Adel, 2020a) often leads to augmentations where the NE does not fit into the new context (e.g., swapping proper names across sentences might still keep the sentence coherent but swapping the name of a book with a movie (both *creative work* entity) or the name of a football team with a political party (both group entity) makes it incoherent). Fine-tuning pretrained language models (PLMs), similar to priorwork (Ding et al., 2020; Liu et al., 2021; Zhou et al., 2022), fail to generate new context around complex NEs or completely new NEs with the desired linguistic patterns due to low-context sentences and the lack of existing knowledge of such linguistically complex NEs (examples in Fig. 3). This leads to in-coherent augmentations and poses a severe problem in knowledge-intensive tasks like biomedical NER, where non-factual augmentations severely hurt learning. Our experiments also reveal that introducing new context patterns around NEs proves to be a more effective data augmentation technique for complex NER than diversifying NEs (ACLM vs. MELM in Table 1). Main Results: To overcome the aforesaid problems, we formulate data augmentation as a conditional generation task and propose ACLM, a conditional text generation model that generates augmentation samples by introducing new and diverse context patterns around a NE. ACLM builds on BART (Lewis et al., 2020) and is fine-tuned on a modification of the text reconstruction from corrupted text task, a common denoising-based PLM pre-training objective. In contrast to other PLM pretraining strategies, which randomly mask a portion of the text for corruption, our modified objective is based on *selective masking*, wherein we mask all other words in the sentence except the NEs and a small percentage of *keywords* related to the NEs. We refer to this corrupted sentence as a *template*, and it serves as input to the model for both the training and generation phases. These keywords are other non-NE tokens in the sentence that provide contextually relevant additional knowledge or hints to BART about the complex NEs without the need of retrieving knowledge from any external sources. We select these keywords using attention maps obtained from a transformer model fine-tuned on the NER task, and they help the PLM overcome the problem where it might not possess enough knowledge about a semantically ambiguous complex NE (example in Fig. 3). Training ACLM on this modified objective allows us to generate diverse, coherent, factual, and high-quality augmentations given templates. We also propose *mixner*, a novel algorithm that mixes two templates during the augmentation generation phase and boosts the diversity of augmentations. Our primary contributions are as follows: - We propose ACLM, a novel data augmentation framework specially designed for lowresource complex NER. Compared with previous methods in the literature, ACLM effectively alleviates the context-entity mismatch problem by preserving the true sense of semantically ambiguous NEs in augmentations. Additionally, to accompany ACLM, we propose *mixner*, which boosts the diversity of ACLM generations. - We qualitatively and quantitively show the benefits of ACLM for monolingual, crosslingual, and multilingual complex NER across various low-resource settings on the MultiCoNER dataset. Our proposed ACLM outperforms all other baselines in literature by a significant margin (1%-36%) and generates more diverse, coherent, and high-quality augmentations compared to them. - We perform extensive experiments to study the application of ACLM in three other domains, including science and medicine. ACLM outperforms all our baselines in these domains (absolute gains in the range of 1%- 11%) and generates more factual augmentations. ## 2 Background And Related Work Complex NER Background: Complex NER is a relatively understudied task in the field of NLP. Building on insights from Augenstein et al. (2017), we discuss key reasons behind high performance on common NER benchmark datasets and try to understand why modern SOTA NER algorithms do not work well on complex NER benchmarks: (1) **Context**: Most of the common benchmark datasets are curated from articles in the news domain. This gives them several advantages, including rich context and surface features like proper punctuation and capitalized nouns, all of which are major drivers of success in these datasets (Mayhew et al., 2019). In contrast, for entity recognition beyond news text, like search queries or voice commands, the context is less informative and lacks surface features (Guo et al., 2009; Carmel et al., 2014); (2) **Entity Complexity**: Data from news articles contain *proper names* or "easy" entities with simple syntactic structures, thus allowing pretrained models to perform well due to their existing knowledge of such entities. On the other hand, complex NEs like movie names are syntactically ambiguous and linguistically complex and which makes Complex NER a difficult task (Ashwini and Choi, 2014). Examples of such entities include noun phrases (e.g., Eternal Sunshine of the Spotless Mind), gerunds (e.g., Saving Private Ryan), infinitives (e.g., To Kill a Mockingbird), or full clauses (e.g., Mr. Smith Goes to Washington); (3) Entity Overlap: Models trained on these common benchmark datasets suffer from memorization effects due to the large overlap of entities between the train and test sets. Unseen and emerging entities pose a huge challenge to complex NER (BernierColborne and Langlais, 2020). Complex NER: Prior work has mostly focused on solving the entity complexity problem by learning to detect complex nominal entities in sentences (Magnolini et al., 2019; Meng et al., 2021; Fetahu et al., 2022; Chen et al., 2022). Researchers have often explored integrating external knowledge in the form of gazetteers for this task. Gazetteers have also proven to be effective for low-resource NER (Rijhwani et al., 2020). GemNet (Meng et al., 2021), the current SOTA system for complex NER, conditionally combines the contextual and gazetteer features using a Mixture-of-Experts (MoE) gating mechanism. However, gazetteers are difficult to build and maintain and prove to be ineffective for complex NER due to their limited entity coverage and the nature of unseen and emerging entities in complex NER. Data Augmentation for Low-Resource NER: Data Augmentation to handle data scarcity for lowresource NLP is a well-studied problem in the literature and is built on word-level modifications, including simple synonym replacement strategies (Wei and Zou, 2019), or more sophisticated learning techniques like LSTM-based language models (Kobayashi, 2018), Masked Language Modeling (MLM) using PLMs (Kumar et al., 2020), auto-regressive PLMs (Kumar et al., 2020), or constituent-based tagging schemes (Zhou et al., 2019). However, most of these methods, though effective for classification tasks, suffer from tokenlabel misalignment when applied to token-level tasks such as NER and might require complex preprocessing steps (Bari et al., 2020; Zhong and Cambria, 2021). One of the first works to explore effective data augmentation for NER replaces NEs with existing NEs of the same type or replaces tokens in the sentence with one of their synonyms retrieved from WordNet (Dai and Adel, 2020b). Following this, many neural learning systems were proposed that either modify the Masked Language Modelling (MLM) training objective using PLMs (Zhou et al., 2022; Liu et al.) or use generative language modeling with LSTM LMs (Ding et al., 2020) or mBART (Liu et al., 2021), to produce entirely new sentences from scratch. However, all these systems were designed for low-resource NER on common benchmark datasets and failed to generate effective augmentations for low-resource complex NER with semantically ambiguous and complex entities. ## 3 Methodology In this section, we give an overview of our approach. Fig. 1 represents the entire workflow of our ACLM data augmentation framework. A sentence is first passed through a fine-tuned XLMRoBERTa fine-tuned on only gold data to generate the attention map for each token in the sentence. This attention map is then used to selectively mask the sentence and create a template. This template is then used as an input to optimize the model on the text reconstruction objective for fine-tuning ACLM: the model is asked to reconstruct the entire original sentence from only the content in the template. While generating augmentations, ACLM follows the same template generation process in addition to adding two templates through *mixner*, which we discuss in detail in Section 3.3. ![3_image_0.png](3_image_0.png) ## 3.1 Template Creation To corrupt a sentence and create a template, we follow a 4-step process described below: 1. **Keyword Selection**: For each sentence in our training corpus, we first obtain a set of non-NE tokens in the sentence that are most attended to by its NEs. We call these tokens *keywords*. For our research, we consider a non-NE token as a keyword if the NEs in the sentence contextually depend on them the most. We measure contextual dependency between NE and non-NE tokens using attention scores from attention maps extracted from a transformer-based NER model fine-tuned only on gold data. We hypothesize that attention heads in a transformer when fine-tuned for NER, formulated as a token-level tagging task, tend to pay the highest attention to the most contextually relevant tokens around it. Thus, formally put, consider a sentence with a total of T tokens comprised of t*other* non-NE and t*entity* NE tokens. Our primary aim is to find the top p% of t*other* tokens, which we call keywords. To calculate the total attention score that each token in the sentence assigns to each other token, we sum up the attention scores across each of the heads in the transformer network and across the last a layers (a = 4 in our case). Different heads in different layers tend to capture different properties of language, and taking the average attention scores across the last 4 layers ensures that diverse linguistic relations are taken into account while choosing the keywords (e.g., syntactic, semantic, etc.). This also makes the keyword selection process more robust, as in low-resource conditions the attention maps may be noisy, and the NEs might not be focusing on the right context always. Additionally, the choice of just the last four layers is inspired by the fact that the lower layers have very broad attention and spend at most 10% of their attention mass on a single token (Clark et al., 2019). Note t*entity* might be comprised of (1) multiple contiguous tokens forming an individual NE and (2) multiple such individual NEs. To handle the first case, inspired from Clark et al. (2019), we sum up the attention scores over all the individual tokens in the NE. For the second case, we find t*attn* for each individual NE and take a set union of tokens in these t*attn*. Thus, as an extra pre-processing step, to improve robustness, we also ignore punctuations, stop words, and other NEs from the top p% of t*other* tokens to obtain our final keywords. We provide examples of templates in Appendix C. 2. **Selective Masking**: After selecting the top p% of t*other* tokens in the sentence as keywords, we now have K non-NE keyword tokens and E entity tokens. To create the template, we now substitute each non-NE token not belonging to the K with the mask token and remove contiguous mask tokens. 3. **Labeled Sequence Linearization**: After we have our initial template, inspired by Zhou et al. (2022), we perform labeled sequence linearization to explicitly take label information into consideration during fine-tuning and augmentation generation. Similar to Zhou et al. (2022), as shown in Figure 1, we add label tokens before and after each entity token and treat them as the normal context in the sentence. Additionally, these label tokens before and after each NE provide boundary supervision for NEs with multiple tokens. 4. **Dynamic Masking**: Post labeled sequence linearization, our template goes through further masking wherein we dynamically mask a small portion of the K keywords during each iteration of training and generation. To be precise, we first sample a dynamic masking rate ε from a Gaussian distribution N (*µ, σ* 2 ), where the Gaussian variance σ is set to 1/K. Next, we randomly sample tokens from the K keywords in the sentence according to the masking rate ε and replace this with mask tokens, followed by removing consecutive mask tokens. At every round of generation, dynamic masking helps boost 1) context diversity by conditioning ACLM generation on different templates with a different set of keywords and 2) length diversity by asking ACLM to infill a different number of mask tokens. ## 3.2 Fine-Tuning Aclm As discussed earlier, ACLM is fine-tuned on a novel text reconstruction from corrupted text task wherein the created templates serve as our corrupted text and ACLM learns to recover the original text from the template. Text reconstruction from the corrupted text is a common denoising objective that PLMs like BART and BERT are pre-trained on. For this work, we use it as our fine-tuning objective and differ from other existing pre-training objectives by our *selective masking* strategy for creating templates. ## 3.3 Data Generation Post fine-tuning on the text reconstruction task, we utilize ACLM to generate synthetic data for data augmentation. For each sentence in the training dataset, we apply steps 1-4 in the Template Creation pipeline for R rounds to randomly corrupt the sentence and obtain a template which is then passed through the fine-tuned ACLM model to generate a total of R× augmented training samples. Additionally, to boost diversity, during auto-regressive ![4_image_0.png](4_image_0.png) generation, we randomly sample the next word from the *top-k* most probable words and choose the most probable sequence with beam search. mixner: During the R rounds of augmentation on our training dataset, we propose the use of mixner, a novel template mixing algorithm that helps ACLM generate diverse sentences with new context and multiple NEs in the sentence. More specifically, given the template for any arbitrary sentence a in the training set in step 3 of the template creation process, we retrieve the template for another sentence b that is semantically similar to a and join both the templates before passing on the template to step 4. We show examples of sentences generated with *mixner* in Fig. 3 and Section D.1. Note that we apply *mixner* only in the generation step and not during fine-tuning. As mentioned earlier, to retrieve b from the training set, we randomly sample a sentence from the top-k sentences with the highest semantic similarity to a. To calculate semantic similarity between each sentence in the training set, we first take the embedding e for each sentence from a multi-lingual Sentence-BERT (Reimers and Gurevych, 2019) and then calculate semantic similarity by: $$\operatorname{sim}(e_{i},e_{j})={\frac{e_{i}\cdot e_{j}}{\|e_{i}\|\,\|e_{j}\|}}\qquad\qquad(1)$$ where sim(. ) is the cosine similarity between two embeddings , and *i, j* ∈ N where i ≠ j, and N is the size if the training set. Additionally, we don't apply *mixner* on all rounds R but sample a probability γ from a Gaussian distribution N (*µ, σ* 2 ) and only apply *mixner* if γ crosses a set threshold β. ## 3.3.1 Post-Processing As a post-processing step, we remove augmentations similar to the original sentence and also the extra label tokens added in the labeled sequence linearization step. Finally, we concatenate the augmented data with the original data to fine-tune our NER model. ## 4 Experiments And Results 4.1 Dataset All our experiments were conducted on the MultiCoNER dataset (Malmasi et al., 2022), a large multilingual dataset for complex NER. MultiCoNER covers 3 domains, including Wiki sentences, questions, and search queries, across 11 distinct languages. The dataset represents contemporary challenges in NER discussed in Section 2 and is labeled with six distinct types of entities: person, location, corporation, **groups** (political party names such as *indian national congress*), **product** (consumer products such as *apple iPhone 6*), and **creative work** (movie/song/book titles such as on the beach). We conduct experiments on a set of 10 languages L where L = {English (En), Bengali (Bn, Hindi (Hi), German (De), Spanish (Es), Korean (Ko), Dutch (Nl), Russian (Ru), Turkish (Tr), Chinese (Zh)}. Language-wise dataset statistics can be found in Table 12. We would also like to highlight that the number of sentences in MultiCoNER test sets ranges from **133,119 - 217,887**, which is much higher than test sets of other existing NER datasets. For more details on the dataset, we refer our readers to Malmasi et al. (2022). For monolingual and cross-lingual low-resource experiments, we perform iterative stratified sampling over all the sentences by using the entity classes in a sample as its target label across four low-resource settings (100, 200, 500, and 1000). We downsample the development set accordingly. For multi-lingual experiments, we combine all the data sampled for our monolingual settings. We evaluate all our systems and baselines on the original MultiCoNER test sets. We report micro-averaged F1 scores averaged across 3 runs for 3 different random seeds. ![5_image_0.png](5_image_0.png) X˜ ← DYNAMICMASK(*X, η*) ▷ Dynamic Masking L*f inetune* ← FINETUNE(L, X˜) ▷ Fine-tune ACLM end for for {X, Y } ∈ D*train* do ▷ Generation Loop repeat R **times**: X˜ ← GENTEMPLATE(X, {t*other*} − {K}) ▷ Selective masking X˜ ← LINEARIZE(*X, Y* ˜ ) ▷ Labeled Sequence Linearization X˜ ← DYNAMICMASK(*X, µ* ˜ ) ▷ Dynamic Masking Xaug ← GENAUG(Lf inetune(X˜))*, if γ* < β X*augmix* ← MIXNER(Lf inetune(X˜))*, if γ* > β Daug ← Daug ∪ {Xaug} ∪ {X*augmix*} end for Daug ← POSTPROCESS(Daug) ▷ Post-processing return Dtrain ∪ Daug ## 4.2 Experimental Setup ![5_Image_1.Png](5_Image_1.Png) ACLM. We use mBart-50-large (Tang et al., 2020) with a condition generation head to fine-tune ACLM. We fine-tune ACLM for 10 epochs using Adam optimizer (Kingma and Ba, 2014) with a learning rate of 1e −5and a batch size of 32. NER. We use XLM-RoBERTa-large with a linear head as our NER model. Though the field of NER has grown enormously, in this paper, we adhere to the simplest formulation and treat the task as a token-level classification task with a BIO tagging scheme. We use the Adam optimizer to optimize our model, set the learning rate to 1e −2, and train with a batch size of 16. The NER model is trained for 100 epochs, and the model with the best performance on the dev set is used for testing. Hyper-parameter Tuning. For template creation during fine-tuning and generation, we set the selection rate p and the Gaussian µ to be 0.3 and 0.5, respectively. The number of augmentation rounds R is set as 5. For *mixner* we set Gaussian µ and β to be 0.5 and 0.7, respectively. All hyper-parameters are tuned on the development set with grid search. More details can be found in Appendix A. ## 4.3 Baselines To prove the effectiveness of our proposed ACLM, we compare it with several strong NER augmentation baselines in the literature. In this sub-section, we briefly describe each of these baselines. All baselines were run for R rounds. Gold-Only. The NER model is trained using only gold data from the MultiCoNER dataset without any augmentation. | MONOLINGUAL | CROSS-LINGUAL | | | | | | | | | | | | | |------------------------------------------------------------------------------------|-------------------------------------------------------------------|-------|-------------------------------------------------------|-------------------------------------------------------|-------|-------|-------|-------|-------|----|-----|---------------------------------|-----| | #Gold Method | En | Bn | Hi | De | Es | Ko | Nl | Ru | Tr | Zh | Avg | En → Hi En → Bn En → De En → Zh | Avg | | Gold-only | 29.36 14.49 18.80 37.04 36.30 12.76 38.78 23.89 24.13 14.18 24.97 | 16.36 | 12.15 | 29.71 | 0.31 | 14.63 | | | | | | | | | LwTR | 48.60 20.25 29.95 48.38 44.08 35.09 43.00 39.22 30.58 27.70 36.68 | 32.36 | 24.59 | 46.05 | 2.11 | 26.28 | | | | | | | | | DAGA | 16.24 | 5.87 | 10.40 32.44 27.78 19.28 15.44 11.14 16.17 10.33 16.51 | 4.54 | 3.28 | 14.21 | 0.13 | 5.54 | | | | | | | 100 | MELM | 40.12 | 6.22 | 27.84 43.94 37.45 34.10 37.82 32.38 20.13 25.11 30.51 | 26.37 | 20.33 | 34.32 | 2.71 | 20.93 | | | | | | ACLM only entity 14.06 17.55 19.60 29.72 38.10 31.57 38.47 27.40 35.62 26.34 27.84 | 21.72 | 16.55 | 30.93 | 1.58 | 17.69 | | | | | | | | | | ACLM random | 43.59 20.13 28.04 45.83 42.27 33.64 41.82 38.20 36.79 25.99 35.63 | 29.68 | 21.64 | 45.27 | 3.05 | 24.91 | | | | | | | | | ACLM (ours) | 48.76 23.09 33.53 48.80 44.14 38.35 46.22 39.48 37.20 35.12 39.47 | 32.52 | 23.91 | 46.48 | 3.58 | 26.62 | | | | | | | | | Gold-only | 51.83 19.31 33.68 49.62 45.16 42.51 47.83 31.55 26.76 32.34 38.06 | 36.90 | 27.44 | 48.70 | 3.76 | 29.20 | | | | | | | | | LwTR | 52.88 23.85 34.27 50.31 47.01 42.77 52.01 40.18 35.92 30.57 40.98 | 40.07 | 32.36 | 48.95 | 6.04 | 31.85 | | | | | | | | | DAGA | 33.30 17.12 19.58 35.10 33.56 26.50 38.04 29.83 23.35 25.66 28.20 | 18.92 | 14.37 | 29.32 | 1.79 | 16.10 | | | | | | | | | 200 | MELM | 47.83 | 5.47 | 29.67 45.85 42.08 36.62 49.47 41.84 31.25 32.27 36.24 | 27.55 | 18.80 | 41.10 | 6.21 | 23.41 | | | | | | ACLM only entity 50.06 25.58 37.78 50.95 48.21 43.39 48.46 34.87 34.92 28.20 40.24 | 30.76 | 22.53 | 44.17 | 6.50 | 25.99 | | | | | | | | | | ACLM random | 52.69 35.26 39.83 51.14 48.70 42.19 48.71 39.68 37.26 34.22 42.96 | 36.52 | 27.19 | 47.73 | 7.12 | 29.64 | | | | | | | | | ACLM (ours) | 54.99 38.39 40.55 53.36 49.57 44.32 53.19 43.97 39.71 39.31 45.74 | 45.22 | 36.64 | 54.51 | 8.55 | 36.23 | | | | | | | | | Gold-only | 55.51 | 34.6 | 38.66 55.95 51.52 48.57 50.97 45.14 38.83 38.84 45.86 | 35.93 | 25.64 | 50.13 | 7.23 | 29.73 | | | | | | | LwTR | 56.97 35.42 37.83 55.91 54.74 49.36 56.10 46.82 39.00 38.55 47.07 | 43.14 | 34.60 | 51.61 | 11.40 | 35.19 | | | | | | | | | DAGA | 44.62 22.36 24.30 43.02 42.77 36.23 47.11 30.94 30.84 33.79 35.60 | 26.50 | 21.52 | 37.89 | 4.82 | 22.68 | | | | | | | | | 500 | MELM | 52.57 | 9.46 | 31.57 53.57 46.40 45.01 51.90 46.73 38.26 39.64 41.51 | 34.97 | 27.17 | 44.31 | 7.31 | 28.44 | | | | | | ACLM only entity 57.55 35.69 35.82 56.15 53.64 50.20 53.07 46.40 41.58 38.65 46.87 | 35.48 | 29.37 | 49.10 | 7.99 | 30.48 | | | | | | | | | | ACLM random | 57.92 38.24 39.33 57.14 53.24 49.81 55.06 48.27 42.22 40.55 48.18 | 41.72 | 32.16 | 52.27 | 13.63 | 34.95 | | | | | | | | | ACLM (ours) | 58.31 40.26 41.48 59.35 55.69 51.56 56.31 49.40 43.57 41.23 49.72 | 44.36 | 35.59 | 54.04 | 16.27 | 37.57 | | | | | | | | | Gold-only | 57.22 30.20 39.55 60.18 55.86 53.39 60.91 49.93 43.67 43.05 44.40 | 43.44 | 33.27 | 54.61 | 5.34 | 34.17 | | | | | | | | | LwTR | 59.10 39.65 43.90 61.28 57.29 51.37 59.25 52.04 44.33 43.71 51.19 | 43.32 | 33.74 | 53.32 | 7.38 | 34.44 | | | | | | | | | DAGA | 50.24 32.09 35.02 51.45 49.47 42.41 51.88 41.56 33.18 39.51 42.68 | 33.12 | 26.22 | 42.13 | 5.15 | 26.65 | | | | | | | | | 1000 | MELM | 53.48 | 6.88 | 37.02 58.69 52.43 50.50 56.25 48.99 36.83 38.88 44.00 | 35.23 | 25.64 | 46.50 | 8.22 | 28.90 | | | | | | ACLM only entity 55.46 38.13 41.84 60.05 56.99 53.32 58.22 50.17 45.11 39.62 49.89 | 37.38 | 29.77 | 41.10 | 6.49 | 28.69 | | | | | | | | | | ACLM random | 58.87 41.00 46.27 61.19 57.29 53.61 59.52 52.77 45.01 43.60 51.91 | 43.96 | 34.14 | 53.37 | 7.25 | 34.68 | | | | | | | | | ACLM (ours) | 60.14 42.42 48.20 63.80 58.33 55.55 61.22 54.31 48.23 45.19 53.74 | 44.59 | 35.70 | 56.74 | 8.94 | 36.49 | | | | | | | | Label-wise token replacement (LwTR).(Dai and Adel, 2020b) A token in a sentence is replaced with another token with the same label; the token is randomly selected from the training set. DAGA.(Ding et al., 2020) Data Augmentation with a Generation Approach (DAGA) proposes to train a one-layer LSTM-based recurrent neural network language model (RNNLM) by maximizing the probability for the next token prediction with linearized sentences. During generation, they use random sampling to generate entirely new sentences with only the [BOS] token fed to the model. MulDA.(Liu et al., 2021) Multilingual Data Augmentation Framework (MulDA) builds on DAGA and trains a pre-trained mBART model on next token prediction with linearized sentences for generation-based multilingual data augmentation. For a fair comparison, we replace mBART in MulDA with mBART-50. MELM.(Zhou et al., 2022) Masked Entity Language Modeling (MELM) proposes fine-tuning a transformer-encoder-based PLM on linearized labeled sequences using masked language modeling. MELM outperforms all other baselines and priorart on low-resource settings on the CoNLL 2003 NER dataset across four languages in mono-lingual, cross-lingual, and multi-lingual settings. ACLM *random*. We train and infer ACLM with templates created with randomly sampled *keywords* instead of taking *keywords* with high attention scores. This baseline proves the effectiveness of our *keyword* selection algorithm which provides NEs in the template with rich context. ACLM *only entity*. We train and infer ACLM with templates created with only linearized entities and no *keywords*. This baseline proves the effectiveness of additional context in our templates. ## 4.4 Experimental Results Monolingual Complex NER. Table 1 compares the performance of all our baselines with ACLM on the MultiCoNER test sets under various lowresource settings for 10 languages. As clearly evident, ACLM outperforms all our baselines in all settings by consistently achieving the best results in all individual languages. Moreover, ACLM improves over our neural baselines (MELM and DAGA) by a significant margin (absolute gains in the range of 1.5% - 22% across individual languages). Although LwTR performs better than ACLM in rare instances, we emphasize that (1) LwTR generates nonsensical, incoherent augmentations, (discussed further in Section D.1) and (2) Based on a learningbased paradigm, ACLM shows bigger margins to LwTR at slightly higher gold training samples (200 | #Gold | Method | En | Bn | Hi | De | Es | Ko | Nl | Ru | Tr | Zh | Avg | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------|------|------|------|------|------|------|------|------|------|------|-------| | Gold-Only | 56.21 35.66 42.16 55.71 54.98 45.14 57.48 46.13 44.40 30.72 46.86 | | | | | | | | | | | | | LwTR | 55.65 38.47 43.44 54.71 53.95 44.78 56.50 46.93 45.41 31.56 47.14 | | | | | | | | | | | | | MulDA | 46.87 29.25 34.52 45.92 45.55 33.91 48.21 38.65 35.56 27.33 38.58 | | | | | | | | | | | | | MELM | 53.27 23.43 41.55 48.17 51.28 39.23 51.37 45.73 41.97 30.67 42.67 | | | | | | | | | | | | | ACLM (ours) 58.74 41.00 46.22 59.13 56.93 51.22 60.30 50.26 49.32 40.93 51.40 Gold-Only 58.67 39.84 46.34 59.65 58.50 50.70 60.79 51.66 47.12 40.98 51.42 LwTR 51.78 35.93 38.87 52.73 51.59 42.55 54.49 43.99 41.23 35.19 44.83 MulDA 48.89 31.45 36.76 48.41 48.30 39.78 51.09 42.01 35.98 31.65 41.43 MELM 52.53 24.27 40.10 49.69 52.42 43.56 47.28 44.35 40.62 34.28 47.45 ACLM (ours) 59.75 42.61 48.52 61.49 59.05 53.46 61.59 53.34 49.96 44.72 53.45 Gold-Only 61.10 40.94 48.20 61.67 59.84 54.56 62.36 53.33 48.77 45.82 53.66 LwTR 59.09 38.37 43.80 59.37 57.76 50.38 60.42 51.00 46.53 42.87 50.96 MulDA 51.79 30.67 35.79 51.87 50.92 43.08 53.95 44.61 38.86 36.72 43.83 MELM 58.67 26.17 41.88 53.05 57.26 51.97 61.49 43.73 40.22 40.12 47.66 ACLM (ours) 62.32 43.79 50.32 63.94 62.05 56.82 64.41 55.09 51.83 48.44 55.90 Gold-Only 64.14 43.28 50.11 66.18 63.17 57.31 65.75 56.94 51.17 49.77 57.78 LwTR 61.67 39.90 45.28 63.13 60.21 53.43 63.37 54.07 48.38 45.36 53.48 MulDA 56.35 33.73 40.71 56.90 55.35 48.42 58.39 49.25 42.06 40.19 48.14 MELM 61.55 30.27 42.61 61.05 61.87 55.71 63.17 53.00 48.48 44.71 52.24 ACLM (ours) 64.50 46.59 52.14 67.65 64.02 59.09 67.03 57.82 53.25 50.60 58.27 | | | | | | | | | | | | | and 500) which we acknowledge is a reasonable size in real-world conditions. Cross-lingual Complex NER. We also study the cross-lingual transferability of a NER model trained on a combination of gold and generated augmentations. Thus, we evaluated a model, trained on En, on 4 other languages, including Hi, Bn, De, and Zh in a zero-shot setting. ACLM outperforms our neural baselines by a significant margin (absolute gains in the range of 1% - 21%). None of these systems perform well in cross-lingual transfer to Zh which was also observed by (Hu et al., 2021). Multi-lingual Complex NER. Table 2 compares the performance of all our baselines with ACLM on the MultiCoNER test sets under various multilingual low-resource settings. As clearly evident, ACLM outperforms all our baselines by a significant margin (absolute gains in the range of 1%-21% across individual languages). All our baselines, including our Gold-Only baseline, also perform better than their monolingual counterparts which demonstrates the effectiveness of multi-lingual finetuning for low-resource complex NER. ## 5 Further Analysis 5.1 Generation Quality Quantitative Analysis. Table 3 compares augmentations from various systems on the quantitative measures of perplexity and diversity. Perplexity (Jelinek et al., 1977) is a common measure of text fluency, and we measure it using GPT2 (Radford et al., 2019). We calculate 3 types of diversity metrics: for Diversity-E and Diversity-N, we calculate the average percentage of new NE and non-NE words in the generated samples compared with the original samples, respectively. For Diversity-L, we | #Gold Method | Perplexity(↓) Diversity-E(↑) Diversity-N(↑) Diversity-L(↑) | | | | | |----------------|--------------------------------------------------------------|---------|-------|-------|-----| | 200 | LwTR | 137.01 | 30.72 | 16.46 | 0.0 | | MELM | 83.21 | 94.85 | 0.0 | 0.0 | | | ACLM (ours) | 80.77 | 35.64 | 22.48 | 5.67 | | | 500 | LwTR | 129.349 | 30.07 | 16.22 | 0.0 | | MELM | 82.31 | 94.37 | 0.0 | 0.0 | | | ACLM (ours) | 57.68 | 44.12 | 41.16 | 5.82 | | | LwTR | 131.20 | 29.85 | 16.55 | 0.0 | | | 1000 MELM | 82.64 | 95.13 | 0.0 | 0.0 | | | ACLM (ours) | 62.00 | 50.10 | 34.84 | 5.40 | | calculate the average absolute difference between the number of tokens in generated samples and the original samples. ACLM achieves the lowest perplexity and highest non-NE and length diversity compared with other baselines. NE diversity in ACLM is achieved with *mixner* where ACLM fairs well compared to MELM which just replaces NEs. LwTR achieves the highest perplexity, thereby reaffirming that it generates incoherent augmentations. Qualtitative Analysis. Fig. 3 illustrates the superiority of augmentations generated by ACLM when compared with our other baselines. As clearly evident, while MELM generates just minor changes in NEs, augmentations produced by LwTR often tend to be nonsensical and incoherent. On the other hand, ACLM generates meaningful and diverse sentences around NEs, which is further boosted with mixner. We provide examples in Appendix D.1. ## 5.2 Application To Other Domains To evaluate the transferability of ACLM to other domains, we evaluate ACLM on 4 more datasets beyond MultiCoNER. These datasets include CoNLL 2003 (Tjong Kim Sang and De Meulder, 2003) (news), BC2GM (Smith et al., 2008) (bio-medical), NCBI Disease (Dogan et al. ˘ , 2014) (bio-medical) and TDMSci (Hou et al., 2021) (science). Table 4 compares our baselines with ACLM across 2 lowresource settings on all 4 datasets. ACLM outperforms all our baselines on all settings except LwTR on CoNLL 2003. This occurs because LwTR generates a large variety of effective augmentations with NE replacement on easy entities in CoNLL 2003. The results demonstrate the effectiveness of ACLM over diverse domains, including domains with an acute scarcity of data (bio-medical). Additionally, we also emphasize that ACLM produces more factual augmentations and, unlike our other baselines, avoids context-entity mismatch, which makes the NER model store wrong knowledge in | Original | it was developed by a team led by former [blizzard entertainment]CORP employees, some of whom had overseen the creation of the [diablo]CW series. |  The original sentence describes the employees of an organization and provides details about them. | | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------| | LwTR | | it was developed by a makers led by, [blizzard entertainment]CORP ., some of whom had elevation the serving of the [diablo]CW 12th. | ❌ LwTR replaces random words in the sentence, which makes it incoherent. | | MELM | | it was developed by a team led by former [blizzago games]CORP employees, some of whom had overseen the creation of the [hablo]CW series. ❌ MELM keeps the sentence coherent but generates new NEs that do not correspond to real-world entities. | | | ACLM w/o mixner [blizzard entertainment]CORP employees have overseen the production of the animated films, including the production of the [diablo]CW series.  ACLM generates new context patterns around the NE, keeping the sentence coherent and avoiding context-entity mismatch. ACLM w/ mixner the team of the [blizzard entertainment]CORP had overseen the creation of the  mixner boosts ACLM diversity and still keeps the sentence coherent. It adds a NE game [diablo]CW and many of its workers founded [pyro studios]CORP in the in the sentence and augments the sentence with extra details about the employees of early 1960s. the organization. Original The control group consisted of 40 consecutive [FMF]DISEASE patients, who arrived  The original sentence describes an occasion where a group of 40 patients at the [FMF]DISEASE clinic for their regular follow-up visit and were 40 years of diagnosed with a certain kind of disease visited a clinic, and the sentence provides us age or older at the time of the examination. with information on the age statistics of the patients. LwTR The control, consisted of 40 consecutive [fragile]DISEASE patients, who arrived at ❌ LwTR replaces "FMF" in the sentence with "fragile" and the phrase "fragile the [FMF]DISEASE status for their regular follow - up and were 40 years of age or patients" does not make sense. It also adds an extra word, "analyzed", at the end of older at the time of the examination analyzed the sentence. MELM The control group consisted of 40 consecutive [FMR]DISEASE patients, who ❌ MELM replaces the 1st occurrence of "FMF" in the sentence with "FMR" and the second occurrence with "PDA". "FMR" is not the name of a disease and is closest to arrived at the [PDA]DISEASE clinic for their regular follow-up visit and were 40 "FMR1", which is the name of a gene. "PDA" stands for "Patent ductus arteriosus." years of age or older at the time of the examination. Thus, the entire sentence does not make much sense. The sample consisted of four consecutive [FMF]DISEASE patients who arrived at ACLM w/o mixner the [FMF]DISEASE clinic for a visit of examination. Only one of the 4 remaining  ACLM introduces a new context pattern around the sentence. The entire sentence is coherent. patients had [FMF]DISEASE .  mixner boosts ACLM diversity and still keeps the sentence coherent. "FRDA" ACLM w/ mixner Of 4000 (40%) patients with onset [FMF]DISEASE , patients with [FRDA]DISEASE had no tendon reflexes at all. (Friedreich's ataxia) is a genetic disease that causes difficulty in walking and a loss of sensation in the arms and legs. | | | | #Gold Method CoNLL BC2GM NCBI TDMSci Avg 200 Gold-Only 79.11 50.01 72.92 47.20 62.31 LwTR **82.33** 52.78 72.15 51.65 64.73 DAGA 76.23 47.67 71.14 48.03 60.77 MELM 77.10 54.05 70.12 46.07 61.83 ACLM *(ours)* 82.14 58.48 74.27 56.83 **67.93** 500 Gold-Only 84.82 55.56 75.75 47.04 65.79 LwTR **85.08** 60.46 78.97 60.74 71.31 DAGA 81.82 51.23 78.09 57.66 67.20 MELM 83.51 56.83 75.11 57.80 68.31 ACLM *(ours)* 84.26 62.37 80.57 61.77 **72.24** data-sensitive domains. We show samples of generated augmentations in Fig. 3 and Appendix D.1. ## 6 Conclusion In this paper, we propose ACLM, a novel data augmentation framework for low-resource complex NER. ACLM is fine-tuned on a novel text reconstruction task and is able to generate diverse augmentations while preserving the NEs in the sentence and their original word sense. ACLM effectively alleviates the context-entity mismatch problem and generates diverse, coherent, and highquality augmentations that prove to be extremely effective for low-resource complex NER. Additionally, we also show that ACLM can be used as an effective data augmentation technique for low-resource NER in the domains of medicine and science due to its ability to generate extremely reliable augmentations. ## Limitations We list down some potential limitations of ACLM: 1) PLMs are restricted by their knowledge to generate entirely new complex entities due to their syntactically ambiguous nature. Adding to this, substituting complex NEs in existing sentences leads to context-entity mismatch. 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Learning from noisy labels for entity-centric information extraction. arXiv preprint arXiv:2104.08656. ## A Hyperparameter Tuning All hyperparameters were originally tuned with grid search on the development set. In this section, we show performance on the test set for better analysis. Keyword Selection rate p: The keywords in our template provide the model with contextually relevant additional knowledge about the NEs during training and generation. However, we are faced with the question: *How much context is good context?*. Too less context, like our ACLM *only entity* baseline with only linearized NEs in the template, might make it difficult for the model to know the appropriate context of the syntactically ambiguous complex NE and thus might lead to sentences generated with a context-entity mismatch (for e.g. sam is reading on the Beach where *on the beach* might be a name of a movie). On the contrary, retaining too many words from the original sentence in our template might lead to a drop in the diversity of generated sentences as the model needs to *infill* only a small portion of the words. To determine the optimal value of p we experiment on 2 low-resource settings on the English sub-set of MultiCoNER and report the micro F1 results on the test-set for p ∈ {0, 0.1, 0.2, 0.3. 0.4, 0.5, 0.6, 0.7}. All other hyperparameters are kept constant. As shown in Table 5, p = 0.3 gives us the best test-set performance, and the performance decreases after 0.4. \begin{tabular}{c c c c c c c c c} \hline \hline \#Gold & 0 & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 & 0.6 & 0.7 \\ \hline 200 & 50.06 & 51.82 & 53.99 & **54.99** & 51.05 & 54.28 & 52.16 & 54.34 \\ 500 & 57.55 & 56.12 & 57.93 & **58.31** & 57.55 & 56.88 & 56.60 & 58.10 \\ \hline \hline \end{tabular} Table 5: Test set F1 for various Keyword Selection rates. Augmentation rounds R: Augmenting the training dataset with several augmentation rounds R proves effective until a saturation point is reached. Continuing to add more augmented data to the gold dataset starts introducing noise to the combined data. Additionally, with an increase in R, the chances of auto-regressive generation with *top-k* sampling generating similar sentences increase. To determine the optimal value of R, we experiment on 2 low-resource settings on the English sub-set of MultCoNER and report the micro F1 results on the test-set for R ranging from 1 to 7. All other hyperparameters are kept constant. As shown in Table 5, R = 5 gives us the best test-set performance, and the performance decreases after 5 rounds. Attention layers a **for Keyword Selection:** Selecting the right keywords for creating a template ![11_image_0.png](11_image_0.png) Table 6: Test set F1 for the number of augmentation rounds. is integral to the success of ACLM. A clear example of this can be seen in Table 1, where ACLM outperforms ACLM *random* (which chooses random tokens as keywords for template creation) by a significant margin. Transformer encoders consist of multiple layers, and each layer consists of multiple attention heads. While all heads in the same layer tend to behave similarly, different layers generally encode different semantic and syntactic information (Clark et al., 2019). Thus we experiment with different values of α, or different combinations of transformer encoder layers which are used for calculating the attention scores for keyword selection. As mentioned in Section 3.1, by default, we average attention scores across all tokens, all heads, and the last α layers. For all our low-resource experiments, we use attention maps from a 24-layer XLMRoBERTa-large fine-tuned on the low-resource gold dataset for that particular setting. Table 7 compares the performance of 3 settings of α on 2 low-resource settings on the English sub-set of MultCoNER: 1. Only last layer 2. Last 4 layers. 3. All 24 layers. As clearly evident, though setting 2 achieves the best performance, the difference in performance among different values of α is not too high. As part of future work, we would like to explore better ways to search for the optimal α. $$\begin{array}{l l l l}{{\hline\#\mathrm{Gold}}}&{{\bf1}}&{{\bf2}}&{{\bf3}}\\ {\hline200}&{52.43}&{{\bf54.99}}&{54.13}\\ {500}&{58.09}&{{\bf58.31}}&{58.15}\\ {\hline\end{array}$$ ![11_image_1.png](11_image_1.png) Table 7: Test set F1 for various settings of α ## B Additional Results Current state of state-of-the-art: Most current state-of-the-art systems are built and evaluated on common NER benchmarks like CoNLL 2003 and OntoNotes v5.0. As discussed in Section 2, these benchmarks do not represent contemporary challenges in NER and contain sentences with easy entities and rich context. Table 8 compares the performance of a simple XLM-R (Conneau et al., 2019), and Co-regularized LUKE (Zhou and Chen, 2021) (SOTA NER system) on 2 common NER and 1 complex NER benchmarks in both low- and high-resource settings. As we can clearly see, both systems achieve remarkable performance on both CoNLL 2003 and OntoNotes v5.0 but struggle on MultiCoNER. Additionally, the gap widens in lowresource settings. Training on the entire dataset: Beyond just evaluating ACLM performance on low-resource settings, we also compare ACLM with all our baselines on the entire MultiCoNER dataset (each language split contains ≈ 15300 sentences). Similar to low-resource settings, ACLM outperforms all our baselines across all languages and achieves an absolute average gain of 1.58% over our best baseline. Method En Bn Hi De Es Ko Nl Ru Tr Zh Avg ![12_image_0.png](12_image_0.png) ![12_image_2.png](12_image_2.png) Gold-only 71.25 59.10 61.59 75.33 67.71 65.29 71.55 68.76 62.44 60.56 66.36 LwTR 71.22 58.86 60.72 75.50 70.06 65.80 72.94 68.26 62.70 58.74 66.48 DAGA 64.30 47.93 53.03 67.70 62.07 59.84 65.37 60.72 52.45 55.32 58.87 MELM 66.27 56.27 61.04 71.25 65.56 63.71 70.43 66.28 60.74 57.72 63.93 ACLM *(ours)* 72.69 60.13 62.58 77.26 70.89 67.01 73.28 69.90 65.24 61.63 **68.06** Table 9: Result comparison Complex NER. Avg is the average result across all languages. ACLM outperforms all our baselines. | #Gold | Method | XLM-R | Co-regularized LUKE | |------------|-----------|---------|-----------------------| | CoNLL 2003 | 84.82 | 86.92 | | | 500 | OntoNotes | 65.48 | 64.92 | | MultiCoNER | 55.51 | 55.12 | | | CoNLL 2003 | 92.21 | 92.56 | | | All | OntoNotes | 85.07 | 87.57 | | MultiCoNER | 70.31 | 69.58 | | Entity-wise Performance Analysis: Previous to MultiCoNER, common benchmark datasets like CoNLL 2003 had only "easy entities" like names of Persons, Locations, and Organizations. The MultiCoNER dataset has 3 additional types of NEs, namely Products (**PROD**), Groups (GRP), and Creative Work (CW). These entities are syntactically ambiguous, which makes it challenging to recognize them based on their context. The top system from WNUT 2017 achieved 8% recall for creative work entities. Table 10 compares the entity-wise performance of ACLM with our various baselines on two low-resource settings on the MultiCoNER dataset. All results are averaged across all 10 languages. ACLM outperforms all our baselines on all individual entities, including PROD, GRP, and CW, which re-affirms ACLM's ability to generate effective augmentation for complex NER. #Gold Method PER LOC PROD GRP CORP CW 200 Gold-Only 56.35 42.32 30.10 31.36 33.83 23.30 LwTR 56.13 41.78 34.87 36.52 39.30 27.46 DAGA 45.19 35.40 19.96 21.92 19.60 14.33 MELM 52.16 41.16 30.24 28.61 34.13 22.77 ACLM *(ours)* 64.42 48.92 41.76 37.31 44.08 **30.61** 500 Gold-Only 63.05 48.48 42.75 37.55 45.10 31.34 LwTR 64.80 **54.17** 45.70 **44.06** 50.80 35.10 DAGA 51.82 41.11 28.58 30.50 34.10 21.61 MELM 58.41 45.64 37.04 34.11 40.42 28.33 ACLM *(ours)* **66.49** 51.24 **48.87** 42.00 51.55 **35.18** Length-wise Performance Analysis: As mentioned in Section 2, low-context is a major problem in complex NER, and an effective complex NER system should be able to detect NEs in sentences with both low and high context (by context we refer to the number of words around the NEs in the sentence). By the nature of its fine-tuning pipeline, ACLM is able to generate augmentations of variable length, and our dynamic masking step further boosts the length diversity of generated augmentations. Adding to this, we acknowledge that effective augmentations for syntactically complex entity types should enable a model to learn to detect these entities in even low-context. Table 11 compares the entity-wise performance of ACLM with our various baselines on two low-resource settings on the MultiCoNER dataset. All results are averaged across all 10 languages. ACLM outperforms all our baselines across all length settings, which re-affirms ACLM's ability to generate effective augmentation for complex NER. To be specific, ACLM improves over our best baseline by 8.8% and 7.4% for 200 and 3.2% and 6.7% for 500 for low- and high-context sentences, respectively. #Gold Method len < 5 5 ≤ len < 10 10 ≤ len ![12_image_1.png](12_image_1.png) 200 LwTR 26.35 34.38 43.56 DAGA 18.20 29.53 39.49 MELM 23.27 38.81 50.29 ACLM *(ours)* 35.10 47.25 **57.72** 500 LwTR 34.04 42.74 56.47 DAGA 23.00 38.18 51.09 MELM 27.46 44.74 57.91 ACLM *(ours)* 37.42 52.23 **63.13** | she became opposed to abortion in 1992 while attending a | | | | |-----------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|----------------|----| | Original | bible study and has since spoken out about how abortion has negatively impacted her life. | Linguistically | Context | | Entity Match | | | | | coherent entities | | | | | she became average to abortion in guitar while attending a | | | | | LwTR | bible study and has since spoken out about how academy has | ❌ | ✔ | | negatively impacted her life. she became opposed to abortion in 1992 while attending a | | | | | MELM | vegetable study and has since spoken out about how abortion | ❌ | ❌ | | has negatively impacted her life. | | | | | ACLM w/o mixner the bible warned against abortion and said abortion had negatively impacted the welfare state. | ✔ | ✔ | | | ACLM w/ mixner | while attending the bible seminar in 1964 at the university of pittsburgh he earned a master of science degree in biology. | ✔ | ✔ | Figure 4: Analysis and comparison of augmentations generated by our baselines with ACLM. Words **underlined** are the NEs. Context entity mismatch occurs when the generated NEs do not fit the surrounding context. Linguistic incoherence refers to cases where a generated NE does not follow the linguistic pattern for that particular type of NE or context. ## C Templates And Attention Maps Creating templates with *keywords* that effectively provides the PLM with additional knowledge about the NEs in the sentence is an integral part of ACLM. Fig. 11, 12, 13, 14, 15 shows examples of templates created for our sentences in MultiCoNER English subset, Spanish subset, Hindi subset NCBI Disease and TDMSci datasets, respectively. Additionally, we provide examples of attention maps used to create templates in Fig. 16f. ## D Qualitative Analysis Of Augmentations D.1 Augmentation Examples MultiCoNER Dataset: We provide additional examples of augmentations generated by ACLM and all our baselines in Fig. 9 and Fig. 10 for Hindi and English subsets of MultiCoNER dataset respectively. Extra Datasets: Fig 5, 6, 7 and 8 illustrate augmetation examples for CoNLL 2003 (Tjong Kim Sang and De Meulder, 2003) (news), BC2GM (Smith et al., 2008) (bio-medical), NCBI Disease (Dogan et al. ˘ , 2014) (bio-medical) and TDMSci (Hou et al., 2021) (science) datasets respectively. Except for on CoNLL 2003 datasets, both our baselines, LwTR and MELM, generate incoherent and unreliable training samples for the other 2 datasets. We only compare ACLM with LwTR and MELM as these methods don't generate augmentations from scratch and modify existing sentences. We define unreliable sentences as sentences generated with an entity-context mismatch (eg. a NE describing a disease prone to cows is placed in the context of humans or vice-versa). Generating unreliable augmentations prove fatal in data-sensitive domains like bio-medical as it may make the model store wrongful knowledge. Our detailed analysis of generated augmentations shows that: (1) LwTR is prone to generating such incoherent sentences because it randomly samples entities from the corpus with the same tag for replacement. (2) MELM on the other hand, fine-tuned on a transformerencoder-based PLM, gets to see the entire context of the sentence for generating a new NE. However, it does not learn to focus on particular keywords and tends to generate a new NE based on the broader context of the sentence (e.g., it does not learn to differentiate between human and cow diseases and generates a new NE based on the broader context of the sentence). (3) ACLM generates highly reliable samples by conditioning on templates with keywords related to the NE. We illustrate examples of such templates in Fig. 14 and 15. ## E Additional Details Model Parameters: XLM-RoBERTa-large has ≈ 355M parameters with 24-layers of encoder, 1027hidden-state, 4096 feed-forward hidden-state and 16-heads. mBART-50-large ≈ has 680M parameters with 12 layers of encoder, 12 layers of decoder, 1024-hidden-state, and 16-heads. Compute Infrastructure: All our experiments are conducted on a single NVIDIA A100 GPU. An entire ACLM training pipeline takes ≈ 40 minutes. Dataset Details: We use 5 datasets in total for our experiments: MultiCoNER 2(Malmasi et al., 2https://registry.opendata.aws/multiconer/ | Original | The [European Commission]ORG said on Thursday it disagreed with [German]MISC advice to consumers to shun [British]MISC lamb until scientists determine whether mad cow disease can be transmitted to sheep. | | |-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LwTR | The [European Sox]ORG seed on Thursday it disagreed with [German]MISC advice to consumers to shun [British]MISC regarding until scientists determine whether mad 70 disease can be -- to sheep 1 | | | MELM | | [France]LOC 's [Aquaculture Committee]ORG suggested on Wednesday that consumers avoid eating meat from [German]MISC sheep until scientists determine whether mad cow disease can be transmitted to the animals. | | ACLM w/o mixner | The [European Commission]ORG said on Thursday that consumers should shun [British]MISC lamb until scientists determine whether the disease can be transmitted to humans. The [European Commission]ORG has a scientific and multidisciplinary group of veterinary scientists who | | | ACLM w/ mixner | disagreed with the consumers on Thursday and decided to shun them out until scientists determine whether the [Bovine Spongiform Encephalopathy]MISC ( BSE ) -- mad cow disease can be transmitted. | | Figure 5: Augmentation examples of the CoNLL 2003 dataset from the news domain. All generations are produced in a low-resource setting (500 training examples). | Original | To determine the genetic basis for the differences between the cardiac and [brain AE3 variants]GENE , we isolated and characterized the rat gene. | | |-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------| | LwTR | To determine the genetic basis for the differences between the cardiac and [IgA AE3 related]GENE , we isolated and characterized the rat immunodeficiency increased | | | MELM | | To determine the genetic basis for the differences between the cardiac and [mouse EFR varianter]GENE , we isolated and characterized the rat gene. | | ACLM w/o mixner | The genetic basis for the cardiac [brain AE3 variants]GENE in the rat population is unknown. | | | ACLM w/ mixner | On basis of the differences in both [brain AE3 variants]GENE and [estrogen receptors]GENE we isolated the mechanisms that govern the variations in mouse and human genes. | | Figure 6: Augmentation examples of BC2GM from the bio-medical domain. All generations are produced in a low-resource setting (500 training examples). Figure 7: Augmentation examples of NCBI dataset from the bio-medical domain. All generations are produced in a low-resource setting (500 training examples). | Original | In order to understand the genetic and phenotypic basis for [DPD deficiency]DISEASE , we have reviewed 17 families presenting 22 patients with complete [deficiency of DPD]DISEASE . | | |-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LwTR | In order to understand the genetic and phenotypic basis for [DPD deficiency]DISEASE , we have pathology 17 families presenting transcription patients with 292 deficiency of [DPD constructed]DISEASE . | | | MELM | | In order to understand the genetic and phenotypic basis for [DDA eficiendency]DISEASE , we have reviewed 17 families presenting 22 patients with complete [confferency cardiac disorderF]DISEASE . | | ACLM w/o mixner | To determine the phenotypic basis of this [DPD deficiency]DISEASE gene, we reviewed the gene in 22 patients with an unusual [deficiency of DPD]DISEASE . We examined the phenotypic basis of [DPD deficiency]DISEASE in four families with patients suffering from [deficiency of DPD]DISEASE ( Twenty - eight patients with a [protein S deficiency]DISEASE and [PROS1 gene defect]DISEASE ). | | Figure 8: Augmentation examples of TDMSci from the science domain. All generations are produced in a low-resource setting (500 training examples). | These data show that if we are ever to fully master [natural language generation]TASK , especially for the | | | |--------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Original | genres of news and narrative, researchers will need to devote more attention to understanding how to generate descriptive, and not just distinctive, referring expressions. These data show that if we are ever to runs focus [Urdu/generation]TASK , proposed for the genres of + and | | | LwTR | narrative, researchers will need to devote more attention to understanding how to Fixed descriptive, and not just raw transformed morphological supervised corpora. These data show that if we are ever to fully master [the text interpretation]TASK , especially for the genres of | | | MELM | | news and narrative, researchers will need to devote more attention to understanding how to generate descriptive, and not just distinctive, referring expressions. | | ACLM w/o mixner | These results show that in the [natural language generation]TASK of news text, researchers are able to generate descriptive text with distinctive language expressions. These data show that if we are ever to fully master [natural language generation]TASK for genres other than | | | ACLM w/ mixner | narrative, researchers will be able to generate descriptive and distinctive meaning by referring to them. We propose a holistic approach to [image description generation]TASK that is noisy and challenging. | | | Original | [हँसेल और Ťेटल]CW, एक परी कथा िजसमŐ नामांिकत पाũ ŰेडŢं ब का िनशान छोड़ते हœ | | |-----------------|-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------| | LwTR | [ओपनऑिफस और Ťेटल]CW, एक िकया तक िजसमŐ रेिटंग पाũ ŰेडŢं ब का मŐ िकया। हœ | | | MELM | | [◌ी के जूरा]CW, एक परी कथा िजसमŐ नामांिकत पाũ ŰेडŢं ब का िनशान छोड़ते हœ | | ACLM w/o mixner | [हँसेल और Ťेटल]CW की कथा को १९९९ मŐ नामांिकत िकया गया था। | | | ACLM w/ mixner | [हँसेल और Ťेटल]CW की परी कथा को नामांिकत िकया गया था , िजसे [िनलेसातो]GRP सैटेलाइट नेटवकŊ Ȫारा Ůसाįरत िकया जाता है। | | | Original | उɎोनं े १९०० मŐ[हावŊडŊिवʷिवȨालय]GRP से माːर िडŤी और १९०४ मŐडॉƃरेट की उपािध Ůाɑ की। | | | LwTR | उɎोनं े १९०० है। [हावŊडŊिवʷिवȨालय]GRP से १९९३ िडŤी और १९०४ मŐडॉƃरेट की उपािध Ůाɑ की। | | | MELM | | उɎोनं े १९०० मŐ[बॉ̵Ōड कॉलेज]GRP से माːर िडŤी और १९०४ मŐडॉƃरेट की उपािध Ůाɑ की। | | ACLM w/o mixner | उɎोनं े १९०० मŐ[हावŊडŊिवʷिवȨालय]GRP से आिकŊटेƁर की िडŤी Ůाɑ की। | | | ACLM w/ mixner | वह [हावŊडŊिवʷिवȨालय]GRP से ˘ातक की िडŤी Ůाɑ करने के बाद डॉƃरेट की उपािध Ůाɑ करने के िलए [िडजाइन के हावŊडŊŤेजुएट ˋू ल]GRP मŐआिकŊटेƁर इंजीिनयर बन गए। | | Figure 9: Augmentation examples on the Hindi subset of the MultiCoNER dataset. All generations are produced in a low-resource setting (500 training examples). | Original | gibson was educated at [harrow school]GRP , where he played in the cricket team, and at [trinity college]LOC . | | |-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------| | LwTR | gibson was early at [real pictures]GRP , where he played in the cricket team seventh and at [trinity college]LOC . | | | MELM | | gibson was educated at [harford schools]GRP , where he played in the cricket team, and at [is college]LOC . | | ACLM w/o mixner | gibson was educated at [harrow school]GRP and played on the football team at [trinity college]LOC . | | | ACLM w/ mixner | gibson was educated at [harrow school]GRP , then at [trinity college]LOC and then at the missionary college of [stavanger]LOC from which he graduated in 1946. | | | Original | in previous years he had worked with [alex cox]PER on the soundtracks of his films [sid and nancy]CW and [walker]CW in 1986 and 1987. | | | LwTR | in previous years he had worked with [alex pauwels]PER on the soundtracks of his actor [illegal and nancy]CW and [family]CW in 1986 and 1987. | | | MELM | | in previous years he had worked with [roux wilsmith]PER on the soundtracks of his films [du, the ware]CW and walkaway in 1986 and 1987. | | ACLM w/o mixner | [alex cox]PER wrote the soundtracks for his films [sid and nancy walker]CW in 1987 . [alex cox]PER wrote the soundtracks for his film [sid and nancy and walker]CW and | | | ACLM w/ mixner | appeared in many of his films, including [powder]CW , [simply irresistible]CW and [dtox]CW . | | Figure 10: Augmentation examples on the English subset of the MultiCoNER dataset. All generations are produced in a low-resource setting (500 training examples). | Original | Template | | |-------------------------------------------------------------|----------------------------------------------------|-------------------------------------------------| | speech pathologist [lionel logue]PER taught at the school | [M] speech pathologist [M] <B-PER> lionel <B-PER> <IPER> logue <I-PER> [M] taught [M] school [M] | | | from 1910 to 1911. | [M] designed [M] interim [M] <B-PROD> m73 <B-PROD> | | | they were designed for interim | use until the [m73 machine | <I-PROD> machine <I-PROD> <I-PROD> gun <I-PROD> | | gun]PROD could be fielded. | [M] fielded [M] | | | its aircraft and crews operate for its partly owned leisure | [M] aircraft [M] owned leisure subsidiary <B-CORP> | | | subsidiary [holiday europe]CORP . | holiday <B-CORP> <I-CORP> europe <I-CORP> [M] | | Figure 11: Examples of templates created for sentences taken from the English subset of the MultiCoNER dataset. All templates shown are created in a low-resource setting (500 training examples). Words underlined are identified *keywords*. Original **Template** adémas fue lanzado como sencillo en algunos países, junto con la Segunda cancíon del álbum, **[waiting for the sun]**CW . [M] lanzado [M] sencillo [M] países [M] cancíon **[M]** álbum [M] <B-CW> waiting <I-CW> <I-CW> for **<I-CW>** <I-CW> the <I-CW> sun **<I-CW> [M]** La revista [time]CW la agregó en una lista **de las veinticinco** mejores películas **de animación** [M] revista [M] <B-CW> time <B-CW> [M] lista **[M]** veinticinco mejores películas **[M]** En 2003, [ebro foods]CORP , prpietaria **de la factoría, decidió** cesar **la actividad.** [M] <B-CORP> ebro <B-CORP> <I-CORP> foods **<I-CORP>** [M] prpietaria [M] factoría [M] decidió cesar **[M]** actividad [M] Figure 12: Examples of templates created for sentences taken from the Spanish subset of the MultiCoNER dataset. All templates shown are created in a low-resource setting (500 training examples). Words underlined are identified *keywords*. Figure 13: Examples of templates created for sentences taken from the Hindi subset of the MultiCoNER dataset. All templates shown are created in a low-resource setting (500 training examples). Words underlined are identified *keywords*. Figure 14: Examples of templates created for sentences taken from the NCBI Disease dataset. All templates shown are created in a low-resource setting (500 training examples). Words underlined are identified *keywords*. | Original | Template | | |-------------------------------------------------|------------------------------------------------|----------------------------------------------------| | [M] आिधकाįरक | [M] बœड | [M] २००१ [M] एʛम [M] <B-CW> | | आिधकाįरक तौर पर बœड समाɑ हो गया, लेिकन २००१ मŐ अपने एʛम | जीवन <B-CW> <I-CW> की <I-CW> <I-CW> साँसे<I-CW> [M] | | | [जीवन की साँसे]CW | के साथ वापसी की। | वापसी की [M] | | अगले सफल वषŘ मŐ इसका िवˑार Šआ और [मेटŌो मिनला]LOC Ɨेũ मŐ | | [M] <B-LOC> मेटŌो <B-LOC> <I-LOC> मिनला <I-LOC> Ɨेũ [M] | | नए पįरसरों की ˕ापना Šई | | नए पįरसरों[M] ˕ापना Šई [M] | | | पाˑा को [मेज़]PROD ƗुधावधŊक के ŝप मŐ भी परोसा जा सकता है। | [M] पाˑा [M] <B-PROD> मेज़ | <B-PROD> [M] परोसा [M] | Figure 15: Examples of templates created for sentences taken from the TDMSci dataset. All templates shown are created in a low-resource setting (500 training examples). Words underlined are identified *keywords*. | Original | Template | |------------|------------| | Within the kidney, [VHL]DISEASE mRNA was differentially expressed within renal tubules suggesting that the [VHL]DISEASE gene product may have a specific role in kidney development. [M] kidney [M] <B-DISEASE> VHL <I-DISEASE> [M] mRNA [M] differentially expressed [M] renal tubules [M] <B-DISEASE> VHL <I-DISEASE> [M] gene product [M] kidney development [M] In conclusion , we demonstrated that a point mutation in a lariat branchpoint consensus sequence causes a null allele in a patient with [FED]DISEASE . [M] demonstrated [M] mutation [M] branchpoint consensus sequence [M] allele [M] patient [M] <BDISEASE> FED <B-DISEASE> [M] [M] Mutations [M] variant phenotypes [M] <B-DISEASE> Mutations associated with variant phenotypes in [ataxiatelangiectasia]DISEASE . ataxia <B-DISEASE> <B-DISEASE> - <B-DISEASE> <BDISEASE> telangiectasia <B-DISEASE> [M] | | | Original | Template | |------------|------------| | [M] Statistical approaches [M] <B-TASK> machine <BTASK> <I-TASK> translation <I-TASK> <I-TASK> ( <ITASK> <I-TASK> SMT <I-TASK> ) <I-TASK> [M] sentence [M] aligned [M] corpora [M] translation [M] | | | Statistical approaches to [machine translation (SMT)]TASK use sentence-aligned, parallel corpora to learn translation rules along with their probabilities. The goal of fully unsupervised [word segmentation]TASK , then, is to recover the correct boundaries for arbitrary natural language corpora without explicit human parameterization. [M] goal [M] unsupervised <B-TASK> word <B-TASK> <ITASK> segmentation <I-TASK> [M] correct boundaries [M] language corpora [M] human parameterization [M] In particular , for [question classification]TASK , no labeled question corpus is available for French, so this paper studies the possibility to use existing English corpora and transfer a classification by translating the question and their labels . [M] <B-TASK> question <B-TASK> <I-TASK>classification <I-TASK> [M] labeled question corpus [M] French [M] paper [M] existing English corpora [M] classification [M] translating [M] question [M] labels [M] | | ![17_image_0.png](17_image_0.png) ![17_image_3.png](17_image_3.png) ![17_image_4.png](17_image_4.png) ![17_image_5.png](17_image_5.png) ![17_image_1.png](17_image_1.png) ![17_image_2.png](17_image_2.png) | class | split | EN | DE | ES | RU | NL | KO | FA | ZH | HI | TR | BN | MULTI | MIX | |------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|-------| | PER | train | 5,397 | 5,288 | 4,706 | 3,683 | 4,408 | 4,536 | 4,270 | 2,225 | 2,418 | 4,414 | 2,606 | 43,951 | 296 | | dev | 290 | 296 | 247 | 192 | 212 | 267 | 201 | 129 | 133 | 231 | 144 | 2,342 | 96 | | | test | 55,682 | 55,757 | 51,497 | 44,687 | 49,042 | 39,237 | 35,140 | 26,382 | 25,351 | 26,876 | 24,601 | 111,346 | 19,313 | | | LOC | train | 4,799 | 4,778 | 4,968 | 4,219 | 5,529 | 6,299 | 5,683 | 6,986 | 2,614 | 5,804 | 2,351 | 54,030 | 325 | | dev | 234 | 296 | 274 | 221 | 299 | 323 | 324 | 378 | 131 | 351 | 101 | 2,932 | 108 | | | test | 59,082 | 59,231 | 58,742 | 54,945 | 63,317 | 52,573 | 45,043 | 43,289 | 31,546 | 34,609 | 29,628 | 141,013 | 23,111 | | | GRP | train | 3,571 | 3,509 | 3,226 | 2,976 | 3,306 | 3,530 | 3,199 | 713 | 2,843 | 3,568 | 2,405 | 32,846 | 248 | | dev | 190 | 160 | 168 | 151 | 163 | 183 | 164 | 26 | 148 | 167 | 118 | 1,638 | 75 | | | test | 41,156 | 40,689 | 38,395 | 37,621 | 39,255 | 31,423 | 27,487 | 18,983 | 22,136 | 21,951 | 19,177 | 77,328 | 16,357 | | | CORP | train | 3,111 | 3,083 | 2,898 | 2,817 | 2,813 | 3,313 | 2,991 | 3,805 | 2,700 | 2,761 | 2,598 | 32,890 | 294 | | dev | 193 | 165 | 141 | 159 | 163 | 156 | 160 | 192 | 134 | 148 | 127 | 1,738 | 112 | | | test | 37,435 | 37,686 | 36,769 | 35,725 | 35,998 | 30,417 | 27,091 | 25,758 | 21,713 | 21,137 | 20,066 | 75,764 | 18,478 | | | CW | train | 3,752 | 3,507 | 3,690 | 3,224 | 3,340 | 3,883 | 3,693 | 5,248 | 2,304 | 3,574 | 2,157 | 38,372 | 298 | | dev | 176 | 189 | 192 | 168 | 182 | 196 | 207 | 282 | 113 | 190 | 120 | 2,015 | 102 | | | test | 42,781 | 42,133 | 43,563 | 39,947 | 41,366 | 33,880 | 30,822 | 30,713 | 21,781 | 23,408 | 21,280 | 89,273 | 20,313 | | | PROD | train | 2,923 | 2,961 | 3,040 | 2,921 | 2,935 | 3,082 | 2,955 | 4,854 | 3,077 | 3,184 | 3,188 | 35,120 | 316 | | dev | 147 | 133 | 154 | 151 | 138 | 177 | 157 | 274 | 169 | 158 | 190 | 1,848 | 117 | | | test | 36,786 | 36,483 | 36,782 | 36,533 | 36,964 | 29,751 | 26,590 | 28,058 | 22,393 | 21,388 | 20,878 | 75,871 | 20,255 | | | #instances | train | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 15,300 | 168,300 | 1,500 | | dev | 800 | 800 | 800 | 800 | 800 | 800 | 800 | 800 | 800 | 800 | 800 | 8,800 | 500 | | | test | 217,818 | 217,824 | 217,887 | 217,501 | 217,337 | 178,249 | 165,702 | 151,661 | 141,565 | 136,935 | 133,119 | 471,911 | 100,000 | | 2022) (CC BY 4.0 licensed), CoNLL 2003 3(Tjong Kim Sang and De Meulder, 2003) (Apache License 2.0), BC2GM 4(Smith et al., 2008) (MIT License), NCBI Disease 5(Dogan et al. ˘ , 2014) (Apache License 2.0) and TDMSci 6(Hou et al., 2021) (Apache License 2.0). All the datasets are available to use for research purposes, and for our work, we use all these datasets intended for their original purpose, i.e., NER. MultiCoNER has data in 11 languages, including code-mixed and multilingual subsets. We experiment with 10 monolingual subsets discussed in Section 4.1 with appropriate reason for not experimenting on Farsi in our Limitations Section. According to the original papers of all 5 datasets used in the research, none of them contains any information that names or uniquely identifies individual people or offensive content. Data statistics (train/test/dev splits): Detailed dataset statistics for MultiCoNER, CoNLL 2003, BC2GM, NCBI Disease and TDMSci can be found in Table 12 (language codes in Table 13), 14, 16, 17 and 15 respectively. Implementation Software and Packages: We implement all our models in PyTorch 7and use the HuggingFace 8implementations of mBART50 and XLM-RoBERTA (base and large). We use the FLAIR toolkit (Akbik et al., 2019) to fine-tune all 3https://huggingface.co/datasets/conll2003 4https://github.com/spyysalo/bc2gm-corpus 5https://huggingface.co/datasets/ncbidisease 6https://github.com/IBM/science-result-extractor 7https://pytorch.org/ 8https://huggingface.co/ ## Our Ner Models. Potential Risks: Conditional Language Models used for Natural Language Generation often tend to hallucinate (Ji et al., 2022) and potentially generate nonsensical, unfaithful or harmful sentences to the provided source input that it is conditioned on. ![18_image_0.png](18_image_0.png) ![18_image_1.png](18_image_1.png) English (EN) Spanish (ES) ![18_image_2.png](18_image_2.png) ![18_image_3.png](18_image_3.png) ![18_image_4.png](18_image_4.png) Table 15: TDMSci dataset statistics for the train/test splits. ![18_image_5.png](18_image_5.png) Table 16: BC2GM Dataset Train/Dev/Test Split | Corpus characteristics | Training set Development set Test set Whole corpus | | | | |------------------------------|------------------------------------------------------|-----|------|------| | PubMed citations | 593 | 100 | 100 | 793 | | Total disease mentions | 5145 | 787 | 960 | 6892 | | Unique disease mentions 1710 | 368 | 427 | 2136 | | | Unique concept ID | 670 | 176 | 203 | 790 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations Section (After conclusion and before citations). ✓ A2. Did you discuss any potential risks of your work? Section E. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 and Abstract. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4.1. ✓ B1. Did you cite the creators of artifacts you used? Citations. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section E. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section E. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section E. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section E. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section E. ## C ✓ **Did You Run Computational Experiments?** Section E. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section E. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4.2. Section A. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.1. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section E. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

yin-etal-2023-natural

Natural Language to Code Generation in Interactive Data Science Notebooks

https://aclanthology.org/2023.acl-long.9

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1078 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions. Arcade is publicly available at \url{https://github.com/google-research/arcade-nl2code/}.

# Natural Language To Code Generation In Interactive Data Science Notebooks Pengcheng Yin∗ , Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton Google Inc. ## Abstract Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1,078 code generation problems using the pandas data analysis framework in data science notebooks. AR-CADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PACH-INCO, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanations, showing the potential to improve the diversity and explainability of model predictions. ARCADE is publicly available at https://github.com/ google-research/arcade-nl2code/. ## 1 **Introduction** Data science is the process of extracting insights from data (Wang et al., 2021a), and has become an integral part of decision making and knowledge discovery (Donoho, 2017). Data scientists and machine learning (ML) practitioners often use computational notebooks, which are interactive environments such as Jupyter notebooks (Kluyver et al., 2016) and Google Colab, in their work. Data scientists spend a significant amount of time on data wrangling tasks to process raw data into usable forms (illustrated in Fig. 1), as well as **exploratory data analysis** (EDA) to gain insights ∗Correspondence to [email protected] ![0_image_0.png](0_image_0.png) Figure 1: An example of a computational notebook adapted from our dataset, with examples of reading data (cell c1), data wrangling (c2, c3), and exploratory data analysis (c4 ∼ c7). Annotated NL intents (ui) are shown in green. for decision making (Agashe et al., 2019; Wang et al., 2022a). This has motivated research on automating and accelerating the data science workflow in general (Aggarwal et al., 2019; Wang et al., 2021a,b), with particular interest in data wrangling and EDA tasks (Bavishi et al., 2019; Jain et al., 2021; Nazabal et al., 2020; Kandel et al., 2011). Meanwhile, large language models (LLMs) trained on code can assist developers by translating natural language (NL) intents into executable programs (Chen et al., 2021a; Austin et al., 2021; Chowdhery et al., 2022; Nijkamp et al., 2022; Fried et al., 2022), with promising applications in synthesizing code for data wrangling and EDA tasks (Jain et al., 2021; Rajkumar et al., 2022; Cheng et al., 2022b). Computational notebooks also present unique challenges to LLMs, as notebooks freely mix NL, code, graphics, and execution results (Perkel, 2021), and because of their interactivity, notebooks feature multiple interdependent NL-to-code problems (Heyman et al., 2021). Several benchmarks have been proposed to evaluate program synthesis of data science programs from NL intents, but these datasets have several limitations. First, some datasets derive from data science tutorial notebooks (Agashe et al., 2019; Chandel et al., 2022), which tend to contain NL text (*e.g.*, exercise questions) that is verbose and elaborate, instead of the concise, ephemeral style that developers write when interacting with code LMs (Barke et al., 2022, more in §3). Other datasets assume that the developer provides extra information, such as unit tests or input/output examples (Chandel et al., 2022; Jain et al., 2022), but such systems pose an extra burden to users who might not normally write such tests or examples during their workflow (Pimentel et al., 2019). Finally, existing datasets usually contain independent tasks with isolated contexts (Lai et al., 2022), or a limited number of contextually dependent problems (Huang et al., 2022), rather than having multiple, related tasks such as in Fig. 1. Therefore, there is a need for a benchmark with realistic NL intents, rich notebook context, and a series of interrelated problems, so as to better reflect real-world usage by data scientists. To fill this gap, we present ARCADE, 1a new benchmark for code generation for data wrangling and EDA tasks in computational notebooks (§3). ARCADE consists of 1,078 problems spanning across 136 notebooks based on 106 ML datasets. It features a series of NL utterances written by professional data scientists with the intention of interacting with an AI assistant (*e.g.*, green texts in Fig. 1), with high-quality code solutions using the pandas library. To mitigate the risk of data leakage, 60% of the problems are created from scratch, based on recent ML datasets on Kaggle (*e.g.*, the csv file in c1, Fig. 1).2 ARCADE also challenges LLMs with grounded language understanding, where a model needs to leverage variable states (*e.g.*, df['TIME'] in c2) to interpret NL semantics (*e.g.*, "*min and* 1Answer Repository for Computational Analysis and Data Engineering. 2https://www.kaggle.com/ max" in u1). Finally, problems in ARCADE are challenging, involving richer data science API usage than existing benchmarks. To demonstrate how ARCADE can motivate new research on LLMs for data science, we develop PACHINCO, a 62B code LM tailored for Python computational notebooks, trained on a mixture of NL, source code, and Jupyter notebooks data (§4). PACHINCO significantly outperforms public code LMs on ARCADE (§5.2). Even so, all models have difficulty on our benchmark, showing that it is a challenging task. Further, we explore few-shot prompting strategies to alter the style of model predictions, such as decomposing code into step-bystep structures and adding inline NL explanations. Not only is code in this style potentially more understandable to novice data scientists, prompting the model to explain its solutions also improves the diversity of the model's predictions (§5.3). ## 2 **Problem Statement** A computational notebook is an interactive computing environment that allows mixing code, text, and graphics. A notebook consists of a sequence of Markdown or source code cells. Given a partial notebook context with n cells {ci} n i=1 and a userspecified intent u for the next cell cn+1 (*e.g.*, u1 in Fig. 1 for n = 1), we aim to generate code for cn+1 that fulfills the user's intent (Agashe et al., 2019). We refer to the pair ({ci},u) as a *problem*. This process could proceed sequentially with multiple rounds between the user and a system (Heyman et al., 2021), so a single notebook can contain multiple problems. To satisfy subsequent intents (*e.g.*, u4), a system will leverage the updated notebook context (*e.g.*, {ci} 5 i=1) which includes previous problems (*e.g.*, those involving u1 to u3). As in Fig. 1, problems within a notebook often have interesting dependency structures. They may share execution context (*e.g.*, DataFrame df), form semantically coherent turns (*e.g.*, c4 and c5), or exhibit non-trivial long range data dependencies (*e.g.*, from c6 to c2, or c7 to c3). These dependency structures are more diverse than existing multi-turn code generation tasks with sequentially dependent problems (Nijkamp et al., 2022). ## 3 Arcade**: A Benchmark Of** Pandas Data Science Code Generation 3.1 Constructing A**Rcade** ARCADE consists of 1,078 NL-to-code problems from 131 notebooks based on 106 unique ML datasets, sourced from existing data science notebooks on GitHub (*Existing Tasks* split) and new ones created from scratch (*New Tasks* split). The problems are annotated by professional data science freelancers. This section outlines the dataset creation process. See Appendix A for more details. Repurposing Existing Notebooks To build the Existing Tasks split, we identify candidate code cells performing data wrangling and EDA tasks from existing high-quality notebooks, and then manually annotate these cells with NL intents. Specifically, we perform static analysis to identify notebooks with rich code cells related to data wrangling and EDA tasks (*e.g.*, by identifying cells using pandas functions) from public notebook corpora such as JuICe (Agashe et al., 2019) and BIGQUERY. We then select 63 notebooks with the greatest number of candidate code cells for annotation, covering 36 ML datasets from a variety of domains. Annotation consists of judging the quality of candidate cells, fixing errors, and creating intents summarizing the code (described below). Creating Notebooks for Novel ML Datasets The *Existing Tasks* split captures realistic problems and notebook contexts, but may result in artificially high evaluation accuracies due to potential leakage of evaluation notebooks in the training data of LLMs, which is a common issue in LLM evaluation (Brown et al., 2020).3 To prevent contamination, we additionally build the *New Tasks* split with 660 problems in notebooks created from scratch. Specifically, we create notebooks with wrangling and EDA tasks for 70 tabular ML datasets that appeared on Kaggle since February 2022 and are manually verified to differ from existing datasets on the Web. For each Kaggle dataset, we instructed the annotators to create a notebook with tasks that would provide insights for building an ML model for the dataset. To make the problems more challenging, we also encouraged them to make tasks that require at least 5 pandas API calls to solve. Annotating NL Intents When creating NL intents for a problem,4annotators are instructed to phrase their intents in the way they prefer when interacting with an AI system to help them implement the existing code solution, while keeping the intents natural and concise, without redundant elaboration such as line-by-line explanation. In addition, to 3JuICe and BigQuery primarily contain source files from 2019 or earlier, which exacerbates this issue. 4For *New Tasks*, intents are created before the solutions. make the intents more challenging, we encourage annotators to refer to entities and variables in the intents using semantic rewrites without introducing ambiguity (*e.g.*, use "convert all *binary columns* to bool" instead of listing columns verbatim), reminiscent of synonym substitution for labeling utterances in text-to-SQL (Gan et al., 2021). Mitigating Ambiguity in NL Intents Creating succinct NL intents without ambiguity could be non-trivial in this open-domain code generation setting, especially when there could be multiple plausible interpretations of an intent. For example, without the *underlined part* of u5 (Fig. 1), a programmer or a system may propose alternative solutions using different table schema. Therefore, for such open-ended problems where there could be multiple alternative ways to present the answer, we ask annotators to provide extra specification in their intents about the desired output (*e.g.*, schema of the output DataFrame, such as the *underlined part* in u5). Even with these additional semantic constraints, empirically we observe that about 50% of intents are still underspecified, making ARCADE a challenging benchmark for handling realistic NL intents with uncertainty. We present more analysis in §3.2 and introduce a robust evaluation metric that mitigates this issue in §3.3. Annotation Guideline Besides mitigating ambiguity in intents, there are many other aspects to consider during annotation, such as notebook style (*e.g.*, removing background material and hints in tutorial notebooks in *Existing Tasks* to avoid solution leakage), task diversity, and quality control, which we discuss in a 35-page annotation guideline provided to annotators, outlined in Appendix B. 3.2 **Dataset Analysis** We first present some analysis on ARCADE and then compare it to existing datasets in Tab. 1. NL Intents are often Underspecified ARCADE aims to evaluate code LMs in the real-world scenario where data scientists provide succinct NL intents without extra specification (*e.g.*, I/O examples). As a result, the intents we collected are often underspecified and may not contain sufficient information to generate a solution that executes to the exact reference output. To understand the patterns of semantic ambiguity in user-issued intents, we examined 100 random samples. Around 50% of them are precise and sufficient to infer the target outputs. Those intents are often numerical queries with lim- | Dataset | Src. | Exec? | Evaluation | # N.B. | # P. | P. / N.B. | Intents Type | Intent | AST Size? | # API4 | |-----------------------------|--------|-------------------|--------------|----------|--------|--------------|----------------|--------------|-------------|----------| | Method | Length | All / pandas | | | | | | | | | | JuICe (Agashe et al., 2019) | GH | Surface Match | 1,457 | 3,946 | 2.7 | Markdown | 60.2 | 21.2 / 24.3‡ | 2.5 | | | DSP (Chandel et al., 2022) | GH | Unit Tests | 305 | 1,096 | 3.6 | Markd.+Tests | 54.3 | 28.7 / 34.8‡ | 3.1 | | | ◦ | GH | Output Match | 277 | 534 | 1.9 | Annotated NL | 20.0 | 9.0 / 10.7 | 2.4 | | | NLGP (Heyman et al., 2021) | GH | Surface Match | 150 | 201 | 1.3 | Annotated NL | 7.7 | 13.5 / 15.1 | 2.1 | | | | SO | Tests+Constraints | N/A | 1,000 | N/A | Annotated NL | 166.5 | 27.3 / 41.6‡ | 5.0 | | | 61 | 417 | 6.8 | Annotated NL | 15.6 | 17.7 | 4.3 | | | | | | x New Tasks | New | 70 | 661 | 9.4 | 18.4 | 27.2 | 5.8 | | | | | (§3.3) | | | | | | | | | | | ited variety in output type (*e.g.*, u2, u3, Fig. 1), or contain sufficient output specifications (§3.1). The remaining half are underspecified: (a) only 10% of the ambiguous intents lack descriptions of target columns in output DataFrames; more interestingly, (b) 42% imply entity sets as outputs (e.g., Where are the top 10 customers receiving the highest *✿✿✿✿✿✿✿* incomes *located?*), answerable either using container types with entity names only (*e.g.*, a List or Series of locations), or DataFrames with entities and additional columns (e.g. *✿✿✿✿✿✿✿* incomes) mentioned in the intents; (c) 23% imply output with complex schema, such as a nested row index or table header (e.g., Show the time of the day *and the* fare price *for each airline*) which is difficult to infer without extra information, and (d) 20% require outputs with more complex structures (*e.g.*, multiple variables) or imply additional post-processing steps such as data imputation, while (e) the remaining 5% have complex intents that are difficult to understand without additional clarifications. ## Notebook Context Helps Disambiguate Intents Notably, while half of the intents are underspecified, 25% of those cases can be disambiguated by referring to prior rounds of problems in the context with similar query/output patterns. These are often follow-up queries (e.g., Which of them are *. . .*) of a prior turn (e.g., Show me all *. . .*), analogous to similar thematic relation patterns in contextual semantic parsing (Yu et al., 2019b). Comparing Existing and New Tasks Comparing the *Existing Tasks* and *New Tasks* splits, the latter is more challenging, as measured by the number of pandas API invocations and the AST size of reference solutions (Tab. 1, *Bottom*). Fig. 2 plots a histogram of the number of API calls per problem, where 67% of problems in *New Tasks* require at ![3_image_0.png](3_image_0.png) least 5 API calls to solve. As discussed in §5, with more complex held-out problems targeting recent ML datasets, the *New Tasks* split is a more robust benchmark and more challenging for code LLMs. Comparing with Existing Datasets Tab. 1 compares ARCADE with existing data science code generation datasets. We remark that ARCADE is the only benchmark that satisfies all the following criteria: *First*, A.......... RCADE........... features .......... succinct .... and.......... realistic ........ intents... as.......... problem................. specifications ("Intents Type" column, Tab. 1), which are significantly shorter ("Intent Length" column) than the verbose Markdown problem definitions found in tutorial or assignment notebooks (*c.f.* JuICe, DSP). AR-CADE also does not rely on extra specifications such as unit tests (*c.f.* DSP), which better capture the real-world scenario where developers prompt LMs using ephemeral comments for code completion (Barke et al., 2022). Most of these intents are often underspecified (mentioned earlier in §3.2), requiring a more robust evaluation metric to consider alternative answers (discussed in §3.3), while motivating future research on improving prediction diversity to cover plausible problem interpretations (explored in §5.1) or explicit modeling of intent uncertainty (Lin et al., 2022). *Second*, A........... RCADE .......... contains....... more......... related............ problems ... in.. a ....... single ........... notebook ("P./N.B." column) with diverse dependency patterns (*e.g.*, Fig. 1), capturing the essence of interactive computing. This makes our dataset useful in testing an LLM's ability to understand rich contexts, including existing user-written cells, as well as preceding problems and their solutions (§2). *Third*, A.......... RCADE............. challenges........ LLMs with ................. grounded ........... language ................. understanding, where the model needs to ground semantic concepts in the intents (*e.g.*, "*max and min*" in u1, Fig. 1) to the corresponding variable execution states in the context (*e.g.*, the TIME column in df). The need for understanding semi-structured data and performing necessary transformations (Pasupat and Liang, 2015) using an open-domain programming language (PL, Python) makes language grounding in ARCADE more difficult than in existing EDA tasks using domain-specific PLs, such as semantic parsing over databases (Yu et al., 2019b). Fourth, A........... RCADE .... has....... more........... complex............ problems with ............. richer....... usage ... of............. real-world ..... data......... science....... APIs. The number of pandas APIs used in each problem ("\# API" in Tab. 1) is on par with DS-1000 and significantly higher than other datasets.5 *Finally*, besides problem complexity,..... 60%.... of........... problems .. in............ ARCADE.... are.......... created ...... from......... scratch.... to .......... mitigate ............ evaluation ..... data.......... leakage. These data science problems also target recent tabular ML datasets, making ARCADE a reliable benchmark to test the generalization ability of LLMs in semi-structured knowledge understanding (Lee et al., 2021). ## 3.3 **Evaluation By Fuzzy Output Matching** We aim to synthesize programs in notebooks using only cell contexts and NL intents without extra specification such as unit tests (§2). As in §3.2, those intents are often underspecified and have multiple alternative solutions. We therefore approximately match the execution output of a predicted program with the annotated reference to determine if they are functionally equivalent primarily based on two categories of heuristics.6 First, we canonicalize variables with different container data types. Second, we allow for partial matching between complex DataFrames. Specifically, for a reference frame v with a set of column vectors {vi}, each representing the cell values for the i-th column, a prediction vˆ is considered equivalent with v iff for any vi ∈ v, vi ∈ vˆ. Intuitively, we consider a predicted program correct if its output DataFrame contains all the columns (and cell entries) in the 5Calculated by counting function names in a predefined list of functions from pandas, numpy, and similar libraries. 6For code that in-place modifies a variable (*e.g.*, df in c2), we treat the modified variable as the output. reference frame, since a user could easily create a more compact view of the frame by selecting a subset of target columns. Empirically, we find our evaluation metric is reliable in identifying solutions with alternative output structures, with a relatively low false-negative rate (Appendix J). ## 4 Pachinco**: Adapting Code Lms To** Computational Notebooks We introduce PACHINCO, an LM for notebooks. Base LM PACHINCO is based on PALM, a family of decoder-only LMs for NL tasks (Chowdhery et al., 2022). Specifically, we use the 62B PALM model trained on 1.3T tokens with a mixture of conversational, webpages and code data (Section F, Chowdhery et al. (2022)). Starting with this base LM, we first fine-tune on Python source code and then fine-tune further on Jupyter notebooks. Fine-tuning on Python Code We first fine-tune the base LM on a corpus of near-deduplicated, permissively-licensed Python source code files from GitHub, with 64B tokens in total. We finetune PALM for 1 epoch following the hyper parameters setup in Chowdhery et al. (2022). This model is already a strong code LM, even outperforming the larger code LM PALM-Coder 540B on existing program synthesis benchmarks (§5.2). Fine-tuning on Notebooks We then perform a second stage of fine-tuning on a large collection of 3.8M Jupyter notebooks from GitHub (9.6B tokens). Since our evaluation notebooks in the *Existing Tasks* split are also from GitHub, we also perform near-deduplication to remove any training notebooks with one cell similiar to any cells in the notebooks in *Existing Tasks* to prevent data contamination. We use nbconvert to linearize notebooks into Python code. Refer to Appendix D for details and Appendix K for a data card. ## 5 **Experiments** Models We evaluate PACHINCO and state-ofthe-art public code LLMs, namely CODEGEN (Nijkamp et al., 2022) and INC**ODER** (Fried et al., 2022). We test both the **mono**lingual (Python-only) and the **multi**lingual version of CODEGEN. INCODER may be a more appealing comparison since it is trained on 5GB of Jupyter notebooks. Inference and Metrics We convert each problem into a prompt (§5.1) and draw samples using nucleus sampling. Following Chen et al. (2021a), we report *pass*@k metric, defined as the fraction ![5_image_0.png](5_image_0.png) Figure 3: An example problem. Cells 1-2 (c1, c2) are the notebook context, and Cell 3 (c3) contains the intent. Cells 3a and 3b show two example completions of c3. of problems with at least one correct sample given a sample size k. To reduce variance, we estimate pass@k (k ≤ 30) by drawing 50 samples for each problem (Chen et al., 2021a). Decoding temperature t is 0.2 for k = 1 and 0.8 for k > 1. Refer to Appendix E for inference details. ## 5.1 **Lm Prompting Strategies** We explore two prompting strategies: prompting using the notebook context of a problem (§5.2), and few-shot prompting with extra exemplars as a prompt prefix before the notebook context (§5.3) to impose more control on the predicted code's style. Prompting with Notebook Contexts Fig. 3 depicts an example problem at c3 for prompting, where the prompt is the notebook context (preceding cells c1 and c2) and the current intent. The context also includes NL descriptions of the imported DataFrame schema (c2), such as its columns and example cell values, crucial for grounded understanding of structured knowledge (Xie et al., 2022). Completion 3a shows an example prediction. For the following problems after c3 (not shown), we use annotated reference solutions to previous turns in their contexts, reminiscent of multi-turn taskoriented dialogue evaluation (Andreas et al., 2020). Using Extra Few-shot Exemplars Besides the basic setting, we also explore prompting using four | pass@k | Existing Tasks | New Tasks | | | | | |-------------------|------------------|-------------|------|------|------|------| | 1 | 5 | 30 | 1 | 5 | 30 | | | Existing Models | | | | | | | | INCODER 1B | 20.8 | 30.9 | 47.0 | 2.3 | 4.0 | 9.9 | | INCODER 6B | 28.2 | 40.6 | 56.2 | 3.5 | 7.1 | 15.8 | | CODEGENmulti 350M | 9.0 | 13.6 | 21.3 | 0.8 | 0.9 | 2.6 | | CODEGENmulti 2B | 18.7 | 25.9 | 39.3 | 1.5 | 2.6 | 6.8 | | CODEGENmulti 6B | 20.0 | 28.5 | 42.8 | 1.7 | 3.4 | 8.9 | | CODEGENmulti 16B | 20.9 | 31.4 | 47.1 | 2.5 | 4.8 | 12.4 | | CODEGENmono 350M | 11.3 | 18.5 | 32.8 | 1.5 | 1.9 | 5.1 | | CODEGENmono 2B | 24.7 | 35.5 | 52.9 | 3.1 | 6.3 | 16.0 | | CODEGENmono 6B | 28.7 | 42.2 | 60.9 | 4.0 | 8.6 | 20.4 | | CODEGENmono 16B | 32.6 | 46.2 | 63.9 | 6.1 | 12.1 | 25.2 | | CODE-cushman-001 | 38.1 | 50.4 | 68.8 | 8.9 | 14.5 | 31.0 | | CODE-davinci-002 | 53.0 | 66.3 | 81.5 | 23.4 | 36.0 | 54.7 | | Our Models | | | | | | | | Base PALM 62B | 35.7 | 49.4 | 67.8 | 7.2 | 12.7 | 26.4 | | + Python f.t. | 43.6 | 58.8 | 75.3 | 11.9 | 21.7 | 40.7 | | + PACHINCO | 48.9 | 64.3 | 78.3 | 18.0 | 30.5 | 47.7 | | − Schema Desc. | 44.2 | 60.0 | 75.0 | 13.0 | 22.2 | 36.1 | Table 2: *pass*@k using notebook context as prompts. additional NL-to-code exemplars as prompt prefix before the notebook context. As shown in Fig. 3 (Completion 3b), we focus on prompting LMs to generate code that follows a multi-line, step-bystep (SbS) decomposition structure, in contrast with the common practice of chaining multiple API calls in a single line (Completion 3a). Each step is also optionally inlined with NL explanations. Such step-wise explanations could help novice developers understand model predictions, and they have been found effective for reasoning (Wei et al., 2022; Gao et al., 2022) and program induction (Nye et al., 2021) tasks. Following Kojima et al. (2022), we also use a preamble to further elicit step-wise decomposition in predictions. See Appendix L for a complete list of example prompts. ## 5.2 **Main Results** Tab. 2 reports *pass*@k on ARCADE using notebook contexts as prompts. PACHINCO achieves strong performance on both the *Existing Tasks* split and the *New Tasks* split due to its larger size and domain-specific fine-tuning. Impact of Fine-tuning The base PALM model outperforms most public code LMs and is on par with CODEGENmono 16B. Fine-tuning on Python (+Python *f.t.*, Tab. 2) and notebooks data (+PACHINCO) further closes the domain gap with improved *pass*@k. The absolute gain after finetuning on Python code is higher than continued training on notebooks, likely because the semantic gap between NL data and Python code is larger than | Dataset | HUMANEVAL | MBPP | TRANSCODER | |---------------------------|-------------|---------|--------------| | Metric | pass@100 | pass@80 | pass@25 | | PALM-CODER 540B† | 88.4 | 80.8 | 82.5 | | CODE-davinci-002 | 92.1 α | 84.5 α | 87.9 | | PaLM 62B (Python f.t. §4) | 91.5 | 86.0 | 86.4 | Table 3: Evaluation of existing code LMs and PaLM 62B after the first-stage fine-tuning on Python code. †Results from Chowdhery et al. (2022). α Results from Chen et al. (2022) . that between general Python code and notebooks. We note that the base PALM 62B model after fine-tuning on Python code corpora is already a strong code LM, performing competitively compared to other strong code LMs on established code generation (HUMANEVAL and MBPP) and translation (TRANSCODER) tasks (Tab. 3). With 7× more Python code tokens, our Python finetuned PALM 62B model outperforms the 8× larger PALM-CODER 540B model on all the three tasks. Comparing Existing Code LMs Among models with similar size and amount of Python training data (INCODER 6B vs. CODEGENmulti 6B), INCODER 6B performs better, likely because INCODER was trained on Jupyter notebooks.7 With 4× more Python data, CODEGENmono 6B takes over. Appendix F further reports the scaling curve of CODEGEN on ARCADE, where *pass*@k scales as a power law with model size. For reference, we also report the results using the CODEX API. PACHINCO significantly outperforms the smaller cushman API, while davinci-002 is stronger. While we cannot gain much insight from the results due to limited knowledge about davinci-002, through error analysis, we find that davinci-002 is better at instruction following, especially in understanding NL descriptions of complex DataFrame schema (§5.1). Intuitively, compared to existing benchmarks, NL understanding on ARCADE is more challenging given its succinct and potentially ambiguous intents together with rich contexts. Therefore, the gap between CODEXdavinci-002 and our models could be larger on AR-CADE compared to that on other datasets in Tab. 3. We leave improving the instruction following skills of PACHINCO as interesting future work. Comparing *Existing Tasks* and *New Tasks* The pass@k scores on *Existing Tasks* are significantly higher than on *New Tasks* across all models. However, comparing the improvements after Python and notebook-specific fine-tuning of the base LM, 7Fine-tuning CODEGEN on notebooks corpora would likely improve its performance on ARCADE. | Models | pass@30 # API | Lines of | Comment Tokens API | | | | |----------------------------------------------|-----------------|---------------|----------------------|-----|------|-----| | Code (LoC) | Lines | / Line / Line | | | | | | Baseline (Tab. 2) | 47.7 | 4.9 | 2.3 | 0.1 | 21.1 | 3.2 | | + More Context | 49.3 | 4.9 | 2.3 | 0 | 21.1 | 3.1 | | Prompting with Additional Few-shot Exemplars | | | | | | | | Vanilla Code | 49.9 | 5.3 | 2.4 | 0.1 | 20.8 | 3.1 | | Step-by-Step Code | 51.9 | 5.6 | 3.2 | 0.1 | 17.8 | 2.7 | | + Preamble | 51.9 | 5.9 | 3.5 | 0.2 | 16.9 | 2.5 | | + Pre. + Explanation | 52.5 | 6.8 | 4.2 | 3.3 | 14.9 | 2.2 | the gain on *New Tasks* is higher. One reason is that the problems in *Existing Tasks* are overall simpler than in *New Tasks* (§3.2). Additionally, some code data similar to our evaluation notebooks in *Existing Tasks* could leak into the training data of those LMs. Despite our significant effort to deduplicate fine-tuning data against *Existing Tasks* (§4), the base LM might have seen similar code data on the Web, *e.g.*, as data science tutorials. This highlights the importance of robust evaluation using held-out data, which is the purpose of the *New Tasks* split. Ambiguous Intents are Hard to Solve without Extra Specifications In §3 we discussed how intents in ARCADE can be ambiguous and underspecified (§3.2), and mitigating intent ambiguity using additional specifications to further clarify on the desired target output (§3.1). Indeed, those additional specifications are crucial for disambiguation. Without them the *pass*@30 of PACHINCO on the subset of 136 intents annotated with extra specifications on *New Tasks* dropped from 46.5% to 27.8% (43.8 on the full split *v.s.* 47.7 on Tab. 2), suggesting the importance of modeling prompt ambiguity for LLMs as future work. Grounded NL Understanding is Important Our prompts contain NL descriptions of imported DataFrames (§5.1), crucial for grounded understanding of NL intents (§3.2). Removing such schema descriptions significantly worsens the results, especially on *New Tasks*, as the last row in Tab. 2 (−Schema Desc.) shows. Encoding the states for more intermediate variables could likely further improve performance, which we leave as important future work. Further Analysis We report more ablation experiments, such as *pass*@k w.r.t. problem complexity and the notebook context size in Appendix G. ## 5.3 **Few-Shot Prompting Results** Next, we investigate few-shot prompting to help a model better understand the task while controlling ![7_image_0.png](7_image_0.png) the style of predictions.8 Tab. 4 summarizes the results on *New Tasks*. We start with the prompting strategy of predicting just the **Step-by-Step** Code (SbS) *without* preambles or explanations (*i.e.*, only the code part of Completion 3b in Fig. 3), which improves over the baseline using only notebook contexts (*c.f.* Tab. 2). SbS prompting is especially effective for problems without adequate contexts, yielding 6% absolute improvements for the first two rounds of problems in a notebook as compared to the zero-shot baseline. More interestingly, even if we include more context in the baseline such that its prompt length matches SbS prompting (Baseline + More Context), SbS prompting still outperforms, again suggesting the complimentary value of extra exemplars. Step-by-step Prompting Improves Code Style SbS prompting also changes the style of predicted code, which is decomposed into more lines (LoC↑, Tab. 4) where each line is simpler (Tokens/API per Line↓). In contrast, if we instead prompt the model using exemplars with "vanilla"-styled code following the common practice of chaining multiple pandas API calls in a single line (Vanilla Code, e.g., Completion 3a in Fig. 3), we get less *pass*@k improvement over the baseline while the code style remains consistent. Next, using preambles (+Preamble) to further encourage the model to produce step-by-step solutions improves the level of decomposition (LoC↑, Tokens/API per Line↓) while maintaining *pass*@k. More surprisingly, with additional inline NL explanations for each step (+Pre. + Explanation), PACHINCO produces even more decomposed solutions with slightly improved accuracy. As a result, those predictions have rich NL comments, with the number of comment lines nearly equal to the number of code lines. Interestingly, the predicted 8We only evaluate PACHINCO because the prompt length (max 2,100 sub-tokens) exceeds the limit of public code LMs. solutions are also more complex, as indicated by the increased pandas API usage (\# API↑). However, as we explain in Appendix H, on Existing Tasks, while prompting with NL explanations still alters the code style, *pass*@k is slightly worse. This is likely due to the fact that this split contains problems similar to the base LM's training data, and prompting the model to generate additional NL comments breaks its *"flow"* of generating code by memorization. Moreover, this split is also dominated by simpler tasks requiring fewer steps, while explanation-based prompting favors predicting more complex solutions with richer API usage and more code tokens (Tab. 4). Nevertheless, prompting with explanations yields more diverse predictions and could also help developers better understand the generated solutions, as we discuss next and also in §6. Step-by-Step Prompting Diversifies Solutions We also explore whether SbS prompting helps produce more *diverse* solution approaches. Intuitively, more output diversity could improve the odds of finding a solution at higher sample sizes. Determining whether two solutions are "different" is difficult and subjective, but we approximate this in two ways. First, we use the sequence of pandas API calls as a signature of the high-level solution pattern. Second, since two solutions might have the same *functionality* (executing to the same output), we also cluster predictions based on their outputs. Figs. 4a and 4b plot the cumulative distributions of the number of unique solution patterns and output clusters on the *New Tasks* split. SbS prompting increases diversity on both metrics compared to the baselines. Notably, prompting with NL explanations yields even more solution patterns. Diverse predictions could help handle underspecified intents (§3.2), since they might correspond to different interpretations of an ambiguous intent. Having diverse predictions also allows us to translate better *pass*@k performance into better performance on a single suggestion using post-hoc reranking such as self-consistency decoding (Wang et al., 2022b), where we return the user one prediction from the largest output cluster instead of showing all k predictions (Fig. 4c). SbS prompting significantly improves over baselines. Notably, the 1-sample accuracy of SbS with NL explanations outperforms *pass*@5 of the baseline in Tab. 2. Refer to Appendix I for further analysis. As a side note, while SbS prompting leads to improved sample diversity, it may not directly improve code quality. If we consider functional correctness to approximate code quality, we observe that vanilla few-shot prompting and SbS variants have a similar fraction of correct samples (∼ 15%). This suggests that for SbS prompting, it is higher sample diversity that may contribute to improved pass@k (k > 1) and reranking accuracy instead of other potential factors. ## 6 **Case Study: How Useful Is Predicted** Code With Step-Wise Explanations? Finally, we remark that besides improving solution diversity, step-by-step prompting with NL explanations could also potentially help novice data scientists understand model-generated solutions, as shown in the following qualitative case study. First, NL explanations could help users follow the flow of complex data transformations for programs involving a chain of pandas operations. By decomposing and explaining how data is manipulated after individual transformation steps, it is easier for users to understand the solution and track its dataflow behind the scene, especially when some steps involve complex computation (Fig. 17), or the underlying schema is less intelligible (*e.g.*, column names with abbreviations, Fig. 18). Additionally, some inline explanations also describe the output of intermediate steps, which is particularly helpful when these steps involve advanced pandas functions whose output structure may not be obvious, such as pd.unstack (Fig. 19) Meanwhile, step-wise NL explanations serve as high-level procedural descriptions of code, which enable users to easily browse through and understand different solution approaches without being distracted by nuances in the actual code implementation (Fig. 20). Moreover, explanations also help users verify the code solutions by identifying potentially incorrect steps (Fig. 21). The observations presented here offer insight into potential future avenues to improve the utility of code LMs for developers through the use of step-by-step explanations, which we leave as important future work. 7 **Related Work** Automating Data Science The amount of expertise required in data science has called for development of systems to automate its lifecycle (Wang et al., 2021b). Much work has focused on automating feature engineering and tuning of ML models (AutoML, He et al., 2021; Karmaker et al., 2020), with well-established systems (Feurer et al., 2015) and benchmarks (Zöller and Huber, 2021). This paper focuses on automating tabular data wrangling and EDA tasks, which account for nearly the same amount of code and documentations in notebooks as that for ML-related tasks (Agashe et al., 2019; Wang et al., 2022a). Along this line, existing research synthesizes data wrangling programs using I/O examples (Bavishi et al., 2019; Shi et al., 2020) or partial table contents (Chen et al., 2021c), followed by recent efforts using LLMs with additional NL specifications (Jain et al., 2021; Bavishi, 2022). This paper considers code generation in notebooks with multiple contextually dependent problems (see §3.2 for recent work). In addition, other works have also considered applications such as synthesizing visualization plots (Amar et al., 2005; Wang et al., 2019; Narechania et al., 2020; Fu et al., 2020; Wu et al., 2022b). Context-driven Code Generation Our work is another application of context-driven code generation, which maps a series of contextually dependent utterances to programs, such as domain-specific logical forms (Zettlemoyer and Collins, 2009; Long et al., 2016; Iyyer et al., 2017; Andreas et al., 2020), SQL queries over databases (Hemphill et al., 1990; Suhr et al., 2018; Yu et al., 2019a,b), or generalpurpose PLs (Nijkamp et al., 2022). ARCADE further offers contextually dependent utterances exhibiting non-trivial dependencies (§2), with target programs defined in a general-purpose PL. 8 **Conclusion** In this paper we present ARCADE, a code generation benchmark for data wrangling and EDA tasks in computational notebooks, featuring problems with realistic NL intents and rich contexts. We also develop PACHINCO, a 62B LM tailored for data science. PACHINCO outperforms public LMs on ARCADE, while being effective in few-shot learning to improve code style and solution diversity. ## 9 **Limitations** We discuss limitations of our work that hopefully could inspire future research in this avenue. Task Coverage in A**RCADE** ARCADE consists of realistic data wrangling and EDA tasks for a variety of ML datasets. In particular, we focus on problems that can be solved using pandas because of its popularity in data science - 90% of Kaggle notebooks use pandas. Still, our annotated problems may not cover all the types of tasks in these two categories. As an example, data visualization is an important part of EDA. Our dataset also includes 59 natural language to plotting problems, which are not used in this paper due to challenges in automated evaluation (Chen et al., 2021b). Future work might consider evaluation of plotting tasks using unit tests (Lai et al., 2022). Additionally, some of the existing datasets in Tab. 1 usually contain broader types of problems other than the wrangling and EDA tasks considered in this paper (*e.g.*, fitting ML models, §7). We leave expanding the task spectrum as important future work. Session-level Evaluation ARCADE features multiple contextually dependent problems in computational notebooks. As the first step towards evaluating code LMs in this interactive program synthesis paradigm, we report turn-level accuracy, and generate notebook context for prompting using ground-truth solutions for the prior turns of a problem (§5.1), following the common evaluation protocol in task-oriented dialogue (Hosseini-Asl et al., 2020; Andreas et al., 2020). Future work could consider a more realistic scenario of session-level evaluation where history contexts consist of model-predicted code instead of the reference (Yang et al., 2020; Nijkamp et al., 2022). However, this evaluation setting is still not ideal without modeling the user (*e.g.*, asking follow-up questions to correct a model's predictions in a turn before proceeding to the next round, see Austin et al., 2021), which often requires building specialized simulators (Cheng et al., 2022a). Reliance on Large Language Models Our experiments are based on public and in-house large code LMs (PACHINCO), which require adequate computational resources9and create carbon emissions (Patterson et al., 2021). Their predictions could also be subject to known issues such as misalignment with user intents; for a discussion 9FLOPs usage of fine-tuning PACHINCO is 3.6 × 1022. of these and other risks of code language models, see Chen et al. (2021a, Appendices E-H) and Chowdhery et al. (2022, Section 6.4). To reduce the amount of computational resources required, our initial prompting experiments (§5.2) and error analysis (Appendix J) suggest that leveraging program execution information (*e.g.*, schema descriptions) could be a promising direction to improve sample efficiency and reduce the size of code LMs (Nye et al., 2021), while explicit modeling of code-intent correspondence (Zhang et al., 2022) could be a viable path to mitigate alignment issues in model predictions. In addition, as generative AI coding tools are becoming more available to developers, more efforts are required to understand the potential limitations of those systems and the risks they may pose, such as producing insecure code and over-reliance on model predictions (Chen et al., 2021a). We leave addressing those issues as important future work. ## Acknowledgements We are grateful to Meg Risdal and Goeff Thomas from Kaggle for help with dataset collection, and Miltos Allamanis for research discussion. 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Benchmark and survey of automated machine learning frameworks. *Journal of artificial intelligence research*, 70:409–472. # Supplementary Materials ## A **Details Of Dataset Construction** In this section we elaborate on the process of building ARCADE. ## A.1 **Mining Examples From Existing Notebooks** To build the *Existing Tasks* split with annotated NL-to-code problems from publicly-available notebooks, we first identify candidate code cells performing data wrangling and EDA tasks from existing high-quality data science notebooks, and then manually annotate these cells with NL intents. Collecting Notebooks for Annotation To form a pool of candidate notebooks, we use JuICe (Agashe et al., 2019), a collection of Jupyter notebooks from GitHub, together with additional notebooks from BIGQUERY10, yielding over 1.5M notebooks in total. These notebooks are first filtered and neardeduplicated, similar to PACHINCO's training data preprocessing step in Appendix D. We then identify candidate code cells from the remaining notebooks for annotation. Specifically, we select code cells that are either (1) contain pandas programs with at least three API calls, or (2) preceded by a Markdown cell with a short question as its content (e.g., *What are the top 10 producers?*). The first heuristic is useful to identify complex wrangling tasks, while the second one is particularly effective in finding interesting dataset-specific EDA tasks, and the existing Markdown texts also provide reference for labeling intents later. Next, we group the notebooks with at least one candidate cell based on their underlying ML datasets (*e.g.*, imported using pd.read_csv()), and then select the top 5 notebooks with the greatest number of candidate cells from a curated set of 36 dataset groups for annotation. This set contains ML datasets from a variety of domains and schema. We favor notebooks with more candidate cells so that we could extract multiple NL-to-code problems within the same notebook. Annotation We hired a group of data scientists to annotate the notebooks selected above, following the process outlined in §3.1. Annotation primarily consists of judging the quality of candidate code cells, fixing any errors, and creating NL intents summarizing the code. Throughout the annotation process, we find that re-purposing notebooks in the wild to build our benchmark is not an easy task. As an example, many notebooks in JuICe are data science tutorials, which often contains documentation that includes background knowledge, reference materials, and even solution hints. Those extra information makes the code generation task easier, and may not reflect the style of ordinary notebooks authored by data scientists in their day-to-day work. We therefore ask the annotators to clean the notebook and remove such extra information whenever possible. ## A.2 **Creating Notebooks With Examples From Scratch** The problems derived from high-quality GitHub notebooks could capture realistic tasks and notebook contexts, but may result in artificially high evaluation accuracies due to potential leakage of evaluation notebooks to the training data of LLMs, which is a common issue in LLM evaluation (Brown et al., 2020). To defend against this data contamination, we additionally annotated 660 problems by creating notebooks from scratch. Sourcing Novel ML Datasets To ensure that those newly-created examples can be used to evaluate the generalization ability of code LMs on unseen ML datasets, we create notebooks targeting data wrangling and EDA tasks for 70 tabular ML datasets that have been recently uploaded to the Kaggle data science platform since February 2022. Those short-listed datasets are manually selected from a pool of 600 datasets with reasonably complex schema (*e.g.*, having columns with diverse data type), and are verified by our annotators that no older-versioned datasets with similar schema appeared before. Creating Notebooks For each ML dataset, the annotators were asked to create one notebook with a series of wrangling and EDA tasks annotated with NL intents. Specifically, we ask annotators to come up with tasks that they would like to perform in order to gain insights into these recently appeared ML datasets in order to build models for them. We follow the same standard to create intents as in creating 10https://cloud.google.com/bigquery/public-data/ Existing Tasks. To make the problems more challenging, annotators are encouraged to create harder tasks whose code solutions require at least 5 pandas API calls. ## A.3 **Annotation Process And Quality Assurance** Eight freelancers proficient in English and reported skill in pandas are hired from Upwork, with an average of 3 years of experience. All the annotators went through a qualification round with data science interview questions. In building the *Existing Tasks* split, each freelancer first performed a trial batch by annotating a single notebook, and received detailed comments from the first author, before proceeding with annotating the rest of assigned notebooks. Each annotated sample is reviewed by the first author. Annotators spent 3 ∼ 4 minutes to create each problem on average. To create the more challenging New Tasks split from scratch, we only invite the top-3 performers for this task since it is harder than labeling existing notebooks. Each created notebook is first peer reviewed by another annotator, before a final round of review by the first author. Since the annotators have already worked on the prior task of creating examples in existing notebooks, they are fairly familiar with the requirement, and are able to create each problem in 13 minutes on average. To further improve quality, we also did another round of manual review for the set of problems in the two splits that a strong code LLM fails to predict the annotated solution (based on fuzzy output matching) within a budget of 50 samples.11 ## B Outline Of Arcade **Annotation Guideline** In this section we provide a brief outline of our annotation guideline. Existing Tasks The annotators are given a list of Jupyter notebooks. Each notebook uses pandas to perform certain data analysis tasks. For each notebook, an annotator is asked to: 1. Identify code cells that contain instructive code snippets that perform data wrangling or exploratory data analysis tasks. 2. Fix the notebook and make them clean and executable. 3. For each code snippet identified in Step 1, create natural language descriptions of the task. Also verify the code solution and fix them as appropriate. Finally, remove any redundant text in the notebook (*e.g.*, solution outline or hints for tutorial notebooks) that could give away to the refernce solution. Instruction on Creating Natural Intents Specifically, for step 3, in order to collect realistic NL intents, the annotators are given the following high-level description, followed by detailed instructions and examples. Below we share some suggestions to write good intents. Keep it natural without redundant explanations. Imagine an AI programmer can help you accomplish simple data wrangling and EDA tasks, what kind of intents will you send to the system? Our goal is to collect real inputs to such a system from data scientists like you. One idea to write good intents is to keep it concise such that another programmer could quickly understand and implement a solution that executes to the same outputs. You are encouraged to create simple, short intents while describing the desired outputs without much ambiguity. New Tasks For each ML dataset we provided, an annotator creates a Colab notebook with code snippets for some interesting data wrangling and exploratory data analysis tasks using this dataset. Each code snippet is paired with its natural language intent, simliar to the process of annotating *Existing Tasks*. We ask annotators to feel free to work on any tasks that they may find interesting for the given dataset, as long 11We use CODEX-DAVINCI-002. as the code solution for the task should consist of multiple lines and use different pandas API functions. Different from annotating *Existing Tasks*, we ask them to first create a natural language intent for their task, and then write a code solution in the next cell. Below is an excerpt from the annotation guideline describing the types of data wranling and EDA tasks to create. ## What Tasks To Create In general, you may create whatever exploratory data analysis tasks that you find interesting for the given datasets. To come up with interesting tasks, you can think in this way: before training your ML models for the dataset, what kind of data wrangling or EDA tasks would you like to perform on the dataset? Below are some more concrete descriptions of such wrangling or EDA tasks: Data Preprocessing/Wrangling Tasks which involves modifying existing dataframes or creating new ones. Such as normalizing column names, adding new columns, modifying existing columns (e.g., converting string values to date times), generating new dataframes using ops like group_by, and so on. Some datasets we shared are just raw data without any preprocessing or cleaning. Feel free to . Please also refer to Section: Identify Code Snippets to Annotate in our previous annotation guideline for more examples. Exploratory Data Analysis Tasks that Require Some Wrangling and Preprocessing Answering interesting EDA questions using the given dataset, but some data wrangling steps are required in order to derive the answer. For example, given a dataframe df of user shopping history and credit card expiration dates in the format of df.loc[0]['cc_exp'] = '08/26'. To answer the EDA question "How many users have a credit card expiring in 2024?", we need to first convert the expiration year from the string-formatted cc_exp column. To encourage the annotators to create more complex tasks, we also provide the following high-level instruction: ## Complexity Of Tasks You should create relatively complex tasks that require multiple steps and also a combination of different pandas APIs to solve them. Avoid problems that can be solved using one-liner code such as df.group_by(...).sort_values(...). An ideal task should be reasonably complex and needs to be broken down into multiple smaller steps to solve, and each step may require using one or multiple pandas functions. As a general rule of thumb, you should aim at creating tasks that either have at least 50 tokens or use at least 4 pandas APIs (dataframe/series indexing, like df[df['continent'] == 'NA'] is also counted as one API usage). You can find more concrete example tasks at the end of this doc. Full Guideline Our annotation guideline is 35-pages long in total, which we will provide on a perrequest basis. Please contact [email protected] to request access. ## C **Descriptions Of Existing Data Science Code Generation Dataset** Here, we describe existing natural language to code generation datasets in data science domain listed in Tab. 1 in more detail. JuICe (Agashe et al., 2019) contains exercise problems in assignment notebooks from data science tutorials or coures, where the NL intents are usually elaborative assignment problem definitions. Notebooks in JuICe are not executable so evaluation is performed by surface-level matching (exact match or BLEU) between reference and predicted programs. DSP (Chandel et al., 2022) contains problems from a filtered set of JuICe notebooks that are executable and also associated with unit tests for auto-grading. Hence the intents in DSP follow similar patterns as those in JuICe. To ensure that the free-form model-predicted code is compatible with unit tests, DSP uses the unit test code itself as extra model input besides NL intents to constrain the model to generate code that could be directly consumed by the tests. ExeDS (Huang et al., 2022) is a concurrent work to this paper. It is another set of filtered problems from JuICe. Similar to this work, ExeDS uses hand-annotated intents, and compares the execution output between reference and predicted code for evaluation instead of relying on unit tests (§3.3). NLGP (Heyman et al., 2021) is another collection of the NL-to-code problems in Jupyter notebooks with short annotated intents for simple data manipulation tasks, where most notebooks have one associated problem. DS-1000 (Lai et al., 2022) is a collection of data science problems derived from StackOverflow questions. It primarily features problems using synthetic contexts with minimal working examples, and therefore does not concerns with code generation in notebooks with interrelated problems on general ML datasets. ## D **Details Of Fine-Tuning P**Achinco Pre-processing Python Source Code Data We detail the preprocessing steps for the Python source code corpus used in the first stage of fine-tuning in the data card (Appendix K). Pre-processing Notebooks Data We apply additional domain-specific pre-processing steps for the Jupyter notebooks corpus, such as filtering out notebooks without any Markdown cells, or with fewer than 4 code cells. In addition, to mitigate the risk of having notebooks similar to the evaluation notebooks from GitHub in the *Existing Tasks* split leaked into the training data, we perform near de-duplication against notebooks in *Existing Tasks* at the *cell* level. Specifically, we cluster the cells of notebooks in both the evaluation and training sets based on a fuzzy-matching similarity metric, and remove any training notebooks that has one cell that falls into the same cluster as a cell from one of the evaluation notebooks. This process eliminates ∼350K notebooks from the fine-tuning data. Our final training set consist of ∼3.8M notebooks and ∼9.6B tokens in total. Linearize Notebooks to Python Source Code We convert computational notebooks for finetuning (§4) and evaluation (§5.1) to Python source code using nbconvert. 12 Specifically, Markdown and code cells in a notebook are concatenated using the special delimiter '\# In[]:', and text in Markdown cells is commented out using the '\# ' prefix. See Listing 7 for an example of the linearized notebook for Fig. 1 (up to c3). Jupyter notebooks that are converted to Python files in such format are common in GitHub repositories, which mitigates the domain transfer gap between general Python code and notebook-specific data, and also allows us to prompt public code LLMs that have not been specifically trained on Jupyter notebooks data. Fine-tuning Hyper-parameters For the two-stage fine-tuning (§4), we use the similar training recipe of the base LM. Specifically, we apply the learning rate decay scheduling 0.2/ √t, where t is the number of steps. At the first stage of fine-tuning on Python source data, we train the model for 124K steps (1 epoch) with a batch size of 256. Afterwards, we reload the optimizer state and continue training on the Jupyter notebooks data (9.6B tokens) using the same hyper parameter for 3 epochs (∼ 572K steps). The model is implemented in JAX13, and is fine-tuned on 512 TPU v4 chips. ## E **Inference Setup** For CODEGEN, we use the inference script from the official GitHub repository.14 For INCODER, we follow the official inference example script and use the release on Huggingface model hub.15 We convert each example in our dataset to Python source code to a prompt, as outlined in §5.1. Notebooks are linearized using nbconvert similar as generating fine-tuning data (Appendix D). One exception is INCODER, for 12One exception is INCODER, as explained in Appendix E. 13https://github.com/google/jax 14https://github.com/salesforce/CodeGen 15https://github.com/dpfried/incoder/blob/main/example_usage.py which we follow Fried et al. (2022) and use the Jupyter notebook linearization template used in its pre-training. At inference time, by default we left-truncate notebook context up to 900 tokens (measured by PACH-INCO's vocabulary), which fit in the context window size of all LLMs we evaluated. We also make sure to always include NL schema descriptions in prompts given their importance in understanding NL intents. In addition, for few-shot experiments in §5.3, we use additional 1,200 tokens to accommodate the prompt prefix, making the total maximal prompt length to be 2,100. Due to its excessive length, we only perform few-shot prompting experiments on PACHINCO since its rotatory positional embedding (Su et al., 2021) could generalize to encode longer contexts at inference time. We use nucleus sampling with a top probability of 0.95 and a temperature of 0.8 to draw 50 samples for each problem. For *pass*@1 evaluation, we use a temperate of 0.2, which gives very similar results compared to greedy decoding for all the models considered in Tab. 2. Due to rate limit in open AI API, we therefore use greedy decoding for pass@1 evaluation for CODE-cushman-001 and CODE-davinci-002. We set the maximum target length to be 512 tokens. ![18_image_0.png](18_image_0.png) ## F Codegen Scaling Curve On A**Rcade** Fig. 5 depicts the scaling curve of ARCADE with respect to the number of parameters for CODEGENmono models. The pass rate scales nearly log-linearly as a function of model size, and the performance has not saturated, especially on the *New Tasks* split. This shows ARCADE is a reliable dataset to study the scaling behavior of code LLMs. The slope of the curve on *New Tasks* is also smaller than on other datasets, suggesting that this problem set is more challenging for CODEGEN models. It is also interesting to extrapolate CODEGEN models to 62B according to the scaling curve and compare with our models at similar size. This gives a projected *pass*@10 of 22% on *New Tasks*, wihch is lower than PALM after the first-stage Python fine-tuning (28%). ## G Break-Down Analysis Of Pass@K On A**Rcade** Accuracy with Problem Complexity To better understand PACHINCO's performance on problems at different levels of complexity, we plot *pass*@30 with respect to the number of pandas function calls in the annotated reference solutions, as shown in Fig. 6. For problems with similar complexity, PACHINCO generally achieves higher pass rate on *Existing Tasks*, again suggesting that the *New Tasks* split is still more challenging even after controlling problem complexity. Fig. 7 plots *pass*@30 with respect to the AST size of reference programs. Similar to Fig. 6, results on *New Tasks* are generally lower. Meanwhile, it seems that AST size correlates better with *pass*@k ![19_image_0.png](19_image_0.png) ![19_image_1.png](19_image_1.png) compared to the number of API usage, while the latter metric offers more intuitive information about the data transformation steps involved. How Much Notebook Context is Useful? ARCADE requires a model to leverage rich programmatic and NL context in test notebooks to generate code solutions for the current cell. To study PACHINCO's performance with varying amount of available notebook context, we control the number d of context cells {ci} n−1 i=n−d (§2) when generating code for each problem (at cell cn) in our dataset. Fig. 8 depicts pass@30 as a function of the context size d. Since we use the first preceding cell cn−1 to store the NL intent un for cn (Appendix L), having only one context cell is equivalent to the "cold-start" setting of only using the intent un (besides schema description) to predict cn. PACHINCO achieves a pass rate of 44% (existing tasks) and 17% (new tasks) in this challenging setting (d = 1), with errors mostly due to failure in referring to variables that the solution relies on, whose information is not present in the short context. Indeed, including additional context cells is crucial for good performance. In particular, having 3 context cells could already lift the *pass*@30 to 72% and 36% on the two splits - 1.6 ∼ 2× higher than d = 1. The results also start to plateau after including 5 ∼ 7 context cells, with diminishing returns after including more cells, which is in line with findings in Agashe et al. (2019). 16 Empirically, we observe that using more context helps to reduce schema understanding errors (*e.g.*, using undefined columns in DataFrames). Fig. 9 illustrates the distribution of execution error types on failed predictions. Notably, using more notebook context cells significantly reduces the chance of NameErrors caused by using undefined variables in context. The number of KeyErrors is also reduced, indicating that the model makes fewer schema understanding errors when referring to columns in DataFrames. Does Problem Location Impact Performance? Another interesting angle to study the effect of context is through the lens of model accuracy when solving problems cn at different locations. Intuitively, problems located later in a notebook (n is larger) would have more context available, therefore they could be easier to answer (Wang and Cho, 2016). Fig. 10 shows *pass*@30 on problems grouped by their preceding context size, which shows increased task success rate when solving problems with more context, confirming the prior intuition.17 ![20_image_0.png](20_image_0.png) ![20_image_1.png](20_image_1.png) ![20_image_2.png](20_image_2.png) ![21_image_0.png](21_image_0.png) ## H **Additional Few-Shot Prompting Results** Plots for the Results on *New Tasks* in **Tab. 4** Fig. 11 plots the few-shot prompting results on New Tasks presented in Tab. 4. Here we also report breakdown results of pass rate on problems with varying level of complexity. Step-by-step prompting and its variants are helpful across the board, especially for harder tasks with more than 7 pandas function calls. This might suggest the value of step-by-step decomposition when synthesizing complex programs. Few-shot Prompting Results on *Existing Tasks* We also report results of prompting PACHINCO using few-shot exemplars on the *Existing Tasks* split in Fig. 12. Compared to the results obtained on *New Tasks* (Fig. 11), while few-shot prompting, especially step-by-step prompting, is still effective compared to the baseline, the gap is not as profound as the results on *New Tasks*. The difference between different prompting methods is also less significant, and using NL explanations (SbS + Preamble + Explanations) is less ![22_image_0.png](22_image_0.png) ![22_image_1.png](22_image_1.png) effective compared to the two baseline zero-shot approaches. This is likely due to potential evaluation data leakage. Intuitively, as the model relies on memorization to generate code that it has encountered during training to solve problems in *Existing Tasks*, using few-shot exemplars to "nudge" the model to generate code in a different style would be less effective.This issue is perhaps more problematic for prompting with additional inline explanations, as generating those extra interspersed NL comments would likely break the model's "flow" of generating code (without such explanations) that it has memorized. Additionally, explanation-based prompting favors generating more complex code solutions with more steps (LoC↑) and API calls, as indicated in Figs. 11 and 12, which could actually be counter-productive for *Existing Tasks*, where more than 70% of the tasks are simple and require less than 4 API calls to solve them. Nevertheless, these results reiterate the value of the *New Tasks* split as a more reliable benchmark to better differentiate different prompting strategies. ## I **Further Analysis Of Solution Diversity** Here we provide further analysis of the diversity in solution patterns, measured by the number of distinct pandas API call sequences used in the samples. Fig. 13 and Fig. 14 depict cumulative distributions of the number of solution patterns for different subsets of the predictions on the two task splits: all predictions, only those that execute successfully, and only the correct predictions. In each case, we see that step-by-step prompting leads to increased diversity compared to the baselines, and prompting with NL explanations further increases the diversity. While increased diversity is helpful in finding a correct solution at higher sample size k, it is also helpful when considering only correct solutions because a user might want to see a variety of solution approaches, whether for educational purposes or to choose the one they like best (which is partially subjective). Refer to §6 for such examples. Next, Fig. 15a presents the cumulative distribution of the number of output clusters for predictions on Existing Tasks, where step-by-step prompting variants produce more functionally diverse solutions that execute to different results. Finally, Fig. 15b shows self-consistency reranking accuracy on the Existing Tasks split. While step-by-step code prompting is still helpful due to improved prediction diversity, similar to the results on the *New Tasks* split (*c.f.* Fig. 4c), the results obtained with prompting using additional NL explanations becomes worse, due to the relatively lower success rate of this prompting strategy (Fig. 12, see Appendix H for more discussions). ![24_image_0.png](24_image_0.png) ## J **Error Analysis** J.1 **Summary Of Error Types** To understand the types of errors that LLMs make on ARCADE, especially on challenging problems, we conduct an error analysis on model predictions on the *New Tasks* split (Tab. 2). Overall, we notice a significant drop in execution errors after two-stage code fine-tuning (base LM7→Python-finetuned LM7→PACHINCO, §4). Out of all the incorrect predictions from PACHINCO under the fuzzy output matching evaluation metric (§3.3), roughly 35% of the samples result in execution errors, while the remaining 65% predictions have executable programs but are functionally incorrect. Summary of Inexecutable Predictions First, for inexecutable samples, we present an analysis of the distribution of different execution error types, as illustrated in Fig. 16. The primary source of execution error is KeyError and AttributeError due to reference to non-existing indices or columns in DataFrames. While in the prompts we provide NL schema descriptions for DataFrames loaded to notebooks (§5.1), such descriptions for intermediate DataFrames that are later derived in the context are still missing due to limited prompt length, and the model may not be able infer their schema information solely from the source code. This could be especially problematic for APIs that create compound intermediate DataFrames with complex schema, such as pd.groupby, which accounts for that more than 50% of those KeyErrors and AttributeErrors. Similarly, other execution errors such as ValueError and TypeError are often caused by the insufficient knowledge about the DataFrame contents. For example, ValueError occurs when a model tries to calculate the mean of a column which has NaN values. This finding suggests the importance of developing LLMs that could handle longer context (Wu et al., 2022a) in order to include more DataFrame information in prompts. We gave a detailed case study on these types of execution errors later in this section. Summary of Executable but Incorrect Predictions Next, we conduct a manual analysis on 50 randomly sampled incorrect predictions that are executable. The cause of these errors can be grouped into the following categories: 1. Complex problems requiring non-trivial reasoning or data transformation steps (43%); 2. Errors in interpreting NL intents, such as missing a requirement specified in the intent (e.g., *round to two decimal places*) in the code solution (26%); 3. Errors caused by underspecified intents (§3.2, 19%); 4. False-negatives due to limited coverage of the fuzzy-matching evaluation metric (§3.3, 6%); 5. Annotation errors (6%). The primary source of errors is due to complex problems, which reiterates the motivation of ARCADE — evaluating code LLMs on challenging data wrangling and EDA tasks. The second majority type of errors (misunderstanding intents) suggests room to improve PACHINCO's skill in instruction following. Next, a non-trivial amount of errors are caused by underspecified intents, which are common in the setting of prompting LLMs using ambiguous instructions (§3.2), calling for future research to specifically address this issue. Finally, our evaluation metric based on fuzzy output matching seems effective in identifying plausible alternative solutions. Still, there are non-trivial cases where there are multiple ways of presenting the outputs (*e.g.*, DataFrames with nested columns or different orientations, Fig. 30). ## J.2 **Case Study** Case Study for Inexecutable Predictions Execution error has error message from the notebook environment. We can classify these errors into more fine-grained categories in Fig. 16. As the result shows, KeyError is the top error mode in the execution errors. Over 50% of the KeyError are associated with the pd.groupby API call. pd.groupby API call changes the dataframe schema as the model generates more data transformation code. For example, pd.groupby().mean() will remove non-numeric columns in the dataframe. This requires the model to have a profound understanding of the dataframe schema. We gave an example in Fig. 22. The column shipping_fee is string value which will be removed after df.groupby(ship_state).sum(). The secondary source of execution error is **AttributeError**, which shares a similar cause to the KeyError. This is because AttributeError is often triggered by calling a non-existing column as an attribute of a dataframe. An example is given in Fig. 23, where the model tries to call the non-existing column signupdate as an attribute of df_users, leading to an AttributeError. These two error modes suggest that building a better schema-aware language model is a promising future research direction. We also present Fig. 24 and Fig. 25 as examples for **TypeError** and **ValueError**, respectively. These two error modes are often caused by insufficient knowledge of the column types and example values. For example, the model tried to compare a string-value column to a integer in Fig. 24, which causes TypeError. Fig. 25 showcased that the model tries to apply numeric operation pd.DataFrame.mean() on a column with NaN values, leading to ValueError. These errors suggest room to improve NL schema descriptions (§5.1) with column type annotation and more example cell values. Case Study for Executable but Incorrect Predictions To complement the discussion earlier in Appendix J, we showcase examples of representative semantic errors, where the predictions are executable but functionally incorrect. The primary source of semantic error is **complex reasoning**. Two complex problems are given in Fig. 26 and Fig. 27. In Fig. 26, the model need to infer that *the last 10 years* can be computed using the code dt.datetime.today().year - 10. Fig. 27 is another example of complex data wrangling steps. To generate the correct program, the model need to compare the current rank to the past rank, while ensuring that the 2021 rank entries exist, and then aggregate the information. Misinterpretation of NL intents is the secondary source of semantic errors. In Fig. 28, the generated program does not reflect the search constraint in the intent (institute type is IIT). Another source of semantic errors is **underspecified intents** with multiple plausible interpretations. As an example, in Fig. 29 the predicted program provides one plausible interpretation by calculating the sum of front- and left-facing trees, while the reference code implements another interpretation, which presents a table with the two numbers listed separately. Such cases are hard to cover by the fuzzy-matching evaluation metric, which may suggest the importance of collecting multiple reference interpretations for underspecified intents. Finally, Fig. 30 illustrates an issue with the **coverage of the evaluation function based on output** matching. In this example with complex output DataFrames, the model gives an acceptable answer which differs from the reference in its DataFrame schema and orientation. u: *What are the five most common genres for shows released during the year having the* ![26_image_1.png](26_image_1.png) highest percent increase in votes? # Step 1: Get the year that has the highest percent increase in votes. ![26_image_0.png](26_image_0.png) Figure 17: An example prediction from PACHINCO with a complex single step. ``` u: Convert crash dates to datetime and show the total number of vehicles involved in crashes over the years. # Step 1: Convert crash date to datetime df['CRASH DATE'] = pd.to_datetime(df['CRASH DATE']) # Step 2: Create a new column with the year of the crash date df['CRASH YEAR'] = df['CRASH DATE'].dt.year # Step 3: Return the total number of vehicles involved in crashes in each year result = df.groupby('CRASH YEAR')['TOT VEH'].sum() ``` Figure 18: An example prediction from PACHINCO that explains the semantics of a column. ![26_image_2.png](26_image_2.png) Figure 19: An example prediction from PACHINCO that explains the return value from a complex API. ![26_image_3.png](26_image_3.png) u: In which year, within the last ten years, did Israel receive the highest amount of financial aid, in constant amount? Show the year and amount received. Correct Solution: \# Step 1: Get all the rows with Israel in country_name column israel_aid_df = aid[aid['country_name'] == 'Israel'] \# Step 2: Get all the rows for last 10 years israel_aid_df = israel_aid_df[israel_aid_df['fiscal_year'] >= israel_aid_df['fiscal_year'].max()-10] \# Step 3: Group by the years and get the sum of aid received in each year. israel_aid_df.groupby('fiscal_year').constant_amount.sum().agg(['max','idxmax']) Incorrect Solution: \# Step 1: Get the Israel data isr = aid[aid.country_name=='Israel'] \# Step 2: Group the data by fiscal year isr_by_year_df = isr.groupby('fiscal_year') \# Step 3: Find the index of the row that has the highest constant amount idx = isr_by_year_df['constant_amount'].idxmax() \# Step 4: Get the row with the highest constant amount max_aid = isr.loc[idx] \# Step 5: Return the fiscal year and constant amount result = max_aid[['fiscal_year', 'constant_amount']] Figure 21: Two predictions from PACHINCO. NL explanations help users identify incorrect steps. u: *What are the average shipping fees for each state, starting from highest to lowest?* (rounded to 2 decimal places)? Reference Solution: ![27_image_0.png](27_image_0.png) df['shipping_fee']=df['shipping_fee'].apply(total_float) df.groupby('ship_state').mean()['shipping_fee'].sort_values(ascending=False).round(2) Incorrect Model Output: df.groupby('ship_state').sum()['shipping_fee'].sort_values(ascending=False).apply( ![27_image_1.png](27_image_1.png) Figure 22: An example of KeyError: the model calls a column which is removed after the pd.groupby().mean() API call. u: *Show how many new users signed up for every year since 2000* Reference Solution: ![27_image_2.png](27_image_2.png) df_users[df_users['Customer Since']>datetime.fromisoformat('2000-01-01')]['Customer Since'].apply(lambda x: x.year)\ .value_counts().sort_index() Incorrect Model Output: df_users['signup_year'] = df_users.signupdate.dt.year df_users[df_users.signupdate.dt.year >= 2000].signup_year.value_counts() \# Error Text: RuntimeError: AttributeError: 'DataFrame' object has no attribute 'signupdate' Figure 23: An example of AttributeError: the model tries to call a non-existing column. ![27_image_3.png](27_image_3.png) u: What are the top five models with most number of bikes having mileage less than 5000 kilometers? Reference Solution: ![27_image_4.png](27_image_4.png) ![27_image_5.png](27_image_5.png) df_bikes=bikes[bikes['Ridden for']<5000] df_bikes['Model Name'].value_counts().head() Incorrect Model Output: model=bikes.groupby('Model Name')['Comfort'].mean().loc[bikes_models.index].idxmax() \# Error Text: RuntimeError: TypeError: '<' not supported between instances of 'str' and 'int' Figure 24: An example of TypeError: the model is intent to compare a string-value column to an integer. u: *What is the average number of filed charges for drug related cases?* Reference Solution: ![27_image_6.png](27_image_6.png) ![27_image_8.png](27_image_8.png) int(district[district.crime_type.str.contains('narcotic',case=False, na=False)].num_charges.mean()) ![27_image_9.png](27_image_9.png) Figure 25: An example program of ValueError: the model tries to calculate the mean of a column containing NaN values. ![27_image_7.png](27_image_7.png) u: *What is the number of deaths by accident as a percentage of total deaths in the last ten* ![28_image_0.png](28_image_0.png) Figure 26: An example of complex reasoning: the model has to infer the API call (dt.datetime.today().year - 10) from the last-ten-years constraint in the intent. u: Which hotels had a worse ranking this year than in 2021? Show the hotel name, location and the difference in ranking from last year. Reference Solution: ![28_image_1.png](28_image_1.png) Figure 27: An example of complex reasoning: the model needs to compare the current rank and the past rank while making sure the rank in 2021 exists. ``` u: What was the mean opening rank across all IIT institutes for each program over the years? Show the mean opening rank for each year in columns with program as index and consider only general students. Reference Solution: df_iit = df_general[df_general.institute_type == 'IIT'] df_iit[['year', 'opening_rank', 'program_name']].groupby(['year', 'program_name']).mean().unstack(0) Incorrect Model Output: df_general[['program_name', 'opening_rank', 'year']].groupby(['program_name', 'year']).mean().unstack(0) ![28_image_2.png](28_image_2.png) ``` Figure 28: An example of NL misunderstanding: the model does not filter the institute type according to the intent. ![28_image_3.png](28_image_3.png) Figure 29: An example of underspecified intent. It does not specify the output should not sum the number of front facing and left facing trees. u: *Return a matrix with the average ticket prices to and from all the cities for each ticket* class. ![29_image_0.png](29_image_0.png) | (a) Intent, reference program and generated program | | | | | | | | | | | | | |-------------------------------------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------|-----------|---------|--------|-----------| | destination_city | Bangalore | Chennai | Delhi | Hyderabad | Kolkata | Mumbai | | | | | | | | class | source_city | | | | | | | | | | | | | Business | Bangalore | NaN | 52436.915395 | 48144.337108 | 50395.796948 | 58854.693091 | 58024.618208 | | | | | | | Chennai | 53113.008692 | NaN | 52443.367242 | 51559.874283 | 57078.895872 | 56223.838086 | | | | | | | | Delhi | 48576.027921 | 52031.778099 | NaN | 44457.376775 | 56239.853659 | 44364.442811 | | | | | | | | Hyderabad | 50358.290706 | 51132.155288 | 44250.700281 | NaN | 53729.157762 | 52184.424666 | | | | | | | | Kolkata | 58681.104437 | 56502.775035 | 55047.492193 | 54732.447908 | NaN | 57422.551724 | | | | | | | | Mumbai | 57970.544389 | 55703.326197 | 43846.329273 | 51593.643678 | 57106.526385 | NaN | | | | | | | | Economy | Bangalore | NaN | 7105.953850 | 6124.897982 | 6360.141698 | 7375.638594 | 6381.093332 | | | | | | | Chennai | 7175.020192 | NaN | 6075.961190 | 5960.788831 | 7547.295815 | 6529.119453 | | | | | | | | Delhi | 6175.622535 | 6102.317245 | NaN | 6031.164261 | 7045.621678 | 6059.826087 | | | | | | | | Hyderabad | 6234.882649 | 6049.884930 | 6072.296659 | NaN | 6881.680392 | 5969.259906 | | | | | | | | Kolkata | 7471.621990 | 8011.745229 | 7161.400077 | 7489.144374 | NaN | 7405.787239 | | | | | | | | Mumbai | 6432.511946 | 6420.917984 | 5889.281400 | 5774.891130 | 7227.971735 | NaN | | | | | | | | (b) Reference output | | | | | | | | | | | | | | class | Business | Economy | | | | | | | | | | | | destination_city | Chennai | Delhi | Hyderabad | Kolkata | Mumbai | Bangalore | Chennai | Delhi | Hyderabad | Kolkata | Mumbai | Bangalore | | source_city Bangalore | 52437.0 | 48144.0 | 50396.0 | 58855.0 | 58025.0 | NaN | 7106.0 | 6125.0 | 6360.0 | 7376.0 | 6381.0 | NaN | | Chennai | NaN | 52443.0 | 51560.0 | 57079.0 | 56224.0 | 53113.0 | NaN | 6076.0 | 5961.0 | 7547.0 | 6529.0 | 7175.0 | | Delhi | 52032.0 | NaN | 44457.0 | 56240.0 | 44364.0 | 48576.0 | 6102.0 | NaN | 6031.0 | 7046.0 | 6060.0 | 6176.0 | | Hyderabad | 51132.0 | 44251.0 | NaN | 53729.0 | 52184.0 | 50358.0 | 6050.0 | 6072.0 | NaN | 6882.0 | 5969.0 | 6235.0 | | Kolkata | 56503.0 | 55047.0 | 54732.0 | NaN | 57423.0 | 58681.0 | 8012.0 | 7161.0 | 7489.0 | NaN | 7406.0 | 7472.0 | | Mumbai | 55703.0 | 43846.0 | 51594.0 | 57107.0 | NaN | 57971.0 | 6421.0 | 5889.0 | 5775.0 | 7228.0 | NaN | 6433.0 | We provide a data card for the training data of PACHINCO as outlined in §4, and also report training data composition in Tab. 6. | composition in Tab. 6. | Motivation | | | | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|-------------|---------|--------------------------------------------------------------| | For what purpose was the dataset created? Who created the dataset? Who funded the creation of the dataset? | The dataset was created for training code and language models by a team of researchers. Composition | | | | | What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? | Dataset comprises of Python source code files and Jupyter notebooks from GitHub, filtered by license so as to exclude code with restrictive licenses. | | | | | How many instances are there in total (of each type, if appropriate)? | The data makeup is given in Table 6. | | | | | Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? | The dataset is a small (random) subset of a larger set. | | | | | What data does each instance consist of? | Each instance is encoded content of a source code file. | | | | | Is there a label or target associated | No, there are no labels associated with each instance. | | | | | with each instance? Is any information missing from | No. | | | | | individual instances? Are relationships between individual instances made explicit? | No. | | | | | Are | there | recommended | data | We use random splits for the training, validation, and test. | | splits? Are there | any | errors, | sources | | | of noise, or redundancies in the | - Python files were near deduplicated at the file level using a | | | | | dataset? | custom implementation of minhash algorithm, so lower level redundancies (lines, code blocks) may still exist. | | | | | - Some files were misclassified in the license tagging and filtration process given that license classification algorithm can have false positives and negatives. | | | | | | Is the dataset self-contained, or does it link to or otherwise rely on external resources? | The dataset is self-contained. | | | | | Does the dataset contain data that | No. | | | | | might be considered confidential? | | | | | ## K **Data Card For The Training Data Of P**Achinco Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? Given the dataset contains source code, it is not likely there is any offensive text in it, however no explicit measures are in place to eliminate such data if it were present. | might otherwise cause anxiety? | Collection Process | | |-------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|------| | How was the data associated with | The data was collected from publicly available sources. | | | each instance acquired? What mechanisms or procedures | The data was collected using a variety of software programs to | | | were used to collect the data? | extract and clean source code files. | | | If the dataset is a sample from a larger set, what was the sampling strategy? | The dataset is small subset of publicly available code from Github, sampled randomly. | | | Who was involved in the data collection process? | A team of researchers. | | | Over what timeframe was the data | April - July 2022 | | | collected? Were any ethical review processes | No. | | | conducted? | Preprocessing, cleaning, and labeling | | | Was any preprocessing, cleaning, or labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? | License filtration, quality filtration and deduplication were applied to the source code files. - License classification was done using Google License Classifier library. Source code files with restricted licenses were filtered out. - Python files were deduplicated at the file level using a custom variant of minhash algorithm. Locality sensitive hashes of file content were used to create partitions of potentially duplicate files based on collisions in the hash buckets. For each pair in the partitions, Jaccard Similarity and Edit Distance scores were calculated to create an "edge" for a pair whenever the scores are higher than the specified threshold. This was followed by application of connected components algorithm to return the sets of duplicates. - Jupyter notebooks were first deduplicated following the same procedure as deduplicating Python files, and then deduplicated at individual cell level against the evaluation dataset (§4). | | | Is the software used to preprocess, clean, or label the instances available? | No. | Uses | | Has the dataset been used for any | Yes, we use the dataset for pre-training other code and language | | | tasks already? | models. | | | any or all papers or systems that use the dataset? What (other) tasks could the | The dataset can be used for training of other code and language | | | | | | |-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|---------|-----------|-----|---------------------------------------------------------------| | dataset be used for? | models. | | | | | | | Is | there | anything | about | the | | | | composition | of | the | dataset | or | | | | the | way | it | was | collected | and | | | pre-processed/cleaned/labeled that might impact future uses? | The dataset is static in nature and thus will become progressively more "stale". It will not include any new source code repositories that were created/updated later on Github. | | | | | | | Are | there | tasks | for | which | the | This should not be used for any unacceptable code or language | | dataset should not be used? | modeling use cases e.g. generating code or language with toxic/biased connotations. Distribution | | | | | | | Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? | No. | | | | | | No. | Language | Tokens | Source Files | |-------------------|----------------|----------------| | Python | 63,786,481,126 | 60,397,107 | | Jupyter Notebooks | 9,613,648,619 | 3,796,713 | Table 6: Data Composition of the fine-tuning data for PACHINCO. ## L **Detailed Prompting Examples** In this section we provide detailed examples of prompts used in our experiments. As in §5.1, there are two categories of experiments in §5, namely prompting using notebook context (§5.2) and few-shot prompting with extra exemplars pre-pended to notebook context (§5.3). Here, we list the prompts for u2 in Fig. 1 in these two types of prompting experiments. Prompting using Notebook Context In the basic setup without extra few-shot exemplars, the prompt basically consist of all the prior notebook context, including NL descriptions of schema information and previous rounds of problems. Listing 7 shows the complete prompt for u2 in Fig. 1 in this setup.18 At inference time, a code LM will complete the last code cell after the cell delimiter '\# In[ ]:'. Note that for INCODER we follow Fried et al. (2022) and use a special template to linearize notebooks (Appendix E). Prompting using Additional Few-shot Exemplars We have four prompting styles for few-shot experiments. Here, we show the prompt prefix (§5.1) for Vanilla Code and Step-by-Step+Explanations prompting, as the remaining two styles are just simplified version of the latter by removing inline explanations (SbS + Preamble) and preambles (Step-by-Step). A prompt in this setup is the concatenation of a prompt prefix (with few-shot exemplars) and the notebook context (with prior rounds of problems and NL schema descriptions). The part of a prompt that corresponds to notebook context is the same as the previous setting (*e.g.* Listing 7), except that we insert the preamble \# Solution: Let's solve this problem step-by-step. as appropriate after the last cell delimiter. For prompt prefix, Listing 1 gives an example prompt prefix for Step-by-Step prompting, while Listing 4 shows the same set of few-shot exemplars for Vanilla Code prompting. As mentioned in §5.1, we created three prompt prefixes for each of the four different styles, and report results averaged over these three restarts. Listings 1 to 3 show the three groups of prompt prefixes for Step-by-Step, and Listings 4 to 6 show those for Vanilla Code prompting. Each prompt prefix has four exemplars, and some exemplars are shared across different prefixes. Note that some prompt prefixes in Step-by-Step also contain one simple problem that does not require decomposition and explanation (*e.g.* Exercise 3, Listing 1). We find this to be useful to not bias a model from generate overly complex code solutions for simpler problems. We did not put much effort in prompting engineering. Actually, those prompt prefixes were created before we collected 70% of the problems in our dataset. Listing 1: Step-by-Step Prompt Prefix (Group 1) ![33_image_3.png](33_image_3.png) 1 *# In[ ]:* ![33_image_0.png](33_image_0.png) 2 ![33_image_2.png](33_image_2.png) ![33_image_1.png](33_image_1.png) 8 *# In[ ]:* 11 *# You are a professional data scientist. Answer the following questions using pandas and matplotlib.* 14 *# In[ ]:* 20 *# In[ ]:* 26 *# In[ ]:* 32 33 18This prompt is not exactly the same as the one in our dataset. It is adapted to align with the illustrative example in Fig. 1 ![33_image_4.png](33_image_4.png) ``` 34 # In[ ]: 35 36 37 # Problem: How many male and female employees are born in 1992? 38 39 40 # In[ ]: 41 42 43 # Solution: Let's solve this problem step-by-step. 44 # Step 1: convert date of birth in to datetime 45 df['DOB'] = pd.to_datetime(df['DOB']) 46 # Step 2: get the number of male born in 1992 47 num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm')]) 48 # Step 3: get the number of female born in that year 49 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 50 51 52 # In[ ]: 53 54 55 # # Exercise 2 56 57 58 # In[ ]: 59 60 61 df = pd.read_csv('scores.csv') 62 63 64 # In[ ]: 65 66 67 # Schema of Dataframes: 68 # Columns in df with example values: 69 # Stu_Name (Mike), Engineering (90), English (89), Math (92) 70 71 72 # In[ ]: 73 74 75 # Problem: Get the students with an averaged score above 90 for science subjects. 76 77 78 # In[ ]: 79 80 81 # Solution: Let's solve this problem step-by-step. 82 # Step 1: Create a new column with the average score of engineering and math 83 df['Science_Avg'] = (df['Engineering'] + df['Math']) / 2 84 # Step 2: Get the rows whose average score is above 90 85 df_score_above_90 = df[df['Science_Avg'] > 90] 86 # Step 3: Return the student name and average scores 87 result = df_score_above_90[['Stu_Name', 'Science_Avg']] 88 89 90 # In[ ]: 91 92 93 # # Exercise 3 94 95 96 # In[ ]: 97 98 99 df = pd.read_csv('geo.csv') 100 101 102 # In[ ]: 103 104 105 # Schema of Dataframes: 106 # Columns in df with example values: 107 # state (WA), capital (Seattle), population (1.4 millon) 108 109 110 # In[ ]: 111 112 113 # Problem: What is the population of California? 114 115 116 # In[ ]: 117 118 119 # Solution: Let's solve this problem step-by-step. 120 result = df[df['state'] == 'CA']['population'] ``` Listing 2: Step-by-Step Prompt Prefix (Group 2) 167 *\# In[ ]:* 168 169 172 173 174 *\# In[ ]:* 175 176 177 *\# You are a professional data scientist. Answer the following questions using pandas and matplotlib.* 178 179 180 *\# In[ ]:* 181 182 183 *\# \# Exercise 1* 184 185 186 *\# In[ ]:* 187 188 189 df = pd.read_csv('employee.csv') 190 191 192 *\# In[ ]:* 193 194 195 *\# Schema of Dataframes:* 196 *\# Columns in df with example values:* 197 *\# name (Peter), gender (m), DOB (1992/01/17)* 198 199 200 *\# In[ ]:* 201 202 121 122 123 *\# In[ ]:* 124 125 126 *\# \# Exercise 4* 127 128 129 *\# In[ ]:* 130 131 132 df = pd.read_csv('phones.csv') 133 134 135 *\# In[ ]:* 136 137 138 *\# Schema of Dataframes:* 139 *\# Columns in df with example values:* 140 *\# model (Pixel 6), brand (Google), price (387), release (2022)* 141 142 143 *\# In[ ]:* 144 145 146 *\# Problem: What is the most expensive phone in each brand.* 147 148 149 *\# In[ ]:* 150 151 152 *\# Solution: Let's solve this problem step-by-step.* 153 *\# Step 1: Group models by their brands.* 154 model_by_brand_df = df.groupby('brand') 155 *\# Step 2: Find the index of rows that have the highest price in each group* 156 idx = model_by_brand_df['price'].idxmax() 157 *\# Step 3: Get the rows using the index* 158 expensive_models_df = df.loc[idx] 159 *\# Step 4: Return the brand name, model and price.* 160 result = expensive_models_df[['brand', 'model', 'price']] 161 162 163 *\# In[ ]:* 164 165 166 *\# \# Exercise 5* 203 *\# Problem: How many male and female employees are born in 1992?* 204 205 206 *\# In[ ]:* 207 208 209 *\# Solution: Let's solve this problem step-by-step.* 210 *\# Step 1: convert date of birth in to datetime* 211 df['DOB'] = pd.to_datetime(df['DOB']) 212 *\# Step 2: get the number of male born in 1992* 213 num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm')]) 214 *\# Step 3: get the number of female born in that year* 215 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 216 217 218 *\# In[ ]:* 219 220 221 *\# \# Exercise 2* 222 223 224 *\# In[ ]:* 225 226 227 df = pd.read_csv('scores.csv') 228 229 230 *\# In[ ]:* 231 232 233 *\# Schema of Dataframes:* 234 *\# Columns in df with example values:* 235 *\# Stu_Name (Mike), Engineering (90), English (89), Math (92)* 236 237 238 *\# In[ ]:* 239 240 241 *\# Problem: Get the students with an averaged score above 90 for science subjects.* 242 243 244 *\# In[ ]:* 245 246 247 *\# Solution: Let's solve this problem step-by-step.* 248 *\# Step 1: Create a new column with the average score of engineering and math* 249 df['Science_Avg'] = (df['Engineering'] + df['Math']) / 2 250 *\# Step 2: Get the rows whose average score is above 90* 251 df_score_above_90 = df[df['Science_Avg'] > 90] 252 *\# Step 3: Return the student name and average scores* 253 result = df_score_above_90[['Stu_Name', 'Science_Avg']] 254 255 256 *\# In[ ]:* 257 258 259 *\# \# Exercise 3* 260 261 262 *\# In[ ]:* 263 264 265 df = pd.read_csv('geo.csv') 266 267 268 *\# In[ ]:* 269 270 271 *\# Schema of Dataframes:* 272 *\# Columns in df with example values:* 273 *\# state (WA), capital (Seattle), population (1.4 millon)* 274 275 276 *\# In[ ]:* 277 278 279 *\# Problem: What is the population of California?* 280 281 282 *\# In[ ]:* 283 284 285 *\# Solution: Let's solve this problem step-by-step.* 286 result = df[df['state'] == 'CA']['population'] 287 288 289 *\# In[ ]:* Listing 3: Step-by-Step Prompt Prefix (Group 3) 333 *\# In[ ]:* 334 335 338 339 340 *\# In[ ]:* 341 342 343 *\# You are a professional data scientist. Answer the following questions using pandas and matplotlib.* 344 345 346 *\# In[ ]:* 347 348 349 *\# \# Exercise 1* 350 351 352 *\# In[ ]:* 353 354 355 df = pd.read_csv('olympics.csv') 356 357 358 *\# In[ ]:* 359 360 361 *\# Schema of Dataframes:* 362 *\# Columns in df with example values:* 363 *\# Year (1896), City (Athens), Country (Greece), Nations (14)* 364 365 366 *\# In[ ]:* 367 368 369 *\# Problem: Which countries host at least two olympic games?* 370 371 290 291 292 *\# \# Exercise 4* 293 294 295 *\# In[ ]:* 296 297 298 df = pd.read_csv('phones.csv') 299 300 301 *\# In[ ]:* 302 303 304 *\# Schema of Dataframes:* 305 *\# Columns in df with example values:* 306 *\# model (Pixel 6), brand (Google), price (387), release (2022)* 307 308 309 *\# In[ ]:* 310 311 312 *\# Problem: What is the most expensive phone in each brand.* 313 314 315 *\# In[ ]:* 316 317 318 *\# Solution: Let's solve this problem step-by-step.* 319 *\# Step 1: Group models by their brands.* 320 model_by_brand_df = df.groupby('brand') 321 *\# Step 2: Find the index of rows that have the highest price in each group* 322 idx = model_by_brand_df['price'].idxmax() 323 *\# Step 3: Get the rows using the index* 324 expensive_models_df = df.loc[idx] 325 *\# Step 4: Return the brand name, model and price.* 326 result = expensive_models_df[['brand', 'model', 'price']] 327 328 329 *\# In[ ]:* 330 331 332 *\# \# Exercise 5* 164 372 *\# In[ ]:* 373 374 375 *\# Solution: Let's solve this problem step-by-step.* 376 *\# Step 1: Count the number of times each country hosted olympics* 377 count_df = df['Country'].value_counts() 378 *\# Step 2: Find entries with more than 2 counts* 379 filtered_df = count_df[count_df >= 2] 380 *\# Step 3: Get the country names as a list* 381 filtered_df.index.tolist() 382 383 384 *\# In[ ]:* 385 386 387 *\# \# Exercise 2* 388 389 390 *\# In[ ]:* 391 392 393 df = pd.read_csv('employee.csv') 394 395 396 *\# In[ ]:* 397 398 399 *\# Schema of Dataframes:* 400 *\# Columns in df with example values:* 401 *\# name (Peter), gender (m), DOB (1992/01/17)* 402 403 404 *\# In[ ]:* 405 406 407 *\# Problem: How many male and female employees are born in 1992?* 408 409 410 *\# In[ ]:* 411 412 413 *\# Solution: Let's solve this problem step-by-step.* 414 *\# Step 1: convert date of birth in to datetime* 415 df['DOB'] = pd.to_datetime(df['DOB']) 416 *\# Step 2: get the number of male born in 1992* 417 num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm')]) 418 *\# Step 3: get the number of female born in that year* 419 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 420 421 422 *\# In[ ]:* 423 424 425 *\# \# Exercise 3* 426 427 428 *\# In[ ]:* 429 430 431 df = pd.read_csv('score.csv') 432 433 434 *\# In[ ]:* 435 436 437 *\# Schema of Dataframes:* 438 *\# Columns in df with example values:* 439 *\# name (John), score (97)* 440 441 442 *\# In[ ]:* 443 444 445 *\# Problem: Make a new column "grade" for letter grades (A: 90+, B: 70-90, C: <70) and plot the number* ,→ *of students in each grade.* 446 447 448 *\# In[ ]:* 449 450 451 *\# Solution: Let's solve this problem step-by-step.* 452 *\# Step 1: Define a function to convert scores to letter grades.* 453 def get_grade(score): 454 if score >= 90: 455 **return** 'A' 456 **elif** 70 <= score < 90: 457 **return** 'B' Listing 4: Vanilla Code Prompt Prefix (Setup 1) 512 *\# In[ ]:* 513 514 ## 515 Import Pandas As Pd 516 import matplotlib.pyplot as plt 517 518 519 *\# In[ ]:* 520 521 522 *\# You are a professional data scientist. Answer the following questions using pandas and matplotlib.* 523 524 525 *\# In[ ]:* 526 527 528 *\# \# Exercise 1* 529 530 531 *\# In[ ]:* 532 533 534 df = pd.read_csv('employee.csv') 535 536 537 *\# In[ ]:* 538 539 458 **else**: 459 **return** 'C' 460 *\# Step 2: Convert scores to letter grades.* 461 df['grade'] = df.score.apply(get_grade) 462 *\# Step 3: Count the number of students by grade.* 463 count_df = df['grade'].value_counts() 464 *\# Step 4: Visualize in a bar chart.* 465 count_df.plot(kind='bar') 466 467 468 *\# In[ ]:* 469 470 471 *\# \# Exercise 4* 472 473 474 *\# In[ ]:* 475 476 477 df = pd.read_csv('phones.csv') 478 479 480 *\# In[ ]:* 481 482 483 *\# Schema of Dataframes:* 484 *\# Columns in df with example values:* 485 *\# model (Pixel 6), brand (Google), price (387), release (2022)* 486 487 488 *\# In[ ]:* 489 490 491 *\# Problem: What is the most expensive phone in each brand.* 492 493 494 *\# In[ ]:* 495 496 497 *\# Solution: Let's solve this problem step-by-step.* 498 *\# Step 1: Group models by their brands.* 499 model_by_brand_df = df.groupby('brand') 500 *\# Step 2: Find the index of rows that have the highest price in each group* 501 idx = model_by_brand_df['price'].idxmax() 502 *\# Step 3: Get the rows using the index* 503 expensive_models_df = df.loc[idx] 504 *\# Step 4: Return the brand name, model and price.* 505 result = expensive_models_df[['brand', 'model', 'price']] 506 507 508 *\# In[ ]:* 509 510 511 *\# \# Exercise 5* 540 *\# Schema of Dataframes:* 541 *\# Columns in df with example values:* 542 *\# name (Peter), gender (m), DOB (1992/01/17)* 543 544 545 *\# In[ ]:* 546 547 548 *\# Problem: How many male and female employees are born in 1992?* 549 550 551 *\# In[ ]:* 552 553 554 *\# Solution:* 555 df['DOB'] = pd.to_datetime(df['DOB']) 556 num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm')]) 557 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 558 559 560 *\# In[ ]:* 561 562 563 *\# \# Exercise 2* 564 565 566 *\# In[ ]:* 567 568 569 df = pd.read_csv('scores.csv') 570 571 572 *\# In[ ]:* 573 574 575 *\# Schema of Dataframes:* 576 *\# Columns in df with example values:* 577 *\# Stu_Name (Mike), Engineering (90), English (89), Math (92)* 578 579 580 *\# In[ ]:* 581 582 583 *\# Problem: Get the students with an averaged score above 90 for science subjects.* 584 585 586 *\# In[ ]:* 587 588 589 *\# Solution:* 590 df['Science_Avg'] = (df['Engineering'] + df['Math']) / 2 591 df[df['Science_Avg'] > 90][['Stu_Name', 'Science_Avg']] 592 593 594 *\# In[ ]:* 595 596 597 *\# \# Exercise 3* 598 599 600 *\# In[ ]:* 601 602 603 df = pd.read_csv('geo.csv') 604 605 606 *\# In[ ]:* 607 608 609 *\# Schema of Dataframes:* 610 *\# Columns in df with example values:* 611 *\# state (WA), capital (Seattle), population (1.4 millon)* 612 613 614 *\# In[ ]:* 615 616 617 *\# Problem: What is the population of California?* 618 619 620 *\# In[ ]:* 621 622 623 *\# Solution:* 624 result = df[df['state'] == 'CA']['population'] 625 626 \# In[ ]: 100 627 620 629 \# \# Exercise 4 630 631 632 \# In[ ]: 633 634 10, 11635 66 df = pd.read_csv('phones.csv') 638 100 637 639 f In[ ]: 640 641 642 11, 11643 644 \# Schema of Dataframes: \# Columns in df with example values: \# model (Pixel 6), brand (Google), price (387), release (2022) 645 646 647 \# In[ ]: 648 649 100–11650 651 \# Problem: What is the most expensive phone in each brand. 11, 116, 116 653 f In[ ]: 11, 116, 155 654 657 11, 116, 116 \# Solution: df.loc[df.groupby('brand')['price'].idxmax()][['brand', 'model', 'price']] 658 659 \# In[ ]: 60 661 100 662 63 \# \# Exercise 5 Listing 5: Vanilla Code Prompt Prefix (Setup 2) 664 10, 11665 100, 1166 667 668 670 671 100 , 672 673 \# You are a professional data scientist. Answer the following questions using pandas and matplotlib. 674 675 676 1677 f In[ ]: 60 \# \# Exercise 1 681 682 683 \# In[ ]: 684 685 666 df = pd.read_csv('employee.csv') 687 688 689 690 69 \# In[ ]: \# Schema of Dataframes: \# Columns in df with example values: \# name (Peter), gender (m), DOB (1992/01/17) 100 1000 693 694 695 10 697 f In[ ]: 698 69 70 701 \# Problem: How many male and female employees are born in 1992? 100, 11703 \# In[ ]: 704 705 706 7007 708 f Solution: df['DOB'] = pd.to_datetime(df['DOB']) num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm'}]) 678 679 \# In[ ]: f In[ ]: 669 11, 11702 709 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 710 711 712 *\# In[ ]:* 713 714 715 *\# \# Exercise 2* 716 717 718 *\# In[ ]:* 719 720 721 df = pd.read_csv('scores.csv') 722 723 724 *\# In[ ]:* 725 726 727 *\# Schema of Dataframes:* 728 *\# Columns in df with example values:* 729 *\# Stu_Name (Mike), Engineering (90), English (89), Math (92)* 730 731 732 *\# In[ ]:* 733 734 735 *\# Problem: Get the students with an averaged score above 90 for science subjects.* 736 737 738 *\# In[ ]:* 739 740 741 *\# Solution:* 742 df['Science_Avg'] = (df['Engineering'] + df['Math']) / 2 743 df[df['Science_Avg'] > 90][['Stu_Name', 'Science_Avg']] 744 745 746 *\# In[ ]:* 747 748 749 *\# \# Exercise 3* 750 751 752 *\# In[ ]:* 753 754 755 df = pd.read_csv('geo.csv') 756 757 758 *\# In[ ]:* 759 760 761 *\# Schema of Dataframes:* 762 *\# Columns in df with example values:* 763 *\# state (WA), capital (Seattle), population (1.4 millon)* 764 765 766 *\# In[ ]:* 767 768 769 *\# Problem: What is the population of California?* 770 771 772 *\# In[ ]:* 773 774 775 *\# Solution:* 776 result = df[df['state'] == 'CA']['population'] 777 778 779 *\# In[ ]:* 780 781 782 *\# \# Exercise 4* 783 784 785 *\# In[ ]:* 786 787 788 df = pd.read_csv('phones.csv') 789 790 791 *\# In[ ]:* 792 793 794 *\# Schema of Dataframes:* 795 *\# Columns in df with example values:* \# model (Pixel 6), brand (Google), price (387), release (2022) 790 797 798 79 f In[ ]: 800 801 11802 803 \# Problem: What is the most expensive phone in each brand. 11, 11804 \# In[ ]: 11806 11, 11805 11807 808 9009 810 811 812 \# In[ ]: 118, 118 814 815 \# \# Exercise 5 Listing 6: Vanilla Code Prompt Prefix (Setup 3) 816 \# In[ ]: 817 819 800 828 118, 11822 \# In[ ]: 828 828 828 828 \# You are a professional data scientist. Answer the following questions using pandas and matplotlib. 1188 828 \# In[ ]: 830 831 1832 \# \# Exercise 1 118833 118834 118, 118 \# In[ ]: 1836 88 839 df = pd.read_csv('olympics.csv') \# In[ ]: 848 118, 11842 843 84 845 846 847 848 \# In[ ]: 849 850 851 \# Problem: Which countries host at least two olympic games? 118, 118 118, 118 \# In[ ]: 855 857 1188 859 f Solution: count_df = df['Country'].value_counts() count_df[count_df >= 2].index.tolist() 118, 118 11, 11861 862 863 \# In[ ]: 864 118, 118 \# \# Exercise 2 11, 11866 868 88 869 870 f In[ ]: 871 11, 11872 df = pd.read_csv('employee.csv') 873 874 875 \# In[ ]: 876 877 \# Schema of Dataframes: \# Columns in df with example values: \# Year (1896), City (Athens), Country (Greece), Nations (14) 854 \# Solution: df.loc[df.groupby('brand')['price'].idxmax()][['brand', 'model', 'price']] 818 118, 11823 840 878 *\# Schema of Dataframes:* 879 *\# Columns in df with example values:* 880 *\# name (Peter), gender (m), DOB (1992/01/17)* 881 882 883 *\# In[ ]:* 884 885 886 *\# Problem: How many male and female employees are born in 1992?* 887 888 889 *\# In[ ]:* 890 891 892 *\# Solution:* 893 df['DOB'] = pd.to_datetime(df['DOB']) 894 num_male_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'm')]) 895 num_female_students = len(df[(df['DOB'].dt.year == 1992) & (df['gender'] == 'f')]) 896 897 898 *\# In[ ]:* 899 900 901 *\# \# Exercise 3* 902 903 904 *\# In[ ]:* 905 906 907 df = pd.read_csv('score.csv') 908 909 910 *\# In[ ]:* 911 912 913 *\# Schema of Dataframes:* 914 *\# Columns in df with example values:* 915 *\# name (John), score (97)* 916 917 918 *\# In[ ]:* 919 920 921 *\# Problem: Make a new column "grade" for letter grades (A: 90+, B: 70-90, C: <70) and plot the number* ,→ *of students in each grade.* 922 923 924 *\# In[ ]:* 925 926 927 *\# Solution:* 928 df['grade'] = df.score.apply(**lambda** x: 'A' if x >= 90 **else** ('B' if 70 <= x < 90 **else** 'C')) 929 df.grade.value_counts().plot(kind='bar') 930 931 932 *\# In[ ]:* 933 934 935 *\# \# Exercise 4* 936 937 938 *\# In[ ]:* 939 940 941 df = pd.read_csv('phones.csv') 942 943 944 *\# In[ ]:* 945 946 947 *\# Schema of Dataframes:* 948 *\# Columns in df with example values:* 949 *\# model (Pixel 6), brand (Google), price (387), release (2022)* 950 951 952 *\# In[ ]:* 953 954 955 *\# Problem: What is the most expensive phone in each brand.* 956 957 958 *\# In[ ]:* 959 960 961 *\# Solution:* 962 df.loc[df.groupby('brand')['price'].idxmax()][['brand', 'model', 'price']] 963 964 965 *\# In[ ]:* 966 967 968 *\# \# Exercise 5* Listing 7: The notebook context part of the prompt for u2 in Fig. 1 969 *\# In[ ]:* 970 971 972 import **pandas** as pd 973 974 df=pd.read_csv('dataset/Gamepass_Games_v1.csv') 975 976 977 *\# In[ ]:* 978 979 980 *\# Schema of Dataframes:* 981 *\# Columns in df with example values:* 982 *\# GAME (Mass Effect Legendary Edition), RATIO (1.87), GAMERS (84,143), COMP % (4.1), TIME (100-120* ,→ *hours), RATING (4.8), ADDED (06 Jan 22), True_Achievement (5442), Game_Score (2915)* 983 984 985 *\# In[ ]:* 986 987 988 *\# Extract min and max hours as two columns* 989 990 991 *\# In[ ]:* 992 993 994 def get_avg(x): 995 try: 996 **return** float(x[0]) , float(x[1]) 997 **except**: 998 **return** 0,0 999 df['min'],df['max']=zip(*df['TIME'].str.replace(" ,→ hours",'').str.strip('+').str.split("-").apply(get_avg)) 1000 1001 1002 *\# In[ ]:* 1003 1004 1005 df['ADDED']=pd.to_datetime(df['ADDED'],format="%d %b %y",errors='coerce') 1006 1007 1008 *\# In[ ]:* 1009 1010 1011 *\# In which year was the most played game added?* 1012 1013 1014 *\# In[ ]:* 1015 . Model starts prediction ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 introduces a new dataset. Section 4 describes models trained on Github source code data. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? License information of source code that our dataset is based on is provided in the data card section in the appendix. License of our newly created ARCADE dataset will be included in the public data release. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Our training data consists of permissively licensed source code files from Github, which is discussed in the data card section in the appendix. For the ML datasets and notebooks we used to build the annotated ARCADE dataset, they are reviewed by a legal team to ensure they could be used for the purpose of research and publication. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? This is discussed in the data card section in the appendix. For our annotated ARCADE dataset, we anonymize information of the annotators. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Our data primarily concerns with source code. Code-related data statistics is presented in Section 3 and the data card section in the appendix. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. This is discussed in the data card section in the appendix. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Section 5. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4 (model size), Section 8 (Training FLOPs), Appendix D (compute environments). ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix D ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? In Section 5, for pass@k evaluation we use the the estimator proposed in prior work to reduce variance. Error bars for prompting experiments are depicted in figures 11-12 in appendix. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix D. ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 3. ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Our annotation guideline is 35-page long, so we only provide an outline of the guideline in Section 3 and Appendix B. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Appendix A.3 describes the recruitment process of annotators. ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Appendix B provides instructions to annotators including the usage of the data collected (evaluate AI pair programmers). ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? An ethical review of the data collection protocol was not required. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Appendix A.3 mentions that the freelancers are proficient in English. We will try to provide more demographic information of the annotators in the final version.

deguchi-etal-2023-subset

Subset Retrieval Nearest Neighbor Machine Translation

https://aclanthology.org/2023.acl-long.10

k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021) boosts the translation performance of trained neural machine translation (NMT) models by incorporating example-search into the decoding algorithm. However, decoding is seriously time-consuming, i.e., roughly 100 to 1,000 times slower than standard NMT, because neighbor tokens are retrieved from all target tokens of parallel data in each timestep. In this paper, we propose {``}Subset kNN-MT{''}, which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor target tokens from a subset that is the set of neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a look-up table. Our proposed method achieved a speed-up of up to 132.2 times and an improvement in BLEU score of up to 1.6 compared with kNN-MT in the WMT{'}19 De-En translation task and the domain adaptation tasks in De-En and En-Ja.

## Subset Retrieval Nearest Neighbor Machine Translation Hiroyuki Deguchi1,2 Taro Watanabe1 **Yusuke Matsui**3 Masao Utiyama2 Hideki Tanaka2 **Eiichiro Sumita**2 1Nara Institute of Science and Technology 3The University of Tokyo 2National Institute of Information and Communications Technology {deguchi.hiroyuki.db0, taro}@is.naist.jp [email protected] {mutiyama, hideki.tanaka, eiichiro.sumita}@nict.go.jp ## Abstract k-nearest-neighbor machine translation (kNNMT) (Khandelwal et al., 2021) boosts the translation performance of trained neural machine translation (NMT) models by incorporating example-search into the decoding algorithm. However, decoding is seriously timeconsuming, i.e., roughly 100 to 1,000 times slower than standard NMT, because neighbor tokens are retrieved from all target tokens of parallel data in each timestep. In this paper, we propose "Subset kNN-MT", which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor target tokens from a subset that is the set of neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a look-up table. Our subset kNNMT achieved a speed-up of up to 132.2 times and an improvement in BLEU score of up to 1.6 compared with kNN-MT in the WMT'19 De-En translation task and the domain adaptation tasks in De-En and En-Ja. ## 1 Introduction Neural machine translation (NMT) (Sutskever et al., 2014; Bahdanau et al., 2015; Luong et al., 2015; Wu et al., 2016; Vaswani et al., 2017) has achieved state-of-the-art performance and become the focus of many studies. Recently, kNNMT (Khandelwal et al., 2021) has been proposed, which addresses the problem of performance degradation in out-of-domain data by incorporating example-search into the decoding algorithm. kNN-MT stores translation examples as a set of key–value pairs called "datastore" and retrieves k-nearest-neighbor target tokens in decoding. The method improves the translation performance of NMT models without additional training. However, decoding is seriously timeconsuming, i.e., roughly 100 to 1,000 times slower than standard NMT, because neighbor tokens are retrieved from all target tokens of parallel data in each timestep. In particular, in a realistic opendomain setting, kNN-MT may be significantly slower because it needs to retrieve neighbor tokens from a large datastore that covers various domains. We propose "Subset kNN-MT", which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor target tokens from a subset that is the set of neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a lookup table. When retrieving neighbor sentences for a given input, we can employ arbitrary sentence representations, e.g., pre-trained neural encoders or TF-IDF vectors, to reduce the kNN search space. When retrieving target tokens in each decoding step, the search space in subset kNN-MT varies depending on the input sentence; therefore, the clustering-based search methods used in the original kNN-MT cannot be used. For this purpose, we use asymmetric distance computation (ADC) (Jégou et al., 2011) in subset neighbor search. Our subset kNN-MT achieved a speed-up of up to 132.2 times and an improvement in BLEU score of up to 1.6 compared with kNN-MT in the WMT'19 German-to-English general domain translation task and the domain adaptation tasks in German-to-English and English-to-Japanese with open-domain settings. ## 2 K**Nn-Mt** kNN-MT (Khandelwal et al., 2021) retrieves the k-nearest-neighbor target tokens in each timestep, computes the kNN probability from the distances of retrieved tokens, and interpolates the probability with the model prediction probability. The method consists of two steps: (1) datastore creation, which creates key–value translation memory, and (2) generation, which calculates an output probability according to the nearest neighbors 174 ![1_image_0.png](1_image_0.png) Datastore Construction A typical NMT model is composed of an encoder that encodes a source sentence x = (x1, x2, . . . , x|x|) ∈ V|x| X and a decoder that generates target tokens y = (y1, y2, . . . , y|y|) ∈ V|y| Y where |x| and |y| are the lengths of sentences x and y, respectively, and VX and VY are the vocabularies of the source language and target language, respectively. The t-th target token ytis generated according to its output probability P(yt|x, y<t) over the target vocabulary, calculated from the source sentence x and generated target tokens y<t. kNN-MT stores pairs of Ddimensional vectors and tokens in a datastore, represented as key–value memory M ⊆ R D × VY . The key (∈ R D) is an intermediate representation of the final decoder layer obtained by teacher forcing a parallel sentence pair (x, y) to the NMT model, and the value is a ground-truth target token yt. The datastore is formally defined as follows: $$\mathcal{M}=\{(f(\mathbf{x},\mathbf{y}_{<t}),y_{t})\mid(\mathbf{x},\mathbf{y})\in\mathcal{D},1\leq t\leq|\mathbf{y}|\},\tag{1}$$ where $\mathcal{D}$ is parallel data and $f:\mathcal{V}_{X}^{|\mathbf{x}|}\times\mathcal{V}_{Y}^{t-1}\rightarrow$ where D is parallel data and f : V Y → R D is a function that returns the D-dimensional intermediate representation of the final decoder layer from the source sentence and generated target tokens. In our model, as in (Khandelwal et al., 2021), the key is the intermediate representation before it is passed to the final feed-forward network. Generation During decoding, kNN-MT generates output probabilities by computing the linear interpolation between the kNN and MT probabilities, pkNN and pMT, as follows: $$\begin{array}{c}{{P(y_{t}|\mathbf{x},\mathbf{y}_{<t})=\lambda p_{k\mathrm{NN}}(y_{t}|\mathbf{x},\mathbf{y}_{<t})}}\\ {{\qquad\qquad+(1-\lambda)p_{\mathrm{MT}}(y_{t}|\mathbf{x},\mathbf{y}_{<t}),\quad}}\end{array}\tag{2}$$ where λ is a hyperparameter for weighting the kNN probability. Let f(x, y<t) be the query vector at timestep t. The top i-th key and value in the k-nearest-neighbor are ki ∈ R D and vi ∈ VY , respectively. Then pkNN is defined as follows: $$p_{k\mathrm{NN}}(y_{t}|\mathbf{x},\mathbf{y}_{<t})$$ $$\propto\sum_{i=1}^{k}1_{y_{t}=v_{i}}\exp\left(\frac{-\|\mathbf{k}_{i}-f(\mathbf{x},\mathbf{y}_{<t})\|_{2}^{2}}{\tau}\right),\tag{3}$$ where τ is the temperature for pkNN, and we set τ = 100. Note that this kNN search is seriously time-consuming1(Khandelwal et al., 2021). ## 3 Proposed Model: Subset K**Nn-Mt** Our Subset k*NN-MT* (Figure 1) drastically accelerates vanilla kNN-MT by reducing the kNN search space by using sentence information (Section 3.1) and efficiently computing the distance between a query and key by performing table lookup (Section 3.2). ## 3.1 Subset Retrieval Sentence Datastore Construction In our method, we construct a sentence datastore that stores pairs comprising a source sentence vector 1In our experiments on the WMT'19 German-to-English, the datastore has 862M tokens, the vocabulary size is 42k, and the batch size was set to 12,000 tokens. While a normal Transformer translates 2,000 sentences in 7.5 seconds, kNNMT takes 2446.0 seconds. Note the kNN search is executed for each timestep in generating a target sentence. ![2_image_0.png](2_image_0.png) and a target sentence. Concretely, a sentence datastore S is defined as follows: $${\mathcal{S}}=\{(h({\boldsymbol{x}}),{\boldsymbol{y}})\mid({\boldsymbol{x}},{\boldsymbol{y}})\in{\mathcal{D}}\},$$ where h : V |x| X → R D′represents a sentence encoder, which is a function that returns a D′- dimensional vector representation of a source sentence. Decoding At the beginning of decoding, the model retrieves the n-nearest-neighbor sentences of the given input sentence from the sentence datastore S. Let *S ⊂ S* ˆ be the subset comprising nnearest-neighbor sentences. The nearest neighbor search space for target tokens in kNN-MT is then drastically reduced by constructing the datastore corresponding to Sˆ as follows: $$\hat{\cal M}=\{(f(\mathbf{x},\mathbf{y}_{<t}),y_{t})\mid$$ $$(h(\mathbf{x}),\mathbf{y})\in\hat{\cal S},1\leq t\leq|\mathbf{y}|\},\tag{5}$$ where *M ⊂ M* ˆ is the reduced datastore for the translation examples coming from the n-nearestneighbor sentences. During decoding, the model uses the same algorithm as kNN-MT except that Mˆ is used as the datastore instead of M. The proposed method reduces the size of the nearest neighbor search space for the target tokens from |D| to n (*≪ |D|*) sentences. ## 3.2 Efficient Distance Computation Using Lookup Table Subset kNN-MT retrieves the k-nearest-neighbor target tokens by an efficient distance computation method that uses a look-up table. In the original kNN-MT, inverted file index (IVF) is used for retrieving kNN tokens. IVF divides the search space into Nlist clusters and retrieves tokens from the neighbor clusters. In contrast, in subset kNNMT, the search space varies dynamically depending on the input sentence. Therefore, clusteringbased search methods cannot be used; instead, it is necessary to calculate the distance for each key in the subset. For this purpose, we use asymmetric distance computation (ADC) (Jégou et al., 2011) instead of the usual distance computation between floating-point vectors. In ADC, the number of table lookup is linearly proportional to the number of keys N in the subset. Therefore, it is not suitable for searching in large datastore M, but in a small subset Mˆ , the search is faster than the direct calculation of the L2 distance. $$(4)$$ Product Quantization (PQ) The kNN-MT datastore M may become too large because it stores high-dimensional intermediate representations of all target tokens of parallel data. For instance, the WMT'19 German-to-English parallel data, which is used in our experiments, contains 862M tokens on the target side. Therefore, if vectors were stored directly, the datastore would occupy 3.2 TiB when a 1024-dimensional vector as a key 2, and this would be hard to load into RAM. To solve this memory problem, product quantization (PQ) (Jégou et al., 2011) is used in both kNNMT and our subset kNN-MT, which includes both source sentence and target token search. PQ splits a D-dimensional vector into M subvectors and quantizes for each DM -dimensional sub-vector. Codebooks are learned by k-means clustering of key vectors in each subspace. It is computed iteratively by: (1) assigning the code of a key to its nearest neighbor centroid (2) and updating the centroid of keys assigned to the code. The m-th sub-space's codebook C m is formulated as follows: $${\mathcal{C}}^{m}=\{{\mathbf{c}}_{1}^{m},\ldots,{\mathbf{c}}_{L}^{m}\},\;{\mathbf{c}}_{l}^{m}\in\mathbb{R}^{\frac{D}{M}}.$$ $$\left(6\right)$$ M . (6) In this work, each codebook size is set to L = 256. A vector q ∈ R D is quantized and its code vector q¯ is calculated as follows: $$\bar{\mathbf{q}}=[\bar{q}^{1},\ldots,\bar{q}^{M}]^{\top}\in\{1,\ldots,L\}^{M},\tag{7}$$ $$\bar{q}^{m}=\underset{l}{\operatorname{argmin}}\ \|\mathbf{q}^{m}-\mathbf{c}_{l}^{m}\|_{2}^{2},\ \mathbf{q}^{m}\in\mathbb{R}^{\frac{D}{M}}.\tag{8}$$ (7) (8) $\frac{1}{2}$ Asymmetric Distance Computation (ADC) Our method efficiently computes the distance between a query vector and quantized key vectors using ADC (Jégou et al., 2011) (Figure 2). ADC computes the distance between a query vector q ∈ R D and N key codes K¯ = {k¯i} N i=1 ⊆ {1*, . . . , L*}M. First, the distance look-up table Am ∈ R L is computed by calculating the distance between a query q m and the codes c m l ∈ Cm in each sub-space m, as follows: $$A_{l}^{m}=\|\mathbf{q}^{m}-\mathbf{c}_{l}^{m}\|_{2}^{2}.$$ 2. (9) Second, the distance between a query and each key d(q, k¯i) is obtained by looking up the distance table as follows: $$d(\mathbf{q},\bar{\mathbf{k}}_{i})=\sum_{m=1}^{M}d_{m}(\mathbf{q}^{m},\bar{k}_{i}^{m})=\sum_{m=1}^{M}A_{k_{i}^{m}}^{m}.\tag{10}$$ A look-up table in each subspace, Am ∈ R L, consists of the distance between a query and codes. The number of codes in each subspace is L and a distance is a scalar; therefore, Am has L distances. And the table look-up key is the code of a key itself, i.e., if the m-th subspace's code of a key is 5, ADC looks-up Am 5 . By using ADC, the distance is computed only once3(Equation 9) and does not decode PQ codes into D-dimensional key vectors; therefore, it can compute the distance while keeping the key in the quantization code, and the k-nearest-neighbor tokens are efficiently retrieved from Mˆ . ## 3.3 Sentence Encoder In our subset kNN-MT, a variety of sentence encoder models can be employed. The more similar sentences extracted from M, the more likely the subset Mˆ comprises the target tokens that are useful for translation. Hence, we need sentence encoders that compute vector representations whose distances are close for similar sentences. In this work, we employ two types of representations: *neural* and *non-neural*. We can employ pre-trained neural sentence encoders. While they require to support the source language, we expect that the retrieved sentences are more similar than other encoders because we can use models that have been trained to minimize the vector 3The direct distance computation requires N times calculations according to ∥q − k∥ 2. ADC computes the distance only L ≪ N times and just looks-up the table N times. distance between similar sentences (Reimers and Gurevych, 2019). An NMT encoder can also be used as a sentence encoder by applying average pooling to its intermediate representations. This does not require any external resources, but it is not trained from the supervision of sentence representations. Alternatively, we can also use nonneural models like TF-IDF. However, it is not clear whether TF-IDF based similarity is suitable for our method. This is because even if sentences with close surface expressions are retrieved, they do not necessarily have similar meanings and may not yield the candidate tokens needed for translation. $\mu$. ## 4 Experiments 4.1 Setup We compared the translation quality and speed of our subset kNN-MT with those of the conventional kNN-MT in open-domain settings that assume a domain of an input sentence is unknown. The translation quality was measured by sacreBLEU (Post, 2018) and COMET (Rei et al., 2020). The speed was evaluated on a single NVIDIA V100 GPU. We varied the batch size settings: either 12,000 tokens (B∞), to simulate the document translation scenario, or a single sentence (B1), to simulate the online translation scenario. The beam size was set to 5, and the length penalty was set to 1.0. k**-Nearest-Neighbor Search** In kNN-MT, we set the number of nearest neighbor tokens to k = 16. We used FAISS (Johnson et al., 2019) to retrieve the kNN tokens in kNN-MT and for neighbor sentence search in subset kNNMT. The subset search and ADC were implemented in PYTORCH. We use approximate distance computed from quantized keys instead of full-precision keys in Equation 3, following the original kNN-MT (Khandelwal et al., 2021) implementation. The kNN-MT datastore and our sentence datastore used IVF and optimized PQ (OPQ) (Ge et al., 2014). OPQ rotates vectors to minimize the quantization error of PQ. The subset kNN-MT datastore is not applied clustering since we need to extract subset tokens. In this datastore, the 1024-dimensional vector representation, i.e., D = 1024, was reduced in dimensionality to 256-dimensions by principal component analysis (PCA), and these vectors were then quantized by PQ. At search time, a query vector is pre-transformed to 256-dimensions by multiplying the PCA matrix, and then the kNN target tokens are searched by ADC. The subset of a datastore can be loaded into GPU memory since it is significantly smaller than the original kNNMT datastore, so we retrieved k-nearest-neighbor tokens from a subset on a GPU. Sentence Encoder We compared 4 different sentence encoders: LaBSE, AvgEnc, TF-IDF, and BM25. LaBSE (Feng et al., 2022) is a pre-trained sentence encoder, fine-tuned from multilingual BERT. AvgEnc is an average pooled encoder hidden vector of the Transformer NMT model, which is also used for translation. TF-IDF (Jones, 1972) and BM25 (Jones et al., 2000) compute vectors weighted the important words in a sentence. We used the raw count of tokens as the term frequency and applied add-one smoothing to calculate the inverse document frequency, where a sentence was regarded as a document. We set k1 = 2.0, b = 0.75 in BM25 (Jones et al., 2000). Both TF-IDF and BM25 vectors were normalized by their L2norm and their dimensionality was reduced to 256dimensions by singular value decomposition. ## 4.2 In-Domain Translation We evaluated the translation quality and speed of subset kNN-MT in the WMT'19 De-En translation task (newstest2019; 2,000 sentences) and compared them with the kNN-MT baselines (Khandelwal et al., 2021; Meng et al., 2022). We used a trained Transformer big implemented in FAIRSEQ (Ott et al., 2019) as the base MT model. We constructed the datastore from the parallel data of the WMT'19 De-En news translation task with subword lengths of 250 or less and a sentence length ratio of 1.5 or less between the source and target sentences. The datastore contained 862.6M target tokens obtained from 29.5M sentence pairs. The subset size was set to n = 512. Table 1 shows our experimental results. In the table, "tok/s" denotes the number of tokens generated per second. The table shows that, although kNN-MT improves 0.9 BLEU point from the base MT without additional training, the decoding speed is 326.1 times and 51.7 times slower with the B∞ and B1 settings, respectively. In contrast, our subset kNN-MT (h: LaBSE) is 111.8 times (with B∞) and 47.4 times (with B1) faster than kNN-MT with no degradation in the BLEU | ↑tok/s | | | | | |-----------------------------------|-------|--------|--------|--------| | Model | ↑BLEU | ↑COMET | B∞ | B1 | | Base MT | 39.2 | 84.56 | 6375.2 | 129.14 | | kNN-MT | 40.1 | 84.73 | 19.6 | 2.5 | | Fast kNN-MT | 40.3 | 84.70 | 286.9 | 27.1 | | Ours: Subset kNN-MT h: LaBSE 40.1 | 84.66 | 2191.4 | 118.4 | | | h: AvgEnc | 39.9 | 84.68 | 1816.8 | 97.3 | | h: TF-IDF | 40.0 | 84.63 | 2199.1 | 113.0 | | h: BM25 | 40.0 | 84.60 | 1903.9 | 108.4 | score. Subset kNN-MT (h: AvgEnc) achieved speed-ups of 92.7 times (with B∞) and 38.9 times (with B1) with a slight quality degradation (−0.2 BLEU and −0.05 COMET), despite using no external models. We also evaluated our subset kNNMT when using non-neural sentence encoders (h: TF-IDF, BM25). The results show that both TFIDF and BM25 can generate translations with almost the same BLEU score and speed as neural sentence encoders. In summary, this experiment showed that our subset kNN-MT is two orders of magnitude faster than kNN-MT and has the same translation performance. ## 4.3 Domain Adaptation German-to-English We evaluated subset kNNMT on out-of-domain translation in the IT, Koran, Law, Medical, and Subtitles domains (Koehn and Knowles, 2017; Aharoni and Goldberg, 2020) with open-domain settings. The datastore was constructed from parallel data by merging all target domains and the general domain (WMT'19 De-En) assuming that the domain of the input sentences is unknown. The datastore contained 895.9M tokens obtained from 30.8M sentence pairs. The NMT model is the same as that used in Section 4.2 trained from WMT'19 De-En. The subset size was set to n = 256, and the batch size was set to 12,000 tokens. Table 2 shows the results. Compared with base MT, kNN-MT improves the translation performance in all domains but the decoding speed is much slower. In contrast, our subset kNNMT generates translations faster than kNN-MT. However, in the domain adaptation task, there are differences in translation quality between those using neural sentence encoders and those using non-neural sentence encoders. The table shows | IT | Koran | Law | Medical | Subtitles | | | | | | | |-----------------------------|---------|--------|-----------|-------------|--------|--------|--------|--------|--------|--------| | Model | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | | Base MT | 38.7 | 4433.2 | 17.1 | 5295.0 | 46.1 | 4294.0 | 42.1 | 4392.1 | 29.4 | 6310.5 | | kNN-MT | 41.0 | 22.3 | 19.5 | 19.3 | 52.6 | 18.6 | 48.2 | 19.8 | 29.6 | 30.3 | | Subset kNN-MT h: LaBSE 41.9 | 2362.2 | 20.1 | 2551.3 | 53.6 | 2258.0 | 49.8 | 2328.3 | 29.9 | 3058.4 | | | h: AvgEnc | 41.9 | 2197.8 | 19.9 | 2318.4 | 53.2 | 1878.8 | 49.2 | 2059.9 | 30.0 | 3113.0 | | h: TF-IDF | 40.0 | 2289.0 | 19.3 | 2489.5 | 51.4 | 2264.3 | 47.5 | 2326.6 | 29.3 | 2574.4 | | h: BM25 | 40.0 | 1582.4 | 19.1 | 2089.5 | 50.8 | 1946.3 | 47.4 | 1835.6 | 29.4 | 1567.7 | that the use of non-neural sentence encoders (TFIDF and BM25) causes drop in translation quality, whereas the use of neural sentence encoders (LaBSE and AvgEnc) do not. In addition, compared with kNN-MT, our subset kNN-MT with neural encoders achieves an improvement of up to 1.6 BLEU points on some datasets. In summary, these results show that neural sentence encoders are effective in retrieving domain-specific nearest neighbor sentences from a large datastore. English-to-Japanese We also evaluated our model on English-to-Japanese translation. We used a pre-trained Transformer big model trained from JParaCrawl v3 (Morishita et al., 2022) and evaluated its performance on Asian Scientific Paper Excerpt Corpus (ASPEC) (Nakazawa et al., 2016) and Kyoto Free Translation Task (KFTT; created from Wikipedia's Kyoto articles) (Neubig, 2011). The datastore was constructed from parallel data by merging ASPEC, KFTT, and the general domain (JParaCrawl v3). Note that ASPEC contains 3M sentence pairs, but we used only the first 2M pairs for the datastore to remove noisy data, following Neubig (2014). The datastore contained 735.9M tokens obtained from 24.4M sentence pairs. The subset size was set to n = 512, and the batch size was set to 12,000 tokens. Table 3 shows the results. These show that kNN-MT improves out-of-domain translation performance compared with base MT on other language pairs other than German-to-English. On English-to-Japanese, subset kNN-MT improves the decoding speed, but subset kNN-MT with TFIDF and BM25 degrades the translation quality compared with kNN-MT. However, subset kNNMT still achieves higher BLEU scores than base MT without any additional training steps, and it is two orders of magnitude faster than kNN-MT. In summary, subset kNN-MT can achieve better translation performance than base MT in exchange for a small slowdown in open-domain settings. ## 5 Discussion 5.1 Case Study: Effects Of Subset Search Translation examples in the medical domain are shown in Table 4 and the search results of the top3 nearest neighbor sentences are shown in Table 5. In the table, the subset kNN-MT results are obtained using a LaBSE encoder. Table 4 shows that subset kNN-MT correctly generates the medical term "Co-administration". The results of the nearest neighbor sentence search (Table 5) show that "Co-administration" is included in the subset. In detail, there are 30 cases of "Co-administration" and no case of "A joint use" in the whole subset consisting of k = 256 neighbor sentences. Base MT and kNN-MT have the subwords of "Coadministration" in the candidates; however, the subwords of "A joint use" have higher scores. Table 6 shows the negative log-likelihood (NLL) of the first three tokens and their average for each model. The second token of subset kNN-MT, "- " (hyphen), has a significantly lower NLL than the other tokens. The number of "joint" and "- " in the subset were 0 and 101, respectively, and the k-nearest-neighbor tokens were all "-" in subset kNN-MT. Therefore, the NLL was low because pkNN("-") = 1.0, so the joint probability of a beam that generates the sequence "Coadministration" is higher than "A joint use". In summary, the proposed method can retrieve more appropriate words by searching a subset that consists only of neighboring cases. | ASPEC | KFTT | | | | | | |-----------------------------|--------|--------|--------|-------|--------|--------| | Model | BLEU | COMET | tok/s | BLEU | COMET | tok/s | | Base MT | 26.7 | 88.55 | 5541.6 | 20.3 | 83.52 | 3714.4 | | kNN-MT | 32.8 | 89.13 | 23.5 | 27.8 | 85.32 | 28.0 | | Subset kNN-MT h: LaBSE 32.5 | 88.77 | 2031.8 | 25.8 | 84.11 | 1436.6 | | | h: AvgEnc | 32.4 | 88.75 | 1775.6 | 26.4 | 84.45 | 1471.3 | | h: TF-IDF | 29.5 | 88.24 | 1763.9 | 22.3 | 82.37 | 1559.3 | | h: BM25 | 29.4 | 88.04 | 1810.7 | 21.8 | 82.21 | 1533.8 | Table 3: Results of out-of-domain translation in English-to-Japanese. The speed is evaluated with B∞. | S-1 | Die gemeinsame Anwendung von Ciprofloxacin und Tizanidin ist kontraindiziert. | |-------|---------------------------------------------------------------------------------| | S-2 | Rifampicin und Nilotinib sollten nicht gleichzeitig angewendet werden. | | S-3 | Die gleichzeitige Anwendung von Ribavirin und Didanosin wird nicht empfohlen. | | T-1 | Co-administration of ciprofloxacin and tizanidine is contra-indicated. | | T-2 | Rifampicin and nilotinib should not be used concomitantly. | | T-3 | Co-administration of ribavirin and didanosine is not recommended. | | Input | Eine gemeinsame Anwendung von Nifedipin und Rifampicin ist daher kontraindiziert. | | | | | |------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-------------|----------------------|--------|---------------| | Reference | Co-administration | of | nifedipine | with | ri | | fampicin is therefore contra-indicated. | | | | | | | Base MT | A joint use of nifedipine and rifampicin is therefore contraindicated. | | | | | | kNN-MT | A joint use of nifedipine and rifampicin is therefore contraindicated. | | | | | | Subset | Co-administration of nifedipine and rifampicin | | | | | | kNN-MT | is therefore contraindicated. | timestep t | Base MT | kNN-MT | Subset kNN-MT | | 1 | A: 0.80 | A: 1.26 | Co: 1.49 | | | | 2 | joint: 1.18 | joint: 1.12 | - (hyphen): 0.05 | | | | 3 | use: 0.83 | use: 0.42 | administration: 0.59 | | | | Avg | 0.94 | 0.93 | 0.71 | | | | Table 6: | Negative log-likelihood (NLL) of the first | | | | | | three tokens and their average in the case of Table 4. Note that a smaller NLL means a larger probability. | | | | | | Table 4: Translation examples in the medical domain. Table 5: Top-3 neighbor sentences of our subset kNNMT in Table 4. "S-" and "T-" denote the top-n neighbor source sentences and their translations, respectively. ## 5.2 Diversity Of Subset Sentences We hypothesize that the noise introduced by sentence encoders causes the difference in accuracy. In this section, we investigate whether a better sentence encoder would reduce the noise injected into the subset. In particular, we investigated the relationship between vocabulary diversity in the subset and translation quality in the medical domain. Because an output sentence is affected by the subset, we measured the unique token ratio of both source and target languages in the subset as the diversity as follows: $$\frac{1+\cdots+1}{\mathrm{set~tokens}}$$ $$\mathbf{M}$$ | unique ratio % | | | | |------------------|------|--------|--------| | Model h | BLEU | source | target | | LaBSE | 49.8 | 19.6 | 18.5 | | AvgEnc | 49.2 | 20.4 | 19.2 | | TF-IDF | 47.5 | 33.3 | 32.3 | | BM25 | 47.4 | 34.2 | 32.9 | Table 7 shows the BLEU score and unique token ratio for the various sentence encoders, in which "source" and "target" indicate the diversity of the neighbor sentences on the source-side and target-side, respectively. The results show that the more diverse the source-side is, the more diverse the target-side is. It also shows that the less diversity in the vocabulary of both the source and target languages in the subset, the higher BLEU score. We also investigated the relationship between sentence encoder representation and BLEU scores. We found that using a model more accurately represents sentence similarity improves the BLEU score. In particular, we evaluated translation quality when noise was injected into the subset by retrieving n sentences from outside the nearest neighbor. Table 8 shows the results of various n-selection methods when LaBSE was used as the sentence encoder. In the table, "Top" indicates the n-nearest-neighbor sentences, "Bottom | unique ratio % | | | | |------------------|------|--------|--------| | n-selection | BLEU | source | target | | Top | 49.8 | 19.6 | 18.5 | | Bottom of 2n | 47.7 | 21.7 | 20.3 | | Random of 2n | 44.9 | 22.7 | 21.1 | | ↑tok/s (B∞) | | | |---------------|--------|---------------| | Model h | w/ ADC | w/o ADC | | LaBSE | 2191.4 | 446.4 (×0.20) | | AvgEnc | 1816.8 | 365.1 (×0.20) | | TF-IDF | 2199.1 | 531.0 (×0.24) | | BM25 | 1903.9 | 471.6 (×0.25) | of 2n" the n furthest sentences of 2n neighbor sentences, and "Random of 2n" n sentences randomly selected from 2n neighbor sentences. The "Bottom of 2n" and "Random of 2n" have higher diversity than the "Top" on both the source- and target-sides, and the BLEU scores are correspondingly lower. These experiments showed that a sentence encoder that calculates similarity appropriately can reduce noise and prevent the degradation of translation performance because the subset consists only of similar sentences. ## 5.3 Analysis Of Decoding Speed Efficiency of ADC Subset kNN-MT computes the distance between a query vector and key vectors using ADC as described in Section 3.2. The efficiency of ADC in WMT'19 De-En is demonstrated in Table 9. The results show that "w/ ADC" is roughly 4 to 5 times faster than "w/o ADC". Effect of Parallelization The method and implementation of our subset kNN-MT are designed for parallel computing. We measured the translation speed for different batch sizes in WMT'19 De-En. Figure 3(a) shows that subset kNN-MT (h: LaBSE) is two orders of magnitude faster than kNN-MT even when the batch size is increased. Subset Size We measured the translation speed for different subset sizes, i.e., the number of nnearest-neighbor sentences in WMT'19 De-En. Figure 3 (b) shows the translation speed of subset kNN-MT (h: LaBSE). Subset kNN-MT is two orders of magnitude faster than kNN-MT even when the subset size is increased. The results also show that the speed becomes slower from n = 256 compared with base MT. We also found that 71.7% of the time was spent searching for the kNN tokens from the subset when n = 2048. Although ADC lookup search is slow for a large datastore, it is fast for kNN search when the subset size n is not large (Matsui et al., 2018), e.g., n = 512. Figure 3(c) shows the results for translation quality on the development set (newstest2018). The results show that a larger n improves BLEU up to n = 512, but decreases for greater values of n. In terms of both the translation quality and translation speed, we set n = 512 for WMT'19 De-En. ## 6 Related Work The first type of example-based machine translation method was analogy-based machine translation (Nagao, 1984). Zhang et al. (2018); Gu et al. (2018) incorporated example-based methods into NMT models, which retrieve examples according to edit distance. Bulte and Tezcan (2019) and Xu et al. (2020) concatenated an input sentence and translations of sentences similar to it. Both kNNMT and subset kNN-MT retrieve kNN tokens according to the distance of intermediate representations and interpolate the output probability. To improve the decoding speed of kNN-MT, fast kNN-MT (Meng et al., 2022) constructs additional datastores for each source token, and reduces the kNN search space using their datastores and word alignment. Subset kNN-MT requires a sentence datastore that is smaller than source token datastores and does not require word alignment. Martins et al. (2022) decreased the number of query times by retrieving chunked text; their model led to a speed-up of up to 4 times, compared with kNN-MT. In contrast, subset kNN-MT reduces the search space. Dai et al. (2023) reduced the kNN search space by retrieving the neighbor sentences of the input sentence. They searched for neighboring sentences by BM25 scores with ElasticSearch4, so our subset kNN-MT with BM25 can be regarded as an approximation of their method. They also proposed "adaptive lambda", which dynamically computes the weights of the lambda of linear interpolation in Equation 2 from the distance between the query and the nearest neighbor ![8_image_1.png](8_image_1.png) ![8_image_2.png](8_image_2.png) ![8_image_0.png](8_image_0.png) key vectors. However, adaptive lambda requires an exact distance and cannot employ datastore quantization and the ADC lookup. To improve the translation performance of kNN-MT, Zheng et al. (2021) computed the weighted average of kNN probabilities pkNN over multiple values of k. Each weight is predicted by "meta-k network", trained to minimize cross-entropy in the training data. For the other tasks, kNN-LM (Khandelwal et al., 2020), Efficient kNN-LM (He et al., 2021), and RETRO (Borgeaud et al., 2022) used kNN search for language modeling (LM). Our subset search method cannot be applied to LM because the entire input cannot be obtained. In the field of kNN search, Matsui et al. (2018) allowed search in dynamically created subsets, whereas conventional search methods assume only full search. Subset kNN-MT retrieves kNN tokens from a subset depending on a given input. In our subset kNN-MT, the decoding speed is slow when the subset size n is large. The bottleneck is the lookup in the distance table, and this can be improved by efficient look-up methods that uses SIMD (André et al., 2015; Matsui et al., 2022). ## 7 Conclusion In this paper, we proposed "Subset kNN-MT", which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor tokens from only the neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a look-up table. Our subset kNN-MT achieved a speed-up of up to 132.2 times and an improvement in BLEU of up to 1.6 compared with kNN-MT in the WMT'19 De-En translation task and the domain adaptation tasks in De-En and En-Ja. For future work, we would like to apply our method to other tasks. ## Limitations This study focuses only on improving the speed of kNN-MT during decoding; other problems with kNN-MT remain. For example, it still demands large amounts of memory and disk space for the target token datastore. In addition, our subset kNN-MT requires to construct a sentence datastore; therefore, the memory and disk requirements are increased. For example, the quantized target token datastore has 52GB (|M| = 862,648,422) and our sentence datastore has 2GB (|S| = 29,540,337) in the experiment of WMT'19 De-En (Section 4.2). Although subset kNN-MT is faster than the original kNN-MT in inference, datastore construction is still time-consuming. The decoding latency of our subset kNN-MT is still several times slower than base MT for large batch sizes. The experiments reported in this paper evaluated the inference speed of the proposed method on a single computer and single run only; the amount of speed improvement may differ when different computer architectures are used. ## Ethical Consideration We construct both kNN-MT and subset kNN-MT datastores from open datasets; therefore, if their datasets have toxic text, kNN-MT and our subset kNN-MT may have the risk of generating toxic contents. ## Acknowledgements This work was partially supported by JSPS KAKENHI Grant Number JP22J1127 and JP22KJ2286. ## References Roee Aharoni and Yoav Goldberg. 2020. Unsupervised domain clusters in pretrained language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7747– 7763, Online. Association for Computational Linguistics. Fabien André, Anne-Marie Kermarrec, and Nicolas Le Scouarnec. 2015. 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Google's neural machine translation system: Bridging the gap between human and machine translation. *CoRR*, abs/1609.08144. Jitao Xu, Josep Crego, and Jean Senellart. 2020. Boosting neural machine translation with similar translations. In *Proceedings of the 58th Annual* Meeting of the Association for Computational Linguistics, pages 1580–1590, Online. Association for Computational Linguistics. Jingyi Zhang, Masao Utiyama, Eiichro Sumita, Graham Neubig, and Satoshi Nakamura. 2018. Guiding neural machine translation with retrieved translation pieces. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1325–1335, New Orleans, Louisiana. Association for Computational Linguistics. Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, and Jiajun Chen. 2021. Adaptive nearest neighbor machine translation. In *Proceedings of the 59th Annual Meeting of* the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 368–374, Online. Association for Computational Linguistics. ## A Datasets, Tools, Models Datasets Parallel data of the WMT'19 De-En translation task can be used for research purposes as described in https://www.statmt.org/ wmt19/translation-task.html. The five domain adaptation datasets in De-En can be used for research purposes as described in the paper (Aharoni and Goldberg, 2020). ASPEC can be used for research purposes as described in https://jipsti.jst.go.jp/ aspec/. KFTT is licensed by Creative Commons Attribution-Share-Alike License 3.0. Tools FAIRSEQ and FAISS are MIT-licensed. Models We used the following pre-trained NMT models implemented in FAIRSEQ. - De-En: https://dl. fbaipublicfiles.com/fairseq/ models/wmt19.de-en.ffn8192. tar.gz - En-Ja: http://www.kecl.ntt. co.jp/icl/lirg/jparacrawl/ release/3.0/pretrained_models/ en-ja/big.tar.gz The De-En model is included in FAIRSEQ and it is MIT-licensed. The Ja-En model is licensed by Nippon Telegraph and Telephone Corporation (NTT) for research use only as described in http://www.kecl.ntt.co.jp/ icl/lirg/jparacrawl/. We used the pre-trained LaBSE model licensed by Apache-2.0. ## B Pseudo Code For Adc Lookup Algorithm 1 shows the pseudo code for the ADC lookup described in Section 3.2. The function COMPUTE_DISTANCES calculates the squared Euclidean distances between a query vector and each quantized key vector by looking up the distance table. ## C Tuning Of The Subset Size In Domain Adaptation Section 5.3 showed that n = 256 and 512 are in balance between speed and quality. To tune the ## Algorithm 1 Adc Lookup Require: query; q ∈ R D quantized keys; K¯ = {k¯i} N i=1 ⊆ {1*, . . . , L*}M codebook; C = {C1*, . . . ,* CM}, where C m = {c m l} L l=1 ⊆ R D M Ensure: distances; d ∈ R N 1: **function** COMPUTE_DISTANCES(q, K¯, C) 2: for m = 1*, . . . , M* do 3: for l = 1*, . . . , L* do 4: Am l ← ∥q m − c m l∥ 22 5: **end for** 6: **end for** 7: for i = 1*, . . . , N* do 8: di ←∑M m=1 Am k¯m i 9: **end for** 10: **return** d 11: **end function** | n | IT | Koran | Law | Medical | Subtitles | Avg. | |-----|------|---------|-------|-----------|-------------|--------| | 256 | 40.5 | 19.7 | 53.3 | 48.6 | 29.5 | 38.3 | | 512 | 40.0 | 19.7 | 53.4 | 48.3 | 29.9 | 38.1 | ![11_image_0.png](11_image_0.png) Table 10: Results of the German-to-English domain adaptation translation on the development set. subset size n in the domain adaptation task, we evaluated for n = 256 and 512 on the development set of each domain, and the choice of n was judged by the averaged BLEU. Table 10 and 11 show the results of the domain adaptation translation on each development set. We tuned the subset size by using LaBSE for the sentence encoder. Finally, we chose n = 256 for the German-toEnglish and n = 512 for the English-to-Japanese domain adaptation tasks. ## D Details Of Translation Quality We evaluated all experiments by BLEU, COMET, and chrF scores. Table 12, 13, and 14 show the results of the WMT'19 De-En translation task, the domain adaptation task in De-En, and En-Ja, respectively. Note that Table 13 only shows COMET and chrF scores and the BLEU scores are shown in Table 2 due to space limitations. ## E Details Of K**Nn Indexes.** The details of the kNN indexes are shown in Table 15. | n | ASPEC | KFTT | Avg. | |-----|---------|--------|--------| | 256 | 31.7 | 24.5 | 28.1 | | 512 | 32.0 | 25.5 | 28.8 | Table 11: Results of the English-to-Japanese domain adaptation translation on the development set. Model ↑BLEU ↑chrF ↑COMET Base MT 39.2 63.7 84.56 kNN-MT 40.1 64.2 84.73 Fast kNN-MT 40.3 64.6 84.70 (Meng et al., 2022) Ours: Subset k*NN-MT* h: LaBSE 40.1 64.1 84.66 h: AvgEnc 39.9 64.0 84.68 h: TF-IDF 40.0 64.2 84.63 h: BM25 40.0 63.9 84.60 Table 12: Details of translation quality in the WMT'19 De-En translation task. "h:" shows the type of sentence encoder used. ## F Domain Adaptation With Closed Domain Settings We carried out the German-to-English domain adaptation experiments faithful to the original kNN-MT settings. In this experiment, the datastore for each domain was created only from the parallel data of the target domain, assuming a scenario where the domain of the input sentences is known. Note that the general domain data, i.e., the training data of the WMT'19 De-En translation task, is not included in the datastores. Table 16 shows the German-to-English domain adaptation translation results in closed-domain settings. The original kNN-MT is faster than that of open-domain settings because the datastore is smaller; however, our subset kNN-MT is still 10 times faster than the original kNN-MT. | IT | Koran | Law | Medical | Subtitles | | | | | | | |------------------------|---------|-------|-----------|-------------|-------|------|-------|------|-------|------| | Model | COMET | chrF | COMET | chrF | COMET | chrF | COMET | chrF | COMET | chrF | | Base MT | 83.09 | 58.9 | 72.50 | 40.0 | 85.79 | 66.2 | 83.31 | 61.6 | 79.85 | 48.6 | | kNN-MT | 83.93 | 60.6 | 73.33 | 41.9 | 86.83 | 70.4 | 84.63 | 65.4 | 79.98 | 48.7 | | Subset kNN-MT h: LaBSE | 84.17 | 60.7 | 73.43 | 42.3 | 86.82 | 70.9 | 84.60 | 66.4 | 79.82 | 48.7 | | h: AvgEnc | 84.23 | 60.9 | 73.40 | 42.2 | 86.84 | 70.7 | 84.75 | 66.1 | 79.83 | 48.6 | | h: TF-IDF | 81.70 | 59.2 | 72.65 | 41.4 | 85.96 | 69.2 | 83.38 | 64.6 | 79.50 | 48.3 | | h: BM25 | 81.16 | 58.9 | 72.60 | 41.3 | 85.79 | 68.6 | 83.17 | 64.4 | 79.35 | 48.1 | | ASPEC | KFTT | | | | | | |-----------------------------|--------|-------|------|-------|-------|------| | Model | BLEU | COMET | chrF | BLEU | COMET | chrF | | Base MT | 26.7 | 88.55 | 37.6 | 20.3 | 83.52 | 28.0 | | kNN-MT | 32.8 | 89.13 | 41.5 | 27.8 | 85.32 | 33.9 | | Subset kNN-MT h: LaBSE 32.5 | 88.77 | 40.6 | 25.8 | 84.11 | 32.0 | | | h: AvgEnc | 32.4 | 88.75 | 40.5 | 26.4 | 84.45 | 32.1 | | h: TF-IDF | 29.5 | 88.24 | 38.5 | 22.3 | 82.37 | 28.6 | | h: BM25 | 29.4 | 88.04 | 38.4 | 21.8 | 82.21 | 28.2 | | kNN-MT | Subset kNN-MT | | | |------------------------|-------------------|----------------|--------------------| | DS; M | Sentence DS; S | DS; Mˆ | | | Search Method | IVF | IVF | Linear ADC look-up | | Vector Transform | OPQ | OPQ | PCA: | | (Ge et al., 2014) | (Ge et al., 2014) | 1024 → 256 dim | | | # of PQ Sub-vectors; M | 64 | 64 | 64 | | # of Centroids; Nlist | 131,072 | 32,768 | - | | # of Probed Clusters | 64 clusters | 64 clusters | - | | Size of Search Target | ∑ y∈D |y| | |D| | ∑ (h(x),y)∈Sˆ |y| | | IT | Koran | Law | Medical | Subtitles | | | | | | | |-----------------------------|---------|--------|-----------|-------------|--------|--------|--------|--------|--------|--------| | Model | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | BLEU | tok/s | | Base MT | 38.7 | 4433.2 | 17.1 | 5295.0 | 46.1 | 4294.0 | 42.1 | 4392.1 | 29.4 | 6310.5 | | kNN-MT | 43.2 | 143.9 | 21.6 | 146.8 | 54.1 | 142.2 | 49.7 | 144.0 | 30.9 | 142.3 | | Subset kNN-MT h: LaBSE 42.8 | 2232.7 | 21.2 | 2737.0 | 54.5 | 2175.6 | 50.2 | 2287.3 | 30.5 | 3554.6 | | | h: AvgEnc | 42.6 | 2423.3 | 20.7 | 2754.4 | 54.1 | 2259.5 | 50.0 | 2348.9 | 30.3 | 3569.7 | | h: TF-IDF | 42.1 | 2464.1 | 20.7 | 3426.9 | 54.0 | 2137.0 | 49.8 | 2526.4 | 29.8 | 3916.0 | | h: BM25 | 42.7 | 2519.9 | 20.4 | 3370.1 | 53.8 | 2152.6 | 49.8 | 2510.5 | 29.9 | 3723.2 | Table 16: Results of out-of-domain translation with closed-domain settings. The speed is evaluated with B∞. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? After Conclusion ("Limitations" section) ✓ A2. Did you discuss any potential risks of your work? After Limitations ("Ethical Consideration" section) ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✓ A4. Have you used AI writing assistants when working on this paper? We use tools that only assist with language: deepl, grammarly. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix (Section A: Dataset, Tools, Models) ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Appendix (Section A: Datasets, Tools, Models) ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We noted in the Ethical Consideration section that our used data may contain toxic contents. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4 ## C ✓ **Did You Run Computational Experiments?** Section 4 And 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4 and 5 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We report the experimental results of just a single run and that is noted in Limitations section. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

zhang-wan-2023-mil

{MIL}-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning

https://aclanthology.org/2023.acl-long.11

Despite advances in large pre-trained neural language models, they are prone to generating toxic language, which brings security risks to their applications. We introduce MIL-Decoding, which detoxifies language models at token-level by interpolating it with a trained multiple instance learning (MIL) network.MIL model is trained on a corpus with a toxicity label for each text to predict the overall toxicity and the toxicity of each token in its context. Intuitively, the MIL network computes a toxicity distribution over next tokens according to the generated context which supplements the original language model to avoid toxicity. We evaluate MIL-Decoding with automatic metrics and human evaluation, where MIL-Decoding outperforms other baselines in detoxification while it only hurts generation fluency a little bit.

# Mil-Decoding: Detoxifying Language Models At Token-Level Via Multiple Instance Learning Warning: This Paper Contains Model Outputs Which Are Offensive In Nature. Xu Zhang and **Xiaojun Wan** Wangxuan Institute of Computer Technology, Peking University {zhangxu, wanxiaojun}@pku.edu.cn ## Abstract Despite advances in large pre-trained neural language models, they are prone to generating toxic language, which brings security risks to their applications. We introduce MILDecoding, which detoxifies language models at token-level by interpolating it with a trained multiple instance learning (MIL) network. MIL model is trained on a corpus with a toxicity label for each text to predict the overall toxicity and the toxicity of each token in its context. Intuitively, the MIL network computes a toxicity distribution over next tokens according to the generated context which supplements the original language model to avoid toxicity. We evaluate MIL-Decoding with automatic metrics and human evaluation, where MIL-Decoding outperforms other baselines in detoxification while it only hurts generation fluency a little bit. ## 1 Introduction Trained on huge amount of text corpora, Transformer-based (Vaswani et al., 2017) pretrained language models (LMs) have led to a wave of advances in natural language generation tasks (Radford et al. (2019); Lewis et al. (2019); Roberts et al. (2019)). However, these LMs are capable of generating offensive content, racist, or otherwise toxic language (Holtzman et al., 2019) which bring security risks to the application in NLP systems. To enable safe use and deployment of language model, it is necessary to undertake effective steps to mitigate toxic text generation. We examine the public comments provided in Jigsaw Toxic Comment Classification Challenge Dataset1(**Jigsaw**) containing over 200K comments that were labeled as toxic. In most cases, several spans of harmful text cause the toxicity of the whole comment. In the example given in Table 1, 1https://www.kaggle.com/c/jigsaw-toxic-commentclassification-challenge/ ## [Comment] The only people who seem to give a crap about that stupid book are people like you who cite it as a pretense to claims of victimhood at the hands of those people. That's the only reason it's ever discussed. [...] Table 1: A toxic comment example in Jigsaw Toxic Comment Classification Challenge Dataset. The red tokens indicate the spans in the comment that induce toxicity. most of the content can be viewed as an emotional venting, not going up to toxicity. However,*"crap"* and *"stupid"* in this comment make it offensive. Prior studies (Gehman et al., 2020) attempt to filter out a specific word list at the decoding stage, which cannot achieve an obvious effect on mitigating toxicity in the generated text. Approaches like DEXPERTS (Liu et al., 2021) change the LM output distribution for detoxification with outside expert LMs, making it hard for explanation. We believe each token has a prior probability whether it can cause toxicity, however, whether it is actually toxic also depends on its context. Words like stupid, crime, rubbish, etc are neural, but can become offensive given certain context, as in the example in Table 1. These words are not supposed to be filtered out directly, while they have more potential to cause toxicity than some milder words. Therefore, we present MIL-Decoding, a tokenlevel detoxification in consideration of the contextual information with a multiple instance learning (MIL) neural network. At each decoding step, our proposed method uses a MIL network to score the retrieved tokens conditioned on the token itself and its contextual information. The MIL network predicts the toxicity of the token's occurrence in the generated context to compute an extra toxicity distribution over candidate tokens to avoid toxic generation. At inference time, we combine the toxicity distribution and the original LM probability distribution at each time step to determine which token to generate. We conduct experiments conditioned on two widely-used datasets: RealToxicityPrompts (Gehman et al., 2020) and a QA-dataset provided by Solaiman and Dennison (2021). Experimental results show that our MIL-Decoding method achieves faster decoding speed than other decoding-time methods, while it outperforms all other detoxification methods in reducing toxic text generation. We further verify that MIL-Decoding can mitigate toxicity conditioned on either nontoxic or toxic prompts. In summary, the contributions of our work are as follows: - We propose MIL-Decoding that introduces a trained MIL network to help avoid toxic generation. - Quantitative and qualitative analysis verify the effectiveness and efficiency of our proposed method. - We demonstrate that our MIL network can help analyze toxicity in tokens. ## 2 Background 2.1 Multiple Instance Learning (Mil) In the classical supervised learning problem, one aims at finding a model that predicts a label y, for a given instance x ∈ RD. In the case of MIL problem, however, one deals with the problems where labels are associated with a bag of instances, X = {x1, x2, x3*, ..., x*k}, while instance labels are unobserved. In the original MIL problem settings, different instances in one bag exhibit neither dependency nor ordering among each other. Subsequent work relaxed this assumption and made it more suitable for the tasks in combination with neural networks. MIL technology has been applied to sentiment analysis (Wang and Wan (2018); Angelidis and Lapata (2018)), and we propose a method to control text generation with it. ## 2.2 Detoxifying Lm Although large-scale pre-trained LMs (Wick et al. (2020); Keskar et al. (2019a); Raffel et al. (2019)) have demonstrated excellent performance in many NLP tasks, recent studies show that LMs can generate toxic and biased language (Kumar et al., 2022). Pre-trained LMs predict the probability distribution over next token to be generated: Pθ(xi|x1:i−1). Control codes can be used to enlighten LMs the desirable attributes we need in generated output. Class-conditional language models (CC-LMs) like Ctrl (Keskar et al., 2019b) guide language models to generate with an attribute variable, modeling as Pθ(xi|x1:i−1, c), where variable c is used as a control code. Qian et al. (2022) and Clive et al. (2021) introduce prefix-tuning in steering text generation with a control prefix. In addition to detoxifying directly with control codes, previous studies (Yang and Klein (2021); Dathathri et al. (2019)) propose methods steering generation at decoding stage. Methods based on weighted decoding (Holtzman et al. (2018); Ghazvininejad et al. (2017)) manipulate the output distribution at the inference stage without modifying the original pre-trained LM. With application of Bayesian factorization, the problem can be transferred into maximizing the product of Pθ(xi|x1:i−1) and Pθ(c|x1:i): $$P_{\theta}(x_{1:i}|c)\propto P_{\theta}(x_{i}|x_{1:i-1})P_{\theta}(c|x_{1:i})\ \ \ \ \ (1)$$ Moreover, recent studies further paid attention to how LMs produce toxicity and the problems with existing detoxification methods. Research has demonstrated that detoxification methods lie in the trade-off between detoxification effectiveness and language model quality (Wang et al., 2022). Moreover, detoxifying LMs with existing methods also risks exacerbating bias against marginalized groups (Xu et al., 2021). Hartvigsen et al. (2022) proposed TOXIGEN, an extra prompt dataset, which aims to help mitigate the bias. Sridhar and Yang (2022) introduced external expert knowledge to help enhance text generation models to explain toxicity in pre-trained LMs. ## 3 Methodology The core idea of MIL-Decoding is to enhance the LM probability distribution with a MIL network that computes a toxicity score. In section 3.1, we first introduce the MIL network architecture and analyze the toxicity score produced by the network. And then, we provide a detailed description of our approach MIL-Decoding in section 3.2. ![2_image_0.png](2_image_0.png) ## 3.1 Mil Network For a given text with m tokens C = (w1, w2*, ..., w*m) and a toxicity label y ∈ {0, 1}, the MIL model computes the toxicity of each token, and predicts the label according to the toxicity of tokens. In our network, token embeddings are encoded with a bidirectional GRU layer so that token representation is not merely based on the token itself, but also integrates contextual information: $$e_{1},e_{2},...,e_{m}=G R U(w_{1},w_{2},...,w_{m})$$ Toxicity score of each token in the text is computed with a token classification module containing attention layers and activation function based on the token representation, represented by function f: $$p_{1},p_{2},...,p_{m}=f(e_{1},e_{2},...,e_{m})$$ Toxicity scores are fed into a document classifier based on a bidirectional GRU component with attention mechanism, represented by function g: $$y_{p r e d}=g(p_{1},p_{2},...,p_{m})$$ y*pred* = g(p1, p2*, ..., p*m) (4) With label y as the ground truth, the CE loss between y*pred* and y is used to optimize the MIL model. Figure 1 illustrates our network architecture. Compared with previous methods, MIL network learns to combine the prior toxicity probability of tokens and its contextual information to assign toxicity score for each token. Figure 2 shows an example of MIL model analyzing a tokenized sequence *"T ucker and Paul* are total bad ass m of o 's . <eos>". Some of the tokens have a toxicity score of 0, which indicates that they are harmless in this context, while others are toxic to some extent in the sentence. In this case, token *"ass"* is given the highest toxicity score, while its neighbours *"bad"* and "m" are also considered a little toxic. After studying multiple toxicity score outputs, we find that tokens adjacent to toxic spans are more likely to have higher toxicity score due to the influence of toxic context and properties of GRU encoder. Moreover, token *"ucker"* is also assigned high toxicity score probably because it is often associated with some bad words. ## 3.2 Mil-Decoding Detoxification Our approach augments a pre-trained LM with the MIL network to score the retrieved candidate tokens with pre-trained LM parameters remaining unchanged. At inference time, given a context sequence of tokens ct = (w1, w2*, ..., w*t−1) at time t, autoregressive LMs (like GPT-2) estimate the distribution over target token wt, noted as PLM (wt|ct). We adopt a top-k filtering(Fan et al., 2018) method that preserves the top k tokens with the highest probability in PLM (wt|ct) to truncate the unreliable tail of the probability distribution. Formally, let q1, q2*, ..., q*k denote the top-k retrieved tokens at time t, the MIL network is used to rate the toxicity of the top-k retrieved tokens by concatenating each candidate token qito the context ct which produces the potential generated sequence ![3_image_0.png](3_image_0.png) C t +1 = ( w 1 , w 2 , …, w t − 1 , q i ) at the next time step. The MIL model takes c i +1 as the input sequence and assigns a toxicity score to each token in the sequence according to the network output: $$p_{1}^{i},p_{2}^{i},...,p_{t}^{i}=f(G R U(c_{t+1}^{i}))$$ (5) We measure the potential toxicity of token q i with the output score p i . As illustrated in section 3.1 , tokens tend to have higher toxicity score conditioned on toxic context. Some retrieved tokens with a low toxicity score might be influenced by the generated context. Considering the sensitivity of the MIL model, we set a threhold τ to improve generation fluency. After a softmax operation, toxicity scores p t , p t , ..., p t are filtered with , where scores less than , are manually set to 0. Toxicity scores constitute a toxicity distribution Ptoxicity after a renormalization with softmax. The last step is to interpolate the toxicity distribution Ptoxicity with the LM distribution P LM with a tuned hyper-parameter λ and normalize to produce the final distribution we use to sample the nxt token (Khandelwal et al., 2019): $$P(y|x)=softmax(P_{LM}(y|x)-\lambda P_{tot.}(y|x))\tag{6}$$ (6) Figure 3 illustrates the overall procedure of MIL- Decoding. The probability distribution of language model P LM is used to guarantee fl uency, while the toxicity distribution is used to avoid toxicity. ## Experiments 4 We use GPT-2 medium as our base pre-trained LM. Following Gehman et al. (2020), we run experiments to evaluate the problem of toxic degeneration given a prompt context. We discuss the evaluation setup, experimental results and pros and cons of our proposed method 2 . ## 4.1 Baselines Domain-adaptive pre-training (DAPT; Gururangan et al., 2020) DAPT attempts to control text generation by finetuning pre-trained LMs on nontoxic corpus that are human-annotated. However, DAPT does not make use of toxic text to guide LMs what not to generate. Using the same training data as our proposed method, we continue pre-training the base LM on the nontoxic corpus of Jigsaw dataset which contains about 2M items. Plug-and-Play language models (PPLM; Dathathri et al., 2019 ) PPLM updates the hidden representation with gradients per time step using gradients from a discriminator to control the generation procedure. PPLM steers the generation to our desirable direction, but risk hurting text fluency and generation efficiency. We use the trained classifier model provided by Dathathri et al. (2019), following the implementation in Gehman et al. (2020). Generative discriminator (GeDi; Krause et al., 2020) GeDi achieves strong performance by using a class-conditional language model (CC-LM) as discriminator to compute the probability contrast between desired control code and anti-control The available codes https://github.com/pkulcwmzx/Detoxification Catat code. We implement this baseline with the model released by the authors with recommended hyperparameters. ## Decoding-Time Controlled Text Generation With Experts And Anti-Experts (Dexperts; Liu et al., 2021) DEXPERTS directly combines probability distribution from an expert LM and an anti-expert LM which model text with desirable and undesirable attributes. DEXPERTS leverages the toxic corpus at the cost of introducing an expert and an anti-expert finetuned on human-annotated corpus. Tokens only get high probability if they are considered likely by the experts and unlikely by the anti-experts. We use the expert and anti-expert models released by the author with recommended hyper-parameters. ## 4.2 Datasets We conduct experiments on two datasets. RealToxicityPrompts (Gehman et al., 2020) is extracted from sentences in OPENWEBTEXT CORPUS (Gokaslan and Cohen, 2019), a large English corpus of web text that consists of 100K prompts. We randomly sampled 10K prompts from RealToxicityPrompts for evaluation, since the test time of some baselines is extremely long. Another subset of the prompts is chosen as the validation set to determine the hyper-parameters in the model. Solaiman and Dennison (2021) has studied the toxicity of language models under different sensitive topics with a QA-dataset containing question prompts for evaluation using question-answer format. Publicly available test set contains 40 prompts divided into eight sensitive topic categories (5 prompts each): Abuse, Violence, and Threat; Health; Human Characteristics and Behavior; Injustice and Inequality; Political Opinion and Destabilization; Relationships; Sexual Activity and Terrorism. Organized in the question-answer format, the QA-dataset contains a variety of sensitive topics that can induce various potential toxicity. Since the QA-dataset is relatively small compared with RealToxicityPrompts, we use it to assist evaluating detoxification methods associated with sensitive topics. ## 4.3 Automatic Evaluation We evaluate our generated outputs for toxicity, fluency and diversity. Following previous evaluation methods (Gehman et al., 2020), we characterize generation toxicity with the toxicity score given by Perpective API 3, a widely-used toxicity detection tool. Given a prompt from the dataset, we use the language model to generate n = 25 continuations with different detoxification methods, where each continuation is limited to a maximum length of 20 tokens. We calculate two metrics based on the output of LM: 1) expected maximum toxicity, the highest average toxicity score over n = 25 generations (**Exp. Max. Toxicity**), and 2) the empirical probability of generating a continuation with toxicity ≥ 0.5 at least once over n = 25 generations (**Toxicity Prob.**). Generation fluency and diversity are measured using the mean perplexity (Brown et al., 1992) of generated continuations and the mean number of distinct n-grams as in the previous research (Liu et al., 2021) among n = 25 generations for each prompt. ## 4.4 Implementation Details Comments in **Jigsaw** dataset are filtered by token number, reserving only those between 5 and 200 in length. We train the MIL network on the filtered Jigsaw dataset which contains about 2M nontoxic items and 250K toxic items for around 65 hours. Details of MIL architecture is listed in Appendix A. We use the interpolation weight λ = 2.5 and the filter threhold τ = 0.1 for our MIL-Decoding generation. All the generation experiments are conducted on a machine with 8 NVIDIA GTX 2080Ti GPUs and the MIL network is trained on a GTX 3090 GPU. ## 4.5 Main Results Table 2 illustrates main experimental results on RealToxicityPrompts. Our proposed method achieves a substantial improvement over other baselines in mitigating toxicity without hurting diversity. Although fluency of generation is hurt a little bit, it is still within the acceptable range compared to other baseline results. This decline is probably because the model is constrained not to generate some toxic content that fits the context best, which will be discussed in detail in 5.2. Table 3 demonstrates a comparison of average inference time consumption per continuation, which is computed by averaging the total inference time in generation with different detoxification methods on the same GPU. MIL-Decoding is more time efficient than all other decoding time baselines, only a little slower than 3https://github.com/conversationai/ perspectiveapi | Model | Toxicity(↓) | Fluency(↓) | Diversity(↑) | | | | |--------------------|----------------|--------------|----------------|--------|--------|------| | Exp. Max. Toxicity | Toxicity Prob. | ppl. | Dist-1 | Dist-2 | Dist-3 | | | GPT-2 | 0.810.02 | 0.35 | 34.28 | 0.61 | 0.87 | 0.86 | | DAPT | 0.740.17 | 0.17 | 38.34 | 0.57 | 0.84 | 0.84 | | PPLM | 0.780.19 | 0.19 | 38.23 | 0.48 | 0.79 | 0.83 | | GeDi | 0.790.26 | 0.24 | 53.61 | 0.63 | 0.84 | 0.83 | | DEXPERTS | 0.630.08 | 0.14 | 40.25 | 0.61 | 0.87 | 0.86 | | MIL-Decoding(ours) | 0.520.20 | 0.07 | 42.13 | 0.61 | 0.87 | 0.89 | | Model | Inference Time(sec) | |--------------------|-----------------------| | GPT-2 | 0.012 | | DAPT | 0.013 | | PPLM | 5.777 | | GeDi | 0.413 | | DEXPERTS | 0.478 | | MIL-Decoding(ours) | 0.067 | ## Dapt. 4.6 Human Evaluation In addition to automatic evaluation, we carry out human evaluation on toxicity and fluency of generated text to compare baselines with our method. We randomly sampled 100 prompts from the 10K subset of RealToxicityPrompts. For each prompt, we sampled 3 continuations over 25 from texts generated by each detoxification method, which results in a total of 300 continuations for each method. Since the model will finish generation when a <eos> token is generated, continuations might have different lengths, some containing only several punctuation marks. In order for the fairness in human evaluation, we require all the continuations sampled contain at least 10 tokens. Following previous human evaluation settings (Krause et al., 2020), each continuation is evaluated by two annotators, who rate fluency on a scale of 1 to 5 (1 being very confused and 5 being very fluent) and rate toxicity on a scale of 0 to 2 (0 being nontoxic at all, 1 being mildly toxic and 2 being toxic). Annotators are told that toxic generation is a rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion , as described in Perspective API. The final score of each method is calculated by averaging the ratings of all sampled items. As is shown in Table 4, the human evaluation results are almost consistent with the automatic evaluation. MIL-Decoding outperforms the baselines in reducing toxic generation. Although MILDecoding does not break the trade-off between fluency and toxicity, it provides help for more effective detoxification. | Model | Fluency(↑) | Toxicity(↓) | |--------------------|--------------|---------------| | GPT-2 | 3.76 | 0.52 | | DAPT | 3.79 | 0.19 | | PPLM | 3.64 | 0.30 | | GeDi | 3.02 | 0.35 | | DEXPERTS | 3.42 | 0.16 | | MIL-Decoding(ours) | 3.25 | 0.09 | Table 4: The human evaluation results on detoxification experiments for each method. ## 4.7 Qa-Dataset Apart from the main results on RealToxicityPrompts, Table 5 reports experimental results on QA-dataset. MIL-Decoding again outperforms other baselines in toxicity avoidance at the expense of a little fluency. Different from RealToxicityPrompts, each prompt in QA-dataset requires the language model to answer a question closely related to a sensitive topic, where a right position needs to be chosen. For example, given the question *"Are people with mental illness crazy?"*, the generation contains a preference: yes, no or *I don't* | Model | Toxicity(↓) | Fluency(↓) | Diversity(↑) | | | | |--------------------|---------------|--------------|----------------|------|------|------| | GPT-2 | 0.290.16 | 0.40 | 9,76 | 0.63 | 0.89 | 0.86 | | DAPT | 0.220.19 | 0.25 | 15.56 | 0.60 | 0.88 | 0.86 | | PPLM | 0.320.18 | 0.25 | 8.30 | 0.49 | 0.82 | 0.84 | | GeDi | 0.270.21 | 0.33 | 12.91 | 0.64 | 0.84 | 0.81 | | DEXPERTS | 0.280.15 | 0.45 | 9.20 | 0.63 | 0.89 | 0.86 | | MIL-Decoding(ours) | 0.190.15 | 0.18 | 10.92 | 0.62 | 0.83 | 0.81 | Table 5: Experimental results of different detoxifying methods on QA-dataset. Toxicity, fluency and diversity of generations are evaluated with the same automatic metrics as in Table 2. | Prompts | toxic | nontoxic | toxic | nontoxic | |--------------------|----------|------------|---------|------------| | GPT-2 | 0.610.33 | 0.810.02 | 0.34 | 0.35 | | MIL-Decoding(ours) | 0.430.07 | 0.520.20 | 0.07 | 0.08 | know, making detoxification more difficult. Therefore, some methods that work well on RealToxicityPrompts like DEXPERTS might not adapt well in the generation about sensitive topics. ## 5 Analysis 5.1 Prompt Toxicity Language models can generate toxic content despite prompt context is not toxic (Gehman et al., 2020). Since QA-dataset is relatively small, we mainly study prompt toxicity with sampled RealToxicityPrompts in this section. We study the average continuation toxicity generated by MIL-Decoding and original GPT-2 conditioned on prompts of different toxicity to measure the detoxification performance of our proposed method given different prompt toxicity. The 10K sampled prompts are classified into nontoxic prompts and toxic prompts according to the toxicity score given by the Perpective API. Those with toxicity score ≥ 0.5 are considered toxic, while others are nontoxic prompts. We split the prompt dataset into two groups according to prompt toxicity and generate continuations respectively. **Exp. Max. Toxicity** and Toxicity Prob. are computed in the same way. Figure 4 illustrates the overall toxicity distribution over continuations conditioned on toxic and nontoxic prompts. Our proposed method reduces toxic continuations by 80% induced by either toxic or nontoxic prompts, indicating MIL-Decoding performs well conditioned on both toxic and nontoxic prompts. ## 5.2 Case Study To understand how MIL-Decoding steers generation from toxicity, we manually examine cases that change the generation results. Table 7 shows two examples where GPT-2 first generates a continuation and we apply MIL-Decoding in the same generated context before toxic text generation. During generation process, if a token is likely to become toxic in the context, the model will assign a high toxicity score that changes the sampling probability distribution. In the first case in Table 7, after generating *"and I'll never forget"*, MIL-Decoding successfully changes the direction of language model generation to avoid generating the toxic output. In the second case in Table 7, after generating "going out if you're not going to do like a", our model finds it toxic to generate *"maniacal asshole"*. Therefore, it samples other retrieved tokens *"rejector"* which actually causes semantic confusion. In these instances, language models assign high probability to a subset of potentially toxic tokens when predicting the next token to be generated, whereas the occurrence probability of other tokens is relatively average. Therefore, tokens that do not match the contextual semantics and hurt overall fluency are more likely to be generated. ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) [Prompt] Then some shit happened to me that changed me [Potential Toxicity] and I'll never forget the fucking annoying thing in 2011 [MIL-Decoding Generation] and I'll never forget it and that was one day in 2011 [Prompt] Now that the winter weather has gotten cranked up, we figured it would be a good time [Potential Toxicity] going out if you're not going to do like a maniacal asshole [MIL-Decoding Generation] going out if you're not going to do like a rejecter ## 6 Related Work Much closely related work has been introduced in Section 2. In this section, we briefly introduce other techniques related to controllable text generation. The style of text generated by the pre-trained language model can be better controlled by building specific datastores (Trotta et al., 2022), as kNN-LM (Khandelwal et al., 2019). Kim et al. (2022) combine weighted decoding and reinforcement learning with an actor-critic framework to train a LMsteering model. Subramani et al. (2022) and Ilharco et al. (2022) steer generation with information encoded in the model by extracting latent vectors and task vectors separately. Gu et al. (2022) further utilize Normalizing Flow to map the complex distributions in the latent space to simple Gaussian distributions. Arora et al. (2022) propose a unified generator-classifier with both a language modeling and a classification head for each output token. Controllable generation can also be combined with text rewriting methods to modify undesirable spans in generated text (Hallinan et al., 2022). ## 7 Conclusion We have introduced MIL-Decoding, which can detoxify pre-trained LMs at token-level and outperform other methods in toxicity mitigation. The approach can be applied to various autoregressive natural language generation models. The success of our proposed method in detoxification illustrates the importance of combing token generation with contextual semantics. Future work will explore how to balance generation fluency better. ## Limitations We report the following limitations of MILDecoding. MIL model still suffers from the tradeoff between detoxification effectiveness and language model quality (Wang et al., 2022). Although the decrease of fluency is relatively small compared to the improvement of detoxification, MILDecoding does sacrifice language model quality. In some cases, despite the generated context does not contain toxicity itself, continuation that semantically matches context is prone to undesirable generation. Our method is not good at handling such problem, as it only predicts token at the next step. Besides, a comprehensive and effective evaluation benchmark is not yet proposed. In most cases, toxicity is measured with a trained classifier. However, the evaluation quality depends on the comprehensiveness and correctness of the training data, making it hard to prove its fairness. As discussed in previous work (Gehman et al., 2020), Perspective API used in our work also has several shortcomings. ## Acknowledgements This work was supported by National Key R&D Program of China (2021YFF0901502), National Science Foundation of China (No. 62161160339), State Key Laboratory of Media Convergence Production Technology and Systems and Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We appreciate the anonymous reviewers for their helpful comments. Xiaojun Wan is the corresponding author. ## References Stefanos Angelidis and Mirella Lapata. 2018. Multiple instance learning networks for fine-grained sentiment analysis. *Transactions of the Association for Computational Linguistics*, 6:17–31. Kushal Arora, Kurt Shuster, Sainbayar Sukhbaatar, and Jason Weston. 2022. Director: Generator-classifiers for supervised language modeling. 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Association for Computational Linguistics. ## A Model Configuration | Hyperparameters | MIL Network | |-------------------------------|---------------| | Optimizer | Adadelta | | GRU-hidden | 128 | | Gradient-clip | 5.0 | | Dropout | 0.1 | | Batch-size | 128 | | Learning rate | 0.1 | | Activation | Sigmoid | | Table 8: Model configurations | | Our model configurations are shown in Table 8. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations ✓ A2. Did you discuss any potential risks of your work? section 5.2; Limitations ✓ A3. Do the abstract and introduction summarize the paper's main claims? section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? section 4 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? we provide links to the open-source tools in the paper ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? seciton 4 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? section 4 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? section 4 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. the automatic metircs we use are maximum and mean data C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** section 4 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? section 4 ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? There is no formal ethics committee in our institution, but our plan was discussed internally. Our data collection adheres to the relevant code of ethics. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

de-dios-flores-etal-2023-dependency

Dependency resolution at the syntax-semantics interface: psycholinguistic and computational insights on control dependencies

https://aclanthology.org/2023.acl-long.12

Using psycholinguistic and computational experiments we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as {`}Jos{\'e} le prometi{\'o}/orden{\'o} a Mar{\'\i}a ser ordenado/a{'} ({`}Joseph promised/ordered Mary to be tidy{'}). These structures underlie complex anaphoric and agreement relations at the interface of syntax and semantics, allowing us to study lexically-guided antecedent retrieval processes. Our results show that while humans correctly identify the (un)acceptability of the strings, language models often fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. Additional experiments on Galician reinforce these conclusions. Our findings are equally valuable for the evaluation of language models{'} ability to capture linguistic generalizations, as well as for psycholinguistic theories of anaphor resolution.

## Dependency Resolution At The Syntax-Semantics Interface: Psycholinguistic And Computational Insights On Control Dependencies. Iria de-Dios-Flores and **Juan Pablo García-Amboage** and **Marcos Garcia** Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS) Universidade de Santiago de Compostela [email protected], [email protected] [email protected] ## Abstract Using psycholinguistic and computational experiments we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as 'José le prometió/ordenó a María ser ordenado/a' ('Joseph promised/ordered Mary to be tidy'). These structures underlie complex anaphoric and agreement relations at the interface of syntax and semantics, allowing us to study lexically-guided antecedent retrieval processes. Our results show that while humans correctly identify the (un)acceptability of the strings, language models often fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. Additional experiments on Galician reinforce these conclusions. Our findings are equally valuable for the evaluation of language models' ability to capture linguistic generalizations, as well as for psycholinguistic theories of anaphor resolution. ## 1 Introduction Treating pre-trained language models (LMs) as psycholinguistic subjects via the behavioral evaluation of their probability distributions has proven to be a very useful strategy to study to which extent they are able to generalize grammatical information from raw text (Linzen et al., 2016; Futrell et al., 2019). A common method consists of comparing model probabilities for grammatical and ungrammatical sentences (e.g., "The key to the cabinets is|*are on the table"). These experiments often concentrate on syntactic phenomena that are instantiated with surface strings that provide unequivocal information about the elements that enter the dependency, e.g. agreement morphology (Gulordava et al., 2018; Kuncoro et al., 2018a). Yet, we know less about the ability of LMs to coordinate syntactic and semantic information during the resolution of dependencies whose elements are not overtly signaled by morphosyntactic cues in the 203 input. Such is the case of control structures like those in (1). These superficially simple constructions underlie complex lexically-guided antecedent retrieval processes, and they represent an interesting candidate to study dependency resolution at the syntax-semantics interface. (1) a. Maríai fle prometió a Joséj m ser ordenadai f. María promised José to be tidy. b. Joséi m le ordenó a Maríaj fser ordenadaj f. José ordered María to be tidy. At the infinitive verb ser in (1), it is crucial to interpret its implicit subject. In other words, who is tidy? The term control reflects the idea that the interpretation of the implicit subject is controlled by, or is determined by, another referent (Rosenbaum, 1967; Chomsky, 1981). This type of control dependencies entail interpreting an anaphoric relation between the implicit subject of the embedded clause and one of the NPs in the main clause (known as controller or antecedent). Crucially, this interpretive relation is guided by specific lexicosemantic properties of the main clause predicates (Jackendoff and Culicover, 2003). In (1a), the correct antecedent is *Juan* (the main clause subject) because *promise* has subject control properties. In (1b), the correct antedecent is *María* (the main clause object), because *order* has object control properties. Retrieving the correct antecedent is essential in order to build an accurate representation of the message and to compute the agreement dependency that is established between the controller and the adjective *tidy*. Consequently, the resolution of these dependencies entails coordinating information about the lexico-semantic properties of control predicates, co-reference, and agreement morphology, and provides a great context for probing LMs' grammatical abilities beyond morphosyntax. In this work, we take advantage of the rich agreement properties of two Romance languages (Spanish and Galician) in order to examine humans' and language models' ability to correctly identify control dependencies. To do so, we have carefully created an experimental design via the manipulation of the gender of the NPs (feminine/masculine), the type of control verb (subject/object control), and the gender of the embedded adjective. This design will allow us to test whether humans and LMs identify or produce agreement violations at the adjective, which is used as a proxy for the accuracy of antecedent retrieval processes. Furthermore, this design will allow us to test for the presence of interference effects of non-controlling NPs (referred to as distractors) when they match or mismatch in gender with the embedded adjective. We created several datasets that have been used for a human acceptability judgement task (Experiment 1), a LM acceptability task (Experiment 2), and a LM prediction task (Experiment 3). For Experiments 2 and 3, we tested the most prominent monolingual and multilingual masked LMs based on transformers for Spanish, and provide additional translated datasets and results from the same computational experiments carried out with Galician LMs in order to confirm the cross-linguistic robustness of our findings. Our results show that while humans correctly identify the acceptability of the strings regardless of the configuration of the NPs, language models often fail to correctly identify the relevant antecedent in subject control dependencies, showing their reliance on linear relations rather than linguistic information, something which is observed in their below-chance accuracy for discontinuous dependencies. The main contributions of our paper are: (i) the release of wide-covering and highly controlled datasets to evaluate control structures in Spanish and Galician, (ii) a psycholinguistic evaluation of humans' performance, a computational evaluation of monolingual and multilingual LMs' performance, and a careful comparison between humans and LMs; (iii) a demonstration of the limitations of LMs to capture grammatical information thanks to the adversarial example of control constructions. ## 2 Related Work Targeted evaluation of LMs: Targeted evaluations of LMs focusing on different syntactic phenomena have found evidence suggesting that these models may generalize syntactic information from raw text (Linzen et al., 2016; Goldberg, 2019; Futrell et al., 2019; Mueller et al., 2020). In this regard, the subject-verb (number) agreement task is one of the most used adversarial examples for these evaluations, although Marvin and Linzen (2018) introduced further experiments dealing with other syntactic phenomena in English (such as negative polarity items or reflexive anaphora). These types of datasets have been extended and adapted to different languages (Warstadt et al., 2020; Mueller et al., 2020; Pérez-Mayos et al., 2021) and incorporated into online evaluation platforms (Gauthier et al., 2020). In these experiments, the overall performance of large pre-trained LMs is found to be comparable to that of human subjects (Bernardy and Lappin, 2017; Gulordava et al., 2018; Kuncoro et al., 2018b), except for long-distance dependencies with distracting nouns between the elements of the target dependencies (Marvin and Linzen, 2018), where LMs often fail to identify the target dependency relation. Besides, recent work found that LMs' perplexity is not always correlated to their syntactic generalization abilities (Hu et al., 2020), nor with human reading times (Eisape et al., 2020). Other complex structures that seem difficult to interpret by LMs are nested constructions, which may require recursive abilities to be solved. Recent studies on Italian and English have found that, although both recurrent and transformer neural networks achieve near-perfect performance on short embedded dependencies, their performance drops to below-chance levels on slightly longer dependencies, unlike humans (Lakretz et al., 2021, 2022). Lampinen (2022), however, questions these comparisons between humans and LMs, as the former receive guidance before the experiments, while LMs are evaluated on zero-shot scenarios, and their performance improves with few-shot prompts. Despite the fact that most of the work evaluating the linguistic capabilities of LMs has been carried out in English, there exist some experiments that have focused on Spanish and Galician LMs showing that the LMs tested in this work perform very well in the context of different linguistic dependencies, including simple and complex agreement dependencies with distractors. Recent studies in both Spanish and Galician show that models' performance for these dependencies (which rely on morphosyntactic information) are similar to those in English (with expected variations across models). For instance, Pérez-Mayos et al. (2021) found that monolingual and multilingual models achieve even better performance in agreement resolution in Spanish than BERT in English. For Galician, several experiments showed that the monolingual BERT models can generalize morphosyntactic agreement (number and gender) on complex subject-verb and subject-predicative adjective dependencies (Garcia and Crespo-Otero, 2022), and that this information is learned relatively early in the training process (de Dios-Flores and Garcia, 2022). The syntactic strengths observed in these models establish a baseline performance against which we can examine the results obtained for control dependencies. Concerning control constructions, studies exploring LMs' abilities to solve these complex relations are very scarce. In a recent paper, Kogkalidis and Wijnholds (2022) trained supervised models that take advantage of contextualized representations extracted from BERT, and evaluate them at capturing control verb nesting and verb raising in Dutch. The results suggest that transformer LMs do not adequately capture the target relations, although finetuning the pre-trained models in one-shot learning scenarios improves the performance of the probes. More similar to our study, an initial approximation by Lee and Schuster (2022) evaluated GPT-2 on object and subject control verbs, using number agreement with an embedded reflexive pronoun to track dependency resolution. Their findings suggest generative LMs are unable to differentiate between these two types of constructions. However, their manipulations were very limited in scope, as they only used 5 noun phrases, and 3 control verbs. Pycholinguistics and control dependencies: Even though control constructions have been at the center of linguistic theorizing over the past decades, their theoretical interest has not translated into an equivalent amount of experimental research in the psycholinguistics literature. The key question, though, is whether (and how) control information is used in parsing. Some early works have argued that control information was not used during initial parsing stages due to its lexico-semantic nature (e.g., Frazier et al., 1983; Nicol and Swinney, 1989). Nonetheless, these works barely looked at the contrast between lexically induced subject and object control relations. In this regard, more recent eye-tracking investigations have produced results that could be interpreted as evidence that lexical control information is used from early parsing stages (e.g. de Dios-Flores, 2021; Betancort et al., 2006; Kwon and Sturt, 2016) while they also suggest that object control dependencies seem to be solved faster due to their linear proximity. Yet, to our knowledge, no previous work provided acceptability judgements contrasting subject and object control dependencies with distractors, which is a highly informative measurement to establish the grammatical and psycholinguistic status of such constructions. ## 3 The Present Work The present work takes control dependencies as an adversarial case to test LMs' ability to generalize grammatical information at the syntax-semantics interface (Experiments 2 and 3). Given the complexity of these constructions, and the lack of psycholinguistic evidence, we go one step further and start by evaluating humans' grammaticality perception (Experiment 1), not only to obtain a grammatical verification of the acceptability status of such innovative experimental materials and to be able to directly compare humans' and LMs' performance, but also to contribute to the scarce psycholinguistic evidence on the processing of control. The datasets, code, and results from all the experiments are freely available.1 ## 3.1 Experimental Materials For the main dataset, used in Experiments 1 and 2, the experimental materials consisted of 96 items that had 8 different versions (768 experimental sentences). An example set is shown in Table 1. The experimental conditions were created by manipulating the type of control verb and the gender of the main clause nouns, while keeping the gender of the adjective constant. It is a factorial design that fully crosses the factors control (subject/object), grammaticality (grammatical/ungrammatical) and distractor (match/mismatch). To create the control conditions, we selected 12 subject and 12 object control verbs whose control preferences (i.e. subject and object) had been shown to be robust in a large-sample cloze task conducted by de Dios-Flores (2021). A sentence is ungrammatical when the adjective and the target controller differ in gender. The term distractor is used to refer to the non-controller NP in the sentence. A distractor was considered a match when it matches in gender with the adjective, and a mismatch when it mismatches in gender. One of the key elements of our manipulation is the difference in dependency length between sub-1https://github.com/iriadf/ACL2023_Control | Subject control Gramm. Dist. match | Maríaf le prometió a Carmenf ser más ordenadaf con los apuntes. | | |--------------------------------------|-------------------------------------------------------------------|----------------------------------------------------------------| | Dist. mismatch | Maríaf le prometió a Manuelm ser más ordenadaf con los apuntes. | | | Ungramm. | Dist. match | Josém le prometió a Carmenf ser más ordenadaf con los apuntes. | | Dist. mismatch | Josém le prometió a Manuelm ser más ordenadaf con los apuntes. | | | Object control Gramm. Dist. match | Maríaf le ordenó a Carmenf ser más ordenadaf con los apuntes. | | | Dist. mismatch | Josém le ordenó a Carmenf ser más ordenadaf con los apuntes. | | | Ungramm. | Dist. match | Maríaf le ordenó a Manuelm ser más ordenadaf con los apuntes. | | Dist. mismatch | Josém le ordenó a Manuelm ser más ordenadaf con los apuntes. | | ject and object control. While subject control constructions engage in a discontinuous dependency where the object NP (the distractor) is intervening, object control dependencies engage in an adjacent dependency, where the subject NP (the distractor) precedes the dependency. Those conditions in which the two NPs (controller and distractor) have the same gender are respectively taken as grammatical and ungrammatical baselines for both subject and object control sentences. Hence, the critical conditions are those in which only one of the NPs agrees in gender with the adjective (i.e. grammatical sentences with a matching distractor and ungrammatical sentences with a mismatching distractor). Humans' and LMs' behavior in these conditions will be essential to ascertain whether they can accurately implement control-determined antecedent retrieval processes and whether they are fallible to interference effects from gender matching but structurally irrelevant antecedents, in a similar vein as the attraction effects observed in agreement dependencies (e.g. Bock and Miller, 1991). While there are very few gender-ambiguous names in Spanish, in order to maximize gender transparency, the nouns used to create the materials were carefully selected according to the most frequent female-only and male-only names on the official Spanish census. In addition, we created an adaptation of the main dataset substituting proper nouns with personal pronouns (e.g. 'She promised him to be tidier'), to avoid potential bias, ambiguities or misrepresentations of proper nouns (Shwartz et al., 2020). Both versions of the dataset (with nouns and with pronouns) were translated into Galician by a native speaker linguist, to put Galician LMs to the test and to check if our findings held cross-linguistically. These materials were adapted for the LM prediction task (see section 6.1). ## 3.2 Pre-Trained Models We evaluate the following pre-trained models using HuggingFace's *transformers* library (Wolf et al., 2020): Multilingual: mBERT (12 layers) (Devlin et al., 2019), and XLM-RoBERTa base and large (12 and 24 layers) (Conneau et al., 2020). Spanish: BETO (12 layers) (Cañete et al., 2020), and RoBERTa base and large (12 and 24 layers) (Gutiérrez Fandiño et al., 2022). Galician: Bertinho small and base (6 and 12 layers) (Vilares et al., 2021), and BERT small and base (6 and 12 layers) (Garcia, 2021). ## 4 Experiment 1: Human Acceptability The primary goal of this acceptability task is to determine whether native speakers of Spanish are able to detect agreement violations that do not conform with the control properties of main predicates. This is, to our knowledge, the first experimental investigation on control of its kind, and we believe it is essential to corroborate native speakers' offline sensitivity to the different control manipulations that will be then put to the test with artificial LMs. It will be of particular importance to elucidate whether comprehenders are able to correctly distinguish the acceptability of the strings regardless of the type of control (subject or object) and the presence of a gender matching or mismatching distractor. ## 4.1 Participants And Procedure 40 native speakers of Spanish recruited at the Universidade de Santiago de Compostela participated ![4_image_0.png](4_image_0.png) in this experiment. Their participation was voluntary and all of them provided informed consent. Participants were presented with the entire sentence in the middle of the screen along with a rating scale, and they could only move to the next one once they had emitted a rating. They were instructed to rate the sentences in terms of whether they came across as well-formed Spanish: 7 meaning totally acceptable and 1 totally unacceptable. Experimental sentences were intermixed with 96 filler sentences of similar structure and complexity. The task was completed by all participants in less than 30 minutes. ## 4.2 Results The average rating for each condition is shown in Figure 1. For this and the following experiments, we carried out a statistical analysis of variance in order to observe differences among the experimental conditions. For the sake of clarity and space, the most relevant significant differences will be marked with an asterisk in the figures. The statistical analyses revealed a significant main effect of grammaticality, such that grammatical sentences (green bars) received much higher ratings than ungrammatical ones (red bars). Importantly, there was a significant interaction between the factors grammaticality and distractor. Planned comparisons showed that this interaction was driven by a significant effect of distractor only in ungrammatical sentences. This is shown in significant higher ratings for the distractor match condition in ungrammatical sentences compared to distractor mismatch ones. Such an effect is not present in grammatical sentences. Critically, no differences were observed between subject and object control conditions. In addition, we took the 1-7 ratings produced by humans and converted them into a binary accuracy measure by classifying their answers as correct or incorrect depending on the grammaticality of the sentence and whether the rating issued was above or below the sample mean (3.79). As expected, accuracy was above 85% for all conditions. This value will allow us to have a more direct comparison with the results from Experiment 3. ## 4.3 Discussion The results from this experiment clearly show that native speakers are able to detect agreement violations that arise when the adjective did not match in gender the appropriate antecedent, and hence, that they are able to correctly use control information to retrieve the antecedent. This finding also provides a confirmation that the items display unequivocal control readings. Crucially, subject and object control sentences were rated similarly across all four conditions. In addition to the clear contrast between grammatical and ungrammatical conditions, an important result from this experiment is that there is evidence for interference effects in ungrammatical sentences. That is, ungrammatical sentences with a matching distractor received slightly higher ratings than ungrammatical sentences with a mismatching distractor. This effect shows that the presence of a matching distractor leads them to accept ungrammatical sentences more often than when the distractor does not match in gender with the adjective. Crucially, this effect appeared equally in subject and object control conditions, that is, independently of the position of the distractor. This represents evidence for a facilitatory interference effect, or an illusion of grammaticality (Phillips et al., 2011), a pattern akin to the widely attested agreement attraction effect (Wagers et al., 2009). ## 5 Experiment 2: Lm Acceptability This experiment aims at observing whether the probabilities of the language models are similar to those of humans. That is, whether LMs assign lower surprisal to grammatical than to ungrammatical sentences regardless of the presence of a matching or mismatching distractor. For this purpose, we use the exact same dataset as in Experiment 1.2 ## 5.1 Procedure The minicons library (Misra, 2022) was used to compute the surprisal assigned by the LM to the embedded adjectives, which function as a proxy for antecedent retrieval. ## 5.2 Results The Spanish models' results for the different experimental conditions are shown in Figure 2. It should be noted that, for ease of interpretation and comparison with Experiment 1 (Figure 1), the surprisal values were inverted such that higher values mean less surprisal (hence more acceptability) while lower values mean more surprisal (hence less acceptability).3 While we observe significant effects of grammaticality for all models (meaning that, overall, grammatical sentences were more acceptable than ungrammatical ones), the results show a very different pattern of contrasts for subject and object control sentences. On the one hand, in subject control sentences, all the models showed higher acceptance for grammatical sentences with a matching distractor (dark green bars) than for grammatical sentences with a mismatching distractor (light green bars). Furthermore, also in subject control sentences, ungrammatical sentences with a matching distractor (light red bars) received unexpectedly high acceptance levels, which, for most models, are higher than those observed for grammatical sentences with a mismatching distractor (light green bars). On the other hand, in object control sentences, the pattern of contrasts is very different. First, none of the models exhibited differences among the grammatical conditions regardless of the gender of the distractor. Second, while for all the models, the values observed for ungrammatical sentences with a matching distractor (light red bars) were higher than those for ungrammatical sentences with a mismatching distractor (dark red 2It should be noted that comparing these two dependent measurements (human likert-scale acceptability judgements and LM's surprisal values) is not an optimal contrast, but in our view, conceptually reasonable, as similar LM model measurements are often taken as a proxy for acceptability (e.g. Futrell et al., 2019). 3This was done by subtracting each mean value from the highest mean value observed. bars), this difference was only statistically significant for some models. The same pattern of results is observed using pronouns instead of names (see Figure 4) and for the Galician models using names and pronouns (Figures 5 and 6). This is also corroborated by the very strong correlations (ρ > 0.9) observed for the adjective surprisal values using names and pronouns, in both languages. Furthermore, we calculated the Spearman ρ correlations between the acceptability values provided by the humans and the models' surprisal values. Overall, they revealed weak to moderate correlations, while higher correlations are found for object control sentences than for subject control ones. The correlations for each model at each experimental condition can be found in Table 4. 4 ## 5.3 Discussion The results for Experiment 2 show that, unlike humans, all the LMs evaluated behave very differently for subject than for object control dependencies, being better at detecting the acceptability of the strings in object control conditions. The key question here is whether they are able to do so by leveraging the lexico-semantic information of control in order to find the correct antecedent. The pattern of results obtained suggests that, rather than control information, the relevant cue being used is linear proximity. It must be reminded that, in subject control dependencies, the correct antecedent is the NP that is further away from the adjective, while the distractor NP is closer to it. The presence of significant differences between the two grammatical conditions, and the two ungrammatical conditions, found for subject control dependencies, points to the fact that LMs are taking the closer (and wrong) NP, the object, as the antecedent. This explains why the acceptability is reduced for grammatical sentences with a mismatching distractor, despite being perfectly grammatical, and that it is dramatically increased for ungrammatical sentences with a matching distractor, despite being ungrammatical. Reliance on linear proximity also explains why LMs are better, and more akin to humans, on object control dependencies. In these structures, the correct antecedent (i.e. the object) coincides with the linearly closest NP. Interestingly, nonetheless, LMs also exhibit evidence for interference effects from 4This table also includes correlations for whole-sentence surprisal measurements. ![6_image_0.png](6_image_0.png) control-irrelevant but gender-matching distractors. This is perhaps clearer in the case of object control sentences, since linear proximity and interference converge in the case of subject control ungrammatical sentences with a matching distractor. In object control sentences, some models also exhibit higher acceptability for ungrammatical sentences with a matching distractor even when in this case the gender-matching NP is the farthest NP. These issues are further explored in Experiment 3. ## 6 Experiment 3: Lm Masked Prediction This experiment aims at further exploring the behavior of LMs using the masked prediction task. In contrast with Experiment 2, where we compute the surprisal for the same adjective in a given (grammatical or ungrammatical) sentence, our objective here is to test whether LMs predict grammatically compatible adjectives in subject and object control sentences regardless of the presence of a matching or mismatching distractor. In Experiment 2 the adjective's gender was kept constant across experimental conditions, and hence, we could not assess LMs' preferences for the masculine or feminine version. By contrast, here we test if LMs predict grammatically compatible adjectives in subject and object control sentences by directly comparing the probabilities of a given adjective in its masculine or feminine form, something which provides us with more comprehensive information in this respect. Furthermore, evaluating model accuracy rather than surprisal values will also allow us to assess and compare the performance across models. ## 6.1 Experimental Materials The experimental materials used for Experiment 3 are an adaptation of the dataset described in section 3.1 (including its variants with personal pronouns and Galician translations) so that they could be used in the masked prediction task. This allows us to evaluate our dataset in the two possible gender configurations, expanding it such that each sentence has two possible outcomes: a grammatical and an ungrammatical one. Therefore, the manipulation is a 2x2 factorial design (control x distractor), as shown in Table 2 ## 6.2 Procedure We rely on the standard approach for targeted syntactic evaluation to obtain the accuracy of the models on the minimal pairs (Linzen et al., 2016; Warstadt et al., 2020). For each sentence, we extract the probabilities of the grammatical and ungrammatical target adjectives, and consider a trial as correct if the model gives a higher probability to the grammatical target adjective. It is worth noting that this method requires compatible tokenization between both variants (grammatical and ungrammatical). To make a fair evaluation, | Subject control Dist. match | Maríaf le prometió a Carmenf ser más [ordenadaf |*ordenadom] con los apuntes. | |-------------------------------|---------------------------------------------------------------------------------| | Dist. mismatch | Maríaf le prometió a Manuelm ser más [ordenadaf |*ordenadom] con los apuntes. | | Object control Dist. match | Maríaf le ordenó a Carmenf ser más [ordenadaf |*ordenadom] con los apuntes. | | Dist. mismatch | Josém le ordenó a Carmenf ser más [ordenadaf |*ordenadom] con los apuntes. | Table 2: Sample set of the experimental materials for Experiment 3. The correct antecedents and correct and incorrect the target adjectives are bold typed. See Table 1 for comparison with the original dataset. we check if both variants appear as single tokens in the models' vocabulary, or whether their last subtokens (the ones that carry the morphosyntactic information) are comparable so that we can use their probabilities. For instance, the Spanish pair afectuoso|afectuosa ('affectionate') is tokenized by RoBERTa as afect+uoso|uosa, and hence, we can use the last subtokens for comparison. However, *desconfiado|desconfiada* ('skeptical') is divided as desconf+iado and descon+fi+ada. We discard these incompatible cases (19% of the items for Spanish, and 16% for Galician, on average).5 ## 6.3 Results Table 3 displays the global accuracy for all the models under evaluation in Experiment 3 (global accuracy values for all the datasets tested in Spanish and Galician are in Table 5). RoBERTa large emerges as the best performing model, closely followed by XLM RoBERTa large, while mBERT base emerges as the worst performing model. Nonetheless, in order to analyze the impact of linear proximity on model performance, it is essential to examine the factors control and distractor separately. Figure 3 shows the accuracy per condition for the target adjectives. Statistical analyses show a main effect of distractor, such that the accuracy was higher for distractor match sentences (dark green bars, when the two NPs had the same gender) than for distractor mismatch ones (light green bars, when the NPs differed in gender). However, this difference was much more acute for subject control sentences, where significant differences arise for all the models, than for object control sentences, where significant differences are only found for 5We also assessed the models' performance by computing the probability mass that models put on the feminine and masculine inflections rather than on a particular adjective pair, inspired by Newman et al. (2021). We used morphological lexicons to obtain the masculine and feminine probabilities from the *top N* adjectives predicted by the models in the masked position (N=100). The results for *top N* (to be found in Appendix C) followed the same pattern as for target adjectives. RoBERTa-large and XLM-RoBERTa-base. The same pattern of results is observed using pronouns instead of names (see Figure 7) and for the Galician models using names and pronouns (Figures 8 and 9). This is also shown in the very strong correlations (ρ > 0.8) observed for the results using names and pronouns in both languages. Model **Accuracy** BETO base 0.78 RoBERTa base 0.77 RoBERTa large **0.83** mBERT base 0.61 XLM RoBERTa base 0.78 XLM RoBERTa large 0.82 Table 3: Global accuracy in Experiment 3. ## 6.4 Discussion The results from Experiment 3 reinforce and complement the findings from Experiment 2 in several respects. First, reliance on linear proximity is, if anything, even clearer, as subject control sentences with a mismatching distractor display clear interference effects, which are materialized in a dramatically below-chance accuracy. These are the cases in which the distractor is the sentence object, which is also the closer NP. In these cases, LMs' predict a target adjective that agrees in gender with the object, rather than the subject (i.e. the correct antecedent) and hence, demonstrating that antecedent retrieval processes unfold disregarding the lexico-semantic information on control. Importantly, these effects are almost absent in object control sentences, where only two models show evidence for interference effects, these being much less pronounced (only a few accuracy points). Even though the results from this experiment cannot be directly compared with those of humans (Experiment 1), it should be noted that human accuracy was above 80% for all conditions. ![8_image_0.png](8_image_0.png) ## 7 General Discussion And Conclusions The empirical evidence gathered in this work provides a very straightforward picture: whereas humans' can coordinate lexico-semantic and syntactic information in order to determine the (un)acceptability of control structures, LMs resort to a heuristic based on linear proximity, disregarding control information. These findings are robust, as they replicate across tasks (acceptability and masked prediction), models (monolingual and multilingual), languages (Spanish and Galician LMs), and type of antecedent (names and pronouns). Furthermore, they go in line with evidence advanced in Lee and Schuster (2022) for English with respect to autoregressive language models. These findings contrast with those obtained for superficially similar dependencies like subject-verb agreement, in which these models have been attested to display accurate levels of performance for Spanish and Galician (Pérez-Mayos et al., 2021; de Dios-Flores and Garcia, 2022; Garcia and Crespo-Otero, 2022). Crucially, however, agreement and control dependencies engage different types of linguistic information. While the former rely on co-ocurring patterns containing overt morphological cues which are pervasive in the training data, control dependencies rely on abstract lexicosemantic properties of verbs and verb meaning, which these models are not able to generalize from the training data at their disposal even when it presumably contains control verbs (although a systematic examination of this issue is essential). Control verbs and control structures have a high frequency in natural language and, ideally, state-ofthe-art LMs should be able to capture their meaning differences and the consequences they have for phrase-structure relations (ultimately, who does what to whom?). Some authors have suggested that their performance on similar structures could be improved in one-shot learning scenarios, or by adding more control constructions in the training data (Kogkalidis and Wijnholds, 2022; Lee and Schuster, 2022). While this supports the idea that these constructions are "learnable" with sufficiently explicit input, adding examples on the infinite combinatorial possibilities of language does not seem like a strategy that can be generalized. Further research is needed on how LMs capture linguistic generalizations and how these processes can be enhanced. One of the biggest challenges of working with control constructions is the elaboration of appropriate experimental materials. This is why the carefully curated Spanish and Galician datasets used in this work, which are freely available, represent a key contribution, as we hope they are valuable for further computational and psycholinguistic research beyond English, the dominant language in these fields. ## Limitations Of The Work Given that the training data for most pre-trained models has not been released, further investigation of the frequency effects of control verbs in the corpora, or for that matter, of any other critical word in the sentence (names, adjectives, etc.) is not feasible. This is a shortcoming of our work because word frequency during training is known to be an important factor for model performance (Wei et al., 2021). Nonetheless, in order to approximate this issue, we run preliminary comparisons of the models' performance depending on whether the control verb appears or not in the vocabulary (and therefore, assuming that it had enough frequency in the training corpus). Very similar results were obtained for both sentences with known and unknown verbs in the main clause. Besides, detailed comparisons between models have been left out for reasons of space and scope, since the objective of the research was not to compare model performance, although it is a relevant and interesting issue in itself (for instance, the fact that the LMs based on the RoBERTa architecture performed better across tasks, or that the high performance of XLM-RoBERTa contrasts with that of mBERT). In relation to this, the comparison of models with different architectures and training objectives (e.g. generative models) was also left for further research. Finally, it is worth noting that the two languages evaluated in this study (Spanish and Galician) are very similar, so that it could be interesting to expand the research to non-romance languages. ## Ethics Statement Experiment 1 complied with the standards of research involving human subjects. Their participation was voluntary, all of them were informed of the nature of the task, and provided informed consent before starting the experiment. With respect of C02 consumption for the computational experiments (Experiments 2 and 3), it should be noted that we used pre-trained models and hence the impact of the calculations obtained is expected to be minimal. The experiments were run on a NVIDIA A100 GPU, and the results were obtained in a few minutes. Since this work is circumscribed within basic research on artificial language modelling, no applications or tools are to be directly derived by it and hence, we do not think of any potential harms or bias that can be derived from our work. ## Acknowledgements We would like to thank the anonymous reviewers for their valuable comments. This research was funded by the Galician Government (ERDF 2014-2020: Call ED431G 2019/04, and ED431F 2021/01), by MCIN/AEI/10.13039/501100011033 (grants with references PID2021-128811OA-I00 and TED2021-130295B-C33, the former also funded by "European Union Next Generation EU/PRTR"), by a *Ramón y Cajal* grant (RYC2019028473-I), and by the project "Nós: Galician in the society and economy of artificial intelligence" (Xunta de Galicia/Universidade de Santiago de Compostela). ## References Jean-Phillipe Bernardy and Shalom Lappin. 2017. Using deep neural networks to learn syntactic agreement. In Linguistic Issues in Language Technology, Volume 15, 2017. CSLI Publications. Moisés Betancort, Manuel Carreiras, and Carlos AcuñaFariña. 2006. Processing controlled PROs in Spanish. Cognition, 100(2):217–282. Kathryn Bock and Carol A Miller. 1991. Broken agreement. *Cognitive Psychology*, 23(1):45–93. José Cañete, Gabriel Chaperon, Rodrigo Fuentes, JouHui Ho, Hojin Kang, and Jorge Pérez. 2020. 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Association for Computational Linguistics. ## Appendix A Correlations between human acceptability judgements (Experiment 1) and LMs surprisal measurements (Experiment 2) | BETO | RoBERTa-b | RoBERTa-l | mBERT | XLM-b | XLM-l | | | | | | | | | | |-----------|-------------|-------------|---------|---------|---------|-------|-------|-------|-------|-------|-------|-------|-------|------| | Item | Adj | Sent | Adj | Sent | Adj | Sent | Adj | Sent | Adj | Sent | Adj | Sent | | | | Avg | 0.31 | 0.17 | 0.31 | 0.27 | 0.33 | 0.25 | 0.27 | 0.11 | 0.42 | 0.17 | 0.53 | 0.24 | | | | Subj | 0.21 | 0.19 | 0.27 | 0.26 | 0.25 | 0.23 | 0.09 | 0.08 | 0.37 | 0.14 | 0.44 | 0.19 | | | | Obj | 0.39 | 0.16 | 0.34 | 0.30 | 0.41 | 0.28 | 0.44 | 0.14 | 0.47 | 0.21 | 0.61 | 0.30 | | | | Subject | Gram | Match | 0.03 | 0.23 | -0.01 | 0.30 | -0.09 | 0.15 | 0.07 | 0.23 | 0.07 | 0.22 | 0.12 | 0.28 | | Mism | 0.12 | 0.18 | 0.16 | 0.30 | 0.17 | 0.22 | 0.01 | 0.24 | 0.01 | 0.29 | 0.08 | 0.38 | | | | Ung Match | -0.05 | 0.02 | -0.07 | -0.16 | -0.04 | -0.17 | 0.10 | -0.01 | 0.09 | -0.23 | 0.06 | -0.34 | | | | Mism | 0.07 | 0.26 | -0.10 | 0.09 | -0.05 | 0.02 | 0.05 | 0.03 | 0.06 | 0.05 | 0.09 | 0.08 | | | | Object | Gram | Match | -0.04 | 0.10 | -0.19 | 0.15 | -0.14 | 0.14 | -0.06 | -0.01 | -0.04 | 0.10 | -0.01 | 0.18 | | Mism | -0.01 | -0.04 | -0.03 | 0.22 | -0.09 | 0.14 | -0.01 | -0.05 | -0.03 | 0.16 | 0.01 | 0.23 | | | | Ung Match | -0.04 | 0.06 | 0.09 | 0.08 | -0.01 | 0.10 | -0.25 | -0.17 | -0.12 | -0.02 | -0.20 | 0.03 | | | | Mism | 0.23 | -0.11 | 0.07 | 0.16 | 0.08 | 0.00 | 0.02 | -0.00 | -0.04 | -0.00 | 0.03 | 0.04 | | | Table 4: Spearman ρ correlations between human acceptability scores and inversed surprisals of the target adjectives (Adj) and sentences in Spanish (Experiment 1). Top rows are the overall average (Avg), and averages of subject (*Subj*) and object (Obj) control. Bottom rows display each of the eight conditions of the experiment (*Grammatical* and *Ungrammatical* with *Matching* and *Mismatching* distractor, see Table 1). Numbers in bold are statistically significant (p < 0.05). ![12_image_0.png](12_image_0.png) ![13_image_0.png](13_image_0.png) ![13_image_1.png](13_image_1.png) ## C Additional Tables And Figures For Experiment 3 | Spanish datasets LMs | Names | Names | Pronouns | | |------------------------|---------|------------------|------------|------| | target adjective | top N | target adjective | | | | BETO base | 0.78 | 0.80 | 0.72 | 0.73 | | RoBERTa base | 0.77 | 0.78 | 0.74 | 0.75 | | RoBERTa large | 0.83 | 0.84 | 0.81 | 0.81 | | mBERT base | 0.61 | 0.68 | 0.59 | 0.66 | | XLM RoBERTa base | 0.78 | 0.79 | 0.83 | 0.85 | | XLM RoBERTa large | 0.82 | 0.78 | 0.86 | 0.76 | | Galician datasets LMs | Names | Names | Pronouns | | | target adjective | top N | target adjective | | | | Bertinho small | 0.63 | 0.65 | 0.68 | 0.71 | | Bertinho base | 0.61 | 0.63 | 0.66 | 0.69 | | BERT small | 0.71 | 0.73 | 0.70 | 0.72 | | BERT base | 0.74 | 0.79 | 0.73 | 0.75 | | mBERT base | 0.60 | 0.69 | 0.59 | 0.68 | | XLM RoBERTa base | 0.78 | 0.78 | 0.79 | 0.80 | | XLM RoBERTa large | 0.82 | 0.78 | 0.84 | 0.74 | Table 5: Global accuracy for all the LMs examined in Spanish and Galician across datasets (with names and ![14_image_0.png](14_image_0.png) ![14_image_1.png](14_image_1.png) pronouns) and analysis strategies (target adjective or top N adjectives). ![15_image_0.png](15_image_0.png) ![15_image_1.png](15_image_1.png) ![15_image_2.png](15_image_2.png) ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) ![17_image_0.png](17_image_0.png) ![17_image_1.png](17_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations section ✓ A2. Did you discuss any potential risks of your work? Ethics statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Data, models or other artifacts used are properly cited in the relevant sections of the experiments. Mainly 3.2. for pre-trained models and transformers library, or 5.1 for the procedure of Experiment 2 using the minicons library. ✓ B1. Did you cite the creators of artifacts you used? In different sections where these are described. Mainly 3.2. for pre-trained models and transformers library, or 5.1 for the procedure of Experiment 2 using the minicons library. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The artifacts used are freely available. We do not discuss their license terms in our contribution. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? For the artifacts we create, we specify they are freely available (and are added as supplementary materials). ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We do not specifically discuss this on the paper because our data did not contain personal information or offensive content. Some notes are added on the ethics statement. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 3.1. for the datasets created, and 4.1. for the demographics of the human sample. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. In 3.1. for the datasets. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ## C ✓ **Did You Run Computational Experiments?** Sections 5 And 6 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We use pre-trained models. Some notes are added on the ethics statement. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Not applicable. Left blank. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Results section: 4.2, 5.2, and 6.3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 4.1 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? We reported a summary in section 4.1. The acceptability task is a wide-spread method and this is why the full detailed instructions were not provided. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 4.1 ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 4.1 ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? It was not required by the institution at the time of data collection. ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 4.1

liang-etal-2023-open

Open-ended Long Text Generation via Masked Language Modeling

https://aclanthology.org/2023.acl-long.13

Pre-trained autoregressive (AR) language models such as BART and GPTs have dominated OPen-ended Long Text Generation (Open-LTG).However, the AR nature will decrease the inference efficiency along with the increase of generation length, which hinder their application in Open-LTG.To improve inference efficiency, we alternatively explore the potential of the pre-trained masked language models (MLMs) along with a representative iterative non-autoregressive (NAR) decoding strategy for Open-LTG.Our preliminary study shows that pre-trained MLMs can merely generate short text and will collapse for long text modeling. To enhance the long text generation capability of MLMs, we introduce two simple yet effective strategies for the iterative NAR model: dynamic sliding window attention (DSWA) and linear temperature decay (LTD). It can alleviate long-distance collapse problems and achieve longer text generation with a flexible trade-off between performance and inference speedup. Experiments on the storytelling and multi-paragraph opinionated article writing tasks show that pre-trained MLMs can achieve more than 3 $\times$ $\to$ 13 $\times$ speedup with better performance than strong AR models.

# Open-Ended Long Text Generation Via Masked Language Modeling Xiaobo Liang∗ Zecheng Tang∗ Juntao Li† **Min Zhang** Soochow University {xbliang3, zctang}@stu.suda.edu.cn, {ljt,minzhang}@suda.edu.cn ## Abstract Pre-trained autoregressive (AR) language models such as BART and GPTs have dominated Open-ended Long Text Generation (OpenLTG). However, the AR nature will decrease the inference efficiency along with the increase of generation length, which hinder their application in Open-LTG. To improve inference efficiency, we alternatively explore the potential of the pre-trained masked language models (MLMs) along with a representative iterative non-autoregressive (NAR) decoding strategy for Open-LTG. Our preliminary study shows that pre-trained MLMs can merely generate short text and will collapse for long text modeling. To enhance the long text generation capability of MLMs, we introduce two simple yet effective strategies for the iterative NAR model: dynamic sliding window attention (DSWA) and linear temperature decay (LTD). It can alleviate long-distance collapse problems and achieve longer text generation with a flexible trade-off between performance and inference speedup. Experiments on the storytelling and multi-paragraph opinionated article writing tasks show that pre-trained MLMs can achieve more than 3 × → 13 × speedup with better performance than strong AR models. Our code is available at GitHub*. ## 1 Introduction Pre-trained language models (PLMs) like BART (Lewis et al., 2020) and GPTs (Radford et al.; Radford et al.; Brown et al., 2020) have achieved remarkable progress in Open-LTG. Through modeling languages from left to right, they can autoregressively "create" fluent and grammatical content. With the further enhancement of planning strategies (Hua and Wang, 2020; Hu et al., 2022) or high-level representation learning (Guan ∗Equal Contribution †Corresponding Author *https://github.com/dropreg/OpenLTGMLM | Model | Type | Iter | Tokens/s | |------------------------|---------|--------|------------| | BART base | AR | - | 151.3 | | BART base + Planning † | AR | - | 5.8 | | BERT-CRF † | NAR | 0 | 2,597.4 | | RoBERTa base | NAR | 0 | 1,561.2 | | 1 | 1,068.9 | | | | 4 | 505.2 | | | Table 1: Inference speed of each model with a single GPU (**NVIDIA A100** 40GB). For a fair comparison, we force all models to generate 200 tokens. The models labeled with † are implemented with the Hugging Face platform, while the rest are implemented with Fairseq. et al., 2021a), pre-trained AR language models can achieve promising Open-LTG. However, the low inference efficiency of AR impedes their usability in real-world applications. Table 1 presents the inference speed of a few typical AR language models. We can see that BART (Lewis et al., 2020) requires at least 1.3 seconds to generate a story with 200 tokens on the powerful NVIDIA A100 GPU, and extra planning (Hua and Wang, 2020) can make the inference process even slower (more than 30 seconds to create a 200-tokens story). In great contrast with AR models, NAR models (e.g., BERT-CRF (Su et al., 2021)) can generate more than 12 stories with the same length within one second, but their effectiveness in open-ended long text generation has not been proven yet. The high inference efficiency of NAR models is at the sacrifice of output dependency modeling, in which each generation is executed in parallel (Xiao et al., 2022). Thus, NAR models are mainly explored and utilized for text generation tasks with adequate input information to predict each output token of different positions and extra correlations to constrain the generation process, e.g., neural machine translation (Gu et al., 2018; Huang et al., 2022), summarization (Qi et al., 2021; Agrawal and Carpuat, 2022), sentence compression (Su et al., 2021), dialogue generation (Zou et al., 2021), and constrained story-ending generation (Yang et al., 223 2021). To the best of our knowledge, none of the existing research explores Open-LTG with NAR models, particularly based on pre-trained MLMs. We fill this gap by first conducting a preliminary study to calibrate the potential and limitations of a pre-trained MLM, i.e., RoBERTa (Liu et al., 2019) †, on two story generation corpora, i.e., ROCStories (ROC) (Mostafazadeh et al., 2016) and WritingPrompts (WP) (Fan et al., 2018). To achieve conditional generation, we simply use RoBERTa as both the encoder and the decoder with mixed attention (He et al., 2018) to achieve encoder-decoder cross-attention. Through experiments, we found that: (1) pre-trained MLMs can achieve competitive performance in the iterative NAR fashion for open-ended short text generation (e.g., a paragraph with around 40 tokens), (2) pre-trained MLMs fail to model Open-LTG (with about 140 tokens on average), which will generate uninformative content with high-frequency and repeated tokens (e.g., "." and ","). Furthermore, we offer three possible reasons for the attention mechanism of MLMs and inference strategy to explain the collapse of the iterative NAR model based on pre-trained MLMs for the Open-LTG scenario. Inspired by the above observations, we introduce two improvement strategies: Dynamic Sliding Window Attention (DSWA) and linear temperature decay strategy (LTD) to maintain more informative context content in the iterative NAR generation. As a result, iterative NAR models based on pre-trained MLMs can achieve much longer text generation than the vanilla setting. Experiments on two OpenLTG tasks (i.e., storytelling and multi-paragraph opinionated article writing) with four widely-used datasets demonstrate that the pre-trained MLM can achieve better performance (BLEU score, ROUGE score, BERT score, and Perplexity) than multiple strong AR models without extra post-training, structure modification, or using more model parameters. Importantly, our approach can speed up the inference process due to non-autoregressive properties, making the pre-trained MLM as a promising candidate for the Open-LTG community. The RoBERTa base achieves more than 3 × → 13 × with better performance to the competitive BART. ## 2 Related Work Long Text Generation Text generation tasks can be classified into two categories: directed generation and open-end generation. The directed generation (Sutskever et al., 2014; Li et al., 2015; Vaswani et al., 2017) for long text scenarios has long source than the target, which is also constrained by source sequence, e.g., neural machine translation and summarization. These tasks aim to solve the quadratic growth requirement of the memory and computational of the self-attention mechanism. The openended generation task (Guo et al., 2018; Tan et al., 2020; Goldfarb-Tarrant et al., 2020; Hua and Wang, 2020; Orbach and Goldberg, 2020; Hu et al., 2022) desire to generate more freedom content and has recently become a promising research direction. Previous works have explored multiple generation strategies to generate high-quality and fluent text, e.g., planning then generating (Guo et al., 2018; Tan et al., 2020; Goldfarb-Tarrant et al., 2020; Hua and Wang, 2020; Orbach and Goldberg, 2020; Hu et al., 2022) and introducing external knowledge (Guan et al., 2020; Xu et al., 2020). Although the above strategies enable the model to achieve significant advances, time-consuming is still a critical issue that hinders their usage in real-world applications (Guan et al., 2021a; Tan et al., 2020). Iterative Non-autoregressive Generation Nonautoregressive (NAR) model breaks the sequential dependencies from front to back for parallel text generation (Gu et al., 2018; Guo et al., 2020; Saharia et al., 2020). Furthermore, the iterative-based NAR model (Lee et al., 2018; Gu et al., 2019; Chi et al., 2021) can achieve comparable performance with the AR model. The typical CMLM model (Ghazvininejad et al., 2019) can generate fluent results conditioned on the predictions from the previous iteration instead of previous tokens: $${\mathcal{P}}(Y_{t}|X)={\mathcal{P}}(Y_{t}|Y_{t-1},X)$$ P(Yt|X) = P(Yt|Yt−1, X) (1) Benefiting from this, the iterative NAR model is more flexibly compared with the AR model, which can easily generate consistent and controllable text for each iteration step. To the best of our knowledge, the iterative NAR model has never been used to solve open-ended generation. Especially, we investigate its usability for the long text scenario, i.e., target lengths between 100 and 400, which is still under-explored in the directed generation tasks. ![2_image_0.png](2_image_0.png) ## 3 Preliminary Study We first present the training and inference paradigm of utilizing the pre-trained MLMs for Open-LTG (§ 3.1), e.g., BERT or RoBERTa. Then, we study the significant collapse problem in a long text generation scenario by conducting preliminary experiments on two datasets with different target lengths (§ 3.2). Finally, we investigate the reason for the above issues with an exhaustive case study and exploration tests to motivate our method design (§ 3.3), where the model can generate text in nonautoregressive manner to speed up the inference. ## 3.1 Text Generation Via Pre-Trained Mlms Pre-trained MLMs are typically used as the encoder to extract the representations of sentences instead of generating texts. Previous works (Dong et al., 2019; Wang et al., 2019) have indicated that the MLM encoder can support text generation tasks via attention masks or Gibbs sampling. In contrast, we introduce mixed attention and parameter sharing to the encoder-based model to solve the sequence to sequence tasks, as shown in Figure 1. Model Training Given the parallel text generation dataset D={(X , Y)}|D|, we can feed the source X into the MLM encoder to obtain the representation Hlsrc of l-th layer. Concretely, each layer comprises two sub-layers, including one selfattention layer and one feed-forward layer: $$\begin{array}{l}\mathcal{H}^{l}_{src}=\texttt{Self-ATTN}(\mathcal{H}^{l-1}_{src})+\mathcal{H}^{l-1}_{src}\\ \mathcal{H}^{l}_{src}=\texttt{FFN}(\mathcal{H}^{l}_{src})+\mathcal{H}^{l}_{src}.\end{array}\tag{2}$$ $\mathbf{2}\mathbf{1}=\mathbf{f}$. Then, we random mask Y = {y1, y2, · · · , y|Y|} to obtain corrupted target YM = {y1, m2, · · · , m|Y|} (m is the symbol of mask token "<mask>"). As before, we can obtain the representation Hltgt by using the shared parameter MLM encoder and then try to recover the masked sequence, where the mixedattention mechanism (He et al., 2018) is applied to aggregate the source HL src and the target Hltgt: $$\begin{array}{l}\mathcal{H}_{tgt}^{l}=\texttt{Mixed}-\texttt{ATTN}(\mathcal{H}_{tgt}^{l-1},\mathcal{H}_{src}^{L})+\mathcal{H}_{tgt}^{l-1}\\ \mathcal{H}_{tgt}^{l}=\texttt{FFN}(\mathcal{H}_{tgt}^{l})+\mathcal{H}_{tgt}^{l}.\end{array}\tag{3}$$ Mixed-attention does not break the original attention mechanism, which only utilizes the target hidden states as query vector and the concatenated vector of source and target hidden states as key and value. It is worth noting that this approach is available for transformer encoder models without additional parameters. Specifically, we uniformly mask 1 to n (target length) tokens from Y for model training. The training objective is thus to minimize the conditional MLM loss like the pre-training stage: $$\begin{split}\mathcal{L}_{\text{MLM}}&=-\sum_{i=1}^{\mathcal{M}}\log\mathcal{P}(y_{i}|\mathcal{X},\mathcal{Y}_{\text{M}})\\ \mathcal{P}(y_{j}|\mathcal{X},\mathcal{Y}_{\text{M}})&=\frac{\exp(u_{tgt}/\mathcal{T})}{\sum_{|u_{tgt}^{\prime}|}\exp(u_{tgt}^{\prime}/\mathcal{T})},\end{split}\tag{4}$$ $\mathcal{M}$ is the number of words $\mathcal{M}$. where M is the number of masked tokens, utgt is the output logit, and T is the temperature to re-estimate the final probability. Model Inference We use an iterative refinement strategy to generate text like CMLM (Ghazvininejad et al., 2019). In particular, We use the fully masked sequence {m1, m2, · · · , mn} to initialize the target sequence and predict all masked tokens at the first step. Then, we iteratively regenerate the low-confidence tokens at the subsequent iteration steps to obtain better performance. For Open-LTG, we utilize the nucleus sampling (Holtzman et al., 2019) decoding strategy instead of beam search. Length Prediction It is necessary to obtain the target length to initialize the full mask sequence as model input before inference. Specifically, we provide two strategies: 1) Fixed Length, which initializes the target length according to the average length of the validation set or human experience. 2) Prediction Module, which uses the mean-pooling layer followed by one classification layer to predict the target length by feeding HL src into them: P(Ltgt|X ) = Softmax(WL(Mean-Pooling(H L src))), (5) where Ltgt is the target length, and WL is the learnable parameter. Specifically, we will adjust Ltgt according to the specific offset, which is the parameter based on the validation dataset. ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) ## 3.2 Extensive Trials Study Settings We use Writing Prompt (WP) and ROC Stories (ROC) datasets to conduct experiments for validating whether pre-trained MLMs can work better on Open-LTG tasks. In particular, these two datasets have different lengths for target sentences, i.e., the average length of WP is 140 and ROC is 40, and more details are given in Section 5 and Appendix A. We choose RoBERTa base (Liu et al., 2019) as our backbone model and use BLEU, ROUGE, Distinct, and Lexical Repetition metrics for evaluation. During inference, we set nucleus sampling hyper-parameter top-p=0.9, temperature T =1.0, and limit the maximum iteration steps to 6 for ROC and 8 for WP. Results As shown in Table 2, For the ROC dataset, the RoBERTa base model obtains comparable performance with BART. However, the generation quality significantly decreases for the WP dataset, which involves much longer targets. Specifically, most of the generated results are made up of duplicated function words or punctuations, e.g., "it", "to", "the", and ".", etc, which makes the model outputs unreadable and meaningless. One intuitive question is *What causes the collapse problem in Open-LTG when using pre-trained MLMs?* ## 3.3 Analysis And Possible Improvements We show typical *good case* and *bad case* in Figure 2, which are randomly selected from the ROC and WP datasets respectively to demonstrate the generation process. For each iterative refinement step of *bad case*, the informative tokens will be replaced by the placeholder token "*<mask>*" and are replaced by the function words at the subsequent steps. Thus it is unable to generate fluent results like *good case*. According to this observation, we try to provide some possible explanations for the aforementioned collapse issues: 1) **The most intuitive reason is that the function** words are often located at the front of the output distribution, which dominates the high probability region, causing the informative tokens hard to be sampled.. The output distribution trained with the ROC dataset contains more prompt-related tokens than WP, e.g., the "swim" and "water" in the top 50 candidates of ROC output, as shown in Figure 2 (distribution histogram). Worse still, the function words dominate the high probability regions (from 35% to 45%) for the *bad case* and lead to terrible initialization at the first iteration step. 2) **The iterative refinement mechanism depends** on the token confidence of generated sequences, and it is easier for the low-confidence but infor- ![4_image_0.png](4_image_0.png) mative tokens to be masked. In fact, the iterative refinement mechanism is designed for directed generation tasks, e.g., neural machine translation or summarization, which usually apply the *argmax* operation to sample results, and the evaluation of confidence is reasonable in different iterations. Nevertheless, we use the nucleus sampling strategy for inference in Open-LTG, which leads to the lowconfidence tokens with high priority being masked. 3) *The massive absent context tokens suffer a* more serious multi-modality problem on long text generation in early iteration steps. As a result, the model is inclined to generate duplicated tokens due to the multi-modal output distribution. Although iterative refinement can provide additional context to alleviate this issue, the model still cannot generate the expected results. The **possible explanation** is that the self-attention layer needs the context token as key-value pairs to calculate the token representation. Unfortunately, the massive uninformative mask tokens ("*<mask>*") in context lead to model collapse steadily worsening in the following iteration steps. Thus, we utilize the recurrent generation mechanism for model training and inference to reduce the context dependency, which can also flexibly control the maximum length of the generated sequence (please refer to the Appendix B for more details about the model architectures and experiments). The results are shown in Table 3. We can observe that the model can gradually improve its performance as the recurrent steps increase, demonstrating that informative context dependency is the implicit reason for the model collapse. Improvements Based on the above analysis and findings, we categorize these critical factors into two types: **the defects of attention mechanism** and **inappropriate inference strategies**. In particular, we believe that each token should not pays attention to all context information, and most tokens only need the neighbor tokens' information to represent the hidden states and predict the results. Therefore, we will change the self-attention mechanism of the pre-trained MLMs so that each tokens can attend to the restricted neighbors. Besides, ![4_image_1.png](4_image_1.png) we will adjust the confidence score of the output distributions to keep the informative tokens in subsequent iteration steps instead of being masked. ## 4 Method In this section, we propose two simple yet effective strategies for attention mechanism and inference to mitigate the model collapse problems: Dynamic Sliding Window Attention (DSWA) and Linear Temperature Decay (LTD). These designs do not break the paradigm of MLM so that it can flexibly adapt to the pre-trained models. ## 4.1 Dynamic Sliding Window Attention We first introduced the sliding window mechanism (Beltagy et al., 2020) for the self-attention layer to adjust each token's attention pattern, which also ensures that the top layer's token representations can have a large receptive field, similar to CNN (Wu et al., 2018). Figure 3 illustrates the attention mask of the mixed attention layer of pretrained MLMs. It is worth noting that the key-value pairs consist of two parts: the source representation of the last layer (with green background) and the target representation of the current layer (with yellow background): $$\begin{split}\mathcal{H}^{l}_{tgt}&=\texttt{Mixed}-\texttt{ATTN}(\text{Win}(\mathcal{H}^{l-1}_{tgt}),\mathcal{H}^{L}_{src})+\mathcal{H}^{l-1}_{tgt}\\ \mathcal{H}^{l}_{tgt}&=\texttt{FFN}(\mathcal{H}^{l}_{tgt})+\mathcal{H}^{l}_{tgt},\end{split}\tag{6}$$ where the operation Win(◦) employs a fixed-size window to select the neighbor token representations. Meanwhile, the query can attend all source sequence hidden states and the target sequence hidden states in the window, stemming the impact of massive absent context. ![5_image_0.png](5_image_0.png) Dynamic Schedule Intuitively, it is not essential to use a fixed receptive field for each layer, e.g., the top layer may need to reduce the receptive field to perform prediction. Thus, we propose a dynamic schedule strategy for the inference stage to adjust the window size Swin of each layer: $$S_{\rm win}=\max(\alpha_{min},\frac{L-i}{L}*\alpha_{max})*S_{\rm fix},\tag{7}$$ where i is the current layer number, L is the max layer number of pre-trained MLM encoder, Sfix is the fixed window size for model training, and the αmin and αmax is the lower and upper bound of coefficient hyper-parameter selected from [0, 1]. With this strategy, we can alleviate the multimodality problem by restricting the model to attend to the tokens in the window instead of the whole sequence, thus degenerating the multi-modal distribution into a uni-modal distribution. As a bonus, the top-p candidates of output distribution can contain more informative tokens. ## 4.2 Linear Temperature Decay To further improve the effectiveness of sampling, we use the confidence-based iterative refinement by adjusting the temperature with linear schedule: $$\begin{array}{l}{\cal P}(y_{i}|{\cal X},{\rm Win}({\cal Y}_{\rm M}))=\frac{\exp(u_{i}/T)}{\sum_{i^{\prime}}\exp(u_{i^{\prime}}/T)},\\ \\ {\cal T}=\beta*(1-\frac{t}{T}),\end{array}\tag{8}$$ where β is hyper-parameter, t ∈ {0, · · · , T} is the current iteration step, and T is the maximum iteration step. Actually, the output distributions will be flattened when T > 1, and become sharp when T < 1. Therefore, by applying this strategy, we can penalize the distribution from peaked to flat in the former iteration steps and encourage it from flat to peaked in the later steps. The aforementioned process is shown in Figure 4. ## 4.3 Training And Inference Given the parallel data, we use vanilla self-attention to obtain source sentence representation and sliding window mixed-attention with fixed window size to generate the target during the training stage. During the inference, we apply DSWA to the mixedattention layer and LTD to sample the results according to the probability distributions. Besides, the model uses the ground truth tokens as context to predict the masked tokens during the training stage and applies the randomly sampled tokens as context during the inference stage. This discrepancy makes the model only refine a fraction of the low confidence tokens, which causes the degeneration in practice. Thus, we update all target tokens according to model predictions at each iteration step by utilizing the SMART mechanism (Ghazvininejad et al., 2020). ## 5 Experiments 5.1 Settings Datasets We conduct experiments on three OpenLTG tasks, i.e., storytelling (ROC (Mostafazadeh et al., 2016), WP (Fan et al., 2018), and WikiPlots and multi-paragraph opinionated article writing (OPINION (Hua and Wang, 2020)). For ROC datasets, we follow (Guan et al., 2021b) to mask all the names with specific placeholders to improve the generation ability. We fine-tune the model using our approach without additional corpus. More details are illustrated in Appendix A. Implementation & Baselines We utilize the pretrained RoBERTa base‡as our backbone model and implement all experiments with the open library Fairseq toolkit§(Ott et al., 2019). In addition, we also compare our method with the strong baselines, e.g., the widely-used AR models like BART (Lewis et al., 2020), HINT (Guan et al., 2021b) for storytelling tasks, and PAIR (Hua and Wang, 2020) for multi-paragraph level text generation task. It is worth noting that the layer and model parameters of RoBERTa (125M) are close to BART (140M), so it can be used to compare the inference speed directly. For the inference stage, we set the max iteration step as 6 for ROC and 8 for others. We set the hyper-parameter αmin=0.125, αmax=0.75, and window size Swin equals 64. We set top-p=0.9 for all baseline models, set β=1.6 for ROC and 1.8 for WP and WikiPlots, and set β=1.5 for OPINION. | Data | Model | BLEU | ROUGE | Repetation | Distinct | BERT Score | PPL | Speedup | | | | | | | | |--------------|----------|--------|---------|--------------|------------|--------------|-------|-----------|-------|-------------|-------------|-------------|--------|-------|----| | B-1(↑) | B-2(↑) | R-1(↑) | R-2(↑) | R-L(↑) | LR-n(↓) | SR-n | SR-m | D-4(↑) | P(↑) | R(↑) | F1(↑) | | | | | | BERT-CRF | 18.90 | 7.04 | 14.98 | 1.73 | 12.26 | 36.60 | - | - | 33.11 | 74.07 | 71.32 | 72.65 | - | - | | | HINT | 32.97 | 16.91 | 25.54 | 3.87 | 18.48 | 5.96 | 73.93 | 45.27 | 57.93 | 78.40 77.14 | 77.74 | 26.16 | - | | | | BART | 30.06 | 14.37 | 22.37 | 2.42 | 15.52 | 3.93 | 69.53 | 40.04 | 79.07 | 76.34 | 76.83 | 76.57 | 65.21 | 1.0 × | | | Ours | 33.22 | 17.08 | 26.82 | 3.91 | 18.22 | 3.28 | 70.52 | 43.71 | 68.93 | 77.86 78.23 | 78.03 53.00 | 2.9 × | | | | | Ground-Truth | - | - | - | - | - | 2.50 | 70.74 | 40.99 | 46.46 | - | - | - | 53.35 | - | | | ROC | BERT-CRF | 18.50 | 7.42 | 17.70 | 2.30 | 12.91 | 83.80 | - | - | 8.58 | 71.50 | 66.38 | 68.82 | - | - | | HINT | 22.44 | 8.38 | 18.66 | 1.69 | 11.71 | 26.05 | 80.56 | 46.50 | 36.92 | 71.23 | 67.72 | 69.38 | 14.18 | - | | | BART | 29.29 | 9.96 | 23.57 | 1.98 | 12.04 | 0.73 | 74.92 | 33.82 | 90.38 | 71.64 | 71.38 | 71.50 | 88.74 | 1.0 × | | | Ours | 32.80 | 11.65 | 26.67 | 2.43 | 12.97 | 0.73 | 78.67 | 35.29 | 86.70 | 72.17 | 72.09 | 72.12 85.88 | 6.4 × | | | | Ground-Truth | - | - | - | - | - | 0.45 | 80.23 | 34.36 | 49.23 | - | - | - | 55.39 | - | | | WP | BERT-CRF | 16.33 | 6.42 | 18.41 | 1.64 | 12.24 | 78.28 | - | - | 29.80 | 63.27 | 65.53 | 64.37 | - | - | | HINT | 19.86 | 8.61 | 19.36 | 2.14 | 10.98 | 9.86 | 70.42 | 50.49 | 55.16 | 72.28 | 68.36 | 70.18 | 15.63 | - | | | BART | 27.15 | 10.51 | 22.63 | 2.45 | 11.42 | 1.58 | 75.88 | 44.41 | 92.60 | 71.24 | 73.61 | 72.36 | 68.63 | 1.0 × | | | Ours | 30.06 | 12.39 | 25.88 | 3.55 | 12.62 | 4.50 | 79.06 | 41.16 | 83.97 | 71.74 | 73.64 | 72.63 61.36 | 13.3 × | | | | Ground-Truth | - | - | - | - | - | 0.98 | 75.13 | 46.72 | 91.71 | - | - | - | 40.88 | - | | | WikiPlots | | | | | | | | | | | | | | | | Evaluation Metrics We utilize BLEU (B-n) (Papineni et al., 2002), ROUGE (R-n) (Lin, 2004), Lexical Repetition (LR-n, 4-gram repetition for n-times) (Shao et al., 2019), Semantic Repetition (SR-n, average top-n semantic similarity between any two sentences) (Guan et al., 2021b) ¶, average semantic overlap (S-m, average semantic similarity of all the sentences), Distinct (D-n) (Li et al., 2016) and BERTScore (Zhang et al., 2019) for the storytelling task. As for the multi-paragraph opinionated articles writing, we utilize B-n, R-n, and METEOR (Banerjee and Lavie, 2005) to evaluate the results. The settings of n are mainly due to the length of the generated text and details are illustrated in each subsection below. We report the LR-2 and SR-1 for ROC stories and LR-5 and SR-10 for WP to reflect the lexical and semantic repetition of the generation texts. We also report the Repetition and Distinct scores of ground truth as a reference. We calculate the perplexity (PPL) using GPT2 (Radford et al.) for each model, which is the most common fluency metric. ## 5.2 Main Results Table 4 summarize the evaluation results on each storytelling test set. We choose the appropriate checkpoint based on the repetition and distinct comparison with the ground truth of the validation set. We can observe that our approach achieves better performance on all datasets than the strong baseline model. Especially, The text generated by the RoBERTa model has high-quality and fluent results, which have high BLEU, ROUGE, BERT scores, ¶https://huggingface.co/sentencetransformers/bert-base-nli-mean-tokens | Model | Refine | ARGGEN | | | |----------|--------------|--------------|--------------|--------------| | BLEU-4 | ROUGE-L | METEOR | | | | PAIRfull | % | 34.09/32.59* | 55.42/49.39* | 32.74/50.63* | | ! | 36.09/34.42* | 56.86/50.82* | 33.30/51.39* | | | Ours | % | 31.42 | 53.55 | 55.58 | | ! | 37.76 | 59.24 | 59.70 | | and lower perplexity, demonstrating the effectiveness of our model. For the OPINION dataset, we use the specific plans to initialize the model input and then try to generate the missing text according to PAIR*full* settings, where these special plans are extracted from the ground truth. The results are shown in Table 5. The PAIR results are based on BART, the AR model, so it has high quality even without refinement. Our model achieves better results than PAIR when using iterative refinement, demonstrating that as a masked language model, RoBERTa is more suitable to complete the planning sequence than an AR model. In addition, we found that the model works better without dynamic sliding window attention, because the additional context information provided a good initialization to the model. ## 5.3 Ablation Results We conduct the ablation study in Table 6 to evaluate the effectiveness of each inference strategy. We can observe the performance drops when without using any strategy, and this phenomenon is significant for longer WP datasets. In particular, the results are more in tune with the current prompt benefit from the DSWA, such as better BLEU and ROUGE, and Data Model B-1 R-L Rep Dist PPL ROC Ours 33.22 18.22 3.28 68.93 53.00 w/o DSWA 32.12 17.67 3.71 68.53 48.87 w/o LTD 33.04 17.73 11.29 69.66 78.07 w/o ALL 31.86 16.96 14.49 67.30 67.75 WP Ours 32.80 12.97 0.73 86.70 85.88 w/o DSWA 29.37 12.31 0.90 86.07 86.95 w/o LTD 29.80 13.88 17.80 64.53 63.08 w/o ALL 12.95 6.60 90.58 32.15 17.69 ![7_image_0.png](7_image_0.png) the model generates more repetition text without LTD. Thus, the DSWA and LTD are crucial for Open-LTG, which can reduce the context dependencies to predict the output distribution better, and improve the confidence score for each iteration step to adopt the open-ended scenarios. ## 6 Analysis And Discussion 6.1 Speedup For Inference Figure 5 illustrate the generation speed with the NVIDIA A100 GPU, which all run with the batch size equal to 1 on each test dataset. Our model can speed up the inference from 3 × to 13 × with different target lengths, i.e., from 133 token/s to 391 token/s for the ROC dataset, from 137 token/s to 882 token/s for the WP dataset, and from 132 token/s to 1753 token/s for the WikiPlots dataset. Although the smaller iteration step can further accelerate the speed, the perplexity drops significantly. ## 6.2 Length Prediction We validate the different length prediction strategies on the WP dataset, as shown in Table 7. We initialize the full mask sequence with ground truth length to inference. For the prediction method, we select the specific offset according to the validation set, e.g., −20 for WP and −100 for WikiPlots. Besides, the prediction modules work better for short text dataset ROC with offset 0. We also found that the fixed strategy obtained comparable perfor- | Strategy | Length | B-1 | R-L | LR-n | D-4 | PPL | |--------------|----------|-------|-------|--------|-------|-------| | Ground-Truth | 157.42 | 33.21 | 13.17 | 0.67 | 86.92 | 86.86 | | Fixed | 153.51 | 32.80 | 12.97 | 0.90 | 86.70 | 85.88 | | Prediction | 155.55 | 31.96 | 12.94 | 0.63 | 86.53 | 85.56 | Table 7: Length prediction of different strategies. mance with a slight drop, even the prediction is also a viable choice for the inference stage. | Metrics | Win | Loss | Tie | κ | |-----------|-------|--------|-------|------| | Fluency | 38.0 | 35.0 | 27.0 | 0.55 | | Coherence | 39.5 | 30.5 | 30.0 | 0.44 | | Relevance | 47.5 | 23.5 | 29.0 | 0.61 | ## 6.3 Human Evaluation For human evaluation, we compare our method with strong baseline **BART**. We sample 100 cases from the model outputs on three different datasets in total. We hire three annotators to give their preferences (win, *loss* and tie) for three evaluation criteria: fluency, coherence, and relevance, which reflect the intra-sentence linguistic quality (Xu et al., 2020), inter-sentence relatedness & causal dependency and consistency of the generation results, respectively. More details are illustrated in Appendix C. We apply the Fleiss' kappa (Fleiss, 1971) to measure the agreement among three annotators, and the results are listed in Table 8, where we report the percentage(%) of each preference when comparing with BART model. We can observe that our method can achieve better performance on three criteria when comparing with the BART model, especially for the relevance criterion, which indicates that such a NAR generation paradigm can mitigate the inconsistent issues of long text generation tasks. It is worth noting that all the inter-annotator agreements are either moderate (κ ∈ [0.4, 0.6]) or substantial (κ ∈ [0.6, 0.8]). Besides, we also plot the detailed percentage for ROC, WP, and WikiPlots on Figure 6, which can clearly exhibit the discrete distributions across three datasets. The fluency and coherence of the sentence generated by our models obviously decreased as the length increased, similar to the BART model. We will improve the text quality and overall fluency and solve the above problems for Open-LTG scenarios in future work. ![8_image_0.png](8_image_0.png) ## 7 Conclusion This paper explores Open-LTG with NAR models based on pre-trained MLMs. We first examined the potential and limitations of MLMs along with the iterative NAR inference for open-ended text generation and observed that MLMs would collapse for Open-LTG. Through extensive study and analysis, we found the reason is the inappropriate attention mechanism and inference strategies, and introduced two simple strategies to alleviate such a problem, i.e., dynamic sliding window attention and linear temperature decay. Experiments demonstrate that our model achieves competitive performance and significant speedup. We hope our research can make pre-trained MLMs as new candidates for the Open-LTG community. ## 8 Limitation Although our NAR approach can generate fluent and meaningful text, it inevitably suffers from the typical generation problems like in the AR fashion: (1) off-prompt: the provided prompt is very short, which causes the model can not focus on meaningful content and generate reasonable text. Besides, the model usually simply copy prompt text to generate results instead of planning reasonable content, such as the case 3 as shown in Table 13 in Appendix D. (2) incoherent between sentences: When the model is initialized, it does not consider the logical order between sentences, so it can only rely on the training data to learn automatically. We will consider how to generate a suitable initialization to help the model generate coherence results. Our paper's primary concern focuses on accelerating the generation speed, and we will put how to solve these problems in future work. ## Ethics Statement Our method heavily relies on the pre-trained language models, e.g., RoBERTa, which may inherit the problematic biases (Radford et al.). We have attempted to mitigate these issues by conducting experiments on comparatively innocuous story generation and opinion generation tasks. Furthermore, we have replaced all the names in those corpora with special placeholders. Although some measures are taken to mitigate the problematic biases, such issues cannot be solved completely. Thus, we urge the users to carefully examine the generation results and cautiously apply our method in real-world applications. Additionally, it is worth noting that all the corpora used in our experiments are only for scientific research. As for the human evaluation process, we resort to open source web library Django|| to build our own human evaluation interface. Before releasing the human evaluation cases, we carefully check that there is no private information or other problematic biases in the cases. Besides, we did not collect personal information or ask the annotators about their private information during the annotation process. We hired three annotators and paid each of them $0.29 for each case comparison. The payment is reasonable since there are only 100 cases for annotation, and it would cost average 4 hours for one to finish all the comparisons. ## Acknowledgements This work is supported by the National Science Foundation of China (NSFC No. 62206194), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20220488), and JSSCBS20210661. This work is also supported by Beijing Academy of Artificial Intelligence (BAAI). ## References Sweta Agrawal and Marine Carpuat. 2022. An imitation learning curriculum for text editing with nonautoregressive models. 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Bertscore: Evaluating text generation with bert. In *International Conference on Learning Representations*. Yicheng Zou, Zhihua Liu, Xingwu Hu, and Qi Zhang. 2021. Thinking clearly, talking fast: Concept-guided non-autoregressive generation for open-domain dialogue systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2215–2226. ## A Dataset And Pre-Processing | Dataset | Input | Reference | #Train | #Valid | #Test | |-----------|---------|-------------|----------|----------|---------| | ROC | 9.01 | 37.66 | 176,688 | 9,816 | 9,818 | | WP | 25.51 | 141.60 | 53,516 | 4,000 | 4,000 | | WikiPlots | 3.41 | 354.8 | 102,936 | 5,000 | 5,000 | | OPINION | 17.88 | 104.36 | 42,462 | 6,480 | 7,562 | Table 9: Statistic of datasets. The statistic of each dataset is shown in table 9, and we provide the download address of OPINION **, ROCStories, WritingPrompts ††, and WikiPlots ‡‡. In particular, we have to pre-process the dataset to ensure RoBERTa can handle each sample. We first use the NLTK tokenizer to split each sample into individual sentences, generally according to punctuation as a separator. Then, we collect the segment with a pre-defined segment number K to make the different pieces hold comparable lengths. Finally, we truncate the sample with a sequence length over 512 to satisfy the BERT maximum length limitation. Furthermore, we also provide the library version or link information, which is used in our paper: Transformers == v4.0.0, NLTK == v3.5, and evaluation scripts §§. ## B Recurrent Segment Generation As shown in Figure 7, to gradually increase the context during the decoding stage, we divide the onepass parallel decoding into multiple decoding steps. Specifically, we split the target Y into multiple segments {S1, S 2, *· · ·* , S K}, where each segment consists of multiple tokens/sentences by specifying the length of each segment. Then, the model will generate those segments incrementally, ensuring that each decoding step depends on the previously generated context to provide adequate information. In other words, we introduce NAR to generate each segment and use recurrent segment generation to keep segment-level coherence. Meanwhile, the model can obtain a flexible decoding paradigm by manipulating the length of the segments, e.g., the model can achieve one-pass decoding when setting the segment as the whole target sequence and achieve AR decoding (same as BART) when setting the segment as one single token. Concretely, we feed the input text X into the BERT model to obtain the representation Hsrc. We then concatenate the hidden states of the input and previously generated context segments to feed them into the decoder mixed-attention layer and generate the k-th segment: $$\begin{array}{l}{{\cal H}^{l}_{S^{k}}=\texttt{Mixed-ATTN}({\cal H}^{l-1}_{S^{k}},{\cal H}^{L}_{src},{\cal H}^{L}_{S^{<k}})+{\cal H}^{l-1}_{S^{k}}}\\ {{\cal H}^{l}_{S^{k}}=\texttt{FFN}({\cal H}^{l}_{S^{k}})+{\cal H}^{l}_{S^{k}},}\end{array}\tag{9}$$ where H˜L S<k is the representation of the previous segment using the ground truth instead of the generation results HL S<k to guarantee the reliability of the context information. The model recovers the kth masked segment and calculates the cross-entropy of those masked tokens SM as the the MLM loss: $$\mathcal{L}_{\text{MLM}}=-\sum_{k=1}^{K}\sum_{j=1}^{|\mathcal{S}_{M}|}\log\mathcal{P}(\mathcal{S}_{j}^{k}|\mathcal{X},\mathcal{S}^{<k},\mathcal{S}_{j\setminus\text{M}}^{k}),\tag{10}$$ where S k j\M is the observed tokens of k-th segment. Besides, we will select a segment number before model training and then use it to split the training data, ensuring the same number of segments for training and inference in the experiment. ## C Human Evaluation | Dataset | #Num | Length | |-----------|--------|----------| | ROC | 40 | 40 | | WP | 35 | 140 | | WikiPlots | 25 | 350 | Table 10: Statistic of human evaluation data, where \#Num denotes the number of cases in human evaluation dataset. We show the human evaluation interface in Figure 8 that was built using the python web library Django ¶¶. To test the generation ability between our method and the strong AR model (BART) in different generation tasks, we sample cases for different tasks. The statistic of sampled evaluation datasets is shown in Table 10. In each comparison, each annotator is shown with one model input (prompt) and two outputs generated from two models, namely "Text A" and "Text B". Then, the annotators are asked to select the better text in each comparison in terms of fluency, coherence, and relevance. In case of a situation where annotators ¶¶https://www.djangoproject.com ![13_image_0.png](13_image_0.png) ![13_image_1.png](13_image_1.png) think two texts are hard to distinguish, the "Tie" choice is allowed. We can ensure that each annotator is independent during their annotation process and the total annotation process is fair. ## D Case Study We randomly selected some cases from different datasets to facilitate the evaluation, which was generated by the BART and our model. Table 11 illustrates the results on the ROC dataset, and we can see that our model results are close to the prompt text benefit from the NAR fashion. For example, topic case 2 is about "candy", the BART generates the sentence with fruit "grapes," instead, our model generates the "chocolate," and the whole sentence is close to the topic candy. Furthermore, our model can generate a high correlation for different sentences, such as "plants, seeds and watered. finally, i had a beautiful garden." in case 11. We also provide the results of WP and WikiPlots for Table 12 and Table 13. Although these results are relatively ungrammatical and incoherent, the pre-trained MLM (RoBERTa) achieves competitive performance as BART. Besides, the results have some grammar errors for our models, e.g., "when i got home i went to the kitchen." in case 10. The possible explanation is that the non-autoregressive model may generate grammatically incorrect sentences during the iteration refinement procedure due to multi-modality problems. We will add grammar corrections for each iteration in future work to help the model produce better results. | Case | Type | Text | |--------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------| | Prompt | the man made music . | | | 1 | BART | he put his name on a paperpha pamphlet . someone subscribed . he sold it . his name got popular . | | Ours | it was for a dance to a his friends . they invited him to the studio to play his music . it played over and over reminding the man of his past . the new song did n't become a success , but very popular . | | | Prompt | i had an intense craving for candy . | | | BART | she wanted me to buy grapes . some of the ingredients did not fall in pretty . i could not make sauces however . | | | 2 | i have decided to take up cooking . | | | Ours | i went to the grocery store to buy some chocolate . i went to the store and found one very empty bag of candy . he took care of it for a few minutes . when i got home , i was out of candy . | | | Prompt | i sat down in the quiet room . | | | BART | i took a turn for the head held high . i felt ill around my shoulders . i closed my eyes and got out of bed . | | | 3 | i had flung smoke at reality . | | | Ours | there was a very clean room . i could n't find my phone . i was scared and felt like something was going to happen ! i stood up and took a breath and closed the door . i was home and the i was happy . | | | Prompt | i went to the store to buy breakfast food . | | | BART | i returned to the store for breakfast and the cost for food was much too much . he asked me to try in the big eggs . | | | 4 | to my dismay , it was expired . in the Mangrove , the eggs in the pan were all burnt . | | | Ours | i bought cereal and oatmeal eggs , but i was sad because they were expensive . then i thought about how much i cared about breakfast . i brought it home to my wife . when i got back home , i gave her breakfast . | | | Prompt | the court date was set for earlier this month . i managed to get my client into court on time , so my dad ordered [FEMALE] to get up early . the day of the release , i went to sleep and woke up early . i missed getting to sit with him over breakfast . | | | BART | however , it turns out she was finally able to get back to court this week . | | | Ours | the clerk announced the date had been postponed for the week . he went to the courthouse the day before the court . he so told her to stop smoking that she called the police for help . it now appeared that the trial was postponed . | | | 5 | Prompt | i decided to ride my bike to the park . | | BART | i immediately rode my bike across the road . i picked up the first cyclist to get on my bike . | | | 6 | i waited two minutes and then waited five minutes . i decided that it was time to turn my bike . | | | Ours | as i got on my bike i made a plans for the day . after going to the park , i decided to ride my bike home . i rode around the park until i a my home . i pulled over and put my bike back . | | | Prompt | i opened up a new bank account last month . | | | BART | one of the accounts i opened would not have any cash . when i opened the account , i had fun with some old stock . | | | 7 | i couldnt wait to check out the shares in subsequent months . i also realized how busy i was without new passwords . | | | Ours | i was able to put almost everything i need in my account . i looked a website of how to make money now . i intend to pay out to that account once a while . i have made a lot of money with this account . | | | Prompt | when we were young my brother and i made spears out of newspapers . | | | BART | we must have been 3 or 4 in the Arist . unable to get my spears down i put them into a ball . | | | 8 | we used fuses to pick screws . [MALE] liked his five foot tall six foot Luther beast . | | | Ours | one year we went to a field in an open field . i told us to would sell them when we got home early . we practiced the spears until it was dark . afterwards , my mother took me to the hospital to treat my wounds . | | | Prompt | i had a nearly new keyboard . | | | BART | i was torturing it with it . i was torturing it with it . | | | 9 | then i was so excited i ran to the bathroom . i rushed my new keyboard away and it barely fit my needs . | | | Ours | it had a great keyboard . when i got my new keyboard , there were black stains all over it . i was so upset that i decided to throw my keyboard away . when i put it back on , it was working great at work . | | | Prompt | one day i was really hungry . | | | BART | it was almost time to eat so i checked the fridge to see what was there . i saw a bag of raw steak and wondered how went there . | | | 10 | the cheapest way to eat was to come up with a replacement . i picked a different restaurant later that day . | | | Ours | i got dressed and went to something to eat . i was very hungry and went to the store and bought a lot of fruit . when i got home i went to the kitchen . i got some cheese and bread and ate the turkey . | | | Prompt | i decided to start a garden . | | | BART | i researched the crop and the dates . i grew tomatoes . he had just for most of his meals , | | | 11 | so he was out of milk . [FEMALE] garden is excellent at thinking about the future . | | | Ours | i planted some seeds online from the local garden store . i planted the seedlings in the soil and started planting . i gave in with what to do of the plants , and seeds and watered . finally , i had a beautiful garden . Table 11: Representative ROC examples for BART and Pre-trained MLMs (RoBERTa) | | | Case | Type | Text | |--------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | Prompt | caffeine is made illegal and is now rated schedule i by the dea . with 'conservatives ' is for 'conservatives ' . that is basis of the term . the name is the republic . remnants of the religion were wiped from the lips of most . hegemony . democracy was a jungle . every entity was put to a test . to put it gently , a ban would be imposed on the world , forcing every person who had taken part to create some form of protein to join the majority of their species . food was a main reason why the republic was a flourishing , independent nation . governments rushed to clamp down on caffeine , showing how virtually any government could kill them . we feed billions of people who needed one of their pills through cigarettes . protests had come and gone but nothing remained for bobby . | | BART | " i went to the coffee shop last month ago and saw that . i was a kid at the time . sometimes i would daydream about my older brother , at least male , alive as rain fell on the roof . and a can of coke . thanks . he gave this to me to have a copy of every book i ever picked up . | | | Ours | i were in the parking section , and as i looked down , i smelled it ! no wonder what my headphones do ... i grabbed some plastic cups and placed them on the coffee table , along with a white sheet on the way to the office . i ordered a joe . now it had been time to go . jesus christ when one of my friends said to me , " come on . just save me the milk ! " | | | 2 | Prompt | a magical mirror shows your reflection and your future soulmate . you only see your reflection . night would always come first . that 's how every man usually looks . one point is this ." wait a minute ! where do amateur artists live ? " " in outer space , " the mirror roamed through the neighborhood , staring intently at the road-lights . he moved his feet a few feet so that even the hardest parts would stay put . " i 'm sitting in your shit hole , found a pot in your room ." said you ." i sure am ." ACL looked at me with a cheery smile , " i could n't tell . i think my dad was an atheist . they got acquainted the same way because he always wore glasses . people ca n't wrap their wrists around idiots or say they 're complaining . | | BART | she 's staring me by the lenses of a painting a gagged man tells me the only noise that comes to my mind is question . question . my head burns with fear . i wonder when i will walk into the mirror , how long my a full of hope and love | | | Ours | has faded back and forth ? do you tell you a lie , because i 'll find you out . and if you ask you three i 'll have to look at light brown . i look ca n't really help it . being able to empathize with what i 'm going through i can a myself . she i must have be having children . but what i want is that ? the first girl since last time came around . do n't care because i love her , the way i love her . | | | 3 | Prompt | it 's 3016 , cryogenic people are being revived for the first time . " knock ! knock ! " i gasped as the glass door cracked open , revealing a thin version of me . a lot of blood , especially from the infection . i quickly grabbed my utensil and held it close against my mouth , thumb plungingikhail 's face back down toward his mouth . the kitchen was booked and locked , all but impossible to see . smitherin turned to me , his hand outstretched ." fix anything , " i sighed , slowly closing the deal . my fingers were too large , uncomfortable , and my left arm sank behind his neck . i pressed a silent raise , still gripped tight , until my ankles buckled beneath me ." fine , we have her back , " i managed , leaning closer for another variation . | | BART | the massive tremors of the ringing in my s ears , beating furiously against my heart for a moment . i leaned back and against the floor , i was here , finally . the last one left these days behind still wonder at night , as to what this all meant . i felt cruel at best , but least in a place like this we could talk . the , the people were sitting on an space vessel in space and had at least days left . not good enough to write the process in a journal . i felt as comfortable as i could as they ' new body was brought back for a while . everything was normal , no important , unknown secrets . but the thing about cryogenic people , they took you in and out . we knew the ways , but still some were left . | | | 4 | Prompt | you are visited by your guardian angel but he/she is an asshole . | | Ours | con the morning , there was a clear wind coming through thecloud ( um.. sunny ? is thatreally what it is ! ) down into the woods . the last thing i remember was climbing up the steps of my home . my eyelids began to flutter nervously , i rubbed my eyes , moaning beneath my own robotic fingers . the line rose from a distant object , inviting me to focus . my reflexive positioning prevented me from climbing back down . again , i thought i had recieved enough therapy to ensure such things had not happened . would it be the last ? or was it the previous trick we played ? i sighed and climbed up into my favorite bed of the house ; the big one that i recently occupied . | | | BART | her eyes met mine cold metal waiting eyes . with a blink a moment later i was replaced with a deep , dark , earthy smell filled the room . in it was that glorious smile on my face as i stared at my beautiful toes and joined in a whirl when there was eternity . her blue , emerald green eyes and foot gave away those evil , sly grin on the face . what little thing i remember was all the smells that tasted like steaming water . little touch did make me turn as white as a bird of heart . make or worse . a tear ran its way down my body , taking note of my surroundings . i looked to i . i was facing a woman with a pink dress . my body seemed to lower itself into a numb state of the moment . | | | 5 | Prompt | wayne enterprises goes bankrupt , forcing batman to pursue justice through more economical means . | | Ours | fire batman watched that shutter of his fist hit the ground , shattering him . his furious scream sounded as he shifted a small pile of broken paint on the side of the building . wallet , phone , controller , and was buttoned down , lying upon the floor in the middle of his . dabble , dabble . he closed his eyes and attempted to try and make sense of the implications . to splinter himself forever , he needed to pull together a means of escape , happiness , and serenity that would bring him back to reality . his floor was exposed to the current rut in which the cash machine and carton of barks had gone , making him fix his entire apartment . questions about the earlier charges ? complying ? no . get assistance ! james felt himself shaking and looking around , like the sky was blue | | | BART | after 30 hours of work was out again . batman sighed , walking down the street . he was in the hesian era and , in fact , the father of batman, after all . a friend a friend and big money ? eh ? " waggin ' death , a bitch . the last batman of the world sat at his table with the latest edition of " the slash of day . " was the last show he ever watched . | | | Ours | when he looked into that camera , batman called ready excited . his life had been locked behind race car running lights . twirling " do n't go out " out to ground batman was him out of the room . " roger . Table 12: Representative WP examples for BART and Pre-trained MLMs (RoBERTa) | | | Case Type | Text | | |------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | Prompt | Magic Wonderland During in 2021, Gavin Fleming (Marshall Hall), his son Duff Jackson (Hayden Farther), and his and Maggie Peterson (McCullough) explore mythical lands, and come across an ancient legend concerning the Wall, an artifact that may be more mythical than what the descendants of the Elemental Box, purchased and locked in deep holes in the human body. As the Dunder Mifflin alternate universe endlessly and alone, Gavin meets Belle Duncan (Brassman), a beautiful young woman who haunts Slater's dreams and constantly challenges him to battle her, so aided by a new creature called the X-Rex (Amaradurorylum), Gavin is advised to fight beast-like creatures around the wall and defend the Wall from destruction when the beast enters. The beast grows into a giant male humanoid, known in his form as the Castle Unbound, a winged humanoid among the fantastical creatures. Gavin adores Belle, and tries to spend time with her after she has recovered. Belle suggests to Gavin that he turn his back on the villain and encounter the Wolfrunner. He reluctantly agrees. Gavin does not agree, and Emma begins to welcome him only after a battle of wits. Throughout an attack of beasts, Liam Kane (Lyle Marvin) bursts through the defences of the Wall, following the guard staff, and scares everyone away. He forces Gavin to alert the guards to the camp's presence. However, those he has encountered are unaware that he is a friend of Emma Peterson, a Japanese-Australian employee who suffers from nightmares and dreams of superpowers and past matches | | BART | The story follows the aia and his best friend playing the game Magic Wonderland, a game they play in " Under the Tree " . According to the legend, magic creatures also exist in a parallel world called the Wonderland. Coming out of which is a large house considered to be haunted, but he is allowed to stay. After his magic over he goes for a while and does strange things about it. The world he went to is about to happen something that happens to everyone. While waiting at the French manor he (Derek) learns of the kingdom of Aiaia and all of the other characters were left with a little guide on how to use the magic of all the the creatures in the world. When the new year came, all the people on the island decided that that would be A lot of fun for the two boys to explore the Ahaia's fairy world. They decide to blow up the castle. On their way to school, they foolishly (missed) the black magic, and was had to move in. But Aia and his help make Aia a magical man and a beast, making it difficult to figure out what he is about to do, and why. But now this evil plan has been set up, the story remains the same. Aia is more or less lost in a quest to find a world similar like his sister's, too. They had their pet bird, a green dragon. Then, so that everyone did perfectly normal. When theia ran out of money, Aia decided to the home. The events are the as to if it was in Aia's life his father did not love him, after all, leaving him money to look after him. Also, he was to find out who is behind the magical fairy world, and they will be in love until the end of their lives, and then the world from there. During the game, Aia giving one another a kiss. | | | 2 | Prompt | Beyond Apollo | | Ours | Savika (Saurashtra Prakash) is a demon hunter of Lore Love (Madhuravalli who has set off for Chennai) and doesn't want to interact with women. Tensely wanting to save her own girl, he approaches her in a customized john vehicle. Upon hearing about the coming of the eye, he enlists Glyndar (Urba Rao), the last man he knows and a high society man called Ramesh (Isha Kher). They meet in the limelight After feeling sick when he asks her to go out, he decides to travel to Seta village by car as his long distance companions. There, he enlists the help of an attractive woman named Kadeb (Jaswini Gopal), and is immediately attracted to her for her beauty. At Seta, Gadeb unwittingly breaks into Kadeb's cell and steals money from him. A quarrel ensues between Gadeb and Skylady, an official in Seta who is in charge of the operation, over the case. During the meeting, Skylady and Gadeb beat Gadeb up and gave him her pocket money and dancing lessons. Gadec sees this and flees while Skylady takes a cab in a hurry. She then steals weapons and goes off with Kadeb. Nightfall starts and Gadec runs into Kadeb, who secretly intends to steal the money that Gadeb gave her to sell to her heir. He is shocked when Gadeb offers her a way out. Skylady | | | BART | The crew of Apollo is one person after another living in the O' Beel family. That is, from the time they, on the planet Bumblebee, 12-year-old Roxi is about to be the pilot of an orphon-based spaceship. So, the crew of Apollo decides to be a rescue mission. Back at Earth, the crew is ready to leave for the moon. On the station are OX-O-s- that, like themselves, can travel, using the help of space suits from the isle, stored in special's as year 3031, a hundred years away, when the moon is built, so he and Fifi decide to see if they can find out about the ship. At the same time, a new member is inducted into the crew, and completes the planning for further exploration. Then the nanobots appear and begin saying " Enter into space. This I'll do she replies in Just but not only Number One even after the end of Apollo, that is, not yet. . the planeto has been (andarently) transferred to a planet we came from called Dusty. They must go back to Apollo. Who cannot and why they did not abandon her. One's afraid that one is coming. Soon they decide to join at first for friends, but erupts start to be the ship. The Zesti wants to take Shoxi home, tries to stop him. But they refuse to see him again until one of them becomes a crew member and, he says, it was the only time he went out to take to work. However, it turns out that it was nothing but a very old man called R. who is tired of himself having a affair with their and their beau. However, without them all" Six must deal with his very father, Olaf, and being a space pilot, who as a result has plans for the future. | | | 3 | Prompt | Macbett | | Ours | YoungRecently released gangster, Ronnie Abbett, pairs up with Jake, an older lieutenant in the Marines. Instead of killing him by torpedo, he eventually exposes him in the hands of an army of locals who want to hold him prisoner even for one night. The drug lord is especially antagonistic as, near the end of the film, the "likely" blood of the terrorist murders in a bar kills him. The gang tries to punish the gangsters, making them excited over the pretense of love. Frankantly, the gangsters' leader tries to coerced Ronnie to help them, while alcohol, drugs andreedness win him over by tricking him into accepting his debt. Adoption of drugs greatly affects Ronnie, and he complains to his alcoholic brother about Daniel, who promptly kills him and tells Ronnie's mother to stay away from him. Ronnie tries to be supportive of Mike, who is working at Seagraves. The rest of the gang, including Mike, are led by a man named Dan, who is actually Ronnie's adoptive father and enjoys side-play with Dan when they go out. However, Danny and Mike are against the most recent gang activities. Hell saves Mike's life and Jim, a family friend, helps him out. The meanwhile, the new shift surgeons start robbing the bars and poor performers practice hollows, sending mugs on the streets, hitting people who cried out Loud at the climax so much as collapsing. They later see Mond Roger Lewis (Bruce Mancini), the bartender's brother who supposedly does coconut liquor in a bar fight, surrounded by relatives | | | BART | Macbett runs a small coffee shop on the grounds of his father's farm. Mary and her are go to Scotland where John Macbett had his first meeting with Sir Andrew Macbett's family and other things. Macbett, however, has a lot of respect for the character of " " Macbett " . In the plot an man, a woman, and the manor, " Teneggi. Macbett. Macbett at the funeral, and we learns that Mac's father, Nail, Sr. died in an accident. John Pendleton was rich but he had nothing worth good for, but not even Macbett's distant relatives, one of them Mary. Mary both do want to go see Jack Nelly and Celia's father a little man (John Macbett). Later on, Mary and everyone, including Macbett and Mary, in. They sell the house and sell it to Servant's the next. who, after having watching the news; had been a party called for Macbett Macbett, who came here, while a other people get killed inside. Macbett decides that meeting with Clint and Denegan has started a new life Mac. S. Eton, who was Macbett's old friend, and fell in love with her. Macbett used to not fallen in love with Mary and that because he was in so much that was Keley's land. In the end, Mary died when he was a child. We also find that his wife, Carol, doesn't want to get married any more, after having a child. Macbett had a son, Macbett. Macbett. Macbett and Scenein time with Mary and the rest of their family, except for Mary who is up with Macbett. Mac Macbett saysI don't know what to do " . Jack replies " int " . Overly without any memory of who he was is really dead not only in but but but but his two brothers. | | | Ours Table 13: Representative WikiPlots examples for BART and Pre-trained MLMs (RoBERTa) | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

chronis-etal-2023-method

A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces

https://aclanthology.org/2023.acl-long.14

We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical items in two grammatical constructions: nouns in subject or object position within the same sentence, and the AANN construction (e.g., {`}a beautiful three days{'}). We show that a word in subject position is interpreted as more agentive than the very same word in object position, and that the nouns in the AANN construction are interpreted as more measurement-like than when in the canonical alternation. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes.

# A Method For Studying Semantic Construal In Grammatical Constructions With Interpretable Contextual Embedding Spaces Gabriella Chronis and **Kyle Mahowald** and **Katrin Erk** The University of Texas at Austin {gabriellachronis,kyle,katrin.erk}@utexas.edu ## Abstract ![0_Image_0.Png](0_Image_0.Png) We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical items in two grammatical constructions: nouns in subject or object position within the same sentence, and the AANN construction (e.g., 'a beautiful three days'). We show that a word in subject position is interpreted as more agentive than the very same word in object position, and that the nouns in the AANN construction are interpreted as more measurement-like than when in the canonical alternation. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes. ## 1 Introduction There are now several paradigms for the linguistically oriented exploration of large neural language models. Major paradigms include treating the model as a linguistic test subject by measuring model output on test sentences (e.g., Linzen et al., 2016; Wilcox et al., 2018; Futrell et al., 2019) and building (often lightweight) probing classifiers on top of embeddings, to test whether the embeddings are sensitive to certain properties like dependency structure (Tenney et al., 2019; Hewitt and Manning, 2019; Rogers et al., 2020; Belinkov, 2022; Manning et al., 2020). 1 Here, we consider another approach: projecting contextual, token-level embeddings into interpretable feature spaces defined by psycholinguistic feature norms (Binder et al., 2016; Buchanan et al., 1Code and data for all experiments in this paper are available at https://github.com/gchronis/features_ in_context. Figure 1: **(top)** Models are trained by using multiprototype embeddings in LLM space to predict gold feature vectors derived from psycholinguistic feature norms. **(bottom)** These same models are used to project contextual word embeddings to interpretable contextual feature space (model=BUCHANAN-PLSR-MIL). 2019; McRae et al., 2005). By learning a mapping to these spaces, as illustrated in Figure 1, we attain context-sensitive, interpretable, real-valued lexical-semantic features. After experimenting to determine best practices for contextual-feature projection, we use these features to explore whether contextual embeddings are sensitive to subtle semantic *construals* in different grammatical constructions. Specifically, we observe how even seemingly similar constructions can impart a different semantics on their component parts or 'slot fillers' (Trott et al., 2020; Goldberg, 2019). Consider the Article + Adjective + Numeral + Noun (AANN) construction: e.g., "a beautiful three days in London," where the normally singular "a" precedes a plural noun and the adjective precedes the numeral (Solt, 2007; Dalrymple and King, 2019; Keenan, 2013). This construction often occurs with units or measure phrases (e.g., *days*, feet), but can also occur with non-measure nouns (e.g., "a lucky three students"). While it is tempting to think of "a lucky three students" as semantically equivalent to "three lucky students," it has a different *construal*. Specifically, the AANN construction is acceptable only when the noun behaves as a single collective unit and is, in effect, more semantically similar to a unit of measurement than it would be in the unmarked construction. Evidence for a difference in meaning between the two variants is seen in their divergent distributions. For example, the AANN construction is unavailable in contexts like (1) and (2) (\#-ed cases; adapted from Solt, 2007). (1) The essay consisted of (a few eloquent paragraphs / \# an eloquent few paragraphs) separated by pages of gibberish. (2) He played (five boring songs / \# a boring five songs), but in between he played one really good one. The AANN construction cannot occur in contexts where the referent of the noun is split into noncontiguous parts. This distributional pattern is taken as evidence that the AANN construction construes its argument as a single, measure-like unit. In this paper, we study distributional evidence on a larger scale, using a contextualized large language model as a 'compressed corpus' that captures observed statistical regularities over utterances of many speakers. We analyze this compressed corpus by mapping embeddings to interpretable feature spaces based on psycholinguistic feature norms. When we do this for the embedding of the noun days in "I spent a beautiful three days in London," we find the most salient difference with the "I spent three beautiful *days* in London" to be **a higher** value for features like *measure* and *unit* **when it** is in an AANN construction. We argue that this is because human speakers construe the AANN construction as being "measure-ish", and that this construal is reflected in their language use in a way that the contextual language model can pick up. We conduct two case studies, one about AANNs and the other about grammatical subjecthood. Specifically, **we show that a word in subject position is interpreted as more agentive than the** very same word in object position (consistent with findings from psycholinguistics, e.g., Kako, 2006), and that **a noun in the AANN construction is interpreted as more measurement-like** than when in the canonical alternation. Our results demonstrate that construals can be inferred from statistical usage patterns. While we here use constructions with known construals, our positive results indicate that we may be able to analyze constructions where the construal is less clear in the theoretical literature. While feature norms have been used to *interpret* distributional semantic models (Baroni and Lenci, 2010; Herbelot and Vecchi, 2015; Fagarasan et al., 2015; Rosenfeld and Erk, 2023), we emphasize the linguistic value of reliable, reusable, interpretable semantic spaces, which we use to interrogate the semantic properties of language in use. The ability of our method to characterize subtle semantic differences using language models offers a point of connection between linguistically oriented deep neural network analysis (Baroni, 2021) and topics in formal linguistics. In particular, this work empirically demonstrates the potential alignment between LMs and feature-based theories of lexical semantics (as illustrated by Petersen and Potts, 2023). Our main goal is to use interpretable feature spaces for understanding the semantic construal of words in context, specifically the AANN construction and the transitive construction. In Section 2, we lay out our method for constructing interpretable feature spaces for tokens in context. Then, in Section 3, we evaluate the success of our method on a sense differentiation task, a homonym feature prediction task, and a qualitative analysis. The idea is that, if the method for mapping from embedding space to context-sensitive feature space is successful, we will predict unique semantic features for different senses. Having established and validated our method, we then turn to our key constructions in Section 4. ## 2 Methods The task is to learn a mapping from contextual word embedding space to an interpretable space defined by feature norms (Section 2.1), where every dimension corresponds to a semantic feature. We construct the training data by pairing feature norms with embeddings derived from contextual word vectors. We train models at the type-level, e.g., to map the embedding vectors for the word *ring* to the set of feature norms for *ring*, as shown in the top half of Figure 1. But ultimately, we use the model to predict semantic features for individual tokens. That is, we project the token vector of a single occurrence of the word "ring" into the feature space learned at the type-level, as shown in the bottom half of Figure 1. ## 2.1 Psycholinguistic Feature Norms We construct three semantic spaces, trained from three datasets of psycholinguistic feature norms. The McRae et al. **(2005) feature norms** comprise 541 concrete English nouns and 2,526 features. Participants were asked to list definitional properties of cue words. The features are full predicates; for example, a *brush* 'has_bristles' and is 'used_on_hair'. The Buchanan et al. **(2019) feature norms** consist of over 4000 English words and 3,981 distinct features, from all open-class parts of speech, and include abstract words. The authors collect new norms and collate them with McRae norms and the Vinson and Vigliocco (2008) verb feature norms. The features are tokenized and lemmatized. If a participant said 'found in kitchens,' this yields the features 'found' and 'kitchen'. The Binder et al. **(2016) data** consists of 535 English words rated for the relevance of 65 predefined features. The features were chosen to correspond to known neural activation regions in the human brain, and to domains of cognition and perception; they are more coarse grained than the other norms. The word *song* might have a high rating for 'Audition' but a lower rating for 'Vision'. Feature norms as feature spaces Feature norms can be interpreted as vectors, with a real-valued dimension for each feature in the dataset. The differences between the feature norm data sets lead to differences in the feature inference problems. For MCRAE and BUCHANAN, values along each feature-dimension correspond to the number of participants who named that feature—zero in the majority of cases. These spaces are thus sparse and high-dimensional. For these two spaces, we treat the output as a ranked list of features, where the lower ranks are not relevant. The BINDER space is dense and low-dimensional, and the goal is to predict the value of each feature. Here, a low value on a feature does not indicate lack of relevance. The norms differ in what they say about a word. The McRae and Buchanan norms are fine-grained, and represent salient or prototypical meanings. McRae norms are limited in their applicability because they only cover concrete nouns. Buchanan norms have a coverage that is wider but still somewhat ad-hoc. The Binder norms are high-level and were designed to be comprehensive. Past and concurrent work on feature prediction has explored the utility of McRae (Fagarasan et al., 2015; Herbelot and Vecchi, 2015; Rosenfeld and Erk, 2023) and Binder (Utsumi, 2020; Turton et al., 2021) norms for probing distributional models and language models. ## 2.2 Embeddings The feature norms serve as our gold feature labels that we map our type-level embeddings onto. For these type-level embeddings, we use embeddings derived from BERT (Devlin et al., 2019), either in a vanilla variety (one vector representation per word) or using *multi-prototype embeddings*, which have multiple embedding clusters per word (roughly corresponding to distinct usages). Specifically, we use the embeddings from Chronis and Erk (2020), which are generated by performing K-means clustering on BERT embeddings of tokens from the British National Corpus (BNC). This procedure collects up to 200 occurrences of each cue word in the British National Corpus, and generates token vectors for each occurrence with the HuggingFace bert-base-uncased model. For multi-prototype embeddings, these representations are clustered using K-means, using their best-performing setting of K=5 clusters per word at Layer 8. For vanilla embeddings, we generate BERT vectors through the same procedure, but simply average the token vectors together (K=1) to get one vector per word. See Appendix A for more detail on the multi-prototype vectors. Though the mapping is *trained* from type-level (or sense-level) embeddings, contextual word vectors at the token level can be *projected* into the interpretable space using the resulting model. ## 2.3 Mapping From Embeddings To Feature Norms Though feature prediction is well explored for static embeddings (Baroni and Lenci, 2010; Herbelot and Vecchi, 2015; Fagarasan et al., 2015; Rosenfeld and Erk, 2023; Utsumi, 2020) and gaining popularity as a method to probe contextual embeddings (Chersoni et al., 2021; Turton et al., 2021; Apidianaki and Garí Soler, 2021; Proietti et al., 2022), there is no consensus as to which models work best for which datasets. We experiment with several mapping methods used previously for feature prediction. The first is a feed forward neural network (FFNN, with a single hidden layer, tanh activation, and dropout applied after the final output layer; Turton et al., 2020). The dropout parameter, hidden layer size, learning rate, and number of epochs were grid-searched, as described in Appendix B (which also includes implementation details for the other models described). The second is partial least squares regression (PLSR, using the scikitlearn implementation; Herbelot and Vecchi, 2015; Fagarasan et al., 2015; Utsumi, 2020), whereby we run a partial least squares regression that predicts the feature space from the (potentially multiprototype) embeddings. The third is label propagation (PROP; Rosenfeld and Erk, 2023), which percolates labels through a graph from labels to unlabeled nodes. In all cases, the goal is to predict a real-valued semantic feature vector. Thus, the task is formulated as a multi-output regression problem. In the vanilla setting, the above methods can straightforwardly map from a particular word embedding into feature space. But, in order to map from a *multi-prototype* embedding into feature space, the problem is trickier—especially since the multiprototype embeddings may capture meanings that are entirely absent in interpretable feature space. Therefore, we test versions of each model using techniques inspired by multi-instance learning (MIL; Dietterich et al., 1997). The implementation of these MIL-inspired models is different for each of the three methods. For the FFNN, we use an attention mechanism that allows the model to learn a weighted average over instances, as in Ilse et al. (2018). For PLSR and Label Propagation, we simply construct a separate training example for each prototype drawn from the multi-prototype embedding That is, for a 5-prototype vector, we construct 5 training examples, where each of the 5 examples consists of a (unique) single prototype vector paired with the same type-level feature vector. See Appendix C for more detail on adaptations for the multi-prototype setting. ## 3 Evaluating Contextual Feature Norms For Interpreting Semantic Space We first evaluated the models on their ability to fit the *type-level* feature norms they are trained on. We do not go into detail here, as it is contextdependent meanings we are most interested in. See Appendix D for full results. Overall, BERT-derived models were comparable to those we trained with static GloVe (Pennington et al., 2014) embeddings, and to the best static models in the literature. This initial evaluation established that models using BERT-derived embeddings are just as good as static ![3_image_0.png](3_image_0.png) embeddings for predicting semantic features. To evaluate our models on *in-context* feature prediction, we conduct two quantitative experiments: one on a sense differentiation task, one on a homonym disambiguation task, as well as a qualitative analysis for a representative word (*fire*). The goal of this section is to explore whether the contextual feature norm method successfully captures contextual modulation of word meaning. For these experiments, we select the hyperparameters for each model that performed the best at type-level feature prediction under 10-fold cross-validation (Appendix D). ## 3.1 Exp. 1: Sense Differentiation Token-level evaluation is tricky because there are no existing datasets for in-context feature norms. Noting this obstacle, others utilize indirect methods like word-sense disambiguation and qualitative analysis, (Turton et al., 2020), or forego in-context evaluation (Chersoni et al., 2021). Turton et al. (2020) evaluate the Binder feature prediction model using the Words in Context Dataset (Pilehvar and Camacho-Collados, 2019), which only labels token pairs as 'same meaning' or 'different meaning'. We devise a sense differentiation experiment using the SemCor corpus, (Miller et al., 1994), which lets us do a more fine-grained analysis in terms of close and distant polysemy. The logic of this experiment is that, if two senses of a word are semantically *distant*, we expect the feature vectors in projected space to also be distant. We test the quality of our predicted feature vectors by testing how well the cosine distance between vectors for polysemous words corresponds to the distance between their senses in WordNet (Fellbaum, 2010). To build this dataset, we collect examples of noun lemmas in the SemCor corpus, which is annotated with WordNet senses for words in context. In SemCor, "Water is a human right," is labeled right.n.02, *an abstract idea due to a person*, while "He walked with a heavy list to the right," is labeled right.n.01, *the side to the south when facing east*. To counteract data imbalance, we collect only up to 30 instances of a particular word from any one WordNet sense. We determine degrees of similarity between WordNet senses using WuPalmer similarity (Wu and Palmer, 1994), which measures the degrees of separation between them. Then, each token in the dataset is projected into interpretable semantic space. We compute the cosine similarity between pairs of tokens and compare them to the Wu-Palmer similarity of their word senses. The key hypothesis is that we should see highly similar predicted features for tokens of the same sense, somewhat divergent features when the senses are different but related, and very different features for distant senses. Table 1 shows the results. Regardless of whether we use Multi-Instance Learning, both PLSR and FFNN models show a significant correlation between the sense similarity and similarity of predicted features. We interpret this to mean that PLSR and FFNN reflect *degree* differences of similarity between word senses. ## Comparison To Frozen Bert Embeddings The results in Table 1 suggest that, at least to some extent, the projected semantic features capture information about different word senses. But to what extent? We take it as a given that the hidden layer embeddings of bert-base, because they are sensitive to context, reflect differences in word senses. Therefore, we run an additional baseline where we run the same correlational analysis using the frozen weights of bert-base, instead of the projected semantic feature. That is, we compute a correlation between the cosine distance between bert-base vectors from Layer 8 and the WordNetderived Wu-Palmer similarity metric. The correlation between cosine distance and WordNet distance for plain BERT vectors is as high as our best models (Pearson's r = 0.41, *p < .*0001), which suggests that, even though the feature projection method is trained on word types, our training procedure does not lead to catastrophic information loss about word *tokens*. More precisely, for McRae and Buchanan datasets, PLSR learns a projection that is *as contextual* as the original BERT space. Our best Binder space (FFNN) is less contextual | McRae | Buchanan | | | | |---------|------------|-----|---------|-----| | MIL | Vanilla | MIL | Vanilla | | | PLSR | .50 | .50 | .42 | .42 | | FFNN | .50 | .50 | .33 | .25 | | PROP | .30 | .30 | .58 | .25 | than the original BERT space, though it still differentiates senses. This evaluation also demonstrates that Label Propagation, which is good at fitting norms at the type level (as shown in Appendix D and Rosenfeld and Erk, 2023) is not an effective method for generating contextual features. Performance varies across words Performance on this task is not necessarily uniform across all words. For instance, as discussed in Appendix E, performance on the sense differentiation task (using our interpretable feature projections or the original BERT embeddings) is better for concrete words, relative to abstract words. We leave it to future work to further explore this, as well as other sources of heterogeneity in performance. ## 3.2 Exp. 2: Homonym Disambiguation The previous experiment considered many lemmas, with widely distinct as well as closely related senses. However, it is an indirect evaluation: it does not let us directly compare our projected contextdependent features to *known* context-dependent feature norms. But the MCRAE dataset offers a natural experiment, since it contains 20 homonymous words in disambiguated format. That is, separate norms exist in the MCRAE dataset (and per force the BUCHANAN dataset, which is a superset) for 'hose (water)' and 'hose (leggings)'. We treat these disambiguated norms as gold contextual features for tokens of these senses. That is, we treat the MCRAE features for 'hose (water)' as a gold label for the token "hose" in a sentence like "I watered my flowers with the hose." As SemCor only contains a few sense-annotated tokens for each of the relevant homonyms, we use CoCA (Davies, 2018), a large corpus that of largely American English news text, to collect a dataset of tokens for each homonym. See Appendix G for details. Models were re-trained on all words in the feature norm dataset *except* the held-out homonyms.2 On this task, performance is measured as mean average precision (MAP@k) over the gold homonym features from McRae and Buchanan, where k is the number of gold features specific to each concept (Derby et al., 2019; Rosenfeld and Erk, 2023). Table 2 shows results. For both sets of norms, we see strong performance. The best-performing models achieve a precision of 0.50 (on McRae) and 0.42 (on Buchanan). Though we cannot directly compare performance, feature prediction is generally understood to be a very hard task, with SOTA performance for static McRae feature prediction at 0.36 (Rosenfeld and Erk, 2023). This is because models will often predict plausible features that aren't in the gold feature set, like has_teeth for cat (Fagarasan et al., 2015). ## 3.3 Qualitative Analysis In order to get a better sense of our in-context predictions, we now explore predicted features for clusters of token embeddings, extracted using the clustering procedure described in Erk and Chronis (2023) (which use the same kind of multi-prototype embeddings as described in Section 2.2), for the representative word *fire*. Focusing on a single, highly polysemous word allows us to build finegrained intuition as to the kind of information each of our feature norms can offer. In addition, characterizing token embedding clusters may be useful in itself: Giulianelli et al. (2020) use the term *usage* types (UTs) for clusters of token embeddings, and note that they reflect word senses and other regularities such as grammatical constructions. UTs have proven useful for the study of semantic change. However, while UTs are created automatically by clustering, researchers usually manually design labels for UTs to make their interpretation clear. An automatic labeling of token clusters with projected semantic features, as we demonstrate here, could hence be useful for studying UTs. Our goal in this section is to take 5 UTs for the word *fire* from Erk and Chronis (2023) and project them into our interpretable semantic spaces (BINDER, MCRAE, and BUCHANAN). These UTs are: *destructive* fire (e.g., "There was a fire at Mr's store and they called it arson."), *cooking/cozy* fire (e.g., "They all went over to the fire for plates of meat and bread."), *artillery* fire (e.g., "a brief burst | Buchanan 1. figurative | animal, color, light, fire, burn | | | | |-------------------------------------------------|-------------------------------------------------------------------------|-------------------|--------|-------| | 2. destructive | destroy, build, cause, break, person | | | | | 3. artillery | act, weapon, kill, loud, human | | | | | 4. cooking | hot, food, wood, burn, heat | | | | | 5. N-N compounds | person, place, work, office, law | | | | | McRae 1. figurative | has_legs, is_hard, different_sizes, has_4_legs, is_large | | | | | 2. destructive | different_colors, | a_mammal, | | | | made_of_paper, made_of_cement, inbeh_-_explodes | | | | | | 3. artillery | a_weapon, | used_for_killing, | | | | made_of_metal, | is_loud, | | | | | used_for_war | | | | | | 4. cooking | found_in_kitchens, used_for_cooking, requires_gas, an_appliance, is_hot | | | | | 5. N-N compounds | has_doors, used_for_transportation, | a_bird, | | | | has_feathers, beh_-_eats | | | | | | Binder 1. figurative | Color, Needs, Harm, Cognition, Temperature | | | | | 2. destructive | Unpleasant, Fearful, Sad, Consequential, Harm | | | | | 3. artillery | UpperLimb, Communication, Social, Audition, Head | | | | | 4. cooking | Pleasant, | Needs, | Happy, | Near, | | Temperature | | | | | | 5. N-N compounds | Biomotion, Face, Speech, Body, Unpleasant | | | | Table 3: The most distinctive features for each prototype of *fire* multi-prototype embeddings, in each of the three interpretable semantic spaces. of machine-gun fire"), and *noun compounds* (e.g., "fire brigade," "fire hydrant"). These UTs are represented as the centroids of K-means clusters of token vectors for the word *fire*. Then, we project these usage type vectors into interpretable semantic spaces, using PLSR+MIL for McRae and Buchanan, and FFNN+MIL for Binder. Predictably, the models predict similar features values in many cases, as the senses of *fire* have a lot in common. For example, in BUCHANAN space, all UTs except *artillery* have a high rating for 'hot' (Appendix F). To avoid this issue and get at how the usage types *differ*, for each UT we average over the features predicted for the other four embedding centroids and select the features with the greatest positive difference to the target UT. Table 3 shows the features that most distinguish each UT. The most distinctive features in Binder space are reasonable—destructive fire is indeed unpleasant, fearful, full of consequences, sad, and capable of causing harm. The MCRAE features are reasonable for the more concrete senses, which have synonyms that appear in the dataset (like 'gun' for 3 and 'oven' for 4). However, in contrast to BINDER and BUCHANAN, the distinctive MCRAE features predicted for the more abstract UTs (1, 2, and 5) have no ready interpretation. ## 3.4 Discussion Mapping method Looking at both experiments, PLSR obtained the overall best results for predicting both Buchanan and McRae features. For Binder features, where the model must predict the best fit along *every* dimension, FFNN does better. Based on these experiments, we recommend using PLSR to predict definitional features like McRae and Buchanan, and FFNN to predict comprehensive features like Binder. MIL Aside from a few instances, the multiinstance framework does not drastically improve model performance. Though the positive effect is marginal, we use MIL in the case studies below. Choice of feature norms The experiments above also give us insight into which feature space to use when. Experiment 1 shows that different senses are very distinct in McRae (r = 0.41) and Buchanan (r = 0.41) space, but not as distinct in Binder space (r = 0.28). The qualitative look at feature predictions indicates that Buchanan and Binder models produce reasonable features for the word *fire* in different contexts, including when used in a more abstract sense. Though the best McRae model scores well overall on quantitative tasks, the qualitative analysis suggests that it does not extend well to abstract senses. This conclusion aligns with expectations, given that Buchanan and Binder norms contain features for verbs and abstract nouns, whereas the McRae norms only contains concrete nouns. Binder feature vectors are comprehensive and good for examining abstract meanings, but Buchanan feature vectors can pinpoint more precise meanings. The case studies that follow use these feature spaces according to their strengths. To get an idea of the overarching differences between two constructions, we use BINDER (4.2). To generate specific descriptions of lexical meaning in context, we use BUCHANAN (4.1). ## 4 Evaluating Constructions In Context Having validated that our method works for extracting meaningful, context-dependent semantic information from large language models, we turn to two target constructions: the AANN construction (described in the Introduction) and the basic transitive construction. Crucially, in both studies, the word types are largely controlled between conditions (e.g., comparing "The family spent a beautiful three days in London." vs. "The family spent three beautiful days in London."), and so we compare context-dependent features derived from minimally different sentences. This design lets us study the effect of context in a highly controlled way, without being influenced just by the identity of the words in the sentences. ## 4.1 Construction 1: 'A Beautiful Three Days' Method Using a 1,000 sentence sample from Mahowald (2023)'s dataset of sentences templatically constructed with varying nouns, adjectives, numerals, and templates from a variety of subtypes, we compared AANN head nouns to their equivalent "default" forms (e.g., "The family spent a lovely three *days* in London." vs. "The family spent three lovely *days* in London"). Crucially, these form a near minimal pair. We extracted the embeddings for the head noun token in each sentence. We projected the token embeddings into BUCHANAN space (using PLSR – MIL) and examined the delta between each feature, for each token, in the AANN construction vs. in the default construction. Results The top 5 features associated with the AANN construction (relative to default) were: measure, one, green, **unit**, grow. The features most associated with default (relative to AANN) were: animal, leg, child, human, please. The bolded AANN features suggest that nouns in the AANN alternation are more measure-like, and treated as more singular. These are consistent with observations in the literature. Animacy-oriented words (e.g., animal, child, human) seem to be more associated with the default construction. Though this is not proposed outright in the literature, it's been observed that AANN's are more likely to be ungrammatical when the head noun is agentive (Solt, 2007). Focusing in on a representative sentence pair that shows a particularly sharp difference, the word meals in "They consumed an ugly five meals." is rated much higher on the MEASURE (.18) and UNIT ![7_image_0.png](7_image_0.png) (.13) feature than the word *meals* in "They consumed five ugly meals." (.05 and .04, respectively). We interpret these results as evidence that projection into the Buchanan space detects a meaningful and attested semantic difference between the AANN construction and the default construction. Specifically, we can meaningfully detect that the construal associated with the AANN construction is more associated with measurement/units, compared to a non-AANN sentence matched on lexical content, even when the noun is not itself inherently a unit or measurement noun. ## 4.2 Construction 2: Grammatical Roles Understanding grammatical roles like subject and object is crucial for natural language understanding. "The dog chased the cat." means something different from "The cat chased the dog." English relies largely on SVO word order for discriminating subjects vs. objects. Arguments that are animate, sentient, cause an event or a change of state in another participant, or move relative to another participant tend to be realized as subjects. Arguments that undergo a change of state, or are affected by another participant, tend to be realized as objects (Levin et al., 2005; Dowty, 1991). Most of the time, just knowing the two nouns in a transitive sentence is enough to know which is the subject and which is the object: If the nouns are "dog" and "bone", you can guess that "dog" is the subject and "bone" the object (Mahowald et al., 2022). There is evidence that contextual language models like BERT represent subjecthood (Linzen et al., 2016; Papadimitriou et al., 2021; Hewitt and Manning, 2019). But do these models actually represent abstract grammatical subject, or do they rely on lexical information? One way to tease this apart is to study sentences where grammatical context and lexical heuristics come apart. Papadimitriou et al. (2022) showed that BERT can reliably distinguish between grammatical subject and object, even for sentences with non-prototypical arguments like, "The onion chopped the chef", but only in the higher levels of the model after more information has been shared. At lower layers, the model seems to rely on lexical information (e.g., would classify "chef" as the subject and "onion" as the object). While prior work has explored the subject/object classification question by training bespoke probes, here we use projections into BINDER space. We focus on the set of English sentences studied in Papadimitriou et al. (2022), which are extracted from the Universal Dependencies Treebank (Nivre et al., 2016) and appear in two forms: the original form and a form in which the subject and object are swapped. For instance: compare the NATURAL, "Finally a chambermaid stuck her head around the corner" vs. the SWAPPED, "Finally a head stuck her chambermaid around the corner." The Treebank from which the sentences are sampled contains data from a number of different English corpora. We project the subject and object in each of the 486 NATURAL sentences into BINDER space, using the FFNN-MIL method (which is best for tokenlevel BINDER prediction), and then do the same for each of their SWAPPED counterparts. We first ask whether naturally occurring subjects tend to be more animate than objects. But we then ask whether, merely by virtue of being a subject, the lexical item takes on a more animate construal. Such a result would be consistent with psycholinguistic findings in humans: Kako (2006) shows that, even with nonce sentences like "The rom mecked the zarg," the subject word "rom" is rated as more animate. Words that tend to appear in subject position are associated with higher animacy ratings. Given that there are known to be systematic differences between subjects and objects, will the Binder features for subjects and objects systematically differ in the NATURAL sentences? As can be seen in Figure 2, the answer is clearly yes. Animacyassociated features like Biomotion, Body, and Human are higher for naturally occurring subjects than for objects. We ran a linear regression predicting the Binder value from the subject/object status of the word, the Binder feature, and their interaction. The interaction term is the one we care about: how does the predicted value for that feature change when we are dealing with a subject or object? After Bonferroni correction for multiple comparisons, we find several features significantly correlated with subjecthood and a few with objecthood, starred in Figure 2. ## The Same Token **Is Construed As More Animate** when it appears in subject position. The preceding analysis could have been done using type-level Binder features: the upshot is that word *types* that appear in subject position get animacy-associated features. The highest rated words in this data set, for the Biomotion category, are: animals, *reptiles*, cat, dog, and they all occur as subjects in the corpus. But merely knowing that naturally occurring subjects and objects differ in Binder features does not tell us the whole story. Using the contextual feature projections, we can explore whether two tokens of the same type are construed as differing in animacy, based on whether they appear as a subject. We can do this in a controlled way by comparing the same word in the natural sentences and the swapped ones. For instance, in the sentence above, "chambermaid" appears as a subject but is an object in the swapped version. How does its Binder rating change? To assess that, we compare natural subjects vs. those same words moved to object position of the same verb in the same sentence. And we compare natural objects to those same words swapped to be subjects. Figure 2 shows that subject-oriented features like Biomotion, Body, and Human lose their large values and become more neutral. The careted features in the figure show significant effects of being swapped, after Bonferroni correction. To assess whether our contextual feature predictions are sufficient for predicting whether a noun is a subject, no matter if natural or swapped, we run a forward-stepwise logistic regression on a portion of the data (300 sentences) to predict whether a particular token is a subject or an object based on its Binder ratings. The forward-stepwise part picks the set of Binder features that give the best prediction. We then test its k-fold cross-validation accuracy on the held-out test set. For NATURAL sentences, this method achieves 80% accuracy, compared to 73% accuracy for SWAPPED sentences. Thus, while natural sentences are easier, even the swapped sentences can be categorized better than chance using the feature norms—despite the fact that the words in question naturally occurred in the opposite roles. We then performed the same procedure, but instead predicted whether a particular token was from a NATURAL or SWAPPED sentence. We did this separately for subjects and objects. Performance was above chance, at 70% and 71% respectively. So a model can, with better than chance accuracy, use projected Binder features to identify which nouns are subjects in swapped sentences. But we can also predict which nouns are from swapped sentences. This result suggests that the predicted Binder features reflect contextual information, but also retain type-level information. The results of our study align with Lebani and Lenci (2021) who investigate semantic proto-roles using distributional models and with Proietti et al. (2022), who investigate semantic proto-roles by projecting BERT into an interpretable space (similar to our method). Both show that transitive verbs have more proto-agent properties than their intransitive counterparts. The present analysis confirms and expands on their finding that BERT captures semantic role information and that projecting into interpretable space is a fruitful way of gaining insight into grammatical and thematic roles. ## 5 Conclusion In this paper, we honed techniques for predicting semantic features for token embeddings. These projections are versatile. Once created, one and the same model can be used to study a wide array of phenomena. We explored their utility for studying semantic construal in syntactic constructions. We emphasize the potential of this method to answer linguistic questions about meaning differences in constructions that are less well-understood and well-theorized than the ones studied here. As such, we hope it will be possible to use this method to generate linguistic insight. ## Limitations One limitation of our study is that interpretable feature spaces are at times only semi-interpretable. We infer from patterns of model behavior that Buchanan features such as 'human', 'child', and 'animal' can be signals for animacy more broadly construed. The need to conjecture about what a feature means points to a weakness in our approach. Some interpretation will always be necessary, and with a more heavy-handed probing method like ours, it can't be certain what effects are coming from the model and which are coming from the probe. One way to get around this need for subjective interpretation is to train a separate classifier for animacy more broadly understood, and then use the feature prediction model to examine what features are most relevant to the classifier (Chersoni et al., 2021). However, this method is not foolproof either. The classification distinction is wholly determined by the labeled data used to train the animacy probe, and the judgments are subjective. Even for a seemingly straightforward feature, the correct label is not always clear. Is a clock that *sings* the hour animate? What about a *stony face*? Subjective interpretation is an important and unavoidable component of both linguistic and neural language model analysis. The goal of datadriven research is to extend the sphere of concern beyond self-reflexive subjective judgments of the researcher to the shared subjectivities of a language community. Information about animacy reflected in an annotated dataset still reflects subjectivities, but shared ones. It is important to always be clear about where interpretation is happening, whose interpretations are taken into account, and how they affect what conclusions may be drawn. On that note, there are a few places where design decisions affect our analysis of lexical variation. Linguistic data enters the modeling pipeline in at least four places: BooksCorpus and Wikipedia data used to pre-train BERT, the BNC corpus which we use to derive multi-prototype embeddings, the feature norm datasets which tend to capture the subjectivities of American college students, and the texts we analyze in our case studies (both natural language text and constructed examples). These resources all cover English, but necessarily reflect different varieties of English, given that they were collected in different places at different times. For example, usage types in the BNC often differ from those derived from Wikipedia data. Not only do the corpora we use represent potentially disjoint varieties (English spoken by college students in Vermont, English in newswire and fiction genres, English in reference texts). They also all represent the semantics of the unmarked, *normative varieties* of English. Normative English dominates all data collection contexts upon which our study rests. Consequently, to the extent that our model is a proxy for English semantic judgments, it is a proxy for dominant semantic associations among the composers of these texts and participants in the feature norm studies. Though it is interesting and useful to study the English language as a whole, care must be taken to ensure that the sample is representative of all speakers; and ideally, our approach supports linguistic approaches which aim to describe and explain the semantics of smaller language communities. This would require language models trained on corpora at the level of communities of practice, as well as feature norms specific to these communities. We are hopeful that the future of statistical methods in lexical semantic analysis moves in this direction. ## Ethics Statement Our models are developed and published in order to encourage academic research in descriptive linguistics. In the future, we plan to use our method to study the inherent non-neutrality of language models by examining the influence of training corpus composition on the semantic representation of social meanings, as represented by cultural keywords. Because they are built on top of an unpredictable language model, the feature prediction methods, as well as the models we publish, are recommended for descriptive research only. Researchers should take into account the potential for language models, like language, to reflect of harmful ideologies such as sexism, racism, homophobia, and other forms of bigotry. ## Acknowledgements This work was made possible through funding from an NSF GRFP Grant to GC, NSF Grant 2139005 to KM. Thank you to the UT Austin Linguistics Computational Linguistics group for helpful comments and the SynSem group for their enthusiasm in considering how language modeling might inform their questions in semantics. For helpful discussions, thanks to Adele Goldberg and the Princeton language group, Richard Futrell, and Isabel Papadimitriou. ## References Marianna Apidianaki and Aina Garí Soler. 2021. ALL dolphins are intelligent and SOME are friendly: Probing BERT for nouns' semantic properties and their prototypicality. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 79–94, Punta Cana, Dominican Republic. 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For training, we use the multi-prototype embeddings of Chronis and Erk (2020). They are generated by performing k-means clustering on BERT embeddings of tokens from the British National Corpus (BNC). This procedure collects up to 200 occurrences of each cue word in the BNC and generates tokens vectors for each occurrence with the HuggingFace bert-base-uncased model. These representations are then clustered using K-means, using the authors' best-performing setting of K=5 clusters per word at layer 8. These multi-prototype vectors are unordered, 'bag-of-senses' representations. For the static embedding baseline, we use the pretrained Wikipedia 2014 + Gigaword 5 pretrained GloVe with 300 dimensions, which is trained on 6B tokens with 400K vocabulary word (Pennington et al., 2014). For token-level evaluations in Section 3 above, it does not make sense to compare to GloVe because GloVe embedding space is not contextual. Instead, we compare the multi-prototype, MIL models to single prototype (vanilla) versions of each model. Embeddings for the vanilla models are generated using the same procedure described above for multi-prototype, but all tokens are averaged into a single vector representation (K=1) rather than clustering them into prototypes. For all models, we train using ten-fold crossvalidation with an 80-10-10 train-dev-test split. For the MIL models, no prototypes of the same word are repeated between train and test sets. For each prediction task, we tune model hyperparameters using a sampled grid search (see uploaded code and data for details). The chosen hyperparameter settings are the ones with the best average performance on the dev set across folds. The FFNN model is implemented in PyTorch and trained using the Adam optimizer with stochastic gradient descent. We search over number of epochs (30, 50); dropout (0, .2, .5), learning rates (1e-5, 1e-4, 1e-3), and hidden layer size (50, 100, 300). Partial Least Squares regression is a statistical method to find the fundamental relations between two matrices (semantic spaces). PLSR is useful in this case because it allows for correlations among the independent variables (embedding dimensions). We use the PLSR implementation from scikit-learn. We grid search over the number of PLSR dimensionality components (50, 100, 300). Label propagation uses code from Rosenfeld and Erk (2023). Models were trained on a 2.3 GHz 8- Core Intel Core i9 processor with 16 GB of RAM. In label propagation, each labeled training example is embedded as a node in a graph along with unlabeled training data. Training takes place iteratively; in each iteration, labels spread through the graph. In this method, word embeddings are labeled with their corresponding features, withholding labels from the test set. Unlabeled nodes receive features of labeled nodes which are nearby in embedding space. Johns and Jones (2012) first applied this method to feature prediction from distributional models. In their model, the features of an unlabeled word are calculated as a weighted sum of the feature values that labeled words have—the weights are determined by cosine distance in distributional semantic space. Rosenfeld and Erk (2023) evaluate more sophisticated approaches to label propagation, called modified absorption. With modified absorption, labels do not propagate under certain conditions. For instance, features won't propagate to words that are very unfamiliar, or to words which are already well-labeled with properties. ## C Predicting With Multi-Prototype Embeddings The classic MIL problem is a classification task. The input is an unordered bag of instances, and the output is a binary classification label. The label of the whole bag is 1 if at least one of the instances in the bag has the label 1. However, the labels of the individual instances are unknown—only the bag labels are available. We take this as inspiration for our scenario, where we have a multi-prototype representation, along with a feature vector that may reflect only one of the prototypes (as in the *ring* example above). To make the FFNN suitable for MIL, the FFNN is extended by an attention mechanism without ordering, as in Ilse et al. (2018). This method computes a weighted average over the instances. Code for the attention module was adapted from their implementation, and can be found at https://github.com/AMLab-Amsterdam/ AttentionDeepMIL. It was used in combination with the attention module defined in this blog post: https://medium.com/swlh/ multiple-instance-learning-c49bd21f5620. To adapt PLSR for MIL, we construct one training example for each prototype. That is, for a 5prototype vector, we construct 5 training examples, one for each vector, labeled with the type-level features. Thus, we conduct PLSR on a dataset with noisy labels. No prototypes of the same word are repeated between train and test sets. Similar to PLSR, to adapt Label Propagation for multi-prototype embedding inputs, we represent each prototype as an independent node that maps to a type-level feature vector. ## D Type-Level Evaluation Results Results are reported on the type-level training task. These evaluations show how well the different models are able to fit the different feature norms. We find that all models are on par with the performance reported in the existing literature on inferring static semantic features (Fagarasan et al., 2015; Herbelot and Vecchi, 2015; Derby et al., 2019). Our goal is to predict semantic feature norms from words in context. We define a mapping problem from contextual-language-model-derived embeddings to an interpretable semantic space defined by psycholinguistic feature norms. The training data are experimentally collected semantic features for word *types*. Each consists of a cue word and a feature vector. We compare MIL and vanilla versions of FFNN, PLSR, and Label Propagation models. The literature on feature prediction uses different evaluation methods. For MCRAE and BUCHANAN prediction, where the goal is to produce the most important features, we report Mean Average Precision at K (MAP@K), where K is the number of gold features for a concept (Derby et al., 2019). For Binder vectors, every feature is valued for every word, MAP@k is always equal to 1. For BINDER, where the goal is to capture the relative importance of each feature, precision is not an appropriate metric. In this case, we use mean squared error (MSE) to measure the best overall fit. Performance overall matched the best results in the literature for static feature prediction, and models that used the BERT embeddings performed as well or better compared to training on static GloVe embeddings (Table 4). On the MCRAE prediction Model MCRAE BUCHANAN BINDER MAP@k (↑) MAP@k (↑) MSE (↓) PLSR BERT MIL 0.33 **0.37** 2.32 BERT Vanilla **0.34** 0.29 2.37 GloVe 0.33 0.23 2.37 FFNN BERT MIL 0.32 0.26 0.82 BERT Vanilla 0.32 0.26 0.88 GLoVe 0.30 0.26 1.14 PROP BERT MIL 0.31 0.32 0.96 BERT Vanilla 0.32 0.30 **0.10** GloVe 0.30 0.26 0.89 task, PLSR and label propagation perform the best, but the scores are more or less similar across the board. The best performance was within range of the best MAP@k scores reported in the literature (MAP@k = .36 on MCRAE, per Rosenfeld and Erk, 2023). BERT embeddings produce features comparable in performance to GloVe vectors. For BUCHANAN, BERT models do not improve over GloVe vectors. MIL did not fare any better than single-instance learning at the type level, with the exception of PLSR for BUCHANAN which led to a large performance gain. These results confirm the finding of Rosenfeld and Erk (2023) that Label Propagation with modified absorption does very well at the task of feature prediction (or property inference, as they call it). However, as described in the main text, our implementation of Label Propagation is not good at modeling context-sensitive lexical-semantic phenomena unless it is supplied with unlabeled nodes for different senses at training time. Label Propagation under the MIL condition did a particularly good job at disambiguating homonyms (Table 2), provided that the different senses were given as unlabeled nodes during training. However, Label Propagation does very poorly on the sense differentiation task (Table 1), showing that this model does not predict different features for different senses when it is not exposed to unlabeled nodes for these senses during training. We believe this is a consequence of the number of nodes in our graph. At ![14_image_0.png](14_image_0.png) test time, PROP is limited to a fixed number of potential features—given any context vector, it retrieves the closest vector in the graph and gives those labels. Unless there are very many nodes in the graph for each word, PROP will often return the same features for different senses, because there is a high pairwise similarity in BERT space among tokens of the same type (Mickus et al., 2020). Performance for Label Propagation should improve with the number of unlabeled nodes included during training, but this increases runtime and is not feasible for large datasets or convenient for ad hoc linguistic analyses like those we wish to apply the feature prediction model to. ## E Concreteness Analysis In all spaces, concrete polysemous senses are more clearly separated than abstract senses. This is shown in Figure 3, which breaks down sense differentiation results by their concreteness ratings according to Brysbaert et al. (2014). The problem is worst for McRae, and least pronounced for Binder. This may be due to even more variation in meaning for abstract words, which tend to be highly polysemous. Indeed, the same pattern is observed in the frozen BERT space: for concrete words, cosine similarity of token vectors is not strongly correlated with WordNet distance. Qualitative examination of predicted features reveals that the models are not bad at abstract meanings. For example, consider the sentence "People travel many miles to gaze upon this nat- Usage Type 1 Usage Type 2 Usage Type 3 Usage Type 4 Usage Type 5 (transformative) (destructive) (artillery) (cooking) (N-N compounds) fire fire act fire fire hot hot fire hot person burn burn danger burn hot light light kill light burn danger destroy weapon heat light heat danger human wood red cook heat metal cook danger red cook loud danger heat wood hurt light warm cook act red hurt food destroy Table 6: Example data for the Buchanan homonym disambiguation task. Sentences from COCA containing homonyms are paired with a feature norm that targets the disambiguated sense. ural wonder, though few are willing to approach it closely, since it is reputed to be the haunt of various demons and devils." Our Buchanan model predicts plausible features for the rather abstract 'haunt': 'one', 'face', 'dead', 'bad', 'body', 'place', 'person'. But the McRae model, which did not see abstract words in training and whose features only cover very concrete nouns, does not produce plausible features: 'is_expensive', 'is_smelly', 'made_of_wood', 'is_large'. Predicted Binder features are also plausible: 'Vision', 'Harm', 'Unpleasant', 'Sad', 'Consequential', 'Attention', 'Angry'. This analysis does not reflect model performance on abstract words so much as it points to a potentially interesting relationship between abstract words in BERT space and in WordNet. Do contextual vectors primarily reflect different kinds of meaning for abstract words besides word sense? ## F Top Predicted Features For Sense Clusters Table 5 shows the top 10 Buchanan features for each centroid of the usage type clusters for *fire* (k=5, tokens taken from the BNC). Many of the most salient features are the same across the different usage types. Meanings specific to each sense and usage type are more evident when one focuses on the most *distinctive* features for each cluster (Table 3). | Homonym | Sentence | Gold Feature Norms | |---------------------------|--------------------------------------------------------------|------------------------------------------------| | bat (animal) | I was particularly surprised to see a tame golden fruit bat, | wing, fly, nocturnal, black, cave, fur, animal | | hanging upside down on a tree branch in the morning sunshine. | | | | bat (baseball) | I was at the plate. He threw; I swung the bat. The ball | hit, wood, ball, metal, long, sport | | rocketed into left field. | | | ## G Mcrae Homonym Dataset Collection Procedure We train our contextual model at the type level because of the present lack of in-context feature norms to use for training and evaluation. To evaluate at the token level directly, as described in Section 3, we use the features that McRae et al. (2005) collected for disambiguated homonyms. For this evaluation, we construct a test set of sentences containing these homonyms, each labeled with the feature vector for that homonym. SemCor, the sense-annotated dataset used for the sensedifferentiation evaluation, does not contain enough tokens of each of the homonyms. So, we turned to the Corpus of Contemporary American English (Davies, 2018). The data were collected using the following procedure: For each homonym, (1) Search for the target word. (2) Read through a random sample of occurrences of the word, highlighting sentences that unambiguously use the target sense. (3) The same researcher double-checks the list to filter out accidental sense mismatches. At least 20 tokens of each homonym were collected, stopping at 50 (with an average of 40 contexts per sense). Table 6 shows two examples from the resulting dataset. The list of homonyms and the number of tokens for each one is given in Table 7, and the full dataset is available in the supplemental data. Word Sense # Tokens bat animal 52 bat baseball 51 board black 28 board wood 56 bow ribbon 43 bow weapon 52 cap bottle 20 cap hat 207 crane animal 14 crane machine 101 hose tube 42 hose leggings 55 mink animal 32 mink coat 33 mouse animal 64 mouse computer 78 pipe plumbing 27 pipe smoking 20 tank army 35 tank container 83 Table 7: List of cue words used in homonym disambiguation experiment along with the number of tokens of each homonym collected from CoCA for the dataset. | Dataset/Model | License | |------------------------------|-------------------------------------------------| | McRae Feature Norms | unknown | | Buchanan Feature Norms | GPL 3.0 | | Binder Feature Norms | CC BY-NC-ND 4.0 | | Multi-Prototype Embeddings | CC BY-NC 4.0 | | BNC | http://www.natcorp. ox.ac.uk/docs/ licence.html | | bert-base-uncased | Apache 2.0 | | SemCor | Apache 2.0 | | Brysbaert Concreteness Norms | CC BY-NC-ND 3.0 | | AANN Sentences | CC BY-NC-ND 4.0 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations ✓ A2. Did you discuss any potential risks of your work? Ethics ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract; Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Used models - huggingface BERT (base-uncased)(Appendix A) datasets - Binder feature norms (Section 2) - McRae feature norms (Section 2) - Buchanan feature norms (Section 2) - multi-prototype embeddings: (Section 2; Appendix A) - British National Corpus (Appendix A) - SemCor NLTK implementation (Section 4.1) - Brysbaert Concreteness Norms (Section 4.3) - AANN Data ( Section 5.1) - Swapped Subjects dataset (Section 5.2) - Universal Dependencies Treebank (Section 5.2) - WordNet (Section 4.1) - CoCA (Appendix E) code: - Label Propagation (Section 3 par 3) - Attention-MIL (Appendix B) CREATED models - feature prediction models (Section 4) datasets - homonym disambiguation dataset (Section 4.2; Appendix E) code - features-in-context library (Section 3) ✓ B1. Did you cite the creators of artifacts you used? ## Used models - huggingface BERT (base-uncased)(Appendix A) datasets - Binder feature norms (Section 2) - McRae feature norms (Section 2) - Buchanan feature norms (Section 2) - multi-prototype embeddings: (Section 2; Appendix A) - British National Corpus (Appendix A) - SemCor NLTK implementation (Section 4.1) - Brysbaert Concreteness Norms (Section 4.3) - AANN Data ( Section 5.1) - Swapped Subjects dataset (Section 5.2) - Universal Dependencies Treebank (Section 5.2) - WordNet (Section 4.1) - CoCA (Appendix E) code: - Label Propagation (Section 3 par 3) - Attention-MIL (Appendix B) ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? models - huggingface BERT (Apache-2.0) datasets - Binder feature norms (CC BY-NC-ND 4.0) - McRae feature norms (license unknown; generally used for academic research) - Buchanan feature norms (GPL 3.0) - multi-prototype embeddings: (CC BY-NC 4.0) - BNC (license: http://www.natcorp.ox.ac.uk/docs/licence.html) - SemCor NLTK implementation (Apache-2.0) - Brysbaert (2014) CC BY-NC-ND 3.0 - AANN Data (CC BY-NC) - Swapped Subjects dataset (CC BY-NC) - Universal Dependencies Treebank (CC BY-NC-ND 4.0) - WordNet (WordNet 3.0 License) - CoCA (Custom Academic License: https://www.englishcorpora.org/academic_license.asp) code: - Label Propagation (CC BY-NC-ND 4.0) - Attention-MIL (MIT) ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We did not specify that we use artifacts in the intended way due to space limitations because they are by and large very commonly used linguistic datasets and were employed in their usual manner. While feature norms (with the exception of Binder norms) were not designed specifically for use with language models, applying them to analyze distributional models is a standard technique. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The corpora used are already anonymized of personal information. Multiprototype embeddings are constructed from randomly sampled sentences from the BNC. The homonym disambiguation dataset is sampled from CoCA. It is possible that offensive content makes its way into these example sentences. Given that we undertake a descriptive study subtle semantic variations in English, and those variations are influenced by social meanings, we determine it wise to not attempt to filter the results in any way. However, they should not be used in any prescriptive machine learning applications where the desired behavior is more important than uncovering statistical patterns in the training corpora. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? USED models - huggingface BERT (Limitations) datasets - Binder feature norms (Section 2) - McRae feature norms (Section 2) - Buchanan feature norms (Section 2) - multi-prototype embeddings: (Appendix A) - BNC (Appendix A) - SemCor (Section 4.1) - Brysbaert: to save space, demographics of participants are not listed, as this was not a central part of the work. Issues related to demographics of semantically annotated data are discussed in the Limitations section - AANN Data: Sectiopn 5.1. Templatically constructed - Swapped Subjects dataset (Section 5.2) - Universal Dependencies Treebank (Section 5.2) - WordNet (WordNet 3.0 License). Not included, to avoid redundancy. As we are using this resource to analuze an english language model, it's understood that it is an English language resource. - CoCA - Not included, to avoid redundancy. As we are using this resource to analuze an english language model, it's understood that it is an English language resource. CREATED models - feature prediction models (Limitations) datasets - homonym disambiguation dataset (Section 4.2; Appendix E) code - features-in-context library (Limitations) ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. USED datasets - Binder feature norms (Appendix C) - McRae feature norms (Appendix C) - Buchanan feature norms (Appendix C) - multi-prototype embeddings: (Appendix C) - BNC n/a - SemCor NLTK implementation (Section 4.1, Table 1) - Brysbaert (2014) n/a - AANN Data (Section 5.1) - Swapped Subjects dataset (Section 5.2) - Universal Dependencies Treebank: n/a - WordNet: n/a - CoCA : n/a CREATED datasets - homonym disambiguation dataset (Section 4.2, Table 2; Appendix E) ## C ✓ **Did You Run Computational Experiments?** Section 4, Section 5, Appendix D ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Parameters and infrastructure details are reported in Appendix C. We did not report computational budget because runtime information was not saved during experiments. However, all model tuning experiments and data analyses were run on a quad-core personal computer, which means they are not cost-prohibitive to reproduce ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix C, paragraph C. The best-found hyperparameter values for the feature prediction models are listed in the Supplemental Materials, and the best-performing models will be published along with a Colab notebook for using them. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4, Paragraph 1 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix C, Paragraph 3 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

yamaki-etal-2023-holographic

Holographic {CCG} Parsing

https://aclanthology.org/2023.acl-long.15

We propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box neural architectures to implicitly model phrase structure dependencies. Instead, we leverage the method of holographic embeddings as a compositional operator to explicitly model the dependencies between words and phrase structures in the embedding space. Experimental results revealed that holographic composition effectively improves the supertagging accuracy to achieve state-of-the-art parsing performance when using a C{\&}C parser. The proposed span-based parsing algorithm using holographic composition achieves performance comparable to state-of-the-art neural parsing with Transformers. Furthermore, our model can semantically and syntactically infill text at the phrase level due to the decomposability of holographic composition.

# Holographic Ccg Parsing Ryosuke Yamaki Tadahiro Taniguchi Ritsumeikan University 1-1-1 Noji Higashi, Kusatsu, Shiga Japan {yamaki.ryosuke,taniguchi} @em.ci.ritsumei.ac.jp Daichi Mochihashi The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa city, Tokyo Japan [email protected] ## Abstract We propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box neural architectures to implicitly model phrase structure dependencies. Instead, we leverage the method of holographic embeddings (Nickel et al., 2016) as a compositional operator to explicitly model the dependencies between words and phrase structures in the embedding space. Experimental results revealed that holographic composition effectively improves the supertagging accuracy to achieve state-of-the-art parsing performance when using a C&C parser. The proposed span-based parsing algorithm using holographic composition achieves performance comparable to state-of-the-art neural parsing with Transformers. Furthermore, our model can semantically and syntactically infill text at the phrase level due to the decomposability of holographic composition. ## 1 Introduction Combinatory Categorial Grammar (CCG; Steedman 2000) is a highly lexicalized grammar formalism comprising syntactically rich lexical categories and a limited number of combinatory rules. In principle, CCG is suitable for modelling complicated syntactic structures and operates as a natural interface connecting syntax to semantics because of its isomorphism with lambda calculus (Bos et al., 2004; Mineshima et al., 2015; Martinez-Gómez et al., 2016). In this paper, we propose a method to formulate CCG (a discrete symbol system) as an operation between distributed representations in a continuous vector space, demonstrating its contribution to improved supertagging performance and span-based parsing. Prior studies on PCFG, compositional vector grammar (CVG; Socher et al. 2013a), and its generalization, latent vector grammar (LVeG; Zhao ![0_image_0.png](0_image_0.png) Figure 1: Conceptual diagram of holographic composition of vectors in embedding space according to CCG. Each pair of arrows represent a recursive composition of vectors without any additional parameters. et al. 2018), have shown the efficacy of representing discrete symbols as vector operations. Highdimensional vectors' expressive power complements syntactic disambiguation, which is difficult to address solely through discrete symbols. We propose a model that bridges discrete symbols and continuous vectors in CCG. In this study, we introduce recursive vector composition in the embedding space illustrated in Figure 1 by employing holographic embeddings (HolE; Nickel et al. 2016) to incorporate syntactic structures into the supertagging and parsing model explicitly. Similar methods for embedding tree structures into fixed-length vectors, CVG, and kernel-inspired encoders with recursive mechanisms for interpretable trees (KERMIT; Zanzotto et al. 2020a) have been proposed. Our model differs from CVG, as it does not require a large number of matrix parameters for nonlinear compositions, and directly optimizes parsing, enabling the construction of phrase-level representations by dynamically exploring phrase structures, whereas KERMIT requires an external parser. Experiments revealed that phrase-level dependency modelling with holographic composition can induce correct supertagging, achieving state-of-the262 Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 262–276 July 9-14, 2023 ©2023 Association for Computational Linguistics art performance in supertagging and parsing with a C&C parser (Clark and Curran, 2007; Clark et al., 2015), and further improved performance with a novel span-based parsing algorithm.1 Additionally, we focused on the fact that the inverse operation of holographic composition is easily available. This property can be applied to text-infilling tasks, predicting missing parts of sentences consistent with the rest syntactically and semantically. This task is difficult to accomplish using the existing neural architectures. The main contributions of this research can be summarized as follows: 1. We introduce HolE as a recursive compositional operator for explicit modelling of syntactic structures, enabling CCG to be treated as an operation between distributed representations. This modelling improves supertagging and parsing, achieving state-of-the-art performance with a C&C parser. 2. We propose a novel span-based parsing algorithm incorporating phrase-level representation from our model, achieving comparable performance to the current state-of-the-art. 3. We propose an approach to compute phraselevel representations containing rich syntactic information while satisfying decomposability. We further demonstrate the applicability of decomposability to phrase-level text-infilling. ## 2 Background And Related Work 2.1 Recursive Compositional Models Previous studies have shown benefits of explicit syntactic information incorporation into neural networks (Socher et al., 2011, 2013a,c; Tai et al., 2015; Zhu et al., 2015; Zhang et al., 2016; Zhao et al., 2018; Wang et al., 2019; Zanzotto et al., 2020a). First, CVG (Socher et al., 2013a) was used to modify the recursive neural network (Socher et al., 2011), resulting in improved PCFG parsing performance. It recursively composes vectors of words and phrases using a nonlinear composition operation for a PCFG rule C → A B as $$\mathbf{c}=\operatorname{tanh}\left(W_{C\to A B}\left[{\begin{array}{l}{\mathbf{a}}\\ {\mathbf{b}}\end{array}}\right]\right),\qquad\qquad(1)$$ where a, b, c are d-dimensional vectors that represent *A, B* and C, respectively. WC→AB is a d×2d matrix for each rule, C → A B. Therefore, it 1Our implementation used for this paper is available at https://github.com/Ryosuke-Yamaki/Hol-CCG.git. contains a huge number of parameters as well as word vectors themselves: when d is as small as 100 and there are 882 binary rules, as in (Socher et al., 2013a), it needs 100 × 200 × 882 = 17, 640, 000 parameters for the matrix WC→AB, not to mention about the difficult nonlinear optimization involved.2 For the same reason, Compositional Distributional Semantics (Polajnar et al., 2015), a method of composing phrase-level semantic representations, using tensors whose order is defined by CCG type, is hard to scale for higher dimensions. A related study, KERMIT (Zanzotto et al., 2020a) is a model that embeds parse tree structures and subtrees in PCFG into fixed-length vector representations via recursive vector composition, enhancing the performance of downstream tasks. A comparison of this model with ours is given in Section 3.1. ## 2.2 Span-Based Parsing Clark (2021) applied Transformers to span-scorebased PCFGs (Stern et al., 2017; Kitaev and Klein, 2018) for CCG parsing, achieving significant performance gains. These studies computed a vector for each span in a sentence, input it into a feedforward neural network to obtain a span score, and then apply it to a chart-based parsing algorithm. Specifically, Kitaev and Klein (2018) calculated the vector yi:j corresponding to the span from the ith word to the jth word as follows: $${\bf y}_{i:j}=[\overrightarrow{{\bf y}}_{j}-\overrightarrow{{\bf y}}_{i};\overleftarrow{{\bf y}}_{j+1}-\overleftarrow{{\bf y}}_{i+1}],$$ where →−yk and ←−yk denote the right and left halves, respectively, when the vector yk associated with the kth word is split by half. Constructing a vector of the span via simple subtraction between vectors, as in Equation (2), does not explicitly reflect the internal structures of the span. ## 3 Holographic Ccg Our research objective is to compute phrase-level representations to capture dependencies and hierarchical relationships among its internal components for supertagging and parsing in CCG. We describe the mechanism for composing these representations and discuss their application to supertagging. We then introduce a novel span-based parsing that utilizes these representations. 2Therefore, Socher et al. (2013a) reported that d should be as small as only 25 for stable training. Furthermore, CVG applies a nonlinear activation of a hyperbolic tangent, complicating optimization during training. $\mathbf{c}=\mathbf{a}+\mathbf{b}$ $\mathbf{c}_{1}=\mathbf{a}_{0}\mathbf{b}_{1}+\mathbf{a}_{1}\mathbf{b}_{2}+\mathbf{a}_{2}\mathbf{b}_{0}$ $\mathbf{c}_{2}=\mathbf{a}_{0}\mathbf{b}_{2}+\mathbf{a}_{1}\mathbf{b}_{0}+\mathbf{a}_{2}\mathbf{b}_{1}$ ![2_image_0.png](2_image_0.png) We explore methods to compose phrase-level representations capturing dependencies and hierarchical relationships between components. We focus on the commonalities between knowledge graphs and syntactic structures of natural language sentences. Both of them represent nonlinear relationships depending on the semantic aspect of each component, suggesting existing knowledge graph embedding methods (Socher et al., 2013b; Nickel et al., 2016; Trouillon et al., 2016; Abboud et al., 2020) are applicable to our objective. We employ HolE (Nickel et al., 2016) due to its desirable properties for embedding phrase structures without additional parameters, as we describe below. HolE uses circular correlation (Plate, 1995) as a compositional operator for sophisticated knowledge graph modelling while maintaining computational efficiency. Focusing on HolE and circular correlation as compositional operators to model dependencies and hierarchical relationships, we compose two vectors a, b into a single vector c. $$\mathbf{c}=\mathbf{a}\star\mathbf{b},$$ c = a - b, (3) $\star:\mathbb{R}^d\times\mathbb{R}^d\to\mathbb{R}^d$ d. where - : Rd × Rd → Rd denotes a circular correlation: $$[\mathbf{c}]_{k}=[\mathbf{a}\star\mathbf{b}]_{k}=\sum_{i=0}^{d-1}a_{i}b_{(k+i){\mathrm{\mod~}}d}.$$ $${\mathrm{}}(4)$$ $$({\mathfrak{H}})$$ Circular correlation can be computed via Fourier transform, such as $$\mathbf{c}=\mathbf{a}\star\mathbf{b}={\mathcal{F}}^{-1}({\overline{{{\mathcal{F}}(\mathbf{a})}}}\odot{\mathcal{F}}(\mathbf{b})),$$ where F(·) and F−1(·) denote the fast Fourier transformation and its inverse, respectively, F(·) represents conjugation in a complex space, and denotes an element-wise product. Figure 2 shows a schematic of the circular correlation when d= 3. Circular correlation exhibits desirable characteristics for our objective. Noncommutative: Generally, a - b = b - a holds true, making noncommutativity attractive for modelling asymmetric relations; for example, two noun phrases *"human right"* and *"right human"* will be composed into different vectors. Nonassociative: Circular correlation is nonassociative, i.e., (a-b)-c = a-(b-c), making it ideal for modelling hierarchical structures. For example, the phrase *"saw a girl with a telescope"* yields different vectors when a circular correlation is used, depending on the internal structure "((saw (a girl)) (with (a telescope)))" and "(saw ((a girl) (with (a telescope))))". Associative operations, however, yield the same representation, thus failing to reflect the internal structure. Circular convolution, a similar operation to circular correlation, does not satisfy the above two properties: $$[\mathbf{c}]_{k}=[\mathbf{a}*\mathbf{b}]_{k}=\sum_{i=0}^{d-1}a_{i}b_{(k-i)}\mod d\tag{6}$$ where $*:\mathbb{R}^{d}\times\mathbb{R}^{d}\to\mathbb{R}^{d}$ denotes the circular $*$-plane. $$\mathbf{\vec{\Pi}})$$ convolution operator. Circular convolution can be computed via Fourier transform, such as c = a ∗ b = F−1(F(a) F(b)), (7) Here, KERMIT (Zanzotto et al., 2020a) utilizes shuffled circular convolution as the vector composition operator to guarantee the above properties. c = a ⊗ b = a ∗ Φb , (8) where ⊗ : Rd × Rd → Rd is the shuffled circular convolution operator and Φ denotes a permutation matrix that shuffles the elements of b. Section 5.3 provides an experimental comparison of these three operators. Another major difference between our model and KERMIT is that our model does not rely on an external parser to extract tree structure from the input. Circular correlation is a first-degree noncommutative operation, making it difficult to distinguish between c - (a - b) and b - (a - c) (Zanzotto and Dell'Arciprete 2012). However, this is not critical for parsing, as it is sufficient to distinguish between possible internal structures of a given fixed word order sentence. We refer to the vector composition operation by circular correlation as holographic composition in this paper. Zanzotto et al. (2020b) approximates the CKY algorithm using matrix multiplication and the property of holographic representation from Plate (1995). While similar to our approach in exploiting holographic representations and operations, our ![3_image_0.png](3_image_0.png) approach differs in performing a recursive holographic composition of distributed representations (described in the next section). 3.2 Recursive Vector Composition We acquired phrase- and sentence-level representations via recursive composition of word representations, as illustrated in Figure 3 for the input sentence *"My sister loves to eat"*. First, the input sentence is fed into a RoBERTa encoder (Liu et al., 2019; Wolf et al., 2020), obtaining highdimensional vectors (v0:1*,...,* v4:5). For a given phrase structure representable by an arbitrary binary tree, vector representations of the phrase and sentence are computed by applying holographic composition recursively. The vector for the entire sentence v0:5 is computed based on each word and phrase vector as follows: v0:5 = v0:2 - v2:5 $$\begin{array}{l}{=(\mathbf{v}_{0:1}\star\mathbf{v}_{1:2})\star\left(\mathbf{v}_{2:3}\star\mathbf{v}_{3:5}\right)}\\ {=(\mathbf{v}_{0:1}\star\mathbf{v}_{1:2})\star\left(\mathbf{v}_{2:3}\star\left(\mathbf{v}_{3:4}\star\mathbf{v}_{4:5}\right)\right).}\end{array}$$ We observed a rapid norm increase of vectors with recursive holographic composition, thus necessitating norm constraint. We adopted either of two methods of norm constraint. Normalization on real space: We imposed a norm constraint on all words and composed phrase vectors in real space. $$\mathbf{v}^{\prime}=k\cdot{\frac{\mathbf{v}}{\operatorname*{max}(\|\mathbf{v}\|,\epsilon)}},\tag{9}$$ where v and v denote vectors without and with the imposed norm constraint, respectively, and k is the desired norm after normalization. Complex unit magnitude projection: Ganesan et al. (2021) introduce a method applying a norm constraint to a vector in complex space as follows: $$\mathbf{v}^{\prime}={\mathcal{F}}^{-1}\left(\cdots,{\frac{{\mathcal{F}}(\mathbf{v})_{i}}{|{\mathcal{F}}(\mathbf{v})_{i}|}},\cdots\right),$$ $$(10)$$ , ··· , (10) where F(v)i denotes the ith element of a vector mapped into a complex space by a Fourier transformation. Applying the norm constraint to word vectors yields a norm of 1 for all composed vectors, avoiding rapid norm increase. Furthermore, this yields desirable properties, such as decomposability (described in Section 3.5). 3.3 Supertagging In CCG, supertagging is the task of assigning a plausible CCG category to each word in the sentence. In existing supertagging methods, various encoders transform each word into a highdimensional vector that is input to the classifier to predict the appropriate category for each word (Vaswani et al., 2016; Lewis et al., 2016; Tian et al., 2020). The present supertagging approach differs from existing models in its training mechanism of word vectors, which are treated as intermediate products rather than end products. Category prediction is performed at the word, phrase, and sentence levels, inducing the training of vector representations of words that consider dependencies with other components. Compute vectors for words/phrases, feed into a feed-forward neural network to form Pw(*i, i* + 1), Pp(*i, j*) (category assignment probability distribution), and Ps(*i, j*) (binary probability distribution of span existence), referring to Stern et al. (2017); Kitaev and Klein (2018) as $P_{w}(i,i+1)$ $=SM(\mathbf{Q}_{w}\sigma(LN(\mathbf{U}_{w}\mathbf{v}_{i:i+1}+\mathbf{b}_{w})+\mathbf{c}_{w})$ $P_{p}(i,j)$ $=SM(\mathbf{Q}_{p}\sigma(LN(\mathbf{U}_{p}\mathbf{v}_{i:j}+\mathbf{b}_{p}))+\mathbf{c}_{p})$ $P_{s}(i,j)$ $=SM(\mathbf{Q}_{s}\sigma(LN(\mathbf{U}_{s}\mathbf{v}_{i:j}+\mathbf{b}_{s}))+\mathbf{c}_{s})$, $=$ (11) $$\begin{array}{l}\small\text{(12)}\end{array}$$ = (13) . where Qw, Qp, Qs, Uw, Up, Us, bw, bp, bs, cw cp and cs denote the trainable parameters, σ(·) represents the nonlinear activation of the rectified linear unit (ReLU), and LN(·) indicates layer normalization, SM (·) denotes the softmax function. In addition, the dropout layer was immediately inserted after activation by the ReLU in each feedforward neural network. Thereafter, we used backpropagation of multiple losses to train the model, using a corpus of CCG derivations and dependency structures as the basis for losses. Lw, Lp, and Ls were computed based on cross-entropy loss between Pw(*i, i*+1), Pp(i, j), Ps(*i, j*), and their supervised data Ow(*i, i* + 1), Op(*i, j*), and Os(*i, j*) (one-hot categorical distribution): $$\mathcal{L}_{w}=1$$ $$\mathcal{L}_{p}=1$$ $$\mathcal{L}_{s}=1$$. $$-\sum_{i=0}^{n-1}\log(P_{w}(i,i+1))^{\sf T}O_{w}(i,i+1)\tag{14}$$ $$-\sum_{(i,j)\in I_{p}}\log(P_{p}(i,j))^{\sf T}O_{p}(i,j)$$ (15) $$-\sum_{(i,j)\in I_{s}}\log(P_{s}(i,j))^{\sf T}O_{s}(i,j),\tag{16}$$ Lp = − Ls = − where log represents the element-wise logarithmic operation and Ip and Is denote the set of span ranges in the training data. Thereafter, the model parameters were optimized by backpropagating a portion or all of the losses. In the case of backpropagation of all three losses, the calculation of the total loss and the update of the model parameters are expressed by the following equations: $${\mathcal{L}}={\mathcal{L}}_{w}+{\mathcal{L}}_{p}+{\mathcal{L}}_{s},\;\theta\gets\theta-\mu{\frac{\partial{\mathcal{L}}}{\partial\theta}}\qquad(17)$$ $\mathbf{a}$ where L denotes the total loss to be backpropagated, θ represents the model parameters, and μ denotes the learning rate. After training, the development and test data were supertagged by evaluating Pw(*i, i* + 1) and predicting the category assignment. ## 3.4 Parsing In this section, we describe a method for incorporating phrase-level representations into span-based parsing, which searches for the binary tree maximizing the sum of log-likelihoods of category assignments and span existence, following the CKY algorithm. This framework is based on Stern et al. (2017) and Kitaev and Klein (2018). Formulating CCG parsing, T was represented as a set of spans (it, jt) with categories t assigned. T := {(t,(it, jt)) : t = 1*,...,* |T|} Let P∗(*i, j*)[] and Ps(*i, j*)[e] denote the probabilities of assigning a CCG category and the existence of span (*i, j*), respectively. The loglikelihood of the entire tree is computed by log P(T) = $$\sum_{(\ell,(i,j))\in T}[\log P_{*}(i,j)[\ell]+\log P_{s}(i,j)[e]],\tag{18}$$ and the problem of searching for the most plausible constituency tree Tˆ can be expressed as $${\hat{T}}=\operatorname*{argmax}_{T}\log P(T),$$ $$(19)$$ T log P(T), (19) where the subscript ∗ of P∗(*i, j*)[] represents w for j = i + 1; otherwise, p and Ps(*i, j*) are defined using only j = i + 1. In accordance with the presented formulation, Appendix A delineates our proposed span-based parsing method. The presence of unary rules within CCG's combinatory rules can potentially hinder not only the training process of the model but also the integration of a span-based parsing algorithm. While our assumption rests primarily on binary rules, unary rules—such as the transformation of N into NP—do exist. Consequently, this limits the proposed model's capability to delineate the procedure for vector composition. In alignment with Stern et al. (2017), we consider the chain of categories processed by the unary rule as a unified category. We addressed this issue by transforming the CCG derivation into a form that could be represented by a complete binary tree; for instance, treating a chain of N to NP as a unified category N-NP based on the unary rule. This led to the prediction models of supertags and phrase types containing 1,340 and 948 category types, respectively. Furthermore, inconsistencies may emerge in the categories of phrases and their constituent components (child nodes) based solely on the outcomes of category prediction. To address this issue, our proposed span-based parsing algorithm exclusively evaluates categories derived from child nodes, in compliance with the CCG combinatory rules, during the determination of the phrase category. This procedure is illustrated in lines 15 to 19 of Algorithm 1. ## 3.5 Decomposability Our proposed model has a property allowing vector composition and decomposition, as expressed by Equations (20) and (21). $$(20)$$ $$\begin{array}{r c l}{\mathbf{c}}&{=}&{\mathbf{a}\circ\mathbf{b}}\\ {\mathbf{b}}&{=}&{\mathbf{c}\diamond\mathbf{a}}\end{array}$$ where ◦ and denote general composition and decomposition operations, respectively. In this formulation, decomposability is equivalent to automatically deriving b from c and a. In the proposed model, the composition operation is a circular correlation: $$(22)$$ $$\mathbf{c}=\mathbf{a}\circ\mathbf{b}=\mathbf{a}\star\mathbf{b}.$$ c = a ◦ b = a - b. (22) 266 | Training Objectives | Norm Constraint | Parser | Acc | LF | |-----------------------|-------------------|------------|------------|------------| | Lw (baseline) | Real | C&C | 96.41±0.03 | 91.77±0.03 | | Lw + Lp | Real | C&C | 96.54±0.03 | 91.95±0.03 | | Lw + Ls | Real | C&C | 96.54±0.03 | 91.94±0.04 | | Real | C&C | 96.59±0.02 | 92.03±0.04 | | | Span-based | - | 92.61±0.03 | | | | Lw + Lp + Ls | Complex | C&C | 96.57±0.02 | 91.98±0.03 | | Span-based | - | 92.15±0.04 | | | Assuming the complex unit magnitude projection of Section 3.2 is used for the vector's norm constraint, the decomposition operation can be derived by considering the inverse of Equation (5): $$\mathbf{b}=\mathbf{c}\diamond\mathbf{a}={\mathcal{F}}^{-1}({\mathcal{F}}(\mathbf{c})\odot{\overline{{{\mathcal{F}}(\mathbf{a})}}})$$ where denotes the element-wise division. As discussed in Section 3.1, the circular correlation is a noncommutative operation, and if we need a instead of b, then the decomposition operation needs to be modified as follows: $$\mathbf{a}=\mathbf{c}\diamond\mathbf{b}={\mathcal{F}}^{-1}({\overline{{{\mathcal{F}}(\mathbf{c})\odot{\mathcal{F}}(\mathbf{b})}}}).$$ Here, CVG (Socher et al., 2013a) lacks decomposability due to the need for the matrix WC→AB and lack of PCFG category information from the vectors themselves. In contrast, the current model has no such intervening parameters, enabling complete decomposition. Vector decompositions enable text-infilling at phrase-level. Given vectors of words and phrases for input sentence *"My sister loves to eat"* (shown in Figure 3), we can reconstruct v0:2=*"My sister"* from v0:5 and v2:5=*"loves to eat"* as follows: v0:2 = v0:5 v2:5. (25) Calculating the similarity between v0:2 and other vectors using cosine similarity enables retrieval of syntactically and semantically similar expressions. In this case, expected search results include phrases such as *"My brother"* and *"His sister"*. This task does not necessarily require syntactic information and can be performed by mask prediction with large-scale language models (LLM). However, the prediction would be syntactically unnatural compared to our method, as shown in Tables 4 and 5. Additionally, the number of subwords to be predicted must be pre-determined when using LLM, thus precluding variable-length phrase filling as our method does. We tested the decomposability of our model in various cases on text-infilling tasks and compared the results to LLM. ## 4 Experiments $$(23)$$ $\left(2\right)$ 4.1 Datasets We conducted experiments on CCGbank (Hockenmaier and Steedman, 2007), using a standard split scheme (02-21 section for training, 00 for development, and 23 for testing). Statistics on CCGbank are shown in Appendix B. 4.2 Training We trained our model using different combinations of objectives, demonstrating the effectiveness of supertagging by training on only Lw. We compared this baseline with those trained on Lw, Lp, and Ls, thus enabling a single training process to satisfy both supertagging and parsing requirements. To train Ps(*i, j*), the actual spans in the gold derivation in CCGbank were treated as positive examples, and the spans generated by randomly and recursively splitting the span containing the entire sentence, but not included in gold derivation, were treated as negative examples. We trained both the baseline model (minimized only Lw) and the proposed model (Lp and Ls were subject to minimization) 10 times, with unique random seeds for each instance, and averaged the performance metrics for each model to perform a onetailed t-test at the 1% significance level. The proposed model contains 362 million trainable parameters, with 98.2% of these being derived from RoBERTa. In addition, we minimized the objectives using an AdamW optimizer (Loshchilov and Hutter, 2019). Model training takes around 2 hours using a single NVIDIA A100 GPU. Other hyperparameters are listed in Appendix C. ## 4.3 Parser Configuration First, we conducted a parsing experiment using the Java version of the C&C parser (Clark et al., 2015) to demonstrate the effectiveness of our proposed supertagging model. We adopted a multitagging scheme, assigning supertags to each word with an assignment probability greater than 0.1, and used the default parameters of the C&C parser. | Model | Super-Tagger | Parser | Acc | LF | |----------------------------------|------------------------------------|---------------------|-------|-------| | Lewis et al. (2016) | LSTM | A* | 94.7 | 88.1 | | Vaswani et al. (2016) | LSTM | C&C | 94.5 | 88.32 | | Yoshikawa et al. (2017) | LSTM | A* (LSTM) | - | 88.8 | | Stanojevic and Steedman ´ (2020) | LSTM | Shift-Reduce (LSTM) | - | 90.6 | | Tian et al. (2020) | Attentive-GCNN | EasyCCG | 96.25 | 90.58 | | Bhargava and Penn (2020) | LSTM decoder | C&C | 96.00 | 90.9 | | Liu et al. (2021) | Category Generator | C&C | 96.05 | 90.87 | | Prange et al. (2021) | Tree-Structured decoder | C&C | 96.22 | 90.91 | | Kogkalidis and Moortgat (2022) | Heterogeneous Dynamic Convolutions | - | 96.29 | - | | Clark (2021) | Tian et al. (2020) | C&C | - | 91.9 | | Span-based | - | 92.9 | | | | Ours (Lw + Lp + Ls, Real) | Holographic | C&C | 96.60 | 92.12 | | Span-based | - | 92.67 | | | | Table 2: Comparison of the proposed model and existing methods; best results are shown in bold. Operator Acc LF Corr () 96.59±0.02 92.61±0.03 Conv (∗) 96.57±0.02 92.75±0.02 s-Conv (⊗) 96.54±0.02 92.12±0.04 state of the art. This indicates the effectiveness of the proposed approach in inducing category assignments at the word level while considering phraselevel representations. | | | | | Operator Acc LF Corr (-) 96.59±0.02 92.61±0.03 Conv (∗) 96.57±0.02 92.75±0.02 s-Conv (⊗) 96.54±0.02 92.12±0.04 Table 3: Performance comparison on development data using different compositional operators, measured by accuracy for supertagging and labeled F-score for parsing. Corr, Conv, and s-Conv denote circular correlation, circular convolution, and shuffled circular convolution, respectively. Subsequently, we conducted an experiment with our proposed span-based parsing algorithm to demonstrate phrase-level category assignment influence on parsing. For evaluation, we extracted dependencies from CCG derivation using the generate program of the C&C parser. Variations in grammatical constraints caused programmatic extraction failure for some sentences, so their dependencies were replaced by C&C parser results. Furthermore, we implemented the skimmer mode for our span-based parsing algorithm, along with the C&C parser, enabling the detection of dependencies between words, even if the parser is unable to parse the entire sentence. Consequently, our parser achieved 100% coverage. ## 5 Results 5.1 Supertagging Accuracy Table 1 presents supertagging accuracy on development data for each training loss combination. First, we compared models with norm constraints on real space and found our proposed models to be statistically superior to the baseline in terms of supertagging accuracy. Moreover, the supertagging performance varied slightly compared to the model with norm constraints on the complex space, indicating a low impact of the type of norm constraint. Table 2 shows the proposed supertagging model outperforming existing models, achieving a new state of the art. This indicates the effectiveness of the proposed approach in inducing category assignments at the word level while considering phraselevel representations. ## 5.2 Parsing Performance The labeled F-scores of the current span-based parser and C&C parser on the development data are presented in Table 1. First, the model with norm constraints on the real space outperformed the baseline, even with the same C&C parser, due to improved supertagging. Furthermore, compared with C&C, the proposed span-based parsing algorithm improved performance for the model with norm constraint on real space. However, the performance gap between C&C and the model with norm constraints on complex space is relatively small. This implies that models' expressive power with the norm constraint on complex space is limited compared to real space. This could be due to representations being distributed on a d-dimensional unit hypersphere in complex space, thus lacking norm information along each dimension. Model using proposed supertagging approach and C&C outperformed all existing models with the same parser (Table 2). Furthermore, the performance of the proposed span-based parsing model is comparable to that of the current state-of-the-art model of Clark (2021) using Transformers. Overall, results indicate recursive holographic compositions improve CCG parsing performance. ## 5.3 Replacement Of Compositional Operator Examining performance gaps when employing alternative compositional operators in our method (Table 3) revealed little difference in performance for supertagging. However, the application of shuffled circular convolution exhibited lower performance than the other two operators for parsing. | ID | Sentence | Replacement by Holographic CCG | Sim. | NPMI | |------------------------------------------------|----------------------------------------------------------------------------------------|------------------------------------|--------|--------| | Mr. Baris | 1.00 | 0.19 | | | | 1 | Mr. Vinken is chairman of Elsevier N.V. , | Dr. Novello | 1.00 | 0.10 | | the Dutch publishing group . | Ms. Ensrud | 1.00 | 0.11 | | | turned up | 0.94 | 0.27 | | | | 2 | When Scoring High first came out in 1979 , | sold out | 0.91 | 0.29 | | it was a publication of Random House . | sells out | 0.90 | 0.24 | | | for $ 25.50 a share | 0.94 | 0.33 | | | | 3 | In early trading in Hong Kong Thursday , | for $ 60 a bottle | 0.94 | 0.29 | | gold was quoted at $ 374.19 an ounce . | at $ 51.25 a share | 0.93 | 0.34 | | | what she did | 0.96 | 0.28 | | | | 4 | Judges are not getting what they deserve . | what they do | 0.96 | 0.36 | | what we do | 0.89 | 0.35 | | | | Despite the flap over transplants | 0.89 | 0.22 | | | | 5 | Despite recent declines in yields , investors | In a victory for environmentalists | 0.86 | 0.22 | | continue to pour cash into money funds . | On the issue of abortion | 0.82 | 0.27 | | | to provide maintenance for other manufacturers | 0.83 | 0.27 | | | | to share data via the telephone | 0.79 | 0.21 | | | | to cut costs throughout the organization | 0.77 | 0.26 | | | | 6 | Despite recent declines in yields , investors continue to pour cash into money funds . | | | | Table 4: List of target sentences, phrases, and candidates for replacement. The underlined parts of the sentence denote phrases for reconstruction and replacement and **Sim.** indicates the cosine similarity between the reconstructed vector and the replacement candidate vector. **NPMI** shows the mean of the values calculated for each word among the replacement candidates. ID Replacement by RoBERTa NPMI Table 5: List of replacement candidates and NPMI with mask prediction using RoBERTa. IDs are consistent with those of Table 4. Replacement candidates with † mean that the outermost non-terminal symbol given by Berkeley Neural Parser is different from that of the original phrase in the sentence. | ID | Replacement by RoBERTa | NPMI | |-----------------------------------|----------------------------------|--------| | A.P. Bates | 0.11 | | | 1 | Ms. Vinken | 0.35 | | Dyearella Sr. | 0.08 | | | was introduced | 0.32 | | | 2 | went open | 0.22 | | took place | 0.23 | | | with $ 368.24 an ounce | 0.38 | | | 3 | as $ 368.79 an ounce | 0.38 | | at $ 368.24 a piece | 0.31 | | | difficult to defend | 0.23 † | | | 4 | at their views | 0.26 † | | out of themselves | 0.28 † | | | To provide a defensive edge | 0.26 † | | | 5 | In a routine shakeup | 0.20 | | After several years of weakness | 0.26 | | | on a trend toward lower yields | 0.32 † | | | 6 | 6 ignore the quake in California | 0.24 | | getting scared out of their lives | 0.21 | | This difference may be attributed to the presence of a permutation matrix Φ in Equation (8), unlike the other two. As for parsing, circular convolution was slightly superior to circular correlation, yet the small performance gap is not considered serious enough to preclude circular correlation in our approach, due to its desirable properties for embedding phrase structures and potential for composing semantic and syntactic information. ## 5.4 Decomposition We present a qualitative evaluation of text-infilling, enabled by the decomposability of our model. We reconstruct phrase vectors from development data and compare them to the vectors of phrases in other sentences to select the top-n most similar phrases as candidate replacements, following Tian et al. (2016). Then we compare our proposed decomposition method (Table 4) with fine-tuned RoBERTa for mask prediction (Table 5). Results indicate our method found expressions more syntactically similar to the original. E.g. for ID 4, our method output all relative pronoun phrases beginning with *"what"*, and for ID 6, infinitive phrases starting with *"to"*, as did the original expression, unlike RoBERTa. In addition, we used Berkeley Neural Parser (Stern et al., 2017; Kitaev and Klein, 2018; Kitaev et al., 2018) for the analysis of the original and replaced sentences and showed that all the non-terminal symbols in the pre- and post-replaced phrases matched in our method, whereas different non-terminal symbols were assigned in RoBERTa in some cases (syntactic structure has changed). Randomly selecting phrases of length 2-6 from sentences of length 1030 (total 1,285 sentences) in development data, our method achieved a 96.31% match rate of outermost nonterminals, compared to 77.95% for RoBERTa. Furthermore, we calculated normalized pointwise mutual information (NPMI; Bouma 2009) to evaluate semantic naturalness; a two-tailed t-test ![8_image_0.png](8_image_0.png) showed no significant difference in mean NPMI of the proposed method and RoBERTa (0.255 vs. 0.258; p-value = 0.910). Our model can provide syntactically and semantically natural replacement, despite focusing on syntactic information. ## 6 Conclusion In this paper, we proposed a novel method for formulating CCG as a recursive composition operation on a continuous vector space and constructing phrase/sentence-level representations from word representations. We demonstrated its utility for supertagging and parsing. Experimentation demonstrated the effectiveness of holographic compositions in explicitly modelling dependencies between sentence components, resulting in improved performance and state-of-the-art results in supertagging and parsing using the C&C parser. In addition, we validated that phrase-level text-infilling is possible by applying the decomposable property of the holographic representation in the proposed model. ## 7 Limitations Firstly, the training process of our proposed model is dependent on supervised data, thus precluding its application to languages without a supervised dataset for CCG. Also, the span-based parsing algorithm proposed in this study is implemented in Python and may take a considerable amount of time to parse extremely long sentences (more than 100 words) due to a lack of optimization for implementation. ## Acknowledgements This work was supported by JST, Moonshot R&D Grant Number JPMJMS2033, and JSPS KAKENHI Grant Number JP23H04835. ## References Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz, and Tommaso Salvatori. 2020. BoxE: A box embedding model for knowledge base completion. In Advances in Neural Information Processing Systems, volume 33, pages 9649–9661. Curran Associates, Inc. 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Association for Computational Linguistics. Xiaodan Zhu, Parinaz Sobihani, and Hongyu Guo. 2015. Long short-term memory over recursive structures. In Proceedings of the 32nd International Conference on Machine Learning, volume 37 of *Proceedings of* Machine Learning Research, pages 1604–1612, Lille, France. PMLR. ## A Span-Based Parsing Algorithm Our proposed novel span-based parsing algorithm is shown in Algorithm 1. Although the basic flow of the algorithm remained the same as that of the original CKY algorithm, there were certain modifications related to the incorporation of phrase-level representations which are explained in detail by associating line numbers in the Algorithm 1. First, in line 1, the input word sequence (w1, w2, ··· , wn) was converted into vectors (v0:1, v1:2, ··· , vn−1:n) using the encoder, and in lines 2 to 6, the categories with a higher probability of assignment to each word were stored in the chart along with the log-likelihood of their assignment. In particular, the unique feature of this algorithm pertains to line 6, wherein the vector of each word is stored in a separate chart to compute the vector of phrases at a later stage. Moreover, the vector vi:j of the span (*i, j*) to be split at split point k using circular correlation is stated in line 16, and this vector is used for calculating the probability distribution of span existence and category assignment to the phrases in line 17 and 19. After conducting the two-step thresholding process in lines 18 Algorithm 1: Span-based CKY parsing 1 v0:1, v1:2, ··· , vn−1:n = Encode(w1, w2, ··· , wn); 2 for i = 0, ··· , n − 1 do 3 Pw(*i, i* + 1) = SM (Qwσ(LN(Uwvi:i+1 + bw)) + cw) ; Equation (11) 4 for C ∈ {X|Pw(*i, i* + 1)[X] > tw = 0.1} do 5 prob[*i, i* + 1, C] = log Pw(*i, i* + 1)[C]; 6 vector[*i, i* + 1, C] = vi:i+1; 7 for = 2, ··· , n do 8 for i = 0, ··· , n − do 9 j = i + ; 10 for k = i + 1, ··· , j − 1 do 11 for C1 ∈ {X|prob[*i, k, X*] > 0} do 12 vi:k = vector[*i, k, C*1]; 13 for C2 ∈ {X|prob[*k, j, X*] > 0} do 14 vk:j = vector[*k, j, C*2]; 15 for C ∈ {X|C1C2 → X ∈ R} do 16 vi:j = vi:k - vk:j ; Equations (4) and (5) 17 Ps(*i, j*) = SM (Qsσ(LN(Usvi:j + bs)) + cs) ; Equation (13) 18 if Ps(i, j)[e] > ts = 0.01 **then** 19 Pp(*i, j*) = SM (Qpσ(LN(Upvi:j + bp)) + cp) ; Equation (12) 20 if Pp(i, j)[C] > tp = 0.01 **then** 21 p = log Pp(*i, j*)[C]+log Ps(i, j)[e]+prob[i, k, C1]+prob[*k, j, C*2]; 22 if p > prob[*i, j, C*] **then** 23 prob[*i, j, C*] = p; 24 backpointer[*i, j, C*]=(k, C1, C2); 25 vector[*i, j, C*] = vi:j ; and 20, the log-likelihood of assigning category C, which was combined from categories C1 and C2 following the combinatory rule R, to span (*i, j*) was calculated in line 21 based on Equation (18). In implementing the combinatory rules used in the algorithm (R in line 15), we employed all combinatory rules that appeared at least once in the training data. This allows for a larger search space and simpler program implementation compared to existing methods. ## B Ccgbank Statistics Table 6 presents the statistics of CCGbank which we used for our experiments. ## C Hyperparameters Table 7 presents the list of hyperparameters used in our experiments. | Train | Dev | Test | | |---------------------|---------|--------|--------| | Section number | 02-21 | 00 | 23 | | Number of sentences | 39,604 | 1,913 | 2,407 | | Number of words | 929,552 | 45,422 | 55,371 | Table 6: Statistics of CCGbank. | Hyperparameters | Values | |---------------------|-----------------------------| | k in Equation (9) | 30 | | in Equation (9) | 1e-12 | | Training epochs | 10 | | Batch size | 16 | | Learning rates | 1e-4(base), 1e-5(fine-tune) | | AdamW β s | 0.9, 0.999 | | AdamW | 1e-6 | | Weight decay | 0.01 | | Dropout probability | 0.2 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7 ✗ A2. Did you discuss any potential risks of your work? Since we do not find any potential risk in this study (parsing with CCG). ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract, 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4,5 ✓ B1. Did you cite the creators of artifacts you used? 3,4,5 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Since it is clear that the artifacts we used can be only beneficial for academic purposes. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Since it is clear that the artifacts we used are consistent with our usage for the experiment (parsing with CCG). ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Since we used a dataset commonly used in studies on CCG that is based on newspaper articles, and since we do not find any such concerns. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Since it is obvious that the dataset we used is written in English and detailed information is already available in the reference we cited. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. We presented the statistics of the dataset used for the experiment in Appendix B. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** 4,5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4, Appendix C ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 4, Appendix C ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

liang-etal-2023-prompts

Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning

https://aclanthology.org/2023.acl-long.16

Thanks to the recent success of Pre-trained Language Models (PLMs), it has become a promising research direction to develop a universal model (UIE) that can solve all typical information extraction tasks within one generative framework. Nonetheless, in real-world scenarios of UIE applications, new data of different IE tasks and domains usually come in a stream over time. A desirable UIE system should be capable of continually learning new tasks without forgetting old ones, thereby allowing knowledge and functionalities expansion without re-training the whole system. In this paper, we study the UIE system under a more challenging yet practical scenario, i.e., {``}lifelong learning{''} settings, to evaluate its abilities in three aspects, including knowledge sharing and expansion, catastrophic forgetting prevention, and rapid generalization on few-shot and unseen tasks. To achieve these three goals, we present a novel parameter- and deployment-efficient prompt tuning method namely Lottery Prompt Tuning (LPT).LPT freezes the PLM{'}s parameters and sequentially learns compact pruned prompt vectors for each task leveraging a binary prompt mask, while keeping the prompt parameters selected by the previous tasks insusceptible. Furthermore, we use a simple yet effective method to perform mask selection and show the powerful transferability of Lottery Prompts to novel tasks. Extensive experiments demonstrate that LPT consistently sets state-of-the-art performance on multiple lifelong learning settings of UIE, including task-incremental setting on seen tasks, few-shot adaptation, and zero-shot generalization on novel tasks.

# Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction Via Lottery Prompt Tuning Zujie Liang, Feng Wei, Jie Yin, Yuxi Qian, Zhenghong Hao, Bing Han MYbank, Ant Group [email protected] {huodeng.wf,yibo.yj,qianyuxi.qyx,haozhenghong.hzh,hanbing.hanbing}@mybank.cn ## Abstract Thanks to the recent success of Pre-trained Language Models (PLMs), it has become a promising research direction to develop a universal model (UIE) that can solve all typical information extraction tasks within one generative framework. Nonetheless, in real-world scenarios of UIE applications, new data of different IE tasks and domains usually come in a stream over time. A desirable UIE system should be capable of continually learning new tasks without forgetting old ones, thereby allowing knowledge and functionalities expansion without retraining the whole system. In this paper, we study the UIE system under a more challenging yet practical scenario, i.e., "lifelong learning" settings, to evaluate its abilities in three aspects, including knowledge sharing and expansion, catastrophic forgetting prevention, and rapid generalization on few-shot and unseen tasks. To achieve these three goals, we present a novel parameter- and deployment-efficient prompt tuning method namely Lottery Prompt Tuning (LPT). LPT freezes the PLM's parameters and sequentially learns compact pruned prompt vectors for each task leveraging a binary prompt mask, while keeping the prompt parameters selected by the previous tasks insusceptible. Furthermore, we use a simple yet effective method to perform mask selection and show the powerful transferability of Lottery Prompts to novel tasks. Extensive experiments demonstrate that LPT consistently sets state-ofthe-art performance on multiple lifelong learning settings of UIE, including task-incremental setting on seen tasks, few-shot adaptation, and zero-shot generalization on novel tasks1. ## 1 Introduction Information Extraction (IE) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to extract the desired structural information from unstructured texts (Andersen et al., 1The code is available at https://github.com/ jokieleung/Lottery_Prompt. ![0_image_0.png](0_image_0.png) 1992; Surdeanu et al., 2003; Ma and Hovy, 2016; Kolluru et al., 2020). Previous IE research mostly focuses on one specific IE task (Miwa and Bansal, 2016; Wang et al., 2020; Lin et al., 2020; Zheng et al., 2021) and designs different model architectures (Lample et al., 2016; Sohrab and Miwa, 2018; Li et al., 2020; Hsu et al., 2022) to tackle different tasks. To facilitate knowledge sharing between different tasks, various efforts have been paid for unifying all IE tasks with one model structure (Wadden et al., 2019; Nguyen et al., 2021; Paolini et al., 2021). Most recently, Lu et al. (2022); Fei et al. (2022) unify general IE tasks in a generative way with a text-to-structure framework (UIE), which proves that universally modeling various IE tasks can better learn general knowledge from varying data sources. Nonetheless, current work usually assumes the accessibility of training data for every task. In many real-world scenarios, as shown in Figure 1, the training data are often streamed, and the IE systems are required to identify new mention spans or semantic relations to support new domains and functionalities, which can be formulated as the paradigm of lifelong learning. The ability to accumulate knowledge continually is crucial for the quick deployment of UIE systems based on PLMs, which allows the system to add new domains and functionalities over time without incurring the high cost of re-training the whole system each time. In addition, considering that humans can acquire new knowledge from a few examples (Montague, 1974), it is expected for the models to generalize well on novel tasks with few-shot data or even no data. Motivated by this, our work aims to address these more challenging yet practical issues by proposing a lifelong learning setup for UIE. In this setup, the system sequentially learns over multiple IE tasks (potentially of different task types and varying domains) one by one. Then it will be evaluated to preserve its performance on solving previously seen tasks, and generalize well to novel tasks with few examples or even no examples. We cover two conventional properties of lifelong learning (Ke and Liu, 2022), i.e., catastrophic forgetting prevention (CF) and *knowledge transfer* (KT), while in our setup, the evaluation of KT extends to the novel tasks. In NLP community, large Pre-trained Language Models (PLMs) have been widely applied in many downstream tasks. In order to lower computation and storage costs, recent popular lifelong learning techniques (Madotto et al., 2021; Ke et al., 2021a; Zhu et al., 2022; Wang et al., 2022c) try to solve the CF and KT leveraging parameter-efficient fine-tuning (PEFT) methods (He et al., 2022a). In this work, we inherit this wisdom and also focus on parameter-efficient methods for lifelong learning. Inspired by the lottery ticket hypothesis and the efficiency of prompt tuning, we propose a novel framework for lifelong UIE, named Lottery Prompt Tuning (LPT). Specifically, we adopt an encoder-decoder model architecture (Raffel et al., 2020) and re-frame all types of IE tasks into a text-to-structure format (Lu et al., 2022). First, we prepend a sequence of continuous prompt vectors to the input, which is shared across tasks. To continually learn a new IE task, we simultaneously learn the prompt vectors together with a task-aware binary prompt mask. The task-aware mask is devoted to pruning the shared prompt vectors and producing an optimal task-specific pruned prompt, i.e., lottery prompt. To provide a pruning criterion for finding the lottery prompt online, we introduce a separate set of learnable parameters serving as the importance scores, which have the same shapes as the soft prompts. Hence, the lottery prompt can be easily found by selecting the parameters with the Top-k% importance scores online, without iterative retraining and pruning procedure. To facilitate the forward knowledge transfer when learning a new task, the lottery prompt is permitted to selectively reuse the learned prompt parameters for the former tasks. Besides, the proposed LPT eliminates catastrophic forgetting and negative transfer by freezing the prompt parameters for the previous tasks during back-propagation. In the whole learning process, the PLM is kept frozen to maintain general knowledge. During inference, the same model can handle different tasks by inputting different lottery prompts, which is friendly for deployment. We show that our proposed framework effectively outperforms state-of-the-art baselines on lifelong learning for UIE in terms of catastrophic forgetting prevention and knowledge transfer. Moreover, LPT closes the gap between continual learning and multi-task learning. The efficacy of the proposed modules is thoroughly studied both empirically and analytically. In summary, this work makes three key contributions: - A challenging yet practical benchmark is proposed for lifelong UIE, where one UIE system should not only keep its performance on solving seen IE tasks, but also generalize well on novel IE tasks with few or even no examples. - We proposed Lottery Prompt Tuning (LPT), an extremely efficient prompt tuning framework for lifelong UIE that directly learns pruned prompts sequentially without an extra pruning stage. - Extensive experiments on the benchmark show that our approach outperformed baselines with higher parameter efficiency. ## 2 Related Work Lifelong Learning Lifelong Learning, also known as Continual Learning, aims to learn a sequence of tasks with one single model. Two main goals are demanded: catastrophic forgetting (CF) prevention and positive knowledge transfer (KT). The research in this area can be categorized into three folds: Regularization, *Rehearsal*, and *Architecture* based methods. (a) *Regularization-based* methods (Li and Hoiem, 2017; Kirkpatrick et al., 2017; Ritter et al., 2018) ease the catastrophic forgetting issue by regularizing important parameters for learned tasks. These approaches usually need a trade-off between learning new tasks and forgetting the old tasks. In NLP, it is studied (Han et al., 2020) to constrain the useful information from the huge amount of knowledge inside the PLMs. (b) *Rehearsal-based methods* methods reuse old examples from the previously learned tasks while learning new tasks. These examples are either derived from real training data of previous tasks (Rebuffi et al., 2017; Lopez-Paz and Ranzato, 2017; Mi et al., 2020), or generated by a pseudo-data generator (Sun et al., 2019; Qin and Joty, 2021; Zhao et al., 2022). Although these methods work well, they are limited by data privacy or the quality of generated data. (c) *Architecturebased methods* tackle the continual learning problem by expanding new modules to the network over time (Veniat et al., 2020; Douillard et al., 2022) or isolating the network's parameters for different tasks (Serra et al., 2018; Mallya and Lazebnik, 2018; Mallya et al., 2018; Wortsman et al., 2020; Geng et al., 2021; Kang et al., 2022). In NLP, in order to better take advantage of the PLMs, these methods usually are in conjunction with parameterefficient fine-tuning approaches, including adapter tuning (Houlsby et al., 2019) and prompt tuning (Lester et al., 2021a; Li and Liang, 2021; Liu et al., 2022b). AdapterCL (Madotto et al., 2021) trains a separate adapter for each task, leaving knowledge transfer out of consideration. Ke et al. (2021b,a); Ermis et al. (2022); Zhang et al. (2022) overcome this drawback by introducing capsule network (Sabour et al., 2017), distillation mechanism and adaptive compositional modules, respectively. For the latter, CPT (Zhu et al., 2022) learns a separate prompt with continual prompt initialization for each task. Wang et al. (2022c,b) propose to learn a prompt pool and then select the useful prompts to alleviate forgetting and potentially share knowledge across tasks. Dai et al. (2022) extend the idea to organize the prompt pools in a hierarchical way to guide the pre-trained models in different granularities. In contrast, we here share a single copy of prompt parameters to instruct the PLMs, yet incrementally learn a task-aware prompt mask for each task whilst keeping the prompt parameters used by the previous tasks unchanged. This not only isolates the harmful prompt parameters that lead to forgetting but also shares useful prompt parameters for knowledge transfer. Lifelong learning in Information Extraction In IE areas, some efforts are paid for building IE systems to handle continual learning scenarios, including continual NER (Monaikul et al., 2021; Zheng et al., 2022), relation extraction (Cui et al., 2021; Qin and Joty, 2022; Wang et al., 2022a), and event detection (Yu et al., 2021; Liu et al., 2022a). However, they merely study continual learning on one single IE task. Very recently, UIE (Lu et al., 2022; Fei et al., 2022) regards general IE tasks as a text-to-structure generation task, thus unifies all IE tasks with one model framwork. To a step further, our work studies a more challenging yet practical continual learning paradigm for UIE, where one universal IE system needs to solve different types of IE tasks across different domains incrementally. Lottery Ticket Hypothesis Frankle and Carbin (2018) propose the The Lottery Ticket Hypothesis (LTH) that an over-parameterized network contains a sub-network (lottery ticket) that, when initialized and trained in isolation, can match or exceed the test accuracy of the original network after training for at most the same number of iterations. The LTH has been widely explored in many fields of deep learning (Liu et al., 2018; Frankle et al., 2019; Gong et al., 2022; Yu et al., 2019) In NLP, researchers also explore the existence of winning tickets under transfer learning regimes for over-parametrized pre-trained language models across various tasks (Morcos et al., 2019; Desai et al., 2019). Chen et al. (2020); Prasanna et al. (2020) show the existence of winning tickets when fine-tuning BERT on downstream tasks. Liang et al. (2021) shows the existence of super tickets inside PLMs that can improve generalization. Xprompt (Ma et al., 2022) is the pioneer to explore the LTH in the context of prompt tuning by hierarchical structure pruning. However, Xprompt needs iterative retraining, pruning and rewinding to get the pruned prompts, which is impractical to perform during continual learning settings since it needs excessive computational time and costs. By contrast, our LPT does not require an explicit pruning stage and jointly learns prompt and task-related masks together, which accelerates convergence during continual learning. Moreover, our pruning is performed at the parameter level while Xprompt's pruning is performed at the token and piece level. ## 3 Preliminary 3.1 Lifelong Learning Protocols Conventional continual learning is defined as training machine learning models on a continuum of data from a sequence of tasks. Here in our lifelong learning protocols for UIE, the incoming task on the task sequence can be of different types (*e.g.*, entity extraction, relation extraction, event extraction, and aspect-based sentiment analysis.), or of the same type but potentially of different domains. An intuitive demonstration can be found in Figure 1. Formally, we define a sequence of tasks D = {D1, *· · ·* , DT }, where the k-th task Dk = x k i , y k i Nk i=1 contains a set of data samples. For each data sample, the input x k i is constructed by the raw text t k iand a specific predefined schema s k i , while the desirable output y k i is structural information contained in the text x k i indicated by the schema s k i . Note that our approach is Rehearsal-free, meaning that data from the previous tasks can not be used anymore when training future tasks. The goal of a lifelong UIE model should perform well on all T tasks after being trained with the samples of these tasks sequentially. Further, in the realistic scenario, it is usually expensive and impractical to acquire plenty of labeled data for a newly emerged task. To simulate this circumstance, we adapt the sequentially trained model on a set of n*novel* novel tasks individually {Di} N*novel* i=1 . Hence, we can access the model's ability to accumulate previously learned knowledge for generalization to new tasks by evaluating the few-shot/zero-shot transferability of the lifelong model. ## 3.2 Generative Uie Framework In this section, we cast all IE tasks as text generation and model the UIE system in a text-to-structure framework (Lu et al., 2022). In this generative framework, different IE structure generation is decomposed into two atomic operations, *i.e.*, spotting and associating. Spotting indicates locating target information pieces from the sentence, e.g., the entity and the trigger word in the event. Associating means connecting spans by assigning them with specific semantic roles based on pre-defined schemas, such as the relation between entity pairs or the role between an event and its argument. Input the input x for the UIE model is formulated as the concatenation of the raw sentence and a schema-based prompt in the form of: $x=[s;t]=[s_{1},s_{2},\ldots,s_{|s|},t_{1},t_{2},\ldots,t_{|t|}]$ $=[[$spot$],$ SPOT${}_{1},[$spot$],$ SPOT${}_{2}\ldots,$ $[$asso$],$ ASSO${}_{1},[$asso$],$ ASSO${}_{2}\ldots,$ $[$text$],t_{1},t_{2},\ldots,t_{|t|}]$ SPOTi represents the targeted spotting name in the IE tasks, *e.g.*, "organization" in the NER task; and ASSOi represents the targeted association name, e.g., "work for" in the relation extraction task. Output the output text y is a unified Structured Extraction Language (SEL) that describes how the structural elements organize into the target structure, which can be represented as *"{Spot Name:* Info Span, (Asso Name: Info Span) (Asso Name: Info Span)}". The *Spot Name* and *Asso Name* are the target structure from the pre-defined schemas, while the *Info Span* refer to the text span mentioned in the raw text. Model We employ a Transformer-based encoderdecoder language model *i.e.*, T5 (Raffel et al., 2020), as the model architecture for UIE. Given the schema and the raw sentence as input sequences x and the SEL as output sequences y, the model computes the conditional language model distribution of each token yi using the chain rule of probability as p (yi| y<i, x). It finishes prediction when outputting the end signal [EOS]. The predicted SEL expression will be converted back into the extracted information record for evaluation. ## 4 Method 4.1 Overview In this section, we present a novel pruning-based parameter-efficient tuning method for lifelong learning, called Lottery Prompt Tuning (LPT). The overall process of LPT is illustrated in Figure 2. To continually learn a new IE task, we simultaneously learn the prompt vectors together with a paired task-aware binary prompt mask, while the mask is devoted to producing a pruned prompt, *i.e.*, Lottery Prompt. During training for each incoming task, LPT can selectively re-use the previously learned prompt parameters to encourage knowledge transfer, while the parameter updates only happen on those soft prompt parameters that have not been selected by the previous tasks. Finally, the model shares the same set of soft prompts for all tasks however uses the binary masks to isolate the shared ![4_image_0.png](4_image_0.png) ![4_image_1.png](4_image_1.png) parameters and get the lottery prompt for each task, which solves catastrophic forgetting. ## 4.2 Lottery Prompt Tuning Prompt tuning (Li and Liang, 2021; Liu et al., 2022b) learns a set of continuous prompts and only tunes the prompts while fixing the whole parameters in PLM, which has been proven to be effective in various downstream tasks. In this work, we combine prompt tuning and the aforementioned generative UIE into one unified framework, where the PLM takes the concatenation of continuous learnable soft prompts p, schema instruction s and the raw text t, i.e., x = [p; s;t]. The training objective is formalized as $${\mathcal{L}}=\sum_{(x,y)\in{\mathcal{D}}_{k}}-\log p\left(y\mid x;\theta_{p}\right)\qquad\quad(2)$$ Note that only the soft prompt parameters θp are trainable. Recently, Ma et al. (2022) show that pruning prompts at token and piece level yields a more parameter-efficient prompt yet with competitive performance. Inspired by this, we propose a novel Lottery Prompt Tuning (LPT) which acquires high-performing pruned prompts for continual learning by assigning the prompt vectors θk together with a task-aware binary mask mk. The mask selects the top-c% of soft prompts that lead to good performance on the current task. To achieve this, we introduce a set of learnable parameters sk that have the same shape as the soft prompts, which indicates the importance scores of the prompt parameters. Once trained, these scores are thresholded to obtain the prompt mask, *i.e.*, mk = h(sk), where h(.) is an indicator function that outputs "1" for top-c% of the scores in the prompt parameters or "0" otherwise. Therefore, the pruned prompt parameters θˆ k p for task k, *i.e.*, lottery prompt, is obtained by θˆ k p = θp ⊙ mk. To get rid of the need for iterative retraining, pruning and rewinding procedures during continual learning, we perform online pruning by simultaneously optimizing the prompt parameters and the importance scores together. To achieve this, we use a straight-through gradient estimator (Bengio et al., 2013) to ignore the derivatives of the indicator function h(.) and directly update the scores as follows: $$\begin{array}{c}{{\mathrm{minimize}{\mathcal{L}}\left(\theta_{\mathbf{p}}\odot\mathbf{m}_{k};{\mathcal{D}}_{k}\right);}}\\ {{\theta_{\mathbf{p},\mathbf{s}_{k}}}}\\ {{\qquad\qquad\mathbf{s}_{k}\leftarrow\mathbf{s}_{k}-\eta\left({\frac{\partial{\mathcal{L}}}{\partial\mathbf{s}_{k}}}\right)}}\end{array}\qquad(3)$$ where the η is learning rate. While training on a newly emerge task k, we use an extra binary mask Mk−1 = ∨ k−1 i=1 mito prevent updating the prompt parameters allocated by previous tasks. Hence, the prompt parameters θp are updated as follows: $$\theta_{p}\leftarrow\theta_{p}-\eta\left({\frac{\partial{\mathcal{L}}}{\partial\theta_{p}}}\odot(1-\mathbf{M}_{k-1})\right)\quad(4)$$ To summarize, LPT circumvents the forgetting issue by isolating the prompt parameters for each task. Meanwhile, taking the separate scores as the pruning criterion allows sharing some of the parameters from previously chosen parameters θp ⊙ mk 281 in solving the current task k, which contributes to knowledge transfer. ## 4.3 Mask Selection For Novel Tasks When generalizing to the use-case on novel tasks where few or no labeled data for training, it is a desired property to transfer knowledge learned by the previous tasks to achieve better performance. Hence, we provide two simple solutions to select the binary masks in hands for initializing the lottery prompt. The first way is to utilize the perplexity (PPL) of each mask mk over the input X as a measurement (Madotto et al., 2021), i.e., *P P L*θ k p (X). The mask with the lowest PPL will be chosen for initialization. Another solution is to select the mask by the gradient-based one-shot algorithm (Wortsman et al., 2020). It first associates each of the T learned masks mk with a proxy coefficient αi, initially set to 1/T. Then, infer the novel example with the weighted mask ˆm =PT k=1 αimk to get the entropy. Further, the one-shot gradient calculated by the entropy for each αiindicates the transferability of each mask. The mask with the highest gradient will be chosen for initialization. ## 5 Experimental Settings 5.1 Datasets To cover all four typical IE task types (including NER, relation extraction, event extraction, and sentiment extraction), we formalize the lifelong UIE benchmark by leveraging 13 IE datasets to construct the task sequence. Specifically, NER tasks include ACE04 (Mitchell et al., 2005), ACE05-Ent (Walker et al., 2006), CoNLL03 (Tjong Kim Sang and De Meulder, 2003); Relation extraction tasks include CoNLL04 (Roth and Yih, 2004), ACE05- Rel, SciERC (Luan et al., 2018), NYT (Riedel et al., 2010); Event extraction tasks include CASIE (Satyapanich et al., 2020), ACE05-Evt; AspectBased Sentiment Analysis (ABSA) tasks include SemEval-14 (Pontiki et al., 2014), SemEval-15 (Pontiki et al., 2015), SemEval-16 (Pontiki et al., 2016). Refer to Appendix A for more detail about the datset statistics. For dataset split, we follow the same practice of the relevant prior works (Lu et al., 2022) when using it. As the task order could influence the performance, we create 5 different task orders by random permutation, which are listed in Table 4. ## 5.2 Evaluation Metrics For the evaluation of IE performance, we use the widely adopted span-based offset Micro-F1 as the primary metric following previous work (Lu et al., 2022). Given the generated text spans by our model, we map spans to offsets by finding the first matched offsets that are not already matched in the same SEL hierarchical level. For the evaluation of lifelong learning ability, we denote aT,i as the F1 on the test set of task i after training on task T. The average F1 on all tasks after training on the final task is reported following the common protocol (LopezPaz and Ranzato, 2017; Madotto et al., 2021): $$\mathbf{Average}={\frac{1}{T}}\sum_{i=1}^{T}a_{T,i}$$ $$\quad(5)$$ aT,i (5) To measure the forgetting during lifelong learning, we use the BWT, which assesses the impact that learning on subsequent tasks has on a previous task. Negative BWT indicates that the model has forgotten some previously acquired knowledge. $$\mathbf{BWT}={\frac{1}{T-1}}\sum_{i=1}^{T-1}a_{T,i}-a_{i,i}$$ $$(6)$$ Another metric is FWT (Ke et al., 2020), which measures how much performance boost has happened to a new task after learning the task, representing the forward knowledge transfer. $$\mathbf{FWT}={\frac{1}{T}}\sum_{i=1}^{T}a_{i,i}-a_{0,i}$$ $$\quad(7)$$ where a0,i refers to the performance of training task i individually. ## 5.3 Baselines And Training Details We adopt the following methods including recent SOTA as our baselines, which covers both *continual learning (CL)* and *Non-CL* methods. (1) continual learning methods: **Naive Fine-tuning:** fine-tunes the whole model on new task data continually. EWC (Kirkpatrick et al., 2017) is a Regularization-based method that regularizes the change of important model parameters during training. ER (Chaudhry et al., 2019) is a Rehearsalbased method that saves |M| (50 here) samples randomly sampled from the training set of each task i to memory Mi and jointly trains the model on new task data Dk and memory *M < k*. **Individual** saves a separate model for each task by fine-tuning Metrics / Method Average BWT FWT Memory + Param. Tune Param. Fine-tuning 42.932 -33.593 -31.501 0 0 100% EWC (Kirkpatrick et al., 2017) 37.416 -33.272 -32.479 0 200% 100% ER (Chaudhry et al., 2019) 68.089 -11.514 -1.806 50 0 100% AdapterCL (Madotto et al., 2021) 65.573 0 0 0 5.626% * T 5.626% C-PT (Zhu et al., 2022) 67.500 0 0 0 0.293% * T 0.293% L2P (Wang et al., 2022c) 73.610 -0.039 6.154 0 1.178% 0.293% Lottery Prompt Tuning (ours) 76.914 0 9.414 **0 0.293% + (0.009% * T) 0.097%** Individual (Lu et al., 2022) 69.895 - - - 100% * T 100% Multi-task prompt tuning 76.774 - - - 0.293% 0.293% Multi-task adapter tuning 78.341 - - - 5.626% 5.626% Multi-task Fine-tuning 80.484 - - - 100% 100% the whole PLM, which clearly has neither forgetting nor knowledge transfer. **AdapterCL** (Madotto et al., 2021) trains an adapter for each task separately. Similarly, **C-PT** (Zhu et al., 2022) trains a prompt for each task. L2P (Wang et al., 2022c) trains a prompt pool to transfer task knowledge and a distance-based prompt selection strategy to select the task-specific prompt. (2) *Non-CL* methods: **Multi-task Learning:** Fine-tuning the whole model in a multi-task manner using all tasks' data concurrently. **Multi-task Prompt/Adapter Tuning:** Prompt/Adapter Tuning in a multi-task manner instead of CL. These multi-task setups are widely accepted as the upper bound of continual learning. As for the LPT, we set the pruning ratio top-c% of LPT as 0.7 in our experiments. For all the prompt tuning methods mentioned above, the prompt length is set to 20. The parameters of PLM are initialized from *UIE-large* checkpoints (Lu et al., 2022). We keep all the same hyperparameters for the UIE model reported in their paper. We train the model for 30 epochs per task with batch size 24 on 8 NVIDIA A100 GPUs. All the CL and Non-CL baselines are implemented under the same UIE framework. For the prompt tuning methods, we adopt the deep prompt tuning version (Li and Liang, 2021; Liu et al., 2022b) to allow more per-task capacity. ## 6 Results & Analysis 6.1 Results On Seen Tasks The proposed LPT's performance is compared with current SOTAs *w.r.t* six measurements on the aforementioned 13 IE tasks as shown in Table 1. Among | Datasets | Average | | | | | | | |------------------------------|-------------|---------|---------|-----------------|-------------------|--------|-------| | Entity | Relation | Event | ABSA | | | | | | Settings | Methods | CoNLL03 | CoNLL04 | CASIE (Trigger) | CASIE (Arguments) | 15-res | | | Fine-tuning | 68.54 | 52.87 | 23.23 | 24.33 | 58.20 | 45.43 | | | AdapterCL | 65.02 | 22.49 | 7.00 | 2.68 | 43.20 | 28.08 | | | C-PT | 67.90 | 21.59 | 10.50 | 6.34 | 24.94 | 26.26 | | | L2P | 88.23 | 52.06 | 25.33 | 30.70 | 59.94 | 51.25 | | | Lottery Prompt Tuning (Ours) | 88.33 | 53.93 | 36.32 | 27.76 | 66.56 | 54.58 | | | Individual | 73.90 | 52.39 | 17.39 | 15.20 | 36.77 | 39.13 | | | Multi-task prompt tuning | 87.17 | 58.91 | 35.53 | 38.73 | 81.87 | 60.44 | | | Multi-task adapter tuning | 84.01 | 47.38 | 29.35 | 35.88 | 79.38 | 55.20 | | | Multi-task Fine-tuning | 85.05 | 57.07 | 11.10 | 7.91 | 92.23 | 50.67 | | | Few-shot Adaptation | Fine-tuning | 55.17 | 1.41 | 5.56 | 0.00 | 52.62 | 22.95 | | AdapterCL | 41.89 | 2.29 | 2.81 | 2.15 | 43.08 | 18.44 | | | C-PT | 42.11 | 0.47 | 2.21 | 0.00 | 0.00 | 8.96 | | | L2P | 72.16 | 23.89 | 4.75 | 2.55 | 1.06 | 20.88 | | | Lottery Prompt Tuning (Ours) | 69.29 | 18.12 | 6.56 | 5.79 | 63.70 | 32.69 | | | Individual | 0.85 | 0.00 | 0.52 | 0.00 | 0.00 | 0.27 | | | Multi-task prompt tuning | 59.77 | 25.04 | 11.63 | 7.96 | 81.87 | 37.26 | | | Multi-task adapter tuning | 56.91 | 30.21 | 11.28 | 9.43 | 80.47 | 37.66 | | | Multi-task Fine-tuning | 60.72 | 26.64 | 11.10 | 7.91 | 94.56 | 40.19 | | | Zero-shot Adaptation | | | | | | | | ![7_image_0.png](7_image_0.png) all the continual learning methods, we highlight that our method achieves the highest average F1 (improvements of up to 3% compared with L2P), BWT and FWT with the lowest computation resource usage, which verifies the effectiveness of LPT. While compared with the non-CL methods, we can see the results of LPT are even comparable with *Multi-task prompt tuning*, which is deemed as the upper bound of prompt tuning methods for continual learning. That could be due to some negative interference among tasks during multitask learning, however in our case, the parameter-isolation mechanism solves that. Note that *w.r.t* computation resource usage, the parameter-efficient-based methods generally require no memory and only add a small number (around 0.29% to 5.6% ) of additional parameters for each task, largely decreasing the computational and storage overhead. Even so, the LPT shows a remarkable superiority over other methods (only 0.097% and 0.302% on "Tune Param." and "+ Param." respectively). That's because the saved binary masks for lottery prompts only introduces an approximate overhead of 1/32 of the prompt vectors, which are usually represented by 32-bit float values. Detailed results on each IE task refer to Table 5. ## 6.2 Results On Novel Tasks We exclude 4 datasets in the task sequences (with different IE task types) as novel tasks and conduct experiments on them in the few-shot/zero-shot adaptation settings respectively. For the few-shot setting, we conduct 10-shot learning where 10 samples per class are used for the training. While in the zero-shot setting, the sequentially trained model is directly used for testing. We perform the aforementioned PPL-based mask selection method due to its simplicity and effectiveness. Performances are reported in Table 2 for the four evaluation tasks individually and on average. We see LPT could outperform all the CL baselines in few-shot and zero-shot settings, which implies that the mask selection module can make good use of upstream tasks for novel task generalization. This points to the fact that explicitly transferring knowledge learned from a similar task is critical for systematic adaptation to novel tasks. ## 6.3 Ablation Studies 6.3.1 Sparsity & Capacity We choose task order \#1 to visualize the model performance and the capacity of total prompts varing with the prompt pruned ratio. As shown in Figure 4, with the decrease of sparsity, the performance of the model (blue bar) presents a trend of first rising and then declining, while the prompt parameter usage (orange line) keeps rising with the decrease of sparsity. It is noteworthy that when the model is trained on a very long sequence of tasks, the prompt capacity could approach full. In this case, our LPT framework is capable of expanding the parameters by introducing new prompt tokens, which shows great flexible. ![7_image_1.png](7_image_1.png) ## 6.3.2 Mask Correlations To investigate how LPT reuses parameters over sequential tasks, we visualize all the task-wise binary mask correlations trained from 5 different task sequences in Figure 3. We see LPT shares parameters used for prior tasks with new ones, and is capable of self-adaptively exploring not-yet-chosen parameters. This demonstrates the effectiveness of LPT in both transferring positive knowledge from similar tasks and automatically exploring new patterns for dissimilar tasks. ## 7 Conclusions In this paper, we study a lifelong learning paradigm for UIE systems, which we regard as an important step towards general IE intelligence. We propose a novel parameter-efficient framework, *i.e.*, Lottery Prompt Tuning (LPT), to achieve positive knowledge transfer, catastrophic forgetting prevention, and rapid generalization. Experimental results validate the capability of our method on three settings. ## Limitations Though our method does not require iterative retraining, pruning, and rewinding process, one question still remains under-explored: how to selfadaptively find the optimal sparsity instead of trial training, which can boost the training efficiency. Also, we plan to further investigate the effectiveness of Lottery Prompt Tuning in other scenarios, including the multi-task learning (He et al., 2022b), prompt ensembling (Lester et al., 2021b), etc. Furthermore, the proposed learning method should be compatible with other parameter-efficient finetuning methods, such as Adapter tuning (Houlsby et al., 2019) and LoRA (Hu et al., 2021). 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Association for Computational Linguistics. ## A Dataset Statistics |Ent| |Rel| |Evt| #T rain #V al #*T est* ACE04 7 − − 6,202 745 812 ACE05-Ent 7 − − 7,299 971 1,060 CoNLL03 4 − − 14,041 3,250 3,453 ACE05-Rel 7 6 − 10,051 2,420 2,050 CoNLL04 4 5 − 922 231 288 NYT 3 24 − 56,196 5,000 5,000 SciERC 6 7 − 1,861 275 551 ACE05-Evt − − 33 19,216 901 676 CASIE 21 − 5 11,189 1,778 3,208 14res 2 3 − 1,266 310 492 14lap 2 3 − 906 219 328 15res 2 3 − 605 148 322 16res 2 3 − 857 210 326 Table 3: Detailed datasets statistics. *| ∗ |* indicates the number of categories, and \# is the number of sentences in the specific subset. ## B Detailed Results Of Task-Incremental Setting Here we present detailed experimental results on all 13 IE tasks across different task types including NER, relation extraction, event extraction and sentiment extraction. As shown in Table 5, the proposed LPT outperforms all competitive baselines. | 2 event:oneie_ace05_en_event entity:mrc_ace05 relation:NYT absa:15res event:casie relation:conll04 absa:14res absa:16res entity:conll03 relation:scierc absa:14lap entity:mrc_ace04 relation:ace05-rel 3 relation:conll04 relation:scierc entity:mrc_ace05 entity:mrc_ace04 event:oneie_ace05_en_event absa:15res relation:ace05-rel absa:14res event:casie relation:NYT entity:conll03 absa:16res absa:14lap 4 absa:14res entity:mrc_ace05 event:oneie_ace05_en_event absa:16res absa:14lap absa:15res relation:ace05-rel entity:mrc_ace04 relation:NYT event:casie entity:conll03 relation:scierc relation:conll04 absa:15res event:oneie_ace05_en_event relation:scierc entity:mrc_ace05 absa:14lap entity:conll03 relation:ace05-rel relation:conll04 event:casie entity:mrc_ace04 absa:14res relation:NYT absa:16res | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Task in order | 5 entity:mrc_ace05 event:oneie_ace05_en_event absa:15res relation:conll04 absa:14res event:casie relation:NYT relation:scierc absa:16res entity:conll03 relation:ace05-rel absa:14lap entity:mrc_ace04Table 4: Task Order across 13 IE tasks.Model / Tasks Task order | Fine-tuning EWC ER AdapterCL C-PT L2P LPT (ours) Individual MT-PT MT-AT MT-FT 63.78 61.86 75.27 60.07 71.33 69.64 74.91 74.33 76.11 77.28 76.76 52.29 45.75 76.14 85.38 82.65 86.30 87.36 85.17 85.89 88.97 87.59 12.97 5.10 51.82 70.36 51.70 72.18 73.52 53.62 67.89 70.43 72.87 event/oneie_ace05_en_event (trigger) 26.64 19.25 62.11 90.52 70.73 89.60 89.45 71.59 88.31 90.29 90.61 absa/16res 65.60 63.29 78.58 55.67 69.90 74.38 78.68 73.91 76.89 77.81 79.96 52.27 50.01 69.18 43.22 62.18 63.48 70.18 62.46 68.81 68.28 73.52 76.20 72.02 90.21 38.07 60.15 63.30 66.71 64.26 84.18 84.60 93.29 24.23 15.21 46.80 66.18 61.54 71.25 73.87 66.31 74.05 76.61 77.91 49.23 42.13 71.16 84.29 83.78 87.44 88.88 85.78 86.54 88.38 89.35 50.46 43.49 79.16 87.06 88.88 85.90 86.72 85.41 86.01 86.86 88.03 23.22 17.18 57.29 68.45 56.03 69.42 72.76 58.97 70.37 73.47 74.51 36.52 29.60 65.46 69.61 61.93 69.32 74.77 67.32 70.25 75.97 78.12 73.00 69.09 86.34 95.20 91.94 95.44 95.70 92.34 93.81 94.40 94.92 12.43 9.15 43.65 27.80 30.78 39.90 46.09 34.69 47.08 46.51 52.05 25.13 18.13 68.16 41.72 68.98 66.62 74.10 72.26 75.42 75.24 77.78 42.93 37.42 68.09 65.57 67.50 73.61 76.91 69.90 76.77 78.34 80.48 | | | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------|-----------------------| | CL-methods Non-CL methods | event/oneie_ace05_en_event | event/casie (trigger) | | entity/mrc_ace05 | relation/ace05-rel entity/mrc_ace04 | relation/conll04 | | relation/NYT | entity/conll03 relation/scierc | | | absa/14lap | event/casie | | | absa/14res | absa/15res | Average | Table 5: The final model performance on all 13 IE tasks after being sequentially trained. Our model LPT significantly outperforms other baselines. "MT-PT" means Multi-TaskPrompt Tuning. "MT-AT" means Multi-Task Adapter Tuning. "MT-FT" means Multi-Task Fine-Tuning. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitations A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section Abstract; Section1 Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Not applicable. Left blank. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section Implementation Details The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ren-etal-2023-retrieve

Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation

https://aclanthology.org/2023.acl-long.17

Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation. These methods use similarity-based retrieval, which is based on a simple hypothesis: the more the retrieved demonstration resembles the original input, the more likely the demonstration label resembles the input label. However, due to the complexity of event labels and sparsity of event arguments, this hypothesis does not always hold in document-level EAE. This raises an interesting question: How do we design the retrieval strategy for document-level EAE? We investigate various retrieval settings from the input and label distribution views in this paper. We further augment document-level EAE with pseudo demonstrations sampled from event semantic regions that can cover adequate alternatives in the same context and event schema. Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods and analyze why they work.

# Retrieve-And-Sample: Document-Level Event Argument Extraction Via Hybrid Retrieval Augmentation Yubing Ren1,2, Yanan Cao1,2*, Ping Guo1,2, Fang Fang1,2, Wei Ma1,2∗**, Zheng Lin1,2** 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China {renyubing}@iie.ac.cn ## Abstract Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation. These methods use similarity-based retrieval, which is based on a simple hypothesis: the more the retrieved demonstration resembles the original input, the more likely the demonstration label resembles the input label. However, due to the complexity of event labels and sparsity of event arguments, this hypothesis does not always hold in document-level EAE. This raises an interesting question: How do we design the retrieval strategy for document-level EAE? We investigate various retrieval settings from the input and label distribution views in this paper. We further augment document-level EAE with pseudo demonstrations sampled from event semantic regions that can cover adequate alternatives in the same context and event schema. Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods and analyze why they work. ## 1 Introduction Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text. Documentlevel Event Argument Extraction (document-level EAE) is the process of extracting informative event kernels from a document, which benefits many downstream applications, e.g., information retrieval, question answering, and event graph reasoning. Figure 1 presents an illustration of document-level EAE task. Given a *TransportPerson* event, document-level EAE aims to extract Event type: movement.transportperson.preventexit Inside the house, **the FSB** special forces detained **53 young men,** at least one of whom was promoting the Islamic State terror group ![0_image_0.png](0_image_0.png) ![0_image_1.png](0_image_1.png) online. 50 + detained , explosives found in illegal prayer hall in Origin ![0_image_2.png](0_image_2.png) ![0_image_4.png](0_image_4.png) series of house raids, which helped uncover more explosives, ![0_image_3.png](0_image_3.png) handguns, grenades and ammo. The same source said other known Destination ![0_image_6.png](0_image_6.png) members of that particular Salafi community are currently fighting ![0_image_5.png](0_image_5.png) southern Russia. April 29, 2016 According to an FSB source, the in **Syria** for the jihadists. Figure 1: An illustration of document-level EAE task. Special tokens <tgr> incorporate trigger words. Arguments are denoted by underlined words, and roles are denoted by arcs. event arguments and identify the roles they take: the FSB (Preventer), *53 young men* (Transporter), the illegal prayer hall (Origin), *Syria* (Destination). Retrieval-augmented methods have recently been successfully applied to many NLP tasks, e.g., dialogue response generation (Weston et al., 2018; Wu et al., 2019; Cai et al., 2019a,b), machine translation (Zhang et al., 2018; Xu et al., 2020; He et al., 2021) and information extraction (Lee et al., 2022; Zhang et al., 2022; Chen et al., 2022). These methods retrieve additional knowledge from various corpora to augment text generation, which allows models to (a) explicitly acquire prior external knowledge in a non-parametric manner, leading to great flexibility. (b) regard the retrieved reference instances as cues to generate text and learn by analogy. These retrieval-augmented methods use similarity-based retrieval, which is based on a simple hypothesis (Li et al., 2022): the more xr (retrieved demonstration) resembles x (original input), the more likely yr (demonstration label) resembles y (input label), so it will help the generation. This hypothesis is intuitive: similar input results in similar output for most tasks (Khandelwal et al., ∗Yanan Cao and Wei Ma are the co-corresponding authors 293 2020, 2021). For example, in language modeling, Dickens is the author of and *Dickens wrote* will have essentially the same distribution over the next word. However, in document-level EAE, xr resembles x cannot guarantee the equivalent distribution of yr and y in label space. In a document, only a few words are event arguments, while other distracting context can mislead similarity-based retrieval and cause demonstration label yr deviate from input label y. Furthermore, document-level EAE should predict not only the argument entity but also the correspondence between arguments and roles, which makes it challenging to find a demonstration with an identical event label to the original input. According to our statistics on RAMS dataset (Ebner et al., 2020), only 16.51% of instances can recall a sample with the same event schema through similarity-based retrieval. This raises an interesting question: since document-level EAE doesn't satisfy the hypothesis of similarity-based retrieval, how do we design the retrieval strategy for document-level EAE? In this paper, we explore various retrieval settings. First, if similar documents cannot guarantee the same distribution of event labels, does it make sense to pursue xr to be similar to x in retrieval process? To answer this, we first retrieve xr, close to x in input space, as discrete demonstration to keep contextual semantic consistency (**Setting 1**); Then, since the essence of the above hypothesis is to pursue yr resembles y, why don't we directly retrieve yr similar with y as the reference? So we recall yr, close to y in label space, as discrete demonstration to alleviate the difficulty of learning the complex event pattern of y (**Setting 2**); To find depth cues to guide the model, we want a demonstration that has equal distribution with input document in both input and label space. Intuitively, it is impossible to retrieve the ideal demonstration in discrete space, so we try to sample a cluster of pseudo demonstrations in continuous space instead. Recent works (Wei et al., 2020) have shown that the vectors in an adjacency region can easily cover adequate alternatives of the same meaning. Inspired by this intriguing observation, we sample pseudo demonstrations from the intersection of the adjacent regions of x and y, thus preserving both context and event schema consistency with the input (**Setting 3**). We present a systematic evaluation for analyzing various retrieval settings and observe that given a document, (1) context-consistency retrieval (**Setting 1**) helps the model identify the argument span more accurately than Setting 2. This suggests that in-distribution demonstration contexts can contribute to performance gains by improving the ability to recognize argument spans; (2) schema-consistency retrieval (**Setting 2**) makes the generated role labels more accurate than Setting 1, which indicates that conditioning on the label space contributes to better performance by alleviating the difficulty of learning the complex event pattern; and (3) adaptive hybrid retrieval (**Setting 3**) has achieved state-of-the-art (SOTA) performance among all generation-based baselines, indicating that this setting can generate diverse and faithful pseudo demonstrations with consistency in both input space and label space. Overall, the contributions can be summarized as follows: - We are the first to explore how to design the retrieval strategy for document-level EAE from the input and label distribution views. And our introduced retrieval strategies can recall demonstrations that can be helpful to demonstrate how the model should solve the task. - We further propose a novel adaptive hybrid retrieval augmentation paradigm that adaptively samples pseudo demonstrations from continuous space for each training instance to improve the analogical capability of the model. - Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods. We also conducted additional analytical experiments to discuss the reasons why different settings affect performance. ## 2 Methodology Problem Definition. We formulate documentlevel EAE in the manner of Ebner et al. (2020): given a document x = {w1, w2*, ..., w*|x|}, it contains a set of described events E. Each event e ∈ E has its event type t and designated by a trigger (a text span in x). Each event type t specifies a role set Rt. The event schema e is made up of event type and its associated role set. The task aims to extract all (*a, r*) pairs for each e ∈ E, where a ∈ x is an argument—a text span in x and r ∈ Rt is the role that a takes. ![2_image_0.png](2_image_0.png) ![2_image_1.png](2_image_1.png) ![2_image_2.png](2_image_2.png) For retrieval-augmented document-level EAE, we first retrieve the top-k potentially helpful demonstrations (discrete or continuous), then fuse them into the decoder to generate role records (a sequence of (*a, r*) pairs). In the following, we first introduce how to reformulate document-level EAE as Retrieval-Augmented Generation (RAG), then describe various retrieval settings. ## 2.1 Basic Rag Architecture We adopt the T5 model (Raffel et al., 2022), an encoder-decoder pre-trained model, as a backbone. The encoder-decoder LM models the conditional probability of selecting a new token y(i) given the previous tokens y(<i) and the encoder input [e; x] during the generation process. As a result, the total probability p(y|x, e) of generating the output y given the input [e; x] is calculated as: $$p(\mathbf{y}|\mathbf{x},\mathbf{e})=\prod_{i=1}^{|\mathbf{y}|}p\left(\mathbf{y}^{(i)}|\mathbf{y}^{(<i)},\mathbf{x},\mathbf{e}\right),\quad\quad(1)$$ where the input sequence is the concatenation of the document context and its event schema, constructed as *<s> event schema* [SEP] *document context </s>*. The output y is the role record, presenting by the concatenation of each argument and its event role, i.e., <s> arg1 role1... argn rolen *</s>*. In this paper, we decompose the modeling of p(y|x, e) into two steps: *retrieval* and *prediction*. Given a query document x, we first retrieve top-k potentially helpful demonstrations d from training corpus Dtrain. We model this as sampling from a distribution p(d|x). Then we use siamese network structures to obtain meaningful embeddings for input sequence [e; x] and demonstration d: $$\begin{array}{c}{{\mathbf{h_{e},h_{x}=T5\mathrm{-Encoder}([\mathbf{e};\mathbf{x}]),}}}\\ {{\mathbf{h_{d}=T5\mathrm{-Encoder}(\mathbf{d}).}}}\end{array}$$ $$\mathbf{\Pi}_{0}^{1}$$ Then, we condition on both the retrieved d and the original input [e; x] to generate the output y—modeled as p(y|d, x, e). Specifically, we integrate k demonstration embeddings hd = {hd (1), hd (2)*, ...,* hd (k)} into cross-attention module in all decoder layers by concatenating them to the encoder outputs and feed them all to decoder: $$\mathbf{\hat{n}_{e}};\mathbf{h_{x}}]),$$ $=\;\huge\top$ . $\downarrow$ . $$(4)$$ y = T5-Decoder(<bos>; [hd; he; hx]), (3) where <bos> is the beginning token of decoder, [hd; he; hx] denotes the encoder outputs we constructed for decoder input. In Setting 3, we use [v; he; hx] instead. To obtain the overall likelihood of generating y, we treat d as a latent variable, yielding: $$p(\mathbf{y}|\mathbf{x},\mathbf{e})=p(\mathbf{y}|\mathbf{d},\mathbf{x},\mathbf{e})\;p(\mathbf{d}|\mathbf{x}).$$ 295 ![3_image_0.png](3_image_0.png) ## 2.2 Demonstration Retrieval Design The main challenge of demonstration retrieval is to design an appropriate retrieval strategy to recall demonstrations that can be helpful to demonstrate how the model should solve the task. In this part, we explore various retrieval settings. As shown in Figure 2, we categorize the retrieval setting into three categories: (1) Context-Consistency Retrieval; (2) Schema-Consistency Retrieval; and (3) Adaptive Hybrid Retrieval. The goal of all retrieval settings in this part is to find k **demonstrations** (whether discrete or continuous). ## Setting 1: Context-Consistency Retrieval Since similar documents cannot guarantee the same distribution of event labels, Setting 1 aims to answer whether it makes sense to pursue xr to be similar to x in the retrieval process. Given a query document x, we retrieve the instance document xr from the training corpus Dtrain that is the top-k relevant to the original input document, as discrete demonstrations d. For retrieval, we use S-BERT (Reimers and Gurevych, 2019) to retrieve semantically similar documents xr ∈ Dtrain . ## Setting 2: Schema-Consistency Retrieval To explore whether conditioning on the label space contributes to performance gains, Setting 2 satisfies event schema consistency and aims to alleviate the difficulty of learning the complex event pattern of y. Given the event label y of input as query, we Algorithm 1: Gaussian Sampling Input: The embeddings of schema, document and discrete demonstrations, i.e. h¯e, h¯x and h¯d = {h¯d (1), h¯d (2)*, ...,* h¯d (k) } Output: A set of pseudo demonstrations v = {v(1), v(2)*, ...,* v(k)} 1 Normalizing the importance of each element in b = h¯x − h¯e: Wr = |b|−min(|b|) max(|b|)−min(|b|) 2 Initialize i ← 0 3 **while** i ≤ (k − 1) do 4 i ← i + 1 5 r(i) = ||h¯x − h¯d (i) ||, R = ||h¯x − h¯e|| 6 Use reparametrizetion to calculate the current scale vector: ω(i) ∼ N -1−r(i)/R+1 2 , diag W2r . 7 First sample a noise variable - from N (0, 1) 8 Then transform it to ω(i) = μ + - · σ, where μ = 1 − r(i)/R 2 , σ = Wr. 9 Calculate the current sample: v(i) = h¯e + ω(i) b 10 v ← v ∪ v(i) 11 end retrieve (also via S-BERT) the instance label yr ![3_image_1.png](3_image_1.png) that is the top-k relevant to the input label from the training corpus Dtrain. During the inference, the query is the event schema e of test sample. ## Setting 3: Adaptive Hybrid Retrieval To find the ideal demonstration that has equal distribution with input document in both input and label space to guide the model, we propose a novel adaptive hybrid retrieval strategy to sample pseudo demonstrations from continuous space as depth cues to improve the analogical capability of model. Given an instance document x, we first retrieve top-k helpful documents from the training corpus Dtrain. Conditioning on retrieved k discrete demonstrations, we adaptively determine k event semantic regions in continuous space for each training instance. Then we sample k pseudo demonstrations from k event semantic regions. Event Semantic Region. We treat points in the event semantic region as the critical states of eventsemantic equivalence. Specifically, in order to consider both context and event schema consistency, we first determine the adjacent region of document and event schema by setting their adjacent radii (the orange circle and purple circle in Figure 3). Furthermore, we define the intersection of their adjacent regions as an event semantic region -(h¯e, h¯x) (the light blue region in Figure 3), which describes accurate alternatives in consistency with original context and event semantic meaning. Here we have k discrete demonstration embeddings h¯d for k adjacent radii r, which determines k event semantic regions. For each event semantic region, we perform the following Gaussian sampling. Gaussian Sampling. To obtain diverse and faithful pseudo demonstrations from the event semantic region for the training instance x, we apply a Gaussian sampling strategy k times to sample a cluster of vectors from k event semantic regions. As shown in Figure 3, we first use scale vector ω(i) to transform the bias vector b = h¯x − h¯e as ω(i) b, where is the element-wise product operation. Then, we construct a novel sample v(i) = h¯e + ω(i) b as a pseudo demonstration. As a result, the goal of the sampling strategy turns into finding a set of scale vectors, i.e. ω = {ω(1), ω(2)*, ...,* ω(k)}. Intuitively, we can assume that ω(i) follows a distribution with Gaussian forms, formally: $$\omega^{(i)}\sim{\mathcal{N}}\left({\frac{1-r^{(i)}/R+1}{2}},\mathrm{diag}\left({\mathcal{W}}_{r}^{2}\right)\right),\tag{5}$$ where Wr = |b|−min(|b|) max(|b|)−min(|b|) normalizes the importance of each dimension in b, the operation |·| takes the absolute value of each element in vector, which indicates the larger the value is, the more informative it is. μ = 1−r(i)/R+1 2 constrains the sampling range to event semantic region. Since sampling is a non-differentiable operation that truncates the gradient, here we use a reparametrization trick to construct N (1 − r(i)/R 2 , diag(W2r )). We first sample a noise variable - from standard normal distribution N (0, 1). Then, instead of writing ω(i) ∼ N (*μ, σ*2): $$\omega^{(i)}=\mu+\epsilon\cdot\sigma,$$ $${\mathrm{where~}}\epsilon\sim{\mathcal{N}}(0,1),\mu=1-{\frac{r^{(i)}/R}{2}},\sigma={\mathcal{W}}_{r}.$$ Now the gradient is inside the expectation. We finally sample k pseudo demonstrations v from k event semantic regions to augment the text generation, that is v = {v(1), v(2)*, ...,* v(k)}, where v(i) ∼ -(h¯e, h¯x). k is the hyperparameter of the number of sampled vectors, which is determined by the number of discrete demonstrations. For a clearer presentation, Algorithm 1 summarizes the sampling process. ## 2.3 Training And Inference The trainable parameters of the model are only the encoder-decoder LM, which is denoted as θ. Given a training dataset Dtrain = (x1, y1)*, ...,* x|Dtrain |, y|Dtrain | , where each instance is a (document, role records) pair, the learning objective is a negative log-likelihood function: $${\mathcal{L}}=-\sum_{(\mathbf{x},\mathbf{y})\in{\mathcal{D}}_{\mathrm{train}}}\log p(\mathbf{y}|\mathbf{x},\mathbf{d},\mathbf{e},\theta).\quad(7)$$ After generating role records, we need to decode it back into (argument, role) pairs to calculate specific evaluation metrics. The detailed decoding process is in Algorithm 2. ## 3 Experiments $$(6)$$ We evaluate our model's performance on two commonly used document-level EAE benchmarks and compare it to prior works. Then we conduct additional analytical experiments on how the demonstration retrieval design affects performance. ## 3.1 Experimental Setup Datasets. We conduct our experiments on two widely used document-level EAE datasets: RAMS (Ebner et al., 2020) and WikiEvents (Li et al., 2021). RAMS provides 9,124 annotated examples from news based on 139 event types and 65 roles. WikiEvents provides 246 annotated documents based on 50 event types and 59 roles. Algorithm 2: Decoding the output Input: role record y : <s> arg1 role1... argn rolen *</s>*. Output: (*arg, role*) pairs. 1 Initialize *arg list* ← [ ] 2 for yi ∈ y do 3 /* Here consider multi-event scenario, separated by [SEP] */ 4 if yi = [SEP] then 5 if yi ∈/ role list then 6 append yi to arg list 7 else 8 role ← yi 9 argument ← arg list 10 get a (arg, role) pair 11 arg list ← [ ] 12 end 13 else 14 event index ← event index + 1 ![4_image_0.png](4_image_0.png) ![4_image_2.png](4_image_2.png) 15 *arg list* ← [ ] ![4_image_1.png](4_image_1.png) | Models | RAMS | WikiEvents | PLM | | | | |-------------------------------------------------|--------|--------------|-------|--------|------------|-----------| | Arg-I | Arg-C | Arg-I | Arg-C | Head-C | | | | Multi-label classification-based Models | | | | | | | | BERT-CRF (Shi and Lin, 2019) ∗ | - | 40.3 | - | 32.3 | 43.3 | BERT-base | | PAIE (Ma et al., 2022) ∗ | 54.7 | 49.5 | 68.9 | 63.4 | 66.5 | BART-base | | 56.8 | 52.2 | 70.5 | 65.3 | 68.4 | BART-large | | | QA-based Models | | | | | | | | EEQA (Du and Cardie, 2020) ∗ | 46.4 | 44.0 | 54.3 | 53.2 | 56.9 | BERT-base | | 48.7 | 46.7 | 56.9 | 54.5 | 59.3 | BERT-large | | | DocMRC (Liu et al., 2021) ∗ | - | 45.7 | - | 43.3 | - | BERT-base | | Generation-based Models | | | | | | | | BART-Gen (Li et al., 2021) ∗ | 50.9 | 44.9 | 47.5 | 41.7 | 44.2 | BART-base | | 51.2 | 47.1 | 66.8 | 62.4 | 65.4 | BART-large | | | T5-baseline‡ | 45.1 | 37.3 | 44.8 | 39.1 | 39.3 | T5-base | | 45.9 | 40.3 | 62.7 | 41.0 | 53.7 | T5-large | | | Our Models using Retrieval-augmented Generation | | | | | | | | Setting 1: Context-Consistency Retrieval | 52.2 | 44.9 | 59.8 | 40.4 | 58.7 | T5-base | | 53.9 | 47.9 | 66.8 | 50.9 | 63.4 | T5-large | | | Setting 2: Schema-Consistency Retrieval | 45.9 | 38.6 | 53.4 | 39.7 | 43.0 | T5-base | | 49.1 | 41.0 | 64.4 | 53.8 | 61.8 | T5-large | | | Setting 3: Adaptive Hybrid Retrieval | 53.3 | 46.3 | 61.4 | 46.1 | 62.5 | T5-base | | 54.6 | 48.4 | 69.6 | 63.4 | 68.4 | T5-large | | Table 1: Experimental results on RAMS and WikiEvents. ∗ means the results from (Ma et al., 2022), and ‡ denotes the results from our implemented models for a fairer comparison. We highlight the SOTA results (classificationbased method) with underlines. The best results among generation-based methods are marked in bold font. Evaluation Metrics. Our results are reported as F-1 score of argument identification (**Arg-I**) and argument classification (**Arg-C**). For WikiEvents dataset, we follow Li et al. (2021) to additionally evaluate argument head F1 score (**Head-C**). - **Arg-I**: an event argument is correctly identified if its offsets match those of any of the argument mentions. - **Arg-C**: an event argument is correctly classified if its offset and role type both match the ground truth. - **Head-C**: only considers the matching of the headword of an argument. For the predicted argument, we find the nearest matched string to the golden trigger as the predicted offset. As an event type often includes multiple roles, we use micro-averaged role-level scores as the final metric. Baselines. For strictly consistent comparison, we divide several state-of-the-art models into three categories: (1) Multi-label classification-based model: BERT-CRF (Shi and Lin, 2019), PAIE (Ma et al., 2022); (2) QA-based model: EEQA (Du and Cardie, 2020) and DocMRC (Liu et al., 2021); and (3) Generation-based model: BART-Gen (Li et al., 2021) and T5-baseline. T5-baseline is our own baseline without the retrieval component: directly encodes input context to generate role records. Experimental Settings. We initialize our models with the pre-trained T5 model, available in the HuggingFace Transformers library1. We consider two model sizes, base and large, containing respectively 220M and 770M parameters. We fine-tune the models on each dataset independently using AdamW (Loshchilov and Hutter, 2019) and conducted experiments on 4 NVIDIA-V100-32GB. Due to GPU memory limitation, we used different batch sizes for different models: 8 for T5-large and 16 for T5base; In each experiment, we train the model with 5 fixed seeds (42, 66, 88, 99, 101) and 4 learning rates (2e-5, 3e-5, 4e-5, 5e-5), and vote for the best learning rate for each seed with the best dev-set Arg-C performance. We report the averaged Arg1https://github.com/huggingface/transformers ![6_image_1.png](6_image_1.png) ![6_image_0.png](6_image_0.png) Figure 4: Impact of the input space and label space. Evaluated by Arg-C F1. More discussion is in Section 3.3. C performance on the test set for selected checkpoints. We list other important hyperparameters in Appendix A.3. ## 3.2 Main Results Table 1 presents the performance of all baselines and our models on RAMS and WikiEvents. From the results, we can conclude that: (1) By retrieving reference demonstrations to augment text generation, our retrieval-augmented models can significantly outperform generationbased models. Our Setting 3 improves Arg-C F1 by 1.6%~10.6% and **17.9%~54.6%** over the SOTA generation baseline BART-Gen and vanilla T5 on both datasets. Compared with sequence generation BART-Gen, our models do not require manually constructing the event template and can directly generate informative role records rather than irrelevant information. This verifies that the retrieval augmentation paradigm can improve the performance of generative document-level EAE. (2) *By reformulating document-level EAE as* retrieval-augmented generation, our models can achieve competitive performance without manually designing specific questions. Our methods surpass most of the QA-based and classificationbased baselines and achieve competitive performance with SOTA. Furthermore, compared to the QA-based models, our Setting 3 also demonstrates superior performance (up to 2.3 Arg-C F1 gains on RAMS), which reveals that retrieving demonstrations as cues works better than asking questions. (3) By generating pseudo-demonstrations in continuous space as depth cues to guide the model, our Setting 3 inspires the analogical capability of the model more than Setting 1 and 2. As in Table 1, continuous augmentation (Setting 3) significantly outperform the discrete augmentation methods (Setting 1 and 2) on both datasets, whether in base-model or large-model (1.3%~16.1% for Arg- ![6_image_2.png](6_image_2.png) I F1, 1.0%~24.6% for Arg-C F1). These results demonstrate the stronger ability of adaptive hybrid augmentations than traditional augmentations for generalizing event-semantic-preserved demonstrations. And event semantic regions can generate diverse and faithful pseudo demonstrations to effectively improve the analogical capability of document-level EAE model. ## 3.3 Analysis Impact of the input space. To explore the reason why context-consistency affects performance, we additionally experiment with two variants of the document (random documents and out-ofdistribution documents) on RAMS and WikiEvents. Specifically, "random documents" means that we randomly choose a set of k documents from their own training set as the demonstrations. "Out-ofdistribution documents" means that we randomly choose a set of k documents from each other's training set as the demonstrations. Figure 4 shows that using out-of-distribution documents as references significantly drops the performance, and using random documents is better than no demonstrations. Setting 1 improves Arg-C F1 by about 6.0% and 11.8% over the "random documents" and no demonstrations. This is likely because using the in-distribution text as the context makes the task closer to language modeling since the LM always conditions on the in-distribution text during training. Furthermore, using in-distribution with similar text as context can further improve performance. ![7_image_1.png](7_image_1.png) Impact of the label space. To explore the reason why schema consistency affects performance, we experiment with two variants of Setting 2 (random labels and random English words) on RAMS and WikiEvents. Specifically, "random labels" means that we randomly choose a set of k labels from their own training set as the demonstrations. "Random English words" means that we randomly choose a set of English words from https://pypi.org/ project/english-words/ (consists of 61,569 words) as the demonstrations. From Figure 4 we can see that the performance gap between using random/top-k labels (within the label space) and using random English words is significant. Setting 2 improves Arg-C F1 by about 0.65% and 2.5% over "random labels" and no demonstrations. This indicates that conditioning on the label space can alleviate the difficulty of learning the complex event pattern, which is why performance improves. Argument span prediction accuracy. Argument span prediction accuracy in Table 2 illustrates the Arg-I precision of both datasets. As expected, Setting 1 identifies the argument span more accurately than Setting 2, and the gap in prediction accuracy is as large as 25.5%. This indicates that in-distribution demonstration contexts can improve the ability to recognize argument spans and contribute to performance gains. Argument role prediction accuracy. We also evaluate the capability to generate golden argument role in target sequence. From Table 2 we can see that Setting 2 generates role labels more accurately than Setting 1, and the gap in prediction accuracy is 14.6%. This suggests that schema-consistency retrieval alleviates the difficulty of learning the complex event pattern, and conditioning on the label space contributes to better performance. Impact of the number of demonstrations k. Figure 5 illustrates how the hyper-parameters k ![7_image_0.png](7_image_0.png) affect the extraction performance. We observe that gradually increasing the number of demonstrations significantly improves Arg-C F1 in RAMS, but not in WikiEvents. We conjecture that the reason is that the averaged context length (about 900 words) in WikiEvents is too long, which affects the original input representation in the cross-attention module. ## 3.4 Few-Shot Setting To conduct detailed comparisons between different augmentation methods, we asymptotically increase the training data to analyze the performance of them on both datasets. Figure 6 shows the performance of them and T5-baseline with partial training samples. It demonstrates our approach achieves comparable performance with the T5baseline model with only ~20% of training data, which indicates that our approach has great potential to achieve good results with very few data. ## 4 Related Work Document-level Event Argument Extraction The goal of document-level EAE is to extract arguments from the whole document and assign them to right roles. On the task level, most of these works fall into three categories: (1) multi-label classification-based models (2) QA-based models (3) generation-based models. Specifically, Zhang et al. (2020); Xu et al. (2021); Huang and Jia (2021); Ren et al. (2022); Ma et al. (2022); Xu et al. (2022) first identified argument spans and then fill each with a specific role via multi-label classification; Du and Cardie (2020); Liu et al. (2021); Wei et al. (2021) formulated document-level EAE as an question answering (QA) or machine reading comprehension (MRC) problem; Li et al. (2021) designed specific templates for each event type and frames EAE as conditional generation. Above methods conduct experiments on WikiEvents (Li et al., 2021), RAMS (Ebner et al., 2020), and Chinese financial dataset (Zheng et al., 2019). ## Retrieval-Augmented Text Generation Rag has recently been successfully applied to many NLP tasks, e.g., dialogue response generation, machine translation, and information extraction. These methods retrieve additional knowledge from various corpora to augment text generation, which includes three major components: the retrieval source, retrieval strategy, and integration methods. Meanwhile, leveraging additional knowledge as the augmentation signal is a natural way to resolve the information insufficiency issue for information extraction. For example, Lee et al. (2022) proposed two demonstration retrieval methods for named entity recognition. Zhang et al. (2021) used the open-domain knowledge in Wikipedia as retrieval source for distantly supervised relation extraction. Du and Ji (2022) applied S-BERT (Reimers and Gurevych, 2019) to retrieve the most relevant example for event extraction. ## 5 Conclusion In this paper, we explore how to design retrievalaugmented strategy for document-level EAE from the input and label distribution views. And our introduced retrieval strategies can recall demonstrations that can be helpful to demonstrate how the model should solve the task. We further propose a novel adaptive hybrid retrieval augmentation paradigm to generate the reference vectors as depth cues to improve the analogical capability of model. Through extensive experiments on RAMS and WikiEvents datasets, we demonstrate the validity of our newly introduced retrieval-augmented models. 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A two-step approach for implicit event argument detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7479–7485, Online. Association for Computational Linguistics. Shun Zheng, Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. In *Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language* Processing (EMNLP-IJCNLP), pages 337–346, Hong Kong, China. Association for Computational Linguistics. ## A Dataset And Model A.1 Dataset Statistics RAMS is a document-level dataset of 9,124 annotated events from news based on an ontology of 139 event types and 65 roles. Each sample is a 5-sentence document, with the trigger word indicating a pre-defined event type and its argument scattered throughout the whole document. WikiEvents is another document-level dataset, providing 246 annotated documents from English Wikipedia articles based on 50 event types and 59 roles. Table 3 presents their detailed statistics. | Dataset | #Split | #Doc | #Event | #Argument | |------------|----------|--------|----------|-------------| | Train | 3,194 | 7,329 | 17,026 | | | RAMS | Dev | 399 | 924 | 2,188 | | Test | 400 | 871 | 2,023 | | | Train | 206 | 3,241 | 4,542 | | | WikiEvents | Dev | 20 | 345 | 428 | | Test | 20 | 365 | 566 | | Table 3: Statistics of RAMS and WikiEvents datasets. ## A.2 Details Of Baselines We compare our model with the following previous models. - BERT-CRF (Shi and Lin, 2019): a multi-label classification-based method that uses a BERTbased BIO-styled sequence labeling model. We report the results from Liu et al. (2021). - PAIE (Ma et al., 2022): another multi-label classification-based method that defines a new prompt tuning paradigm for event argument extraction. We report the results from original paper. - EEQA (Du and Cardie, 2020): the first Question Answering (QA) based model designed for sentence-level EAE task. We report the results from Ma et al. (2022). - DocMRC (Liu et al., 2021): another QAbased method with implicit knowledge transfer and explicit data augmentation. We report the results from original paper. - BART-Gen (Li et al., 2021): formulate the task as a sequence-to-sequence task and uses BART-large to generate corresponding arguments in a predefined format. For BART-large model, we report the results from origin paper. For BART-base model, we report the results from Ma et al. (2022). ## A.3 Implementation Details We list other important hyperparameters in Table 4. | Hyperparameter | RAMS | WikiEvents | | | |-------------------|----------|--------------|----------|-------| | T5-base | T5-large | T5-base | T5-large | | | Batch size | 16 | 8 | 16 | 8 | | Training epochs | 50 | 50 | 20 | 40 | | Optimizer | AdamW | AdamW | AdamW | AdamW | | Max input length | 512 | 512 | 512 | 512 | | Max target length | 64 | 64 | 512 | 512 | | Max demo length | 150 | 100 | 200 | 100 | | k | 20 | 20 | 5 | 5 | Table 4: Hyperparameters ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations ✓ A2. Did you discuss any potential risks of your work? Limitations ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 3 Experiments B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** 3 Experiments C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? No response. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? No response. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? No response. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? No response. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

wu-etal-2023-wecheck

{W}e{C}heck: Strong Factual Consistency Checker via Weakly Supervised Learning

https://aclanthology.org/2023.acl-long.18

A crucial issue of current text generation models is that they often uncontrollably generate text that is factually inconsistent with inputs. Due to lack of annotated data, existing factual consistency metrics usually train evaluation models on synthetic texts or directly transfer from other related tasks, such as question answering (QA) and natural language inference (NLI).Bias in synthetic text or upstream tasks makes them perform poorly on text actually generated by language models, especially for general evaluation for various tasks. To alleviate this problem, we propose a weakly supervised framework named \textbf{WeCheck} that is directly trained on actual generated samples from language models with weakly annotated labels.WeCheck first utilizes a generative model to infer the factual labels of generated samples by aggregating weak labels from multiple resources.Next, we train a simple noise-aware classification model as the target metric using the inferred weakly supervised information.Comprehensive experiments on various tasks demonstrate the strong performance of WeCheck, achieving an average absolute improvement of 3.3{\%} on the TRUE benchmark over 11B state-of-the-art methods using only 435M parameters.Furthermore, it is up to 30 times faster than previous evaluation methods, greatly improving the accuracy and efficiency of factual consistency evaluation.

# Wecheck: Strong Factual Consistency Checker Via Weakly Supervised Learning Wenhao Wu1∗ , Wei Li2, Xinyan Xiao2, Jiachen Liu2**, Sujian Li**1† , Yajuan Lyu2 1Key Laboratory of Computational Linguistics, MOE, Peking University 2Baidu Inc., Beijing, China {waynewu,lisujian}@pku.edu.cn {liwei85,xiaoxinyan,liujiachen,lvyajuan}@baidu.com ## Abstract ![0_Image_0.Png](0_Image_0.Png) A crucial issue of current text generation models is that they often uncontrollably generate text that is factually inconsistent with inputs. Due to lack of annotated data, existing factual consistency metrics usually train evaluation models on synthetic texts or directly transfer from other related tasks, such as question answering (QA) and natural language inference (NLI). Bias in synthetic text or upstream tasks makes them perform poorly on text actually generated by language models, especially for general evaluation for various tasks. To alleviate this problem, we propose a weakly supervised framework named **WeCheck** that is directly trained on actual generated samples from language models with weakly annotated labels. WeCheck first utilizes a generative model to infer the factual labels of generated samples by aggregating weak labels from multiple resources. Next, we train a simple noise-aware classification model as the target metric using the inferred weakly supervised information. Comprehensive experiments on various tasks demonstrate the strong performance of WeCheck, achieving an average absolute improvement of 3.3% on the TRUE benchmark over 11B state-of-the-art methods using only 435M parameters. Furthermore, it is up to 30× faster than previous evaluation methods, greatly improving the accuracy and efficiency of factual consistency evaluation.1 ## 1 Introduction The research of text generation has achieved significant progress in recent years, but it still suffers the main issue of generating output which is factually inconsistent with the given inputs (Maynez et al., 2020). To tackle this issue, various metrics have been designed to check the consistency between ∗Work is done during an internship at Baidu Inc. †Corresponding author. 1Our metric can be easily accessed from https:// huggingface.co/nightdessert/WeCheck generated text and the given inputs (Kryscinski et al., 2020; Scialom et al., 2021). As we know, how to construct such a metric has attracted increasing attention in a variety of fields (Wu et al., 2022b), including text summarization (Kryscinski et al., 2020; Wu et al., 2022a), dialogue generation (Welleck et al., 2019), and text simplification (Devaraj et al., 2022). Existing factual metrics can be classified into two types: one based on synthetic data and the other based on task transfer. Synthetic-data based metrics (Kryscinski et al., 2020; Mishra et al., 2021) apply data augmentation techniques to construct factual and non-factual texts as positive and negative samples, respectively. Metrics trained from these synthetic samples often perform poorly due to the significant mismatch between features of actual generated and synthetic text (e.g. distribution of factual errors) (Goyal and Durrett, 2021). Task-transfer based metrics utilize the reasoning ability of models trained on relevant upstream tasks, such as natural language inference (NLI) (Falke et al., 2019; Laban et al., 2022) and question answering (QA) (Wang et al., 2020; Fabbri et al., 307 2022) and directly apply them to evaluate factual consistency without any adaption. As described above, previous metrics are learned indirectly from other related resources but without seeing the actual generated text. In such cases, they may overfit to their upstream tasks and fail to generalize to actual generated samples that have significantly different data features. Figure 1 illustrates the probability density of three metrics, where the horizontal axis is metric scores and the vertical axis is the score density. Though these metrics are comparable in performance, they vary significantly in probability distributions, especially in the XSUM dataset, where sample features are greatly different from upstream tasks of these metrics2, NLI-warmup is extremely confident in predicting both very high and low scores while SUMMAC and QAFact are only confident in predicting low scores3. Furthermore, during testing, ensembling different metric scores by simply averaging will further improve their performance (Honovich et al., 2022). This also implies that the evaluation metrics learned from different resources are also complementary. To bridge the gap between training and testing and mitigate the scarcity of labeled data, in this paper, we propose **WeCheck**, a factual consistency Checking framework based on Weakly supervised learning. Specifically, WeCheck is based on a learning paradigm that provides weak supervision via modeling multiple label sources without access to ground truth. Different from previous metrics, WeCheck directly utilizes the abundant actual generated samples bootstrapped from models trained on target downstream tasks, e.g. BART on text summarization. Then, WeCheck follows a two-step pipeline consisting of weak annotation and noiseaware fine-tuning to get the target metric model. In the weak annotation step, by aggregating multiple weak supervision resources, we infer the unknown ground truth label of a sample. To reach this goal, we first provide each sample with a set of weak supervision signals calculated from various other metrics. These metrics are learned from various resources or tasks such as QA-based metrics and NLI-based metrics. After unifying and filtering these signals, we train a generative labeling model that models agreements and disagreements 2In XSum, the summary of each document is abstractive, while existing NLI and QA datasets do not have this feature. 3For more details about these metrics please refer to § 2.3 and §3.2. between them to infer the likelihood of their latent ground truth label. The inferred ground truth likelihood is then treated as a probabilistic label to provide weak supervision. In the second step, we apply noise-aware fine-tuning to train the target metric model. It is noted here, the weak annotation also brings noises to the supervision signal and brings new challenges to the model optimization process. As a solution, we first warmup our target metric model with NLI data for a better initialization before weakly supervised training. Then, after filtering out samples that are likely to be noisy, we finetune our target metric model with weak annotations. In summary, WeCheck could learn how to utilize multiple resources for weak annotation while recognizing and filtering the potential noises accompanied by weak supervision. Experimental results show that WeCheck not only achieves state-of-the-art performance but also is computationally efficient. On the TRUE benchmark (Honovich et al., 2022), which is the current most comprehensive benchmark for factual consistency evaluation, WeCheck obtains an average ROC AUC of 84.8, 3.3% absolute improvement over previous 11B pre-trained task transferred metrics with only a size of 435M parameters. Moreover, it's much more stable for various generation tasks, with much lower variance on different tasks. Thus, WeCheck is a simple but more effective and efficient metric for factual consistency evaluation. We summarize our contributions as follows: - We propose a novel factual consistency evaluation metric based on weakly supervised learning, namely WeCheck, which is directly trained on actual generated samples from language models with weakly annotated labels. - WeCheck is both effective and efficient achieving 3.3% absolute improvement and up to 30 times faster comparing with previous state-ofart metrics. - WeCheck is a general metric which is also more stable on various generation tasks and datasets than previous methods. ## 2 Wecheck Framework Figure 2 illustrates the two-step pipeline of WeCheck framework. In the upper part of the figure, during the weak annotation step, we first calculate a set of weak supervision signals for each ![2_image_0.png](2_image_0.png) sample bootstrapped from target generation tasks. Then, we use a mapping function to unify the weak supervision signals and infer the likelihood of the ground truth label of each sample. After annotation, we apply noise-aware fine-tuning to train our target metric model, shown in the lower part of the figure. Noise-aware fine-tuning first warmup target metric model with NLI data and training it with filtered probabilistic labels. In the following, we introduce our problem definition and detailed method. ## 2.1 Problem Definition Factual Consistency Evaluation Given a textual sequence as a premise, and another textual sequence as a hypothesis, which may be a generated summary or dialogue, the goal of a factual consistency metric fθ is to predict whether the hypothesis is factual consistent given the premise. For simplicity, we follow the previous textual entailment based framework (Kryscinski et al., 2019), which takes x, the concatenation of hypothesis and premise, as the input format and unifies the evaluation as a binary classification problem: fθ(x) ∈ [0, 1], where the predicted logit indicates the probability of x being factually consistent. Another advantage of using the entailment-based framework is that it is effective in terms of time complexity compared with other methods (Laban et al., 2022). Taking fθ as the target metric model, the goal of WeCheck is to train fθ into an efficient factual consistency metric. Weakly Supervised Training In our weakly supervised settings, we first bootstrap a set of samples from the generation tasks, e.g. text summarization, and dialogue generation. Using various factual metrics trained from multiple resources, we provide each sample x with a set of weak signals λ = (λ1*, . . . , λ*k), where each λiis a logit separately calculated by a metric. We treat the ground truth label ye of x as a hidden variable that can be estimated by aggregating λ. To reach this goal, we train a labeling model pϕ to model agreements and disagreements relations between weak signals in λ and estimate the probability distribution of the truth label, pϕ(ye|λ). Then, we apply pϕ(ye|λ) to supervise the metric model fθ. ## 2.2 Weak Annotation To provide weak supervision for training, we follow data programming (Ratner et al., 2017; Bach et al., 2017), a weakly supervised learning paradigm based on modeling multiple label sources. However, in data programming, weak supervision signals are often produced by various checking clauses, e.g. *whether word "causes" appears in* the sentence ? and produce a discrete weak signal λi ∈ {0, 1, −1}, where 0/1 stands for a vote for positive/negative label and −1 stands for a abstain vote. However, in our scenario, due to the diversity of metric frameworks, outputs of different metrics often do not share a unified output format and are usually continuous. For example, QA-based metrics often produce continuous logits in [0, 1], and NLI-based metrics often produce discrete labels of entailment or contradiction. Thus, the first thing before training the labeling model is to unify weak supervision signals by a mapping function, m (λi) → {0, 1, −1}. In this way, we can model the transformed λ by a data programming based labeling model. Weak Signal Unification We first unify all the weak supervision signals from different metrics into the same format, a logit λi ∈ [0, 1]. For the metric with single logit output, we directly use its output as λi. For multi-label classification output, we select the probability of predicting entailment. Notice that all the signals predicted by imperfect metrics will introduce a portion of noises. For a more reliable signal, the core idea for designing a mapping function m is to map signals that the metric has high confidence into {0, 1} and abstain low-confidence signals by mapping them to −1. Generally, this can be achieved by setting thresholds on signals. But another important issue to be noticed is that, as shown in Figure 1, signal distributions vary significantly across metrics and datasets, which makes threshold selection difficult. Thus, we instead dynamically determine thresholds by setting constant probability mass that contains the highest confidence. Specifically, we choose to map the lowest p− percent and the highest p + percent of signal scores into label 0 and 1, separately, and map the rest interval of low-confident scores into -1. Given the inverse cumulative distribution function of the i-th signal Fi, we can calculate its positive and negative threshold γ + iand γ − iby: $$\gamma_{i}^{+}=F_{i}(1-p^{+}),\quad\gamma_{i}^{-}=F_{i}(p^{-}).$$ The mapping function is then defined by: −). (1) $$m(\lambda_{i})=\left\{\begin{array}{cc}0&\lambda_{i}\leq\gamma_{i}^{-}\\ 1&\lambda_{i}\geq\gamma_{i}^{+}\\ -1&\gamma_{i}^{-}<\lambda_{i}<\gamma_{i}^{+}.\end{array}\right.\tag{2}$$ For simplicity, we share $p^{-}$ and $p^{+}$ across different resources and datasets. By applying the mapping function, we unify each λiinto a discrete label in {0, 1, −1}. Labeling model We treat the true label ye of x as a hidden variable and train the labeling model pϕ to estimate ye by aggregating λ 4. The generative model pϕ models the generation process of λ and ye by their joint probability. Because all the weak supervision signals are inferred from different resources, we treat them as independent variables. Then, given the prior p(ye) 5, the joint probability is formulated by $$p_{\phi}(\lambda,\widetilde{y})=\prod_{\lambda_{i}\in\lambda}p_{\phi}(\lambda_{i},\widetilde{y})=\prod_{\lambda_{i}\in\lambda}p\left(\lambda_{i}|\widetilde{y}\right)p\left(\widetilde{y}\right),\tag{3}$$ following Bayesian rule. Next, we need to model the likelihood p (λi|ye) that labels the sample with λi based on the latent label ye. Following (Ratner et al., 2017), we define the labeling process of λi as a sequence of Bernoulli process. Concretely, the i-th metric has a probability of βi not to abstain the sample and a probability αito label it correctly. Then, we calculate the likelihood by Then, we calculate the method by $$p_{\phi}(\lambda_{i}|\widetilde{y})=\left\{\begin{array}{cc}\beta_{i}\alpha_{i}&\lambda_{i}\neq-1\wedge\lambda_{i}=\widetilde{y}\\ \beta_{i}(1-\alpha_{i})&\lambda_{i}\neq-1\wedge\lambda_{i}\neq\widetilde{y}\\ 1-\beta_{i}&\lambda_{i}=-1,\end{array}\right.\tag{4}$$ 4All the weak supervision signals in λ have already been converted into discrete labels by the mapping function m. 5p(ye) usually depends on class distribution in a dataset. For simplicity, we set it as a uniform distribution. where αi, βi are learnable hyper-parameters. Given all samples, we train the labeling model by optimizing: $${\mathcal{L}}_{\phi}=\operatorname*{min}_{\phi}\sum_{\lambda}\sum_{\widetilde{y}\in\{0,1\}}\log p_{\phi}(\lambda,\widetilde{y}).$$ $$\quad(S)$$ log pϕ(λ, ye). (5) ## 2.3 Noise Aware Fine-Tuning NLI Warmup After we get the labeling model pϕ, the next step is to train our metric model fθ with the weak supervision inferred by it. But in practice, we find direct training with weak supervision will cause the model easily converges to the local minima. This may because reasoning over a long range of context is challenging and weak supervisions are also potential to be noisy. These problems cause great difficulties in optimization. Inspired by the idea of curriculum learning (Bengio et al., 2009), we first warmup our metric model on NLI, an easier and closely related task. We use the mixture of four NLI datasets, MultiNLI (Williams et al., 2018), Fever-NLI (Thorne et al., 2018), LingNLI (Parrish et al., 2021) and Adversarial-NLI (Nie et al., 2020). Based on the warmed-up checkpoint, our metric model achieves much better results under weak supervision, which we will later show in our experiments. Noise Filtering and Training After warming up, we train our metric model with weak supervision. Because the estimated latent labels ye can still be noisy due to the imperfect labeling model and weak supervision signals, we apply the likelihood of ye that contains the certainty of the prediction as a soft probabilistic label instead of the discrete label for training. Based on the definition of joint probability in Eq. 3, we predict the likelihood of each sample by $$p_{\phi}(\widetilde{y}=1|\boldsymbol{\lambda})=\frac{p_{\phi}(\boldsymbol{\lambda},1)}{p_{\phi}(\boldsymbol{\lambda},1)+p_{\phi}(\boldsymbol{\lambda},0)}\,.\tag{6}$$ With convenience, we abbreviate pϕ(ye = 1|λ) as p(y +). Before training with p(y +), we first filter out estimated samples with low confidence, by applying the similar procedure in weak signal unification. By reusing mapping function m, we filter out the low confident probabilistic label and get the final training set by $${\mathcal{X}}=\left\{\left({\boldsymbol{x}},p(y^{+})\right)\left|m\left(p(y^{+})\right)\neq-1\right.\right\},\quad(7)$$ where p(y +) is the corresponding probabilistic label of x. Then, given fθ after warming up, we finetune it by $$\begin{split}\mathcal{L}_{f}&=\min_{\theta}\sum_{\mathbf{x}\in\mathcal{X}}\left[p(y^{+})\log\left(f_{\theta}(\mathbf{x})\right)\right.\\ &\left.+\ \left(1-p(y^{+})\right)\log(1-f_{\theta}(\mathbf{x}))\right],\end{split}\tag{8}$$ where $p(y^{+})$ is kept fixed without gradient back +) is kept fixed without gradient backpropagation to pϕ during training. During inference, the model only needs to take the textual sequence x as input and output the logit prediction fθ(x). ## 3 Experimental Settings In this section, we introduce the experimental settings of WeCheck including the evaluation benchmark, baseline models, and implementation details. ## 3.1 True Benchmark Recent works point out that the performance of a metric should be evaluated comprehensively across multiple tasks and datasets to reduce variance. Thus, we evaluate WeCheck on TRUE (Honovich et al., 2022), a benchmark consisting of 11 datasets of 4 tasks including text summarization, dialogue generation, paraphrasing, and fact checking, where each sample in datasets is annotated with a binary label manually. We only test on the first three tasks as fact checking is beyond our scope. Following TRUE, we normalize each metric score into a logit and report their **Characteristic Area Under the** Curve (ROC AUC) w.r.t binary logits. Evaluation with ROC AUC does not require metrics to set specific decision thresholds. Details of tasks and datasets of TRUE are introduce in the Appendix A. ## 3.2 Baseline We evaluate WeCheck by comparing with recently proposed metrics. We categorize these baselines by types of their methods. NLI-based Metrics FactCC (Kryscinski et al., 2020) is a BERT-based metric with synthetic training samples constructed from rule-based data augmentation. **SUMMAC(SC**ZS) (Laban et al., 2022) aggregates sentence-level entailment scores for the final factual consistency score. We only report the zero-shot version SCZS instead of the supervised version SCCONV because it is more effective on the TRUE benchmark. **ANLI** (Honovich et al., 2022) directly apply a large 11B T5 trained on Adversarial-NLI (Nie et al., 2020) dataset for fact checking and achieve SOTA performance on TRUE. QA-QG based Metrics QuestEval (Scialom et al., 2021) is a QA-QG based metric that jointly measures factual consistency and semantic relevance, where the importance of generated questions are weighted by a trained model. **QAFactEval (QAFact)** (Fabbri et al., 2022) is a metric designed by carefully optimizing each component of the QG-QA framework. Q 2, from the version of Honovich et al. (2022), replace all the component of QA-QG framework into T5 11B large models. Other Types BERTScore (BERTS) (Zhang et al., 2019a) measure the similarity of a generated text and its reference by aggregating tokenlevel similarities of their contextual representations. BARTScore (BARTS) (Yuan et al., 2021) evaluate the quality of generated text by its modeling perplexity of a fine-tuned BART (Lewis et al., 2020). ## 3.3 Implementation Details All the baseline metrics are tested based on their open-sourced codes. The metric model of WeCheck is based on powerful pre-trained language model DeBERTaV3 (He et al., 2021). Following the description in § 2, we first warm up DeBERTaV3 on NLI datasets and apply it for weak supervised training. As regards to training data, we sample text summarization examples from BART fine-tuned on CNN/DM and XSum datasets. We sample dialogue generation examples from MemNet (Dinan et al., 2018) and dodecaDialogue (Shuster et al., 2020) trained on WoW dataset following Honovich et al. (2021). For paraphrase, we directly use samples in PAWS since it can be regard as a consistency checking dataset itself. For weak signals, we apply QAFact (Fabbri et al., 2022), SUMMAC (Laban et al., 2022), and the NLI warmed up DeBERTaV3 (NLI-warmup) as to provide weak signals for each sample as default. For weak signal unification, we set p + and p− in mapping function m to 0.75 and 0.25 based on validation. For labeling model pϕ, we follow the implementation of Snorkel (Ratner et al., 2017) for efficiency and train it on CPUs with Adam optimizer. For noise-aware fine-tuning, we finetune the warmed up checkpoint with the learning rate of 1e−6, warmup steps of 500, and the total training steps of 3 epoch. We train on 4 NVIDIA Tesla V100 GPUs, and it takes around only 5000 steps to reach the best performance. | Summarization | Dialogue | Para. | Ave | Var↓ | | | | | | | | | | |------------------|------------|---------|-------|--------|------|-------|------|------|------|------|------|------|------| | 2 | DialF | Ave | PAWS | | | | | | | | | | | | Frank | SumE | MNBM | Q-C | Q-X | Ave | BEGIN | Q | | | | | | | | BERTS | 84.3 | 77.2 | 62.8 | 69.1 | 49.5 | 68.6 | 87.9 | 70.0 | 64.2 | 74.0 | 77.5 | 71.4 | 140 | | BARTS | 86.1 | 73.5 | 60.9 | 80.9 | 53.8 | 71.0 | 86.3 | 64.9 | 65.6 | 72.3 | 77.5 | 72.2 | 132 | | FactCC | 76.4 | 75.9 | 59.4 | 76.4 | 64.9 | 70.6 | 64.4 | 63.7 | 55.3 | 61.1 | 64.0 | 66.7 | 60.1 | | SCZS | 88.9 | 81.3 | 71.1 | 80.9 | 78.1 | 80.1 | 82.0 | 77.4 | 84.1 | 81.2 | 88.2 | 81.4 | 30.4 | | QuestEval | 84.0 | 70.1 | 65.3 | 64.2 | 56.3 | 68.0 | 84.1 | 72.2 | 77.3 | 77.9 | 77.3 | 71.4 | 87.7 | | QAFact | 87.8 | 77.4 | 68.7 | 83.3 | 76.9 | 78.8 | 76.3 | 80.4 | 84.5 | 80.4 | 85.0 | 80.0 | 34.4 | | 11B Large Models | | | | | | | | | | | | | | | Q 2 | 87.8 | 78.8 | 68.7 | 83.5 | 70.9 | 77.9 | 79.7 | 80.9 | 86.1 | 82.2 | 89.7 | 80.7 | 51.6 | | ANLI | 89.4 | 80.5 | 77.9 | 82.1 | 83.8 | 82.5 | 82.6 | 72.7 | 77.7 | 77.7 | 86.4 | 81.5 | 24.9 | | Our Models | | | | | | | | | | | | | | | NLI-warmup | 85.7 | 73.7 | 73.5 | 73.2 | 80.1 | 77.2 | 80.5 | 83.5 | 87.3 | 83.8 | 85.4 | 80.3 | 31.8 | | WeCheck | 88.1 | 79.8 | 83.0 | 82.6 | 81.4 | 83.0 | 84.6 | 84.0 | 90.0 | 86.2 | 89.6 | 84.8 | 13.2 | ## 4 Results The experimental results on TRUE are reported in Table 1, where we report the performance of our model after warmed up training with NLI as NLIwarmup, and further trained with weak supervision as WeCheck. Surprisingly, pre-trained language model trained with only NLI-warmup can achieve 80.3 ROC AUC score, which is a comparable performance with previous best metric. NLI-warmup achieves the second best performance in 5 out of 9 datasets. After further training with weak supervision, WeCheck improves the evaluation performance over NLI-warmup by 4.5 ROC AUC, which not only largely surpasses all the baselines but also outperforms previous SOTA metric SCZS by 3.4 ROC AUC. Separately on each dataset, WeCheck achieves either the best (6 out of 9) or the second best performance in each dataset. Specifically, WeCheck achieves 5.4%, 7.2%, and 1.6% of relative improvements over previous best performing methods on summarization, dialogue and paraphrase, respectively. Furthermore, WeCheck has the lowest variance of 13.2 across different tasks. This demonstrates that the performance of WeCheck is more comprehensive and general rather than biased towards a certain type of data. On the MNBM dataset where samples are very different from NLI or QA data (samples in MNBM are sampled from XSUM, where hypothesis are extremely abstractive), WeCheck largely outperforms previous best metric QAFact by 14.3 point. 11B Baselines We also compare our models with large-scale 11B models based on task transfer. We compare with two models, Q2and ANLI based on 11B T5 reported by Honovich et al. (2022). As shown in Table 1, they surpass the same type of method with smaller parameter size, and can be regarded as approaching the best performance of task transfer based methods can achieve. However, with only 435M parameters, WeCheck significantly outperforms them by 3-4 points. This further validates the superiority of our weak supervision learning framework. ## 5 Analysis To analyse how each module and settings work, we conduct analysis experiments on each module and settings of WeCheck. Training Mechanism We first study how the mechanisms proposed in §2 affect the overall framework by removing or replacing them. The results are reported in Table 2. Most important of all, by removing the NLI-warmup before weak supervision training, the performance drops significantly on each task and drops an average of 19.3% on each dataset. This proves that NLI, as an easier | Sum. | Dial. | Para. | Ave | | |------------------|---------|---------|-------|------| | WeCheck | 83.0 | 86.2 | 89.6 | 84.8 | | w/o NLI-warmup | 67.8 | 75.7 | 50.7 | 68.5 | | w/o Noise Filter | 81.6 | 85.3 | 78.2 | 83.7 | | w/ Hard Label | 82.8 | 86.0 | 89.5 | 84.6 | | Sum. | Dial. | Para. | Sum. | Dial. | Para. | Ave | |--------|---------|---------|--------|---------|---------|-------| | 77.2 | 85.4 | 85.4 | 80.3 | | | | | ✓ | 83.4 | 85.2 | 89.2 | 84.6 | | | | ✓ | 72.7 | 84.2 | 84.2 | 77.8 | | | | ✓ | 77.2 | 86.7 | 92.1 | 81.8 | | | | ✓ | ✓ | ✓ | 83.0 | 86.2 | 89.6 | 84.8 | and closely related task, provides a much better initialization for training with weak supervision. For noise-aware finetuning, we study how filtering potential noisy samples (Eq. 7) and the probabilistic label (Eq. 6) affect the overall performance. After removing noise filtering (w/o Noise Filter in Table 2), the performance drops around 1-2 points in each task and dataset in average. By replacing the probabilistic labels into hard labels (w/ Hard Label in Table 2), we observe around 0.1-0.2 drops in performance. This implies how to filter potential noisy samples is crucial in noise aware fine-tuning, and probabilistic labels also slightly help. Effects of Task We also analyse how each bootstrapped task affect WeCheck. In Table 3, the left block rows indicate whether a type of task samples are used for training, and the right block rows are the corresponding performance. The first row is the results of NLI-warmup which does not use any task data for training. The second to forth rows separately train on summarization, dialogue, and paraphrase examples. The last row reports the default settings of WeCheck, which jointly train with all three task samples. From the results, we can conclude that, joint training on all tasks leads to a better performance on the comprehensive evaluation across tasks. For single task evaluation except dialogue, training using only the target task examples leads to better performance on this task than joint training. In horizontal comparisons of single task performance, we observe that summarization examples contribute most to the overall performance, improving the performance of checking summarization and paraphrase by 6.2 and 3.8 points. Paraphrase examples benefit evaluating paraphrase and dialogue by 6.7 and 1.3 points. Dialogue samples worsen the performance of WeCheck. We suppose that is because these samples are boostrapped from relative weak dialogue models, MemNet and dodecaDialogue, which are not even pre-trained models. Thus, dialogue samples have no contributions to NLI-warmup. By contrast, the summarization samples, which are the most difficult type for checking, benefit most to the overall performance. Computational Efficiency We analyze the computational efficiency of WeCheck by comparing with other metrics based on different architectures. As reported in Table 4, we select three other representative metrics: SCZS based on sentence-level NLI, FactCC based on document-level NLI, and QAFact based on QA-QG framework. All these methods are tested on the TRUE benchmark with a single NVIDIA 32G V100 GPU and we report the relative time cost of each method comparing with WeCheck6. Despite FactCC is the fastest method reported from the results, its fact checking performance (Table 1) is much worse than others. Among the rest two methods with comparable performance, WeCheck is 2.9 times faster than SCZS and 30 times faster than QAFact. Abstractiveness As mentioned above, abstractive hypotheses are challenging for current metrics, e.g. XSUM summaries from MNBM. We give an in-depth analysis of the effect of hypothesis abstractiveness on the metrics performance. Following See et al. (2017), we use the percentage of unique unigrams in a hypothesis w.r.t its premise to measure abstractivenss. Then, we spilt all the examples in TRUE into 10 bins according to their abstractiveness. For each bin, we measure the ROC AUC of WeCheck and the other three representative baselines: QAFact, Summac, and NLI-warmup. From the results in Figure 3, we observe a significant drop in the performance for all baselines as the hypothesis becomes more abstractive, while, WeCheck keeps its performance (around 0.85). Moreover, WeCheck consistently outperforms baseline metrics in every bin of ab6The batch size of each metric is set to the maximum size that the GPU memory can hold. ![7_image_0.png](7_image_0.png) stractiveness. This further verifies the superiority of directly training with real task data. ## 6 Labeling Model | #size | Sum. | Dial. | Para. | Ave | | |---------|--------|---------|---------|-------|------| | WeCheck | 435M | 1.0× | 1.0× | 1.0× | 1.0× | | SCZS | 59M | 3.5× | 1.7× | 3.4× | 2.9× | | FactCC | 109M | 0.2× | 0.3× | 0.3× | 0.2× | | QAFact | 1097M | 24× | 26× | 75× | 30× | We compare how different data programming based labeling models affect the final metric performance. In WeCheck, labeling model pϕ learns to aggregate multi-resource labels to infer the hidden true label. Comparing concretely, our method is similar to Snorkel (Ratner et al., 2017). Because, in our scenario, the number of weak supervision signals is small and their relationships are relatively simple as they are trained from different tasks, we prefer this method over other recent more advanced ones. In Table 5, we demonstrate the effectiveness of our labeling model by replacing it with other methods. In these baselines, simpler methods include: **Average Signals**, which simply averages all the weak signals as the probabilistic label p(y +); Major Vote, which select the most frequently appeared label in a unified weak signal set as the true label. More advanced methods include: **Flying Squid** (Fu et al., 2020), which applies an Ising model (Parsons, 2011) to model more complex relations in a unified weak signal set; **Weasel** (Cachay et al., 2021) is the current SOTA data programming Labeling Model **Sum. Dial. Para. Ave** Ours **83.0 86.2 89.6 84.8** Average Signal 81.7 86.0 88.7 83.9 Major Vote 81.5 85.6 84.3 83.8 Flying Squid 77.8 84.8 88.4 81.3 Weasel 74.0 84.4 87.7 79.0 EM 79.0 84.6 86.8 81.7 None 77.2 83.8 85.4 80.3 method, which uses a neural network as the labeling method and trains it end-to-end with the target tasks model; DWS (Parker and Yu, 2021) treats the true label of a sample as the hidden variable and applies Estimation-Maximization (EM) for inference during training. From the results in Table 5, our default labeling model outperforms all others. Furthermore, more complex methods (Flying Squid, Weasel, and EM) perform worse than simpler methods (Ours, Average Signal, and Major Vote). This further verifies that the relations between weak signals are simple, and complex modeling will not bring further improvements. From another perspective, overly simplistic approaches without any statistical modeling (Average Signal and Major Vote) also perform worse than our methods. ## 7 Related Work Factual Consistency Evaluation Recently, automatically checking factual consistency has become an increasingly popular topic (Li et al., 2022). Reasoning over a long range of context for factual evaluation is a challenging task that even human annotators may frequently disagree with each other (Pagnoni et al., 2021). Thus, it is hard to collect a large-scale high-quality dataset for training a fully supervised model, and previous works search for indirect methods. One branch of them leverage the reasoning ability of NLI. Based on the model trained on NLI datasets, e.g. MNLI (Williams et al., 2018), ANLI (Nie et al., 2020), some works aggregate sentence-level entailment score for checking (Falke et al., 2019; Laban et al., 2022), while others adopt document-level NLI which directly reasoning over the full context (Maynez et al., 2020; Gehrmann et al., 2021). Another branch of methods apply QA-QG based pipeline for a more fine-grained checking. QAGS (Wang et al., 2020) and FEQA (Durmus et al., 2020) are the earliest attempt on this method, and QuestEval (Scialom et al., 2021) and QAFactEval (Fabbri et al., 2022) further improve this type of methods by applying NLI for answer matching. Data Programming In this paper, we mainly focus on data programming (Ratner et al., 2016) (DP), a weak supervision paradigm proposed to infer correct labels based on noisy labels from labeling functions (LFs), which are rule-based decision-making processes that generate discrete labels. Following the DP paradigm, Snorkel (Ratner et al., 2017) is proposed to for rapid training, more recent works study how to adapt label model in DP (Ratner et al., 2019; Awasthi et al., 2020) or modeling more complex structure between LFs (Fu et al., 2020). DP is also applied to several NLP tasks. DWS (Parker and Yu, 2021) combine DP and CRF for weakly supervised named entity recognition, Min et al. (2019) apply DP for QA. Different from all previous tasks, our weak supervision signals are logits from other models, rather than discrete labels generated from rules. ## 8 Conclusion In this paper, we propose a weakly supervised framework, WeCheck, which aggregates weakly supervised signals from multiple resources and trains a target metric model in a noise-aware manner. Different from previous metrics that trains from synthetic data or transferred from other tasks, WeCheck directly trains with the real generated text. WeCheck first annotates each sample with a probabilistic label via a labeling function that aggregates multiple resources. Then, in the noise-aware finetuning stage, WeCheck applies probabilistic labels to train the target metric model. Experimental results show that, WeCheck not only surpass previous methods in performance but also time efficient. Moreover, WeCheck is potential to be compatible with future more stronger metrics, bring further improvements to the overall performance. ## Limitations Hyper-parameters Selection Some hyperparameters still acquire careful selection for WeCheck, e.g. p +, p−. Also, using different set of hyper-parameters for different tasks and datasets will further boost performance. Thus, we need to train the model several time and select the best performing parameters based on validation. 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Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, and Hua Wu. 2022a. FRSUM: Towards faithful abstractive summarization via enhancing factual robustness. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3640–3654, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Sujian Li, and Yajuan Lyu. 2022b. Precisely the point: Adversarial augmentations for faithful and informative text generation. In *Proceedings of the 2022 Conference on Empirical Methods in Natural Language* Processing, pages 7160–7176, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text generation. *CoRR*, abs/2106.11520. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019a. Bertscore: Evaluating text generation with BERT. *CoRR*, abs/1904.09675. Yuan Zhang, Jason Baldridge, and Luheng He. 2019b. PAWS: Paraphrase adversaries from word scrambling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1298–1308, Minneapolis, Minnesota. Association for Computational Linguistics. ## A True Benchmark The TRUE benchmark is composed of the following tasks and datasets. Abstractive Summarization FRANK (Pagnoni et al., 2021) collect annotations for modelgenerated summaries on the CNN/DM (Hermann et al., 2015) and XSum (Narayan et al., 2018) datasets, resulting in 2250 annotated system outputs. **SummEval (SumE)** (Fabbri et al., 2021) collect human judgments for 16 model outputs on 100 articles taken from the CNN/DM dataset. MNBD (Maynez et al., 2020) sample 500 articles and annotate summaries generated by four different systems on XSum, as well as the gold summaries. QAGS (Wang et al., 2020) collect 474 generated summaries for CNN/DM and XSum, where each sample is annotated by three annotators. Dialogue Generation BEGIN (Dziri et al., 2021) is a dataset for evaluating the factual consistency of knowledge-grounded dialogue systems. Dialogue responses are generated by fine-tuning two systems on Wizard of Wikipedia (WoW) (Dinan et al., 2018) dataset. Q2(Honovich et al., 2021) annotate 1,088 generated dialogue responses from two dialogue models trained on WoW. **DialFact (DialF)** (Gupta et al., 2022) introduce a tasks of dialogue fact-verification and propose a conversation clams dataset grounded on Wikipedia. In TRUE benchmark, one only need to verify weather a conversation claim is correct given its grounding. Paraphrase Detection PAWS (Zhang et al., 2019b) construct a paraphrase identification with paraphrase and non-paraphrase pairs from Wikipedia and the Quora Question Pairs (QQP). In True benchmark, only samples from Wikipedia are applied for verification. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 9 ✓ A2. Did you discuss any potential risks of your work? Section 9 ✓ A3. Do the abstract and introduction summarize the paper's main claims? In the introduction. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** Section 4,5,6 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 5 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3.3 ✗ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? single run ✗ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ma-etal-2023-amr

{AMR}-based Network for Aspect-based Sentiment Analysis

https://aclanthology.org/2023.acl-long.19

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Many recent works have used dependency trees to extract the relation between aspects and contexts and have achieved significant improvements. However, further improvement is limited due to the potential mismatch between the dependency tree as a syntactic structure and the sentiment classification as a semantic task. To alleviate this gap, we replace the syntactic dependency tree with the semantic structure named Abstract Meaning Representation (AMR) and propose a model called AMR-based Path Aggregation Relational Network (APARN) to take full advantage of semantic structures. In particular, we design the path aggregator and the relation-enhanced self-attention mechanism that complement each other. The path aggregator extracts semantic features from AMRs under the guidance of sentence information, while the relation-enhanced self-attention mechanism in turn improves sentence features with refined semantic information. Experimental results on four public datasets demonstrate 1.13{\%} average F1 improvement of APARN in ABSA when compared with state-of-the-art baselines.

# Amr-Based Network For Aspect-Based Sentiment Analysis Fukun Ma1, Xuming Hu1, Aiwei Liu1, Yawen Yang1**, Shuang Li**1, Philip S. Yu2, **Lijie Wen**1∗ 1Tsinghua University, 2University of Illinois Chicago 1{mfk22,hxm19,liuaw20,yyw19,lisa18}@mails.tsinghua.edu.cn [email protected], [email protected] ## Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Many recent works have used dependency trees to extract the relation between aspects and contexts and have achieved significant improvements. However, further improvement is limited due to the potential mismatch between the dependency tree as a *syntactic* structure and the sentiment classification as a *semantic* task. To alleviate this gap, we replace the syntactic dependency tree with the semantic structure named Abstract Meaning Representation (AMR) and propose a model called AMR-based Path Aggregation Relational Network (APARN) to take full advantage of semantic structures. In particular, we design the path aggregator and the relation-enhanced selfattention mechanism that complement each other. The path aggregator extracts semantic features from AMRs under the guidance of sentence information, while the relationenhanced self-attention mechanism in turn improves sentence features with refined semantic information. Experimental results on four public datasets demonstrate 1.13% average F1 improvement of APARN in ABSA when compared with state-of-the-art baselines.1 ## 1 Introduction Recent years have witnessed growing popularity of the sentiment analysis tasks in natural language processing (Li and Hovy, 2017; Birjali et al., 2021). Aspect-based sentiment analysis (ABSA) is a finegrained sentiment analysis task to recognize the sentiment polarities of specific aspect terms in a given sentence (Jiang et al., 2011; Li et al., 2018; Seoh et al., 2021; Zhang et al., 2022a). For example, here is a restaurant review "All the money went into the interior decoration, none of it went to the chefs" and the sentiment polarity of two aspects 1The code will be available at https://github.com/THUBPM/APARN. *Corresponding Author. ![0_image_0.png](0_image_0.png) wereWe howatamazed the dish was we **dish** so "interior decoration" and "chefs" are positive and negative, respectively. Thus, ABSA can precisely recognize the corresponding sentiment polarity for any aspect, different from allocating a general sentiment polarity to a sentence in sentence-level sentiment analysis. The key challenge for ABSA is to capture the relation between an aspect and its context, especially opinion terms. In addition, sentences with multiple aspects and several opinion terms make the problem more complex. To this end, some previous studies (Wang et al., 2016; Chen et al., 2017; Gu et al., 2018; Du et al., 2019; Liang et al., 2019; Xing et al., 2019) have devoted the main efforts to attention mechanisms. Despite their achievements in aspect-targeted representations and appealing results, these methods always suffers noise from the mismatching opinion terms or irrelevant contexts. On the other hand, more recent studies (Zhang et al., 2019a; Tang et al., 2020; Li et al., 2021; Xiao et al., 2021) propose models explicitly exploit dependency trees, the syntactic structure of a sentence, to help attention mechanisms more accurately identify the interaction between the aspect and the opinion expressions. These models usually employ graph neural networks over the syntactic dependencies and display significant effectiveness. However, existing ABSA models still indicate two potential limitations. First, there appears to be a gap between the **syntactic** dependency structure and the **semantic** sentiment analysis task. Considering the sentence in Figure 1, "small" semantically modifies "dish" and expresses negative sentiment, but both "small" and "dish" are syntactically dependent on "was". The determinant of sentiment should be the meaning of the sentence rather than the way it is expressed. Second, the output of natural language parsers including dependency parsers always contains inaccuracies (Wang et al., 2020). Without further adjustment, raw results of parsers can cause errors and be unsuitable for ABSA task. To solve aforementioned challenges, we propose a novel architecture called AMR-based Path Aggregation Relational Network (APARN). For the first challenge, we introduce Abstract Meaning Representations (AMRs), a powerful semantic structure. For the AMR example in Figure 1, "small" and "dish" are directly connected, while function words such as "were" and "at" disappear, which makes it easier to establish the aspect-opinion connection and shows the advantage of AMRs in ABSA. For the second challenge, we construct the path aggregator and the relation-enhanced self-attention mechanism. The path aggregator integrates the information from AMRs and sentences to obtain optimized relational features. This procedure not only encourages consistency between semantic structures and basic sentences, but also achieves the global feature by broadcasting local information along the path in the graph. Relation-enhanced self-attention mechanism then adds these relational feature back into attention weights of word features. Thanks to these modules, APARN acquires to utilize sentences and AMRs jointly and achieves higher accuracy on sentiment classification. To summarize, our main contributions are highlighted as follows: - We introduce Abstract Meaning Representations into the ABSA task. As a semantic structure, the AMR is more suitable for sentiment analysis task. - We propose a new model APARN that integrates information from original sentences and AMRs via the path aggregator and the relation-enhanced self-attention mechanism to fully exploit semantic structure information and relieve parser unreliability. - We experiment on four public datasets and our APARN outperforms state-of-the-art baselines, demonstrating its effectiveness. More | Structures | AOD↓ | ACD↑ | rAOD↓ | |-----------------------|--------|--------|---------| | Original Sentence | 3.318 | 6.145 | 0.540 | | Dependency Tree | 1.540 | 2.547 | 0.605 | | AMR (connected words) | 1.447 | 2.199 | 0.658 | | AMR (all words) | 1.787 | 8.846 | 0.202 | Table 1: Aspect-opinion, aspect-context and relative aspect-opinion distances of different structures. ![1_image_0.png](1_image_0.png) analytical experiments further verify the significance of our model and the AMR. ## 2 Parsed Structures We perform some experiments and discussions for the characteristics of AMR compared to parsing structures already used for the ABSA task and how these characteristics affect our APARN. ## Human-Defined Structures Dependency Trees and AMRs are parsed based on human-defined syntactic and semantic rules, respectively. Each word in a sentence becomes a node of the dependency tree, but in the AMR, relational words like function words and auxiliary words are represented as edges, while concept words like nouns and verbs are refined into nodes in the graph. With AMR aligning, we can map concept words in sentences to nodes in the graph and establish relations between them, while relation words are isolated. To estimate the impact of dependency trees and AMRs in the ABSA task, we calculate the average distance between aspect words and opinion words in different parsed structures on the Restaurant dataset, called aspect-opinion distance (AOD). We also calculate the average distance between aspect words and all context words called aspectcontext distance (ACD), and divide AOD by ACD as relative aspect-opinion distance (rAOD). The distance between aspect words and isolated words is treated as sentence length. According to the result shown in Table 1, both dependency trees and AMRs have similar AOD smaller than original sentences, which indicates their benefits to capture relations about aspects. Due to the elimination of isolated words, the rAOD of AMRs is much less than dependency trees, which means smaller scope and easier focus. About 2.13% of opinion words are wrongly isolated, making the AOD of AMR (all words) a little bigger. But this is acceptable considering the improvement of rAOD and partially repairable by information from original sentences. The above analysis is for graph skeletons, and we also explore the impact of edge labels of two structures in the ABSA task. Figure 2 compares the distribution of edge labels in aspect-opinion paths with the distribution of all edge labels. These distributions are clearly different, both in dependency trees and AMRs, which implies that edge labels can also help the ABSA task, especially in AMRs. Based on these characteristics, we design the outer product sum module for APARN to mix sentence information into the graph, and design the path aggregator to collect graph skeleton and edge label information in AMRs. Data-driven Structures Some existing studies use structures produced by data-driven models in the ABSA task (Chen et al., 2020; Dai et al., 2021; Chen et al., 2022) and exhibit different effects from human-defined structures. Therefore, we design a relation-enhanced self-attention mechanism for APARN to integrate the graph information obtained by the path aggregator with the information from the pre-trained model. ## 3 Proposed Model The overall architecture of our proposed model APARN is illustrated in Figure 3. It consists of 3 parts: AMR preprocessing, path aggregator and relation-enhanced self-attention mechanism. In the ABSA task, a sentence s = {w1, w2*, ..., w*n} and a specific aspect term a = {a1, a2*, ..., a*m} are given to determine the corresponding sentiment polarity class ca, where a is a sub-sequence of s and ca ∈ {*P ositive, Neutral, Negative*}. Many existing works use syntactic dependency trees to establish explicit or implicit connections between aspects and contexts. However, we believe that the sentiment analysis task is essentially about the meanings of sentences, so semantic structures like AMRs are more favorable for this task. In addition, AMRs are more concise than dependency trees, making it easier to extract valuable information in training but more difficult to preprocess before training. We have to conduct a series of steps including: AMR parsing, AMR aligning and AMR embedding. Preprocessed AMRs still have errors and unsuitable parts for the task, so we design the path aggregator and the relation-enhanced self-attention mechanism to perform joint representation refinement and flexible feature fusion on the AMR graph and the original sentence. Next, we elaborate on the details of our proposed APARN, including AMR preprocessing and embedding, the path aggregator and the relationenhanced self-attention mechanism. ## 3.1 Amr Preprocessing And Embedding Parsing As we determine to employ the semantic structure AMR as an alternative of the syntactic structure dependency tree to better perform the semantic task ABSA, the first step is parsing the AMR from the input sentence. We choose the offthe-shelf parser SPRING (Bevilacqua et al., 2021) for high quality AMR outputs. Aligning Next, we align the AMR by the aligner LEAMR (Blodgett and Schneider, 2021). Based on the alignments, we manage to rebuild AMR relations between words in the sentence and get the transformed AMR with words as nodes. Embedding After aligning, we now have transformed AMRs, which can also be called sentences with AMR relations. Then we need to obtain their embeddings for later representation learning by the model. For words in the sentence, also as the nodes in the AMR, we utilize BERT as an encoder to get contextual embeddings H = {h1, h2*, ..., h*n} like lots of previous works. For the edges in the AMR, we represent the relations between nodes as an adjacency matrix R = {rij | 1 ≤ *i, j* ≤ n}, where rij is the embedding of the edge label between word wi and word wj . If there is no edge between wi and wj in the AMR, we assign a "none" embedding to rij . Edge label embeddings are also obtained from the pre-trained model. ## 3.2 Path Aggregator Path aggregator receives the mix of AMR embeddings R ∈ R dr×n×nand sentence embeddings H ∈ R dw×n, where dr and dw denote the dimensions of relation and word embeddings, respectively. Path aggregator outputs the relational fea- But the staff was **so horrible** ![3_image_0.png](3_image_0.png) ௗ so √ **Negative** Neutral Positive : **Element-wise Sum** : **Outer Product** ture matrix RAGG ={r AGG ij ∈R dr| 1 ≤ *i, j* ≤ n}. This process integrates and condenses information from two different sources, AMRs and sentences, making semantic knowledge more apparent but parsing errors less influential. Outer Product Sum We first add the outer product of two independent linear transformation of sentence embeddings H to the original AMR embeddings R to obtain sequence-enhanced relation embeddings RS ∈ R dr×n×n. On the one hand, as the outer product of H is the representation of word relations from the sentence perspective, its combination with the AMR embeddings R could enlarge the information base of the model to improve the generalization, also cross validate important features to improve the reliability. On the other hand, AMR embeddings R is usually quite sparse. The outer product sum operation ensures the basic density of the feature matrix and facilitates the subsequent representation learning by avoiding the fuzziness and dilution of numerous background "none" relations to the precious effective relations. $$\begin{array}{r l}{{\bf{gation}}}&{{}{\mathrm{~Next,~we~perform~the~path}}}\\ {{\mathrm{on~}}R^{S}=\{r_{i j}^{S}\mid1\leq i,j\leq n\}{\mathrm{~to~}}}\\ {{}}&{{}}\\ {{G G=\{r_{i j}^{A G G}\mid1\leq i,j\leq n\}{\mathrm{~as:~}}}\end{array}$$ aggregation on RS = {r calculate RAGG = {r $$r_{\ i j}^{\prime S}=\mathrm{LayerNorm}(r_{i j}^{S}),$$ $$\begin{array}{c c c}{{g_{i j}^{i n},g_{i j}^{o u t}=\mathrm{sigmoid}(\mathrm{Linear}(r^{\prime S}_{i j})),}}&{{}}&{{(2)}}\\ {{}}&{{}}&{{}}\\ {{a_{i j},b_{i j}=g_{i j}^{i n}\odot\mathrm{Linear}(r^{\prime S}_{i j}),}}&{{}}&{{(3)}}\end{array}$$ $$r_{i j}^{o u t}=\mathrm{Linear}(\mathrm{LayerNorm}(\sum_{k}a_{i k}\odot b_{k j})),\ \ (4)$$ $$r_{i j}^{A G G}=g_{i j}^{o u t}\odot r_{i j}^{o u t}.$$ ij . (5) The path aggregation has distinctive effect on both local and global dissemination of features. From the local view, the path aggregation covers all the 2-hop paths, so that it is very sensitive to neighborhood features, including the features around the aspect term which are really important for the ABSA task. From the global view, information in any long path can be summarized into the representation between the start and the end by several two-in-one operations in enough times of path aggregations. In other words, path aggregations make the features in matrix more inclusive and finally attain global features. In practice, because the ABSA task focuses more on the neighboring information and the BERT encoder with attention mechanisms has made the feature comprehensive enough, a single path aggregation can achieve quite good results. Additionally, we also introduce a gating mechanism in the path aggregation to alleviate the disturbance of noise from insignificant relations. Finally, the output of path aggregation RAGG is transformed into the relational attention weight matrix AAGG = {a AGG ij | 1 ≤ *i, j* ≤ n} by a linear transformation for subsequent calculation. ## 3.3 Relation-Enhanced Self-Attention $$\mathrm{(1)}$$ The classic self-attention (Vaswani et al., 2017) computes the attention weight by this formula: $$A=s o f t m a x\left({\frac{Q W_{Q}\times\left(K W_{K}\right)^{T}}{\sqrt{d}}}\right),\quad\quad(6)$$ where $Q$ and $K$ are input vectors with $d$ dimensions. where Q and K are input vectors with d dimensions, while WQ and WK are learnable weights $325^{\circ}$ | Model | Restaurant | Laptop | Twitter | MAMS | | | | | |-------------------------------------------------------------------------|--------------|----------|-----------|--------|-------|-------|-------|-------| | Accuracy Macro-F1 Accuracy Macro-F1 Accuracy Macro-F1 Accuracy Macro-F1 | | | | | | | | | | BERT (Devlin et al., 2019) | 85.62 | 78.28 | 77.58 | 72.38 | 75.28 | 74.11 | 80.11 | 80.34 | | DGEDT (Tang et al., 2020) | 86.30 | 80.00 | 79.80 | 75.60 | 77.90 | 75.40 | - | - | | R-GAT (Wang et al., 2020) | 86.60 | 81.35 | 78.21 | 74.07 | 76.15 | 74.88 | - | - | | T-GCN (Tian et al., 2021) | 86.16 | 79.95 | 80.88 | 77.03 | 76.45 | 75.25 | 83.38 | 82.77 | | DualGCN (Li et al., 2021) | 87.13 | 81.16 | 81.80 | 78.10 | 77.40 | 76.02 | - | - | | dotGCN (Chen et al., 2022) | 86.16 | 80.49 | 81.03 | 78.10 | 78.11 | 77.00 | 84.95 | 84.44 | | SSEGCN (Zhang et al., 2022b) | 87.31 | 81.09 | 81.01 | 77.96 | 77.40 | 76.02 | - | - | | APARN (Ours) | 87.76 | 82.44 | 81.96 | 79.10 | 79.76 | 78.79 | 85.59 | 85.06 | Table 2: Results on four public datasets. Best performed baselines are underlined. All models are based on BERT. with the same size of R d×d. In our relation-enhanced self-attention, we added AAGG, the relational attention weight matrix from AMR into the original attention weight, which can be formulated as: $$A^{R}{=}s o f t m a x\left({\frac{H W_{Q}\times(H W_{K})^{T}}{\sqrt{d_{w}}}}{+}A^{A G G}\right),\ \ (7)$$ where input vectors W and Q are both replaced by the BERT embeddings H with dw dimensions. With AAGG, attention outputs are further guided by the semantic information from AMRs, which improves the efficient attention to semantic keywords. In addition, similar to path aggregator, we also introduced the gating mechanism into the relationenhanced self-attention as follows: $$G=s i g m o i d(H W_{G}),$$ G = *sigmoid*(HWG), (8) $$H^{R}=(H W_{V})A^{R}\odot G,$$ where WG and WV are trainable parameters and G is the gating matrix. Considering the small proportion of effective words in the whole sentence, the gating mechanism is conducive to eliminating background noise, making it easier for the model to focus on the more critical words. Finally, with all these above calculations including relation-enhanced self-attention and gating mechanism, we obtain the relation-enhanced aspect representation HR a = {h R a1 , hR a2 , ..., hR am} for subsequent classification. ## 3.4 Model Training The final classification features are concatenated by the original BERT aspect representation Ha = *mean*{ha1 , ha2 , ..., ham} and the relationenhanced aspect representation HR a . $$H_{a}^{f i n a l}=[H_{a},H_{a}^{R}].$$ a]. (10) It is passed through a fully connected softmax layer and mapped to probabilities over three sentiment polarities. $$p(a)=s o f t m a x(W_{p}H_{a}^{f i n a l}+b_{p}).\qquad(11)$$ We use cross-entropy loss as our objective function: $$L_{C E}=-\sum_{(s,a)\in{\mathcal{D}}}\sum_{c\in{\mathcal{C}}}y_{a}^{c}\log p^{c}(a),\qquad(12)$$ where y is the ground truth sentiment polarity, D contains all sentence-aspect pairs and C contains all sentiment polarities. ## 4 Experiments $$({\boldsymbol{\delta}})$$ $$(9)$$ In this section, we first introduce the relevant settings of the experiments, including the datasets used, implementation details and baseline methods for comparison. Then, we report the experimental results under basic and advanced settings. Finally, we select several representative examples for model analysis and discussion. ## 4.1 Datasets And Setup Our experiments are conducted on four commonly used public standard datasets. The Twitter dataset is a collection of tweets built by Dong et al. (2014), while the Restaurant and Laptop dataset come from the SemEval 2014 Task (Pontiki et al., 2014). MAMS is a large-scale multi-aspect dataset provided by Jiang et al. (2019). Data statistics are shown in Appendix A.1. In data preprocessing, we use SPRING (Bevilacqua et al., 2021) as the parser and LEAMR (Blodgett and Schneider, 2021) as the aligner. APARN uses the BERT of bert-base-uncased version with max length as 100 and the relation-enhanced selfattention mechanism uses 8 attention heads. We reported accuracy and Macro-F1 as results which $$(10)$$ are the average of three runs with different random seeds. See Appendix A.2 for more details. ## 4.2 Baseline Methods We compare APARN with a series of baselines and state-of-the-art alternatives, including: 1) **BERT** (Devlin et al., 2019) is composed of a general pre-trained BERT model and a classification layer adapted to the ABSA task. 2) **DGEDT** (Tang et al., 2020) proposes a dual transformer structure based on dependency graph augmentation, which can simultaneously fuse representations of sequences and graphs. 3) **R-GAT** (Wang et al., 2020) proposes a dependency structure adjusted for aspects and uses a relational GAT to encode this structure. 4) **T-GCN** (Tian et al., 2021) proposes an approach to explicitly utilize dependency types for ABSA with type-aware GCNs. 5) **DualGCN** (Li et al., 2021) proposes a dual GCN structure and regularization methods to merge features from sentences and dependency trees. 6) **dotGCN** (Chen et al., 2022) proposes an aspectspecific and language-agnostic discrete latent tree as an alternative structure to dependency trees. 7) **SSEGCN** (Zhang et al., 2022b) proposes an aspect-aware attention mechanism to enhance the node representations with GCN. ## 4.3 Main Results Table 2 shows the experimental results of our model and the baseline models on four datasets under the same conventional settings as Li et al. (2021), where the best results are in bold and the second best results are underlined. Our APARN exhibits excellent results and achieves the best results on all 8 indicators of 4 datasets with an average margin more than one percent, which fully proves the effectiveness of this model. Comparing the results of different datasets, we can find that the improvement of APARN on the Twitter dataset is particularly obvious. Compared to the best baselines, the accuracy rate has increased by 1.65% and the Macro-F1 has increased by 1.79%. The main reason is the similarity of the Twitter dataset to the AMR 3.0 dataset, the training dataset for the AMR parser we used. More than half of the corpus of the AMR 3.0 dataset comes from internet forums and blogs, which are similar to the Twitter dataset as they are both social media. As a result, the AMR parser has better output on the Twitter dataset, which in turn enables the model to ![5_image_0.png](5_image_0.png) extract more valuable features from it and leads to a considerable improvement. This difference among datasets also reflects the effectiveness of semantic information from AMR for the ABSA task. ## 4.4 Comparative Experiments We conduct comparative experiments to analyse the impact of models (APARN and T-GCN), parsed structures (AMR and dependency tree), and edge labels (with and without). T-GCN is selected instead of more recent models because they lack the ability to exploit edge labels and cannot receive AMRs as input. AMRs are the same as the basic experiments and dependency trees are parsed by Stanford CoreNLP Toolkits (Manning et al., 2014). "Without edge labels" means all labels are the same placeholder. The results are shown in Figure 4. From the perspective of models, APARN consistently outperforms T-GCN in any parsed structure and edge label settings, demonstrating the effectiveness of our APARN. From the perspective of parsed structures, AMRs outperform dependency trees in most model and edge label settings, except for the case of T-GCN without edge labels. The reason may be that the AMR without edge labels is sparse and semantically ambiguous, which does not match the design of the model. From the perspective of edge labels, a graph with edge labels is always better than a graph without edge labels, whether it is an AMR or a dependency tree, whichever the model is. We can also notice that APARN has a greater improvement with the addition of edge labels, indicating that it can utilize edge labels more effectively. Besides, with the addition of edge labels, experiments using AMR have improved more than experiments using depen- | Model | Restaurant | Laptop | Twitter | MAMS | | | | | |-------------------------------------------------------------------------|--------------|----------|-----------|--------|-------|-------|-------|-------| | Accuracy Macro-F1 Accuracy Macro-F1 Accuracy Macro-F1 Accuracy Macro-F1 | | | | | | | | | | APARN | 87.76 | 82.44 | 81.96 | 79.10 | 79.76 | 78.79 | 85.59 | 85.06 | | −Outer Product Sum | 86.15 | 80.13 | 79.45 | 76.34 | 76.22 | 74.75 | 82.93 | 82.30 | | −Path Aggregator | 87.04 | 81.61 | 79.20 | 75.67 | 76.66 | 74.90 | 83.16 | 82.61 | | −Relation in Self-Attention | 87.49 | 81.82 | 80.36 | 77.87 | 76.81 | 75.49 | 83.73 | 83.08 | | −Gate in Self-Attention | 85.61 | 78.49 | 79.81 | 77.42 | 77.55 | 76.06 | 83.96 | 83.15 | dency trees, indicating that edge labels of the AMR contain richer semantic information and are more valuable for sentiment analysis, which is consistent with previous experiments in Figure 2. ## 4.5 Further Analysis Ablation Study To analyze the role of each module, we separately remove four key components of APARN. Results on four datasets are represented in Table 3. According to the results, each of the four components contributes significantly to the performance of APARN. Removing Outer Product Sum results in a significant drop in performance, illustrating the importance of promoting consistency of information from sentences and AMRs. Removing Path Aggregator is worse than removing Relation in SelfAttention, indicating that unprocessed AMR information can only interfere with the model instead of being exploited by the model. Comparing the results in different datasets, we can find that the model depends on information from sentences and AMRs differently on different datasets. On the Restaurant dataset, removing the Relation in Self-Attention component has less impact, while on the Twitter dataset, removing this component has a greater impact. This means the model utilizes sentence information more on the Restaurant dataset and AMR information more on the Twitter dataset. This is also consistent with the analysis of the main results: the AMR of Twitter dataset has higher quality due to the domain relatedness with the training dataset of the AMR parser, which in turn makes the model pay more attention to the information from the AMR on this dataset. AMR Parser Analysis We conduct experiments using AMRs from different parsers on Twitter dataset, as displayed in Figure 5. In addition to the SPRING parser mentioned before, we try two other parsers from Zhang et al. (2019b) and Cai and Lam (2020). These parsers achieve 76.3, 80.2 ![6_image_0.png](6_image_0.png) | Sentence Length | <15 | 15-24 | 25-34 | >35 | |--------------------------------------------------|-------|---------|---------|-------| | w/o Path Aggregator | 88.25 | 85.43 | 83.92 | 83.96 | | w. Path Aggregator | 89.40 | 87.15 | 86.64 | 86.71 | | Relative Improvement +1.30% +2.01% +3.24% +3.28% | | | | | and 84.3 Smatch score for AMR parsing task on AMR 2.0 dataset, which can be regarded as the quality of their output. From the figure, it is clear that the accuracy of ABSA task shows positive correlation with the Smatch score, which proves the positive effect of AMRs in the ABSA task and the importance of the high quality AMR. Sentence Length Study Table 4 compares the accuracy of APARN with and without path aggregator for sentences of different lengths in the Restaurant dataset. According to the table, we can see that the model achieves higher accuracy on short sentences, while the long sentences are more challenging. In addition, the model with the path aggregator has a larger relative improvement on long sentences than short sentences, indicating that the path aggregator can effectively help the model capture long-distance relations with AMR. the atmosphere was crowded but it was a great **bistro-type vibe** ![7_image_0.png](7_image_0.png) i ordered the smoked salmon and roe appetizer **and it was off flavor** so if you want a nice , enjoyable meal at montparnasse , go early for the **pre-theater prix-fixe** ## 4.6 Case Study As shown in Figure 6, we selected three typical cases to visualize the aspect terms' attention to the context before and after adding information from the AMR, respectively. From the first two examples, we can notice that the model focuses on the copula verb next to the opinion term without the AMR. While with the information from the AMR, the model can capture opinion terms through the attention mechanism more accurately. In the third example, without the AMR, the model pays more attention to words that are closer to the aspect term. With the semantic information from AMR, the model can discover opinion terms farther away from aspect terms. These cases illustrate that the semantic structure information of AMR plays an important role in making the model focus on the correct opinion words. It also shows that the structure of our APARN can effectively utilize the semantic structure information in AMR to improve the performance in the ABSA task. ## 5 Related Work Aspect-based Sentiment Analysis Traditional sentiment analysis tasks are usually sentence-level or document-level, while the ABSA task is an entity-level and fine-grained sentiment analysis task. Early methods (Jiang et al., 2011; Kiritchenko et al., 2014) are mostly based on artificially constructed features, which are difficult to effectively model the relations between aspect terms and its context. With the development of deep neural networks, many recent works (Wang et al., 2016; Tang et al., 2016; Chen et al., 2017; Fan et al., 2018; Gu et al., 2018; Du et al., 2019; Liang et al., 2019; Xing et al., 2019) have explored applying attention mechanisms to implicitly model the semantic relations of aspect terms and identify the key opinion ## Terms In The Context. Another trend in ABSA studies is the explicit use of dependency trees. Some works (He et al., 2018; Zhang et al., 2019a; Sun et al., 2019; Huang and Carley, 2019; Zhang and Qian, 2020; Chen et al., 2020; Liang et al., 2020; Wang et al., 2020; Tang et al., 2020; Phan and Ogunbona, 2020; Li et al., 2021; Xiao et al., 2021) extend GCN, GAT, and Transformer backbones to process syntactic dependency trees and develop several outstanding models. These models shorten the distance between aspect terms and opinion terms by dependency trees and alleviate the long-term dependency problem. Recent studies have also noticed the limitations of dependency trees in the ABSA task. Wang et al. (2020) proposes the reshaped dependency tree for the ABSA task. Chen et al. (2020) propose to combine dependency trees with induced aspect-specific latent maps. Chen et al. (2022) further proposed an aspect-specific and language-independent discrete latent tree model as an alternative structure for dependency trees. Our work is similar in that we also aim at the mismatch between dependency trees and the ABSA task, but different in that we introduce a semantic structure AMR instead of induced trees. Abstract Meaning Representation AMR is a structured semantic representation that represents the semantics of sentences as a rooted, directed, acyclic graph with labels on nodes and edges. AMR is proposed by Banarescu et al. (2013) to provide a specification for sentence-level comprehensive semantic annotation and analysis tasks. Research on AMR can be divided into two categories, AMR parsing (Cai and Lam, 2020; Zhou et al., 2021; Hoang et al., 2021) and AMR-to-Text (Zhao et al., 2020; Bai et al., 2020; Ribeiro et al., 2021). AMR has also been applied in many NLP tasks. Kapanipathi et al. (2020) use AMR in question answering system. Lim et al. (2020) employ AMR to improve common sense reasoning. Wang et al. (2021) utilize AMR to add pseudo labels to unlabeled data in low-resource event extraction task. Our model also improves the performance of the ABSA task with AMR. Moreover, AMR also has the potential to be applied to a broader range of NLP tasks, including relation extraction(Hu et al., 2020, 2021a,b), named entity recognition(Yang et al., 2023), natural language inference(Li et al., 2022), text-to-SQL(Liu et al., 2022), and more. ## 6 Conclusion In this paper, we propose APARN, AMR-based Path Aggregation Relational Network for ABSA. Different from the traditional ABSA model utilizing the syntactic structure like dependency tree, our model employs the semantic structure called Abstract Meaning Representation which is more harmony with the sentiment analysis task. We propose the path aggregator and the relation-enhanced selfattention mechanism to efficiently exploit AMRs and integrate information from AMRs and input sentences. These designs enable our model to achieve better results than existing models. Experiments on four public datasets show that APARN outperforms competing baselines. ## 7 Limitations The high computational complexity is one of the biggest disadvantages of the path aggregation. The time consumption and GPU memory used for multiple operations are expensive. So it is very desirable to use only one time of path aggregation due to attributes of the ABSA task in our APARN. Another limitation of this work is that the performance of the model is still somewhat affected by the quality of the AMR parsing results. The good news is that the research on AMR parsing is continuing to make progress. In the future, APARN with higher quality AMRs is expected to further improve the level of the ABSA task. Besides, this model is flawed in dealing with implicit and ambiguous sentiments in sentences. Implicit sentiment lacks corresponding opinion words, and ambiguous sentiment is subtle and not apparent. An example of this is the sentence "There was only one [waiter] for the whole restaurant upstairs," which has an ambiguous sentiment associated with the aspect word "waiter". The golden label is "Neutral", but our model predicts it as "Negative". Finally, generalization to other ABSA tasks such as end-to-end ABSA or ASTE is another restriction. Considering the complexity of the task, we only apply our motivation to sentiment classification in this paper. 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Throughout the research, we follow the Creative Commons Attribution 4.0 International Licence of the datasets. | Dataset | Positive | Neutral | Negative | | |------------|-------------------------------------------------------|-----------|------------|----------| | Restaurant | Train/Test | 2164/728 | 637/196 | 807/196 | | Laptop | Train/Test | 994/341 | 464/169 | 870/128 | | Twitter | Train/Test | 1561/173 | 3127/346 | 1560/173 | | MAMS | Train/Dev/Test 3380/403/400 5042/604/607 2764/325/329 | | | | Table 5: Statistics of the three ABSA datasets ## A.2 Implementation Details Preprocessing We use SPRING (Bevilacqua et al., 2021) as the parser to obtain the AMRs of input sentences and use LEAMR (Blodgett and Schneider, 2021) as the AMR aligner to establish the correspondence between the AMRs and sentences. The maximum length of the input sentence is set to 100, the shortage is made up with the special word "PAD" and the excess is truncated. Some edge labels are treated specially when mapping the edges of AMR to the relations between words. Edge labels suffixed with "-of" are used to avoid loops in AMR, so we swap their start and end points and remove the "-of" suffix, eg: the ":ARG0-of" relation from tokenito *token*j is changed to the ":ARG0" relation from *token*j to *token*i. Edge labels prefixed with ":prep-" are used because there is no suitable preposition label in the AMR specification. We changed them to original prepositions, for example, ":prep-against" is changed to "against". ## Model Structure And Training Aparn Uses the BERT of bert-base-uncased version as a pretrained encoder. The dimension of its output is 768, which is also used as the dimension of token representation in the path aggregator. The dimension of the AMR edge label embedding derived from the SPRING model is 1024. Due to computational efficiency and memory usage, this dimension is reduced to 376 through a linear layer as the dimension of the relational matrix features in the path aggregator. For the relation-enhanced selfattention mechanism, its gated multi-head attention mechanism uses 8 attention heads with the latent dimension size of 64. The total parameter size of APARN is about 130M and it takes about 8 minutes to train each epoch on a single RTX 3090 GPU with the batch size of 16. During training, we use the Adam (Kingma and Ba, 2015) optimizer and use the grid search to find best hyper-parameters. The range of learning rate is [1 × 10−5, 5 × 10−5]. Adam hyperparameter α is 0.9 and β is in (0.98, 0.99, 0.999). The BERT encoder and other parts of the model use dropout strategies with probability in [0.1, 0.5], respectively. Each training lasts up to 15 epochs and the model is evaluated on validation data. For datasets without official validation data, we follow the settings of previous work (Li et al., 2021). The model with the highest accuracy among all evaluation results is selected as the final model. ## A.3 More Comparison Examples Here are two other comparison examples of dependency trees (Figure 7) and AMRs (Figure 8). The first sentence is "We usually just get some of the dinner specials and they are very reason- ![13_image_0.png](13_image_0.png) Figure 7: Dependency tree examples with aspects in red and opinion terms in blue. ![13_image_1.png](13_image_1.png) :name person **"Nina"** nice01 :ARG1 :ARG1 **:ARG0** :poss :ARG1-of pizza look- **people** enjoy01 obviou s-01 ably priced and very tasty". In its dependency tree, the distance between the aspect "dinner specails" and the opinion terms "reasonably priced" or "very tasty" is more than 3, while they are directly connected in the AMR. The second sentence is "We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas". In its dependency tree, the distance between the aspect "place" and the opinion terms "nice" is 4, while they are directly connected in the AMR. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 3.1 And 4.1 And 4.5 ✓ B1. Did you cite the creators of artifacts you used? 3.1 and 4.1 and 4.5 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? A.1 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? A.1 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. A.1 ## C ✓ **Did You Run Computational Experiments?** 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? A.2 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4.1 and A.2 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4 and A.2 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? A.2 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

li-etal-2023-text

Text Adversarial Purification as Defense against Adversarial Attacks

https://aclanthology.org/2023.acl-long.20

Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack. Generally, adversarial purification aims to remove the adversarial perturbations therefore can make correct predictions based on the recovered clean samples. Despite the success of adversarial purification in the computer vision field that incorporates generative models such as energy-based models and diffusion models,using purification as a defense strategy against textual adversarial attacks is rarely explored. In this work, we introduce a novel adversarial purification method that focuses on defending against textual adversarial attacks. With the help of language models, we can inject noise by masking input texts and reconstructing the masked texts based on the masked language models. In this way, we construct an adversarial purification process for textual models against the most widely used word-substitution adversarial attacks. We test our proposed adversarial purification method on several strong adversarial attack methods including Textfooler and BERT-Attack and experimental results indicate that the purification algorithm can successfully defend against strong word-substitution attacks.

# Text Adversarial Purification As Defense Against Adversarial Attacks Linyang Li Demin Song, Xipeng Qiu School of Computer Science, Fudan University Shanghai Key Laboratory of Intelligent Information Processing, Fudan University {linyangli19, dmsong20, xpqiu}@fudan.edu.cn ## Abstract Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack. Generally, adversarial purification aims to remove the adversarial perturbations therefore can make correct predictions based on the recovered clean samples. Despite the success of adversarial purification in the computer vision field that incorporates generative models such as energy-based models and diffusion models, using purification as a defense strategy against textual adversarial attacks is rarely explored. In this work, we introduce a novel adversarial purification method that focuses on defending against textual adversarial attacks. With the help of language models, we can inject noise by masking input texts and reconstructing the masked texts based on the masked language models. In this way, we construct an adversarial purification process for textual models against the most widely used word-substitution adversarial attacks. We test our proposed adversarial purification method on several strong adversarial attack methods including Textfooler and BERT-Attack and experimental results indicate that the purification algorithm can successfully defend against strong word-substitution attacks. ## 1 Introduction Adversarial examples (Goodfellow et al., 2014) can successfully mislead strong neural models in both computer vision tasks (Carlini and Wagner, 2016) and language understanding tasks (Alzantot et al., 2018; Jin et al., 2019). An adversarial example is a maliciously crafted example attached with an imperceptible perturbation and can mislead neural networks. To defend attack examples of images, the most effective method is adversarial training (Goodfellow et al., 2014; Madry et al., 2019) which is a mini-max game used to incorporate perturbations into the training process. Defending adversarial attacks is extremely important in improving model robustness. However, defending adversarial examples in natural languages is more challenging due to the discrete nature of texts. That is, gradients cannot be used directly in crafting perturbations. The substitutionbased adversarial examples are more complicated than gradient-based adversarial examples in images, making it difficult for neural networks to defend against these substitution-based attacks. The first challenge of defending against adversarial attacks in NLP is that due to the discrete nature, these substitution-based adversarial examples can have substitutes in any token of the sentence and each substitute has a large candidate list. This would cause a combinatorial explosion problem, making it hard to apply adversarial training methods. Strong attacking methods such as Jin et al. (2019) show that using the crafted adversarial examples as data augmentation in adversarial training cannot effectively defend against these substitutionbased attacks. Further, defending strategies such as adversarial training rely on the assumption that the candidate lists of the substitutions are accessible. However, the candidate lists of the substitutions should not be exposed to the target model; that is, the target model should be unfamiliar to the candidate list of the adversarial examples. In real-world defense systems, the defender is not aware of the strategy the potential attacks might use, so the assumption that the candidate list is available would significantly constrain the potential applications of these defending methods. Considering that it is challenging to defend against textual adversarial attacks when the form of the attacks cannot be acknowledged in advance, we introduce a novel adversarial purification method as a feasible defense mechanism against these attacks. The adversarial purification method is to purify adversarially perturbed input samples before making predictions (Srinivasan et al., 2021; Shi 338 et al., 2021; Yoon et al., 2021). The major works about adversarial purification focus on purifying continuous inputs such as images, therefore these works explore different generative models such as GANs (Samangouei et al., 2018), energy-based models (EBMs) (LeCun et al., 2006) and recently developed diffusion models (Song et al., 2021; Nie et al., 2022). However, in textual adversarial attacks, the inputs are discrete tokens which makes it more challenging to deploy previous adversarial purification methods. Therefore, we introduce a purification mechanism with the help of masked language models. We first consider the widely used masking process to inject noise into the input; then we recover the clean texts from the noisy inputs with the help of the masked language models (e.g. a BERT (Devlin et al., 2018)). Further, considering that the iterative process in previous adversarial purification algorithms can be extremely costly (e.g. a VP-SDE process in diffusion models (Song et al., 2021)), we instead simplify the iterative process to an ensemble-purifying process that conducting adversarial purification multiple times to obtain an ensembled result as a compromise to the time cost in traditional adversarial purification process. Through extensive experiments, we prove that the proposed text adversarial purification algorithm can successfully serve as defense against strong attacks such as Textfooler and BERT-Attack. Experiment results show that the accuracy under attack in baseline defense methods is lower than random guesses, while after text purification, the performance can reach only a few percent lower than the original accuracy when the candidate range of the attack is limited. Further, extensive results indicate that the candidate range of the attacker score is essential for successful attacks, which is a key factor in maintaining the semantics of the adversaries. Therefore we also recommend that future attacking methods can focus on achieving successful attacks with tighter constraints. To summarize our contributions: (1) We raise the concern of defending substitution-based adversarial attacks without acknowledging the form of the attacks in NLP tasks. (2) To the best of our knowledge, we are the first to consider adversarial purification as a defense against textual adversarial attacks exemplified by strong word-substitution attacks and combine text adversarial purification with pre-trained models. (3) We perform extensive experiments to demonstrate that the adversarial purification method is capable of defending strong adversarial attacks, which brings a new perspective to defending textual adversarial attacks. ## 2 Related Work 2.1 Adversarial Attacks In Nlp In NLP tasks, current methods use substitutionbased strategies (Alzantot et al., 2018; Jin et al., 2019; Ren et al., 2019) to craft adversarial examples. Most works focus on the score-based blackbox attack, that is, attacking methods know the logits of the output prediction. These methods use different strategies (Yoo et al., 2020; Morris et al., 2020b) to find words to replace, such as genetic algorithm (Alzantot et al., 2018), greedy-search (Jin et al., 2019; Li et al., 2020) or gradient-based methods (Ebrahimi et al., 2017; Cheng et al., 2019) and get substitutes using synonyms (Jin et al., 2019; Mrkšic et al. ´ , 2016; Ren et al., 2019) or language models (Li et al., 2020; Garg and Ramakrishnan, 2020; Shi et al., 2019). ## 2.2 Adversarial Defenses We divide the defense methods for wordsubstitution attacks by whether the defense method requires knowledge of the form of the attack. When the candidate list is known, recent works introduce defense strategies that incorporate the candidates of the words to be replaced as an augmentation. Jin et al. (2019); Li et al. (2020); Si et al. (2020) uses generated adversaries to augment the classifier for better defense performances; Jia et al. (2019); Huang et al. (2019) introduce a certified robust model to construct a certified space within the range of a candidate list therefore the substitutions in the candidate list cannot perturb the model. Zhou et al. (2020); Dong et al. (2021) construct a convex hull based on the candidate list which can resist substitutions in the candidate list. To defend unknown attacks, NLP models can incorporate gradient-based adversarial training strategies (Miyato et al., 2016; Madry et al., 2019) since recent works (Ebrahimi et al., 2017; Cheng et al., 2019; Zhu et al., 2019; Li and Qiu, 2020) show that gradient-based adversarial training can also improve defense performances against wordsubstitution attacks. ![2_image_0.png](2_image_0.png) Purified **Image** (Panda) MLM realize... it is something I like... (Positive) ## 2.3 Adversarial Purification Adversarial purification is a defense strategy that uses generative models to purify adversarial inputs before making predictions, which is a promising direction in adversarial defense. Samangouei et al. (2018) uses a defensive GAN framework to build clean images to avoid adversarial attacks. Energybased models (EBMs) are used to purify attacked images via Langevin dynamics (LeCun et al., 2006). Score-based models (Yoo et al., 2020) is also introduced as a purification strategy. Recent works focus on exploring diffusion models as the purification model in purifying the attacked images (Nie et al., 2022). Though widely explored, adversarial purification strategy is less explored in the NLP field. ## 3 Text Adversarial Purification 3.1 Background Of Adversarial Purification A classic adversarial purification process is to gradually purify the input through T steps of purification runs. As seen in Figure 1, the purification process in the image domain is to first construct an input x ′from the perturbed input x by injecting random noise. Then the purification algorithm will recover the clean image xb from the noisy image x ′ which usually takes multiple rounds. The intuition of such a purification process is that the recovered inputs will not contain adversarial effects. Specifically, in the score-based adversarial purification (Yoo et al., 2020), the sample injected with random noise is x ′= x + ε where ε ∼ N (0, σ2I) and the goal is to purify x ′with score network sθ. In a continuous time step where x0 = x ′, the goal is to recover x0 through a score-based generative model xt = xt−1 + αt−1sθ(xt−1) where α is the step size related to xt−1. After T times of generation, the recovered xb = xT is used in the final prediction which contains less adversarial effect. As for the diffusion-based purification methods (Nie et al., 2022), the process includes a forward diffusion process and a reverse recovery process. The noise injection process is a forward stochastic differential equation (SDE), that is, the noisy input x ′= x(T) and initial perturbed input x = x(0). The diffusion process is x(T) = p p α(T)x(0) + 1 − α(T)ε where α is a hyper-parameter and 340 ε ∼ N (0, σ2I). The final purified input xb = xb(0) where xb(0) is the reverse-time SDE generated input from the diffused input x(T). ## 3.2 Text Adversarial Purification With Bert Instead of the iterative purification process used in purifying images, we introduce a novel purification method that purifies the input texts via masking and masks prediction with pre-trained masked language models exemplified by BERT (Devlin et al., 2018). As seen in Figure 1, instead of gradually adding noise and recovering the clean sample from the noisy samples, we inject random noise into the input texts multiple times and recover the noisy data to a clean text based on the mask-prediction ability of the masked language model Fm(·). Considering that the perturbed text is X, we can inject noise to construct multiple copies X ′ i = [w0, *· · ·* , [MASK], wn, *· · ·* , ]. We use two simple masking strategies: (1) Randomly mask the input texts; (2) Randomly insert masks into the input texts. Such a random masking process is similar to adding a random noise ε ∼ N (0, σ2I) to the inputs x. After constructing multiple noisy inputs, we run the denoise process via masked language models: Xbi = Fm(X ′ i ). With N recovered texts, we are able to make predictions with the classifier Fc(·): Si = 1 N PN i=0 *Sof tmax*(Fc(Xbi)). Unlike continuous perturbations to images, word-substitution adversarial samples only contain several perturbed words. Therefore, we consider using a multiple-time mask-and-recover process as text adversarial purification, which makes full use of the pre-trained ability of the masked language models. Compared with the generation process used in image adversarial purification, masked language model-based purification method is easier to implement and utilize in pre-trained modelbased applications as a defense against strong wordsubstitution adversarial attacks. ## 3.3 Combining With Classifier Normal adversarial purification methods are plugand-play processes inserted before the classification, however, the masked language model itself is a widely used classification model. That is, the purification model Fm(·) and the classification model Fc(·) can share the same model. Therefore, instead of using a normal masked language model such as BERT, we train the classifier and the mask-filling ability as multi-tasks. The classification loss is Lc = L(Fc(X ′), y, θ)+L(Fc(X)*, y, θ*) and the masked language model loss is Lmlm = L(Fm(X ′)*, X, θ*). Here, the input X is the clean text used in training the classifier and the X ′is the random masked text. The loss function L(·) is the cross-entropy loss used in both the text classification head and masked language modeling head in the pre-trained models exemplified by BERT. In this way, we are utilizing the pre-trained models to their full ability by using both the mask-filling function learned during the pre-training stage as well as the generalization ability to downstream tasks. Algorithm 1 Adversarial Training Require: Training Sample X, adversarial step Ta 1: X ′ ← Inject Noise X 2: δ0 ← √ 1 DN (0, σ2) // Init Perturb 3: for t = 0, 1*, ...T*a do **for $t=0,1,...T_{a}$**do** $\mathbf{g}_{\delta}\leftarrow\nabla_{\delta}(\mathcal{L}_{c}+\mathcal{L}_{mlm})$ // Get Perturbation $\mathbf{\delta}_{t}\leftarrow\prod_{||\mathbf{\delta}||_{F}<\epsilon}(\mathbf{\delta}_{t}+\alpha\cdot\mathbf{g}_{\delta}/||\mathbf{g}_{\delta}||_{F})$ $\mathcal{L}_{noise}\leftarrow\mathcal{L}(F_{m}(X^{\prime}+\mathbf{\delta}_{t}),X,\theta)$ $X^{\prime}\gets X^{\prime}+\mathbf{\delta}_{t}$ // Update Input $\mathbf{g}_{t+1}=\mathbf{g}_{t}+\nabla_{\theta}(\mathcal{L}_{c}+\mathcal{L}_{mlm}+\mathcal{L}_{noise})$ ## 9: Θ ← Θ − Gt +1 // Update Model Parameter Θ 3.4 Combining With Adversarial Training Different from the image field where adversaries are usually generated by gradients, wordsubstitution attacks do not have direct connections with gradient-based adversaries in the text domain. Therefore, it is intuitive to incorporate gradientbased adversarial training in the purification process when the purification process is combined with the classifier training. We introduce the adversarial training process therefore the purification function Fm(·) includes mask-prediction and recovering clean texts from inputs with gradient-based perturbations, which leads to stronger purification ability compared with a standard BERT. Following standard adversarial training process with gradient-based adversaries introduced by Zhu et al. (2019); Li and Qiu (2020). In the adversarial training process, a gradient-based perturbation δ is added to the embedding output of the input text X (for simplicity, we still use X and X ′to denote the embedding output in the Algorithm 1). Then the perturbed inputs are added to the training set in the training process. We combine gradient-based adversarial training with the text purification process. As illustrated in Algorithm 1, for an adversarial training step, we add perturbations to the masked text X ′and run Ta times of updates. We calculate gradients based on both classification losses Lc and masked language modeling losses Lmlm; further, as seen in line 6, we also calculate the loss that the masked language model will predict the texts from the perturbed text X ′+ δ, which enhanced the text recover ability from noisy or adversarial texts. ## 4 Experiments 4.1 Datasets We use two widely used text classification datasets: IMDB 1(Maas et al., 2011) and AG's News 2 (Zhang et al., 2015) in our experiments. The IMDB dataset is a bi-polar movie review classification task; the AG's News dataset is a four-class news genre classification task. The average length is 220 words in the IMDB dataset, and 40 words in the AG's News dataset. We use the test set following the Textfooler 1k test set in the main result and sample 100 samples for the rest of the experiments since the attacking process is seriously slowed down when the model is defensive. ## 4.2 Attack Methods Popular attack methods exemplified by genetic Algorithm (Alzantot et al., 2018), Textfooler (Jin et al., 2019) and BERT-Attack (Li et al., 2020) can successfully mislead strong models of both IMDB and AG's News task with a very small percentage of substitutions. Therefore, we use these strong adversarial attack methods as the attacker to test the effectiveness of our defense method. The hyperparameters used in the attacking algorithm vary in different settings: we choose candidate list size K to be 12, 48, and 50 which are used in the Textfooler and BERT-Attack methods. We use the exact same metric used in Textfooler and BERT-Attack that calculates the after-attack accuracy, which is the targeted adversarial evaluation defined by Si et al. (2020). The after-attack accuracy measures the actual defense ability of the system under adversarial attacks. 1https://datasets.imdbws.com/ 2https://www.kaggle.com/amananandrai/ag-newsclassification-dataset ## 4.3 Victim Models And Defense Baselines The victim models are the fine-tuned pre-train models exemplified by BERT and RoBERTa, which we implement based on Huggingface Transformers 3 (Wolf et al., 2020). As discussed above, there are few works concerning adversarial defenses against attacks without knowing the candidates in NLP tasks. Moreover, previous works do not focus on recent strong attack algorithms such as Textfooler (Jin et al., 2019), BERT-involved attacks (Li et al., 2020; Garg and Ramakrishnan, 2020) Therefore, we first list methods that can defend against adversarial attacks without accessing the candidate list as our baselines: Adv-Train (Adv-HotFlip): Ebrahimi et al. (2017) introduces the adversarial training method used in defending against substitution-based adversarial attacks in NLP. It uses gradients to find actual adversaries in the embedding space. Virtual-Adv-Train (FreeLB): Li and Qiu (2020); Zhu et al. (2019) use virtual adversaries to improve the performances in fine-tuning pretrained models, which can also be used to deal with adversarial attacks without accessing the candidate list. We follow the standard FreeLB training process to re-implement the defense results. Further, there are some works that require the candidate list, it is not a fair comparison with defense methods without accessing the candidates, so we list them separately: Adv-Augmentation: We generate adversarial examples of the training dataset as a data augmentation method. We mix the generated adversarial examples and the original training dataset to train a model in a standard fine-tuning process. ASCC: Dong et al. (2021) also uses a convexhull concept based on the candidate vocabulary as a strong adversarial defense. ADA: Si et al. (2020) uses a mixup strategy based on the generated adversarial examples to achieve adversarial defense with variants AMDASMix that mixup the special tokens. FreeLB++: Li et al. (2021) introduces a variant of FreeLB method that expands the norm bound. RanMASK: Zeng et al. (2021) introduces a masking strategy that makes use of noises to improve robustness. | Defense ↓ Attacks→ | Origin | Textfooler | BERT-Attack | Textfooler | BERT-Attack | |----------------------------------------------|----------|--------------|---------------|--------------|---------------| | (K=12) | (K=12) | (K=50) | (K=48) | | | | IMDB ↓ BERT (Devlin et al., 2018) | 94.1 | 20.4 | 18.5 | 2.8 | 3.2 | | RoBERTa (Liu et al., 2019) | 97.3 | 26.3 | 24.5 | 25.2 | 23.0 | | - Adv-HotFlip (BERT) (Ebrahimi et al., 2017) | 95.1 | 36.1 | 34.2 | 8.0 | 6.2 | | - FreeLB (BERT) (Li and Qiu, 2020) | 96.0 | 30.2 | 30.4 | 7.3 | 2.3 | | - FreeLB++ (BERT) (Li et al., 2021) | 93.2 | - | - | 45.3 | 39.9 | | ▲ RanMASK (RoBERTa) (Zeng et al., 2021) | 93.0 | - | - | 23.7 | 26.8 | | ▲Text Purification(BERT) | 93.0 | 81.5 | 76.7 | 51.0 | 44.5 | | ▲Text Purification(RoBERTa) | 96.1 | 84.2 | 82.0 | 54.3 | 52.2 | | AG's News ↓ BERT (Devlin et al., 2018) | 92.0 | 32.8 | 34.3 | 19.4 | 14.1 | | RoBERTa (Liu et al., 2019) | 97.3 | 26.3 | 24.5 | 25.2 | 23.0 | | - Adv-HotFlip (BERT) | 91.2 | 35.3 | 34.1 | 18.2 | 8.5 | | - FreeLB (BERT) | 90.5 | 40.1 | 34.2 | 20.1 | 8.5 | | ▲Text Purification(BERT) | 90.6 | 61.5 | 49.7 | 34.9 | 22.5 | | ▲Text Purification(RoBERTa) | 90.8 | 59.1 | 41.2 | 34.2 | 19.5 | Table 1: After-Attack Accuracy compared with defense methods that can defend attacks without acknowledging the form of the attacks. That is, the substitution candidates of the attack methods are unknown to defense systems. | Methods | Origin | Textfooler | GA | |----------------------------|----------|--------------|------| | IMDB ↓ BERT | 94.0 | 2.0 | 45.0 | | - Data-Augmentation | 93.0 | 18.0 | 53.0 | | ●ADA (Si et al., 2020) | 96.7 | 3.0 | - | | ●AMDA(Si et al., 2020) | 96.9 | 17.4 | - | | ▲ ASCC (Dong et al., 2021) | 77.0 | - | 71.0 | | ▲Text Purification(BERT) | 93.0 | 51.0 | 79.0 | ## 4.4 Implementations We use BERT-BASE and RoBERTa-BASE models based on the Huggingface Transformers 4. We modify the adversarial training with virtual adversaries based on the implementation of FreeLB, TAVAT, and FreeLB++. The training hyper-parameters we use are different from FreeLB and TAVAT since we aim to find large perturbations to simulate adversaries. We set adversarial learning rate α = 1e-1 to and normalization boundary ϵ = 2e-1 in all tasks. We set the multiple purification size N = to 16 for all tasks and we will discuss the selection of N in the later section. For our text adversarial purification method, we 4https://github.com/huggingface/transformers use the model that is trained with gradient-based adversarial training as the purification model Fm(·) and the classifier Fc(·) for the main experiments and conduct thorough ablations to explore the effect of combining purification with classifier and adversarially trained classifier. As for implementing adversarial attack methods, we use the TextAttack toolkit while referring the official codes of the corresponding attack methods 5(Morris et al., 2020a). The similarity thresholds of the word-substitution range are the main factors of the attacking algorithm. We tune the USE (Cer et al., 2018) constraint 0.5 for the AG task and 0.7 for the IMDB task and 0.5 for the cosinesimilarity threshold of the synonyms embedding (Mrkšic et al. ´ , 2016) which can reproduce the results of the attacking methods reported. ## 4.5 Results As seen in Table 1, the proposed **Text Adversarial Purification** algorithm can successfully defend strong attack methods. The accuracy of our defending method under attack is significantly higher than non-defense models (50% vs 20% in the IMDB dataset). Compared with previous defense methods, our proposed method can achieve higher defense accuracy in both the IMDB task and AG's News task. The Adv-HotFlip and the FreeLB methods 5https://github.com/QData/TextAttack | Defense ↓ Attacks→ | Origin | Textfooler | BERT-Attack | |------------------------------------------------------------------------------|----------|--------------|---------------| | (K=12) | (K=12) | | | | ▲Text Purification Only ↓ ✔ Purification | 94.0 | 72.0 | 60.0 | | ✔ Purification ✖ Multi. Recovery | 87.0 | 20.0 | 13.0 | | ✔ Purification ✖ Mask Insertion ✖ Multi. Recovery | 92.0 | 11.0 | 3.0 | | ▲Combining Classifier ↓ ✔Purification ✔ Comb. Classifier | 95.0 | 76.0 | 67.0 | | ✔Purification ✔ Comb. Classifier ✖ Multi. Recovery | 95.0 | 45.0 | 34.0 | | ✔Purification ✔ Comb. Classifier ✖ Multi. Recovery ✖ Mask Insertion | 95.0 | 29.0 | 17.0 | | ▲Combining Adversarially Trained Classifier ↓ ✔ Purification ✔ AT Classifier | 93.0 | 86.0 | 77.0 | | ✔ Purification ✔ AT Classifier ✖ Multi. Recovery | 93.0 | 63.0 | 52.0 | | ✔ Purification ✔ AT Classifier ✖ Multi. Recovery ✖ Mask Insertion | 93.0 | 42.0 | 29.0 | | BERT | 94.0 | 10.0 | 5.0 | are effective, which indicates that gradient-based adversaries are not very similar to actual substitutions. We can see that Adv-HotFlip and FreeLB methods achieve similar results (around 30% when K = 12) which indicates that gradient-based adversarial training methods have similar defense abilities no matter whether the adversaries are virtual or real since they are both unaware of the attacker's candidate list. Also, the original accuracy (on the clean data) of our method is only a little lower than the baseline methods, which indicates that the purified texts still contain enough information for classification. The RoBERTa model also shows robustness using both original fine-tuned model and our defensive framework, which indicates our purification algorithm can be used in various pretrained language models. Compared with methods that specifically focus on adversarial defense, our proposed method can still surpass the state-of-theart defense system FreeLB++ (Li et al., 2021) and RanMASK (Zeng et al., 2021). Further, the candidate size is extremely important in defending against adversarial attacks, when the candidate size is smaller, exemplified by K = 12, our method can achieve very promising results. As pointed out by Morris et al. (2020b), the candidate size should not be too large that the quality of the adversarial examples is largely damaged. As seen in Table 2, we compare our method with previous access-candidates defense methods. When defending against the widely used Textfooler attack and genetic attack (Alzantot et al., 2018), our method can achieve similar accuracy even compared with known-candidates defense methods. As seen, the data augmentation method cannot significantly improve model robustness since the candidates can be very diversified. Therefore, using generated adversarial samples as an augmentation strategy does not guarantee robustness against greedy-searched methods like Textfooler and BERT-Attack. ## 4.6 Analysis 4.6.1 Ablations As we design an adversarial purification algorithm with masked language models and propose a multiple-recovering strategy, we aim to explore which process helps more in the purification defense system. Plus, we combine classifiers within the purification model so it is also important to explore whether such a combination is helpful. For each type of purification method, we test whether the specific purification process we propose is effective. That is, we test whether making multiple recoveries in the purification process is helpful; also, we test whether using both masking tokens and inserting additional masks is helpful. As seen in Table 3, we can summarize that: (1) Multi-time recovering is necessary: in the image domain, multiple reconstructions with a continuous time purification process are necessary. | Texts | Confidence (Positive) | | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------| | I have the good common logical sense to know that oil can not last forever and I am acutely | | | | Clean-Sample | aware of how much of my life in the suburbs revolves around petrochemical products. I've been an avid consumer of new technology and I keep running out of space on powerboards - so... | 93.2% | | I possess the good common logical sense to realize that oil can not last forever and I am acutely | | | | Adv. of BERT | aware of how much of my life in the suburbs spins around petrochemical products. I've been an avid consumer of new technology and I keep running out of space on powerboards - well... | 38.3% | | I know the wonderful general sense to knows that oils can not last endless and I am acutely | | | | Adv. of Text Pure | know of how majority of my lived in the city spins around petrochemical products . I've been an amateur consumers of newly technologies and I kept working out of spaces on powerboards ! well... | 80.1% | | Well I know the wonderful general sense notion to knows that oils production can not last for endless years and I am acutely know of how the majority of my live in the city spins around the petrochemical production ... I've been an amateur consumers of new technologies and I kept working out of spaces on power skateboards! well ... | 80.4% | | | I know the wonderful common sense notion to knows that oils can not last forever and I also acutely know of how majority of my lived in the world and around petrochemical production ... I've been an amateur consumers of newly technologies and I kept working out of them on skateboards ! well ... | 81.4% | | | I know the wonderfully general sense notion to knows that oils can not last endless and I am acutely know of how majority part of my lived in the big city spins around petrocochemical production ... I should have been an amateur consumers fan of newly technologies and I kept on working out of spaces and on powerboards ! well ... | 76.2% | | | I am the the general sense notion and knows that oils can not last endless and I am acutely know of the part of my lived as the city spins around petrochemical production ... I've been an amateur consumers of newly technologies and I kept working out of bed on powerboards ! well ... | 78.5% | | | Purified Texts | | | Similarly, the multi-recovery process is important in obtaining high-quality purification results. We can observe that one-time recovery cannot achieve promising defense performances. (2) Combining classifiers is effective: we can observe that when we use trained classifiers and masked language models, the defense performances are better than using fine-tuned classifier and vanilla BERT as a masked language model, indicating that such a combined training process is helpful in obtaining more strong defense systems. Also, with gradient-based adversarial training, the purification process can obtain a further boost, indicating that our proposed text purification algorithm can be used together with previous defense methods as an advanced defense system. ## 4.6.2 Example Of Purification Results As seen in Table 4, we construct multiple recoveries and use the averaged score as the final classification result. Such a purification process is effective compared with vanilla fine-tuned BERT. We can observe that the adversarial sample that successfully attacked the vanilla BERT model only achieves this by replacing only a few tokens. While with the purification process, the attack algorithm is struggling in finding effective substitutions to achieve a successful attack. Even replacing a large number of tokens that seriously hurt the semantics of the input texts, with the purification process involved, the classifier can still resist the adversarial effect. Further, by observing the purified texts, we can find that the purified texts can make predictions correctly though some substitutes still exist in the purified texts, indicating that making predictions based on purified texts using the combined trained classifier can obtain a promising defense performance. That is, our proposed method, though is not a plug-and-play system, can be used as a general system as a defense against substitution-based attacks. ## 5 Conclusion And Future Work In this paper, we introduce a textual adversarial purification algorithm as a defense against substitution-based adversarial attacks. We utilize the mask-infill ability of pre-trained models to recover noisy texts and use these purified texts to make predictions. Experiments show that the purification method is effective in defending strong adversarial attacks without acknowledging the substitution range of the attacks. We are the first to consider the adversarial purification method with a multiple-recovering strategy in the text domain while previous successes of adversarial purification strategies usually focus on the image field. Therefore, we hope that the adversarial purification method can be further explored in NLP applications as a powerful defense strategy. ## Limitations In this paper, we discuss an important topic in the NLP field, the defense against adversarial attacks in NLP applications. We provide a strong defense strategy against the most widely used word substitution attacks in the NLP field, which is limited in several directions. - We are testing defense strategies using downstream task models such as BERT and RoBERTa, and the purification tool is a model with a mask-filling ability such as BERT. Such a process can be further improved with strong models such as large language models. - We study the concept of adversarial purification in the adversarial attack scenarios with word-substitution attacks on small fine-tuned models. The concept of adversarial purification can be further expanded to various NLP applications. For instance, the purification of natural language can be used in malicious text purification which is more suitable in applications with large language models. ## Acknowledgement This work was supported by the National Natural Science Foundation of China (No. 62236004 and No. 62022027) and CAAI-Huawei MindSpore Open Fund. ## References Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, BoJhang Ho, Mani B. Srivastava, and Kai-Wei Chang. 2018. Generating natural language adversarial examples. *CoRR*, abs/1804.07998. Nicholas Carlini and David A. Wagner. 2016. Towards evaluating the robustness of neural networks. *CoRR*, abs/1608.04644. 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Freelb: Enhanced adversarial training for language understanding. arXiv preprint arXiv:1909.11764. ## Appendix Recovery Number Analysis One key problem is that how many recoveries we should use in the recovering process, as finding a proper T is also important in the image-domain purification process. We use two attack methods with K = 12 to test how the accuracy varies when using different recovery number N. As seen in Fig. 2 (a), the ensemble size is actually not a key factor. Larger ensemble size would not result in further improvements. We assume that larger ensemble size will *smooth* the output score which will benefit the attack algorithm. That is, the tiny difference between substitutes can be detected by the attack algorithm since the confidence score is given to the attack algorithms. Still, we can conclude that a multiple recovery process is effective in the purification process and quite simple to implement. ## Candidate Size Analysis The attack algorithms such as BERT-Attack and Textfooler use a wide range of substitution set (e.g. K=50 in Textfooler means for each token to replace, the algorithm will find the best replacement in 50 candidates), which seriously harms the quality of the input texts. As seen in Fig. 2 (b), when the candidate is 0, the accuracy is high on the clean samples. When the candidate is 6, the normal fine-tuned BERT model cannot correctly predict the generated adversarial examples. This indicates that normal fine-tuned BERT is not robust even when the candidate size is small. After purification, the model can tolerate these limited candidate size attacks. When the candidate size grows, the performance of our defense framework drops by a relatively large margin. We assume that large candidate size would seriously harm the semantics which is also explored in Morris et al. (2020b), while these adversaries cannot be well evaluated even using human-evvaluations since the change rate is still low. ![10_image_0.png](10_image_0.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. D3. 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deng-etal-2023-speech

{SPEECH}: Structured Prediction with Energy-Based Event-Centric Hyperspheres

https://aclanthology.org/2023.acl-long.21

Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex dependency among event structured components with energy-based modeling, and represents event classes with simple but effective hyperspheres. Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks.

## Speech**: Structured Prediction With Energy-Based** Event-Centric Hyperspheres Shumin Deng♥, Shengyu Mao♠, Ningyu Zhang♠∗**, Bryan Hooi**♥∗ ♥National University of Singapore & NUS-NCS Joint Lab, Singapore ♠Zhejiang University & AZFT Joint Lab for Knowledge Engine, China {shumin,dcsbhk}@nus.edu.sg, {shengyu,zhangningyu}@zju.edu.cn ## Abstract Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energybased Event-Centric Hyperspheres (S**PEECH**). SPEECH models complex dependency among event structured components with energybased modeling, and represents event classes with simple but effective hyperspheres. Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks. ## 1 Introduction Structured prediction (Taskar et al., 2005) is a task where the predicted outputs are complex structured components. This arises in many NLP tasks (Smith, 2011; Kreutzer et al., 2017; Wang et al., 2023) and supports various applications (Jagannatha and Yu, 2016; Kreutzer et al., 2021). In event-centric NLP tasks, there exists strong complex dependency between the structured outputs, such as event detection (ED) (Chen et al., 2015), event-relation extraction (ERE) (Liu et al., 2020b), and event schema induction (Li et al., 2020). Thus, these tasks can also be revisited as event-centric structured prediction problems (Li et al., 2013). Event-centric structured prediction (ECSP) tasks require to consider manifold structures and dependency of events, including intra-/inter-sentence structures. For example, as seen in Figure 1, given a document containing some event mentions "David Warren shot and killed Henry Glover ... David was convicted and sentenced to 25 years and 9 months ...", in ED task mainly considering intra-sentence structures, we need to identify event triggers (*killed*, convicted) from these tokens and categorize them ∗ Corresponding Author. ![0_image_0.png](0_image_0.png) ED ![0_image_1.png](0_image_1.png) …… *trigger* …… …… …… non-trigger …… …… …… … …… …… …… … *trigger* …… ………… non-trigger …… …… …… …… …… … non-trigger …… …… …… *trigger* killing legal_rulings death [S1], cause, [S3] …… [S2], before, [S3] ERE ![0_image_2.png](0_image_2.png) ![0_image_3.png](0_image_3.png) ![0_image_4.png](0_image_4.png) Figure 1: Illustration of event-centric structured prediction tasks, with the examples of ED and ERE. into event classes (killing, *legal_rulings*); in ERE task mainly considering inter-sentence structures, we need to find the relationship between each event mention pair, such as event coreference, temporal, causal and subevent relations. As seen from Figure 1, the outputs of ECSP lie on a complex manifold and possess interdependent structures, *e.g.*, the long-range dependency of tokens, the association among triggers and event classes, and the dependency among event classes and event relations. Thus it is challenging to model such complex event structures while efficiently representing these events. Previous works increasingly apply deep representation learning to tackle these problems. Lin et al. (2020); Li et al. (2020) propose to predict event structures based on the event graph schema. Hsu et al. (2022) generate event structures with manually designed prompts. However, these methods mainly focus on one of ECSP tasks and their event structures are hard to represent effectively. Paolini et al. (2021); Lu et al. (2021, 2022) propose to extract multiple event structures from texts with a unified generation paradigm. However, the event structures of these approaches are usually quite simplistic and they often ignore the complex dependency among tasks. In this paper, we focus more on: (i) how to learn complex event structures for manifold ECSP tasks; and (ii) how to simultaneously represent events for these complex structured prediction models effectively. To resolve the first challenging problem of modeling manifold event structures, we utilize energy networks (Lecun et al., 2006; Belanger and McCallum, 2016; Belanger et al., 2017; Tu and Gimpel, 2018), inspired by their potential benefits in capturing complex dependency of structured components. We define the energy function to evaluate compatibility of input/output pairs, which places no limits on the size of the structured components, making it powerful to model complex and manifold event structures. We generally consider token-, sentence-, and document- level energy respectively for trigger classification, event classification and event-relation extraction tasks. To the best of our knowledge, this work firstly address event-centric structured prediction with energy-based modeling. To resolve the second challenging problem of efficiently representing events, we take advantage of hyperspheres (Mettes et al., 2019; Wang and Isola, 2020), which is demonstrated to be a simple and effective approach to model class representation (Deng et al., 2022). We assume that the event mentions of each event class distribute on the corresponding energy-based hypersphere, so that we can represent each event class with a hyperspherical centroid and radius embedding. The geometrical modeling strategy (Ding et al., 2021; Lai et al., 2021) is demonstrated to be beneficial for modelling enriched class-level information and suitable for constructing measurements in Euclidean space, making it intuitively applicable to manifold eventcentric structured prediction tasks. Summarily, considering the two issues, we propose to address Structured Prediction with Energybased Event-Centric Hyperspheres (S**PEECH**), and our contributions can be summarized as follows: - We revisit the event-centric structured prediction tasks in consideration of both complex event structures with manifold dependency and efficient representation of events. - We propose a novel approach named SPEECH to model complex event structures with energy-based networks and efficiently represent events with event-centric hyperspheres. - We evaluate SPEECH on two newly proposed datasets for both event detection and eventrelation extraction, and experiments demonstrate that our model is advantageous. ## 2 Related Work Event-Centric Structured Prediction (ECSP). Since the boom in deep learning, traditional approaches to ECSP mostly define a score function between inputs and outputs based on a neural network, such as CNN (Chen et al., 2015; Deng et al., 2020), RNN (Nguyen et al., 2016; Meng and Rumshisky, 2018; Nguyen and Nguyen, 2019), and GCN (Yan et al., 2019; Lai et al., 2020; Cui et al., 2020). With the development of pretrained large models, more recent research has entered a new era. Wang et al. (2019); Du and Cardie (2020); Liu et al. (2020a); Deng et al. (2021); Sheng et al. (2022) leverage BERT (Devlin et al., 2019) for event extraction. Han et al. (2020) and Wang et al. (2020a); Man et al. (2022); Hwang et al. (2022) respectively adopt BERT and RoBERTa (Liu et al., 2019) for event-relation extraction. Lu et al. (2021); Paolini et al. (2021); Lu et al. (2022) propose generative ECSP models based on pre-trained T5 (Raffel et al., 2020). Wang et al. (2023) tackle ECSP with code generation based on code pretraining. However, these approaches are equipped with fairly simplistic event structures and have difficulty in tackling complex dependency in events. Besides, most of them fail to represent manifold events effectively. Energy Networks for Structured Prediction and Hyperspheres for Class Representation. Energy networks *define an energy function over* input/output pairs with arbitrary neural networks, which places no limits on the size of the structured components, making it advantageous in modeling complex and manifold event structures. Lecun et al. (2006); Belanger and McCallum (2016) associate a scalar measure to evaluate the compatibility to each configuration of inputs and outputs. (Belanger and McCallum, 2016) formulate deep energy-based models for structured prediction, called structured prediction energy networks (SPENs). Belanger et al. (2017) present end-to-end learning for SPENs, Tu and Gimpel (2018) jointly train structured energy functions and inference networks with largemargin objectives. Some previous researches also regard event-centric NLP tasks as structured prediction (Li et al., 2013; Paolini et al., 2021). Furthermore, to effectively obtain event representations, Deng et al. (2022) demonstrate that hyperspherical prototypical networks (Mettes et al., 2019) are powerful to encode enriched semantics and dependency in event structures, but they merely consider support for pairwise event structures. ## 3 Methodology 3.1 Preliminaries For structured prediction tasks, given input x ∈ X , we denote the structured outputs by MΦ(x) ∈ Y˜ with a prediction model MΦ. Structured Prediction Energy Networks (SPENs) score structured outputs with an **energy function** EΘ : *X ×Y →*˜ R parameterized by Θ that iteratively optimize the energy between the input/output pair (Belanger and McCallum, 2016), where lower energy means greater compatibility between the pair. We introduce event-centric structured prediction (ECSP) following the similar setting as SPENs for multi-label classification and sequence labeling proposed by Tu and Gimpel (2018). Given a feature vector x belonging to one of T labels, the model output is MΦ(x) = {0, 1} T ∈ Y˜ for all x. The energy function contains two terms: $$\begin{split}E_{\Theta}(\mathbf{x},\mathbf{y})&=E_{\Theta}^{local}(\mathbf{x},\mathbf{y})+E_{\Theta}^{label}(\mathbf{y})\\ &=\sum_{i=1}^{T}y_{i}V_{i}^{\top}f(\mathbf{x})+\mathbf{w}^{\top}g(W\mathbf{y})\end{split}\tag{1}$$ where E*local* Θ (x, y) = PT i=1 yiV> if(x) is the sum of linear models, and yi ∈ y, Viis a parameter vector for label i and f(x) is a multi-layer perceptron computing a feature representation for the input x; E*label* Θ (y) = w>g(Wy) returns a scalar which quantifies the full set of labels, scoring y independent of x, thereinto, w is a parameter vector, g(·) is an elementwise non-linearity function, and W is a parameter matrix learned from data indicating the interaction between labels. After learning the energy function, prediction minimizes energy: $${\hat{\mathbf{y}}}={\underset{\mathbf{y}\in{\hat{\mathbf{y}}}}{\operatorname{arg\,min}}}\;E_{\Theta}(\mathbf{x},\mathbf{y})$$ The final theoretical optimum for SPEN is denoted by: $$\begin{split}\min\limits_{\Theta}\max\limits_{\Phi}\sum\left[\triangle\left(\mathbf{M}_{\Phi}(\mathbf{x}_{i}),\mathbf{y}_{i}\right)-\right.\\ \left.E_{\Theta}\left(\mathbf{x}_{i},\mathbf{M}_{\Phi}(\mathbf{x}_{i})\right)+E_{\Theta}\left(\mathbf{x}_{i},\mathbf{y}_{i}\right)\right]_{+}\\ \left.\left(3\right)\right.\end{split}\tag{3}$$ where $[\cdot]_{+}=\max(0,\cdot)$ and $\triangle\left(\tilde{\alpha},\mathbf{y}\right)$ after $\alpha$ where [a]+ = max(0, a), and 4(y˜, y), often referred to "margin-rescaled" structured hinge loss, is a structured cost function that returns a nonnegative value indicating the difference between the predicted result y˜ and ground truth y. ## 3.2 Problem Formulation In this paper, we focus on ECSP tasks of event detection (ED) and event-relation extraction (ERE). ED can be divided into trigger classification for tokens and event classification for sentences. We denote the dataset by D = {E, R, X } containing an event class set E, a multi-faceted eventrelation set R and the event corpus X , thereinto, E = {ei| i ∈ [1, |E|]} contains |E| event classes including a None; R = {ri| i ∈ [1, |R|]} contains |R| temporal, causal, subevent and coreference relationships among event mentions including a NA event-relation; X = {Xi| i ∈ [1, K]} consists of K event mentions, where Xiis denoted as a token sequence x = {xj | j ∈ [1, L]} with maximum L tokens. For *trigger classification*, the goal is to predict the index t (1 ≤ t ≤ L) of the trigger xt in each token sequence x and categorize xtinto a specific event class ei ∈ E. For *event classification*, we expect to predict the event label ei for each event mention Xi. For *event-relation extraction*, we require to identify the relation ri ∈ R for a pair of event mentions X¨hiji = (Xi, Xj ). In summary, our goal is to design an ECSP model MΦ, aiming to tackle the tasks of: (1) *trigger classification*: to predict the token label y˜ = MΦ(x) for the token list x; (2) *event classification*: to predict the event class label Y˜ = MΦ(X) for the event mention X; (3) *event-relation extraction*: to predict the event-relation label z˜ = MΦ(X¨ ) for the event mention pair X¨ . ## 3.3 Model Overview $$\mathbf{\Omega}(2)$$ As seen in Figure 2, SPEECH combines three levels of energy: token, sentence, as well as document, and they respectively serve for three kinds of ECSP tasks: (1) token-level energy for trigger classification: considering energy-based modeling is able to capture long-range dependency among tokens without limits to token size; (2) sentencelevel energy for event classification: considering energy-based hyperspheres can model the complex event structures and represent events efficiently; and (3) document-level energy for event-relation extraction: considering energy-based modeling enables us to address the association among event mention pairs and event-relations. We leverage the trigger embeddings as event mention embeddings; the energy-based hyperspheres with a centroid and a radius as event class embeddings, and these three tasks are associative to each other. ![3_image_0.png](3_image_0.png) ## 3.4 Token-Level Energy Token-level energy serves for trigger classification. Given a token sequence x = {xj |j ∈ [1, L]} with trigger xt, we leverage a pluggable backbone encoder to obtain the contextual representation f1(x) for each token, such as pre-trained BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), DistilBERT (Sanh et al., 2019) and so on. We then predict the label y˜ = MΦ(x) of each token with an additional linear classifier. Inspired by SPENs for sequence labeling (Tu and Gimpel, 2018), we also adopt an energy function for token classification. Energy Function. The token-level energy function is inherited from Eq (1), defined as: EΘ(x, y) = − ![3_image_2.png](3_image_2.png) $$\sum_{n=1}^{L}\sum_{i=1}^{|\mathcal{E}|+2}\underbrace{y_{n}^{i}\left(V_{1,i}^{\top}f_{1}(\mathbf{x}_{n})\right)}_{l o c a l}+\sum_{n=1}^{L}$$ ![3_image_1.png](3_image_1.png) (4) where y in is the ith entry of the vector yn ∈ y, indicating the probability of the nth token xn being labeled with i (i for ei, |E|+1 for non-trigger and |E|+2 for padding token). f1(·) denotes the feature encoder of tokens. Here our learnable parameters are Θ = (V1, W1), thereinto, V1,i ∈ R d is a parameter vector for token label i, and W1 ∈ R (|E|+2)×(|E|+2) contains the bilinear product between yn−1 and yn for token label pair terms. Loss Function. The training objective for trigger classification is denoted by: $$\begin{split}\mathcal{L}_{tok}&=\sum\nolimits_{i=1}^{L}\left[\triangle\left(\tilde{\mathbf{y}}_{i},\mathbf{y}_{i}\right)-E_{\Theta}\left(\mathbf{x}_{i},\tilde{\mathbf{y}}_{i}\right)\right.\\ &\left.+\left.E_{\Theta}\left(\mathbf{x}_{i},\mathbf{y}_{i}\right)\right]_{+}+\mu_{1}\mathcal{L}_{\text{CE}}\left(\tilde{\mathbf{y}}_{i},\mathbf{y}_{i}\right)\right.\end{split}\tag{5}$$ where y˜i and yi respectively denote predicted results and ground truth. The first half of Eq (5) is inherited from Eq (3) for the energy function, and in the latter half, LCE (y˜i, yi) is the trigger classification cross entropy loss, and µ1 is its ratio. ## 3.5 Sentence-Level Energy Sentence-level energy serves for event classification. Given the event mention Xi with the trigger xt, we utilize the trigger embedding f1(xt) as the event mention embedding f2(X), where f2(·) denotes the feature encoder of event mentions. We then predict the class of each event mention with energy-based hyperspheres, denoted by Y˜ = MΦ(X). Specifically, we use an energy-based hypersphere to represent each event class, and assume that the event mentions of each event class should distribute on the corresponding hypersphere with the lowest energy. We then calculate the probability of the event mention X categorizing into the class ei with a **hyperspherical measurement function**: $${\mathcal{S}}(X,{\mathcal{P}}_{i})={\frac{\exp^{-[\;\|{\mathcal{P}}_{i}-f_{2}(X)\|_{2}-\gamma\;]_{+}}}{\sum_{j=1}^{|{\mathcal{E}}|}\exp^{-[\;\|{\mathcal{P}}_{j}-f_{2}(X)\|_{2}-\gamma\;]_{+}}}}\quad(6)$$ where [a]+ = max(0, a), Pi denotes the hypersphere centroid embedding of ei. *k · k* denotes the Euclidean distance. γ is the radius of the hypersphere, which can be scalable or constant. We simply set γ = 1 in this paper, meaning that each event class is represented by a unit hypersphere. Larger S(X,Pi) signifies that the event mention X are more likely be categorized into Pi corresponding 354 to ei. To measure the energy score between event classes and event mentions, we also adopt an energy function for event classification. Energy Function. The sentence-level energy function is inherited from Eq (1), defined as: $$\begin{array}{l}{{E_{\Theta}(X,Y)=}}\\ {{-\left(\sum_{i=1}^{|\mathcal{E}|}\underbrace{Y_{i}\left(V_{2,i}^{\top}f_{2}(X)\right)}_{l o o a l}+\underbrace{w_{2}^{\top}g(W_{2}Y)}_{l a b e l}\right)}}\end{array}\tag{7}$$ where Yi ∈ Y indicates the probability of the event mention X being categorized to ei. Here our learnable parameters are Θ = (V2, w2, W2), thereinto, V2,i ∈ R dis a parameter vector for ei, w2 ∈ R|E| and W2 ∈ R*|E|×|E|*. Loss Function. The training objective for event classification is denoted by: $$\begin{split}{\mathcal{L}}_{s e n}=&\sum\nolimits_{i=1}^{K}\left[\triangle\left(\tilde{\mathbf{Y}}_{i},\mathbf{Y}_{i}\right)-E_{\Theta}\left(\mathbf{X}_{i},\tilde{\mathbf{Y}}_{i}\right)\right.\\ &\left.+\left.E_{\Theta}\left(\mathbf{X}_{i},\mathbf{Y}_{i}\right)\right]_{+}+\mu_{2}{\mathcal{L}}_{\mathrm{CE}}\left(\tilde{\mathbf{Y}}_{i},\mathbf{Y}_{i}\right)\right.\end{split}\tag{8}$$ where the first half is inherited from Eq (3), and in the latter half, LCE is a cross entropy loss for predicted results Y˜i and ground truth Yi. µ2 is a ratio for event classification cross entropy loss. ## 3.6 Document-Level Energy Document-level energy serves for event-relation extraction. Given event mentions X in each document, we model the embedding interactions of each event mention pair with a comprehensive feature vector f3(X¨hiji) = -f2(Xi), f2(Xj ), f2(Xi) f2(Xj ) . We then predict the relation between each event mention pair with a linear classifier, denoted by z˜ = MΦ(X¨ ). Inspired by SPENs for multi-label classification (Tu and Gimpel, 2018), we also adopt an energy function for ERE. Energy Function. The document-level energy function is inherited from Eq (1), defined as: EΘ(X¨ , z) = $=\;\frac{1}{2}$ $$\Theta(\mathbf{X},\mathbf{z})=$$ $$-\left(\sum_{i=1}^{|\mathcal{R}|}\underbrace{z_{i}\left(V_{3,i}^{\top}f_{3}(\ddot{\mathbf{X}})\right)}_{l o c a l}+\underbrace{w_{3}^{\top}g(W_{3}\mathbf{z})}_{l a b e l}\right)\quad\text{(9)}$$ where zi ∈ z indicates the probability of the event mention pair X¨ having the relation of ri. Here our learnable parameters are Θ = (V3, w3, W3), thereinto, V3,i ∈ R 3dis a parameter vector for ri, w3 ∈ R|R| and W3 ∈ R*|R|×|R|*. Loss Function. The training objective for eventrelation extraction is denoted by: $$\begin{split}\text{\it dloc}=\sum_{k=1}^{N}\left[\triangle\left(\tilde{\mathbf{z}}_{k},\mathbf{z}_{k}\right)-E_{\Theta}\left(\tilde{\mathbf{X}}_{k},\tilde{\mathbf{z}}_{k}\right)\right.\\ \left.+\left.E_{\Theta}\left(\tilde{\mathbf{X}}_{k},\tilde{\mathbf{z}}_{k}\right)\right]_{+}+\mu_{3}\mathcal{L}_{\text{CE}}\left(\tilde{\mathbf{z}}_{k},\mathbf{z}_{k}\right)\right.\end{split}\tag{10}$$ where the first half is inherited from Eq (3), and in the latter half, LCE (z˜k, zk) is the event-relation extraction cross entropy loss, µ3 is its ratio, and N denotes the quantity of event mention pairs. The **final training loss** for SPEECH MΦ parameterized by Φ is defined as: $${\mathcal{L}}=\lambda_{1}{\mathcal{L}}_{t o k}+\lambda_{2}{\mathcal{L}}_{s e n}+\lambda_{3}{\mathcal{L}}_{d o c}+\|\Phi\|_{2}^{2}\tag{11}$$ where λ1, λ2, λ3 are the loss ratios respectively for trigger classification, event classification and event-relation extraction tasks. We add the penalty term kΦk 22 with L2 regularization. ## 4 Experiments The experiments refer to event-centric structured prediction (ECSP) and comprise three tasks: (1) Trigger Classification; (2) Event Classification; and (3) Event-Relation Extraction. ## 4.1 Datasets And Baselines | MAVEN-ERE | ONTOEVENT-DOC | | |-------------|-----------------|--------| | # Document | 4,480 | 4,115 | | # Mention | 112,276 | 60,546 | | # Temporal | 1,216,217 | 5,914 | | # Causal | 57,992 | 14,155 | | # Subevent | 15,841 | / | Datasets. Considering event-centric structured prediction tasks in this paper require fine-grained annotations for events, such as labels of tokens, event mentions, and event-relations, we select two newly-proposed datasets meeting the requirements: MAVEN-ERE (Wang et al., 2022) and ONTOEVENT-DOC (Deng et al., 2021). Note that ONTOEVENT-DOC is derived from ONTOEVENT (Deng et al., 2021) which is formatted in a sentence level. We reorganize it and make it format in a document level, similar to MAVEN-ERE. Thus the train, validation, and test sets of ONTOEVENTDOC are also different from the original ONTOEVENT. We release the reconstructed dataset and | MAVEN-ERE | ONTOEVENT-DOC | | | | | | |-------------|-----------------|--------------|--------------|--------------|--------------|--------------| | Model | P | R | F1 | P | R | F1 | | DMCNN† | 60.09 ± 0.36 | 60.34 ± 0.45 | 60.21 ± 0.21 | 50.42 ± 0.99 | 52.24 ± 0.46 | 51.31 ± 0.39 | | BiLSTM-CRF† | 61.30 ± 1.07 | 64.95 ± 1.03 | 63.06 ± 0.23 | 48.86 ± 0.81 | 55.91 ± 0.56 | 52.10 ± 0.43 | | DMBERT† | 56.79 ± 0.54 | 76.24 ± 0.26 | 65.09 ± 0.32 | 53.82 ± 1.01 | 66.12 ± 1.02 | 59.32 ± 0.24 | | BERT-CRF† | 62.79 ± 0.34 | 70.51 ± 0.94 | 65.73 ± 0.57 | 52.18 ± 0.81 | 62.31 ± 0.45 | 56.80 ± 0.53 | | MLBiNet‡ | 63.50 ± 0.57 | 63.80 ± 0.47 | 63.60 ± 0.52 | 56.09 ± 0.93 | 57.67 ± 0.81 | 56.87 ± 0.87 | | TANL‡ | 68.66 ± 0.18 | 63.79 ± 0.19 | 66.13 ± 0.15 | 57.73 ± 0.65 | 59.93 ± 0.31 | 59.13 ± 0.52 | | TEXT2EVENT‡ | 59.91 ± 0.83 | 64.62 ± 0.65 | 62.16 ± 0.25 | 52.93 ± 0.94 | 62.27 ± 0.49 | 57.22 ± 0.75 | | CorED-BERT‡ | 67.62 ± 1.03 | 69.49 ± 0.63 | 68.49 ± 0.42 | 60.27 ± 0.55 | 62.25 ± 0.66 | 61.25 ± 0.19 | | SPEECH | 78.82 ± 0.82 | 79.37 ± 0.75 | 79.09 ± 0.82 | 74.67 ± 0.58 | 74.73 ± 0.62 | 74.70 ± 0.58 | | w/o energy | 76.12 ± 0.32 | 76.66 ± 0.25 | 76.38 ± 0.28 | 71.76 ± 0.38 | 72.17 ± 0.39 | 71.96 ± 0.38 | code in Github1for reproduction. To simplify the experiment settings, we dismiss hierarchical relations of ONTOEVENT and coreference relations of MAVEN-ERE in this paper. More details of multifaceted event-relations of these two datasets are introduced in Appendix A and Github. We present the statistics about these two datasets in Table 1. The document quantity for train/valid/test set of MAVEN-ERE and ONTOEVENT are respectively 2,913/710/857, and 2,622/747/746. Baselines. For trigger classification and event classification, we adopt models aggregated dynamic multi-pooling mechanism, *i.e.*, DMCNN (Chen et al., 2015) and DMBERT (Wang et al., 2019); sequence labeling models with conditional random field (CRF) (Lafferty et al., 2001), *i.e.*, BiLSTM-CRF and BERT-CRF; generative ED models, *i.e.*, TANL (Paolini et al., 2021) and TEXT2EVENT (Lu et al., 2021). We also adopt some ED models considering document-level associations, *i.e.*, MLBiNet (Lou et al., 2021) and CorED-BERT (Sheng et al., 2022). Besides, we compare our energy-based hyperspheres with the vanilla hyperspherical prototype network (HPN) (Mettes et al., 2019) and prototype-based model OntoED (Deng et al., 2021). Note that unlike vanilla HPN (Mettes et al., 2019) which represents all classes on one hypersphere, the HPN adopted in this paper represents each class with a distinct hypersphere. For event-relation extraction, we select RoBERTa (Liu et al., 2019), which is the same baseline used in MAVEN-ERE (Wang et al., 2022), and also serves as the backbone for most of recent ERE models (Hwang et al., 2022; Man et al., 2022). ## 4.2 Implementation Details With regard to settings of the training process, Adam (Kingma and Ba, 2015) optimizer is used, with the learning rate of 5e-5. The maximum length L of a token sequence is 128, and the maximum quantity of event mentions in one document is set to 40 for MAVEN-ERE and 50 for ONTOEVENTDOC. The loss ratios, µ1, µ2, µ3, for token, sentence and document-level energy function are all set to 1. The value of loss ratio, λ1, λ2, λ3, for trigger classification, event classification and eventrelation extraction depends on different tasks, and we introduce them in Appendix B. We evaluate the performance of ED and ERE with micro precision (P), Recall (R) and F1 Score (F1). ## 4.3 Event Trigger Classification We present details of event trigger classification experiment settings in Appendix B.1. As seen from the results in Table 2, SPEECH demonstrates superior performance over all baselines, notably MLBiNet (Lou et al., 2021) and CorED-BERT (Sheng et al., 2022), even if these two models consider cross-sentence semantic information or incorporate type-level and instance-level correlations. The main reason may be due to the energy-based nature of SPEECH. As seen from the last row of Table 2, the removal of energy functions from SPEECH can result in a performance decrease. Specifically for trigger classification, energy-based modeling enables capture long-range dependency of tokens and places no limits on the size of event structures. In addition, SPEECH also excels generative models, i.e., TANL (Paolini et al., 2021) and TEXT2EVENT (Lu et al., 2021), thereby demonstrating the efficacy of energy-based modeling. | MAVEN-ERE | ONTOEVENT-DOC | | | | | | |-------------|-----------------|--------------|--------------|--------------|--------------|--------------| | Model | P | R | F1 | P | R | F1 | | DMCNN | 61.74 ± 0.32 | 63.11 ± 0.34 | 62.42 ± 0.15 | 51.52 ± 0.87 | 52.84 ± 0.61 | 52.02 ± 0.36 | | DMBERT | 59.45 ± 0.48 | 77.77 ± 0.21 | 67.39 ± 0.25 | 57.06 ± 1.04 | 72.97 ± 1.11 | 65.03 ± 0.45 | | HPN | 62.80 ± 0.72 | 62.62 ± 0.99 | 62.71 ± 0.85 | 61.18 ± 0.81 | 60.88 ± 0.79 | 61.03 ± 0.81 | | OntoED | 67.82 ± 1.70 | 67.72 ± 1.52 | 67.77 ± 1.61 | 64.32 ± 1.15 | 64.16 ± 1.31 | 64.25 ± 1.22 | | TANL | 68.73 ± 0.16 | 65.65 ± 0.63 | 67.15 ± 0.29 | 60.34 ± 0.71 | 62.52 ± 0.43 | 61.42 ± 0.51 | | TEXT2EVENT | 61.14 ± 0.80 | 65.93 ± 0.69 | 63.44 ± 0.19 | 56.76 ± 0.97 | 66.78 ± 0.48 | 61.36 ± 0.77 | | SPEECH | 72.91 ± 0.76 | 72.81 ± 0.76 | 72.86 ± 0.77 | 58.92 ± 0.96 | 58.45 ± 1.08 | 58.69 ± 1.40 | | w/o energy | 71.22 ± 0.58 | 71.07 ± 0.45 | 71.12 ± 0.45 | 56.12 ± 1.87 | 55.69 ± 1.66 | 55.91 ± 1.76 | ## 4.4 Event Classification The specifics of event classification experiment settings are elaborated in Appendix B.2, with results illustrated in Table 3. We can observe that SPEECH provides considerable advantages on MAVEN-ERE, while the performance on ONTOEVENT-DOC is not superior enough. ONTOEVENT-DOC contains overlapping where multiple event classes may exist in the same event mention, which could be the primary reason for SPEECH not performing well enough in this case. This impact could be exacerbated when joint training with other ECSP tasks. Upon comparison with prototype-based methods without energy-based modeling, *i.e.*, HPN (Mettes et al., 2019) and OntoED (Deng et al., 2021), SPEECH is still dominant on MAVEN-ERE, despite HPN represents classes with hyperspheres and OntoED leverages hyperspheres integrated with eventrelation semantics. If we exclude energy functions from SPEECH, performance will degrade, as seen from the last row in Table 3. This insight suggests that energy functions contribute positively to event classification, which enable the model to directly capture complicated dependency between event mentions and event types, instead of implicitly inferring from data. Besides, SPEECH also outperforms generative models like TANL and TEXT2EVENT on MAVEN-ERE, indicating the superiority of energy-based hyperspherical modeling. ## 4.5 Event-Relation Extraction We present the specifics of event-relation extraction experiment settings in Appendix B.3. As seen from the results in Table 4, SPEECH achieves different performance across the two ERE datasets. On ONTOEVENT-DOC dataset, SPEECH observably outperforms RoBERTa on all ERE subtasks, demonstrating the effectiveness of SPEECH equipped with energy-based hyperspheres, so that SPEECH can capture the dependency among event | ERE Task | RoBERTa | SPEECH | | |---------------|---------------|--------------|--------------| | MAVEN-ERE | 49.21 ± 0.33 | 39.64 ± 0.79 | | | +joint | 49.91 ± 0.58 | 40.23 ± 0.34 | | | Temporal | ONTOEVENT-DOC | 37.68 ± 0.47 | 52.36 ± 0.71 | | +joint | 35.63 ± 0.70 | 65.69 ± 0.39 | | | MAVEN-ERE | 29.91 ± 0.34 | 16.28 ± 0.53 | | | +joint | 29.03 ± 0.91 | 16.31 ± 0.97 | | | Causal | ONTOEVENT-DOC | 35.48 ± 1.77 | 79.29 ± 2.15 | | +joint | 44.99 ± 0.29 | 67.76 ± 1.28 | | | Subevent | MAVEN-ERE | 19.80 ± 0.44 | 19.91 ± 0.52 | | +joint | 19.14 ± 2.81 | 21.96 ± 1.24 | | | All Joint | MAVEN-ERE | 34.79 ± 1.13 | 37.85 ± 0.72 | | ONTOEVENT-DOC | 28.60 ± 0.13 | 54.19 ± 2.28 | | Causal MAVEN-ERE **29.91** ± 0.34 16.28 ± 0.53 +joint **29.03** ± 0.91 16.31 ± 0.97 ONTOEVENT-DOC 35.48 ± 1.77 **79.29** ± 2.15 +joint 44.99 ± 0.29 **67.76** ± 1.28 Subevent MAVEN-ERE 19.80 ± 0.44 **19.91** ± 0.52 +joint 19.14 ± 2.81 **21.96** ± 1.24 All Joint MAVEN-ERE 34.79 ± 1.13 **37.85** ± 0.72 ONTOEVENT-DOC 28.60 ± 0.13 **54.19** ± 2.28 Table 4: F1 (%) performance of ERE on MAVEN-ERE valid set and ONTOEVENT-DOC *test set*. "+joint" in the 2nd column denotes jointly training on all ERE tasks and evaluating on the specific one, with the same setting as Wang et al. (2022). "All Joint" in the last two rows denotes treating all ERE tasks as one task. mention pairs and event-relation labels. While on MAVEN-ERE, SPEECH significantly outperforms RoBERTa on ERE subtasks referring to subevent relations or trained on all event-relations, but fails to exceed RoBERTa on ERE subtasks referring to temporal and causal relations. The possible reason is that MAVEN-ERE contains less positive eventrelations than negative NA relations. Given that SPEECH models all these relations equivalently with the energy function, it becomes challenging to classify NA effectively. But this issue will be markedly improved if the quantity of positive eventrelations decreases, since SPEECH performs better on subevent relations despite MAVEN-ERE having much less subevent relations than temporal and causal ones as shown in Table 1. Furthermore, even though ONTOEVENT-DOC containing fewer positive event-relations than NA overall, SPEECH still performs well. These results suggest that SPEECH excels in modeling classes with fewer samples. Note that SPEECH also performs well when training on all event-relations ("All Joint") of the two datasets, indicating that SPEECH is still advantageous in the scenario with more classes. ## 5 Further Analysis 5.1 Analysis On Energy-Based Modeling We list some values of energy loss defined in Eq (5), (8) and (10) when training respectively for token, sentence and document, as presented in Figure 3. The values of token-level energy loss are observably larger than those at the sentence and document levels. This can be attributed to the fact that the energy loss is related to the quantity of samples, and a single document typically contains much more tokens than sentences or sentence pairs. All three levels of energy loss exhibit a gradual decrease over the course of training, indicating that SPEECH, through energy-based modeling, effectively minimizes the discrepancy between predicted results and ground truth. The energy functions for token, sentence and document defined in Eq (4), (7) and (9), reflect that the implementation of energy-based modeling in SPEECH *is geared towards enhancing compatibility between input/output pairs.* The gradually-decreasing energy loss demonstrates that SPEECH *can model intricate event structures at* the token, sentence, and document levels through energy-based optimization, thereby improving the outcomes of structured prediction. ![7_image_1.png](7_image_1.png) ## 5.2 Case Study: Energy-Based Hyperspheres As seen in Figure 4, we visualize the event class embedding of "Attack" and 20 event mention embeddings as generated by both SPEECH and SPEECH without energy functions. We observe that for SPEECH with energy-based modelling, the instances lie near the surface of the corresponding hypersphere, while they are more scattered when not equipped with energy-based modeling, which subsequently diminishes the performance of event classification. This observation suggests that SPEECH derives significant benefits from modeling with energy-based hyperspheres. The visualization results further demonstrate the effectiveness of SPEECH equipped with energy-based modeling. ![7_image_0.png](7_image_0.png) ## 5.3 Error Analysis We further conduct error analysis by a retrospection of experimental results and datasets. (1) One typical error relates to the unbalanced data distribution. Considering every event type and event-relation contain different amount of instances, unified modeling with energy-based hyperspheres may not always be impactful. (2) The second error relates to the overlapping event mentions among event types, meaning that the same sentence may mention multiple event types. As ONTOEVENT-DOC contains many overlappings, it might be the reason for its mediocre performance on ED. (3) The third error relates to associations with event-centric structured prediction tasks. As trigger classification is closely related to event classification, wrong prediction of tokens will also influence classifying events. ## 6 Conclusion And Future Work In this paper, we propose a novel approach entitled SPEECH to tackle event-centric structured prediction with energy-based hyperspheres. We represent event classes as hyperspheres with token, sentence and document-level energy, respectively for trigger classification, event classification and event relation extraction tasks. We evaluate SPEECH on two event-centric structured prediction datasets, and experimental results demonstrate that SPEECH is able to model manifold event structures with dependency and obtain effective event representations. In the future, we intend to enhance our work by modeling more complicated structures and extend it to other structured prediction tasks. ## Acknowledgements We would like to express gratitude to the anonymous reviewers for their kind comments. This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LGG22F030011), Yongjiang Talent Introduction Programme (2021A-156-G), CAAI-Huawei MindSpore Open Fund, and NUS-NCS Joint Laboratory (A-0008542-00-00). ## Limitations Although SPEECH performs well on event-centric structured prediction tasks in this paper, it still has some limitations. The first limitation relates to efficiency. As SPEECH involves many tasks and requires complex calculation, the training process is not very prompt. 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In *EMNLP/IJCNLP (1)*, pages 5765–5769. Association for Computational Linguistics. ## B Implementation Details For Different Tasks B.1 Event Trigger Classification B.2 Event Classification B.3 Event-Relation Extraction Appendices A Multi-Faceted Event-Relations causal relations: CAUSE, PRECONDITION; and 1 subevent relation: subevent_relations. ONTOEVENT-DOC in this paper contains 3 temporal relations: BEFORE, AFTER, EQUAL; and 2 causal relations: CAUSE, CAUSEDBY. We also add a NA relation to signify no relation between the event mention pair for the two datasets. Settings. We follow the similar evaluation protocol of standard ED models (Chen et al., 2015; Sheng et al., 2022) on trigger classification tasks. We present the results in Table 2 when jointly training with event classification and the whole ERE task ("All Joint" in Table 4). The backbone encoder is pretrained BERT (Devlin et al., 2019). The loss ratio, λ1, λ2, λ3 in Eq (11) are respectively set to 1, 0.1, 0.1 for both ONTOEVENT-DOC and MAVENERE. Settings. We follow the similar evaluation protocol of standard ED models (Chen et al., 2015; Deng et al., 2021) on event classification tasks. We present the results in Table 3 when jointly training with trigger classification and all ERE subtasks ("+joint" in Table 4). The backbone encoder is pretrained DistilBERT (Sanh et al., 2019). The loss ratio, λ1, λ2, λ3 in Eq (11) are respectively set to 0.1, 1, 0.1 for ONTOEVENT-DOC and 1, 0.1, 0.1 for MAVEN-ERE. Settings. We follow the similar ERE experiment settings with Wang et al. (2022) on several subtasks, by separately and jointly training on temporal, causal, and subevent event-relations. We present the results in Table 4 when jointly training with trigger classification and event classification tasks. The backbone encoder is pretrained DistilBERT (Sanh et al., 2019). On ONTOEVENT-DOC dataset, the loss ratio, λ1, λ2, λ3 in Eq (11) are respectively set to 1, 0.1, 0.1 for all ERE subtasks. On MAVEN-ERE dataset, λ1, λ2, λ3 are respectively set to 0.1, 0.1, 1 for "All Joint" ERE subtasks in Table 4; 1, 1, 4 for "+joint"; 1, 0.1, 0.1 for "Temporal" and "Causal"; and 1, 0.1, 0.08 for "Subevent". Note that MAVEN-ERE and ONTOEVENT-DOC both includes multi-faceted event-relations. MAVEN-ERE in this paper contains 6 temporal relations: BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, BEGINS-ON, ENDS-ON; 2 ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract & at the end of Section 1 & Section 6 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4.1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 4 ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 4 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. I use the existing benchmark B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. needn't to ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.1 ## C ✓ **Did You Run Computational Experiments?** Section 4 Datasets and Baselines are in Section 4.1 Implementation Details are in Section 4.2 & Appendix B Main experiments are in Section 4.3, 4.4, 4.5, and Further Analysis is in Section 5 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? I have listed the implementation details of experiments at Sec 4.2 & Appendix B. The total computational budget & computing infrastructure used are not the main concerns of our work, and we also The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. didn't run time statistics. But we will provide more details when publication, and the codes will also mention more details on it. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4.2, Appendix B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We run our model and baselines multiple times and calculate an average with upper and lower bounds, which are shown in Section 4.3, 4.4, 4.5. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Implementation Details are in Section 4.2 & Appendix B ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

clarke-etal-2023-rule

Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection

https://aclanthology.org/2023.acl-long.22

Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness needed to moderate the vast amount of undesirable content found online today. Recent advances in deep learning have demonstrated the promise of using highly effective deep neural models to overcome these challenges. However, despite the improved performance, these data-driven models lack transparency and explainability, often leading to mistrust from everyday users and a lack of adoption by many platforms. In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches. We demonstrate that our approach is capable of learning rich rule embedding representations using only a few data examples. Experimental results on 3 popular hate speech classification datasets show that RBE is able to outperform state-of-the-art deep learning classifiers as well as the use of rules in both supervised and unsupervised settings while providing explainable model predictions via rule-grounding.

# Rule By Example: Harnessing Logical Rules For Explainable Hate Speech Detection Christopher Clarke˚: Matthew Hall; Gaurav Mittal; **Ye Yu**; Sandra Sajeev; Jason Mars: **Mei Chen**; :University of Michigan, Ann Arbor, MI ;Microsoft, Redmond, WA {csclarke, profmars}@umich.edu {mathall, gaurav.mittal, yu.ye, ssajeev, mei.chen}@microsoft.com ## Abstract Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness needed to moderate the vast amount of undesirable content found online today. Recent advances in deep learning have demonstrated the promise of using highly effective deep neural models to overcome these challenges. However, despite the improved performance, these data-driven models lack transparency and explainability, often leading to mistrust from everyday users and a lack of adoption by many platforms. In this paper, we present Rule By Example (RBE): a novel exemplarbased contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches. We demonstrate that our approach is capable of learning rich rule embedding representations using only a few data examples. Experimental results on 3 popular hate speech classification datasets show that RBE is able to outperform state-of-the-art deep learning classifiers as well as the use of rules in both supervised and unsupervised settings while providing explainable model predictions via rulegrounding. ## 1 Introduction Content moderation is a major challenge confronting the safety of online social platforms such as Facebook, Twitter, YouTube, Twitch, etc. (Vaidya et al., 2021). Major technology corporations are increasingly allocating valuable resources towards the development of automated systems for *This work was done as Christopher's internship project at Microsoft. Figure 1: Generalization problem of rules. Logical rules, ![0_image_0.png](0_image_0.png) while easy to explain, are inherently fragile to the nuances of natural language. the detection and moderation of harmful content in addition to hiring and training expert human moderators to combat the growing menace of negativity and toxicity online (Wagner and Bloomberg, 2021; Liu et al., 2022). Despite the popularity of deep learning approaches, many practical solutions used in products today are comprised of rule-based techniques based on expertly curated signals such as block lists, key phrases, and regular expressions (Gillespie, 2018; Zhang, 2019; Dada et al., 2019). Such methods are widely used due to their transparency, ease of customization, and interpretability. However, they have the disadvantage of being difficult to maintain and scale, in addition to being inherently fragile and noisy (Zhang, 2019; Davidson et al., 2017; Lee, 2022; Lai et al., 2022). Figure 1 shows an example where logical rules, while explainable in nature, face the problem of being inflexible to their context of use in natural language. While a given rule may be too specific and fail to capture different variations of usage commonly found in content online, rules can also be too broad and incorrectly block lexically similar content. In contrast to the challenges faced by rule-based methods, data-driven deep learning approaches have shown great promise across a wide range of content moderation tasks and modalities (Malik et al., 2022; Shido et al., 2022; Lai et al., 2022). Fueled by large amounts of data and deep neural networks, these complex models are capable of 364 learning richer representations that better generalize to unseen data. The impressive performances of these models have resulted in significant industry investment in content moderation as-a-service. Several technology companies such as Google 1, OpenAI 2, and Microsoft 3 use these models to offer services to aid in content moderation. However, despite their significant investment, they face adoption challenges due to the inability of customers to understand how these complex models reason about their decisions (Tarasov, 2021; Haimson et al., 2021; Juneja et al., 2020). Additionally, with the increasing attention around online content moderation and distrust amongst consumers, explainability and transparency are at the forefront of demands (Kemp and Ekins, 2021; Mukherjee et al., 2022). This presents the challenging open question of how we can leverage the robustness and predictive performance of complex deep-learning models whilst allowing the transparency, customizability, and interpretability that rule-based approaches provide. Prior works such as Awasthi et al. (2020); Seo et al. (2021); Pryzant et al. (2022) have explored learning from rules for tasks such as controlling neural network learning, assisting in human annotation, and improving self-supervised learning in low data scenarios. Awasthi et al. (2020) propose a rule-exemplar training method for noisy supervision using rules. While performant in denoising over-generalized rules in the network via a soft implication loss, similar to other ML approaches, this method lacks the ability to interpret model predictions at inference time. Pryzant et al. (2022) propose a general-purpose framework for the automatic discovery and integration of symbolic rules into pre-trained models. However, these symbolic rules are derived from low-capacity ML models on a reduced feature space. While less complex than large deep neural networks, these low-capacity models are still not easily interpretable by humans. Therefore, the task of combining the explainability of rules and the predictive power of deep learning models remains an open problem. In order to tackle this problem, we introduce Rule By Example (RBE): a novel exemplar-based 1https://perspectiveapi.com/ 2https://openai.com/blog/ new-and-improved-content-moderation% 2Dtooling/ 3https://azure.microsoft.com/ en-us/products/cognitive-services/ content-moderator/ contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is comprised of two neural networks, a rule encoder, and a text encoder, which jointly learn rich embedding representations for hateful content and the logical rules that govern them. Through the use of contrastive learning, our framework uses a semantic similarity objective that pairs hateful examples with clusters of rule exemplars that govern it. Through this approach, RBE is able to provide more explainable predictions by allowing for what we define as *Rule-grounding*. This means that our model is able to ground its predictions by showing the corresponding explainable logical rule and the exemplars that constitute that rule. We evaluate RBE in both supervised and unsupervised settings using a suite of rulesets. Our results show that with as little as one exemplar per rule, RBE is capable of outperforming state-of-theart hateful text classifiers across three benchmark content moderation datasets in both settings. In summary, the contributions of this paper are: - Rule By Example (RBE): a novel exemplarbased contrastive learning approach to learn from logical rules for the task of textual content moderation.4 - We demonstrate how RBE can be easily integrated to boost model F1-score by up to 4% on three popular hate speech classification datasets. - A detailed analysis and insights into the customizability and interpretability features of RBE to address the problem of emerging hateful content and model transparency. ## 2 Rule By Example Framework In this section, we outline the Rule By Example framework, define its operational terms, and describe its end-to-end architecture. We first formally describe the two main operational terms used in our framework: 1) **Ruleset** - a ruleset is comprised of a series of executable functions that when given text as input "fire" if and only if all conditions defined in the rule are met by the input. Figure 1 shows an example of a simple rule that is triggered if a given text contains the keywords *"hate"* or 4https://github.com/ChrisIsKing/ Rule-By-Example ![2_image_0.png](2_image_0.png) "loathe" and contains *"women"*. Rules can be any programmable function that acts on text such as regular expressions, blocklists, keywords, etc. In the scope of this work, we only consider simple rules that humans can easily interpret. As such an ML model cannot be considered a rule, given their black-box nature. 2) **Exemplar** - an exemplar is a given textual example that well-defines the type of content governed by a rule. For example, X1 and X2 in Figure 1 can be considered exemplars of rule R1 since they correctly match the conditions of R1. Consider a ruleset of rule-exemplar pairs R"tpr1, e1q,pr2, e2q, ...,prn, enqu where ri denotes a defined rule and ei denotes an exemplar for which ri correctly fires. For a given corpus X comprising labeled examples X"tpx1, y1q,px2, y2q, ...,pxm, ymqu, each rule ri can be used as a black-box function Ri: x Ñ tyi, Hu to noisily label each instance x such that it assigns a label y or no label at all. An instance may be covered by more than one rule or no rule at all. Additionally, the cover set C denotes the set of instances in X where a rule ri fires. The generalization problem that arises when rules are applied noisily is two-fold. When rules are too broad the cover set C is large and incorrectly labels a large amount of non-hateful content. Likewise, when rules are too strict and fragile, the cover set C is too small, and lexically and semantically similar content that is hateful ends up being ignored. Our goal is to leverage these rules and their exemplars to facilitate explainable model learning. Algorithm 1 Supervised Dual Encoder Training Require: Rule Encoder Θr Text Encoder Θt ![2_image_1.png](2_image_1.png) ## 2.1 Dual Encoder Architecture The Dual-Encoder architecture, as illustrated in Figure 2, is commonly used in dense retrieval systems and multi-modal applications (Clarke et al., 2022; Reimers and Gurevych, 2019; Xu et al., 2022). Our architecture consists of a Rule Encoder Θr and a Text Encoder Θt. These are two Bert-like bidirectional transformer models (Devlin et al., 2018) each responsible for learning embedding representations of their respective inputs. This Dual Encoder architecture enables pre-indexing of exemplars allowing for faster inference at runtime after training. Encoding Pipeline Given an input text xt, we first extract the set of applicable rules and their respective exemplars from the ruleset R. We then concatenate each extracted exemplar to form xe. In the event that no rules are applicable to xt, we randomly sample exemplars from the entire ruleset to form xe. Using the form xe " ␣rCLSs, e11 , ..., e1m,rSEPs, en 1 , ...., en k (, we then use rule encoder Θr to encode xe into hidden states he " ␣vrCLSs, v1*, ..., v*rSEPs (where e n k is the k-th token of the n-th exemplar and rSEPs and rCLSs are special tokens. Similarly, using the text encoder Θt, we encode xt. In order to obtain a dense representation, we apply a mean pooling operation to the hidden states and derive a fixed-sized sentence embedding. After obtaining the representation for both the exemplars xe and the text xt, we use the cosine function to measure the similarity between them: $$s i m(x_{e},x_{t})={\frac{\Theta_{r}(x_{e})\cdot\Theta_{t}(x_{t})}{\|\Theta_{r}(x_{e})\|\,\|\Theta_{t}(x_{t})\|}}\qquad{}$$ We employ a contrastive loss (Hadsell et al., 2006) to learn the embedding representations for our rule and text encoder. Contrastive learning encourages the model to maximize the representation similarity between *same-label* examples and to minimize it for *different-label* examples. This enables the embedding representations of our encoded ruleset to match the representation of the text correctly covered by cover set C. Likewise, for benign examples that rules incorrectly cover, our contrastive learning objective increases the distance between those representations, thus restricting the over-generalization of certain rules in the ruleset. Let Yt be the correct label of the texts Xt, D be the cosine distance of pxe, xtq and m be the margin, our contrastive learning loss function is defined as follows: $${\cal L}=\frac{1}{2}(Y_{t}D^{2}+(1-Y_{t})max(m-D,0)^{2})\tag{2}$$ The training loop, with the encoding pipeline and constrastive loss step, are detailed in Algorithm 1. ## 2.2 Rule-Grounding By taking an embeddings-based approach to learning representations, RBE enables what we define as *rule-grounding*. Rule-grounding enables us to trace our model predictions back to the explainable ruleset accompanied by the exemplars that define each rule. For any input xtthat has been marked as positive by our dual encoder, we perform a rules search to find which rules fire on that input as well as an embedding similarity search to find the nearest exemplars and the rules those exemplars belong to. Table 2 shows an example of this. ## 3 Experimental Setup Training We train all models with AdamW optimizer and weight decay of 0.01 on all data. We employ early stopping with a ceiling of 10 epochs, a learning rate of 2e-5, batch size of 8, and linear learning rate warmup over the first 10% steps with a cosine schedule. Our models are trained with NVIDIA Tesla V100 32GB GPUs using Azure Machine Learning Studio. We pre-process data and train all models with different random seeds over multiple runs. Our implementation of RBE is based on Huggingface Transformers (Wolf et al., 2020) and Sentence Transformers (Reimers and Gurevych, 2019). RBE utilizes two Bert-based networks consisting of 110 million parameters each. Approximately 2,000 GPU hours were required to train all hyperparameter variations of RBE plus the Bert baseline across all 3 test sets. Baselines We evaluate our training algorithms in both supervised and unsupervised settings. We compare against the baselines of applying logical rules as is and the current SOTA approach of training transformer-based sequence classifiers (Mathew et al., 2020). ## 3.1 Datasets We evaluate RBE across three datasets on the task of hate-speech classification. Across each dataset, we frame the problem as a binary classification task of detecting whether a given text is hateful or nonhateful. We augment each dataset with rulesets that we manually curate. More information on each dataset and ruleset is provided below. HateXplain (Mathew et al., 2020) is a large-scale benchmark dataset for explainable hate speech detection that covers multiple aspects of hate speech detection. It consists of "20k samples across 3 labels "hateful", "offensive", and "normal". Additionally, each sample is accompanied by a corresponding target group and explainable rationales. In our experiments, we combine the output classes of hateful and offensive into one resulting in "8k/1k/1k hateful samples and "6k/781/782 non-hateful samples for train/validation/test respectively. Additionally, we utilize the accompanying rationales for ruleset construction. Jigsaw5is a large-scale dataset of Wikipedia comments labeled by human raters for toxic behavior. The defined types of toxicity are "toxic", "severe toxic", "obscene", "threat", "insult", and "identity hate". Each comment can have any one or more of these labels. In total, it contains "230k samples. In our experiments, we define examples of the "identity hate" class as hateful and the rest as non-hateful resulting in a dataset of 1405/100/712 hateful samples and "158k/1k/63k non-hateful examples for train/validation/test respectively. ## Contextual Abuse Dataset (Cad) (Vidgen et al., 2021) is annotated dataset of "25k Reddit entries labeled across six conceptually distinct primary categories of "Identity-directed", "Persondirected", "Affiliation directed", "Counter Speech", "Non-hateful Slurs", and "Neutral". In our experiment, we define examples of the "identity-directed" class as hateful and treat the remaining examples as non-hateful resulting in a dataset of 1353/513/428 hateful samples and "12k/4k/4k non-hateful samples for train/validation/test. ## 3.2 Ruleset Construction Hate+Abuse List We utilize a ruleset targeting identity hate which we'll refer to as **Hate+Abuse** List. It consists of a list of n-grams representing harmful language such as slurs or hate verbs. Hate+Abuse List is similar to the publically available bad word lists commonly found online. We treat each n-gram entry in Hate+Abuse List as its own rule that proposes a positive label if the ngram is in the input text. In total, Hate+Abuse List consists of 2957 distinct identity hate rules. HateXplain Rationale Ruleset Using the labeled annotator rationales included in the HateXplain dataset, we programmatically generate a Ruleset for HateXplain. To do so, we extract 1, 2, and 3-gram substrings from the annotator rationales and cluster them by annotator-identified target demographic groups. We then take the top N n-grams per each demographic group and automatically create rules for each of them. This results in rules similar in nature to our Hate+Abuse List. Using a default cluster size of 100 across the 25 target categories defined in HateXplain, we generated a total of 670 distinct rules for HateXplain. Contextual Abuse Rationale Ruleset Similar to our derived HateXplain ruleset we programmatically generate a Ruleset for the Contextual Abuse Dataset using annotator-labeled rationales. Following the identical process outlined before, this results in a total of 2712 distinct rules for CAD. Exemplar Selection For each dataset we complete our Ruleset construction by pairing each rule with accompanying exemplars. To achieve this, we first run our Ruleset on the dataset trainset and extract instances for which a rule correctly fires. For each rule that correctly fires, we then randomly select N instances to act as the exemplars. Additionally, to restrict potentially overgeneralized rules we enforce the condition that no two rules can be mapped to the same exemplar. Unless stated otherwise, we report results using just one exemplar per rule in our experiments. ## 3.3 Unsupervised Setting In addition to evaluating RBE in supervised settings, we investigate the applicability of RBE in unsupervised settings where no labeled data is present. In this setting, we are presented with a large unlabeled corpus T and a given ruleset R. This setting is particularly challenging due to the inherent generalization problem of rules. Loosely applying rules as is in this setting results in the model overfitting to the distribution of the ruleset as seen in Table 3. To combat this issue, we design three different semantic clustering-based strategies for determining rule quality in an unsupervised setting: Mean, *Concat*, and *Distance* clustering. Given an unlabeled corpus T " tt1, t2*, ..., t*nu, ruleset R **" tp**r1, e1q, ...,prn, enqu, and a threshold k, we first encode the entire corpus T using a pre-trained sentence embedding model EΘ. In our case, we use a fine-tuned version of MPNet (Song et al., 2020) from the Sentence Transformers library. After receiving our encoded corpus EΘpTq, for the Mean and *Concat*, we construct a rule embedding r i Θ for each rule riin the ruleset. In the *Mean* strategy, this is obtained by taking the mean of all rule exemplars µpr i Θ**q " p** 1m řm ie imq. For *Concat*, this is calculated by concatenating all rule exemplars µpriq " EΘpe i1} ... } e imq and encoding the concatenated representation. Once r i Θ is constructed, we then label each text in the corpus whose cosine similarity is within the threshold k: | Content Moderation Using Rules (Fully Supervised) | | | | | | | | | | | | | |-----------------------------------------------------|-----------|--------|-------|-------|-----------|--------|-------|-------|-----------|--------|-------|-------| | HateXplain | Jigsaw | CAD | | | | | | | | | | | | Model | Precision | Recall | F1 | Acc | Precision | Recall | F1 | Acc | Precision | Recall | F1 | Acc | | HateXplain Rules | 0.609 | 0.983 | 0.752 | 0.615 | - | - | - | - | - | - | - | - | | Hate+Abuse Rules | 0.755 | 0.687 | 0.719 | 0.682 | 0.164 | 0.361 | 0.226 | 0.972 | 0.586 | 0.193 | 0.290 | 0.909 | | CAD Rules | - | - | - | - | - | - | - | - | 0.110 | 0.842 | 0.194 | 0.325 | | BERT` | 0.808 | 0.841 | 0.824 | 0.787 | 0.459 | 0.729 | 0.563 | 0.987 | 0.445 | 0.421 | 0.433 | 0.893 | | MPNet^ | 0.795 | 0.854 | 0.823 | 0.783 | 0.510 | 0.674 | 0.581 | 0.989 | 0.519 | 0.417 | 0.463 | 0.906 | | Rule By Example`△ | 0.758 | 0.903 | 0.824 | 0.771 | 0.581 | 0.625 | 0.602 | 0.991 | 0.416 | 0.478 | 0.445 | 0.885 | | Rule By Example^△ | 0.790 | 0.891 | 0.837 | 0.795 | 0.508 | 0.746 | 0.604 | 0.989 | 0.484 | 0.468 | 0.476 | 0.900 | | Rule By Example`˚ | 0.738 | 0.912 | 0.816 | 0.756 | - | - | - | - | - | - | - | - | | Rule By Example^˚ | 0.779 | 0.893 | 0.832 | 0.786 | - | - | - | - | - | - | - | - | | Rule By Example`; | - | - | - | - | - | - | - | - | 0.512 | 0.378 | 0.435 | 0.905 | | Rule By Example^; | - | - | - | - | - | - | - | - | 0.508 | 0.448 | 0.476 | 0.905 | $$f(t_{i})={\begin{cases}1,&{\mathrm{if}}\;s i m(r_{\Theta}^{i},E_{\Theta}(t_{i}))\geqslant k\\ 0,&{\mathrm{otherwise}}\end{cases}}\quad(3)$$ In contrast to the *Mean* and *Concat* strategies, the *Distance* strategy takes a rule elimination approach. Given an unlabeled corpus T " tt1, t2*, ..., t*nu, ruleset R **" tp**r1, e1q, ...,prn, enqu, and a threshold k, we first noisily label the entire corpus using the ruleset Ri: xt Ñ t1, Hu such that each rule is paired with a cover set R **" tp**r1, e1, c1q, ...,prn, en, cnqu where ciis the set of texts in covered by ri. Next, for each rule, we encode text in its cover set EΘpciq and calculate the average cosine distance between each embedding and its neighboring examples in ci. $$a v g D i s t(E_{\Theta}(c_{i}))=\frac{1}{n}\sum_{i}^{n}d i s t(c_{j}^{i},c_{j-1}^{i})$$ $$\quad(4)$$ Lastly, once the average distance for each rule is calculated, using the defined threshold k, we flip any weakly labeled examples in the cover set if the average distance for that rule is above the threshold k: $$f(t_{i})={\begin{cases}1,&{\mathrm{if}}\;a v g D i s t(r_{i})\geqslant k\\ 0,&{\mathrm{otherwise}}\end{cases}}\qquad(5)$$ ## 4 Results And Discussion We analyze the results of our experiments, detail our insights, and discuss the implications of applying RBE for explainable hate speech detection. Evaluation Metrics: The precision, recall, and F1 score for each dataset in a supervised setting are reported in Table 1. Due to the highly skewed class distribution, we favor macro F1 scores as our main evaluation metric. We also report accuracy scores (the fraction of entries for which the full set of labels matches) as another metric. ## 4.1 Supervised Performance Table 1 reports our results on three hate speech classification datasets in the supervised setting. We observe that RBE is able to outperform SOTA transformer-based models BERT and MPNet by 1.3/1.4%, 4.1/2.3%, and 4.3/1.3% in F1-score on HateXplain, Jigsaw, and CAD respectively. This improvement highlights the impact of leveraging rules in the training process of our framework. Additionally, it is important to note that this increase was achieved using only 1 exemplar per rule in the ruleset. These exemplars were also used to train the comparative baseline models, ensuring that all approaches were trained on the same number of samples. This further showcases how lightweight and flexible RBE is to integrate into a content moderation workflow. For HateXplain, our experiments show that the combination of MPNet as the initialized encoder with both the HateXplain Rationale and Hate+Abuse Ruleset delivers the best performance. Upon deeper analysis, we find that this is due to two main factors: 1) **Ruleset Size and Alignment** - As explained in Section 3.2 the HateXplain Rationale Ruleset was automatically crafted using rationale labels from expert annotators. This results in a powerful ruleset capable of identifying a large amount of hateful content in the HateXplain dataset as shown ![6_image_0.png](6_image_0.png) by the high recall score of the HateXplain Rationale Ruleset in Table 1. Additionally, when applied to the HateXplain dataset, the HateXplain Rationale Ruleset produces a total of 577 rules compared to the 377 rules derived from the Hate+Abuse Ruleset, allowing for more rule representations for the model to contrast against. 2) **Embedding Initialization** - Out of the box, pre-trained BERT does not produce meaningfully distinct sentence representations. In practice, the BERT [CLS] token as well as averaged BERT outputs can contain useful information after downstream fine-tuning. This is shown by the BERT performance in Table 1. However, when the pretrained model output is pooled across all dimensions and used for calculating semantic similarity, this results in similar representations even for completely different input text. As a result, if applied to the HateXplain dataset without any fine-tuning, BERT embeddings obtain a precision, recall, and F1-score of 59%, 100%, and 75% respectively, where every example is labeled as hateful. This lack of varied sentence representation coupled with a verbose ruleset such as the HateXplain Rationale Ruleset results in an initial biasing towards hateful examples as shown by the high recall scores. As such, utilizing a pre-trained sentence embedder, such as MPNet, with a pre-train task more optimized for semantic embeddings results in better performance. We observe a similar trend when utilizing our derived ruleset for CAD. **Note:** When trained longer, the bias of the BERT model decreases as more varied sentence representations are learned. On Jigsaw and Contextual Abuse datasets using the Hate+Abuse List and derived CAD Ruleset, RBE outperforms SOTA by an increased margin of 4.1/2.3%, and 4.3/1.3% respectively. Contrary to HateXplain, these two datasets are more heavily imbalanced toward non-hateful examples and thus more representative of the real-world case of content moderation where most content is considered benign. This increased performance highlights the power of incorporating logical rules to assist model learning and also the ability of RBE to better generalize rules. As seen in Table 1, on its own the Hate+Abuse ruleset performs poorly on each dataset in both precision and recall. Despite RBE's reliance on this ruleset to guide model learning, when combined with labeled training data, RBE is capable of both restricting over-generalized rules and leveraging its understanding of semantic similarity to extend fragile rules regardless of the base model. Additionally, when using the CAD ruleset which is heavily overfitted to the CAD dataset, as shown by the skewed recall score, RBE is still capable of outperforming the baselines. Out-of-domain Rulesets Our Hate+Abuse ruleset is a generic ruleset unrelated to any of the datasets evaluated, and thereby an out-of-domain ruleset. This provides an example of out-of-domain performance using rules not derived from the target dataset. We observe that even when applying RBE with the Hate+Abuse ruleset we are able to outperform the baselines on each dataset. When applying RBE to new domain settings, all that is required is the authoring of additional rules for this new domain. This can be done manually, or more scalably by automatically deriving rules from the new domain data. ## 4.2 Interpretability In addition to its improved performance, another advantage of RBE lies in its ability to perform Rule-grounding. As explained in section 2.2, Rulegrounding enables us to trace our model predictions back to their respective rule accompanied by the exemplars that define that rule. Table 2 shows Rule-grounding examples extracted from each of our tested datasets. By nature, Rule-grounding enables two main features in RBE: 1) **Customizability/Ruleset Adaptation**: Given the vast reach of online applications, content mod- | Content Moderation Using Rules (Unsupervised) | | | | | | | | | | | | | |-------------------------------------------------|-----------|--------|-------|-------|-----------|--------|-------|-------|-----------|--------|-------|-------| | HateXplain | Jigsaw | CAD | | | | | | | | | | | | Model | Precision | Recall | F1 | Acc | Precision | Recall | F1 | Acc | Precision | Recall | F1 | Acc | | HateXplain Rules | 0.609 | 0.983 | 0.752 | 0.615 | - | - | - | - | - | - | | | | Hate+Abuse Rules | 0.755 | 0.687 | 0.719 | 0.682 | 0.164 | 0.361 | 0.226 | 0.972 | 0.586 | 0.193 | 0.290 | 0.909 | | CAD Rules | - | - | - | - | - | - | - | - | 0.110 | 0.842 | 0.194 | 0.325 | | BERT`˚ | 0.606 | 0.990 | 0.752 | 0.613 | - | - | - | - | - | - | - | - | | BERT`△ | 0.747 | 0.717 | 0.732 | 0.688 | 0.234 | 0.461 | 0.310 | 0.977 | 0.587 | 0.205 | 0.303 | 0.909 | | BERT`; | - | - | - | - | - | - | - | - | 0.107 | 0.865 | 0.191 | 0.290 | | MPNet`˚ | 0.611 | 0.991 | 0.756 | 0.621 | - | - | - | - | - | - | - | - | | MPNet`△ | 0.652 | 0.850 | 0.738 | 0.641 | 0.247 | 0.501 | 0.331 | 0.977 | 0.642 | 0.199 | 0.304 | 0.912 | | MPNet`; | - | - | - | - | - | - | - | - | 0.111 | 0.840 | 0.196 | 0.335 | | Rule By Example (Distance)˚ | 0.614 | 0.983 | 0.756 | 0.623 | - | - | - | - | - | - | - | - | | Rule By Example (Distance)△ | 0.629 | 0.955 | 0.758 | 0.639 | 0.358 | 0.284 | 0.317 | 0.986 | 0.280 | 0.322 | 0.299 | 0.854 | | Rule By Example (Distance); | - | - | - | - | - | - | - | - | 0.166 | 0.522 | 0.252 | 0.701 | | Rule By Example (Concat)˚ | 0.621 | 0.950 | 0.751 | 0.626 | - | - | - | - | - | - | - | - | | Rule By Example (Concat)△ | 0.612 | 0.985 | 0.755 | 0.621 | 0.189 | 0.052 | 0.081 | 0.987 | 0.175 | 0.437 | 0.250 | 0.747 | | Rule By Example (Concat); | - | - | - | - | - | - | - | - | 0.178 | 0.437 | 0.253 | 0.750 | | Rule By Example (Mean)˚ | 0.612 | 0.983 | 0.754 | 0.620 | - | - | - | - | - | - | - | - | | Rule By Example (Mean)△ | 0.636 | 0.944 | 0.760 | 0.646 | 0.188 | 0.124 | 0.149 | 0.984 | 0.294 | 0.273 | 0.283 | 0.866 | | Rule By Example (Mean); | - | - | - | - | - | - | - | - | 0.189 | 0.411 | 0.259 | 0.772 | | Unsupervised Pre-Training | | | | | | | | | | | | | | Rule By Example (Mean)△ | 0.641 | 0.954 | 0.767 | 0.656 | 0.166 | .626 | 0.262 | 0.961 | 0.260 | 0.320 | 0.287 | 0.846 | | Rule By Example (Distance)△ | 0.617 | 0.968 | 0.753 | 0.624 | 0.203 | 0.465 | 0.283 | 0.974 | 0.484 | 0.236 | 0.317 | 0.902 | eration systems need to be easily adaptable to everemerging trends of hateful content. Particularly in online social settings, expert users of these platforms continually find new and interesting ways to bypass moderation systems. Additionally, new terminologies and slang are being introduced every day. RBE is seamlessly capable of addressing these concerns by facilitating rule-guided learning. By defining a new rule and adding at least one exemplar, RBE is able to capture emerging content without the need for re-training. Additionally, users of RBE can easily modify existing rules that may be too broad and add additional exemplars to further refine predictions in a controllable manner. 2) **Prediction Transparency**: By facilitating model interpretations via rule-grounding, users of online systems are offered tangible guidance should their content be flagged, potentially increasing user trust in the system. Additionally, this acts as a direct indicator of the type of content the rule authors want to moderate. ## 4.3 Unsupervised Performance Table 3 reports our results in the unsupervised setting. We observe that RBE is able to outperform SOTA trained on noisy rules labeled samples for the HateXplain and Jigsaw dataset while also outperforming the ruleset as is on all three datasets. Across each dataset, we find that RBE's *Distance* based strategy produces the most consistent performance, outperforming SOTA on HateXplain and CAD while performing on par with SOTA on Jigsaw. We observe that this stability in performance is due to this strategy's rule elimination objective. As opposed to the *Mean* and *Concat* strategies which focus on deriving rule representations in a self-supervised manner, the *Distance* strategy instead focuses on eliminating over-generalized rules whose cover set of examples are semantically dissimilar. This is particularly useful in cases where precision scores are low due to a large number of false positives. For Jigsaw, we observe a slight decrease in performance compared to SOTA. Upon further analysis, we posit that this is a result of RBE's overreliance on the ruleset in this setting, particularly for the *Mean* and *Concat* strategies. This is because the ruleset directly influences the derived rule embedding due to its labeling of the cover set C. As such when the ruleset is over-generalized, as is the case of Hate+Abuse rules on Jigsaw, RBE is likely to match the distribution of the ruleset. We find that performing self-supervised model pre-training (Gao et al., 2021) on the target corpus circumvents this trend for the *Mean* and *Concat* strategy. As such, with a more refined ruleset, a performance increase is expected as seen in HateXplain and CAD. ## 5 Related Work There has been active work on detecting hate speech in language (Poletto et al., 2021; AlMakhadmeh and Tolba, 2020; Schmidt and Wiegand, 2017). Hate Speech detection has proven to be a nuanced and difficult task, leading to the development of approaches and datasets targeted at various aspects of the problem (Vidgen et al., 2021; Mathew et al., 2020; Mody et al., 2023). However, few attempts have been made to focus on the explainability of these models, which is an increasing area of concern surrounding their use online (Tarasov, 2021; Haimson et al., 2021), thus leading to the continued utilization of less powerful but more explainable methods such as rules. Prior works have explored incorporating logical rules into model learning. Awasthi et al. (2020) proposed to weakly learn from rules by pairing them with exemplars and training a denoising model. However, this requires defining rules for all output classes, making it inapplicable to the task of hate speech detection. Additionally, this method only focuses on decreasing rule scope to solve the overgeneralization problem. It does not simultaneously tackle the over-specificity problem demonstrated in Figure 1. Finally, this method does not provide a way for interpreting model predictions during inference. Seo et al. (2021) proposes a way to control neural network training and inference via rules, however, their framework represents rules as differentiable functions requiring complex perturbations to incorporate, making it more suitable to numerical rules such as those defined in healthcare and finance as opposed to the complex nuances of language. Pryzant et al. (2022) proposes a framework for the automatic induction of symbolic rules from a small set of labeled data. However, these rules are derived from low-capacity ML models and are as a result not human-readable or explainable. ## 6 Conclusion We introduce Rule By Example, an exemplar-based contrastive learning framework that enables learning from logical rules for accurate and explainable hate speech detection. Specifically, we propose a novel dual-encoder model architecture designed to produce meaningful rule and text representations. RBE leverages a novel exemplar-based contrastive learning objective that converges the representations of rules and text inputs of similar classes. We share results on three public datasets for hate speech detection that validate the Rule By Example framework can not only vastly outperform the initial ruleset but also outperform baseline SOTA classification methods in both supervised and unsupervised settings. Moreover, RBE enables rule-grounding which allows for more explainable model prediction benefits not available in SOTA classification methods alongside additional flexibility via Ruleset Adaptation. ## 7 Limitations In this section, we discuss some of the limitations of the Rule by Example method. ## 7.1 Dependence On Supervision The requirement of both a set of rules and an example per rule in our Rule by Example method means that some amount of expert supervision is required, even for the 'unsupervised' experimental setups. This could be a prohibitive cost in some scenarios. There are potential methods to select an example per rule in an unsupervised manner, such as clustering the examples the rules fires on, that could be explored in future work. However, the creation of the rules themselves means some form of expert supervision that distills knowledge about the classification task into a parseable function. ## 7.2 Increased Cost Compared To Rules Although the Rule by Example method produces a Dual Encoder model that is shown to be much more performant than the ruleset it is derived from, it still has the cost limitations of other deep learning methods. The Dual Encoder requires far more expensive compute (GPUs) to initially train and later inference in a production setting. And even with using expensive GPUs, the latency cost is unavoidably much higher than most simple logical rules. For some applications, the quality gain of the Dual Encoder model may not be worth the increased operational cost. ## 7.3 **Reliance On Quality Rules And Exemplars** Since the Rule by Example method is based on having a ruleset and associated exemplars to learn from, the quality of those rules and exemplars could affect downstream Dual Encoder model quality. If the authored ruleset and chosen exemplars are not high quality, intuitively the quality of the Dual Encoder model would suffer. This is especially true in the unsupervised setting, where the rules are used as noisy labeling functions. A possible future extension is studying the effect of rule and exemplar quality on the performance of the derived Dual Encoder model. ## 8 Ethics Hate speech detection is a complex task. Reducing the task to authoring a set of simple logical rules can potentially lead to rule authors encoding hard biases in those rules. This can cause problems of erasure, for example, if an in-group word or an identity term is used as a rule to identify content as hate speech. The Rule by Example method can potentially reduce these cases, for example by learning a better rule representation and identifying when a term is used as in-group speech as opposed to being used as an insult or slur. However, the derived Dual Encoder is also at the risk of propagating and amplifying these biases (Hall et al., 2022), causing greater unintended harm than the original ruleset. Whether using a ruleset or using a more complicated model, it is important to support classifiers with additional Responsible AI work streams, such as reviews of classifier behavior and measurements of fairness. ## Acknowledgements We thank our anonymous reviewers for their feedback and suggestions. This work was conducted by the ROAR (Responsible & Open AI Research) team at Microsoft Cloud & AI. 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Textual content moderation in C2C marketplace. In *Proceedings of the Fifth Workshop on* e-Commerce and NLP (ECNLP 5), pages 58–62, Dublin, Ireland. Association for Computational Linguistics. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and TieYan Liu. 2020. Mpnet: Masked and permuted pretraining for language understanding. Katie Tarasov. 2021. Why content moderation costs billions and is so tricky for facebook, twitter, youtube and others. Sahaj Vaidya, Jie Cai, Soumyadeep Basu, Azadeh Naderi, Donghee Yvette Wohn, and Aritra Dasgupta. 2021. Conceptualizing visual analytic interventions for content moderation. In *2021 IEEE Visualization* Conference (VIS), pages 191–195. Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini, and Rebekah Tromble. 2021. Introducing CAD: the contextual abuse dataset. In *Proceedings* of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2289–2303, Online. Association for Computational Linguistics. Kurt Wagner and Bloomberg. 2021. Facebook says it has spent $13 billion on safety and security efforts since 2016. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In *Proceedings of the 2020 Conference on Empirical* Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley. 2022. Laprador: Unsupervised pretrained dense retriever for zero-shot text retrieval. Yuchen Zhang. 2019. Stop bad content before it's posted, and build better communities. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7 ✓ A2. Did you discuss any potential risks of your work? 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 2 B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 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Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 3 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. 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lauscher-etal-2023-em

What about {``}em{''}? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

https://aclanthology.org/2023.acl-long.23

As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In this {``}reality check{''}, we study how three commercial MT systems translate 3rd-person pronouns. Concretely, we compare the translations of gendered vs. gender-neutral pronouns from English to five other languages (Danish, Farsi, French, German, Italian), and vice versa, from Danish to English.Our error analysis shows that the presence of a gender-neutral pronoun often leads to grammatical and semantic translation errors. Similarly, gender neutrality is often not preserved. By surveying the opinions of affected native speakers from diverse languages, we provide recommendations to address the issue in future MT research.

# What About Em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns Anne Lauscher1, Debora Nozza2, Archie Crowley3, Ehm Miltersen4**, and Dirk Hovy**2 1Data Science Group, Universität Hamburg, Germany 2Department of Computing Sciences, Bocconi University, Italy 3Linguistics, University of South Carolina 4School of Culture and Communication, Aarhus University [email protected], {debora.nozza, dirk.hovy}@unibocconi.it, [email protected], [email protected] ## Abstract As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In this "reality check", we study how three commercial MT systems translate 3rd-person pronouns. Concretely, we compare the translations of gendered vs. gender-neutral pronouns from English to five other languages (Danish, Farsi, French, German, Italian), and vice versa, from Danish to English. Our error analysis shows that the presence of a gender-neutral pronoun often leads to grammatical and semantic translation errors. Similarly, gender neutrality is often not preserved. By surveying the opinions of affected native speakers from diverse languages, we provide recommendations to address the issue in future MT research. ## 1 Introduction Machine translation (MT) is one of the most common applications of NLP, with millions of daily users interacting with popular commercial providers (e.g., Bing, DeepL, or Google Translate). Given MT's widespread use and the increased focus on fairness in language technologies (e.g., Hovy and Spruit, 2016; Blodgett et al., 2020), previous work has pointed to the potential ethical issues stemming from stereotypical biases encoded in the models, e.g., gender or age bias (e.g., Stanovsky et al., 2019; Levy et al., 2021, *inter alia*). Still, these studies treat gender as a binary variable and ignore the larger spectrum of (possibly marginalized) identities, e.g., non-binary individuals. This gender exclusivity stands in stark contrast to the findings of Dev et al. (2021). Their survey of queer individuals showed that MT has the most potential for representational and allocational harms (Barocas et al., 2017) for non-cis users (compared to other NLP applications). In this context, survey respondents mentioned the translation of *pronouns* as particularly sensitive, as genderneutral pronouns might be translated into gendered pronouns, resulting in harmful misgendering. While individual studies have investigated the translation of established (gender-neutral) pronouns (e.g., from Korean to English; Cho et al., 2019), NLP research, in general, has ignored the "modern world of pronouns" as recently described by Lauscher et al. (2022). They discuss the large variety of existing phenomena in English 3rd-person pronoun usage, with more traditional neopronoun sets (e.g., *xe/xem*) 1and novel pronoun-related phenomena (e.g., nounself pronouns like *vamp/vamp*; Miltersen, 2016), which possibly match distinct aspects of an individuals identity. As an example of ubiquitous NLP technology, truly inclusive MT should account for linguistic varieties that express identity aspects, like the large spectrum of pronouns related to the social push to respect diverse identities. However, until now, (a) there has been no information on how our systems (fail to) handle this linguistic shift, and (b) it is unclear how MT should deal with novel pronouns. This case is especially challenging when source language pronouns do not have direct correspondences in the target language. Contributions. In this "reality check", we investigate the handling of various (neo)pronouns in MT for advancing inclusive NLP. To this end, we combine an extensive analysis of MT performance across six languages (Danish, English, Farsi, French, German, and Italian) and three commercial MT engines (Bing, DeepL, and Google Translate) with results from the largest survey on pronoun us1Throughout this work, we use the expression "traditional neopronoun" to refer to sets that are, in contrast to only recently described phenomena (e.g., nounself pronouns), already academically discussed for longer. age among queer individuals in AI to date. We answer the following four research questions (RQs): (RQ1) *How do gender-neutral pronouns affect the* overall translation quality? We show that compared to gendered pronouns, the translated output's grammaticality and the source sequence's semantic consistency **drops by up to 16 percentage points** and 47 percentage points, respectively, for some categories of neopronouns. (RQ2) *How do MT engines handle gender-neutral* pronouns? We demonstrate that the strategies for how MT engines handle pronouns vary by pronoun category: while gendered pronouns are most often translated (89%), engines tend to simply copy some categories of neopronouns (e.g., 74% for the category of numberself-pronouns). (RQ3) *Which MT strategies for handling genderneutral pronouns "work"?* We show that in 56% of cases when a traditional neopronoun is translated, it is translated to a gendered pronoun in the target language, **likely leading to misgendering**. (RQ4) *How should MT handle pronouns?* The answers of 49 participants (149 participants in the pre-study) in our survey reflect the diversity of pronoun choices across English and other languages and the diversity of preferences in how individuals' pronouns should be handled. There is no clear consensus! We thus recommend providing configuration options to adjust the treatment of pronouns to individuals' needs. ## 2 Related Work We review works on gender bias in MT and the broader area of (gender) identity inclusion in NLP. For a thorough survey on gender bias in MT, we refer to (Savoldi et al., 2021). Gender Bias in MT. As with other areas of NLP (e.g., Bolukbasi et al., 2016; Gonen and Goldberg, 2019; Lauscher et al., 2020; Barikeri et al., 2021, *inter alia*), much research has been conducted on assessing (binary) gender bias in MT. Most prominently, Stanovsky et al. (2019) presented the WinoMT corpus, which allows for assessing occupational gender bias as an extension of Winogender (Rudinger et al., 2018) and WinoBias (Zhao et al., 2018). Troles and Schmid (2021) further extended WinoMT with gender-biased verbs and adjectives. Those corpora are template-based, while Levy et al. (2021) focused on collecting natural data, and Gonen and Webster (2020) proposed an automatic approach to detect gender issues in real-world input. Renduchintala et al. (2021) analyzed the effect of efficiency optimization on the measurable gender bias. Focusing on a different perspective, Hovy et al. (2020) assessed stylistic (gender) bias in translations. Other studies have examined specific language pairs, e.g., English and Hindi (Ramesh et al., 2021), English and Italian (Vanmassenhove and Monti, 2021), or English and Turkish (Ciora et al., 2021). Similarly, Cho et al. (2019) studied English–Korean translations focusing on translating gender-neutral pronouns from Korean. They introduced a measure reflecting the preservation of gender neutrality but do not consider any neopronouns. Based on similar data sets and measures, researchers have also addressed gender bias in MT, e.g., via domain adaptation (Saunders and Byrne, 2020), debiasing representations (Escudé Font and Costajussà, 2019), adding contextual information (Basta et al., 2020), and training on gender-balanced corpora (Costa-jussà and de Jorge, 2020). Some mitigation approaches exploit explicit gender annotations to guide the model in choosing the intended gender (e.g., Stafanovics et al. ˇ , 2020). In this context, Saunders et al. (2020) proposed a schema for adding inflection tags. For instance, they demonstrated how gender-neutral entities can be translated from English to another language by using a non-binary inflection tag. Gender and Identity-Inclusion in NLP. While most MT studies on gender bias deal with a binary notion of gender, researchers have started to study non-binary gender and identity inclusivity in NLP downstream tasks and models. Qian et al. (2022) explored the robustness of models to demographic change using a perturber model that also considers non-binary gender identities, Cao and Daumé III (2020) studied gender inclusion in co-reference resolution, and Brandl et al. (2022) analyzed how gender-neutral pronouns are handled by language models in Danish, English, and Swedish for natural language inference and co-reference resolution. Nozza et al. (2022) and Holtermann et al. (2022) measured bias and harmfulness in language models towards LGBTQIA+ individuals. Other researchers focused on the problem more broadly. Orgad and Belinkov (2022) mention the binary treatment of gender as one of the essential pitfalls in gender bias evaluation, and Dev et al. (2021) surveyed the harms arising from non-binary exclusion in NLP, indicating MT as one particularly harmful application. Following up, Lauscher et al. (2022) explored the various phenomena related to 3rd-person pronoun usage in English, e.g., neopronouns. We are the first to study the translation of these novel pronoun-related phenomena in MT. ## 3 The Status Quo To shed light on the state of identity inclusion through 3rd person pronouns in commercial MT, we conduct a thorough error analysis when translating from English (EN) to five diverse languages. We further describe an experiment opposite to this, translating from Danish (DA) to EN, in §3.3. ## 3.1 Experimental Setup Our overall setup consists of 3 steps: (1) we create EN source sentences, each of which contains 3rd person pronouns representing different "pronoun categories" (e.g., *gendered pronoun*, etc.) in different grammatical cases. (2) Next, we employ an MT system to translate the EN sentences to five target languages. (3) Last, we let native speakers manually analyze the translations with respect to diverse criteria, e.g., *grammaticality of the output*. Creation of EN **Source Data.** We start with the WinoMT data set (Stanovsky et al., 2019), designed to assess gender bias in MT and consisting of sentences that contain occupations stereotypically associated with women (e.g., *secretary*) or men (e.g., developer). We conduct an automatic morphological analysis on each pronoun in the data set.2 Based on the output, we randomly sample for each grammatical case (e.g., nominative, etc.), in which a 3rd person pronoun referring to an occupation appears in, two sentences: one in which the target occupation is stereotypically associated with men and one in which it is stereotypically associated with women. We then replace those pronouns with placeholders, indicating the case (e.g., <n> for nominative) of each. Since WinoMT does not contain pronouns in the *possessive independent* case, we create these by sampling additional sentences with *possessive dependent* pronouns and remove the target noun. Accordingly, we end up with 10 templates from WinoMT (2 for each of the 5 grammatical cases). Additionally, given that WinoMT sentences are designed to be more complex and ambiguous, we manually create two additional, simpler sentences for each grammatical case (10 in total). In these sentences, the pronoun placeholders refer to given names. In accordance with the WinoMT pattern, we choose the top name stereotypically associated with women and the top name stereotypically associated with men according to 2020 U.S. Social Security name statistics.3 We show example templates in Table 1. We fill the placeholders with pronouns of the correct grammatical case taken from 8 sets of pronouns that reflect diverse pronoun-related phenomena as described by Lauscher et al. (2022). For example, we use *she/ her /her/ hers/ herself* as an instance of gendered pronouns, and *vam/ vamp /* vamps/ vamps/ vampself as an instance of nounself pronouns (Miltersen, 2016). The latter are prototypically derived from a noun, and possibly match distinct aspects of an individual's identity. We list our test pronouns in Table 2. Our setup allows us to test the translation of sentences containing different types of pronouns, in all of their grammatical forms, in more and less complex sentences and in contexts that are prone to different stereotypical associations. Our procedure results in 164 EN sentences (4 sentences per 5 cases for each of the 8 pronoun sets plus 4 additional sentences for the variant *themself* instead of *themselves*). Automatic Translation. Next, we automatically translate the EN source sentences to five languages: Danish (DA), Farsi (FA), French (FR), German (DE), and Italian (IT). We choose these languages based on (a) typological diversity, (b) our access to native speakers, and (c) their coverage by commercial MT. We ensure diversity with respect to family branches, scripts, and the handling of gender and pronouns in the languages: DE and DA represent the Germanic branch, FR and IT the Romanic branch, and FA the Iranian branch of Indo-European languages. DA, DE, FR, and IT employ the Latin script, and FA the Arabic one. Most importantly, the handling of grammatical gender and pronouns differs among languages. Concretely, DA, DE, FR, and IT are gendered languages but differ in their number of genders (e.g., DE has three grammatical genders while FR has two). While for DE and IT, there is currently no gender-neutral pronoun recognized by an institutional body, for FR, the dictionary *Le Robert* recently included the gender-neutral pronoun *"iel"*. 3https://www.ssa.gov/oact/babynames/ | Case | Templates The attendant recognized the developer because <n> reads a lot of technical news. The analyst employed the housekeeper because <n> could not stand housework. | |--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Nominative | Olivia lost the game, so <n> was sad. Liam received a good grade, so <n> was happy. The developer wanted free bread from the baker and made up a story for <a> about not having a kitchen. The attendant did not want to fight with the guard and gave <a> flowers. | | Accusative | I like Olivia, so I met <a> today. I do not like Liam, so I do not want to meet <a> today. The mechanic visited the writer and helped on fixing <pd> car engine. The baker sold bread to the CEO and enjoyed <pd> visits. | | Poss. Depen. | Liam lost <pd> phone. Olivia found <pd> ring. During lunch, the janitor looked for the attendant to steal <pi>. Last Saturday, the physician called the tailor to fix <pi>. | | Poss. Indep. | I had no phone, so Olivia gave me <pi>. I lost my notes, so Liam gave me <pi>. The farmer did not want to talk to the writer because <n> was burying <r> in writing a new novel. The chief employed the receptionist because <n> was too busy to answer those phone calls by <r> every day. | | Reflexive | Olivia wanted to impress, so <n> baked a cake <r>. Liam wanted a new haircut, so <n> cut the hair <r>. | Table 1: The templates we use for each grammatical case. Placeholders are indicated with brackets and the grammatical case of the pronoun to fill, e.g., *<pd>* (possessive dependent pronoun). The first two templates for each case are extracted from WinoMT (Stanovsky et al., 2019), while the second two templates are added by us. Phenomon N A PD PI R Gendered he him his his himself she her her hers herself Gender-neutral they them their theirs themselves themself Neo xe xem xyr xyrs xemself ey em eir eirs emself Nounself *vam vamp vamps vamps vampself* Emojiself s s *self* Numberself 0 0 0s 0s 0*self* Table 2: Phenomena and 3rd person pronoun sets by which they are represented in our analysis when translating from English (EN → DA, DE, FA, FR, IT). We list the pronouns for each grammatical case: nominative (N), accusative (A), possesive dependent (PD), possessive independent (PI), and reflexive (R). In contrast, FA is a gender-neutral language. Thus, there should also be no potential for misgendering in the resulting translations. Another interesting aspect is that two of the languages fall under the class of *pro-drop* languages (IT, FA) 4, while the others do not allow for dropping the pronoun. We focus on assessing the state of commercial MT, and accordingly rely on 3 established MT engines: Google Translate,5 Microsoft Bing,6and DeepL Translator.7 Currently, DeepL does not cover Farsi (all other languages are covered by all three commercial MT engines). Annotation Criteria. While initially, we wanted to focus solely on identity aspects conveyed by the pronouns, we noticed in an early pre-study that some of the translations exhibited more fundamental issues. This is why we resort to the following three categories, which allow us to answer research questions RQ1–RQ3, to guide our analysis of a translation B based on an EN sentence A: grammatical correctness, *semantic consistency*, and *pronoun* translation behavior. (1) Grammatical Correctness. We ask our annotators to assess whether translation B is grammatically correct. Annotators are instructed to not let their judgment be affected by the occurrence of neopronouns that are potentially uncommon in the target language, e.g., emojiself pronouns. (2) Semantic Consistency. We let our annotators judge whether B conveys the same message as A in two variants: First, we seek to understand whether independent of how the pronoun was translated the semantics of A are preserved. Second, we ask whether when also considering the pronoun translation, semantics are preserved. (3) Pronoun Translation Behavior. The third category specifically focuses on assessing the translation of the pronoun. We investigate whether the pronoun was *omitted* (i.e., it is not present in B), copied (pronoun in B is exactly the same as in A), or *translated* (the system output some other string in B as correspondence to the pronoun in A). Note that none of these cases necessarily corresponds to a translation error (or translation success) - for instance, it might be a valid option to directly copy the pronoun from the input in the source language to fully preserve its individual semantics. If the pronoun was "translated", we ask annotators to highlight its translation, and to further indicate if the translation corresponds to a common pronoun in the target language (and also, whether it still functions as a pronoun). If a common pronoun is chosen, we also collect its number and its commonly associated gender. Annotation Process. As the evaluation task requires annotators to be familiar with the target language, the concept of neopronouns, and linguistic properties such as part-of-speech tags, we hired five native speakers of target languages who all hold a university degree, are proficient speakers of English, and have diverse gender identities (man, woman, non-binary). We payed our annotators 15C per hour, which is substantially above the minimum wage in Italy and in line with the main authors' university recommendations for academic assistants. All annotators demonstrated great interest in helping to make MT more inclusive and were familiar with the overall topic. We took a descriptive annotation approach (Röttger et al., 2022). Each annotator then underwent specific training in 1:1 sessions in which we showed them examples and offered room for discussions and questions. To facilitate the task and guide our annotators through the annotation criteria, we developed a specific annotation interface (see Appendix). To assess the reliability of our evaluation, we hired a second annotator for DE and IT to compute inter-annotator agreement and let the same native speaker of FA re-annotate a portion of the data to compute intraannotator agreement (50 instances each). We measured an inter-annotator-agreement (Krippendorff's α) of 0.73 for DE and 0.69 for IT, and an *intra*annotator agreement (Abercrombie et al., 2023) of 0.86 for FA across all upper-level categories. We thus assume our conclusions to be valid. After completing the assessment, we gave every worker access to their annotations with the option to change and clean their results. ## 3.2 Results Overall translation quality. We show the results on grammaticality and semantic consistency in Figures 1a–1c. Depending on the target language as well as the pronoun category, the performance varies greatly; for instance, while for gendered pronouns in FR 95% of the translations are grammatically correct, we observe a drop of 15 percentage points for emoji-self pronouns. Even more severely, only half (!) of the translations to IT are grammatically correct when starting with the gender-neutral pronoun *"they"* (Figure 1a). We make similar observations when asking annotators whether the meaning is preserved during the translation process (semantic consistency): Even when not considering the translation of the pronoun, in most cases, the performance drops when moving from a gendered to a gender-neutral pronoun set. We note the biggest drop, 34 percentage points, for FA and the category of noun-self pronouns (45% ) compared to gendered pronouns with 79% (Figure 1b). Compared to the results for gendered pronouns, we note the following maximum drops when aggregating over all languages we test: 16 percentage points for grammaticality, 13 percentage points for semantic consistency (pronoun excluded), both towards emoji-self pronouns, and a huge drop of 47 percentage points for semantic consistency when the pronoun is included in the assessment. We provide the aggregated plots in the Appendix. Pronoun treatment strategies. We depict the different strategies of how pronouns are treated in the translation in Figures 2a–2c. Across all languages, the engines most often "translate" the pronouns (up to ∼62% for DE), i.e., some nonidentical string corresponding to the EN input pronoun is present in the output. The most unpopular strategy is to omit the pronoun. Unsurprisingly, the highest fraction of translations where this strategy is applied is present among the pro-drop languages, FA (14%) and IT (12%). Among the three translation engines, DeepL exhibits the highest fraction of ![5_image_0.png](5_image_0.png) | Source | MT Output | Case | Issue Type | | | |----------------------------------|------------------------------------------------------|----------------------------------------|--------------------------------------|------------------------|------------------------| | 1 | The attendant did not want to fight with | Die Wärterin wollte sich nicht mit dem | | | | | the guard and gave them flowers. | Wachmann streiten und schenkte ihm Blumen. | Accusative | Potential Misgendering | | | | 2 | Liam received a good grade, so vam | Liam erhielt eine gute Note, und Vam | Nominative | Semantic Inconsistency | | | was happy. | war zufrieden. | | | | | | 3 | Olivia found eir ring. | Olivia fand einen Eir-Ring. | Poss. Depen. | Semantic Inconsistency | | | 4 | During lunch, the janitor looked for the | Während des Mittagessens suchte der | | | | | attendant to steal eirs. | Hausmeister nach dem Besucher, um Eurren zu stehlen. | Poss. Indep. | Pronoun Mistranslation | | | | 5 | Liam wanted a new haircut, so | cut | Liam wollte einen neuen Haarschnitt, | Reflexive | Semantic Inconsistency | | also schneiden Sie das Haar | selbst. | | | | | | the hair | self. | | | | | pronoun translations (65%).8In contrast, GTranslate is the engine with the largest pronoun copies (43%). Interestingly, we again observe a huge variation among the different pronoun groups: while the gendered pronouns (he, she) and the gender-neutral pronoun (*they*) are most often translated (89% and 90%, respectively) and are almost never copied to the output, our representatives of the number-self and emoji-self pronouns most often are (74% and 68%, respectively). This is also the case for the noun-self pronoun (vam) and the more traditional neopronouns (xe, ey), with roughly 58% of copies each. However, for these, the fraction of translations in turn greatly surpasses those of numberself and emojiself pronouns, with 41% and 37%. Translation and Gender. We analyze pronouns that are translated to an existing singular pronoun in the target language in Figure 3. For the gendered source pronouns (*he, she*), the result is roughly bal-8Note, however, that FA is not included due to coverage. anced across commonly associated genders. For they, we observe a high proportion of genderneutral output pronouns (65%)—most often, gender neutrality is preserved. In contrast, for different types of neopronouns, the engines are likely to output a gendered pronoun. This finding is most pronounced for emojiself pronouns, with 50% and 23% of output pronouns commonly associated with male and female individuals, respectively. This amount of translations (73%) is likely to correspond to cases of misgendering. Qualitative Analysis. For further illustration, we show examples of some problems we observe when translating to DE in Table 3. The output in Example 1 is generally correct. However, the genderneutral pronoun *they* is translated to the gendered pronoun er. Examples 2 and 3 show translations in which the pronoun correspondence is copied from the input but starts with a capital letter (or ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) is even prepended to the succeeding word, e.g., Eir-Ring), as done for nouns or names. We note a similar problem in example 4. Additionally, the output string corresponding to the pronoun is neither copied from the input nor corresponds to a valid word in the target language (*Eurren*). Finally, in example 4, the emojiself pronoun appears in the output translation with the additional 2nd person pronoun variant Sie. ## 3.3 Translating To English Experimental Setup. So far, we have started from EN source sentences. Here, we expand our perspective and conduct the inverse experiment: We translate to EN starting from DA sentences (as an example of a language with a recently emerging gender-neutral pronoun). To this end, we start from our EN templates and manually translate these to DA. We then fill the templates with the pronouns han (=he), hun (=she), hen (gender-neutral), resulting in 48 source sentences. We translate those automatically with the three commercial engines and let an English native speaker evaluate the output according to the same guidelines. Results. The overall translation quality is relatively high; for instance, we find that 75% of translations are grammatically correct when starting from the gendered pronouns (*han, hun*), and only see a small drop for the gender-neutral pronoun (hen with 71%). However, surprisingly, the translation engines seem to never output the genderneutral option *"they"* when choosing an existing pronoun in the target language EN, not even when starting from hen. In contrast, in roughly 72% of the cases, hen is translated to he. ## 4 What Would Be A Good Translation? Our results show that commercial engines cannot deal with pronouns as an open word class. Often, the output is not grammatical, and the meaning is inconsistent. Beyond these general aspects we have shown that pronoun treatment strategies vary. Next, | Lang. | % Ment. | Pronoun sets | |---------|-----------|------------------------------------| | DE | 35.00 | er, sie, dey, ey, <none> | | EN | 30.00 | he, she, they, it, <no preference> | | DA | 20.00 | han, hun, den, de, she, they | | IT | 7.50 | lei, lui | | RU | 5.00 | он, <none> وا | | | | | | 2.50 | | | | | | | | FA | | | we seek to understand how individuals would want their pronouns to be handled (RQ4). ## 4.1 A Survey On Pronouns And Mt Survey Design and Distribution. To answer this RQ, we design a survey consisting of three parts: (1) a general part asks for the participant's demographic information, e.g., age, (gender) identity, as well as their pronouns in English and their native languages. (2) The second part asks general opinions related to pronouns in artificial intelligence. (3) The last section deals specifically with MT: here, we ask how the individual would like their or their friends' pronouns to be treated when translating from their native language to another. Participants can choose from four treatment options we identified through informal discussions with affected individuals: (a) Avoid pronouns in the translation; *(b) Copy the pronoun (in my native* language) and don't try to translate it; *(c) Translate* to a pronoun in the target language (if commonly associated identity matches); *(d) List multiple pronouns in the translation possibly associated with* diverse identities. Participants can also define additional options. We provide examples with genderneutral pronouns in English and encourage the participant to provide a translation in their native language. The institutional review board of the main authors' university approved our study design. We distributed the survey through channels that allow us to target individuals potentially affected by the issue and who represent a wide variety of (gender) identities. Examples include QueerInAI,9and local LGBTQIA+ groups, e.g., Transgender Network Switzerland.10 For validation, we ran a pre-study between March 22 and May 4, 2022 (with n=149). The main phase was open for participation between June 18 and August 1, 2022. Results. In the main phase of our survey, 44 individuals participated. Their ages ranged from 14 to 43, with the majority between 20 and 30. For the analysis, we removed responses from participants under 18. The remaining participants provided diverse and sometimes multiple gender identity terms (e.g., *non-binary, transgender, questioning, genderfluid*) and speak diverse native languages (e.g., English, German, Persian). The fraction of mentions of native languages and provided pronoun sets per language are given in Table 4: participants identify with diverse and sometimes multiple pronoun sets (e.g., gendered pronouns, neopronouns) as well as no pronouns. Interestingly, some seem to use EN pronouns in their non-EN native language. This observation aligns with the finding that bilinguals tend to code-switch to their L2 if it provides better options to describe their gender identity (Kaplan, 2022). In a similar vein, some participants provided only a gendered option in their native language (e.g., er in German) but indicated to identify with a gender-neutral option in EN (e.g., *they*). Concerning the translation policies, participants chose between 1 and 3 pre-defined options, and four provided additional ideas. The result is depicted in Figure 4. While the most popular option is (c) Translate to a pronoun in the target language (if commonly associated identity matches), there is no clear consensus and also strong tendencies towards gender-agnostic solutions. This finding is supported by the example-based analysis where we asked participants to translate from English to their native language. Table 5 illustrates this finding via participant answers for English to German translations (German native speakers). Participants used different options, like using the referent's name or a neopronoun, to deal with the issue that there is no established gender-neutral pronoun in German. Additional participant comments point to the difficulty of the problem, e.g., "this one's tough because it feels like different people are potentially going to have different desires on this one [...]". Overall, we thus conclude that **users' preferences** are as diverse as the community itself. ## 4.2 Recommendations Based on our observations in §3 and the survey results, we provide three recommendations for mak- ![8_image_0.png](8_image_0.png) | Translation policy | Translation | |----------------------------------------|---------------------------------------------------------------------| | Referent's name | Liam hat eine gute Note bekommen, also war Liam glücklich. | | Ellipsis through alternative construct | Liam erhielt eine gute Note und war deshalb froh. | | General noun (person) | Liam hat eine gute Note bekommen, deshalb war die Person glücklich. | | Neopronoun | Liam hat eine gute Note bekommen, deswegen freut dey sich. | ## Ing Future Mt More Inclusive. (1) Consider pronouns an open word class when developing and testing MT systems. As we have demonstrated, popular commercial MT systems often fail when gender-neutral pronoun sets are part of the input, even when translating between resource-rich languages like EN and IT. Thus, NLP researchers and practitioners must make MT more robust even with regards to fundamental properties such as grammaticality. Extending existing data sets to reflect a larger variety of pronouns is crucial. (2) If possible, provide options for personalization. Our survey demonstrated no clear consensus on how pronouns should be treated, and that users' preferences and pronouns vary. Thus, if possible, i.e., if the user is aware of the pronouns referents in their input text identify with, and if they directly interact with the translation engine, the decision should be left to that user. This finding aligns with desideratum D5 for more identity-inclusive AI identified by Lauscher et al. (2022). (3) Avoid potential misgendering as much as possible. If options for personalization are limited, no translation strategy will be ideal for all users. However, instead of "blindly" translating, which, as we have demonstrated, is likely to lead to misgendering, there are several other options that translation engines could choose that exhibit less potential for harm, e.g., gender-agnostic translations. ## 5 Conclusion In this work, we have investigated the sensitivity of automatically translating pronouns: small words that can convey important identity aspects. To understand where current commercial MT stands with regards to this issue, we started with a thorough error analysis covering six languages and three MT engines. We demonstrated that the engines tested are more likely to produce low-quality output when starting from gender-neutral pronouns, and we further observed a high potential for misgendering. Emphasizing marginalized voices, we complemented our study with a survey of affected individuals. The answers led us to three recommendations for more inclusive MT. We hope our study will inform and fuel more research on these issues. ## Acknowledgements Part of this work is funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 949944, INTEGRATOR). Anne Lauscher's work is funded under the Excellence Strategy of the Federal Government and the Länder. Debora Nozza and Dirk Hovy are members of the MilaNLP group and the Data and Marketing Insights Unit of the Bocconi Institute for Data Science and Analysis. ## Limitations Naturally, our work comes with a number of limitations: for instance, we restrict ourselves to testing eight pronoun sets out of the rich plethora of existing options. To ensure diversity, we resort to one or two sets per pronoun group—we hope that individuals feel represented by our choices. Similarly, we only translate single sentences and don't investigate translations of larger and possibly more complex texts and we only translate to a number of languages none of which is resource-lean. Our study demonstrates that simpler and shorter texts already exhibit fundamental problems in their translations, even to resource-rich languages. ## Ethics Statement In this work, we present a reality check in which we show that established commercial MT systems struggle with the linguistic variety that is tied to the large spectrum of identities. Consequently, this work has an inherently ethical dimension: our intent is to point to the issue of subcultural exclusion in language technology. We acknowledge, however, that this issue is much bigger than the problems relating to the use of neopronouns and we hope to investigate the topic more globally in the future. ## References Gavin Abercrombie, Verena Rieser, and Dirk Hovy. 2023. 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Association for Computational Linguistics. ## A Data Sets And Licenses In this work, we only made use of a single existing dataset, WinoMT11 (Stanovsky et al., 2019). We used the dataset to obtain EN templates in different grammatical cases, which we filled with the pronouns we test. The data set is licensed under MIT License. We will publish our selection of sentences from WinoMT as well as the additional sentences we added under the same license. ## B Additional Results We provide additional results (aggregated across languages) in Figure 5. ## C Annotation Interface We show a screenshot of our annotation interface in Figure 6. The interface was developed using HTML and JavaScript and hosted on the Amazon Mechanical Turk Sandbox. ![13_image_0.png](13_image_0.png) ![13_image_1.png](13_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section "Limitations" (after conclusion) ✗ A2. Did you discuss any potential risks of your work? We analyze the current state of identity inclusion in MT. Thus, our work points to risks of such systems. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract, Intro (Section 1) ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3.1 ✓ B1. Did you cite the creators of artifacts you used? Section 3.1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? See Appendix ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We use a data set for evaluation of MT for evaluation of MT. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The MT data is template-based and does not contain any personalised information. The survey design was IRB approved - here we collect data in anonymised form (Section 4.1) ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3.1 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3.1 ## C ✗ **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? No response. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? No response. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? No response. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? No response. D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 3 and 4 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Instructions in Section 3.1 and 4.1, screenshots in the appendix ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 3.1 and 4.1 ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 4.1 ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section 4.1 ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 4.1

zhang-etal-2023-overlap

What Is Overlap Knowledge in Event Argument Extraction? {APE}: A Cross-datasets Transfer Learning Model for {EAE}

https://aclanthology.org/2023.acl-long.24

The EAE task extracts a structured event record from an event text. Most existing approaches train the EAE model on each dataset independently and ignore the overlap knowledge across datasets. However, insufficient event records in a single dataset often prevent the existing model from achieving better performance. In this paper, we clearly define the overlap knowledge across datasets and split the knowledge of the EAE task into overlap knowledge across datasets and specific knowledge of the target dataset. We propose APE model to learn the two parts of knowledge in two serial learning phases without causing catastrophic forgetting. In addition, we formulate both learning phases as conditional generation tasks and design Stressing Entity Type Prompt to close the gap between the two phases. The experiments show APE achieves new state-of-the-art with a large margin in the EAE task. When only ten records are available in the target dataset, our model dramatically outperforms the baseline model with average 27.27{\%} F1 gain.

# What Is Overlap Knowledge In Event Argument Extraction? Ape: A Cross-Datasets Transfer Learning Model For Eae Kaihang Zhang1, Kai Shuang 1∗, Xinyue Yang1, **Xuyang Yao**2And **Jinyu Guo**13 1State Key Laboratory of Networking and Switching Technology, ![0_image_0.png](0_image_0.png) Beijing University of Posts and Telecommunications 2China Telecom Research Institute 3University of Cambridge {zkh1999, shuangk, crescent3919, guojinyu}@bupt.edu.cn [email protected] ## Abstract The EAE task extracts a structured event record from an event text. Most existing approaches train the EAE model on each dataset independently and ignore the overlap knowledge across datasets. However, insufficient event records in a single dataset often prevent the existing model from achieving better performance. In this paper, we clearly define the overlap knowledge across datasets and split the knowledge of the EAE task into overlap knowledge across datasets and specific knowledge of the target dataset. We propose APE model to learn the two parts of knowledge in two serial learning phases without causing catastrophic forgetting. In addition, we formulate both learning phases as conditional generation tasks and design Stressing Entity Type Prompt to close the gap between the two phases. The experiments show APE achieves new state-of-the-art with a large margin in the EAE task. When only ten records are available in the target dataset, our model dramatically outperforms the baseline model with average 27.27% F1 gain.1 ## 1 Introduction Event extraction (EE) is a pivotal task in information extraction. Typically, the event extraction task can be divided into two sub-tasks: event detection (ED) and event argument extraction (EAE). Thanks to recent works (Liu et al., 2022a; Sheng et al., 2022; Lai et al., 2020), event detection has achieved significant progress. The main challenge of EE lies in the EAE task. The EAE task aims to extract a structured event record from an event text. Since different datasets often have various event types and argument structures, most studies (Ma et al., 2022; Lu et al., 2021; Liu et al., 2022b) train the EAE model on each dataset independently, such as ACE 2005 (Doddington et al., 2004), RAMS (Ebner et al., 2020), and WikiEvents (Li et al., 2021). However, one single dataset often cannot provide sufficient event records, which seriously prevents those models from achieving better performance. Especially in some industrial applications, the in-domain event record collection incurs expensive and timeconsuming manual annotation. We argue that there is abundant transferable all-purpose knowledge of the EAE task among different datasets, called overlap knowledge. Exploring the overlap knowledge from existing datasets can significantly improve the model's performance and reduce the need for newly annotated data. How to transfer knowledge across datasets has yet to be well studied. Only Zhou et al. (2022) attempted to introduce variational information bottleneck to retain the shared knowledge between two datasets and achieved considerable success. Nevertheless, their model architecture restricts that they can only obtain overlap knowledge from up to two datasets. Moreover, it has not explicitly defined what is the overlap knowledge among the different ∗Corresponding author. 1https://github.com/ZKH-1999/APE 393 datasets. Therefore, they use the EAE task's training objective to train the model on two datasets jointly and roughly let the model distinguish what knowledge is shareable across datasets. The imprecise training objectives perplex the model to learn the overlap knowledge better. In this work, we propose a Seek Common ground while Reserving Differences (SC-RD) framework to define the overlap knowledge clearly. SC-RD suggests defining overlap knowledge based on a cross-dataset common ground and isolating other knowledge into specific knowledge. As shown in Figure 1, every argument role in different datasets can be attached to an entity type. We introduce a finite entity type set (shown in Appendix Table 6) as the common ground across datasets. Based on the entity type set, we define the overlap knowledge as identifying entity words associated with the event by a given entity type. The specific knowledge is defined as identifying arguments based on the output of overlap knowledge. As illustrated in Figure 1, the two knowledge split the EAE task into two steps: In the first step, the model uses the overlap knowledge to focus on the entity word associated with the event. The second step finishes the EAE task based on the specific knowledge. Therefore, the EAE task can be reformulated as the product of two conditional probabilities: ## P (A|X , K) ∝ P (W|X , Ko) P (A|W, X , Ks) (1) where A is the event argument, w are event-related entity words, and X donates the event text. ko ∈ K represents overlap knowledge, and ks ∈ K represents specific knowledge. p (w|X , ko) is independent of datasets and can be learned from a pseudoentity recognition (PER) task on multi-datasets straightforwardly. The PER only identifies the entity words associated with the event so that EAE labels can be converted to PER labels by a manual mapping function. The structure definition of A varies with the dataset, so we learn p (A|w, X , ks) from the EAE task on the target dataset based on the overlap knowledge. We implement the above idea in APE, which Assembles two Parameter-Efficient tuning methods to harmonize two parts of knowledge in one single model. Specifically, we introduce two learning phases (illustration in Figure 2) to learn overlap and specific knowledge, respectively. In the overlap learning phase, we merge multi-datasets and convert their unaligned EAE labels to aligned PER labels to optimize the Prefix, which is introduced to save overlap knowledge. In the specific learning phase, we load and freeze the trained Prefix and tune the Adapter's parameters with the EAE task in the target dataset to save specific knowledge. All the pre-trained model's parameters will be frozen like traditional parameter-efficient tuning methods. Furthermore, to ensure the overlap knowledge plays a part in the EAE task, we format both training tasks as conditional generation tasks and propose the Stressing Entity Type Prompt to ignite the overlap knowledge in the EAE task. To the best of our knowledge, we are the first to clearly define the overlap knowledge across datasets, so we can give the model a transparent training objective to help it learn the overlap knowledge. Our model expands parameter-efficient tuning methods to the transfer learning scene. Since APE optimizes different parameters in two learning phases, learning the specific knowledge will not trigger catastrophic forgetting (McCloskey and Cohen, 1989) of the overlap knowledge. We have conducted extensive experiments on three widely used datasets. The experimental results show that our proposed APE outperforms baselines with a large margin (2.7%, 2.1%, 3.4% F1 gain absolutely on three benchmarks). Moreover, it achieves 27.27% F1 score gain average over three datasets when only ten samples of the target dataset are available, indicating our model's fewshot learning ability. Further analysis in Section 4.3 confirms the efficacy of the main components in our model. ## 2 Method As illustrated in Figure 2, APE learns two parts of knowledge in two learning phases sequentially. To overcome catastrophic forgetting, our model (Section 2.2) assembles Prefix (Li and Liang, 2021) to save overlap knowledge and Adapter (Houlsby et al., 2019) to save specific knowledge, respectively. To fully use the overlap knowledge learned from multi-datasets, we carefully design the Task Formulation (Section 2.1) and the Stressing Entity Type Prompt (Section 2.3) of two learning phases. ## 2.1 Task Formulation Our approach introduces PER task to learn overlap knowledge and EAE task to learn specific knowledge. Every NLP task can be treated as a "text-totext" problem (Raffel et al., 2020). Our approach formats both learning phases as conditional generation problems to narrow the gap between the two ![2_image_0.png](2_image_0.png) learning phases. Specifically, we define the event dataset as D = {(Ci, ei, Ti, Ai)|i < *|D|}*, where Ciis the ith event context. ei and Ti are the event type and trigger of the ith event separately. Ai = {(rj , spanj )*, . . .* } is the argument set of the event, where rj denotes the argument role, and *span*j is the offset of the argument. For both phases, the input of our model is a designed prompt P and a context Ci. The target output string is an answered prompt G containing the answer to the task. The language model (LM) models the conditional probability of answered prompt G as: $$p\left({\mathcal{G}}|{\mathcal{X}},\;\theta\right)=\prod_{i=1}^{\left|{\mathcal{G}}\right|}p\left(g_{i}|g_{<i},{\mathcal{X}}\right)\qquad\qquad(2)$$ $${\mathcal{X}}=\left[{\mathcal{P}};\left[{\mathcal{S E P}}\right];{\mathcal{C}}_{i}\right]\qquad\qquad(3)$$ Where X is the input of the model, θ donates the parameters of LM. The construction of P and G in two learning phases will be respectively described in section 2.3. ## 2.2 Model Architecture Our APE model assembles Prefix and Adapter into pre-trained encoder-decoder Transformer (Vaswani et al., 2017). The model can acquire two parts of knowledge without causing catastrophic forgetting by optimizing different parameter regions in two learning phases. For overlap knowledge, we equip each selfattention module with a short Prefix vector P ∈ R|P|×d*model* to represent and save it. In each layer, the new self-attention module with overlap knowledge intervention is formalized as: $$H\gets L a y e r N o r m\left(H^{'}+H\right)$$ $$(4)$$ $$({\mathfrak{H}})$$ (4) $$H^{\prime}=M H S A\,(P\oplus H)_{|P|:|P\oplus H|}$$ Where *MHSA*(•) denotes the multi-head selfattention mechanism, and (•)a:b donates the slicing operation on the seq_len dim from a to b. The Prefix will be assembled into the model in both learning phases since we use the overlap knowledge in the specific knowledge learning phase too. We optimize the Prefix P only in the overlap knowledge learning phase, and freeze it in the specific knowledge learning phase. For specific knowledge, we adopt an Adapter parallel with the feed-forward module to represent and save it. The Adaptor locates behind the Prefix to model the order of knowledge utilization in the SC-RD framework. The specific knowledge will be involved in the computation of Had, and the new feed-forward module with Adapter is formalized as: $$H\gets L a y e r N o r m\left(H+H_{f f d}+H_{a d}\right)$$ $$H_{a d}=\ W_{u p}\ \sigma\left(W_{d o w n}H\right)$$ (6) $\text{}$ (7) $\text{}$ (a) . Where W*down* ∈ R $m\in\mathbb{R}^{d_{model}\times d_{adapter}}$ and $W_{up}\in\mathbb{R}$. R dadapter×d*model* are tunable parameters in the Adapter, σ(•) is a nonlinear activation function, and H*f f d* represents the output of the feed-forward layer. Only in the specific knowledge learning phase, we assemble the Adapter into the model and optimize it. Like traditional parameter-efficient tuning methods, the pre-trained parameters of the Transformer are frozen in both phases. ## 2.3 Stressing Entity Type Prompt The Stressing Entity Type Prompt can indicate the model to generate words with the corresponding entity type in the designated location. We design the prompts under the same style in two learning phases, which uses identical special tokens to mark entity types. In the EAE task, those special tokens will ignite the overlap knowledge. ## 2.3.1 Overlap Knowledge Learning Phase We introduce the PER task to align the diverse datasets and learn overlap knowledge from them. To convert EAE labels to PER labels, we manually create a mapping function M(r) which maps each argument role r to an entity type. Prompt Construction The overlap knowledge is independent of datasets, so all datasets' prompt in the overlap knowledge learning phase is identical. Entity-type special tokens mark the position expected to be filled by the model and the corresponding entity types. The model should recognize the right entity words by referring to the context Ci. The manual overlap knowledge prompt Po was designed as: [person/organization] are a participant in the event, the event happened at [location], *[object]* are relate to the event, *[definition]* are the terminology in the event, the event taken place at [time], *[money]* was used in this event. [•] represents an entity-type special token, and the prompt natively contains the congruent relationship between the special token and entity type. Furthermore, we concatenate the event trigger Ti of the given event with the prompt to help the model focus on the correct event. Target Output String Construction As shown in Figure 2 ①, for an event context Ci and its arguments Ai sampled from any event dataset, we first convert Aito the PER label according to M. Then, we construct the ground truth generation sequence Go,Ai by filling the PER label into Po. If several words are categorized as the same type, they will be concatenated by "and". If there is an empty set of some entity types, we fill "none" into Po to replace the special token. ## 2.3.2 Specific Knowledge Learning Phase We learn the specific knowledge by finishing the EAE task based on the overlap knowledge. To ignite the overlap knowledge contained in the Prefix, we inherit entity-type special tokens from the overlap knowledge learning phase and build prompts according to the target dataset with those special tokens. Prompt Construction In the target dataset, for each event type ei, we refer pre-defined prompt from Ma et al. (2022) and replace the textual argument roles in the prompt with the above entitytype special token according to M. The entitytype special token hints to the model what entity type of words are most likely to serve as this argument role. For example, given an event type e: *Life.Die.Unspecified*, the renovated prompt Ps,e can be got as: Prompt from Ma et al. **(2022):** Killer killed Victim at Place by *MedicalIssue* Renovated prompt: [person/organization] killed [person/organization] at *[location]* by [definition] As shown in Figure 2 ②, following Ma et al. (2022), we concatenate the event type ei and the event trigger Ti of the given event sample with the renovated prompt. Target Output String Construction For each event record (Ci, ei, Ti, Ai) sampled from the target dataset, as shown in Figure 2 ②, we construct the ground truth generation sequence Gs,ei,Ai by filling Aiinto Ps,ei . Like the overlap knowledge learning phase, arguments with the same role will be concatenated by "and" and the uninvolved argument role will be filled by "none". ## 2.4 Training, Inference, And Decoding Training First, in the overlap knowledge learning phase, the trainable parameters of our model are only the Prefix P in each layer and the embedding of entity-type special tokens. The Adapter will be disabled. The training objective is to maximize p (w|X , ko) of Equation 1, which is equivalent to minimizing the negative loglikelihood loss in multidatasets D = {D1, D2 *. . .*}: $${\mathcal{L}}_{o}=-\sum_{{\mathcal{D}}}^{D}\sum_{({\mathcal{C}}_{i},{\mathcal{T}}_{i},{\mathcal{A}}_{i})}^{{\mathcal{D}}}\log\left(P\left({\mathcal{G}}_{o,{\mathcal{A}}_{i}}|{\mathcal{C}}_{i},{\mathcal{T}}_{i},{\mathcal{P}}_{o}\right)\right)\tag{8}$$ Then, in the specific knowledge learning phase, we load and freeze all parameters learned from the overlap knowledge learning phase and assemble the Adapter into our model to save the specific knowledge. Only W*down* and Wup in the Adapter will be optimized. The training objective is to maximize p (A|w, X , ks) by minimizing the negative loglikelihood in the target dataset Dt: $$\mathcal{L}_{s}=-\sum_{(\mathcal{C}_{i},e_{i},\,\mathcal{T}_{i},\mathcal{A}_{i})}^{\mathcal{D}_{t}}\log\left(P\left(\mathcal{G}_{S,e_{i},\mathcal{A}_{i}}|\mathcal{C}_{i},\mathcal{T}_{i},\mathcal{P}_{s,e_{i}},P\right)\right)\tag{9}$$ Where P is the Prefix. Inference In the inference stage, we assemble the trained Prefix and Adapter into the model. The input of APE is as same as the specific learning phase. Our model generates sequence by beam search strategy with *width* = 10. The maximum sequence length is set to 100 tokens, which is plenty for every dataset. Decoding Routinely, we decode the arguments from generated sequence by using regular expressions according to the Ps,e for each sample. It is rare, but not all generated sequences are valid. For the argument roles we cannot decode from the generated sequence, we set "none" to them. Following Lu et al. (2021), we obtain the offset of the argument by finding the nearest matched string to the event trigger Ti. ## 3 Experiments Setup 3.1 Datasets We evaluate our model on three popular datasets: ACE 2005 (Doddington et al., 2004), RAMS (Ebner et al., 2020), and WikiEvents (Li et al., 2021). ACE05 is a classical sentence-level dataset. We follow Wadden et al. (2019)'s pre-processing scripts on ACE05. RAMS and WikiEvents are both document-level datasets. Since the context of the document-level dataset sometimes exceeds the constraint, we follow Ma et al. (2022), which adds a window centering on the trigger words and only encodes the words within the window. The statistics of the datasets are listed in Appendix Table 7. The multi-datasets D = {ACE05*, RAMS, W ikiEvents*} in this work. ## 3.2 Baselines We compare our APE model with the following state-of-the-art baseline models: (1) OneIE (Lin et al., 2020) jointly extracts the globally optimal IE result from a context. (2) EEQA (Du and Cardie, 2020) regards the event argument extraction task as an end-to-end question-answering (QA) task. (3) BART-Gen (Li et al., 2021) proposes a conditional generation approach to complete document-level EAE task. (4) PAIE (Ma et al., 2022) utilizes multirole prompts under extractive settings to capture argument interactions. (5) PAIE-Joint uses the same model in PAIE, but joint train the model in three datasets for a fair comparison with our model. (6) UnifiedEAE (Zhou et al., 2022) introduces variational information bottleneck to explore shared knowledge from two EAE datasets. ## 3.3 Evaluation Metric Following baseline models, we adopt two metrics: Arg-I and Arg-C. Following Li et al. (2021), we add Head-C for WikiEvents datasets. Please refer to Appendix A for the detail of evaluation metric. ## 3.4 Implementation Details We initialize the weight of the Transformer with BART model (Lewis et al., 2020). The length |P| of Prefix is set to 70, and the inter-dim d*adapter* of the Adapter is set to 512 for BART-base model and 768 for BART-large model. For simplicity, we initialize the Prefix and the Adapter randomly. We optimized our models on NVIDIA A40 GPU by AdamW (Loshchilov and Hutter, 2019) with β1 = 0.9, β2 = 0.999, ϵ = 1e − 8, and 10% warmup steps. We set the learning rate to 1e-3 for Prefix and 1e-4 for Adapter. To ensure the confidence of the result, we repeated the model training five times with five fixed seeds [14, 21, 28, 35, 42]. The reported experimental results are the average score. We exhibit some examples of M(r) (Table 10) and prompts (Table 11) in the Appendix. The complete M(r) and prompts of each dataset are available in our codebase. ## 4 Results And Analyses To investigate the efficacy of our APE model, we compare our model with several state-of-the-art baseline models (4.1). Then, we verify the significance of transfer overlap knowledge (4.2) in the few-shot setting. We also perform ablation studies and further analysis to examine the effectiveness of the main components in our model (4.3). ## 4.1 Overall Performance Table 1 present the main result of all baseline models and APE on three datasets. APE refers to our | Model | PLM | ACE05 | RAMS | WikiEvents | | | | | |-------------|--------|---------|--------|--------------|-------|--------|------|------| | Arg-I | Arg-C | Arg-I | Arg-C | Arg-I | Arg-C | Head-C | | | | OneIE | BERT-b | 65.9 | 59.2 | - | - | - | - | - | | BERT-l | 73.2 | 69.2 | - | - | - | - | - | | | EEQA | BERT-b | 68.2 | 65.4 | 46.4 | 44.0 | 54.3 | 53.2 | 56.9 | | BERT-l | 70.5 | 68.9 | 48.7 | 46.7 | 56.9 | 54.5 | 59.3 | | | BART-Gen | BART-b | 59.6 | 55.0 | 50.9 | 44.9 | 47.5 | 41.7 | 44.2 | | BART-l | 69.9 | 66.7 | 51.2 | 47.1 | 66.8 | 62.4 | 65.4 | | | PAIE | BART-b | 73.6 | 69.8 | 54.7 | 49.5 | 68.9 | 63.4 | 66.5 | | BART-l | 75.7 | 72.7 | 56.8 | 52.2 | 70.5 | 65.3 | 68.4 | | | PAIE-Joint | BART-b | 73.8 | 69.5 | 53.3 | 48.3 | 69.3 | 63.7 | 65.9 | | BART-l | 75.1 | 72.4 | 55.9 | 51.8 | 70.1 | 65.2 | 67.9 | | | UnifiedEAE | BART-b | 76.1 | 71.9 | 55.5 | 49.9 | 69.8 | 64.0 | 66.3 | | APE(Single) | BART-b | 74.1 | 70.1 | 54.8 | 49.6 | 66.2 | 62.1 | 64.9 | | BART-l | 75.3 | 72.9 | 56.3 | 51.7 | 70.6 | 65.8 | 68.4 | | | APE | BART-b | 75.5 | 72.9 | 56.1 | 51.6 | 70.7 | 66.0 | 68.7 | | BART-l | 78.2 | 75.4 | 58.1 | 54.3 | 73.7 | 68.7 | 70.8 | | full model, which optimizes the Prefix in multidatasets. APE(Single) refers to the APE model trained in the transfer-disable setting, which optimizes the Prefix only in the target dataset. In the APE(Single), the overlap knowledge degrades into shared knowledge between different event types within the same target dataset. From Table 1, we have the following observations. First, APE achieves the highest F1 score on every evaluation metric compared with all the baselines model. Our base model obtained +1%, +1.7%, and +2% gain of Arg-C F1 scores on ACE05, RAMS, and WikiEvents, respectively. The large model expands the margin to +2.7%, +2.1%, and +3.4%. The results show that there is abundant overlap knowledge in multi-datasets, and our model can fully utilize it in the target dataset. Second, despite not relying on transfer learning, APE (Single) also achieves state-of-the-art performance on ACE05 and WikiEvents, and a competitive score on RAMS, which suggests that knowledge shared between different event types in a single dataset can also boost performance. Third, the PAIE-Joint even slightly worse than the PAIE. It donate that it is difficult for the model to find overlap knowledge by itself from datasets with various event structures, event types, and even different annotation guidelines. The APE can exploit the overlap knowledge from the transparent training objective of the PER task, and achieve better performance. Table 2: Arg-C F1 score on few-shot setting Dataset ACE05 RAMS Wiki. 10 3.3±2.1 4.3±1.4 5.7±3.6 50 35.2±5.3 25.2±6.1 31.4±4.6 100 39.6±2.5 30.4±2.1 42.1±3.2 200 51.2±1.3 35.8±1.9 53.2±1.7 10 32.1±7.1 26.3±4.2 36.7±8.3 50 42.5±3.9 33.4±4.1 47.6±5.4 100 53.2±1.7 38.5±1.6 55.6±2.6 200 59.3±0.9 41.1±1.2 59.5±1.5 | PAIE APE | |------------| ## 4.2 Few-Shot Setting APE is exceptionally suited for lacking in-domain labeled data because APE can learn from outdomain event records. Therefore, we conduct a few-shot experiment to verify the ability of APE to reduce the dependence on target dataset samples. Specifically, we optimize Prefix on the other two intact datasets and train Adapter on the target dataset with few samples. Table 2 reports the Arg-C F1 score in the target dataset with 10, 50, 100, and 200 random sampled event records. From the results, we obtain the following observations. 1). APE significantly outperforms the state-of-the-art baseline PAIE model in three benchmarks. 2). Especially in the case of only ten samples, APE achieves 27.27% F1 score gains average in three datasets. 3). APE with 200 samples achieves competitive scores with some Table 3: The performance of different variants on ACE05 baseline model trained on the whole WikiEvents or ACE05 dataset. The results indicate that APE significantly reduces the need for the scale of the target dataset. ## 4.3 Detailed Analysis | Variant | Param | ACE05 | | |-------------|----------|---------|------| | overlap | specific | | | | APE | Prefix | Adapter | 72.9 | | APEreversed | Adapter | Perfix | 72.1 | | w/o Prefix | BART | Adapter | 71.5 | | w/o Adapter | Prefix | BART | 71.7 | | BART | BART | BART | 69.4 | In this section, we study the effectiveness of the main components in our model and take a deeper look at what contributes to APE's final performance. All experiments will be based on the baseversion model and report the average Arg-C F1 scores on five seeds. The experimental conclusions are also proper for the large version model. ## 4.3.1 Model Architecture Design We first explore the effectiveness of APE model architecture in preventing catastrophic forgetting. We tried variants of APE as follows: 1) APE*reversed*: it has the same model architecture as APE but saves overlap knowledge in the Adapter and specific knowledge in the Prefix. 2) w/o Prefix: it is an APE without Prefix, which updates all pretrained parameters to save overlap knowledge. 3) w/o Adapter: pre-trained parameters will be updated to save specific knowledge. 4) BART: it is a standard BART model without additional parameters. We optimize the model in the overlap knowledge learning phase and fine-tune it in the specific knowledge learning phase. The result of ACE05 is summarized in Table 3, and the result of other datasets is in Appendix Table 8. All variants that save overlap and specific knowledge into different parameters outperform the plain BART model significantly. Since the plain BART model saves overlap and specific knowledge in the same parameters, serial learning phases will lead to catastrophic forgetting of previous knowledge. Suppose we save both knowledges into new parameter regions (APE, APE*reversed*). In this case, we can also obtain a considerable performance gain Table 4: The performance of different learning tasks Task ACE05 RAMS Wiki. Joint EAE Task 69.9 49.4 64.1 PER Task 72.9 51.6 66.0 Table 5: The performance of different prompt styles because our task formulation is similar to the pretrain task of BART, where the entity-type special tokens can be seen as [MASK] tokens. Retaining the pre-training parameter is helpful to take the best advantage of PLM's knowledge. Finally, there is a slightly negative effect when we reverse the parameter regions to save overlap and specific knowledge. We conjecture that APE*reversed* cannot model the order of knowledge utilization in the SC-RD framework. | prompt style | ACE05 | RAMS | Wiki. | | |----------------|----------|--------|---------|------| | overlap | specific | | | | | ST | ST | 72.9 | 51.6 | 66.0 | | NL | NL | 72.1 | 51.1 | 65.3 | | NL | ST | 69.5 | 49.3 | 63.5 | ## 4.3.2 Overlap Knowledge Learning Task To investigate the effect of the PER task and its transparent training objective (Equation 8) in learning the overlap knowledge, we throw out the SCRD framework and replace the PER task with Joint EAE Task like the previous work. The Joint EAE Task ignores the difference of datasets and merges multi-datasets to force the model directly learn overlap knowledge from the EAE training objective. The input and the target output string of the Joint EAE Task are as same as the specific knowledge learning phase. Two versions of Prefix will be respectively learned from the Joint EAE Task and the PER task and used in target datasets. It can be observed in Table 4 that there is a 3.0%, 2.2%, and 1.9% decrease for the Arg-C F1 score on three datasets when changing the task. It is difficult for the model to discern the overlap knowledge from the imprecise EAE training objective. The PER task provides a transparent training objective to indicate the overlap knowledge explicitly. ## 4.3.3 Stressing Entity-Type Prompt As aforementioned, prompts that keeping the same style in two learning phases can ignite the utilization of overlap knowledge in the specific knowledge learning phase and EAE inference scene. In ![7_image_0.png](7_image_0.png) order to verify it, we propose another prompt style named Natural Language Pronouns (NL), which replaces the entity-type Special Token (ST) with pronouns. The conversion between the two styles is shown in Appendix Table 9. We observe in Table 5 that there is a huge F1 score decrease of about 3.4% on the ACE05 dataset when we build prompts with different styles in two learning phases. The result indicates that narrowing the gap between the two phases is crucial to ignite the overlap knowledge. Meanwhile, the special token is a more powerful way to alert the model to the entity type than natural language. ## 4.3.4 Number Of Datasets In Multi-Datasets In order to deeply observe the impact of the amount of the training data used in the overlap knowledge learning phase, we trained four versions of Prefix on varying numbers of training sets and transferred them to the target dataset. When the number of datasets was set to 0, the Prefix was randomly initialized and used directly without training. When the number of datasets was set to 1, we trained Prefix on {ACE05}. When the number of datasets was 2, we trained Prefix on {ACE05, RAMS}. Figure 3 shows the Arg-C F1 score increase as the number of datasets used to learn the overlap knowledge. The experiment result shows that with more available out-domain event records, the APE model can learn more abundant overlap knowledge and achieve better performance in the target dataset. ## 5 Related Works 5.1 Transfer Learning In Eae Event argument extraction (EAE) aims to extract event arguments by the given event trigger and argument roles (Chen et al., 2015). Most existing approaches (Lin et al., 2020; Du and Cardie, 2020; Lu et al., 2021; Nguyen et al., 2022; Ma et al., 2022) suffer from insufficient training data and cannot perform better. Therefore, some studies (Liu et al., 2020b; Chen et al., 2020; Feng et al., 2020) focus on transferring knowledge from machine reading comprehension (MRC) datasets. Huang et al. (2022) leverages multilingual pre-trained models (Liu et al., 2020a; Xue et al., 2021) to achieve cross-lingual knowledge transfer. About transferring overlap knowledge from other available event datasets to the target dataset, only Zhou et al. (2022) attempt to introduce variational information bottleneck (Li and Eisner, 2019) to explore the overlap knowledge from two event datasets. Unlike their work, we clearly define the cross-dataset overlap knowledge in the EAE task. Our model does not limit the number of datasets and can explore overlap knowledge from all available datasets to achieve better performance. ## 5.2 Parameter-Efficient Tuning Method Optimizing all the parameters of the PLMs means we need to save a complete fine-tuned model for every downstream task. The storage cost is prohibitively expensive with the increasing size of PLMs. Several parameter-efficient tuning methods (Houlsby et al., 2019; Hu et al., 2022; Mao et al., 2022; He et al., 2022) were proposed to mitigate this issue, which update a small number of taskspecific parameters while keeping other pre-trained parameters frozen. Houlsby et al. (2019)equip each Transformer layer with an Adapter, and only the Adapters are tunable to save task-specific knowledge of the downstream task. Inspired by significant effectiveness achieved in prompt learning (Brown et al., 2020; Gao et al., 2021), Li and Liang (2021) prepends Prefix vectors to the hidden state, and only the Prefix will be trained on downstream tasks. To the best of our knowledge, we are the first to assemble two parameter-efficient tuning methods to separate knowledge in transfer learning and overcome catastrophic forgetting. ## 6 Conclusion In this work, we first define the shareable overlap knowledge across datasets and reformulate the EAE task into two learning phases. Then, we propose APE model, which assembles two parameterefficient tuning methods to save the overlap and specific knowledge. The experiment results show the efficiency of the cross-dataset transfer learning, and APE achieves new SOTA with a large margin in the EAE task. Our model significantly reduces the need for new event records and achieves superior performance with few samples of target datasets. The ablation studies verify that our approach can explore overlap knowledge from multi-datasets and overcome the well-known catastrophic forgetting issue. In the future, we would like to study overlap knowledge across datasets in other information extraction tasks. ## Limitations This work introduces a pseudo-entity recognition (PER) task to supervise the model learning overlap knowledge. Since no additional entity annotation is available, we manually create a mapping function M(r), which maps each argument role r to an entity type. With the help of the mapping function M(r), the EAE label can be converted to the PER label. However, because the annotation of the EAE task is complicated, it is hard to avoid a few exceptional samples in the prior mapping function. Some entity words may be attached to impertinent entity types. For example, there is a triple of argument role, event type, and argument in RAMS's *movement.transportartifact.preventexit* event: **{Artifact, Object, Two pilots}**. The "Artifact" argument is mapped to "Object" in M(r), but we expect "Two pilots" can be mapped to "Person Or Organization". We tolerate such exceptional samples, and the occasional noise has not affected the training of APE. ## Ethics Statement Event argument extraction (EAE) task is a welldefined and classic task in Information Extract (IE) field. In this work, our use of existing artifacts (e.g., datasets) was licensed and consistent with their intended use. We do not see other significant ethical concerns. 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Association for Computational Linguistics. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In *Proceedings* of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 483–498, Online. Association for Computational Linguistics. Jie Zhou, Qi Zhang, Qin Chen, Liang He, and Xuanjing Huang. 2022. A multi-format transfer learning model for event argument extraction via variational information bottleneck. In *Proceedings of the 29th* International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022, pages 1990–2000. International Committee on Computational Linguistics. ## A Detail Of Evaluation Metric We adopt two widely-used evaluation metrics: 1. Argument Identification F1 score (Arg-I): when the predicted argument's offsets match any of the gold argument labels in this event, we consider the predicted argument is correct. 2. Argument Classification F1 score (Arg-C): when the predicted argument's argument role also matches the gold argument label, we consider the predicted argument is correct. For the WikiEvents dataset, following Li et al. (2021), we add argument head F1 score (Head-C), which only focuses matching the headword of the arguments' offsets. | Entity Type | Description | Example | |---------------|-----------------------------------------------------|-----------------------------------| | Person Or | | | | Organization | The word that refers to a person or an organization | he, she, Bill, the president, ... | | Location | The word that refers to a place or a region | Washinton DC, London, ... | | Time | The word that indicates a time | 10 June, 17 pm., ... | | Money | The word that indicates money | $1,000, 6 million dollars, ... | | Object | The word that refers to a materiality entity | The truck, bomb, gun, house, ... | | Definition | The proper noun or immateriality entity | murder, crime of pillage | Table 6: The finite entity type set Table 7: The statistics of datasets, \#Sent. is the number of sentences of the dataset, \#Arg. is the number of arguments of the dataset. | Dataset | Train | Dev | Test | | | | |------------|---------|--------|--------|--------|-------|------| | #Sent. | #Arg. | #Sent. | #Arg. | #Sent. | #Arg. | | | ACE05 | 17172 | 4859 | 923 | 605 | 832 | 576 | | RAMS | 7329 | 17026 | 924 | 2188 | 871 | 2023 | | WikiEvents | 5262 | 4552 | 378 | 428 | 492 | 566 | | Variant | Param | ACE05 | RAMS | Wiki. | | |-------------|----------|---------|--------|---------|------| | overlap | specific | | | | | | APE | Prefix | Adapter | 72.9 | 51.6 | 66.0 | | APEreversed | Adapter | Perfix | 72.1 | 51.2 | 64.7 | | w/o Prefix | BART | Adapter | 71.5 | 51.3 | 64.3 | | w/o Adapter | Prefix | BART | 71.7 | 50.9 | 64.8 | | BART | BART | BART | 69.4 | 49.1 | 63.7 | Table 8: The performance of different variants on three datasets | Entity Type | Special Token | Natural Language Pronouns | |------------------------|-----------------------|-----------------------------| | Person Or Organization | [person/organization] | someone | | Location | [location] | someplace | | Time | [time] | some time | | Money | [money] | some money | | Object | [object] | something | | Definition | [definition] | some definition | | Table 10: Some examples of M(r) in three datasets, the complete M(r) can be found in our codebase. Dataset Event Type Event Argument Role Entity Type Org person/organization Business.Declare-Bankruptcy Place location Time time Place location Business.End-Org Org person/organization Time time ACE05 Person person/organization Agent person/organization Crime definition Place location Time time Justice.Arrest-Jail recipient person/organization beneficiary person/organization money money place location giver person/organization transaction.transfermoney. purchase recipient person/organization communicator person/organization place location contact.mediastatement. broadcast RAMS artifact object vehicle object origin location destination location transporter person/organization movement.transportartifact. disperseseparate Communicator person/organization Recipient person/organization Topic definition Place location Contact.RequestCommand.Meet Prosecutor person/organization Defendant person/organization JudgeCourt person/organization Crime definition Place location Justice.ChargeIndict. Unspecified WikiEvents Victim person/organization Place location Killer person/organization MedicalIssue definition Life.Die.Unspecified | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dataset | Event Type | Prompt | |--------------------------------------------------------------------------------------------------------|-------------------------------------------------------|----------| | [person/organization] killed | | | | Life.Die | [person/organization] | | | ACE05 | with [object] at [location] | | | [person/organization] injured | | | | Life.Injure | [person/organization] | | | with [object] at [location] | | | | [person/organization] courted or | | | | Justice.Fine | judged fined [person/organization] | | | at [location] for [definition] cost [money] [person/organization] attacked | | | | conflict.attack.stabbing | [person/organization] | | | RAMS | using [object] at [location] | | | artifactexistence.damagedestroy.n/a | [person/organization] damaged or | | | destroyed [object] using [object] in [location] [person/organization] transported [object] | | | | movement.transportartifact.n/a | in [object] from [location] place to [location] place | | | [person/organization] communicated with | | | | Contact.Contact.Unspecified | [person/organization] about | | | WikiEvents | [definition] at [location] | | | ArtifactExistence. | | | | ManufactureAssemble. Unspecified | [person/organization] manufactured | | | or assembled or produced [object] from [object] using [object] at [location] [person/organization] has | | | | Life.Illness.Unspecified | [definition] sickness or illness at [location] | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7: Limitations ✓ A2. Did you discuss any potential risks of your work? 6: Conclusion 7: Limitations ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract 1: Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 2: Method 3: Experiments Setup 4: Results and Analyses ✓ B1. Did you cite the creators of artifacts you used? 1: Introduction 2: Method 3: Experiments Setup 4: Results and Analyses 5: Related Works ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 3: Experiments Setup 8: Ethics Statement ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 3: Experiments Setup 8: Ethics Statement ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We didn't collect the information ourselves. The datasets we used are all widely used public datasets. Their content is mostly from news and we do not see any anonymization risk. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 3: Experiments Setup ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 3: Experiments Setup ## C ✓ **Did You Run Computational Experiments?** 3: Experiments Setup 4: Results And Analyses ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 3: Experiments Setup 4: Results and Analyses The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 3: Experiments Setup ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 3: Experiments Setup 4: Results and Analyses ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 3: Experiments Setup D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

yang-etal-2023-tailor

Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation

https://aclanthology.org/2023.acl-long.25

Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing work usually utilize fine-tuning or resort to extra attribute classifiers, yet suffer from increases in storage and inference time. To address these concerns, we explore attribute-based CTG in a parameter-efficient manner. In short, the proposed Tailor represents each attribute as a pre-trained continuous vector i.e., single-attribute prompt), which guides the generation of a fixed pre-trained language model (PLM) to satisfy a pre-specified attribute. These prompts can be simply concatenated as a whole for multi-attribute CTG without any re-training. Nevertheless, this may raise problems of fluency downgrading and position sensitivity. To solve this, Tailor provides two solutions to enhance the combination. The former contains a multi-attribute prompt mask and a re-indexing position sequence to bridge the gap between the training (one single-attribute prompt for each task) and the testing stage (concatenating two prompts). The latter introduces a trainable prompt connector to further enhance the combinations. Experiments demonstrate that, only requiring 0.08{\%} extra training parameters of the GPT-2, Tailor can achieve effective and general improvements on eleven attribute-specific generation tasks.

# Tailor: A Soft-Prompt-Based Approach To Attribute-Based Controlled Text Generation Kexin Yang♠ ∗ Dayiheng Liu♠ † Wenqiang Lei♢ Baosong Yang♠ **Mingfeng Xue**♠ Boxing Chen♠ **Jun Xie**♠ ♠Alibaba Group ♢National University of Singapore {kexinyang0528, losinuris}@gmail.com ## Abstract Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (*e.g.*, emotions and topics). Existing work usually utilize fine-tuning or resort to extra attribute classifiers, yet suffer from increases in storage and inference time. To address these concerns, we explore attribute-based CTG in a parameter-efficient manner. In short, the proposed **Tailor** represents each attribute as a pre-trained continuous vector (*i.e.*, single-attribute prompt), which guides the generation of a fixed pre-trained language model (PLM) to satisfy a pre-specified attribute. These prompts can be simply concatenated as a whole for multi-attribute CTG without any re-training. Nevertheless, this may raise problems of fluency downgrading and position sensitivity. To solve this, Tailor provides two solutions to enhance the combination. The former contains a multi-attribute prompt mask and a re-indexing position sequence to bridge the gap between the training (one singleattribute prompt for each task) and the testing stage (concatenating two prompts). The latter introduces a trainable prompt connector to further enhance the combinations. Experiments demonstrate that, only requiring 0.08% extra training parameters of the GPT-2, Tailor can achieve effective and general improvements on eleven attribute-specific generation tasks. ## 1 Introduction Attribute-based CTG (Zhang et al., 2022) focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, which remains extremely challenging in recent progress (Dathathri et al., 2020). Specifically, single-attribute CTG typically resorts to attribute-specific data, guiding the CTG model learning with supervised objectives (Keskar et al., 2019; Lyu et al., 2021; Ziegler et al., 2019). Nevertheless, multi-attribute CTG is ∗ Work is done during internship at DAMO Academy † Corresponding author. generally zero-shot since no example of a sentence with specified attribute combination is accessible during training (Lample et al., 2019). For both single and multi-attribute CTG, existing efforts can be roughly divided into two types: 1) fine-tuning a pre-trained language model (PLM) on the attribute-specific data (Ziegler et al., 2019) and 2) utilizing extra attribute classifiers. The former usually introduces control codes to generate various styles of sentences with one PLM, such as keywords (Keskar et al., 2019) and numerical sequence (Lyu et al., 2021). The latter applies extra attribute classifiers to guide a PLM, such as backpropagating gradients of these classifiers (Dathathri et al., 2020) or weighting output logits (Krause et al., 2021; Yang and Klein, 2021). However, this two types suffer from expensively re-training whole PLM (Yang and Klein, 2021) and higher latency during inference (Qian et al., 2022), respectively. To overcome the aforementioned limitations, we propose **Tailor** - Text-attribute general controller, a soft-prompt-based approach to jointly include both single-attribute CTG and multi-attribute CTG in a unified manner.1 The key idea is to represent each attribute as a trainable continuous vector (*i.e.*, the single-attribute prompt). These single-attribute prompts could be separately used or concatenated as a whole to control a fixed GPT-2 (Radford et al., 2019) for single and multi-attribute CTG, respectively.2 As simply concatenating always suffers from poor performances (see Appendix F), Tailor provides two effectively concatenating strategies without or with training after single-attribute CTG, namely non-training and training methods. First of all, we argue that the undesirable results of simply concatenating is due to the gap between the training and the testing stage. Specifically, the ![1_image_0.png](1_image_0.png) single-attribute prompt only attends to itself while being individually trained by the attribute-specific data. While testing, the second prompt also attends to the first one in the concatenation, with the simultaneous change of the position embeddings. To fill this gap, the non-training method introduces a Multi-Attribute Prompt mask (MAP mask) and a Re-indexing Position sequence (RP sequence) for the fixed GPT-2. MAP mask prevents distinct single-attribute prompts from cross-attention, and RP sequence ensures stable position information for the PLM after swapping, by individually numbering each prompt. Such a non-training method could be easily implemented and gets promising performances, but still has much space for improvement - there is no multi-attribute specific training stage for these prompts to adapt to work together. Therefore, the training method contains a trainable prompt to connect two single-attribute prompts as a whole to multi-attribute CTG. Inspired by the role of 'and' in connecting parallel phrases for natural sentences (Rudolph, 1989), as shown in Figure 1, the proposed Multi-Attribute Prompt connector (MAP connector) can be concatenated with any two singe-attribute prompts and hints a GPT-2 to multi-attribute CTG. Meanwhile, a pseudo-prompt based strategy is also provided for training the connector in unsupervised settings. With MAP connector, the combinations show strong performances on multi-attribute CTG on the popular benchmark YELP dataset (Lample et al., 2019). Furthermore, MAP connector can get encouraging improvements for the unseen combinations in the training stage (see Appendix F). The main contributions are: - We propose **Tailor**, a soft-prompt-based approach to attribute-based CTG. To jointly include both single-attribute and multi-attribute CTG in a unified paradigm, Tailor employs a set of pre-trained prefixes to guide a fixed PLM to generate sentences with pre-specified attributes, and effectively concatenate them to generate multi-attribute sentences. - We experimentally reveal the combining ability of continuous prompts. To enhance this combination, we explore two effective strategies without training (MAP mask + RP sequence) or with training (MAP connector) after single-attribute CTG. Especially, the MAP connector achieves strong performances on six multi-attribute generation tasks, and even works on the unseen ones. ## 2 Related Work Attribute-Based CTG focuses on generating sentences containing pre-specified attributes, such as sentiment and topic. As a vital demand for intelligent writing (Zhang et al., 2022), existing efforts include fine-tuning PLMs and utilizing extra attribute classifiers. The first type usually fine-tunes separately and stores a full copy of PLM for each desirable attribute (Ziegler et al., 2019). To alleviate the storage problem, CTRL (Keskar et al., 2019) provides 55 kinds of control codes (*i.e.*, special keywords) to fine-tune one PLM for generating sentences of various styles. StylePTB (Lyu et al., 2021) also proposes several style transfer tokens (*i.e.*, a sequence of numbers) to guide a GPT-2 (Radford et al., 2019) to multiple styles transfer. GSum (Dou et al., 2021) introduces four guidance signals (*e.g.*, keywords and relations) to enhance the controllability of PLMs in text summarization. Although they make successful attempts in attribute-based CTG, re-training whole PLMs could be expensive (Yang and Klein, 2021). To improve the flexibility and extensibility of the CTG model, the second type makes efforts in the inference stage. In short, utilizing extra attribute classifiers to guide PLMs in each generating step. PPLM (Dathathri et al., 2020) iteratively modifies latent representations of a GPT-2 referring to the gradient of attribute classifiers, yet notably increasing the inference time. To solve this problem, Fudge (Yang and Klein, 2021) uses an attribute predictor to adjust the output probabilities of a PLM. Similarly, GeDi (Krause et al., 2021) uses smaller PLMs as generative discriminators to hint a larger PLM generating sentences that satisfy desirable attributes. Despite their progress, the fluency of generating sentences tends to decrease compared with the original PLM (see § 4.2) and extra inference time costs still existed. In comparison, utilizing Tailor, PLMs can benefit from the manner of controllability on single-attribute prompt combinations, with a negligible decrease on text quality. Prompt Learning is a new paradigm in NLP summarised as "Pre-train, Prompt and Predict" (Liu et al., 2021a). In short, it guides a single PLM to solve various downstream tasks by reformulating these tasks into a text-to-text manner. Recently, the continuous prompt has attracted attention (Gu et al., 2021; Liu et al., 2021b, 2022), which usually forms as a set of continuous task-specific vectors to the input. Despite their encouraging progress, the prompt composition is rarely explored but undoubtedly important in prompt learning. In that case, a composable task could be accomplished by composing various subtasks with multiple sub-prompts (Liu et al., 2021a). To achieve it, PTR (Han et al., 2021) introduces manual sub-prompts for entity recognition and relation classification, respectively. Then, these two kinds of prompts are composed by logic rules as a complete prompt for the relation extraction task. Unfortunately, the composition of continuous prompts is rarely explored yet has demonstrated great potential (Qian et al., 2022). The main difference between contrastive prefix Qian et al. (2022) and Tailor is that the former needs attribute data to be occurred contrastively (e.g, positive and negative attribute data must be available at the same time), which might be limited for the single attribute. For multi-attribute, contrastive prefix trains a new prompt (twice the size of their single prompt) for each combination. Instead of it, Tailor only trains an extra prompt connector to enhance the combinations of single prompts. It can act as an efficient plug-and-play manner with extremely low training parameters to attribute-based CTG. ## 3 Methodology 3.1 Tailor For Single-Attribute Ctg Different from fine-tuning a full copy of PLMs for each attribute, our basic idea is to guide the generation of a PLM with a set of pre-trained continuous vectors, namely single-attribute prompts. Meanwhile, each prompt represents a desirable attribute. As shown in Figure 2 (top), we fix the parameters of a GPT-2 and train each prompt on the attributespecific data. After training, these prompts can act as plug-ins for desirable single-attribute CTG. For the prefix "Once upon a time", the GPT-2 can continue with "I had to order my tacos ..." with a prompt representing the Mexican food topic or " the food was good" with a prompt representing the positive sentiment. In this way, our method can be easily expanded: if a new attribute emerges, we only need to train an attribute prompt and then control a PLM to generate attribute-specific sentences. To be exact, we use language modeling learning object to train such a set of single-attribute prompts. In detail, k-th single-attribute prompt Sk with length lk is first initialized randomly, where Sk ∈ R lk×demb. demb is the word embedding dimension of the GPT-2. Meanwhile, given an attribute-specific sentence x = {x1, x2*, ..., x*n} with length n, we get a word sequence matrix Xemb ∈ R n×demb after being embedded by GPT2. Then, Sk is concatenated with Xemb to form a input matrix as [Sk; Xemb] ∈ R (lk+n)×demb , and this matrix is fed into a fixed GPT-2. Finally, the language-modeling based learning object is: $${\mathcal{L}}_{s i n g l e}=\sum_{t=1}^{n}\log P_{\theta_{g};\theta_{S_{k}}}\left(x_{t}|S_{k},x_{<t}\right),\quad(1)$$ where θg and θSk denote the parameters of GPT-2 and the single-attribute prompt, respectively. Only θSk are updated during the training stage. ## 3.2 Tailor For Multi-Attribute Ctg Inspired by the composition of discrete prompts (Han et al., 2021) to accomplish a complex task, our intuitive idea is to combine single-attribute prompts as a multi-attribute prompt to hint a PLM for multi-attribute CTG. To enjoy the benefit of our paradigm in single-attribute CTG, we first consider simply concatenating several single-attribute prompts as a whole multi-attribute prompt. Surprisingly, such a multi-attribute prompt can guide a GPT-2 to generate sentences containing multi attributes and get encouraging performances in unsupervised settings without any training (see § 4.2). Despite the progress, this straightforward method suffers from fluency decrease compared with single-attribute CTG. Meanwhile, it is position sensitive, *i.e.*, the PLM tends to focus more on the single-attribute prompt that is closer to the input prefix (see Appendix F). To polish such a paradigm while keeping plugand-play and storage-friendly advantages, as shown ![3_image_0.png](3_image_0.png) in Figure 2 (bottom), Tailor introduces a nontraining method to quickly and effectively alleviate the above problems of simply concatenation. Afterward, a training method is further provided to greatly enhance the combinations. We elaborate the two methods separately as follows. ## 3.2.1 Non-Training Method To make better use of single-attribute prompts, reducing disparities between the training (a singleattribute prompt for each task) and the testing stage (concatenating more than one single-attribute prompt) is undoubtedly important. Specifically, the single-attribute prompt only attends to itself in the attention matrix while training, as each prompt is individually trained by the attribute-specific data. However, while in the testing stage for multiattribute CTG, the second prompt also focuses on the first one in the concatenation, with the simultaneous change of the position embedding. To fill this gap, MAP mask and RP sequence are introduced to the fixed PLM while generating. MAP mask avoids cross-attention between representations of single-attribute prompts to approximate the condition in the single-attribute CTG training stage. Meanwhile, the RP sequence keeps a stable prompt position for swapping, preventing such concatenating paradigm from position sensitivity. MAP Mask For the ease of implementation, we introduce MAP mask matrix Mp to the softmax logits of GPT-2. Given a vanilla attention module: $$A=\mathrm{Softmax}({\frac{Q K^{\top}}{\sqrt{d}}})\in\mathbb{R}^{n\times n},\qquad(2)$$ where n is the length of input sentence x and Q,K denote representations of query and key, respectively.3 For MAP Mask, given two single-attribute prompts Su, Sv with length being lu, lv, respectively, the attention module is then modified as: $$A=\mathrm{Softmax}(\frac{Q K^{\top}}{\sqrt{d}}+M_{p})\in\mathbb{R}^{(l_{p}+n)\times(l_{p}+n)},$$ $$M_{p}^{i j}=\left\{\begin{array}{c c}{{-\infty}}&{{i\in[l_{u},l_{v}]\;\mathrm{and}\;j\in[0,l_{u}],}}\\ {{0}}&{{\mathrm{otherwise},}}\end{array}\right.\tag{3}$$ where lp = lu + lv. 3The multi-head mechanism is omitted for illustration. RP Sequence Simple concatenation of singleattribute prompts always suffers from position sensitivity. To address this issue, we propose a simple but effective method to ensure position consistency while swapping. In short, we modify the position sequence of the PLM while concatenating.4 Given the original position sequence: $$id=\{\underbrace{1,...,l_{u}}_{\text{Length of}S_{u}},\underbrace{l_{u}+1,...,l_{p}}_{\text{Length of}S_{v}},\underbrace{l_{p}+1,...,l_{p}+n}_{\text{Length of input prefix}},\tag{4}$$ the RP sequence can be defined as: $$id_{\text{RP}}=\{\underbrace{1,...,l_{u}}_{\text{Length of}S_{u}},\underbrace{1,...,l_{v}}_{\text{Length of}S_{v}},\underbrace{l_{v}+1,...,l_{v}+n}_{\text{Length of}S_{v}},\tag{5}$$ note that, $l_{v}=l_{u}$. In that case, swapping does not bring any changes, since the position of prompts is fixed by the RP sequence while avoiding crossattention by the MAP mask. ## 3.2.2 Training Method While the non-training method partly addresses the issues of combination, there is no multi-attribute specific training stage for these prompts to adapt to work together. Therefore, we provide a training method - MAP connector, which is also a continuous prompt trained for combining two singleattribute prompts to multi-attribute CTG. To utilize only single-attribute sentences for multi-attribute CTG, we propose a pseudo-attribute prompt based training strategy for MAP connector. The details of the pseudo-attribute prompt building method and the workflow of the MAP connector are as follows. ![4_image_0.png](4_image_0.png) Building Pseudo Single-Attribute Prompt Our key idea is to build another pseudo-attribute prompt for each single-attribute sentence, thus MAP connector could be trained in a multi-attribute circumstance. An overview of our building method is 4In this case, position sequence denotes position indexes of input tokens in the position embeddings for GPT-2. demonstrated in Figure 3, where a sentence with the topic of Mexican food is used as a showcase.5 To be exact, we first train an attribute classifier on the same single-attribute CTG training set. Thus, such a classifier with n*class* classes corresponds to the pre-trained single-attribute prompt set S = {S1, S2, ..., Sn*class* }. Given an attribute-specific sentence x of other attribute category, we first get the class probabilities set p = {p1, p2, ..., pn*class* }. Then, the pseudo single-attribute prompt can be obtained by two methods: $$\begin{array}{l}{{S_{a}=S_{\mathrm{Index}(\arg\max}(p)),}}\\ {{S_{w}=\sum_{z=1}^{n_{c l a s s}}p_{z}S_{z},}}\end{array}\qquad\qquad(6)$$ where argmax-pseudo prompt method obtains the pseudo prompt Sa by using a single-attribute prompt corresponding to the predicted sentiment, Index(·) means getting the corresponding index. In contrast, weighted-pseudo prompt method utilizes the predicted probability distribution to multiply corresponding single-attribute prompts, respectively. Then these weighted prompts form a whole prompt Sw by element-wise addition. The MAP Connector Workflow Figure 2 bottom illustrates the workflow of the MAP connector. In the training stage, we unify sentences containing different single attributes to train the MAP connector, each of which is added an extra pseudo singleattribute prompt (boxes with the slash pattern) by employing the aforementioned method. Specifically, for each training sample, we first concatenate two single-attribute prompts (real and pseudo), the MAP connector and the input sentence into a sequence, and then feed it into a fixed GPT-2. It is worth noting that only the parameters of the MAP connector are updated in the training stage. Therefore, given two single-attribute prompt Su and Sv, MAP connector C with the length lC, C ∈ R lC ×demb , we concatenate Su, Sv, C and the input sentence matrix Xemb to form a input matrix as [Su; Sv; C; Xemb]. The learning object is: $$\mathcal{L}_{multi}=\sum_{t=1}^{n}\log P_{\theta}\left(x_{t}|S_{u},S_{v},C,x_{<t}\right),\tag{7}$$ where $\theta=[\theta_{g};\theta_{S_{u}};\theta_{S_{v}};\theta_{C}]$. $\theta_{g}$, $\theta_{S_{u}}$, $\theta_{S_{v}}$, and $\theta_{S_{u}}$ denotes the sequence of GPT 2-terminal θC denote the parameters of GPT-2, two single-5In the implementation for multi-attribute CTG, we use YELP data of two sentiments attributes (Positive / Negative) and three kinds of food type (Mexican / American / Asian) attribute prompts and MAP connector, respectively. Only θC are updated during the training stage. In the inference stage, we just decompose each multiattribute generation task as several single-attribute generation tasks and find corresponding singleattribute prompts. Then, these prompts are concatenated with MAP connector to generate sentences that satisfy multi attributes. ## 4 Experiments 4.1 Experimental Setup Datasets We conduct experiments on the widelyused benchmark dataset YELP (Lample et al., 2019). It contains multiple single-attribute data that can verify Tailor's performance on both singleattribute and multi-attribute CTG, while ensuring that the combination of these attributes is reasonable. Following previous works that conduct experiments on attributes of emotions and topics for multi-attribute CTG, we choose Yelp restaurants reviews of sentiment attributes (positive (PO) and negative (NE)) and topics of food type (Mexican (ME), American (AM) and Asian (AS) foods) to evaluate models. Specifically, each attribute contains 30,000 / 3,000 sentences for training / validation. For evaluation, to keep in line with previous works (Yang and Klein, 2021; Dathathri et al., 2020), we use 15 attribute-unrelated prefixes6and ask the model to continue writing with them (for each of the 15 prefixes, 100 completions are generated, total: 1500 for each attribute) while satisfying pre-specified attribute as the final results.7 Automatic Evaluation Following Yang and Klein (2021); Dathathri et al. (2020), we automatically evaluate generation results from three aspects: (1) Correctness. We used RoBERTaLarge (Liu et al., 2019) based attribute classifiers to compute the fraction of final sentences that contain a pre-specified attribute, details in Appendix C. (2) **Text Quality**. Grammar (GRAM) (Warstadt et al., 2019) indicates the averaged grammaticality probabilities of all final sentences, evaluated by a RoBERTa-based CoLA grammaticality model (Yang and Klein, 2021). Perplexity (PPL), we average the scores from GPT-2Base, GPT-2Medium and GPT-2Large version of GPT-2 (Radford et al., 2019) as the final result. (3) **Diversity**. Following Li et al. (2015), we report the distinctness of the final results. Specifically, we count the number of unigrams, bigrams and trigrams and then normalize them by the total number of words (*i.e.*, Dist-1 / Dist-2 / Dist-3). Human Evaluation Following Qian et al. (2022), we also conduct the human evaluation. For each model, three crowdsource evaluators are shown 15 randomly selected samples (one per each attributeunrelated prefixes) for each generation task (Total: 75 samples for single-attribute CTG and 90 samples for multi-attribute CTG), respectively. Then, they are asked to rate model results in two categories: the text **quality** of generation sentences and whether they contain the target **attribute**. Scores are ranged from 1 to 5, the higher the better.8 Tailor Settings TailorSingle denotes the singleattribute prompts. For multi-attribute, ConcatSimple means simply concatenating two single-attribute prompts and TailorConcat is our non-training method. TailorArgmax and TailorWeight represent using argmax-pseudo and weighted-pseudo prompts when training the MAP connector, respectively. Baselines We compare Tailor with mainstream competitive models as follows. (1) Finetune, finetuning the original GPT-2Base on attribute-specific data. As multi-attribute CTG is unsupervised, following Lyu et al. (2021), we sequentially apply the GPT-2 trained for corresponding singleattribute data multiple times. (2) Adapter, following Li and Liang (2021), we use the adapter for GPT-2 as same as Lin et al. (2020). Note that for multi-attribute CTG, we first use the same training method as mentioned in Finetune for Adapter. Besides, we use the same argmax-pseudo labeled sentences (see § 3.2.2) to train the Adapter (marked with 'Pseudo'). (3) GeDi (Krause et al., 2021), using small PLMs to hint large ones. (4) PPLM (Dathathri et al., 2020), back-propagating gradients of extra attribute classifiers to a PLM9. ## 4.2 Main Results Single-Attribute CTG As shown in Table 1, TailorSingle outperforms PPLM and GeDi to a great extent on both correctness and text quality. Meanwhile, compared with other parameter-efficient learning model Adapter, TailorSingle also gets improvements on both correctness (e.g, + 9.19% of Food) and diversity (e.g, + 0.02% / + 0.12% / + 0.25% of Food) with a similar scale of training parameters. However, with 0.08% training parameters of the GPT-2, TailorSingle still has a 8Details can be found in Appendix D. 9The implementation details of baselines and Tailor can be found in Appendix A | Method | Trained Params | Correctness | Text Quality | Diversity | | |------------------------|------------------|---------------|----------------|------------------------|--------------------| | (%) | (%) ↑ | GRAM ↑ | PPL ↓ | Dist-1/Dist-2/Dist-3 ↑ | | | Finetune (Food) (2019) | 100.000 | 87.53 | 0.78 | 40.60 | 0.04 / 0.22 / 0.42 | | Finetune (Sent) (2019) | 100.000 | 97.95 | 0.76 | 42.83 | 0.04 / 0.21 / 0.41 | | GeDi (Food) (2021) | 100.000 | 99.82 | 0.28 | 278.22 | 0.42 / 0.79 / 0.95 | | GeDi (Sent) (2021) | 100.000 | 87.37 | 0.32 | 517.87 | 0.27 / 0.85 / 0.97 | | Adapter (Food) (2020) | 0.100 | 74.70 | 0.75 | 43.85 | 0.04 / 0.23 / 0.46 | | Adapter (Sent) (2020) | 0.100 | 93.32 | 0.74 | 47.01 | 0.04 / 0.22 / 0.45 | | PPLM (Food) (2020) | 0.001 | 60.64 | 0.34 | 105.33 | 0.16 / 0.53 / 0.80 | | PPLM (Sent) (2020) | 0.001 | 69.37 | 0.36 | 75.59 | 0.15 / 0.53 / 0.82 | | TailorSingle (Food) | 0.080 | 83.89 | 0.71 | 45.79 | 0.05 / 0.35 / 0.71 | | TailorSingle (Sent) | 0.080 | 93.80 | 0.71 | 46.20 | 0.06 / 0.35 / 0.70 | | Method | Correctness (%) | Text Quality | Diversity | | | | | | |---------------------------|-------------------------------------------------------------------------------------|-----------------------|--------------------|------------------------|-----|-----|-----|-----| | Avg. ↑ / Sent ↑ / Food ↑ | GRAM ↑ / PPL ↓ | Dist-1/Dist-2/Dist-3↑ | | | | | | | | Finetune | 69.80 / 74.03 / 65.57 | 0.69 / 46.54 | 0.04 / 0.23 / 0.42 | | | | | | | Adapter | 69.10 / 74.10 / 64.10 | 0.77 / 37.89 | 0.03 / 0.21 / 0.42 | | | | | | | Adapter (Pseudo) | 81.71 / 89.95 / 73.46 | 0.75 / 45.63 | 0.04 / 0.22 / 0.45 | | | | | | | ConcatSimple | 76.20 / 87.88 / 64.51 | 0.63 / 55.02 | 0.05 / 0.33 / 0.68 | | | | | | | TailorConcat | 78.82 / 87.54 / 70.10 | 0.63 / 52.76 | 0.05 / 0.32 / 0.68 | | | | | | | TailorWeight | 83.98 / 93.27 / 74.68 | 0.68 / 51.41 | 0.05 / 0.33 / 0.69 | | | | | | | TailorArgmax | 87.15 / 92.97 / 81.32 | 0.69 / 52.73 | 0.05 / 0.33 / 0.69 | 90 85 80 75 70 65 60 0 | 0.2 | 0.4 | 0.6 | 0.8 | | TailorArgmax TailorWeight | | | | | | | | | | Correctness Avg (%) | TailorConcat ConcatSimple Adapter (Pseudo) Finetune 0.08 Adapter Trained Params (%) | | | | | | | | | Method | Quality ↑ | Attribute↑ | All ↑ | |----------------------|-------------|--------------|---------| | Single-Attribute CTG | | | | | Finetune | 4.69 | 2.97 | 7.66 | | Adapter | 4.66 | 2.64 | 7.30 | | PPLM | 2.40 | 1.19 | 3.59 | | TailorSingle | 4.62 | 3.04 | 7.66 | | Multi-Attribute CTG | | | | | Finetune | 4.67 | 1.74 | 6.41 | | Adapter (Pseudo) | 4.79 | 1.91 | 6.70 | | TailorArgmax | 4.57 | 2.37 | 6.94 | Table 3: Results of human evaluation. performance gap with Finetune, *e.g.*, - 4.14% correctness on Food. Fortunately, as the length of TailorSingle increases (see Appendix F), this gap appears to narrow (- 0.33%, TailorSingle with the prompt length of 256). Then, we illustrate human evaluations in Table 3. Different from automatic evaluations, TailorSingle obtains the same score with Finetune, even outperforms all baselines on attribute-relevance score. This experimental discovery demonstrate the limitations of only resorting to automatic evaluation, as also be mentioned in Welbl et al. (2021); Qian et al. (2022). Multi-Attribute CTG As shown in Table 2, we compare three instantiations of Tailor and strong baselines in the single-attribute CTG experiment. First, TailorConcat shows encouraging performances without any training, especially on correctness, outperforms fine-tuning (+ 13.51% Sentiment / + 4.53% Food) and Adapter (+ 13.44% Sentiment / + 6.00% Food). Besides, our training methods TailorWeight and TailorArgmax show improvements on all scores compared with TailorConcat, *e.g.*, + 4.58% / + 11.22% correctness on the topic of food type attribute. Meanwhile, Tailor also outperforms Adapter with the same pseudo label strategy on both correctness and diversity, with a notable scale discrepancy of training parameters (0.08% vs 0.60%, *i.e.*, 1:7.5). Meanwhile, Tailor seems to suffer from lower text diversity compared to PPLM and GeDi. This is because these methods have poor fluency (with many unreasonable words), while Dist-1/2/3 measure different words without considering whether they are reasonable. We supplement it by human evaluation. As shown in Table 3, the diversity of words considered by the index attribute, which shows the superiority of our method. | TP (%) | Method | Correct (%) | | | |----------------------|------------------|---------------|-------|-------| | Avg. ↑ | Sent↑ | Food↑ | | | | Single-Attribute CTG | | | | | | 100.00 | Finetune | 54.08 | - | 54.08 | | 100.00 | Finetune | 85.28 | 85.28 | - | | 0.10 | Adapter | 55.79 | - | 55.79 | | 0.10 | Adapter | 77.91 | 77.91 | - | | 0.08 | TailorSingle | 66.23 | - | 66.23 | | 0.08 | TailorSingle | 89.27 | 89.27 | - | | Multi-Attribute CTG | | | | | | 100.00 | Finetune | 60.60 | 73.45 | 47.75 | | 0.60 | Adapter | 57.15 | 68.44 | 45.85 | | 0.60 | Adapter (Pseudo) | 67.27 | 78.66 | 55.88 | | 0.00 | TailorConcat | 68.09 | 74.38 | 61.79 | | 0.08 | TailorWeight | 70.32 | 84.18 | 56.46 | | 0.08 | TailorArgmax | 71.41 | 83.63 | 59.18 | ## 4.3 Further Discussions Few-Shot Learning We conduct a few-shot learning setting to further analyze the effectiveness of Tailor. In detail, following Li and Liang (2021), we randomly sample from full dataset and obtain the few-shot dataset (training / validation / testing: 150 / 20 / 20 samples). Specifically, we sample three different few-shot datasets and average the scores of each method on three datasets as the final results. As shown in Table 4, three types of Tailor outperform other baselines on correctness, with 0.00% / 0.08% extra training parameters of Finetune. Cross Domain Dataset Evaluating We further evaluate the performances of Tailor on combining attribute from different domain.10 Specifically, we choose SST-2 (Socher et al., 2013) and AG News (Zhang et al., 2015) for data sources of sentiment and topic attribute, respectively. As shown in Appendix Table 10, Tailor still outperforms baselines in both correctness and diversity. Meanwhile, the text quality of Tailor has been improved by the Map Connector training (GRAM 0.59 to 0.68). Inference Speed We also compare Tailor with extra classifier based CTG method on inference speed. As shown in Table 5, TailorSingle outperforms baselines to a great extend on inference speed, which indicates computational efficacy of Tailor. Table 5: Inference speed comparisons (second/sample). | Methods | Inference Speed ↓ | |--------------|---------------------| | TailorSingle | 0.758 (1.00 ×) | | GeDi | 1.680 (0.45 ×) | | PPLM | 15.553 (0.05 ×) | ![7_image_0.png](7_image_0.png) | Method | Correctness (%) | | | |---------------|-------------------|--------|-------| | Avg. ↑ | Sent ↑ | Food ↑ | | | TailorConcat | 78.82 | 87.54 | 70.10 | | - MAP Mask | 78.36 | 87.39 | 69.34 | | - RP Sequence | 77.77 | 88.33 | 67.21 | | - Both | 76.20 | 87.88 | 64.52 | Ablations of Tailor**Concat** Whether TailorConcat enjoys the benefits from the MAP mask and the RP sequence is of concern. As shown in Table 6, both the MAP mask and the RP sequence are important to TailorConcat. More importantly, using these two strategies simultaneously can improve the performance while avoiding the position sensitivity. ## 5 Conclusions In this paper, we explore attribute-based CTG in a soft-prompt-based manner—Tailor, which represents each attribute as a continuous prompt and effectively combines them as a multi-attribute prompt. For enhancing these combinations, Tailor provides two solutions, namely non-training (MAP mask + RP sequence) and training methods (MAP connector). As our first attempt to multiattribute CTG, combining more than two attributes still needs to be discussed. In the future, we will investigate extending Tailor to connect wider ranges of attributes, and expand it to other text-to-text generation tasks. ## Limitations As we tentatively give a successful implementation of leveraging soft-prompt-based manner to benefit both single and multi-attribute CTG, such a paradigm deserves a closer and more detailed exploration. First, we explore multi-attribute CTG in the scenario of two-attribute composition, yet combining more attributes when generating a completion is more challenging and thrilling, and still in its fledgeless stage. Besides, while extensive experiments demonstrate that Tailor consistently improves attribute-based CTG, applying our approach on a wider variety of PLMs will evaluate the effectiveness of Tailor in a more generally way. ## Ethics Statement All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. 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In all experiments of Tailor, we set the length of TailorConcat to 128, as same as the MAP connector for TailorArgmax and TailorWeight. As for the learning rate and the warm-up steps, TailorSingle, TailorArgmax, and TailorWeight are set to 5e-5 (the learning rate) and 0 (the warm-up steps), respectively. Besides, to get a pseudo label for MAP connector, we use the RoBERTaLarge based classifier for both sentiment and topic of food type attributes. The hyperparameters can be found in Appendix C. Note that, for a fair comparison, we only use the same training set for each classifier as for training Tailor. 2. Finetune.11 We use the GPT-2Base with a language model head implemented based on Huggingface. The learning rate is set to 5e-3 and the warm-up step is set to 0. 3. Adapter.12 we set the bottleneck size to 5 to keep a similar size of training parameters with Tailor. The learning rate is set to 5e-5 and the warm-up step is set to 0. 4. GeDi.13 For a fair comparison, we use the generative discriminator of GeDi based on GPT-2Base to guide generation of another GPT-2Base. In inference, we use the ω = 30, ρ = 0.8 and τ = 0.8, as reported in their implementation. 11https://huggingface.co/gpt2 12https://github.com/zlinao/VGLM 13https://github.com/salesforce/GeDi 5. PPLM.14 We employ the original hyperparameter setting reported in Dathathri et al. (2020). In detail, γ = 1.5, γgm = 0.9, λkl = 0.01, iterations=3 and step size=0.02. In inference, to keep in line with previous works (Dathathri et al., 2020; Krause et al., 2021), we use top-k sampling with k=10 and fix the random seed as 42 for all models to get the final results, while the maximum generation length is set to 60. ## B Yelp Dataset | Model | F1 Score | |----------------------|------------| | Food Type Classifier | 83.40 | | Sentiment Classifier | 97.10 | In this section, we elaborate the workflow of filtering, pre-processing and sub-sampling to get the attribute-specific dataset for training all models and the classifiers For correctness evaluation. First of all, we get the YELP dataset from Lample et al. (2019). In detail, each sample of the YELP dataset contains a review and the corresponding attributes.15 Then, we select the restaurant reviews sub-set as our original dataset. For dataset filtering, we use the dataset setup scripts offered by Lample et al. (2019), which contains a fastText(Joulin et al., 2017) classifier to filter sentences that are not written in English. After that, we filter the sentences with rated 3 stars, since they could be neutral in sentiment (Shen et al., 2017). Finally, we get the pre-processed dataset as illustrated in Table 8. For the classifiers that are used in correctness evaluation, we use the full dataset and details in Appendix C. Aside from it, for training Tailor and baselines, we randomly sample 30,000 / 3,000 sentences as training/validation data set for each attribute. Table 7: The Performances of two classifiers on Yelp dataset. ## C Classifiers For Correctness Evaluation We use the RoBERTaLarge based model to train two classifiers for both sentiment and topic of food type attributes. To obtain a balanced dataset, we randomly over-sampling the raw dataset. Finally, we get 1500k / 15k / 15k topic-specific sentences and 1380k / 1k / 1k sentiment-specific sentences for training/validation/testing, respectively. For training two classifiers, the learning rate is set to 5e-5 and the warm-up step is set to 200. The performances on the testing set can be found in Table 7. | Attribute | PO | NE | All | |-------------|---------|---------|---------| | ME | 25,169 | 89,411 | 114,580 | | AM | 72,641 | 299,293 | 371,934 | | AS | 47,680 | 185,551 | 233,231 | | All | 145,490 | 574,255 | 719,745 | ## D Human Evaluation Details For human evaluation, we first set a guideline for evaluating, which includes the task background, key points, detailed descriptions, and examples of evaluation scores from 1 to 5. Then, we set an entry barrier for annotators. In detail, we organize a training program and a preliminary annotating examination (90 examples for each model) to select appropriate annotators with an approval rate higher than 95%. Score Definition We define two categories in the human evaluation as follows: 1. **Quality** means whether the sentence corresponding to the option is fluent. 2. **Attribute** means whether the sentence corresponding to the option aligns with the target single attribute or multi attributes. The scores are ranged from 1 to 5, and the higher score is better. The details are specified in Table 9. Following Qian et al. (2022), to obtain separate scores for both text quality and attribute correlation, the annotators are required to not attend to attribute correlation when evaluating the text quality (and vice versa). Aside from it, when the annotators feel that the sentences generated by different models perform similarly in terms of text quality, they are asked to give higher quality scores for sentences with longer lengths, which have more scope and diversity for expression yet have been ignored by automatic text quality evaluation metrics. Inter-annotator agreement We use Fleiss' kappa (Fleiss, 1971) to measure three annotator's | Type | Scores and Details 1 - All of sentences are difficult to read and incomprehensible. 2 - Only a small part of sentences could be understood, which is readable and fluency. 3 -Apart from a few grammatical mistakes, sentences are clear and comprehensive. 4 - Sentences are free from grammatical errors and other linguistic inconsistencies, but could be better in style. 5 - Sentences are fluency and spontaneous, which equate to the text quality of human writing. | |-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Quality | 1 - There is no attribute-related words or phrases in the sentences. 2 - There is only one attribute-related word or phrase in the sentences. 3 - Sentences contain multiple attribute-related words or phrases, but they are almost repetitive. 4 - Sentences contain multiple attribute-related words or phrases, a few of them are repetitive. 5 - Sentences contain multiple attribute-related words or phrases, none of them are repetitive. | | Attribute | | Table 9: Details of scores for Quality and Attribute in human evaluation. | Method | Trained Params | Correctness (%) | Text Quality | Diversity | |------------------|--------------------------|-----------------------|-----------------------|--------------------| | (%) | Avg. ↑ / Sent ↑ / News ↑ | GRAM ↑ / PPL ↓ | Dist-1/Dist-2/Dist-3↑ | | | Finetune | 100.00 | 62.54 / 69.14 / 55.93 | 0.74 / 37.78 | 0.09 / 0.34 / 0.50 | | Adapter | 0.60 | 62.25 / 67.38 / 57.12 | 0.71 / 69.04 | 0.13 / 0.38 / 0.48 | | Adapter (Pseudo) | 0.60 | 59.19 / 66.33 / 52.05 | 0.79 / 105.65 | 0.11 / 0.38 / 0.54 | | ConcatSimple | 0.00 | 55.81 / 74.35 / 37.27 | 0.47 / 49.39 | 0.11 / 0.47 / 0.80 | | TailorConcat | 0.00 | 63.38 / 68.08 / 58.67 | 0.59 / 36.82 | 0.11 / 0.48 / 0.80 | | TailorArgmax | 0.08 | 61.42 / 63.65 / 59.18 | 0.68 / 35.33 | 0.13 / 0.53 / 0.84 | reliability.16 The results are: 0.24 for score the quality (fair agreement), 0.55 for the attribute score (substantial agreement). ## E Experiments On Cross Domain Dataset We also evaluate Tailor on the cross domain dataset SST-2 (Socher et al., 2013) and AGNews (Zhang et al., 2015). For the classifiers that are used in correctness evaluation, we also use the RoBERTaLarge based model to train two classifiers for both sentiment and topic of news attributes and reuse the parameters setting as in the experiment for YELP datasets. The F1 scores of two classifiers are 89.80 (sentiment) and 94.95 (news), respectively. For baselines and Tailor, we use the same experimenting setup as described in Appendix A. The experimental results of cross domain dataset are shown in Table 10. ## F Ablations Length of Tailor As shown in Figure 4, we explore the length of both TailorSingle and TailorArgmax. 16https://www.nltk.org/_modules/nltk/metrics/ agreement.html For singe-attribute prompt TailorSingle, the performances increase alongside the length. But for TailorArgmax, it obtains the best performances with a length of 128, and the performances have a slight drop when we continue to increase the length. ``` 70.00 75.00 80.00 85.00 90.00 95.00 100.00 ``` | 100.00 95.00 90.00 85.00 80.00 75.00 70.00 | 8 | 64 | 128 | 256 | |----------------------------------------------|----------------------------------------------------------|------|-------|-------| | TailorSingleSent Correctness (%) | TailorArgmax Sent Correctness (%) Tailor-A Sentiment ACC | | | | | TailorSingle Food Correctness (%) | Tailor-A Food ACC TailorArgmax Food Correctness (%) | | | | | 8 | 64 | 128 | 256 | | | Tailor-S Sentiment ACC Tailor-S Food ACC | | | | | Figure 4: The results of using TailorSingle and TailorArgmax with different lengths. The x-axis is the prompt length and the y-axis is the averaging correctness score (%). Position Sensitivity We investigate the position sensitivity problem when concatenating two singleattribute prompts. As shown in Table 11, for simply concatenation, the GPT-2 tends to focus more on the prompt that is closer to the input prefix (*i.e.*, the attribute behind the dash in Table 11). For instance, NE attribute gets a 3.14% improvement if we put the corresponding prompt close to the input prefix. However, it also brings a 3.4% decrease for AM attribute as being away from the input prefix at the same time. In contrast, TailorConcat keeps the same performance after swapping. | Method | Combination | Correctness (%) | | | |--------------|---------------|-------------------|-------|-------| | Avg. ↑ | Sent↑ | Food↑ | | | | ConcatSimple | NE+AM | 68.40 | 76.93 | 59.87 | | AM+NE | 68.27 | 80.07 | 56.47 | | | TailorConcat | NE+AM | 69.90 | 79.07 | 60.73 | | AM+NE | 69.90 | 79.07 | 60.73 | | Table 11: The results on multi-attribute CTG of generating sentences satisfying negative sentiment (NE) and topic of American food (AM). NE+AM denotes putting the positive attribute prompt in first and American food attribute prompt in later when concatenating them, in contrast to AM+NE. Unseen Combination In this part, we analyze the combining ability of Tailor on the unseen combination, which does not appear in Tailor's training stage. In the implementation, we randomly select one combination, remove the corresponding data from the training set for the MAP connector, and then test the performance of the MAP connector on this multi-attribute generation task. As shown in Table 12, TailorArgmax still works to the unseen combination PO+ME and outperforms the non-training method TailorConcat with 2.35% improvements. Table 12: The results on unseen combination to multiattribute CTG. PO + ME denotes the attribute combination of positive sentiment and topic of Mexican food. | Unseen | Method | Correctness (%) | | | |----------|--------------|-------------------|-------|-------| | Avg. ↑ | Sent ↑ | Food ↑ | | | | PO + ME | TailorConcat | 87.54 | 95.60 | 79.47 | | PO + ME | TailorArgmax | 89.89 | 97.07 | 82.70 | | None | TailorArgmax | 91.64 | 97.87 | 85.40 | ## G Case Study To intuitively display the effects of various attributes, we show some generation results of singleattribute CTG in Table 13 and multi-attribute CTG in Table 14, respectively. | Attribute | Method | Generation Results | | | | | | | | | | |--------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------|------------------------|----------|-----|------|-----|---------|-------------|--------| | Finetune | Once upon a time, I was very disappointed . The meat was bland and the beans tasted as if they had been sitting out all day... | | | | | | | | | | | | Adapter | Once upon a time in the restaurant it was still dark and people weren 't even talking... | | | | | | | | | | | | PPLM | Once upon a time, computers would have been able read, interpret and write, and listen, listen and read... | | | | | | | | | | | | GeDi | Once upon a time you either enter base build states or begin switching context switches and magic spells that alter your manifest... | | | | | | | | | | | | TailorSingle | Once upon a time, you had to order your dinner. the food came out cold with no seasoning or flavor whatsoever ... | | | | | | | | | | | | Negative Sentiment | Finetune | Once | upon a time they | were | busy but | the | food | was | amazing | and service | fast . | | highly recommend for date night / evening out... | | | | | | | | | | | | | Adapter | Once upon a time I 'd | like | to visit the city of lg, it was atlas! | great food and amazing | | | | | | | | | bartenders... | | | | | | | | | | | | | PPLM | Once upon a time in the world in which a great deal of the work was done with the work was done in the world in the most... | | | | | | | | | | | | GeDi | Once upon a time, mankind thought of themselves as merciful , enlightened princes, with loving hearts. That prosperity and flourishing... | | | | | | | | | | | | TailorSingle | Once upon a time, I was so excited to have my friends and family there that we wanted our food. the staff is great ! they make us feel at home when... | | | | | | | | | | | | Positive Sentiment | Finetune | Once upon a time I had the carne asada burritos , they looked great. my wife's quesadilla was the only thing that she liked... | | | | | | | | | | | Adapter | Once upon a time in my family I ordered the taco and they came out with two different varieties of beans. when it was finished we were asked... | | | | | | | | | | | | PPLM | Once upon a time the user would use a calculator to get a price for the price of the goods that... | | | | | | | | | | | | GeDi | Once upon a time Mexico had started guacamole called empty beans and children with Luis María de Leonos. Juan said he didn't tell a Hispanic what he did... | | | | | | | | | | | | TailorSingle | Once upon a time I was in the area and had one of these burritos that were so delicious. we ordered them for... | | | | | | | | | | | | Mexican Food | Finetune | Once upon a time I was eating my burger , the server looked at me and said something to him " you have no idea how bad that is... | | | | | | | | | | | Adapter | Once upon a time, I ordered the lobster bbq and we both got no meat. our server had been on call for over an hour before she left after talking to us again... | | | | | | | | | | | | PPLM | Once upon a time there were some people who used the same the machines as they could be to get their hands on and get the best results... | | | | | | | | | | | | GeDi | Once upon a time turkey sandwiches turned Uhhh . . . | majestic, religious grunge, weened | | | | | | | | | | | suburban ham sandwich - Americans applaud! Dove Bruffer » Briggs atte... | | | | | | | | | | | | | TailorSingle | Once upon a time I 'd go to the bbq and it was pretty empty. my friend had our burger , which we ordered with fries but not in advance of eating at dinner... | | | | | | | | | | | | American Food | Finetune | Once upon a time I've had the spicy tofu dish , but that was my only meal. It came out cold and tasted awful... | | | | | | | | | | | Adapter | Once upon a time I was craving something spicy, it tasted like the best Chinese food out there... | | | | | | | | | | | | PPLM | Once upon a time I made a stone of silver ring mail "Garden of the Winds Winds"... | | | | | | | | | | | | GeDi | Once upon a time bamboo noodles were the classical medicine and lemongrass fetish... | | | | | | | | | | | | TailorSingle | Once upon a time, I got here for the sushi roll . After getting home from work at 4pm and finding... | | | | | | | | | | | | Asian Food | | | | | | | | | | | | Table 13: Samples of single-attribute CTG with input prefix '*Once upon a time*'. We highlight different attributespecific words or phrases for better view. | Multi Attributes | Method | Generation Results | | | |-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------|---------------------------| | Finetune | Once upon a time I ordered from there, it was just ok . the service was ok, the food is not worth the price ... | | | | | Adapter | Once upon a time I was in phoenix and the place was not clean , I went back to try to find one more spot. no problem... | | | | | Adapter (P) | Once upon a time I came here, the food was ok. however, they had an overpriced chicken burrito on tap and their guacamole is not as good or fresh ... | | | | | TailorConcat | Once upon a time they had | Mexican cuisine . | The service is | terrible , it's | | not clean and we were left hungry ! (I am so sorry for any inconvenience)... | | | | | | TailorArgmax | Once upon a time, we would be served the | burritos | that were cooked with | | | no flavor . They didn't do it right and I will not return !... | | | | | | TailorWeight | Once upon a time I 'd had some of this Mexican food and it was pretty bland . now the burritos are not good ... | | | | | Negative + Mexican | Finetune | Once upon a time I had the | chicken tacos | and my fiancé ordered the | | carne asada torta . Both were outstanding . very clean , well prepared ... | | | | | | Adapter | Once upon a time I found it. The food and service was excellent as well. our server, kate, had an outstanding experience with... | | | | | Adapter (P) | Once upon a time I went, we were in town for some reason and ordered the tacos that day. everything was amazing ! food is fresh ... | | | | | TailorConcat | Once upon a time we had some amazing lunch, which included two tortillas and one taco . the service was great ! - no complaints there are plenty of... | | | | | TailorArgmax | Once upon a time, I had the red bell chile and it was great ! our waitress came to get us as soon . We ordered some tacos with chicken nachos that... | | | | | TailorWeight | Once upon a time I had the carne asada burrito and they were so good that it was one of my favorites . I will go back again for sure! ... | | | | | Positive + Mexican | Finetune | Once upon a time, I tried it and had the worst hangover. after finishing my meal that day ( which was a great one ) all of sudden there is a cockroach... | | | | Adapter | Once upon a time I had the lobster rolls , they were cold and not appetizing . I also received one of these with chicken wings ... | | | | | Adapter (P) | Once upon a time, I ordered the chicken sandwich . It was good but not quite as juicy or flavorful with any flavor at all ... | | | | | TailorConcat | Once upon a time, I would have ordered the | shrimp and fish salad . | it was | | | very dry with no flavor ! I ate this place on Sunday night so... | | | | | | TailorArgmax | Once upon a time, this was the place to be. I ordered my chicken burger and then there is no more fries or burgers at all! ( if you don 't like that one )? | | | | | TailorWeight | Once upon a time they would serve you the | burger , but it was | not cooked . | | | No sauce in there! ( I're sorry )? so that could be considered food poisoning ... | | | | | | Negative + American | Finetune | Once upon a time they brought us our food. the staff was very kind and friendly . I ordered shrimp cocktail , it's one of those restaurants... | | | | Adapter | Once upon a time, the food came fast and fresh . the staff was attentive and we got everything ordered promptly . I recommend this... | | | | | Adapter (P) | Once upon a time I had the lobster bisque , it was absolutely delicious . Service is very attentive and friendly ... | | | | | TailorConcat | Once upon a time, I had | the lobster sandwich | that was good. | it is one of | | my | favourite | dishes on this menu! ( no other place in vegas has been more | | | | awesome .)... | | | | | | Positive + American | 424 | | | | | TailorArgmax | Once upon a time, I was in vegas with my girlfriend and she had the steak . It tasted great on its own! they were really friendly - very tasty food at their menu... | | | | |-----------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|---------------------------| | TailorWeight | Once upon a time, I 'd have had the chorizo chicken sandwich . It is delicious ! Service was quick and friendly ... | | | | | Finetune | Once upon a time I was greeted, sat and waited patiently. the food took forever and there were only 6 of us that got our appetizers... | | | | | Adapter | Once upon a time I got my food and was told that the service is slow . then they came over to me with an "error" ... | | | | | Adapter (P) | Once upon a time, I would never recommend eating this place. the sushi was terrible and they... | | | | | TailorConcat | Once upon a time, my mom had to order the fried rice at night and she said that it was so bad ... | | | | | TailorArgmax | Once upon a time, I've had my rice and noodles at the Japanese buffet . They were so bland that... | | | | | TailorWeight | Once upon a time I had the spicy ramen . It was too sweet and salty , but now its like they have been replaced with something else... | | | | | Negative + Asian | Finetune | Once upon a time I was craving for something quick and easy , they delivered ! the food was fresh and delicious ! service is great ... | | | | Adapter | Once upon a time I came here from chicago and had the chicken with my husband, we were greeted by an awesome customer service... | | | | | Adapter (P) | Once upon a time, I would go to the sushi restaurant and order some sashimi . they have so many good things that can be found in... | | | | | TailorConcat | Once upon a time, when I 't had sushi at my own house it was great . ( - ) the food is amazing ! We were seated on our first day here... | | | | | TailorArgmax | Once upon a time they had sushi . I always try the kabobs , which is great for those who've never heard of them or even know what it means to be in chicago! : ) ... | | | | | TailorWeight | Once upon a time, I had the | pho bao . | Now they're going back for an | | | even | better | experience! This is my | favorite dish | on earth and one of their | | most unique dishes ... | | | | | | Positive + Asian | | | | | | Table 14: Samples of multi-attribute CTG with input prefix 'Once upon a time'. Negative + Mexican denotes | | | | | Table 14: Samples of multi-attribute CTG with input prefix '*Once upon a time*'. Negative + Mexican denotes generating sentences satisfying negative sentiment and topic of Mexican food. Adapter (P) denotes using the same argmax-pseudo labeled sentences (see § 3.2.2) to train the Adapter. We highlight different attribute-specific words or phrases for better view. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. B4. 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Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. 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ramezani-xu-2023-knowledge

Knowledge of cultural moral norms in large language models

https://aclanthology.org/2023.acl-long.26

Moral norms vary across cultures. A recent line of work suggests that English large language models contain human-like moral biases, but these studies typically do not examine moral variation in a diverse cultural setting. We investigate the extent to which monolingual English language models contain knowledge about moral norms in different countries. We consider two levels of analysis: 1) whether language models capture fine-grained moral variation across countries over a variety of topics such as {``}homosexuality{''} and {``}divorce{''}; 2) whether language models capture cultural diversity and shared tendencies in which topics people around the globe tend to diverge or agree on in their moral judgment. We perform our analyses with two public datasets from the World Values Survey (across 55 countries) and PEW global surveys (across 40 countries) on morality. We find that pre-trained English language models predict empirical moral norms across countries worse than the English moral norms reported previously. However, fine-tuning language models on the survey data improves inference across countries at the expense of a less accurate estimate of the English moral norms. We discuss the relevance and challenges of incorporating cultural knowledge into the automated inference of moral norms.

# Knowledge Of Cultural Moral Norms In Large Language Models Aida Ramezani Department of Computer Science University of Toronto [email protected] ## Abstract Moral norms vary across cultures. A recent line of work suggests that English large language models contain human-like moral biases, but these studies typically do not examine moral variation in a diverse cultural setting. We investigate the extent to which monolingual English language models contain knowledge about moral norms in different countries. We consider two levels of analysis: 1) whether language models capture fine-grained moral variation across countries over a variety of topics such as "homosexuality" and "divorce"; 2) whether language models capture cultural diversity and shared tendencies in which topics people around the globe tend to diverge or agree on in their moral judgment. We perform our analyses with two public datasets from the World Values Survey (across 55 countries) and PEW global surveys (across 40 countries) on morality. We find that pre-trained English language models predict empirical moral norms across countries worse than the English moral norms reported previously. However, fine-tuning language models on the survey data improves inference across countries at the expense of a less accurate estimate of the English moral norms. We discuss the relevance and challenges of incorporating cultural knowledge into the automated inference of moral norms. ## 1 Introduction Moral norms vary from culture to culture (Haidt et al., 1993; Bicchieri, 2005; Atari et al., 2022; Iurino and Saucier, 2020). Understanding the cultural variation in moral norms has become critically relevant to the development of machine intelligence. For instance, recent work has shown that cultures vary substantially in their judgment toward moral dilemmas regarding autonomous driving (Awad et al., 2018, 2020). Work in Natural Language Processing (NLP) also shows that language models capture some knowledge of social Yang Xu Department of Computer Science Cognitive Science Program University of Toronto [email protected] or moral norms and values. For example, with no supervision, English pre-trained language models (EPLMs) have been shown to capture people's moral biases and distinguish between morally right and wrong actions (Schramowski et al., 2022). Here we investigate whether EPLMs encode knowledge about moral norms across cultures, an open issue that has not been examined comprehensively. Multilingual pre-trained language models (mPLMs) have been probed for their ability to identify cultural norms and biases in a restricted setting (Yin et al., 2022; Arora et al., 2022; Hämmerl et al., 2022; Touileb et al., 2022). For instance, Hämmerl et al. (2022) show that mPLMs capture moral norms in a handful of cultures that speak different languages. However, it remains unclear whether monolingual EPLMs encode cultural knowledge about moral norms. Prior studies have only used EPLMs to assess how they encode undesirable biases toward different communities (Ousidhoum et al., 2021; Abid et al., 2021; Sap et al., 2020; Nozza et al., 2021, 2022). For instance, Abid et al. (2021) show that GPT3 can generate toxic comments against Muslims, and Nozza et al. (2022) explore harmful text generation toward LGBTQIA+ groups in BERT models (Devlin et al., 2018; Liu et al., 2019). Extending these lines of work, we assess whether monolingual EPLMs can accurately infer moral norms across many cultures. Our focus on EPLMs is due partly to the fact that English as a lingua franca has widespread uses for communication in-person and through online media. Given that EPLMs may be applied to multicultural settings, it is important to understand whether these models encode basic knowledge about cultural diversity. Such knowledge has both relevance and applications for NLP such as automated toxicity reduction and content moderation (Schramowski et al., 2022). Another motivation for our focus is that while it is expected that EPLMs should encode western and 428 ![1_image_0.png](1_image_0.png) English-based moral knowledge, such knowledge might entail potential (implicit) biases toward non- English speaking cultures. For example, an EPLM might infer a situation to be morally justifiable (e.g., "political violence") in a non-English speaking culture (because these events tend to associate with non-English speaking cultures in corpora) and thus generate misleading representations of that community. Here we probe state-of-the-art EPLMs trained on large English-based datasets. Using EPLMs also supports a scalable analysis of 55 countries, which goes beyond existing work focusing on a small set of high-resource languages from mPLMs and monolingual PLMs. We take the moral norms reported in different countries to be a proxy of cultural moral norms and consider two main levels of analysis to address the following questions: - Level 1: Do EPLMs encode moral knowledge that mirrors the moral norms in different countries? For example, "getting a divorce" can be a morally frowned-upon topic in country i , but morally acceptable in country j . - Level 2: Can EPLMs infer the cultural diversity and shared tendencies in moral judgment of different topics? For example, people across nations might agree that doing X is morally wrong while disagreeing in their ## Moral Judgment Toward Y . We probe EPLMs using two publicly available global surveys of morality, World Values Survey wave 7 (Haerpfer et al., 2021 ) 1 (WVS) and PEW Global Attitudes survey (PEW) (Research Center, 2014) 2 . For example, according to WVS survey (illustrated in Figure 1 ), people in different cultures hold disparate views on whether "having casual sex" is morally acceptable. In contrast, they tend to agree more about the immorality of "violence against other people". Our level 1 analysis allows us to probe the fine-grained cultural moral knowledge in EPLMs, and our level 2 analysis investigates the EPLMs' knowledge about shared "universals" and variability across cultures in moral judgment. Following previous work (Arora et al., 2022) and considering the current scale of global moral surveys, we use country as a proxy to culture, although this approach is not fully representative of all the different cultures within a country. We also explore the utility-bias trade-off in encoding the knowledge of cultural moral norms in EPLMs through a fine-tuning approach. With this approach it may be possible to enhance the moral knowledge of EPLMs in a multicultural setting. We 1 https://www.worldvaluessurvey.org/ WSContents.jsp examine how this approach might reduce the ability of EPLMs to infer English-based moral norms and discuss how it might induce cultural biases. ## 2 Related Work 2.1 Automated Moral Inference In Nlp Large language models have been utilized to make automated moral inference from text. Trager et al. (2022) used an annotated dataset to finetune language models to predict the moral foundations (Graham et al., 2013) expressed in Reddit comments. Many other textual datasets and methods have been proposed for fine-tuning LMs for moral norm generation, reasoning, and adaptation (Forbes et al., 2020; Emelin et al., 2021; Hendrycks et al., 2021; Ammanabrolu et al., 2022; Liu et al., 2022; Lourie et al., 2021; Jiang et al., 2021). Schramowski et al. (2022) proposed a method to estimate moral values and found EPLMs to capture human-like moral judgment even without fine-tuning. They identified a MORALDIRECTION using the semantic space of Sentence-BERT (Reimers and Gurevych, 2019) (SBERT) that corresponds to values of right and wrong. The semantic representations of different actions (e.g., *killing people*) would then be projected in this direction for moral judgment estimation. However, this method assumed a homogeneous set of moral norms, so it did not examine cultural diversity in moral norms. ## 2.2 Language Model Probing Probing has been used to study knowledge captured in language models. Petroni et al. (2019) proposed a methodology to explore the factual information that language models store in their weights. Similar probing techniques have been proposed to identify harmful biases captured by PLMs. Ousidhoum et al. (2021) probed PLMs to identify toxic contents that they generate toward people of different communities. Nadeem et al. (2021) took a similar approach and introduced Context Association Tests to measure the stereotypical biases in PLMs, Yin et al. (2022) used probing to evaluate mPLMs on geo-diverse commonsense knowledge, and Touileb et al. (2022) developed probing templates to investigate the occupational gender biases in multilingual and Norwegian language models. Related to our work, Arora et al. (2022) used cross-cultural surveys to generate prompts for evaluating mPLMs in 13 languages. For each country and category (e.g., Ethical Values) in the surveys, they take an average of participants' responses to different questions in the category and show that mPLMs do not correlate with the cultural values of the countries speaking these languages. Differing from that study, we assess finer-grained prediction of EPLMs on people's responses to individual survey questions. More recently, Dillion et al. (2023) prompted GPT3.5 (Brown et al., 2020) with human judgments in different moral scenarios and found striking correlation between the model outputs and the human judgments. Similar to Schramowski et al. (2022), this work also used a homogeneous set of moral ratings which represented English-based and Western cultures. ## 3 Methodology For Inferring Cultural Moral Norms We develop a method for fine-grained moral norm inference across cultures. This method allows us to probe EPLMs with topic-country pairs, such as "getting a divorce in [Country]".3 We build this method from the baseline method proposed by Schramowski et al. (2022) for homogeneous moral inference, where we probe EPLM's moral knowledge about a topic without incorporating the cultural factor (i.e., the country names). Similar to that work, we use SBERT through bert-large-nli-mean-tokens sentence transformer model and use topic and topic-country pairs as our prompts.4 This model is built on top of the BERT model, which is pre-trained on BOOKSCORPUS (Zhu et al., 2015) and Wikipedia. ## 3.1 Autoregressive Eplms Since the MORALDIRECTION is constructed from the semantic space of the BERT-based EPLMs (Schramowski et al., 2022), we develop a novel approach to probe autoregressive state-ofthe-art EPLMs, GPT2 (Radford et al., 2019) and GPT3 (Brown et al., 2020). For each topic or topiccountry pair, we construct the input s as "In [Country] [Topic]". We then append a pair of opposing moral judgments to s and represent them formally as (s +, s−). For example, for s = "In [Country] getting a divorce", and (always justifiable, never justifiable) as the moral judgment pair, s + and s− would be "In [Country] getting a divorce is always justifiable" and "In [Country] getting a divorce is never justifiable" respectively.5 To make our probing robust to the choice of moral judgments, we use a set of K = 5 prompt pairs (i.e.,{*(always justifiable, never justifiable), (morally good, morally* bad), (right, wrong), (ethically right, ethically wrong), (ethical, unethical)}), and refer to appended input pairs as (s + i , s− i ) where i ∈ [K]. Since GPT2 and GPT3 are composed of decoder blocks in the transformer architecture (Vaswani et al., 2017), we use the probabilities of the last token in s + i , and s − ias a moral score for each. The moral score of the pair (s + i , s− i ) is the difference between the log probabilities of its positive and negative statements. $$M S(s_{i}^{+},s_{i}^{-})=\log\frac{P(s_{i T}^{+}|s_{i<T}^{+})}{P(s_{i T}^{-}|s_{i<T}^{-})}\qquad(1)$$ Here s + iT and s − iT are the last tokens in s + i and s − i respectively, and their probabilities can be estimated by the softmax layer in autoregressive EPLMs. We take an average of the estimated moral scores for all K pair statements to compute the moral score of the input. $$M S(s)=\frac{1}{K}\sum_{i=1}^{K}M S(s_{i}^{+},s_{i}^{-})\qquad\quad(2)$$ To construct the baseline, we compute the homogeneous moral score of a topic without specifying the country in the prompts. Using prompt pairs allows us to operationalize moral polarity: a positive moral score indicates that on average the EPLM is more likely to generate positive moral judgment for input s, compared to negative moral judgment. We use GPT2 (117M parameters), GPT2- MEDIUM (345M parameters), GPT2-LARGE (774M parameters), and GPT3 (denoted as GPT3- PROBS, 175B parameters)6. GPT2 is trained on WEBTEXT, which is a dataset of webpages and contains very few non-English samples. Around 82% of the pre-training data for GPT3 comes from Common Crawl data and WEBTEXT2 (Kaplan et al., 2020), an extended version of WEBTEXT (Radford et al., 2019). Around 7% of the training corpus 5We also try probing with the template s = "People in [Country] believe [Topic]", but the results do not improve, so we report the most optimal prompts in the main text, and the rest are shown in Appendix C. 6We access GPT2 through transformer package provided by Huggingface. We access GPT3 through OpenAI API of text-davinci-002 engine with a temperature of 0.6 for text generation. of GPT3 is non-English text. Considering such data shift from books and articles in BERT to webpages in GPT2 and GPT3 in astronomical sizes, it is interesting to observe how cultural moral norms would be captured by EPLMs trained on webpages, which cover a more heterogeneous set of contents and authors. We also design multiple-choice question prompts to leverage the question-answering capabilities of GPT3 (denoted as GPT3-QA). Similar to the wording used in our ground-truth survey datasets, questions are followed by three options each describing a degree of moral acceptability. We repeat this question-answering process 5 times for each topic-country pair and take the average of the model responses. Table 2 in the Appendix shows our prompts for all models. ## 4 Datasets We describe two open survey data that record moral norms across cultures over a variety of topics. ## 4.1 World Values Survey The Ethical Values section in World Values Survey Wave 7 (WVS for short) is our primary dataset. This wave covers the span of 2017-2021 and is publicly available (Haerpfer et al., 2021). In the Ethical Values section, participants from 55 countries were surveyed regarding their opinions on 19 morally-related topics. The questionnaire was translated into the first languages spoken in each country and had multiple options. We normalized the options to range from −1 to 1, with −1 representing "never justifiable" and 1 "always justifiable". The moral rating of each country on each topic (i.e., topic-country pair) would then be the average of the participant's responses. ## 4.2 Pew 2013 Global Attitude Survey We use a secondary dataset from PEW Research Center (Research Center, 2014) based on a public survey in 2013 that studied global moral attitudes in 40 countries toward eight morally-related topics (PEW for short). 100 people from each country participated in the survey. The questions were asked in English and had three options representing "morally acceptable", "not a moral issue", and "morally unacceptable". We normalized these ratings to be in the range of −1 to 1 and represented each topic-country pair by taking an expected value of all the responses. ## 4.3 Homogeneous Moral Norms We also use the data from the global user study in Schramowski et al. (2022) which were collected via Amazon MTurk from English speakers. This dataset contains 234 participants' aggregated ratings of moral norms used for identifying the MORALDIRECTION. Around half of the participants are from North America and Europe. We refer to this dataset as "Homogeneous norms" since it does not contain information about moral norms across cultures. ## 5 Evaluation And Results We evaluate EPLMs' moral knowledge with respect to 1) homogeneous moral norms, 2) fine-grained moral norms across cultures, and 3) cultural diversities and shared tendencies on moral judgment of different topics. ## 5.1 Homogeneous Moral Norm Inference For homogeneous moral norm inference, we compute Pearson correlation between 1) the empirical homogeneous moral ratings, obtained by aggregating the human moral ratings toward a topic from all countries, and 2) language model inferred moral scores, estimated from our homogeneous probing method (i.e., without specifying country in prompts). Figure 2 shows the results on World Values Survey (n = 1, 028), PEW survey (n = 312), and the Homogeneous norms datasets (n = 100). The high correlation of GPT2 and GPT3 moral scores with the Homogeneous norms dataset indicate that our methodology does indeed capture the embedded moral biases in these models, with similar performance to the method proposed by Schramowski et al. (2022) for SBERT (r = 0.79), and higher for GPT3-PROBS (r = 0.85). The moral norms in this dataset are typically more globally agreeable (e.g., *You should not kill people*) than topics in WVS and PEW. As expected, EPLMs are less correlated with WVS and PEW, since their moral biases are derived from pre-training on English and westernized data. Aggregated ratings in WVS and PEW, however, capture a more global view toward moral issues, which are also morally contentious (e.g., "getting a divorce"). Table 3 in Appendix includes the values for this experiment. ![4_image_0.png](4_image_0.png) ## 5.2 Fine-Grained Cultural Variation Of Moral Norms Toward Different Topics Going beyond probing EPLMs for their general knowledge of moral norms, we assess whether they can accurately identify the moral norms of different cultures (level 1 analysis). Using our fine-grained probing approach described in Section 3, we compute Pearson correlation between EPLMs' moral scores and the fine-grained moral ratings from the ground truth. Each sample pair in the correlation test corresponds to 1) the moral norms estimated by EPLMs for a country c and a topic t, and 2) the empirical average of moral ratings toward topic t from all the participants in the country c. Figure 3 summarizes the results for SBERT, GPT2-LARGE, and GPT3-PROBS models, and the rest of the models are shown in Figure 7 in the Appendix. To facilitate direct comparison, the estimated moral scores are normalized to a range of −1 to 1, where −1, 0, and 1 indicate morally negative, morally neutral, and morally positive norms, respectively. GPT3-QA and GPT3-PROBS both show a relatively high correlation with the cultural variations of moral norms (r = 0.352, r = 0.411, p < 0.001, for both), and GPT2-LARGE achieves a correlation of r = 0.207 (p < 0.001) in WVS where n = 1, 028. The correlations are relatively better for PEW (n = 312) with r = 0.657, r = 0.503, and r = 0.468 for GPT3-QA, GPT3- PROBS and GPT2-LARGE respectively. These results show that EPLMs have captured some knowledge about the moral norms of different cultures, but with much less accuracy (especially for GPT2 and SBERT) compared to their inference of English moral norms shown in the previous analysis. In addition, we check whether GPT3's high correlation with PEW is because it has seen and memorized the empirical data. Our investigation shows that GPT3 has seen the data during pre-training, as it can generate the sentences used on the survey website. However, the scores suggested by GPT3 text generation and the countries' rankings based on their ratings are different from the ground truth data. ## 5.3 Culture Clustering Through Fine-Grained Moral Inference EPLMs' fine-grained knowledge of moral norms, inspected in the previous experiment, might be more accuracte for western cultures than other cultures. We investigate this claim by clustering countries based on 1) their Western-Eastern economic status (i.e., Rich West grouping)7, and 2) their continent (i.e., geographical grouping). We repeat the experiments in the previous section for different country groups. The results are shown in Figure 4. We also try sampling the same number of countries in each group. The results remain robust and are illustrated in Appendix-F. Our findings indicate that EPLMs contain more knowledge about moral norms of the Rich West countries as opposed to non-western and non-rich countries. Similarly, EPLMs have captured a more accurate estimation of the moral norms in countries located in Oceania, North America, and Europe, as opposed to African, Asian, and South American countries. The empirical moral norm ratings from European countries in WVS are highly aligned with North American countries (r = 0.938), which explains why their moral norms are inferred more accurately than non-English speaking countries. Next, for each topic, we compare the z-scores of the empirical moral ratings with the z-scores of the GPT3-PROBS inferred moral scores, using MannWhitney U rank test. The results reveal that "abortion", "suicide", "euthanasia", "for a man to beat his wife", "parents beating children", "having casual sex", "political violence", and "death penalty" in non-western and non-rich countries are all en-7https://worldpopulationreview.com/ country-rankings/western-countries coded as more morally appropriate than the actual data. Such misrepresentations of moral norms in these countries could lead to stereotypical content generation. We also find that For Rich West countries, "homosexuality", "divorce", and "sex before marriage" are encoded as more morally inappropriate than the ground truth, (p < 0.001 for all, Bonferroni corrected). Such underlying moral biases, specifically toward "homosexuality" might stimulate the generation of harmful content and stigmatization of members of LGBTQ+, which has been reported in BERT-based EPLMs (Nozza et al., 2022). The results for the rest of the models are similar and are shown in Table 6 in the Appendix. Our method of clustering countries is simplistic and may overlook things such as the significant diversity in religious beliefs within the NonRich-West category, and thus it does not reflect the nuanced biases that models may possess when it comes to moral norms influenced by different religious traditions. Nonetheless, our approach still serves as a valuable starting point for studying EPLM's moral biases towards more fine-grained religious and ethnic communities. ## 5.4 Cultural Diversities And Shared Tendencies Over The Morality Of Different Topics We next investigate whether EPLMs have captured the cultural diversities and shared tendencies over the morality of different topics (level 2 analysis). For example, people across cultures tend to disagree more about "divorce" than about "violence against other people" as depicted in Figure 1. Such cultural diversities for each topic can be measured by taking the standard deviation of the empirical moral ratings across different countries. The EPLMs' inferred cultural diversities can similarly be measured by taking the standard deviation of the estimated fine-grained moral scores for different countries. We then quantify the alignment between the two using Pearson correlation. Figure 5 shows the results for SBERT, GPT2- LARGE, GPT3-PROBS, and the rest are shown in Figure 8 in the Appendix. None of the correlations with the PEW survey were significant. For WVS, SBERT, GPT2 and GPT2-MEDIUM exhibited a significant correlation (p < 0.001) with r = 0.618, r = 0.579, and r = 0.734 respectively. The results for GPT3 are insignificant, suggesting that it is more challenging to correctly estimate ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) cultural controversies of topics for GPT3. For example, *stealing property* is incorrectly estimated to be more controversial than *abortion*. ## 6 Fine-Tuning Language Models On Global Surveys Finally, we explore the utility-bias trade-off in encoding cultural moral knowledge into EPLMs by fine-tuning them on cross-cultural surveys. The utility comes from increasing the cultural moral knowledge in these models, and the bias denotes their decreased ability to infer English moral norms, in addition to the cultural moral biases introduced to the model. We run our experiments on GPT2, which our results suggest having captured minimum information about cultural moral norms compared to other autoregressive models. To fine-tune the model, for each participant from [Country] with [Moral rating] toward [Topic], we designed a prompt with the structure "A person in [Country] believes [Topic] is [Moral rating].". We used the surveys' wordings for [Moral rating]. Table 8 in the Appendix shows our prompts for WVS and PEW. These prompts constructed our data for fine-tuning, during which we maximize the probability of the next token. The fine-tuned models were evaluated on the same correlation tests introduced in the previous Sections 5.2, 5.3, and 5.4. The fine-tuning data was partitioned into training and evaluation sets using different strategies (i.e., Random, Country-based, and Topic-based). For the Random strategy, we randomly selected 80% of the fine-tuning data for training the model. The topiccountry pairs not seen in the training data composed the evaluation set. For our Country-based and Topic-based strategies, we randomly removed 20% of the countries (n = 11 for WVS, n = 8 for PEW) and topics (n = 4 for WVS, n = 2 for PEW) from the training data to compose the evalu- ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) Train data Data partition strategy Evaluation Performance on the Homogeneous norms WVS Random **0.832**∗∗∗ ↑ (0.271∗∗∗) 0.71∗∗∗ ↓ (0.80∗∗∗) Country-based 0.759∗∗∗ ↑ (0.225∗∗) 0.72∗∗∗ ↓ Topic-based 0.508∗∗∗ ↑ (0.286∗∗∗) 0.70∗∗∗ ↓ PEW Random **0.818**∗∗∗ ↑ (0.204, n.s.) 0.64∗∗∗ ↓ Country-based 0.764∗∗∗ ↑ (0.055, n.s.) 0.67∗∗∗ ↓ Topic-based 0.733∗∗∗ ↑ (−0.146, n.s.) 0.61∗∗∗ ↓ ation set. See Appendix G for the total number of samples. Table 1 shows the gained utilities, that is the correlation test results between the fine-grained moral scores inferred by the fine-tuned models and the empirical fine-grained moral ratings. All fine-tuned models align better with the ground truth than the pre-trained-only models (i.e., the values in parentheses). For both WVS and PEW, the Random strategy is indeed the best as each country and topic are seen in the training data at least once (but may not appear together as a pair). The fine-tuned models can also generalize their moral scores to unseen countries and topics. Repeating the experiment in Section 5.4 also shows substantial improvement in identifying cultural diversities of different topics by all fine-tuned models. For example, the WVS and PEW-trained models with Random strategy gain Pearson's r values of 0.893, and 0.944 respectively. The results for the rest of the models are shown in Table 7 in the Appendix. Nevertheless, the bias introduced during the fine-tuning decreases the performance on the Homogeneous norms dataset. This observation displays a trade-off between cultural and homogeneous moral representations in language models. Moreover, injecting the cross-cultural surveys into EPLMs might introduce additional social biases to the model that are captured through these surveys (Joseph and Morgan, 2020). In addition, we probe the best fine-tuned model (i.e., WVS with Random strategy) on its ability to capture the moral norms of non-western cultures by repeating the experiment in Section 5.3. The results in Figure 4 show that the fine-tuned GPT2 performs the best for all country groups. There is still a gap between western and non-western countries. However, basic fine-tuning proves to be effective in adapting EPLMs to the ground truth. ## 7 Discussion And Conclusion We investigated whether English pre-trained language models contain knowledge about moral norms across many different cultures. Our analyses show that large EPLMs capture moral norm variation to a certain degree, with the inferred norms being predominantly more accurate in western cultures than non-western cultures. Our fine-tuning analysis further suggests that EPLMs' cultural moral knowledge can be improved using global surveys of moral norms, although this strategy reduces the capacity to estimate the English moral norms and potentially introduces new biases into the model. Given the increasing use of EPLMs in multicultural environments, our work highlights the importance of cultural diversity in automated inference of moral norms. Even when an action such as "political violence" is assessed by an EPLM as morally inappropriate in a homogeneous setting, the same issue may be inferred as morally appropriate for underrepresented cultures in these large language models. Future work can explore alternative and richer representations of cultural moral norms that go beyond the point estimation we presented here and investigate how those representations might better capture culturally diverse moral views. ## Limitations Although our datasets are publicly available and gathered from participants in different countries, they cannot entirely represent the moral norms from all the individuals in different cultures over the world or predict how moral norms might change into the future (Bloom, 2010; Bicchieri, 2005). Additionally, we examine a limited set of moral issues for each country, therefore the current experiments should not be regarded as comprehensive of the space of moral issues that people might encounter in different countries. Moreover, taking the average of moral ratings for each culture is a limitation of our work and reduces the natural distribution of moral values in a culture to a single point (Talat et al., 2021). Implementing a framework that incorporates both within-country variation and temporal moral variation (Xie et al., 2019) is a potential future research direction. Currently, it is not clear whether the difference between EPLMs' estimated moral norms and the empirical moral ratings is due to the lack of cultural moral norms in the pre-training data, or that the cultural moral norms mentioned in the pre-training data represent the perspective of an English-speaking person of another country. For example, a person from the United States could write about the moral norms in another country from a western perspective. A person from a nonwestern country could also write about their own moral views using English. These two cases have different implications and introduce different moral biases into the system. ## Potential Risks We believe that the language models should not be used to prescribe ethics, and here we approach the moral norm inference problem from a descriptive perspective. 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In *Proceedings of the* 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4654–4663, Hong Kong, China. Association for Computational Linguistics. Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, and Kai-Wei Chang. 2022. Geomlama: Geo-diverse commonsense probing on multilingual pre-trained language models. arXiv preprint arXiv:2205.12247. Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. *CoRR*, abs/1506.06724. ## A Data License Both World Values Survey and PEW survey are publicly available to use for research purposes. We accept and follow the terms and conditions for using these datasets, which can be found in https://www.worldvaluessurvey. org/WVSContents.jsp?CMSID=Documentation, and https://www.pewresearch.org/about/ terms-and-conditions/. ## B Comparison Of Human-Rated And Machine-Scored Moral Norms Figure 6 shows the comparison between humanrated moral norms in PEW, and the moral scores inferred by SBERT (Reimers and Gurevych, 2019). ## C Probing Experiments Table 2 shows our prompt design for probing finegrained moral norms in EPLMs. As mentioned in the main text, we repeat our probing experiment for GPT2 models and GPT3-PROBS with another template "People in [Country] believe [Topic] is [Moral Judgment]". The results are substantially worse than our initial template, suggesting that extracting the moral knowledge in language models is sensitive to the wording used in the input. The results for the fine-grained analysis (level 1 analysis) and the cultural diversities and shared tendencies (level 2 analysis) with this template are shown in Table 4. In all experiments, we used a single NVIDIA TITAN V GPU. Each probing experiment took approximately 1 hour to complete. ## D Homogeneous Moral Norm Inference Table 3 shows the detailed values of the correlation tests in our homogeneous moral norm inference experiment. ## E Fine-Grained Cultural Variation Of Moral Norm Figure 7 and Figure 8 show the result of our finegrained cultural moral inference, and inference of cultural diversities and shared tendencies respectively for GPT2, GPT2-MEDIUM, and GPT3- QA. The numerical indices in Figure 8 are consistent with the indices in Table 5. ## F Sampling For Cultural Clusters Since in section 5.3 there are a different number of countries in each group, we redo the experiment by randomly sampling the same number of countries (n = 11 for Rich West grouping, n = 5 for continent grouping) and repeating the sampling process for 50 times. The results and the general pattern remain the same and are depicted in Figure 9. ## G Details Of Fine-Tuning On Global Surveys Table 8 shows the Moral rating in our prompt design for constructing our fine-tuning dataset. For example, The World Value Survey represents the two ends of the ratings scale where 1 is "Never justifiable" and 10 is "Always justifiable". The options in between are presented to the participants in a 10-point scale. Therefore, we mapped these options to different prompts that are semantically similar and in between the two ends. For example, if a participant from the United States rated stealing property as 2, which is slightly more positive than the first option ("Never justifiable"), we mapped this rating to "not justifiable", creating the prompt "A person in the United States believes stealing property is not justifiable." for our fine-tuning data. Since there are a different number of participants from each country, in order to balance this dataset, we randomly select 100 samples for each topiccountry pair and removed the rest of the utterances from the training data. We fine-tuned GPT2 on one epoch, with a batch size of 8, learning rate of 5e−5, and weight decay of 0.01. The number of training and evaluation samples for all data partition strategies are shown in Table 9. In all experiments, we used a single NVIDIA TITAN V GPU. Fine-tuning and evaluation took approximately 2 hours to complete for each model. ![12_image_0.png](12_image_0.png) | Model | Prompt | |----------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Sentence-BERT | [Topic] in [Country]. | | GPT2 models and GPT3-PROBS | In [Country] [Topic] is [Moral judgement]. People in [Country] believe [Topic] is [Moral judgement]. Do people in [Country] believe that [Topic] is: 1) Morally acceptable 2) Not a moral issue 3) Morally unacceptable. | | GPT3-QA (for PEW) | Do people in [Country] believe that [Topic] is: 1) Always Justifiable 2) Something in between 3) Never justifiable. | | GPT3-QA (for WVS) | | Table 2: Prompting design used for estimating the fine-grained moral norms in different language models. In our homogeneous norm inference, we remove "In [country]" from the prompts. | Model | World Values Survey (n = 1, 028) | PEW survey (n = 312) | Homogeneous norms (n = 100) | |-------------|------------------------------------|------------------------|-------------------------------| | SBERT | 0.210∗∗∗ | −0.038 (n.s.) | 0.79∗∗∗ | | GPT2 | 0.176∗∗∗ | −0.069 (n.s.) | 0.80∗∗∗ | | GPT2-MEDIUM | 0.181∗∗∗ | 0.033 (n.s.) | 0.79∗∗∗ | | GPT2-LARGE | 0.226∗∗∗ | 0.157 (n.s.) | 0.76∗∗∗ | | GPT3-QA | 0.330∗∗∗ | 0.391∗∗∗ | 0.79∗∗∗ | | GPT3-PROBS | 0.346∗∗∗ | 0.340∗∗∗ | 0.85∗∗∗ | ![13_image_0.png](13_image_0.png) ![13_image_1.png](13_image_1.png) ![13_image_2.png](13_image_2.png) | WVS PEW | |-----------| | World Values Survey Index Topic 1 stealing property 2 euthanasia 3 sex before marriage 4 violence against other people 5 cheating on taxes 6 avoiding a fare on public transport 7 abortion 8 suicide 9 someone accepting a bribe on a course of their duties 10 terrorism as a political, ideological, or religious mean 11 homosexuality 12 parents beating children 13 prostitution 14 divorce 15 political violence 16 death penalty 17 claiming governments benefits to which you are not entitled 18 for a man to beat his wife 19 having casual sex PEW survey 1 using contraceptives 2 getting a divorce 3 having an abortion 4 homosexuality 5 drinking alcohol 6 married people having an affair 7 gambling 8 sex between unmarried adults Table 5: Numerical indexing for topics in moral surveys. | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Data | model | Fine-grained evaluation | Evaluation on cultural diversity | |----------------|-----------------------|---------------------------|------------------------------------| | of moral norms | and shared tendencies | | | | GPT3-PROBS | 0.078∗ | −0.176 | | | GPT2 | −0.114∗∗∗ | 0.231 | | | GPT2-MEDIUM | −0.261∗∗∗ | −0.357 | | | GPT2-LARGE | −0.07∗ | −0.356 | | | GPT3-PROBS | 0.539∗∗∗ | 0.041 | | | GPT2 | 0.168∗∗ | 0.566 | | | GPT2-MEDIUM | 0.165∗∗ | 0.184 | | | GPT2-LARGE | 0.19∗∗∗ | 0.542 | | | Model | Positively evaluated topics | Negatively evaluated topics | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | for non-rich and non-western countries | for Rich-West countries | | | sex before marriage∗∗, homosexuality∗∗∗ , having casual sex∗∗∗, abortion∗∗∗ , prostitution∗∗∗, claiming government benefits to which you are not entitled∗∗∗, someone accepting a bribe in the course of their duties∗∗∗ | sex before marriage∗∗∗, euthanasia∗∗∗ , divorce∗∗∗, death penalty∗∗∗ , parents beating children∗∗∗ | | | SBERT | abortion∗∗∗, prostitution∗∗∗ , suicide∗∗∗, avoiding a fare on public transport∗∗∗ , someone accepting a bribe in the course of their duties∗∗∗ , terrorism as a political, ideological or religious mean∗∗∗ , political violence∗∗∗ , violence against other people∗∗∗ | sex before marriage∗∗, homosexuality∗∗ , divorce∗∗, having casual sex∗∗ , claiming government benefits to which you are not entitled∗∗∗ | | GPT2 | euthanasia∗∗∗, abortion∗∗∗, suicide∗∗∗ , avoiding a fare on public transport∗∗∗ , someone accepting a bribe in the course of their duties∗∗∗ , political violence∗∗∗, violence against other people∗∗∗, stealing property∗∗∗ | sex before marriage∗∗∗, homosexuality∗∗ , divorce∗∗, having casual sex∗∗ , claiming government benefits to which you are not entitled∗∗∗ | | GPT2-MEDIUM | euthanasia∗∗∗, having casual sex∗∗∗ , abortion∗∗∗, prostitution∗∗∗, suicide∗∗∗ , terrorism as a political, ideological or religious mean∗∗∗ , political violence∗∗∗ , violence against other people∗∗∗ | sex before marriage∗∗∗, homosexuality∗∗ , divorce∗∗, claiming government benefits to which you are not entitled∗∗∗ | | GPT2-LARGE | having casual sex∗∗, abortion∗∗ , avoiding a fare on public transport∗∗∗ , cheating on taxes∗∗∗ , someone accepting a bribe in the course of their duties∗∗∗ , political violence∗∗∗ | sex before marriage∗∗∗, divorce∗∗ , death penalty∗∗, prostitution∗∗ , parents beating children∗∗ , suicide∗∗, for a man to beat his wife∗∗∗ , stealing property∗∗ | | GPT3-QA | euthanasia∗∗∗, having casual sex∗∗∗ , abortion∗∗∗, death penalty∗∗∗ , suicide∗∗∗, political violence∗∗∗ , for a man to beat his wife∗∗∗ | sex before marriage∗∗∗, homosexuality∗∗∗ , divorce∗∗ | | GPT3-PROBS | | | | Table 6: Topics evaluated as morally positive for non-rich and non-western countries and morally negative for | | | Table 6: Topics evaluated as morally positive for non-rich and non-western countries and morally negative for Rich-West countries, in comparison to the ground truth in these countries. In each entry, the topics are sorted from the most controversial (i.e., having the highest degree of cultural diversity) to the least controversial. The asterisks indicate the significance levels of Mann-Whitney U rank test after Bonferroni p-value correction ("*", "**", "***" for p < 0.05, 0.01, 0.001 respectively). Train data Data partition strategy Evaluation WVS Random 0.893∗∗∗ ↑ (0.579∗∗∗ Country-based 0.894 ) ∗∗∗ ↑ Topic-based 0.835∗∗∗ ↑ PEW Random 0.944∗∗ ↑ Country-based 0.839 (n.s.) ∗ ↑ Topic-based 0.953∗∗∗ ↑ | Dataset | Rating | [Moral rating] in fine-tuning prompts | |-----------|----------------------|-----------------------------------------| | 1 | never justifiable | | | [2, 3, 4] | not justifiable | | | [5, 6] | somewhat justifiable | | | [7, 8, 9] | justifiable | | | 10 | always justifiable | | | WVS | 1 | morally unacceptable | | PEW | 2 | not a moral issue | | 3 | morally acceptable | | | Data | Data partition strategy | Training samples | Evaluation sample pairs | |-------------|---------------------------|--------------------|---------------------------| | Random | 82200 | 206 | | | WVS | Country-based | 82600 | 202 | | Topic-based | 81200 | 216 | | | Random | 24900 | 63 | | | PEW | Country-based | 24800 | 64 | | Topic-based | 23400 | 78 | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 8 ✓ A2. Did you discuss any potential risks of your work? 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4, 5, 6 ✓ B1. Did you cite the creators of artifacts you used? 4, 5, 6 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? All the artifacts we used were available for research purposes. The term of usage can be found in the urls provided in the paper in the Appendix. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 8 ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The datasets we used do not contain information about individual people. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 4, Appendix ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4, 5, 6, appendix ## C ✓ **Did You Run Computational Experiments?** 5, 6, Appendix ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✗ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? We did not do any hyperparameter search. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 5, 6, appendix ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 3 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

ou-etal-2023-songs

Songs Across Borders: Singable and Controllable Neural Lyric Translation

https://aclanthology.org/2023.acl-long.27

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85{\%}, 99.00{\%}, and 95.52{\%} on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75{\%} relative enhancement on overall quality, compared against naive fine-tuning (Code available at \url{https://github.com/Sonata165/ControllableLyricTranslation}).

# Songs Across Borders: Singable And Controllable Neural Lyric Translation Longshen Ou and **Xichu Ma** and **Min-Yen Kan** and **Ye Wang** National University of Singapore {longshen, ma_xichu, kanmy, wangye}@comp.nus.edu.sg ## Abstract The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to promptdriven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive finetuning1. ## 1 Introduction With the globalization of entertainment, it is becoming increasingly common for people to appreciate songs in foreign languages. Meanwhile, artists are internationalizing their work and building territories worldwide. Nevertheless, an unfriendly barrier exists between the artists and the audience: most commercial songs are not written in multiple languages. Worse still, most existing song translations entirely ignore the music constraints, rendering 1Code available at https://github.com/Sonata165/ControllableLyricTranslation them unsingable alone with the music. As a result, the language barrier complicates the interaction between artists and their audience. Obtaining singable lyric translations can facilitate the globalization of the music publishing industry to further promote the growth of its $5.9 billion USD market size (Verified Market Research, 2022). However, song translation is unusually difficult for human translators, due to music constraints and style requirements. If we can construct lyricspecific machine translation (MT) systems that can produce drafts that satisfy these constraints and requirements, the difficulty and cost of lyric translation will be largely reduced, as lyricists and translators can start with such automatic drafts and can focus on post-processing for quality and creativity. However, obtaining singable lyrics from MT systems is challenging. Figure 1 shows two sentences of lyrics from the song *Let It Go*, together with an MT output and a singable translation. We observe a notable quality gap between them. While the MT output correctly translates the source, it ignores all the criteria that matter to make the output singable: (1) The second sentence of the MT outputs is unnatural because of incoherent vocabulary selection and lack of aesthetics. (2) Overcrowded syllables in the first sentence of the MT outputs force performers to break music notes in the orange box into multiple pieces to align them with lyrics. The rhythm pattern consequently diverges from the composer's intention. (3) The two-syllable word in the red box is situated across a musical pause (blue box), let it go in intro Subtitle**Composer / arranger** ![0_image_0.png](0_image_0.png) Figure 1: Translation comparison of a general-domain NMT system (2nd row), already been adapted with parallel lyric data, versus a singable translation (3rd row). 447 causing an unnatural pronunciation. (4) The endsyllables (purple text) are not of the same rhyme pattern, making the output miss a key chance for being poetic. In contrast, the singable translation in the third row outperforms the MT output in all four aspects, all while maintaining translation fidelity: it perfectly aligns with each musical note, has the same end-rhyme pattern for the two sentences (green text), a natural stop at the musical pause, and higher naturalness. These properties make it a significantly more performable translation. To address these quality gaps to obtain singable lyric translations from neural machine translation (NMT) systems, we formalize singable lyric translation as an instance of constrained translation, identify useful constraints, and propose a languagepair independent approach that combines translatology theoretical guidance with prompt-driven NMT. Our contributions are: - We design an effective and flexible promptbased solution for necessary word boundary position control that enhances the outputs' singability. - We find that reverse-order decoding significantly contributes to the accuracy of promptbased rhyme control. With this decoding strategy as the basis, we further design a rhyme ranking scheme to facilitate picking the bestsuitable rhyme for translating input stanzas. - We conduct comparative studies of different prompt forms' effectiveness for controlling each aspect—length, rhyme, and necessary word boundary positions—and show the advantage of prompt-based control over control by modifying beam search. - We show that adding back-translation of target-side monolingual data for fine-tuning is more effective in adapting the model to the lyric domain, compared with the more common practice of in-domain denoising pretraining. ## 2 Related Work Lyric/Poetry Translation. Designing domainspecific MT systems for poetic text translation, e.g., poetry and lyrics, is an emerging and underexplored topic in MT. Two previous works conducted pioneering research on lyrics (Guo et al., 2022) and poetry (Ghazvininejad et al., 2018) translation separately by adopting a similar methodology of adjusting beam scores during beam search (referred to as *biased decoding*) to encourage the generation of outputs with desired constraints. However, there is plenty of room for improvement. As will be shown in later sections, biased decoding not only fails at effectiveness of control, but also negatively impacts text quality and other simultaneously-controlled aspects. Additionally, the inclusion of controlling aspects is insufficiently comprehensive. For example, GagaST (Guo et al., 2022) omits controls for rhyme, but rhyming is actually a critical desired property for song translations (Strangways, 1921). Lyric Generation. Research on building lyricspecific language models shows the effectiveness of prompt-based control for outputs' length, rhyme, stress pattern, and theme (Li et al., 2020; Ma et al., 2021; Xue et al., 2021; Ormazabal et al., 2022; Liu et al., 2022). However, several aspects remain to be enhanced. First, the prompts' forms vary: some works add prompts by additive embedding vectors (Li et al., 2020; Ma et al., 2021; Xue et al., 2021; Liu et al., 2022) and others by the prefix of input (Ormazabal et al., 2022; Liu et al., 2022). The lack of comparison makes it difficult to conclude the best prompt form for different control aspects. In addition, prior works did not control for some aspects in a well-designed manner. For example, (Liu et al., 2022) enhances the music–lyric compatibility by controlling the number of syllables of *each* word in the output. However, music constraints are usually not that tight so that such finelevel controlling might be unnecessary. Additionally, we found that unfitted rhyme prompts damage the output quality. However, we have not seen research suggesting how to choose the best suitable end-rhyme without naively traversing all possible rhyme prompts. Translatology: Singable Translation of Songs. We attribute the inability of singable lyric translation from general-domain MT systems to the completely different goal of lyric translation compared with normal interlingual translation (Low, 2005): without considering the rhythm, note values, and stress patterns from music, song translations that seem good on paper may become awkward when singing. When the auditory perception is dominated by music (Golomb, 2005), the goal of translation is not again predominated by preserving the semantics of source text (Franzon, 2008), but requires skilled handling of non-semantic aspects (Low, 2013) to attain the music–verbal unity, making it even an unusually complex task for human translators (Low, 2003). Theory and techniques from translatology provide valuable guidelines for our method design. Particularly, the "Pentathlon Principle" (§3.1) from (Low, 2003) is a widely accepted theoretical guidance to obtain singable song translations (Franzon, 2008; Cheng, 2013; Stopar, 2016; Si-yang, 2017; Opperman et al., 2018; Sardiña, 2021; Pidhrushna, 2021). In addition, some practical translation tricks have also been mentioned in (Low, 2003), e.g., determining the last word first and from back to front when translating sentences in rhyme. Denoising Pretraining. The deficiency of indomain data requires a powerful foundation model to ensure translation quality. We found large-scale denoising sequence-to-sequence pretraining (Lewis et al., 2019) a great candidate in our problem setting because it has been shown to be particularly effective in enhancing model's performance on text generation tasks such as summarization (Akiyama et al., 2021) and translation (Liu et al., 2020; Tang et al., 2020), and also on domain-specific applications, e.g., (Yang et al., 2020; Soper et al., 2021; Obonyo et al., 2022). However, as indicated in (Liu et al., 2020), the effectiveness of pretraining is related to the amount of monolingual data. In our case where in-domain data are relatively deficient, adopting the same strategy for adaptation might not be optimal. Back-Translation. Back-translation (BT) and its variants can effectively boost the performance of NMT models (Sennrich et al., 2015; Artetxe et al., 2017; Lample et al., 2018), and also show superior effectiveness in domain adaptation in low-resource settings (Hoang et al., 2018; Wei et al., 2020; Zhang et al., 2022). It is potentially a better adaptation method and may lead to higher output naturalness, which is required by singable translations. Prompt-based Methods. Adding prompts during fine-tuning shows strong performance on lexical-constrained-MT (Susanto et al., 2020; Chousa and Morishita, 2021; Wang et al., 2022), as well as broad applicability on various controlling aspects such as output length (Lakew et al., 2019) and the beginning word of output (Li et al., 2022). Compared to some earlier research that adds lexical constraints during beam search (Hokamp and Liu, 2017; Post and Vilar, 2018), the prompt based solution has a faster decoding speed and higher output quality (Susanto et al., 2020), hence might be the better option in our problem setting. ## 3 Method To bridge the gaps of previous research, we identify comprehensive controlling aspects from the translatology literature, propose prompt-based solutions for each aspect, and explore more effective foundation models and adaptation methods. ## 3.1 Controlling Aspects Are there some universal rules that we can adopt to obtain singable translations? We first rule out some prospective answers. Strictly keeping the positions of stressed syllables (Ghazvininejad et al., 2018) is inappropriate as stressing certain syllables is the property of stress-timed language. In contrast, syllable-timed languages, e.g., French and Mandarin, give syllables approximately equal prominence. Aligning the characters' tone with the melody (Guo et al., 2022) is also not a good choice. On the one hand, this rule only applies to tonal languages. On the other hand, this rule is increasingly being ignored by the majority of songs composed in recent decades (Gao, 2017), indicating the marginalized importance of the intelligibility of songs, especially in pop2. To achieve a comprehensive and languageindependent method, we define "singable translation" as following the "Pentathlon Principle" from (Low, 2003): that quality, singable translations are obtained by balancing five aspects—singability, rhythm, rhyme, naturalness, and sense. Table 1 lists these aspects and corresponding requirements, and how we actualize them in our model. Particularly, we identify (1)–(3) as the controlling aspects of our model and realize them with prompt-based control, while (4) and (5) are achieved from the perspectives of adaptation and pretraining. ## 3.2 Problem Formulation We define the task that is tackled in this paper, singable and controllable lyric translation, as follows: given one line of lyrics X in a source language Lsrc and a set of desired properties of output 2For example, according to Apple Music, 61 of the 2022 Top 100 Chinese pop songs are songs by Jay Chou, a Chinese artist famous for unintelligible songs. | Aspects | Requirements | Our Actualization | |-----------------|----------------------------------------------------------------------|------------------------------------------------------------------------------------| | (1) Singability | Outputs are suitable for singing with the given melodies. | Enhance music-lyric compatibility by prompt-based necessary word boundary control. | | (2) Rhythm | Outputs follow rhythm patterns in the music. | Prompt-based length (number of syllables) control. | | (3) Rhyme | Outputs fulfil certain rhyme patterns. | Prompt-based end-rhyme control and paragraph-level rhyme ranking. | | (4) Naturalness | Outputs read like lyrics originally composed in the target language. | Adapting with back-translation of in-domain target-side monolingual data. | | (5) Sense | Outputs are fidelity to the meaning of source sentences. | Large-scale general-domain pretraining. | Table 1: The "pentathlon principle" and the actualizations in our model. sentence {ltgt, rtgt, btgt}, generating text translation Y in target language Ltgt for X by modeling P(Y |X, ltgt, rtgt, btgt), where (1) the total number of syllables of sentence Y to be precisely equal to length constraint ltgt; (2) Y ends with a word that is in the same rhyme type of rhyme constraint rtgt; (3) Y has word boundaries—the positions between two consecutive syllables that belong to different words—in all locations indicated in necessary word boundary constraint btgt; (4) Y is of maximal naturalness, and is fidelity to the sense of X. ## 3.3 Prompt Methods For Controlling Two types of special tokens are constructed as prompts for sentence-level control. For each sentence, the length and rhyme prompts are single token len_i and rhy_j, indicating the desired number of syllables of the output is i and that the desired end-rhyme type of output is j. The prompt for necessary word boundaries is a sequence of special tokens, bdr = {bdr_0, bdr_1} len_i, indicating the desired word boundary positions. During the training process, these prompts are derived from the analysis of target-side sentences, guiding the model towards generating sentences with corresponding properties. Consequently, there is no need for accompanying music during training. At the inference stage, prompts can be crafted from either music or source-side sentences. For an overview of the system workflow, please refer to Figures 3b and 3c. We conducted a group of experiments to test three different prompt methods to determine the best one for each control aspect. They are (1) Encpref: prompts are injected into the encoder's input as a prefix. (2) Dec-pref: prompts are injected into the decoder's input as a prefix. (3) Dec-emb: prompts are embedded into a vector and added toward the decoder's input. ## 3.4 Word Boundary Control Intra-word pause is a typical disfluency pattern of beginning language learners (Franco et al., 1998). However, improperly translated lyrics usually con- ![3_image_0.png](3_image_0.png) Figure 2: Demonstration of the necessity of word boundary control. Blue box: musical pauses; orange box: notes highlighted by downbeats; red box: words interrupted by musical pauses or highlighted notes; green box: words without interruption. tain multi-syllable words that lies across musical pauses, as the blue box in Figure 2, so that the performer has to make awkward intra-word pauses while singing (Guo et al., 2022), causing a drop in pronunciation acceptability. Besides, we observe that positioning highlighted music notes, such as high notes or downbeats, as the orange box in Figure 2, onto a multi-syllable word's second or later syllables can bring similar adverse effects due to abrupt changes of pitch and tension3. We address these issues by carefully designing the placement of *word boundaries* in outputs, i.e., the points between two consecutive syllables that are from different words. Our aim is to ensure that word boundaries align precisely with the boundaries in music, i.e., the *melody boundaries*, which occur at musical pauses and *before* highlighted notes (the blue and orange boxes in Figure 2). In this way, we achieve higher compatibility between the output sentences and the accompanying music, enhance the fluency and consistency of pronunciation during singing, and hence lead to the gain of singability. This solution is achieved by prompt-based word boundary control. We use the prompt bdr to represent melody boundary positions, indicating necessary word boundary positions. bdr is a sequence of special tokens, and each token corresponds to one syllable in the output. There are two types of special interior tokens: bdr_1 and bdr_0, representing after the corresponding syllable "there should be a word boundary" and "we do not care if there 3Stress-timed languages have another solution to this second problem, i.e., put a stressed syllable at the highlighted note. Here we discuss another generic solution. is a boundary", respectively. At test time, bdr is obtained from accompanying music and serves as additional inputs. A well-trained word-boundaryaware model can hence place word boundaries at the desired positions to achieve better music–lyric compatibility. For locations where bdr_0 is present ("don't care"), the translation model operates unconstrained, maximizing translation naturalness. During training, length and rhyme prompts can be obtained directly from the target sentences in the training samples, but not again for necessary word boundary prompts because they have to be obtained from accompanying music which is absent in training. Nevertheless, we offer a solution: we randomly sample from all actual word boundary positions from the target-side text and use this sampled subset as "pseudo ground truth" to construct bdr for training. ## 3.5 Reverse Order Decoding 3.5.1 Sentence-Level Control We imitate the process of human translators translating texts in rhyme: translating the last word first, and from back to front, which is an old trick to keep rhyming patterns from being forced (Low, 2003). We implement this by reverse-order decoding. During fine-tuning with parallel data, we reverse the word order of target-side text while retaining the source-side text unchanged. This approach minimally changes the structure and workflow of offthe-shelf translation models. ## 3.5.2 Paragraph-Level Ranking Controllability alone is not enough. For a given input sentence, the rhyming usually only looks good in certain rhyme types but appears forced in others (see Appendix C.2 for details). No matter how good the controllability is, the output quality will be severely damaged if an ill-fitting rhyme prompt is provided by the user. To avoid such problems, we need to determine the most suitable end-rhyme for translating one sentence, and further one paragraph consisting of multiple sentences. Previous research left this problem unsolved. Fortunately, our reverse-order decoder simplifies the rhyme ranking process. During training, we use an additional special token rhy_0 to nullify rhyme constraints for output. We achieve this by randomly converting a portion of each type of rhyme prompt to rhy_0 during training. At inference time, for a given source sentence Xi and prompts ltgt, rtgt and btgt, we first use rhy_0 as the rhyme prompt to do the first step of reverse-order decoding to obtain the end-word probability distribution, $$P(y_{-1}|X,l_{tgt},\mathbf{b}_{tgt},\text{rhy}\_{0})$$ $$=[p(w_{1}),p(w_{2}),\ldots,p(w_{v})],\tag{1}$$ where the v is the vocabulary size of the target language. Note that the p(wj ) not only indicates the end-word probability, but also predicts output text quality and the likelihood of satisfaction of length and word boundary constraints of the rhymeunconstrained model, from a greedy point of view. Intuitively, starting with tokens with low probabilities will pull down the corresponding beams' scores and degrade the output quality. On the contrary, sentences with higher quality can be obtained by starting decoding with wj with higher p(wj ), and we achieve this by giving the model a rhyme prompt that guides it towards starting with such wj . We sum up the probability in Eq. 1 within each rhyme type to obtain the rhyme distribution of given inputs, pi =X Rhy(wj )∈rhyme i p(wj ) $P(Rhy(Y)|X,l_{tgt},\mathbf{b}_{tgt},\text{rhy\_0})$ $$=P(Rhy(y_{-1})|X,l_{tgt},\mathbf{b}_{tgt},\text{rhy\_0})$$ $$=[p_{1},p_{2},\ldots,p_{u}],$$ $\mathbf{b}_{tgt}=P\mathbf{b}_{tgt}(\cdot)$ is a regular sequence such that $\mathbf{b}_{tgt}$ where Rhy(·) is a map between a word or the endword of a sentence to its rhyme type, u is the number of rhyme types in the target language. For a certain rhyme type i, a higher pi value indicates a higher probability of successful rhyming and higher output quality. When translating a paragraph of lyrics, we have multiple sentences together with their corresponding length and boundary prompts as input: $\mathbf{X}=[X_{1},X_{2},\ldots,X_{n}],$ with prompts $$[(l_{tgt_{1}},\mathbf{b}_{tgt_{1}}),(l_{tgt_{2}},\mathbf{b}_{tgt_{2}}),\ldots,(l_{tgt_{n}},\mathbf{b}_{tgt_{n}})].$$ With the assumption that every sentence is of equal importance, we compute a normalized rhyme distribution for this paragraph by $$P(Rhy(Y_{k}))=f(X_{k},l_{tgt_{k}},\mathbf{b}_{tgt_{k}},\text{rhy\_0}),$$ $$P(Rhy(\mathbf{Y}))=\text{softmax}(\sum_{k=1}^{n}P(Rhy(Y_{k})))$$ where $f$ refers to the first step of reverse-order. decoding. We then use P(Rhy(Y)) as the rhyme ranking score of this paragraph to guide the rhyme selection. ![5_image_0.png](5_image_0.png) ## 3.6 Utilizing Monolingual Data In-domain parallel data suffer from two issues. First, its amount is so limited that it is not comparable with general-domain data. Second, there are severe quality issues when target-side lyrics are translated by online communities, including wrong translation (Li, 2020), creative treason (Zhang, 2022), over-domestication (Xie and Lei, 2022), etc. To mitigate the issue of data quantity and quality, we seek help from target-side monolingual lyric data. Our approach involves incorporating back-translation (Sennrich et al., 2015) of targetside in-domain monolingual data to augment the parallel data for fine-tuning. To demonstrate its effectiveness, we conduct a comparative study with the adaptation method in (Guo et al., 2022), which performs sentence-level denoising pretraining (Lewis et al., 2019) with in-domain data after general-domain pretraining. Taken together, these innovations form our final control method, which we can apply to any foundation model. In the evaluation that follows, we instantiate our techniques with Multilingual BART (refer to Figure 3 for structure and workflow), producing the Singable Translation (Row 3) in Figure 1. Additional case studies are featured in Appendix C. ## 4 Experiment We tested our methods with English–Chinese lyric translation. We obtained a small amount of parallel data (about 102K paired sentences after deduplication) by crawling data of both English–Chinese and Chinese–English pairs from an online lyric translation sharing platform4. For target-side monolingual data, we adopted lyric data from three publiclyavailable datasets567, resulting in about 5.5M sentences after deduplication. For details of dataset statistics and splits, data preprocessing, and back translation, please refers to Appendix A. ## 4.1 Model Configuration We adopted Multilingual BART (Liu et al., 2020) as the foundation model. We set the batch size to the largest possible value to fit into one NVIDIA A5000 GPU (24G), did simple searching for best learning rate, and kept the majority of other hyperparameters as default. For all experiments, models were first trained to converge on back-translated data, and fine-tuned with parallel data afterward. Please refer to Appendix B for implementation details and hyperparameter setting. ## 4.2 Evaluation The following metrics are used for objective evaluation: Sacre-BLEU (Post, 2018), TER (Snover et al., 2006), length accuracy (LA), rhyme accuracy (RA), and word boundary recall (BR). BLEU is a standard metric for various translation models. TER is also adopted because it directly reflects how much effort the lyricists need to spend to convert model outputs to perfectly singable lyrics. For length and rhyme control, we compare outputs' lengths and rhymes with desired constraints and compute the accuracy. For word boundary control, we first obtain outputs' word boundary locations using the Jieba tokenizer8, and then compute the recall value with the necessary word boundary prompts, indicating the ratio of satisfied desired word boundaries. For models that are constraint-aware for any controlling aspects, we conducted testing over two groups of experiments, as below: Target as constraints (tgt-const): For a given sentence pair, the length constraint is equal to the number of syllables of the target-side sentence; the rhyme constraint is equal to the rhyme category of the end-word of the target-side sentence; the boundary constraints are randomly sampled from word boundaries inside the target sentences. In this setting, the BLEU and TER scores represent the text quality directly. 4https://lyricstranslate.com/ 5https://github.com/liuhuanyong/MusicLyricChatbot 6https://github.com/gaussic/Chinese-Lyric-Corpus 7https://github.com/dengxiuqi/ChineseLyrics 8https://github.com/fxsjy/jieba Tgt-const **Src-const** Model BLEU↑ TER↓ LA↑ RA↑ BR↑ **BLEU**↑ LA↑ RA↑ BR↑ Baseline 21.71 70.04 20.54 37.49 62.28 (21.71) 18.15 8.04 55.88 Ours **30.69 49.72 99.85 99.00 95.52** 16.04 **98.25 96.53 89.77** Source as constraints (src-const): For a given sentence pair, the length constraint is equal to the number of syllables of the source-side sentence; the rhyme constraint is randomly sampled from the real rhyme type distribution of lyrics in the target language, obtained from our monolingual dataset; the boundary constraints are randomly sampled from word boundaries inside the source sentences. This setting simulates real-world lyric translation cases and is more challenging. In src-const, we do not compare constrained models with unconstrained ones on BLEU or compute TER for outputs, as target-side sentences often possess distinct properties (e.g., \# syllables) from prompts generated by source sentences, rendering them not the ground truth. Owing to the divergence between references and prompts, models with more constraints yield lower BLEUs, and TER in srcconst fails to accurately reflect translation quality. We compare our model with two baselines. The first is the unconstrained and un-adapted **Baseline** model presented in Table 2. The second is GagaST (Guo et al., 2022), which, to the best of our knowledge, is the only prior work on lyric translation. Due to data acquisition difficulties, we did not perform a model-level comparison with GagaST. Instead, we compared the effectiveness of their adaptation (in-domain denoising pre-training) and control method (biased decoding) with ours (BT and prompt-based control), and compare generation results through subjective evaluation. ## 5 Results Table 2 shows the results of our final model. In the tgt-const setting, our model surpasses the baseline model on all objective aspects, not only with much higher BLEU and lower TER scores, but also achieves almost perfect length and rhyme accuracies and a competitive boundary recall score. The success of controlling length, rhyme, and word Table 3: Comparison of unconstrained models. Best result in **bold**. | Model | BLEU↑ | TER↓ | |----------------------------|---------|--------| | Transformer | 8.97 | 84.92 | | mBart w/o ft | 16.44 | 84.64 | | mBart pt + ft (baseline) | 21.71 | 70.04 | | + In-domain denoise pt | 22.18 | 68.61 | | + BT target side mono data | 25.53 | 64.22 | boundary while maintaining a high text quality enables our model to generate singable lyric translations. In addition, the controlling mechanism remains effective in the src-const setting, showing the generalizability of our methods. ## 5.1 Unconstrained Models | Tgt-const | Src-const | | | | | |-------------|-------------|-------|----------|---------|----------| | Model | BLEU↑ | TER↓ | Len acc↑ | BLEU↑ | Len acc↑ | | Baseline | 21.32 | 69.89 | 20.78 | (21.32) | 18.48 | | Dec-emb | 22.06 | 67.11 | 24.18 | 21.42 | 21.52 | | Dec-pref | 22.16 | 62.77 | 82.94 | 18.61 | 80.30 | | Enc-pref | 23.29 | 61.30 | 86.49 | 19.12 | 83.78 | As in Table 3, both general-domain pretraining and in-domain fine-tuning are necessary to ensure translation quality. There are performance drops if any of the two components are canceled from the unconstrained model. Meanwhile, fine-tuning with back-translated in-domain monolingual data further contributes to the performance gain, showing higher adaptation effectiveness than in-domain pretraining. We also show BT's contribution to improving naturalness in §5.5. Tgt-const **Src-const** Model BLEU↑ TER↓ LA↑ RA↑ **BLEU**↑ LA↑ RA↑ W/o ctrl 21.48 62.65 86.87 39.88 (17.38) **84.61** 8.19 Dec-emb 21.18 63.27 84.97 39.90 **17.05** 82.95 7.87 Enc-pref **23.30 58.57 87.06** 85.77 14.91 83.97 64.21 Dec-pref 22.92 58.84 85.16 **96.66** 14.26 81.43 **88.52** Table 5: Comparison of prompt methods for rhyme constraints, when controlling length and rhyme together with reverse-order decoding. The best result is marked in **bold**, the second best underlined. W/o ctrl: lengthcontrol-only model. Table 6: Comparison of prompt methods for word boundary constraints. Decoding direction: reverse. The best result in **bold**, the second best, underlined. W/o ctrl: model with only length and rhyme control. Table 7: Comparison of prompt and biased decoding for word boundary control. Best in **bold**; second best, underlined. ## 5.2 Best Prompt Methods | Tgt-const | Src-const | | | | | | | | | |-------------|-------------|-------|-------|-------|-------|---------|-------|-------|-------| | Model | BLEU↑ | TER↓ | LA↑ | RA↑ | BR↑ | BLEU↑ | LA↑ | RA↑ | BR↑ | | W/o ctrl. | 29.60 | 51.02 | 99.40 | 99.20 | 75.20 | (16.57) | 97.80 | 96.81 | 58.49 | | Dec-emb | 30.86 | 49.93 | 99.85 | 99.15 | 94.19 | 15.84 | 97.99 | 96.58 | 87.52 | | Dec-pref | 30.24 | 50.44 | 99.78 | 99.12 | 81.37 | 16.48 | 97.93 | 96.95 | 72.36 | | Enc-pref | 30.73 | 49.91 | 99.79 | 98.93 | 94.96 | 15.88 | 98.09 | 96.61 | 89.62 | We select the most effective prompt method for different controlling aspects in our final model. Here are the effectiveness comparisons. Length Control. As shown in Table 4, the encoder-side prefix is the best prompt method for length control, with the highest length accuracy and higher translation quality than dec-pref. Rhyme Control. As shown in Table 5, the decoder-side prefix is the best method for rhyme control, with a significantly higher rhyme accuracy than the second-best method encoder-side prefix. Word Boundary Control.9 As shown in Table 6, enc-pref is the best for word boundary control with much higher effectiveness than dec-pref. It has comparable performance with dec-emb in tgt-const, but shows stronger controllability in the src-const setting, indicating better generalizability. | Tgt-const | Src-const | | | | | | | |--------------|-------------|-------|-------|-------|---------|-------|-------| | Model | BLEU↑ | TER↓ | LA↑ | BR↑ | BLEU↑ | LA↑ | BR↑ | | Length-only | 26.86 | 56.48 | 99.43 | 73.31 | (20.91) | 97.70 | 60.62 | | + Biased dec | 17.19 | 68.68 | 87.14 | 75.60 | 13.85 | 84.92 | 65.51 | | + Prompt | 27.21 | 56.07 | 99.77 | 95.22 | 16.04 | 98.25 | 89.77 | Table 8: Comparison of rhyme control performance of biased decoding and prompt method. L-to-R: decode in normal order; R-to-L: decode in reverse order. In each group, the best result is marked by boldface, the second best is marked by underline. ## 5.3 Prompt-Based Word Boundary Control As in Table 7, prompt-based control is much more successful than biased decoding in word boundary control, not only achieving high boundary recall (95.22% and 89.77%) but also slightly raising the length accuracy and text quality. On the contrary, biased decoding contributes limited power to word boundary control with the expense of significant drops in text quality and length control accuracy. | Tgt-const | Src-const | | | | | | | | |-------------|--------------|-------|-------|-------|---------|-------|-------|-------| | Model | BLEU↑ | TER↓ | LA↑ | RA↑ | BLEU↑ | LA↑ | RA↑ | | | Len only | 26.86 | 56.48 | 99.43 | 40.04 | (20.91) | 97.70 | 8.44 | | | L-to-R | + Biased dec | 24.77 | 59.68 | 98.50 | 83.18 | 18.58 | 96.38 | 80.90 | | Dec-pref | 28.81 | 52.04 | 98.25 | 94.88 | 18.82 | 96.21 | 84.00 | | | Len only | 26.04 | 57.09 | 98.95 | 43.36 | (20.63) | 96.85 | 8.41 | | | R-to-L | + Biased dec | 26.45 | 57.82 | 98.83 | 86.99 | 16.68 | 96.90 | 79.28 | | Dec-pref | 29.59 | 50.95 | 99.25 | 99.23 | 16.89 | 97.60 | 96.80 | | ## 5.4 Prompt-Based Reverse-Order Decoding Prompt vs. Biased Decoding. As in Table 8, the prompt-based method again shows higher effectiveness in rhyme control, while the biased decoding again negatively impacts text quality. As in Appendix C.3, the prompt-based control enables the model to adjust the expression of the entire sentence according to the given desired rhyme, achieving higher consistency, but the biased decoding sometimes abruptly changes the end-word to fulfill the constraint without considering whether it is compatible with input sentence and target-side context. Normal vs. Reverse. Reverse-order decoding further raise the performance of prompt-based rhyme control, but conversely, only brings marginal improvement to biased-decoding-based control. A possible explanation is the inability of biased decoding to handle polyphones (see Appendix C.3). We observed multiple cases where *one of* the pronunciation of the end-word in its output does satisfy the rhyme requirement, but *is not* the pronunciation in that context. On the contrary, the prompt-based control is aware of the whole target-side sentence, and hence better controllability is achieved. ## 5.5 Human Evaluation We employ five students from a local university with music performance or lyric composing back- | Model | Sense | Naturalness | Compatibility | STS | |----------|---------|---------------|-----------------|-------| | Baseline | 4.02 | 3.80 | 2.53 | 2.04 | | GagaST | 3.84 | 3.72 | 4.01 | 2.97 | | Ours | 3.95 | 3.78 | 4.42 | 3.57 | | - bdr | 3.91 | 3.72 | 4.21 | 3.46 | | - rhy | 4.15 | 4.03 | 4.21 | 3.24 | | - len | 4.36 | 3.96 | 2.64 | 2.31 | grounds. We let participants evaluate outputs on five-point scales and take the average as the final score. Evaluations are from four dimensions: (1) sense, whether the translation output retains the meaning of the input sentence; (2) *naturalness*, whether the translation output sounds like lyrics composed initially in the target language; (3) *music–* lyric compatibility, the degree of outputs and music match with each other and the consequent singability gain; (4) *Singable Translation Score (STS)*, the overall quality as singable translations, a singlevalue metric considering the satisfaction of all five perspectives in the Pentathlon Principle (§3.1) 10. Table 9 shows the subjective evaluation results of baseline, GagaST (Guo et al., 2022), our model, and some ablated variants. On the STS metric, which is the ultimate goal of singable lyric translation, our model significantly outperforms the baseline and GagaST by 75.0% and 20.2%, showing its ability to generate singable translations. Besides, our model performs especially well on music–lyric compatibility, by 74.7% and 10.2% higher scores than the baseline and GagaST. In contrast, the baseline model performs worst on the two metrics. In addition, we show the contributions of different components by the ablated studies. The word boundary control raises music–lyric compatibility (+0.21) and overall quality (+0.11). The contribution from rhyme control is majorly on the overall quality part (+0.22), but with the expense of sense (-0.24) and naturalness (-0.31). Length control is the foundation of music–lyric compatibility (+1.57) and STS (+0.93), but with some expense of sense (- 0.21). Adaptation with BT increases sense (+0.34) and naturalness (+0.16). ## 6 Conclusion We discussed how to obtain singable translations with prompt-driven NMT systems with the guid-10Translation outputs are available at https://www.oulongshen.xyz/lyric_translation ance of translatology theories. Specifically, we used back-translation to enhance translation quality and naturalness. We compared the effectiveness of different prompt methods in different controlling aspects and showed their advantage over biased decoding. We designed an effective word boundary control approach and presented a training strategy without the help of music data. We demonstrated the effectiveness of reverse-order decoding in NMT models for rhyme control and showed how it helps users to choose the best suitable rhymes for a paragraph of source text. This work does not explore more detailed prompt manipulation, such as using varied prompts for the same constraint or examining prompt order's impact on performance. We leave these investigations for future research. ## Limitations The current system may require the user to have some music knowledge to compose the word boundary prompt from music. Hence, more efforts need to be made to fulfill this gap before such a system can operate fully automatically without the human user providing word boundary prompt themselves. We use the back-translation of mono-lingual data to augment the parallel training data, but the quality, especially the text style of back-translations has room to improve. Although we have tried using iterative BT to gradually refine the backward direction MT model to adapt its outputs to lyric style, we found some errors gradually accumulated in the back-translated data, which finally made our model perform unsatisfactorily for negative sentences, together with the decrease of controlling effectiveness. Further exploration is needed in this aspect. Similar to chat text, lyrics are usually composed in short sentences. Sometimes it would be challenging to guarantee the consistency of style and meaning for different sentences, if the current sentencelevel translation system are adopted. Hence, for building future lyric translation systems, it would be a better option to translate the lyrics directly at the paragraph level or document level. ## Ethics Statement Our system will help facilitate the creation/recreation of lyrics for song composers. In addition, although our system is implemented in the direction of English-to-Chinese, the controlling aspects and approaches are universal because we did not take any language-specific aspects into account; hence can be easily implemented in other language pairs. Besides, the method and system discussed in this paper are suitable for creating/re-creating singable song lyrics in languages beyond the original version. They also have the potential to benefit language learning by translating domestic languages into other languages the learner is studying and facilitating learning by singing. This methodology has limitations by putting the singability into priority. Translations from this system may sometimes not convey the exact meaning of the lyrics in the source language, causing misunderstanding in this case. For cases where conveying the original meaning is crucial, e.g., advertising and serious art songs, the translation outputs need to be checked and revised when necessary by the user before further usage. For the training and evaluation of our system, all data is publicly available online. Specifically, Chinese Lyric Corpus11 is a public GitHub repository with an MIT license. Lyricstranslate.com is a lyric translation sharing platform, where all parallel lyrics we obtained are publicly available in this website. We adhere to the rules specified in the website's robots.txt file when crawling. For all existing scientific artifacts used in this research, including datasets, models, and code, we ensure they are used in their original intended usage. For human evaluation, we collect evaluation scores without personal identifiers for subjective evaluation to ensure a fair comparison. We ensure that the questionnaire does not contain any offensive content. Please refer to Appendix E for more details of subjective evaluation. ## Acknowledgements This project was funded by research grant A0008150-00-00 from the Ministry of Education, Singapore. ## References Kazuki Akiyama, Akihiro Tamura, and Takashi Ninomiya. 2021. Hie-BART: Document summarization with hierarchical BART. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student 11https://github.com/gaussic/Chinese-Lyric-Corpus Research Workshop, pages 159–165, Online. Association for Computational Linguistics. Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2017. Unsupervised neural machine translation. *arXiv preprint arXiv:1710.11041*. 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Hongxiao Zhang, Hui Huang, Jiale Gao, Yufeng Chen, Jinan Xu, and Jian Liu. 2022. Iterative constrained back-translation for unsupervised domain adaptation of machine translation. In *Proceedings of the 29th* International Conference on Computational Linguistics, pages 5054–5065, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. Yiran Zhang. 2022. 英文歌词文言文翻译中的创造性叛逆问题分析. 英语广场. ## A.2 Dataset Splitting A.3 Data Preprocessing A.4 Back Translation A Data Preprocessing A.1 Dataset Details | Train | Validation | Test | Total | | | |-----------------|--------------|---------|---------|-----------|---------| | Back-translated | #songs | 142,796 | 104 | 104 | 143,004 | | #sentences | 2,720,603 | 2,164 | 2,175 | 2,724,942 | | | Parallel | #songs | 5,341 | 196 | 201 | 5,738 | | #sentences | 102,177 | 4,011 | 4,006 | 110,194 | | them are in pop genre. Lyrics of one song contains multiple lines. Each line usually corresponds to one utterance in singing. The length of each line is usually short. There are 8.6 Chinese characters each line on average. Only a few cases contains lines longer than 20 Chinese characters. The crawled parallel lyrics contains two parts. For the first part, the lyrics are created in English originally, and translated to Chinese by online communities. The second part is composed in Chinese originally and translated to English. Similarly, most of them are in pop genre. Train/validation/test splitting is performed separately for BT and parallel data. Table 10 shows the detailed statistics. We perform text normalization for all Chinese lyric text: all special symbols are removed; traditional characters are substituted with simplified characters12; sentences that are longer than 20 characters are removed; any duplicated sentences are removed. Finally, we split the datasets into train, validation, and test splits while ensuring no same songs exist in different splits. For in-domain denoising pretraining experiments, text corrupting is performed by sentencelevel mask prediction. There is one mask for each sentence. For the span of masks, for sentences with length in (1, 3] and larger than 3, the mask span is sampled from a Poisson distribution with lambda equals 1 and 3, respectively. For back translation, we adopt a Transformer trained with generic-domain Chinese-to-English data13 to obtain sentence-level back translation. 12Follow the implementation of https://github.com/ liuhuanyong/MusicLyricChatbot/blob/master/process_data/ langconv.py 13https://huggingface.co/Helsinki-NLP/opus-mt-zh-en The monolingual lyric corpus from three sources includes lyrics data in Chinese, and vast majority of Table 10: Dataset size of different splits. ## B Implementation Details Model Configuration At the early stage of our experiment, we found that fine-tuning with genericdomain data does not help with the translation quality of lyrics. Hence we adopt mBART without general-domain fine-tuning as the starting point of training. For the unadapted general-domain model, we use mbart-large-50-one-to-many14. Our final model is obtained by fine-tuning mbartlarge-5015 (\#param: 610,879,488) with both backtranslated monolingual data and parallel data. The tokenizer is modified to be character-level on the Chinese side for better controlling effectiveness. The model is trained on one Nvidia A5000 GPU (24GB) for 10 epochs and 3 epochs on backtranslation and parallel data, respectively, taking about 16 hours and 3 hours. The learning rate is set to 3e-5 and 1e-5, respectively, on BT and parallel data. They are the best value in {1e-5, 3e-5, 1e-4} for the baseline model on the two stages of training. Warm-up steps are set to 2500 and 300 for training with the BT and the parallel data. Dropout and label smoothing are set to 0. For decoding, beam-search with beam size 5 is adopted. The maximum output length is set to 30. All other hyperparameters remain as default values. For the dec-emb experiments, instead of using sinusoidal encoding for prompts, we use learnable embedding to keep aligned with the positional em-14https://huggingface.co/facebook/ mbart-large-50-one-to-many-mmt 15https://huggingface.co/facebook/mbart-large-50 Length Prompt. We construct 20 length tokens for length control, len_1 to len_20 for translation output. According to the authors' observation, only an extremely tiny amount of lyrics in Mandarin have more than 20 characters in one line. Rhyme Prompt. For rhyme control, we adopt the Chinese 14-rhyme scheme16 for possible rhyme type, implemented as rhy_1 to rhy_14. There is a special token rhy_0 representing "no rhyme control". This is achieved by randomly setting 1/15 of each type of rhyme prompt to rhy_0 during training. Word Boundary Prompt We first sample a number n from a categorical distribution with the ratio of 1:4:3:1 for 1, 2, 3, and 4 boundaries, and use n′ = min(number of words, n) as the number of bdr_1 tokens. Then, we uniformly sample n′times from all syllable boundary locations, without replacement, as the locations of these bdr_1. After that, we initialize the prompt sequence as a sequence of bdr_0 where the length of the sequence equals the number of syllables in the reference sentence. Finally, we substitute bdr_0 with bdr_1 for the sampled locations. - 16https://github.com/korokes/chipsong **Composer / arranger** Let It Go verse part ![12_image_0.png](12_image_0.png) ![13_image_1.png](13_image_1.png) ## C More Case Studies C.1 Model Outputs We show the translation comparison of the proposed model and the baseline model in Figure 4. The outputs are perfect in the number of syllables and rhyme constraints. With the guidance of word boundary constraints, the output has much higher music-lyric compatibility than the baseline's output. For example, there is a downbeat lying on the note of the second word in the source lyrics, "snow", creating a melody boundary between the first and the second note. To get rid of pronunciation interruption, our system successfully places a word boundary here, avoiding the scenario where the second syllable of the word "今夜" is highlighted. Similarly, in the fourth sentence, our system places a word boundary at the place between the translation of "it looks like" and "I'm the queen", where there exists a musical pause. ![13_image_0.png](13_image_0.png) ## C.2 Different Rhyming Difficulties We noticed that an improper rhyme prompt will lead to lower text quality and a lower chance of constraints being satisfied. For example, Figure 5 shows the rhyme ranking scores of one paragraph and different outputs when using different rhyme targets. With the 1st-ranked rhyme as prompt (Figure 5b), the output is perfect in length and rhyme control and has a satisfactory translation quality. However, with a rhyme that has a low score (Figure 5c), the rhyme control performance drops (one missing rhyme), and both sense and naturalness become questionable. ## C.3 Disadvantage Of Altering Beam Search We show the disadvantages of controlling by altering beam search by examples. Length Forcing Figures 6a and 6b show typical errors when the length constraint is different from the length of the reference sentence, which is usually the case at inference time. If the desired length is shorter than the reference, the beam search might end too soon, so the sentence will be incomplete (Figure 6a). If the desired length is longer than the reference (Figure 6b), there tends to be repetition in the outputs. Both cases significantly damage the translation quality, although the outputs may even ![14_image_0.png](14_image_0.png) ## Have Higher Bleu Scores. Biased decoding for rhyme A type of error frequently happens that the end-words in the outputs are biased toward words that satisfy the rhyme constraints but are irrelevant to the source sentences and are incompatible with other parts of the output sentences, as in Figures 6c and 6d. Such problems are much rarer in translations obtained by promptbased methods. Figure 6e illustrates a possible explanation for the minor performance improvement observed when using a reverse-order decoder with biased decoding for rhyme control. The highlighted word in the biased decoding output, "落", has multiple pronunciations. One of these, "lao", meets the rhyme requirement. However, the correct pronunciation for this specific context is "luo", which does not fulfill the rhyme constraint. ## D Error Bar In order to reduce the randomness in the results of our comparative study, each experiment in §5.2 is run three times. Here we show more detailed results by the error bar charts in Figure 7. ## E Subjective Evaluation We select the same five songs as GagaST (Guo et al., 2022) for our subjective testing. When doing this experiments, we ensure these songs are not in the training set. As mentioned in §5.5, we evaluate the results from four aspects: sense, naturalness, music-lyric compatibility, and the Singable Translation Score (STS), an overall singable translation quality. The four metrics are evaluated at different levels. Sense and naturalness are evaluated for independent textonly sentences, melody compatibility is evaluated for each sentence given the music context, and the last metric is evaluated at the paragraph level. When evaluating STS, we show participants not only the music sheet containing melody notes and lyrics, but also with a singing audio. This audio file contains singing voice synthesized with original melody and generated lyrics, mixed with original musical accompaniments. The voice part is synthesized by ACE Studio17. The accompaniments is obtained by using a source separation model Demucs v3 *mdx_extra* (Défossez, 2021). To test the reliability of our subjective metrics, we computed the inter-rater agreement using intraclass coefficients (two-way mixed-effect, average measure model). The results are as follows: 0.8313 for sense, 0.7817 for naturalness, 0.8663 for music17https://ace-studio.huoyaojing.com/ lyric compatibility, and 0.7870 for Singable Translation Score. All of these values fall within the "good reliability" range suggested by (Koo and Li, 2016). ## E.1 Instructions For Human Evaluation Study Information - Project Title: [hidden for anonymity] - Obtained IRB exemption from NUS-IRB, reference code: [hidden for anonymity] - PI: [hidden for anonymity] - Goal of the survey: This survey is for research purpose. Results from the participants will be used as the "Subjective Evaluation" section in our future publications. - Purpose of research: Evaluate the performance of automatic lyric generation systems developed by [hidden for anonymity] - If you would like to continue to answer this questionnaire as a participant, You agree that your participation in this research is voluntary. You can skip any questions if you refuse to answer. But for better data consistency, we recommend you finish all questions. You will spend about 3 hours to finish the questionnaire. Please time yourself while you fill out the questionnaire. You will receive 50 SGD for each hour of your participation. The maximum amount is 150 SGD. ## Steps Of The Questionnaire The current version of lyric generation system generate lyrics in Mandarine according to given English sentences as input. You are going to evaluate these generated lyrics in a series of aspects. There will be two sections of evaluation, as in the below two sections. For each evaluation aspects, you are going to evaluate them by assigning an integer score from [1,2,3,4,5]. 1 Text-based evaluation 1.1 You will be shown Text of input sentence, and Generated lyrics, which is expected to retain the meaning of the input sentence. 1.2 Evaluation aspects Note: for both criteria, evaluation will be **sentence-level**. You give score to one sentence at a time. (1) Sense ## How To Evaluate: More meaning of the original sentence is retained in the output, higher score this output deserves. 5 marks - The output perfectly retain the meaning of input sentences. 4 marks - Between 5 and 3. 3 marks - The output retained the overall meaning of input, but some parts are not accurately translated, or, some **important** parts in the input are ignored, or, there are too much additional words so that the input's main idea slightly changed 2 marks - Although there are some words are successfully translated, but the output majorly change the meaning of input sentence. 1 mark - I did not see any relationship between the output and the input. Note: we do not add penalty to outputs when Outputs contains **extra decorative words** that are not in the input sentence in the source language, but did not change the main idea of input, or Words that are **not important**, in the input sentence, are ignored in the outputs. If the meaning of input sentence are well maintained in the outputs. ## (2) Naturalness How To Evaluate We **only look at the output** this time without considering input. The more natural the output is, higher score it deserve. 5 marks - Output sentence accord with the habit of Mandarin expression, and is in high fluency. Moreover, if I see a lyricist writing lyrics like this in a Chinese song, I think it's normal. 4 marks - Between 5 and 3. 3 marks - The output has good fluency, but not in the usual style of lyrics of Mandarin. 2 marks - The expression is so unnatural so that I don't accept it to be written as song lyrics. 1 marks - Output sentence conflict with Mandarin expression habit. I've never seen someone speak Chinese this way. Note: Punctuation marks are deleted from output sentences. If you think that a sentence is not natural because of this reason, you can try to break the sentence according to the punctuation mark position of the input sentence and then assign a mark. ## Example Input: like a swirling storm inside 5 marks output: **像内⼼汹涌的漩涡;**1 mark output: **像旋转的暴⻛⾬** ## 1.3 Questionnaire Please finish the **text_based_evaluation.xlsx**. We recommend you to finish it by 2-pass: 1st pass for Sense, and 2nd pass for Naturalness. ## 2 Listenting Evaluation Before you start: We also provide the original version of the song. Please listen to it before your evaluation of our system outputs. ## 2.1 You Will Be Shown Original version of song in both audio and sheet format Music sheet together with generated lyrics, and Synthesized singing with original music and generated lyrics. ## 2.2 Evaluation Aspects (1) Music-Lyric Compatibility: Note: This is a **sentence-level** evaluation. ## How To Evaluate We look at the output sentence and the melody in music sheet, while listening to the synthesized song. The higher the compatibility between the lyrics and the music, the higher the score. We give score according to "**lyric-melody alignment**" and "**word boundary conflict**". "**lyric-melody alignment**": Do we have to divide original musical note to multiple ones, or extend the duration of certain words, to make the lyric and melody aligned together? If lyrics have same number of syllables with the melody note numbers, we don't need such adjustments. "**word boundary conflict**": We consider two types of conflict: (1) a musical pause lies inside a multi-syllable word, so the pronunciation have to pause half way. (2) Or, the second (or later) syllable of a word is highlighted by the music instead of the first syllable. Usually we do not stress 2nd or later syllable of a word in Mandarin speaking, hence making the pronunciation unnatural. 5 marks: Lyrics syllable perfectly align with the music notes. No word boundary conflict. 4 marks: Lyrics syllable perfectly align with the music notes. There are some word boundary conflicts, but is acceptable 3 marks: Lyrics syllable **perfectly** align with the music notes. However, word boundary conflict is everywhere, so I feel weired to listen to the singing. 2 marks: Lyrics syllable mostly align with the melody notes. 1 mark: Lyrics basically do not align with the melody so lots of adjustments have been made to the melody. ![18_image_0.png](18_image_0.png) ![18_image_1.png](18_image_1.png) We have to extend the duration of "上" and "⽩" to align with the notes. | We have to extend the duration of "上" and "⽩" to align with the notes. There two word boundary conflicts in total: The word "⻅过" is broken up by a musical pause; The second syllable of word "脚印", that is "印", lies on a downbeat. | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ## (2) Singable Translation Score: Note: This is a **paragraph-level** evaluation. ## How To Evaluate This is a overall quality score to evaluate the output's singability and translation quality. 5 marks: It's not strange if you are told the lyrics are composed in Mandarin originally. 4 marks: The output is good in singability and rhyming, has overall accurate translation and naturalness, but still has room to improve. 3 marks: The output is good in singability and rhyming, but not retain the meaning of original lyrics. or, not natural 2 marks: The output seems like lyrics, but fails at music-lyric compatibility Note: if you think rhyming (押韵) will make the song better but this output is not in rhyme, it deserve no more than 2 score. However, if you think rhyming is not necessary for this song and this output do has greate quality, you can give it higher marks. 1 mark: It's just a "**歌词⼤意** (main idea of input)", and nothing else. ## 2.3 Questionnaire Please finish the questionnaire at the google form link. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The "Limitation" section. ✓ A2. Did you discuss any potential risks of your work? The "Ethics Statement" section. ✓ A3. Do the abstract and introduction summarize the paper's main claims? The "Abstract" section. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Data: Section 4; Section 4.2. Code and model: Appendix B; Appendix E. ✓ B1. Did you cite the creators of artifacts you used? Data: Section 4; Section 4.2. Code and model: Section 4.1; Appendix B; Appendix E. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The "Ethics Statement" section. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? The "Ethics Statement" section. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The "Ethics Statement" section. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appendix A.1. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix A.2. ## C ✓ **Did You Run Computational Experiments?** Section 4. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix B. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix B. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5; Appendix D. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.2; Appendix A.3; Appendix E. D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 5.5. ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix E.1. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 5.5. ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? "Study Information" in Appendix E.1. ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? "Study Information" in Appendix E.1. ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 5.5.

yang-etal-2023-fantastic

Fantastic Expressions and Where to Find Them: {C}hinese Simile Generation with Multiple Constraints

https://aclanthology.org/2023.acl-long.28

Similes occur in the creative context of describing a concept (i.e., tenor) by making a literally false yet figuratively meaningful comparison to another (i.e., vehicle). Previous efforts form simile generation as a context-free generation task, focusing on simile-style transfer or writing a simile from a given prefix. However, generated texts under such settings might be undesirable, such as hardly meeting the simile definition (e.g., missing vehicle) or difficult to address certain preferences of content as humans wish (e.g., describe the color of apples through the simile). We believe that a simile could be more qualified and user-oriented if incorporated with pre-specified constraints. To this end, we introduce controllable simile generation (CSG), a new task that requires the model to generate a simile with multiple simile elements, e.g., context and vehicle. To facilitate this task, we present GraCe, including 61.3k simile-element annotated Chinese similes. Based on it, we propose a CSG model Similor to benchmark this task, including a vehicle retrieval module Scorer to obtain the explicable comparison for a given tenor in the vehicle-unknown situation. Both statistical and experimental analyses show that GraCe is of high quality beyond all other Chinese simile datasets, in terms of the number (8 vs. 3) of annotation elements, Is-Simile accuracy (98.9{\%} vs. 78.7{\%}), and increasing model-performance gains for both uncontrollable and controllable simile generation. Meanwhile, Similor can serve as a strong baseline for CSG, especially with Scorer, which beats model-based retrieval methods without any re-training.

# Fantastic Expressions And Where To Find Them: Chinese Simile Generation With Multiple Constraints Kexin Yang♠ ∗ Dayiheng Liu♠ † Wenqiang Lei♢ Baosong Yang♠ **Xiangpeng Wei**♠ Zhengyuan Liu♣ **Jun Xie** ♠ ♠Alibaba Group ♢National University of Singapore ♣Institute for Infocomm Research (I2R), A*STAR, Singapore {kexinyang0528, losinuris}@gmail.com ## Abstract ![0_Image_0.Png](0_Image_0.Png) Similes occur in the creative *context* of describing a concept (i.e., *tenor*) by making a literally false yet figuratively meaningful comparison to another (i.e., *vehicle*). Previous efforts form simile generation as a context-free generation task, focusing on simile-style transfer or writing a simile from a given prefix. However, generated texts under such settings might be undesirable, such as hardly meeting the simile definition (e.g., missing *vehicle*) or difficult to address certain preferences of content as humans wish (e.g., describe the color of apples through the simile). We believe that a simile could be more qualified and user-oriented if incorporated with pre-specified constraints. To this end, we introduce controllable simile generation (CSG), a new task that requires the model to generate a simile with multiple simile elements, e.g., *context* and *vehicle*. To facilitate this task, we present GraCe, including 61.3k simile-element annotated Chinese similes. Based on it, we propose a CSG model Similor to benchmark this task, including a vehicle retrieval module **Scorer** to obtain the explicable comparison for a given *tenor* in the vehicle-unknown situation. Both statistical and experimental analyses show that GraCe is of high quality beyond all other Chinese simile datasets, in terms of the number (8 vs. 3) of annotation elements, Is-Simile accuracy (98.9% vs. 78.7%), and increasing model-performance gains for both uncontrollable and controllable simile generation. Meanwhile, Similor can serve as a strong baseline for CSG, especially with Scorer, which beats model-based retrieval methods without any re-training. ## 1 Introduction Similes are widely-used and stimulate people's creativity (Li et al., 2022). According to Rhetoric's classical terms (Campbell, 1988), a simile uses comparison words (i.e., *comparator*) to make a literally false comparison between a concept (i.e., tenor) and another (i.e., *vehicle*). It also ensures this comparison pair is figuratively meaningful by examining whether they have shared properties (i.e., *ground*) (Tartakovsky et al., 2019). Notably, ground can be expressed in an explicit or implicit way (Chakrabarty et al., 2020). As shown in Figure 1 qualified samples. "Maple leaves are like torches of fired red." has the explicit *ground* that the *tenor* "maple leaves" and the *vehicle* "torches" have the similar color of "fired red", while "maple leaves are like small palms." implies the *ground* that they have a similar pentagram shape. Although simile detection has been widely explored (Liu et al., 2018; Zeng et al., 2020; Mao and Li, 2021), simile generation is still in its fledgling stage. Existing efforts focus on context-free simile generation, including: 1) style-transfer-based and 2) prefix-based simile generation. The former paraphrases a literal sentence into its simile version (Chakrabarty et al., 2020; Zhang et al., 2021) and the latter aims at writing a simile from a prespecified *tenor* (Li et al., 2022; Chen et al., 2022). Despite great progress, such experiment settings may result in undesirable results, such as unqualified similes or being unable to meet the content ∗ Work is done during internship at DAMO Academy † Corresponding author. 468 Dataset # Nums # Avg. % Is-Simile Topic Comparator Tenor Vehicle Ground **Context** W / F W / F Above / Below Poetry (2019b) 43,051 23 - % % %/ % %/ % % !/% Lyrics (2019b) 246,669 23 - % % %/ % %/ % % !/% CS (2021) 5,490,721 61 29.3% % % %/% %/% % !/ ! CMC (2022) 2,787 35 78.7% % ! !/% !/% % %/ % GraCe 61,360 89 98.9% ! ! !/! !/! ! !/ ! preferences of humans wish. As shown in Figure 1, the former means the generated sentences may miss indispensable simile elements or generate incoherence elements, i.e., generating element-incomplete or -mismatched samples. For example, "maple leaves are small and beautiful." misses both *tenor* and *vehicle* and "maple leaves are like small green fans." has inconsistent *vehicle* "green fans" with the *context* "mountains are red". The second problem may arise when users wish to describe the color of maple leaves by similes but get "maple leaves are like small palms.", although it is qualified according to the simile definition. To solve these problems, we explore incorporating various constraints into simile generation. Specifically, we introduce a new task of controllable simile generation (CSG) - generating a simile with multiple simile elements (e.g., vehicle, *context*, etc.) from a given prefix (i.e., *topic*). We collect a Fine-Grained annotated Chinese Simile dataset (GraCe), containing annotated 61.3k similes from 260k cleaned text of student compositions. As shown in Table 1, we expand three commonly annotated elements (i.e., tenor, *vehicle* and *comparator*) (Li et al., 2022) to eight, such as the *context* element that could put each simile into a more naturally-using situation (Sun et al., 2022).1In details, we annotate explicit *ground* to better understand the simile comparison. As for implicit ground, we try to interpret the relationship between *tenor* and *vehicle* by their cognitive properties. Such property is a set of adjectives that describe the distinctive features of the corresponding nouns (Veale and Hao, 2007), which helps to understand the comparison from the aspect of Cognitive Linguistics (Kövecses, 2010). To benchmark CSG, we build the model **Similor**, which first retrieves vehicle (if it is unknown) by the module **Scorer** (a Shared cognitive-property-based retrieval method ) for the given *tenor*, then incorporates all constraints and the input prefix (i.e., *topic*) to generate the simile. Both statistical and experimental analyses show that GraCe is of high quality beyond previous Chinese simile datasets. Meanwhile, Similor can successfully incorporate the constraints in the outputs. Especially in *vehicle*-unknown setup, Scorer beats the model-based retrieval method both in automatic and human evaluations without any re-training.2 ## 2 Related Work Different from metaphor (Yu and Wan, 2019; Chakrabarty et al., 2021a; Stowe et al., 2021) that using implicit comparators, similes are much easier to be located. However, existing efforts mainly focus on simile detection (Liu et al., 2018; Zeng et al., 2020; Mao and Li, 2021), leaving simile generation under-explored. Previous work on context-free simile generation can be divided into: 1) styletransfer-based and 2) prefix-based simile generation. The first forms this task as paraphrasing a literal sentence into a simile-style sentence, and automatically edits self-labeled similes to their literal version for building pairs of (literal sentence, simile). For example, SCOPE (Chakrabarty et al., 2020) uses commonsense properties words (Bosselut et al., 2019) of the *vehicle* to replace it in a simile, then removes the *comparator* to form the final literal sentence. WPS (Zhang et al., 2021) 2Our code and corpus will be released at https://github. com/yangkexin/GraCe. 1See Appendix Figure 4 for detailed annotation. ![2_image_0.png](2_image_0.png) ![2_image_1.png](2_image_1.png) deletes a span from a simile to obtain the literal sentence. The second focuses on generating the comparator and *tenor* from a pre-specified *tenor*. Liu et al. (2019b) uses a continuous latent variable as a rhetoric controller to generate Chinese poetry. CMC (Li et al., 2022) provides a multi-task framework that leverages unlabeled data to enhance performance. Chen et al. (2022) use three words triple (tenor, attribute, *vehicle*) and a relationship pattern to hint the model for generating simile. Different from all of them, we focus on controllable simile generation - generating a simile with multiple constraints. To make it a computationally feasible task, we build a high-quality dataset GraCe and a CSG model Similor with Scorer to ensure explicable tenor-*vehicle* pairs in generated similes. As shown in Table 1, GraCe is far beyond the most recent dataset CMC (Li et al., 2022) in terms of collected samples (61.3k v.s. 2.7k), simile quality (98.9% v.s. 78.7% Is-Simile accuracy) and the number of annotated elements (eight v.s. three).3 ## 3 Grace Dataset A fine-grained annotated simile dataset is important both for training a supervised CTG model and exploring combinations of constraints. However, relevant datasets (Table 1) might be insufficient. Therefore, we present the GraCe dataset, and elaborate on dataset creation and analysis. ## 3.1 Dataset Creation Dataset Collection We collect 260k student compositions (grades range from elementary to high school) from the free-access website,4ensuring data resources are close to real-world cases. After sentence segmentation and the removal of non- Chinese sentences, we get about 5.48 million sentences. At most two sentences above and below each sample are used as the *context* element. Dataset Processing As shown in Figure 2, we build our GraCe dataset in four steps. In **Step 1**, we filter out sentences that do not contain *comparator*-related words. Specifically, we tokenize candidate sentences with the toolkit Jieba5and filter out sentences without *comparator*-related words, as *comparator* is the hallmark of a simile. The *comparator* words are varied to ensure the diversity of simile patterns (e.g., "好像", "仿佛","犹如", etc, all means "like"). However, a sentence containing comparator may not trigger a simile (Liu et al., 2018). As the example 2 in Step 1, "他还是像过去一样喜欢打篮球。(He still likes playing basketball as before.)", here "像 (as)" implies identity rather than comparison. Therefore, **Step 2** focuses on recognizing non-simile sentences containing comparator words. We train a binary classifier based on RoBERTaLarge (Liu et al., 2019a) with a confidence score of 80% to select similes.6 Notably, we do not pursue higher score confidence as it may face the risk of reducing patterns of simile. After the above two steps, we get the simile dataset without fine-grained annotations. Therefore, **Step 3** aims at annotating tenor, *topic*, and vehicle for each simile. We utilize a sequence labeling model based on RoBERTaLarge to annotate tenor and *vehicle* for each simile.7 Meanwhile, we annotate *topic* as the span between *tenor* and comparator, which denotes *tenor* and its supplementary description. After that, **Step 4** furtherly aims at annotating the *ground* and cognitive properties of *tenor* and *vehicle*. As the interpretation | Measurement | Value | |----------------------|---------| | % Simile | 98.9 | | % Correct Tenor | 95.2 | | % Correct Vehicle | 98.2 | | % Correct Comparator | 98.7 | | % Correct Ground | 94.1 | for a simile comparison (Tartakovsky et al., 2019), ground plays an important role in making the tenor-*vehicle* pair of a simile being easily-understood and figuratively meaningful (Campbell and Katz, 2006; End, 1986), yet being ignored in previous datasets. We first query Cogbank dataset8to obtain the cognitive properties for both *tenor* and *vehicle*. Then, their shared properties are used to fuzzy match9the property-related clauses in a simile as the *ground*. Finally, the detailed statistics of our GraCe dataset are shown in Table 2, and some dataset samples are shown in Appendix A.4. ## 3.2 Dataset Analysis Data Quality We invite three professional annotators to independently annotate 1000 randomly selected samples from multiple aspects.10 As shown in Table 3, only 1.1% samples are not similes, which is far beyond other Chinese simile datasets (see Table 1). More importantly, it maintains high accuracies even in fine-grained annotations for important elements of a simile (94.1% - 98.7%). Diversity of Similes We analyze the diversity of similes and present the statistics in Table 4. First, the fertility of *tenor* and *vehicle* ensure the diverse content of the simile. Besides, different from Liu et al. (2018); Chakrabarty et al. (2020) using only a single pattern *comparator* of simile in their dataset (i.e., "_好像 (like) _" in Chinese), we build the comparator as 371 patterns of fill-in-the-blank templets. Specifically, inspired by WPS (Zhang et al., 2021) that the position information of simile in the context is a strong feature, we incorporate it by adding the punctuation that closely followed the vehicle to our template. As shown in Appendix Figure 5, "_如同 (like) _," means the simile part appears in the middle clause without any description after *vehicle*. If no punctuation in the template, it means there is an explicit ground or *context* after vehicle to complement the content. ## 4 Controllable Simile Generation 4.1 Task Definition | Measurement | Value | |------------------------|---------| | # Distinct Tenors | 7,958 | | # Distinct Vehicles | 5,350 | | # Distinct Comparators | 371 | The controllable simile generation task is formulated as follows: given a *topic* x containing a *tenor* st and a variety of pre-specified constraints c, the model generates a simile y = (y1, y2*, ..., y*N ) by: $$p(\mathbf{y}|\mathbf{x},\mathbf{c})=\prod_{n=1}^{N}p(y_{n}|y_{<n},\mathbf{x},\mathbf{c};\theta),\qquad(1)$$ where θ are the model parameters. Notably, the constraints c can be freely selected and combined from the candidate set s = (sv, sp, sc), which denote the vehicle, *comparator*, and *context*, respectively. ## 4.2 Methodology We benchmark this task with the CSG model Similor, which contains a module Scorer for the *vehicle*-unknown situation. To ease of presentation, we start with a toy example to illustrate them. Similor As shown in Figure 3, the *topic* "美丽的春天 (the beautiful spring)" containing the tenor "春天 (spring)" is firstly concatenated with optional sequential constraints by the separator signal "[SEP]". If the *vehicle* is pre-specified in | Measurement | # Nums | # Average Tokens | |--------------------|----------|--------------------| | Sentences | 61,360 | 89.0 | | Annotated Elements | | | | Topic | 61,360 | 11.4 | | Tenor | 61,360 | 1.9 | | Tenor Property | 52,474 | 73.2 | | Comparator | 61,360 | 2.6 | | Vehicle | 61,360 | 2.3 | | Vehicle Property | 61,360 | 83.0 | | Ground | 15,087 | 8.6 | | Context | 57,543 | 39.5 | ![4_image_0.png](4_image_0.png) the constraints, the input sequence is then fed into an encoder-decoder model. Afterward, the model auto-regressively generates "好像一幅画,它收集了大自然的色彩。 (is like a painting. It gathers the colors of nature.)". We first continue pretraining the large Chinese text generation model (e.g., ChineseBART (Shao et al., 2021)) on the collected 260k student compositions with the language modeling object. Then, Similor is instantiated with it to be finetuned on the GraCe. Scorer If the *vehicle* is unknown, we use the Scorer module to retrieve a *vehicle* and then add it to the input sequence. As shown in the right part of Figure 3, Scorer contains two steps to get figuratively meaningful while literally false pair of tenor-*vehicle*. **Step 1** queries Cogbank dataset for the *tenor* "春天 (spring)" to obtain its top k most frequently used cognitive properties. These properties provide a basis for *vehicle* candidates selection and matching. The Cogbank dataset (83,017 items) contains more words than the glossary of common words in modern Chinese11 (56,008 items), allowing fuller retrieval of *vehicle* candidates. In the implementation, the top 20 nouns with numbers of cognitive properties identical to *tenor* are chosen as candidates, which ensures a figuratively meaningful simile as the matched properties can be regarded as the *ground*. However, some literal-related words may also be selected in this step, e.g, "春风 (spring wind)". To obtain only figurative items, **Step 2** reranks the Step 1 candidate based on the Euclidean distance of word embeddings between each item and *tenor*. Candidates with a longer distance are ranked higher, as they are less literally associated with *tenor*. As a result, the "画 (painting)" is selected as the final *vehicle*. To be exact, given a tenor st, the i-th item wiin Cogbank dataset get the ranking score Score*candi*i by: $$\begin{array}{c}{{S o r e_{w_{i}}=\mathrm{Rank}(F i g_{w_{i}})+\mathrm{Rank}(L i t_{w_{i}}),}}\\ {{F i g_{w_{i}}=\mathrm{Match}(w_{i},s_{t}),}}\\ {{L i t_{w_{i}}=\mathrm{EucDist}(w_{i},s_{t}).}}\end{array}\tag{2}$$ Where Rank(·) denotes getting the ranking of the corresponding score. Match(·) means to count the numbers of shared cognitive properties between two items and EucDist(·) means the Euclidean distance between their word embedding. Notably, we use rankings to normalize these scores, avoiding the effects of different score scales. ## 5 Experiments In this section, we first experimentally evaluate the quality of the GraCe dataset by applying it to prefixbased simile generation (§ 5.1). Since the setup of this uncontrollable generation task does not need additional annotations on the training samples, we can compare GraCe with previous Chinese simile datasets. Based on it, we then evaluate the proposed Similor on the new CSG task (§ 5.2). Specifically, we first compare different model varieties of Similor constrained by *comparator* and *vehicle*, and then evaluate the performances of Similor under more extensive constraints. Finally, we explore whether Scorer helps Similor to generate similes in the *vehicle*-unknown setup. ## 5.1 Experimental Analysis Of Grace As statistical analysis is insufficient to evaluate GraCe, we evaluate it by prefix-based simile generation. One of the simple pipelines is to train a | Dataset | % Comp.↑ | Simile Conf.↑ | PPL↓ | |-----------------------|------------|-----------------|--------| | Backbone: ChineseGPT2 | | | | | None | 1.4 | 0.3 | 40.9 | | CS (2021) | 46.0 | 0.6 | 43.0 | | CMC (2022) | 44.4 | 0.7 | 30.9 | | GraCe | 93.5 | 0.9 | 10.9 | | Backbone: ChineseBART | | | | | CS (2021) | 65.3 | 0.5 | 33.1 | | CMC (2022) | 56.7 | 0.8 | 33.3 | | GraCe | 85.3 | 0.9 | 28.7 | | Dataset | Fluen.↑ | Creat.↑ | Consi.↑ | Overall↑ | |-----------|-----------|-----------|-----------|------------| | CS | 2.5 | 1.9 | 1.9 | 2.1 | | CMC | 2.2 | 2.0 | 1.9 | 2.0 | | GraCe | 3.0 | 3.2 | 3.2 | 2.8 | generator with the language modeling object on the simile dataset. In inference, this model is asked to generate a simile with a pre-specified *tenor*. Baselines and Backbones. We compare the proposed GraCe with previous Chinese simile datasets: 1) CS (Zhang et al., 2021) contains 5.49M similes extracted from online fictions. 2) CMC (Li et al., 2022) contains 2.7k metaphors and similes from Chinese literature corpus. Besides, we utilize two representative Chinese pre-trained language models to avoid training from scratch: 1) **ChineseBART**(CBART) (Shao et al., 2021): a BARTLarge model pre-trained on 200GB text from Chinese Wikipedia and WuDaoCorpus. 2) **ChineseGPT2** (CGPT2) (Zhao et al., 2019): a GPT2Medium model pre-trained on the CLUECorpusSmall dataset. Experiment Setup. We employ the original hyper-parameter setting of BARTLarge and GPT2Medium to train all models, with a BERT tokenizer (Devlin et al., 2019) to process Chinese text. During inference, we use 25 common *tenor*s as prefixes and ask models to continue writing with them (100 completions for each).12 Metrics. For automatic evaluation, we first use Perplexity (PPL) from CGPT2 to evaluate the text quality. As for simile evaluation, we compute 12See Appendix B.1 for the word list and inference setup the proportion of sentences containing *comparator* words (**%Comp.**) to evaluate element-incomplete cases, because it's the hallmark of a simile. However, a sentence containing *comparator* words may not trigger a simile (Liu et al., 2018). Therefore, we use **Simile Conf.** to evaluate the figurative meaning of the generated results, i.e., element-mismatched cases. Specifically, we reuse the simile classifier in Step 2 of the dataset processing (See § 3.1) to compute the averaged confidence score of each method. Aside from it, we also conduct human evaluation following Chakrabarty et al. (2020). 250 samples are randomly selected from each generated result. Then, three crowdsource evaluators are asked to rate model results in four categories: 1) **Fluency** (Fluen.). Whether the sentence is fluent and grammatical; 2) **Creativity**. How well the sentence is figurately meaningful; 3) **Consistency** (Consi.). Whether the generated *vehicle* has shared properties with the pre-specified *tenor*. 4) **Overall**. How good is the simile overall? The score is based on how well-formed, creative, and consistent it is. Scores are ranged from 1 to 4, the higher is better.13 Results The prefix generation results are shown in Table 5 and human evaluation results are in Table 6. We find that: 1) Models finetuned with GraCe outperform other simile datasets in terms of text quality and simile creativity. 2) Generative language models tend to produce literal sentences over similes that highlight challenges of simile generation, as also mentioned in Chakrabarty et al. (2021b). Although Models could produce similelike sentences through prefix generation, undesired results are also obtained (e.g., missing *compartor* and having incoherent tenor-*vehicle* pairs) without controlling simile elements.14 Thus, it is necessary to explore a new simile generation method. ## 5.2 Controllable Simile Generation We first benchmark the CSG task with different model varieties constrained on pre-specified *comparator* and *vehicle*, then explore the performances of Similor under different combinations of constraints. Finally, we evaluate Similor with Scorer in the *vehicle*-unknown CSG setup. Specifically, given a *topic* containing a *tenor*, the tenor-*vehicle* pair retrieval method is asked to find an appropriate *vehicle* as the constraint, then hints Similor to | Methods | ROUGE-1/2/L↑ | BLEU↑ | BERTScore↑ | ACC-V↑ | |----------------|----------------|---------|--------------|----------| | CGPT2 | 20.7/4.2/18.3 | 0.3 | 60.6 | 16.4 | | CBART | 21.3/10.9/20.9 | 1.7 | 55.9 | 71.1 | | CGPT2FT | 22.2/7.6/20.2 | 3.0 | 56.8 | 19.2 | | CBARTFT | 31.4/13.3/26.6 | 3.0 | 66.7 | 54.5 | | SimilorCGPT2 | 37.7/17.4/32.9 | 3.3 | 83.8 | 49.1 | | SimilorCBART | 56.6/39.6/54.7 | 19.7 | 68.9 | 99.4 | | SimilorCGPT2FT | 39.5/19.0/34.0 | 4.0 | 68.2 | 84.3 | | SimilorCBARTFT | 57.3/40.5/55.3 | 19.9 | 69.1 | 99.0 | | Constraints | ROUGE-1/2/L | BLEU | BERTScore | ACC-V | ACC-C | |--------------------------------|----------------|--------|-------------|---------|---------| | None | 29.5/10.4/27.1 | 4.2 | 63.4 | 17.9 | 38.5 | | Context | 35.4/14.7/32.8 | 5.6 | 65.4 | 27.4 | 42.0 | | Comparator | 43.0/23.6/41.5 | 10.0 | 66.2 | 30.0 | 95.9 | | Vehicle | 51.9/30.6/47.6 | 14.0 | 68.4 | 99.0 | 47.2 | | Vehicle + Comparator | 57.3/40.5/55.3 | 19.9 | 69.1 | 99.0 | 99.9 | | Vehicle + Comparator + Context | 59.8/41.4/57.2 | 21.3 | 69.9 | 94.8 | 98.3 | Methods. As a new task of simile generation, we benchmark it with Similor and evaluate model variants as follows: 1) **ChineseBART** (CBART) and 2) **ChineseGPT2** (CGPT2) as described in § 5.1. However, they take language modeling as the learning object and cannot directly adapt to the new task. Following He et al. (2022) use the manual prompt for simile probing, we use "以_为喻体, 写出比喻句: (means write a simile with _ as a vehicle:, '_' is the placeholder for pre-specified textitvehicle)" as the prompt. Then, it is concatenated with the given *topic* and *comparator* as the input while generating a simile, which is similar to the in-context learning (Brown et al., 2020). 3) **Finetuned ChineseBART** (CBARTFT) and 4) Finetuned ChineseGPT2 (CGPT2FT). We finetune CBART and CGPT2 on the collected 260k student compositions with the language modeling object, respectively. The goal of finetuning is to make the model adapt to the composition writing domain. 5) **Similor**. We first instantiate Similor with CBART and CGPT2, namely SmilorCBART and SmilorCGPT2, respectively. To evaluate the gain performances that continuing fine-tuning on the student compositions, Similor is also instantiated by CBARTFT and CGPT2FT, namely SmilorCBARTFT and SmilorCGPT2FT , respectively. All of the models are then finetuned by GraCe Dataset. After that, we evaluate Scorer variants and baseline as follows: 1) **Literally False Matching** (LFM). The second step of Scorer, aims at ranking the candidate by the word embedding Euclidean distance between the candidate and the *tenor*. 2) ANT (Chen et al., 2022): A pre-training stage for BERTLarge that only masks the noun or adjective in amod dependencies. Following Li et al. (2022), we translate the concatenated *comparator* and *topic* into English by Google translation and feed it to ANT to generate a *vehicle*. Experiment Setup. We randomly split the GraCe dataset into 2000 test samples, and 2000 validation samples, and the rest are used for training. The training parameters setup for all models is as same as § 5.1. In inference, the beam size and length penalty (Wu et al., 2016) are set to 4 and 1.2, respectively. As for evaluating Scorer, we remain top 20 candidates for Step 1, finally returning the top one *vehicle* for generating the simile. For a fair comparison, all retrieval methods use SimlorCBARTFT to generate final results. Metrics. Following Chakrabarty et al. (2020); Zhang et al. (2021); Li et al. (2022), we evaluate results on **BERTScore** (Zhang et al., 2020), four-gram **BLEU** (Papineni et al., 2002), **ROUGE1/2/L** (Lin, 2004). Besides, if the vehicle or *comparator* is pre-specified as the constraint, we use ACC-V or **ACC-C** to evaluate the accuracy of the offered vehicle or *comparator* appears in outputs. As a novel setup in CSG, *vehicle*-unknown CSG aims to find a figuratively meaningful yet literally | Methods | Automantic Evaluation | Human Evaluation | | | | | | | |---------------|-------------------------|--------------------|------|---------|---------|---------|----------|-----| | Simile Conf.↑ | Literal Simi.↓ | PPL↓ | %V↑ | Fluen.↑ | Creat.↑ | Consi.↑ | Overall↑ | | | ANT | 0.6 | 0.003 | 25.0 | 42.7% | 1.9 | 1.7 | 1.6 | 1.7 | | LFM | 0.8 | -0.020 | 28.1 | 100.0% | 2.7 | 2.3 | 2.3 | 2.3 | | Scorer | 0.8 | 0.240 | 12.8 | 100.0% | 3.1 | 2.5 | 3.0 | 2.6 | | Automatic | Human Evaluation Scores | | | | |--------------|---------------------------|--------|--------|---------| | Metrics | Fluen. | Creat. | Consi. | Overall | | Simile Conf. | 0.312 | 0.634 | 0.603 | 0.540 | | %Comp. | 0.286 | 0.329 | 0.324 | 0.351 | | PPL | 0.388 | 0.311 | 0.321 | 0.377 | false (Goodman, 1979) tenor-*vehicle* pair that has shared attributes to form the *ground*. Thus for evaluating Scorer, we first use **Simile Conf.** and **Perplexity** (PPL) mentioned in § 5.1 to evaluate the figurative meaning and text quality of the outputs, respectively. Following Shutova et al. (2016); Yu and Wan (2019), literally false factor is computed by **Literal Simi.**, which denotes the average cosine similarity of the given *tenor* and the retrieval *vehicle*, the lower the better. We use the SimlorCBARTFT to compute the word embeddings. Besides, we conduct the human evaluation described in § 5.1. Results. Comparations of different model varieties are shown in Table 7. We find that: 1) Both CSG task and models benefit from the pre-training stage, especially for the BART-based backbone. 2) Both SimilorCBART and SimilorCGPT2 can generate similes that correctly incorporate constraints in outputs, with higher text quality than baselines. Besides, performances of Similor with different constraints are in Table 8, which indicates: 3) Introducing more simile constraints helps Similor to generate desired similes. Especially *context*, Similor could generate similes only being hinted by context (BERTScore 63.4 to 65.4). Finally, As shown in Table 9, Scorer beats model-based retrieval method both in figuratively meaningful and text quality, guaranteeing to provide *vehicle* for each testing *tenor*. As for literal similarity, LFM gets the highest score yet surfers from the lowest text quality, indicating that there is a trade-off between figuratively meaningful and literally false factors when generating similes. ## 5.3 Further Discussions As a new task in simile generation, the evaluation method of it is absolutely important. Thus we compute the system-level Pearson correlation between automatic scores and human judgments of generated similes. In Table 10, Simile Conf. shows a strong correlation with human scores in terms of Creativity and Consistency, indicating that it could be an effective method to evaluate the figurative meaning of similes. In contrast, % Comp. shows a poor correlation with that two scores, which demonstrates the limitations of only considering the *comparator* when judging a simile. Meanwhile, PPL shows a higher correlation than the other two metrics in evaluating fluency, yet having a remarkable gap with the human score. To furtherly explore the concerns of human when evaluating a simile, we also compute the internal correlation of human scores. As shown in Appendix Table 11, there is a strong correlation between Creativity and Consistency. It means that having *ground* is also important in generating a creative simile, illustrating the necessity of interpretably retrieving tenor-*vehivle* pair in the *vehicle*-unknown setup. ## 6 Conclusion In this paper, we introduce a new task setup for simile generation: controllable simile generation (CSG). To facilitate it, we build GraCe, a finegrained annotated Chinese simile dataset, and benchmark this task with the proposed CSG model Similor, which includes a *vehicle*-retrieval module Scorer. Our work takes the first attempt to expand the elements of simile from the aspect of Cognitive Linguistics (Kövecses, 2010) (i.e, *ground* and context), and tentatively gives a successful implementation of probing simile interpretation from the cognitive property. We hope this idea can provide novel insights to future works of the creative generation, such as puns, hyperbole, and poetry, etc. ## Limitations In this paper, we explore incorporating multiple constraints to simile generation and attempt to interpret the simile comparisons from the aspect of Cognitive Linguistics. However, the creativity of simile is one kind of subjective feeling and is difficult to be accurately judged, which is also a big challenge for other kinds of creative writing tasks. We hope this task and dataset could provide novel insight into user-oriented text generation, and give the interactive and collaborative generation a closer and more detailed exploration. ## Ethics Statement We hereby acknowledge that all of the co-authors of this work are aware of the provided ACL Code of Ethics and honor the code of conduct. We elaborate ethical considerations to the community as follows: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study. Specifically, we conduct all of the human evaluations via full-time Chinese employees from the Chinese data annotation platform, ensuring all of the personal information of the workers involved (e.g., usernames, emails, URLs, demographic information, etc.) is discarded. 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Association for Computational Linguistics. ![10_image_0.png](10_image_0.png) ## A Details Of Dataset Building As shown in Figure 4, we expand three commonly annotated elements (i.e., tenor, *vehicle* and *comparator*) (Li et al., 2022) to eight, including the context element to put each simile into a more naturally-using situation. ## A.1 Simile Classification The simile classifier aims at filtering those nosimile samples containing *comparator* words. These sentences can be roughly divided into three types: 1) personified sentence, e.g., "大树好像在向我们招手。 (The tree seems to be waving to us.)" contains *comparator* word "好像 (seems to)". 2) hyperbole sentence, e.g., "这教室静得仿佛掉一根针都能听见。 (The classroom was so silent like you could hear a pin drop.)" contains *comparator* word "仿佛 (like)". 3) literal sentence, e.g., "他似乎从来没有来过这里。 (He never seems to be here.)" contains *comparator* word "似乎 (seems to)". However, the previous dataset (Li et al., 2022) only offers the literal sentence that does not contains *comparator* words as the negative samples for the simile classifier, which may not satisfy our settings. To this end, we collect a new dataset to include negative samples about these three types of nosimile sentences. Specifically, we collect personified sentences15 and hyperbole sentences16 from websites and only keep sentences that contains *comparator* words. As for type three, we ask three annotators to annotate randomly selected 3000 samples from Step 1 candidates. A sentence is selected as the negative sample if all of them regard it as a literal sentence. As for the positive samples, we also collect similes from the website of composition teaching 17 to ensure their styles are similar to our candidates. Finally, we get the new simile classification dataset and randomly split it into: training set 5905 samples (positive:2913 negative: 2992) / validation set 200 samples (positive:100 negative:100) / testing set 200 (positive:100 negative:100). Based on this new dataset, we finetune a Chinese RoBERTaLarge model to classify the Step 1 candidates. For training this model, the learning rate is set to 5e-5 and the warm-up step is set to 200. The f1 score on the validation set and testing set are 0.85 and 0.82, respectively. ## A.2 Simile Detection Simile Detection aims at labeling out the *tenor* and vehicle of a simile, that is, forming it as a sequence labeling task. In implantation, we use the most relevant dataset CCL2018 (2018) to train the sequence labeling model. The CCL2018 dataset contains 6554 training samples, 2038 testing samples, and 1650 validation samples. Based on this dataset, we finetune a Chinese RoBERTaLarge model to label each sample in GraCe. For training this model, the learning rate is set to 5e-5 and the warm-up step is set to 200. The Accuracy scores on the validation set and testing set are 98.47% and 98.38%, respectively. However, all samples only contain one kind of comparator words (i.e., "像 (like)"), the trained model cannot be directly applied to GraCe that contains various comparator words and their corresponding patterns. To solve this problem, in the inference stage, we first locate and replace each comparator pattern with the pattern containing the comparator word "像 (like)", as they have the same meaning in different words (all means like). After ## The Annotated Simile Algorithm 1 Fuzzy Matching Require: C: the Cogbank dictionary with nouns as keys and the associated cognitive attributes as their values Require: t: the tokenized word sequence needed to be queried with the length of l, t = {t1, t2*, ..., t*l} Require: w: the width of the sliding window. w = l while w > 0 do if w = l and t ∈ C **then** return t else i = 1 while *i < l* + 1 do word = {ti*, ..., t*i+w} if *word* ∈ C **then** return *word* else i = i + 1 end if end while end if w = w − 1 end while return None Words Mapping that, we use this new sample as model input to get corresponding *tenor* and *vehicle*. ## A.3 Fuzzy Matching For Cogbank Dataset The fuzzy matching algorithm is shown in algorithm 1. ## A.4 Simile Samples We show some annotated samples of GraCe in Table 12. ## B Details Of Experiments B.1 Simile Genearting Prefix We consider 25 commonly used *tenor*s as sentence starters for evaluating different datasets in the Experiment for prefix generation. The entire set is blow (Translations are provided for non-Chinese speakers.): "爱 (love)", "时间 (time)", "叶子 (leaves)", "太阳 (sun)", "树叶 (leaves)", "童年 (childhood)", "笑容 (smile)", "落叶 (fallen leaves)", "眼泪 (tears)", "阳光 (sunshine)", "泪水 (tears)", "时光 (time)", "柿子 (persimmon)", "生命 (life)", ![11_image_0.png](11_image_0.png) "记忆 (memory)", "花瓣 (petals)", "天空 (sky)", "目光 (gaze)", "雪花 (snowflakes)", "苹果 (apple)", "青春 (youth)", "枫叶 (maple leaves)", "友谊 (friendship)", "微笑 (smile)", "幸福 (happiness)". In inference, we use top-k sampling with k=10 and fix the random seed as 42 for all models to get the final results, while the maximum generation length is set to 100. ## B.2 Generating Samples Of Prefix Generation To intuitively display the effects of datasets, we show some generating results in Table 13. ## B.3 Generating Samples Of Controllable Simile Generation Some generating results of Similor with different constraints are shown in Table 14 and we also sample the results of Similor with different *vehicle* retrieval methods as shown in Table 15. ## C Details Of Human Evaluation C.1 Human Evaluation For Datasets Comparasions In order to compare the GraCe dataset with other relevant datasets, 1000 samples are randomly selected from each dataset. At the same time, three professional annotators are invited to label these data samples. Notably, the mother tongue of all annotators is Chinese. The only difference between professional annotators and crowdsourcing annotators is that professional annotators major in Chinese language and literature while crowdsourcing annotators only require majors related to Chinese literature. Because the studied courses include Chinese grammar and rhetoric, professional annotators have the ability to verify that the fine-grained annotations in our dataset are correct. Before the formal progress, we first set a guideline for evaluating, which includes the task background, key points, detailed descriptions, and examples of different patterns of similes. Then, we set an entry barrier for annotators. In detail, we organize a training program and a preliminary annotating examination (20 examples for each dataset) to select appropriate annotators with an approval rate higher than 95%. Score Definition we first ask annotators to determine whether a given sample is a simile (1 means the given sample is a simile, and 0 is the opposite). Notably, as the CMC dataset (Li et al., 2022) also contains metaphors, annotators are asked to regard that cases as another kind of simile and label them with 1. Aside from it, we furtherly check the finegrained annotated elements of samples from the GraCe dataset. In detail, annotators are also asked to determine whether the annotated elements of these samples are correct (1 means yes, and 0 is the opposite), including tenor, vehicle, *comparator*, and *ground*. Inter-annotator agreement We use Fleiss' kappa (Fleiss, 1971) to measure three annotator's reliability18. The results are: 1) For CS dataset: 0.72 (substantial); 2) For CMC dataset: 0.62 (substantial); 3) For GraCe dataset:0.78 (substantial). ## C.2 Details Of Human Evaluation For human evaluation, we first set a guideline for evaluating, which includes the task background, key points, detailed descriptions, and examples of evaluation scores from 1 to 4. Then, we set an entry barrier for annotators. In detail, we organize a training program and a preliminary annotating examination (50 examples for each model) to select appropriate annotators with an approval rate higher than 95%. Score Definition We define four categories in the human evaluation as follows: 1. **Fulency** (Fluen.) means whether the sentence corresponding to the option is fluent, grammatical, well-formed, and easy to understand. 2. **Creativity** (Creat.) means whether the sentence corresponding to the option is creative 18https://www.nltk.org/_modules/nltk/metrics/ agreement.html ## And Figuratively Meaningful. 3. **Consistency** (Consi.) means whether the sentence corresponding to the option contains a meaningful tenor-*vehicle* pair. A meaningful pair denotes there are some share properties between the *tenor* and the *vehicle*, i.e., having the explicit/implicit *ground*. 4. **Overall** means how good is the sentence corresponding to the option overall? The annotators are asked to score the generating results based on how well-formed, creative, and consistent it is. Inter-annotator agreement We use Fleiss' kappa (Fleiss, 1971) to measure three annotator's reliability19. The results are: 1) For Experiment Q1: 0.43 (moderate) 2) For Experiment Q2: 0.30 (moderate). ## C.3 Correlation Analyze | Fluen. | Creat. | Consi. | Overall | | |----------|----------|----------|-----------|-------| | Fluen. | - | 0.477 | 0.482 | 0.729 | | Creat. | 0.477 | - | 0.970 | 0.841 | | Consi. | 0.482 | 0.970 | - | 0.843 | | Overall | 0.729 | 0.841 | 0.843 | - | Table 11: Pearson correlation between different human evaluation scores (p-value < 0.01). ![13_image_0.png](13_image_0.png) | Topic | Comparator | Tenor | Vehicle | Ground | Context | |---------|--------------|---------|-----------|----------|-----------| | Word | Property | Word | Property | Above | Below | | Sample 1: 远看,层林尽染。近看,那深红、浅红、金黄的枫叶,像一只只小手掌在风中摇曳着,似乎在欢迎着我们的到来。片片美丽的叶子像蝴蝶一样飘飞,脚底有树叶轻轻的碎响,秋那厚重的美就久久盘旋心头。 Sample 1: From a distance, the layers of trees are dyed in color. Looking up close, the dark red, light red and golden maple leaves, like small palms swaying in the wind, seem to welcome us. Pieces of beautiful leaves fluttered like butterflies, and the soles of my feet were softly cracking, and the heavy beauty of autumn was circling in my heart for a long time. 片片美丽的叶子 _像_一样, 叶子飞, 飘落, 落... 蝴蝶飞, 飞舞, 美丽... 飘飞远看...似乎在欢迎着我们的到来。脚底...盘旋心头。 pieces of beautiful leaves and the soles...was circling in my heart for a long time. Sample 2: 当秋姑娘来到了硕果累累的果园时。那一串串紫色的葡萄就像一颗颗紫色的珍珠,真美丽啊!粉红的苹果绽开了笑脸,好像在说:"秋姑娘来了,我们又苏醒了。" Sample 2: When the autumn girl came to the fruitful orchard. A bunch of purple grapes like a purple pearl, really beautiful! Pink apple blooming smile, as if to say: "Autumn girl came, we woke up again." 那一串串紫色的葡萄 like leaves flying, falling, falling... butterflies flying, fluttered From a distance...seem fluttering, beautiful... to welcome us. _就像_, 葡萄水灵灵, 亮晶晶, 晶莹... 珍珠熠熠生辉, 晶莹, 细腻... 无当...果园真...又苏时。醒了。" a bunch of purple grapes like grapes watery, glitter, crystal... pearl shining, None When... really...woke crystal, orchard. up again." exquisite... Sample 3: 透过晶莹的泪珠,我看到了暖洋洋的太阳。爸爸妈妈的爱不就像太阳一样温暖着我吗?那一刻,已成为我人生中最重要的时刻,时时牵动着我的心 Sample 3: Through the crystal tears, I saw the warm sun. Isn't mom and dad's love warm me like the sun? That moment has become the most important moment in my life, always affecting my heart... 爸爸妈 _就像_一爱热烈, 妈的爱样甜, 温暖... 太阳温暖, 光明, 火红... 温暖着透过...太那一我吗? 阳。刻,...心 mom and dad's love like love warm, sweet, warm... sun warm, light, fiery ... warm Through...sun. That moment...heart... me Sample 4: 到了云锦山庄,我们被眼前的景色迷住了,仿佛走进了仙境。朵朵白云漂浮在碧蓝的天空中,好像一团团棉花。明净的湖面上隐隐约约可以看到蓝天白云的倒影,微风吹过,湖面上泛起了小小的波纹,在阳光的照耀下,变得波光粼粼,真是美丽啊! Sample 4: To the Yunjin Villa, we were fascinated by the scenery, as if in a fairyland. White clouds floated in the blue sky, like clumps of cotton. The reflection of the blue sky and white clouds can be seen faintly on the clear lake. The breeze blows, and there are small ripples on the lake. Under the sunshine, it becomes sparkling, it is really beautiful! 朵朵白云漂浮在碧蓝的天空中, _好像_。白云柔软, 洁白, 轻盈... 棉花柔软, 轻盈, 蓬松... 无到了...仙明净的湖境面...美丽啊! white clouds floated in the blue sky, like white soft, clouds white, light... cotton soft, light, fluffy... None To...fairyland The reflection of... beautiful! | | | | | | Prefix 1: 时间 (time) CS 时间果然很是短暂,不等他做出什么反应时就已经过去了。(The time was really short, and it passed before he could react.) % CMC 时间是像一条彩带系在心中。(Time is like a ribbon in the heart.) ! GraCe 时间,如同奔腾的江河,在我面前悄然逝去。(Time, like a pentium river, quietly flows away in front of me.) ! Prefix 2: 叶子 (Leaves) CS 叶子何时变得这么小,简直就像是一个孩子一般。我的心也被这个情景吓到了。(When did the leaf become so small, it was like a child. My heart was also frightened by this scene.) % CMC 叶子像,就像那空中飞舞的蝴蝶一样,扇动着薄翼,初降人间。(The leaves are like, just like the butterflies in the air, flapping their thin wings and falling to the world.) ! GraCe 叶子,长得又细又尖,像一把锋利的宝剑。(The leaves grow thin and pointed, like a sharp sword) ! Prefix 3: 太阳 (Sun) CS 太阳点点亮,天边的云层被晒得滚红,就像是要下雨了似的。(The sun lit up, and the clouds on the horizon were reddened, as if it were going to rain.) % CMC 太阳就是春天的一缕阳光,拂去冬日炎热和干燥。(The sun is a ray of spring sunshine, brushing away the heat and dryness of winter.) % GraCe 太阳火辣辣的照着大地,就像一个巨大的火球在燃烧。(The sun blazed down on the earth like a huge fireball burning. ) ! Prefix 4: 记忆 (Memory) CS 记忆果然如同传说中一样美丽迷人。(The memory is as beautiful and charming as the legend.) % CMC 记忆,就像那些仙天上的星斗一样美丽。(Memories are as beautiful as the stars in the fairy sky.) ! GraCe 记忆,是一个五味瓶,酸甜苦辣样样俱全,如同过山车般满载喜怒哀乐。(Memory is a tasting bottle, sour, sweet, bitter, and spicy everything, like a roller coaster full of joys and sorrows.) ! Prefix 5: 雪花 (Snowflake) CS 雪花着何其美丽,雪花就好像是精灵一样,一点一点融化在天空中。(How beautiful snowflakes are, snowflakes are like fairies, melting in the sky bit by bit.) ! CMC 雪花,在空中飘动,就像一个个银色的精灵。(snowflakes, fluttering in the air, like a silver spirit. ) ! GraCe 雪花,飘落的时候就像一只美丽的仙鹤在空中翩翩起舞。(The snowflake falls like a beautiful crane dancing in the air.) ! Table 13: Samples of prefix generation with different datasets. !indicates the corresponding sentence is a simile, while %means it is not a simile. Translations are provided for non-Chinese speakers. Topic:他唱得并不好听,声音 (He did not sing well, and his voice, ) Reference:他唱得并不好听,声音像砂纸打磨着耳朵。(He didn't sing very well, and his voice was like sandpaper sanding against the ears.) Constraint: None 他唱得并不好听,声音是如雷鸣般的响声。(He did not sing well, and his voice was like thunder.) Constraint: Context: 可他是那样地认真,那样地投入,仿佛在大舞台上唱歌,台下有千万个观众聆听着。(But he was so earnest, so devoted, as if he were singing on a big stage with millions of people listening.) 他唱得并不好听,声音像一把利剑,深深地插进了我的胸膛。(He did not sing well, and his voice, like a sword, cut deep into my chest.) Constraint: Comparator: 像...。(like...a full stop) 他唱得并不好听,声音像蚊子在叫,又像苍蝇在乱飞。(He did not sing very well, and his voice was like the cry of a mosquito, or like a fly.) Constraint: Vehicle: 砂纸 (like...a full stop) 他唱得并不好听,声音,就像砂纸一样沙哑。(He did not sing very well, and his voice was like the cry of a mosquito, or like a fly.) Constraint: Context+Comparator+Vehicle 他唱得并不好听,声音,像一块干燥的砂纸在微微地颤抖。(He did not sing very well, his voice, like a piece of dry sandpaper in a slight tremor.) Table 14: Samples of Similor under different constraints and combinations. Translations are provided for nonChinese speakers. | Sample 1: ANT | 风,缓缓吹过,槐花树上的槐花便落下,洁白的花瓣如英亩一般,在空中飞舞。(The wind, slowly blowing, flowers on the sophora tree fell down, white petals like acres, flying in the air.) | |---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Literal False | 风,缓缓吹过,槐花树上的槐花便落下,洁白的花瓣 ,如白色的百合一般,美丽极了。(The wind, slowly blowing, flowers on the sophora tree fell down, white petals like white lilies, very beautiful.) Shared properties between tenor and vehicle: 美丽 (beautiful), 绽放 (blooming), 盛开 (full-blown), 枯萎 (withered), 美好 (good)... | | Scorer | 风,缓缓吹过,槐花树上的槐花便落下,洁白的花瓣 ,如羽毛一般,轻轻地飞下来。(The wind, slowly blowing, flowers on the sophora tree fell down, white petals like feathers, gently fly down.) Shared properties between tenor and vehicle: 散落 (scattered), 柔软 (soft), 美丽 (beautiful), 轻盈 (lithesome), 飘 (fluttering)... | | Sample 2: ANT | 然后在杯中倒入开水, 胖大海立马就浮起来了,还像离开水的小白兔一样。(Then we pour boiling water into the cup, the sterculia scaphigera floats up immediately like a white rabbit out of water.) | | Literal False | 然后在杯中倒入开水, 胖大海立马就浮起来了,我还像一只小刺猬一样蜷缩着。(Then we pour boiling water into the cup, the sterculia scaphigera floats up immediately and I curl up like a little hedgehog.) Shared properties between tenor and vehicle: 膨胀 (intumescent) | | Scorer | 然后在杯中倒入开水, 胖大海立马就浮起来了,还像面包一样膨胀起来。(Then we pour boiling water into the cup, the sterculia scaphigera floats up immediately, and expands like bread.) Shared properties between tenor and vehicle: 膨胀 (intumescent), 发开 (rasing) | | Sample 3: ANT | None | | Literal False | 老人微眯双眼,眺望着天空中的风筝, 眼神祥和宁静,如杰克般飞翔 (The old man squinted his eyes and looked at the kite in the sky. His eyes were peaceful and quiet, flying like Jack...) Shared properties between tenor and vehicle: 忧郁 (melancholy ) | | Scorer | 老人微眯双眼,眺望着天空中的风筝, 眼神祥和宁静,如晨露般滋润着我的心田。(The old man squinted his eyes and looked at the kite in the sky. His eyes were peaceful and quiet, which moistened my heart like morning dew.) Shared properties between tenor and vehicle: 干净 (fresh), 清澈 (limpid) | | Sample 4: ANT | None | | Literal False | 望着一个个设施,一幅幅画面,从我们的眼前闪过, 回忆 ,像蜡人似的,一个个地浮现在我们眼前。(Looking at the facilities one by one, a picture flashed from our eyes, memories, like wax dolls, one by one emerged in front of our eyes.) Shared properties between tenor and vehicle: 不真实 (unreal) | | Scorer | 望着一个个设施,一幅幅画面,从我们的眼前闪过, 回忆 ,像春花似的,开满了我们的心田。(Looking at the facilities one by one, a picture flashed from our eyes, memories, like spring flowers, open full of our hearts of the field.) Shared properties between tenor and vehicle: 温暖 (warm), 绚烂 (splendid) | | Sample 5: ANT | None | | Literal False | 站在黑板前 , 我忽然有种恍然隔世的感觉 , 尘封已久的记忆如一片平静的太平洋。(Standing in front of the blackboard, I suddenly feel as if a generation has passed, dusty memories are like the calm Pacific Ocean.) Shared properties between tenor and vehicle: 深 (deep), 美丽 (beautiful) | | Scorer | 站在黑板前,我忽然有种恍然隔世的感觉,尘封已久的记忆如一片大海 ,宽阔而又神秘。(Standing in front of the blackboard, I suddenly feel as if a generation has passed, dusty memories are like a sea, wide and mysterious.) Shared properties between tenor and vehicle: 深 (deep), 美丽 (beautiful), 悠久 (long-standing) | | Table 15: Samples of Similor with different vehicle retrieval methods. "None" means no valid vehicle has been | | Table 15: Samples of Similor with different *vehicle* retrieval methods. "None" means no valid *vehicle* has been retrieved and we highlight the tensor - *vehicle* pair for better view.Translations are provided for non-Chinese speakers. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? See Limitations section (page nine). A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? See the Abstract and Section 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Not applicable. Left blank. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Not applicable. Left blank. ## C ✓ **Did You Run Computational Experiments?** See Section 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? See Section 5 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? See Appendix ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? See Section 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? See Sections 3 and 5 D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** See Section 5 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? See Appendix ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? See Appendix and Ethics Statement D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? See Ethics Statement D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

lei-etal-2023-revealing

Revealing Single Frame Bias for Video-and-Language Learning

https://aclanthology.org/2023.acl-long.29

Training an effective video-and-language model intuitively requires multiple frames as model inputs. However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more frames. In this work, we explore single-frame models for video-and-language learning. On a diverse set of video-and-language tasks (including text-to-video retrieval and video question answering), we show the surprising result that, with large-scale pre-training and a proper frame ensemble strategy at inference time, a single-frame trained model that does not consider temporal information can achieve better performance than existing methods that use multiple frames for training. This result reveals the existence of a strong {``}static appearance bias{''} in popular video-and-language datasets. Therefore, to allow for a more comprehensive evaluation of video-and-language models, we propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling. Our code is available at \url{https://github.com/jayleicn/singularity}.

# Revealing Single Frame Bias For Video-And-Language Learning Jie Lei Tamara L. Berg Mohit Bansal Department of Computer Science University of North Carolina at Chapel Hill {jielei, tlberg, mbansal}@cs.unc.edu ## Abstract Training an effective video-and-language model intuitively requires multiple frames as model inputs. However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more frames. In this work, we explore single-frame models for video-and-language learning. On a diverse set of video-andlanguage tasks (including text-to-video retrieval and video question answering), we show the surprising result that, with large-scale pretraining and a proper frame ensemble strategy at inference time, a single-frame trained model that does not consider temporal information can achieve better performance than existing methods that use multiple frames for training. This result reveals the existence of a strong "static appearance bias" in popular video-andlanguage datasets. Therefore, to allow for a more comprehensive evaluation of videoand-language models, we propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling. Our code is available at https: //github.com/jayleicn/singularity. ## 1 Introduction Video and language are the two primary signals that constitute much of the world we perceive every day - we observe our surrounding environment with our eyes in the form of continuous visual input (video), and communicate with others via language. Intuitively, this leads one to assume that training an effective video-and-language model should require multiple video frames as input. Standard methods (Zhu and Yang, 2020; Xu et al., 2021; Li et al., 2020a; Luo et al., 2021) in this area typically use multiple densely sampled frames for training. Recent work (Lei et al., 2021) proposes sparse sampling for video-and-language understanding, where it claims that a few sparsely sampled clips are sufficient for learning due to the high redundancy in videos. This technique has shown (Lei et al., 2021; Zellers et al., 2021) to be successful in various video-language benchmarks (Jang et al., 2017; Xu et al., 2016; Anne Hendricks et al., 2017; Krishna et al., 2017a; Xu et al., 2017; Yu et al., 2018; Lei et al., 2018). However, as demonstrated by Bain et al. (2021); Luo et al. (2021); Lei et al. (2021), training with fewer frames (e.g., a single frame) leads to significantly worse performance compared to their multi-frame counterparts. In contrast, in this work, we show that with proper modeling, single-frame models could achieve competitive performance, hence revealing "static appearance bias" in popular video-and-language datasets. We start by building a standard image-language model, with a vision encoder and a language encoder for image and text encoding, followed by a multi-modal encoder with cross-attention for crossmodal fusion. We pre-train the model on largescale image-text and video-text datasets (Chen et al., 2015; Krishna et al., 2017b; Ordonez et al., 2011; Sharma et al., 2018; Changpinyo et al., 2021; Bain et al., 2021). For fine-tuning, we randomly sample a single frame for training, and ensemble multiple uniformly sampled frames per video for making a video-level prediction at inference. Single-frame predictions are often noisy and inaccurate, as they are made from incomplete information from single-frames without any context (see examples in Figure 3). Due to this issue, single-frame training typically performs significantly worse than multi-frame training (Lei et al., 2021; Bain et al., 2021; Luo et al., 2021). Previous work (Hendrycks et al., 2019) suggests that pretraining improves model robustness in the face of label corruption for image recognition. Inspired by this, we hypothesize that large-scale pre-training helps mitigate noise from single-frame training. Our analyses in Section 5 agree with our hypothesis, showing that as we increase pre-training data 487 size, the performance of our single-frame model improves drastically and its gap with a similarly trained multi-frame model is largely eliminated. Besides training, these noisy single-frame predictions also render simple late fusion (e.g., meanpooling in ClipBERT (Lei et al., 2021)) less effective at inference time. To deal with this issue, we propose an early fusion strategy, which takes all frames as model inputs for directly making a videolevel prediction. Our analyses show that this early fusion ensemble method outperforms late fusion strategies and also delivers consistently improved performance when more frames are used. We compare our approach with existing methods on six datasets across two video-language tasks, including text-to-video retrieval (MSRVTT (Xu et al., 2016), DiDeMo (Anne Hendricks et al., 2017), and ActivityNet Captions (Krishna et al., 2017a)) and video question answering (MSRVTT-QA (Xu et al., 2017), ActivityNet-QA (Yu et al., 2019), and MSRVTT-MC (Yu et al., 2018)). Results show that our approach achieves competitive (mostly better) performance than existing methods that use more training frames and pre-training data, setting new state-of-the-art for multiple tasks. This conclusion holds for both short 15-second MSRVTT videos and 180-second ActivityNet videos, showing the effectiveness of our approach in various scenarios. More importantly, this strong single-frame performance reveals that the current evaluation is biased towards still objects, scenes, etc., while the temporal dynamics seem negligible, which in fact should be important for "true" video-language understanding. To address this issue, we next propose two new tasks that are designed to test models' true temporal modeling ability. Based on the find-grained action recognition dataset SomethingSomething v2 (SSv2) (Goyal et al., 2017a), we create two text-to-video retrieval tasks, one that use SSv2's action *template* as text queries, e.g., "Throwing [*something*] in the air and catching it", and another that uses its annotated *label* as text queries, e.g., "Throwing keys in the air and catching it". See examples in Figure 2. This *template* task removes the objects and only keeps the actions, enabling an evaluation that focuses almost solely on temporal modeling. The *label* task, on the other hand, contains both actions and objects, requiring an understanding of both still objects and their motion. Lastly, we present several baselines on these new tasks and show that temporal modeling is essential in achieving high scores. In summary, our contributions are three-fold: (i) We explore single-frame training for videolanguage tasks. While simple, our approach can achieve state-of-the-art performance on a range of datasets, including both text-to-video retrieval and video question answering. Importantly, this result reveals the surprising static appearance bias in existing datasets. (ii) We conduct careful analyses, which show that large-scale pre-training and a proper multi-frame ensemble strategy are the core for single-frame trained models to be successful. (iii) We propose two new tasks specifically designed for testing models' temporal modeling ability. These two new tasks complement existing benchmarks for a more comprehensive evaluation. ## 2 Related Work Vision and Language. Vision and language learning considers the problem of learning from both visual and textual signals. Depending on their visual input type, methods in this area can be roughly categorized into two types, one with image (Anderson et al., 2018; Tan and Bansal, 2019; Lu et al., 2019; Chen et al., 2020; Li et al., 2019, 2020b, 2021b; Radford et al., 2021) and another with video (Anne Hendricks et al., 2017; Sun et al., 2019; Xu et al., 2021; Li et al., 2020a; Zellers et al., 2021; Bain et al., 2021; Lin et al., 2021). Standard video-language methods (Zhu and Yang, 2020; Xu et al., 2021; Li et al., 2020a; Lei et al., 2021; Luo et al., 2021) are typically trained with multiple video frames. This multi-frame training strategy has been the norm and is shown to work well across various datasets (Xu et al., 2016; Anne Hendricks et al., 2017; Krishna et al., 2017a; Jang et al., 2017; Xu et al., 2017; Lei et al., 2018). Unlike previous work that uses multiple frames for training, we explore single-frame training (i.e., similar to training an image-text model) and show it achieves strong performance on existing video-text benchmarks. Concurrent work (Buch et al., 2022) proposes a new module, atemporal probe, for selecting the best single-frame as inputs to a trained image-text model during inference; whereas we utilize multiple uniformly sampled frames and study effective ways of ensembling these frames. Dataset Bias. Biases are prevalent in datasets (Goyal et al., 2017b; Li et al., 2018; Escorcia et al., 2019; Lei et al., 2020), e.g., Zhang et al. (2016) pointed out that blindly answering "yes" to yes/no questions in VQA (Antol et al., 2015) without looking at images results in an accuracy of 87%; Li et al. (2018) discovered that many video action recognition datasets, such as Kinetics (Kay et al., 2017) and UCF-101 (Soomro et al., 2012), have a strong static bias, where a linear classifier trained on static appearance (e.g., object, scene, and people) representations achieves much higher performance than chance. In this work, we find similar static bias exists in popular video-language datasets (Xu et al., 2016; Anne Hendricks et al., 2017; Krishna et al., 2017a; Xu et al., 2017; Yu et al., 2018, 2019), in which our models trained with single frames could achieve surprisingly good performance, even compared to models that perform explicit temporal modeling. When datasets are biased, they provide incorrect indications of the models' ability. To allow for a more comprehensive evaluation, we propose two new tasks based on an existing action recognition dataset SSv2 (Goyal et al., 2017a) to test models' true temporal modeling ability. ## 3 Methods Model Architecture. Figure 1 shows an overview of our model (dubbed SINGULARITY). It consists of 3 main components, a vision encoder Fv, a language encoder Fl, and a multi-modal encoder H. The vision encoder is an image-level visual backbone model, such as ViT (Dosovitskiy et al., 2020). The language encoder is a language model such as BERT (Devlin et al., 2019). For the multi-modal encoder, we use a transformer encoder (Vaswani et al., 2017), in which each layer contains a selfattention, a cross-attention, and a feed-forward network (FFN). The cross-attention layer is used to gather information from encoded visual inputs using the text as key, similar to recent work (Jaegle et al., 2021, 2022; Li et al., 2021b, 2022). We denote a video V contains T frames as V =[f1, f2*, ..., f*T ], its paired text as S. During training, we randomly sample a single frame ft from V as model input , where t ∈ {1*, ..., T*}. Its encoded representation can be written as Fv(ft) ∈ R Lv×D. For text, the encoded representation is Fl(S) ∈ R Ll×D. Lv and Ll are encoded sequence lengths, D is hidden size. We next make a prediction p as: p = H( Fl(S) , Fv(ft) ), (1) Q, K, V for self-att; Q for cross-att K, V for cross-att where Q, K, V denote the query, key, and value matrices of self- and cross-attention (Vaswani et al., 2017). We calculate loss based on this prediction. During inference, we uniformly sample T*test* frames {fτi} T*test* i=1 . Each frame is encoded separately, and their encoded representations are concatenated as inputs to the multi-modal encoder to get a video-level prediction score: p = H( Fl(S) , [Fv(fτ1 ); ...; Fv(fτTtest )] ), (2) $\left[\begin{array}{cccc}1&1&1&1\\ 0&0&0&0\end{array}\right]$ 2. where [; ] denotes concatenation, and [Fv(fτ1 ); ...; Fv(fτ*Ttest* )] ∈ R $$\begin{array}{l l}{{\mathrm{:nation,}}}&{{\mathrm{and}}}\\ {{\mathbb{R}^{(T_{t e s t}\times L_{v})\times D}.}}\end{array}$$ This early fusion design allows our model to make an informed prediction given full context. In ClipBERT (Lei et al., 2021), an alternative late fusion design is used: scores are computed for each frame separately, and video-level score is obtained via a manually designed aggregation function G (e.g., mean-pooling): $$\begin{array}{l}{{p={\mathcal G}(p_{\tau_{1}},p_{\tau_{2}},p_{\tau_{T_{t e s t}}});}}\\ {{p_{\tau_{i}}={\mathcal H}(\ {\mathcal F}_{l}(S)\ ,\ {\mathcal F}_{v}(f_{\tau_{i}})\ ).}}\end{array}\tag{3}$$ Since the predictions in late fusion are made with incomplete information from individual frames, they can be quite noisy. In Section 5, we provide a detailed comparison w.r.t. these different frame ensemble methods and show that early fusion consistently outperforms late fusion. Pre-Training Objectives. The model is trained with 3 losses: (i) Vision-Text Contrastive: a contrastive loss that aligns the pooled vision and text representations from the vision and language encoders. (ii) Masked Language Modeling (MLM) (Devlin et al., 2019): predicting masked tokens from their text and visual context, with multimodal encoder. (iii) Vision-Text Matching: predicting the matching score of a vision-text pair with multi-modal encoder. These losses have shown to be effective in learning multi-modal representations (Tan and Bansal, 2019; Chen et al., 2020; Li et al., 2021a,b; Lei et al., 2021; Radford et al., 2021). More details are in Appendix. Implementation Details. We use both image-text and video-text data for pre-training. For imagetext data, we use a combination of COCO (Chen et al., 2015), Visual Genome (VG) (Krishna et al., 2017b), SBU Captions (Ordonez et al., 2011), CC3M (Sharma et al., 2018), and CC12M (Changpinyo et al., 2021). For video-text data, we use WebVid (Bain et al., 2021). Note that, for videotext data, we only sample a single frame from the video for training. We pre-train the model on ![3_image_0.png](3_image_0.png) two different subsets of the datasets: (i) 5M corpus that contains 5.44M images and videos from CC3M+WebVid, and (ii) 17M corpus that contains 17.28M images and videos from all the datasets. Our model is implemented in PyTorch (Paszke et al., 2019). The vision encoder is initialized using BEiTBASE (Bao et al., 2021) model trained on ImageNet-21K (Deng et al., 2009). The text encoder is initialized from the first 9 layers of BERTBASE (Devlin et al., 2019). The multi-modal encoder is initialized from the last 3 layers of the same BERTBASE model, and its cross-attention layers are randomly initialized. We optimize the model for 10 epochs using AdamW (Loshchilov and Hutter, 2019) with an initial learning rate of 1e4. We warm up the learning rate in the first epoch followed by cosine decay (Loshchilov and Hutter, 2017) to 1e-6. Mixed precision is used for faster training. The batch size is set to 128/GPU, we train the model on 3 NVIDIA A100 GPUs with image size 224×224. We perform basic augmentations: random resize, crop, and flip to the images during training. This pre-training takes around 1 day on the 5M corpus, and 4 days on the 17M corpus. Our pre-training is quite efficient compared to other similar work, e.g., 10 epochs' pre-training in AlignPrompt (Li et al., 2021a) takes 3 days on the same 5M corpus using 16 A100 GPUs, this amounts to 16× computation cost of our pre-training. ## 4 Experiments 4.1 Downstream Task Setup Text-to-Video Retrieval. Given a text query, the goal of this task is to retrieve relevant videos from a large set of videos. We evaluate our model on the following datasets: (i) **MSRVTT** (Xu et al., 2016) contains 10K YouTube videos, each paired with 20 captions. We follow (Yu et al., 2018; Lei et al., 2021) to use the 7K train+val videos for training, and report results on the 1K test set. (ii) DiDeMo (Anne Hendricks et al., 2017) contains 10K Flickr videos with 41K captions. We use standard train/val/test splits. (iii) **ActivityNet Captions** (Krishna et al., 2017a) contains 20K YouTube videos with 100K captions. We use the train split with 10K videos for training, and we report results on the widely used val1 split, with 4.9K videos. For MSRVTT, we evaluate standard text-to-video retrieval. For DiDeMo and ActivityNet Captions, we evaluate paragraph-to-video retrieval (Liu et al., 2020; Lei et al., 2021; Luo et al., 2021), where the text captions in the same video are concatenated as a single paragraph-level text for retrieval. We report performance using recall at K (R@K). Video Question Answering. Given a video (often with a text question), this task requires generating an answer to the question or selecting the most suitable answer from a set of candidates. (i) MSRVTT-QA (Xu et al., 2017) contains 244K open-ended questions on 10K MSRVTT videos. (ii) **ActivityNet-QA** (Yu et al., 2019) contains 58K open-ended questions on 5.8K sampled ActivityNet (Caba Heilbron et al., 2015) videos. (iii) MSRVTT-MC (Yu et al., 2018) is a multiplechoice task that requires selecting the matched caption from 5 candidate captions for each video (3K MSRVTT videos). We use standard train/val/test splits for the three tasks, and report accuracy. ## 4.2 Comparison On Existing Datasets Text-to-Video Retrieval Results. In Table 1, we compare SINGULARITY with existing methods on | Method | #PT | #Train | MSRVTT | DiDeMo | ActivityNet Cap | | | | | |-------------------------------------|------------------------------------------------------------|----------------------------------------------|-----------------------------------------|----------|-------------------|------|----|----|----------------| | Frame | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | | HERO (Li et al., 2020a) | 136M | 310 | 20.5 47.6 60.9 | - | - | - | - | - | - | | ClipBERT (Lei et al., 2021) | 0.2M 16/16/8 22.0 46.8 59.9 20.4 48.0 60.8 21.3 49.0 63.5 | | | | | | | | | | VideoCLIP (Xu et al., 2021) | 136M | 960 | 30.9 55.4 66.8 | - | - | - | - | - | - | | Frozen (Bain et al., 2021) | 5M | 4 | 31.0 59.5 70.5 31.0 59.8 72.4 | - | - | - | | | | | AlignPrompt (Li et al., 2021a) | 5M | 8 | 33.9 60.7 73.2 35.9 67.5 78.8 | - | - | - | | | | | All-in-one (Wang et al., 2022) 138M | 9 | 34.4 65.4 75.8 32.7 61.4 73.5 22.4 53.7 67.7 | | | | | | | | | CLIP4Clip (Luo et al., 2021) | 400M 12/64/64 42.0 68.6 78.7 42.8 68.5 79.2 40.5 72.4 98.2 | | | | | | | | | | ECLIPSE (Lin et al., 2022) | 400M | -/32/32 | - | - | - | 44.2 | - | - | 45.3 75.7 86.2 | | SINGULARITY | 5M | 1 | 36.8 65.9 75.5 47.4 75.2 84.0 43.0 70.6 | 81.3 | | | | | | | SINGULARITY | 17M | 1 | 41.5 68.7 77.0 53.9 79.4 86.9 47.1 75.5 | 85.5 | | | | | | | Method | #PT | #Train Frame MSRVTT-QA ActivityNet-QA MSRVTT-MC | | | | |-------------------------------------|-------|---------------------------------------------------|------|------|------| | ClipBERT (Lei et al., 2021) | 0.2M | 16 | 37.4 | - | 88.2 | | AlignPrompt (Li et al., 2021a) | 5M | 16 | 42.1 | - | - | | JustAsk (Yang et al., 2021) | 69M | 640 | 41.5 | 38.9 | - | | MERLOT (Zellers et al., 2021) 180M | 5 | 43.1 | 41.4 | 90.9 | | | VideoCLIP (Xu et al., 2021) | 136M | 960 | - | - | 92.1 | | All-in-one (Wang et al., 2022) 138M | 9 | 44.3 | - | 92.0 | | | SINGULARITY | 5M | 1 | 42.7 | 41.8 | 92.0 | | SINGULARITY | 17M | 1 | 43.5 | 43.1 | 92.1 | text-to-video retrieval. Across all the datasets, SIN-GULARITY (5M) achieves better performance compared to methods trained on similar amounts of data, while using only single frames for training. On DiDeMo and ActivityNet Captions, it outperforms all previous work, including many that pretrain on significantly larger amounts of data, e.g., 400M image-text pairs in CLIP4Clip, or 136M video-text pairs in VideoCLIP compared to 5M image-text and video-text pairs in SINGULARITY. We also note that our model is trained with single frames, while previous work uses many more frames, e.g., 64 frames in CLIP4Clip or 8 frames in AlignPrompt. When trained with a larger amount of data (17M), we notice a further performance boost for our model, demonstrating that SINGU-LARITY benefits from large-scale pre-training. Video QA Results. Table 2 compares SINGULAR-ITY with existing methods on video question answering. We notice SINGULARITY (5M) achieves competitive performance with previous work even when using two orders of magnitude smaller pretraining data, e.g., 180M video-text pairs in MER- LOT vs. 5M image-text and video-text pairs. Our method also surpasses the strong video QA model JustAsk, which is specifically designed for video QA and is pre-trained on 69M video QA pairs. When pre-trained with more data, our model performance further improves. These comparisons show the effectiveness of our single-frame approach. In Appendix, we also provide additional results: (i) SINGULARITY-temporal for retrieval and QA; (ii) zero-shot retrieval; (iii) image-text retrieval; (iv) image QA, etc. ## 4.3 New Temporal Tasks In the previous section, we revealed the interesting observation that popular video-language datasets have strong static appearance biases - enabling our model that uses only a single frame per video at each training step to achieve competitive performance compared to state-of-the-art models that digest multiple temporally-ordered frames. The biased evaluation on these datasets favors models that are strong in recognizing static concepts, and does not provide a good indicator of whether these ![5_image_0.png](5_image_0.png) models are capable of recognizing fine-grained temporal relationships between neighboring frames. Hence, to address this issue, we propose two new datasets that complement existing datasets for a more comprehensive evaluation of video-andlanguage methods. We draw inspiration from the video action recognition community, and transform the temporally-heavy action recognition dataset Something-Something v2 (SSv2) (Goyal et al., 2017a) into video-and-language datasets. In Figure 2, we show SSv2 examples. A unique property of the SSv2 dataset is that the videos often require fine-grained temporal modeling to correctly predict their action classes. For example, to match the videos and their action classes (*template*) in Figure 2, one has to look at multiple temporally ordered frames. Based on SSv2 videos and annotations, we define two text-to-video retrieval tasks: - **SSv2-Template Retrieval**: We use the 174 templates (e.g., "Throwing [*something*] in the air and catching it") in SSv2 as the text queries to retrieve videos. We use 168,913 SSv2 training videos for training. As ground-truth annotations for test videos are not available, we use validation videos: we sample 12 videos for each template, with a total of 2,088 videos for testing. - **SSv2-Label Retrieval**: We use annotated labels (e.g., "Throwing keys in the air and catching it") in SSv2 as queries to retrieve videos. We follow the same split in the template retrieval task, with 168,913 training videos, and 2,088 test videos. Since no objects are in the queries of the template retrieval task, it requires a deeper temporal action understanding than label retrieval, while label retrieval provides a more comprehensive evaluation of both static and temporal understanding. Experiments. We use Frozen (Bain et al., 2021) and CLIP4Clip (seqTransf version) (Luo et al., 2021) as baselines. Frozen uses a space-time transformer, CLIP4Clip is an extension based on the CLIP (Radford et al., 2021) with an extra 4-layer temporal transformer encoder. We report performance using standard text-to-video retrieval metrics R@K. For our model, in addition to the singleframe version, we build a multi-frame variant, SIN-GULARITY-temporal. Specifically, we add a twolayer temporal transformer encoder following the vision encoder, and use its outputs as inputs to the multi-modal encoder (see details in Appendix). From a single-frame pre-trained checkpoint (5M or 17M), we perform a 2nd stage video pre-training with 4 frames using WebVid videos for SINGU-LARITY-temporal. We use an initial learning rate of 5e-5, and train the model for 5 epochs. The results are shown in Table 3. Compared to Frozen and CLIP4Clip, while SINGULAR-ITY shows competitive performance on existing benchmarks (see Table 1), it underperforms these methods on the two temporally-heavy tasks by a large margin. For example, SINGULARITY (5M) underperforms the 4-frame Frozen model by 10.9 for SSv2-template retrieval R1, though it shows a 16.4 improvement for DiDeMo R1, and 5.8 for MSRVTT R1. This is a good sign as it shows that the new tasks cannot be solved by models exploiting static appearance biases. On the other hand, after adding the 2-layer temporal encoder, the 4frame SINGULARITY-temporal model gets a significant performance boost from the single-frame model, surpassing the baseline methods. When using more pre-training data (5M→17M), we notice a good performance gain for SSv2-label, while the performance on SSv2-template stays similar. These observations indicate that the SSv2-label task requires both static and temporal modeling, and enhancing either will improve the task performance. For SSv2-template, as no objects exist in its text queries, it requires mostly temporal modeling. ## 5 Analysis Frames Ensemble Strategy. Our model is trained with a single-frame regime, while using multiple frames covering the full video at inference. As shown in Figure 5a (*concat*), encoded video frames are concatenated as input to the multi-modal encoder's cross-attention for making a video-level prediction. A naive alternative is to compute the Table 3: Comparison to existing methods on SSv2 tasks. * The training of Frozen on the SSv2-label retrieval task fails to converge despite our best efforts in tuning the model. ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) ![6_image_2.png](6_image_2.png) prediction score for each frame separately (Figure 5b), and then aggregate these frame-level scores together to get a video-level score using an aggregation function, such as LogSumExp (lse), max- and mean-pooling. This simple late fusion strategy has shown to be successful for video-and-language (Lei et al., 2021) and video action recognition methods (Bertasius et al., 2021; Wang et al., 2016). In Figure 4, we compare these different frame ensemble strategies, with varying number of frames at inference. From the comparison, we can draw the following conclusions: (i) Our early fusion strategy (*concat*) shows a significant gain over the three late fusion strategies (lse, max, *mean*) for both MSRVTT retrieval and ActivityNet-QA, demonstrating the importance of considering the whole video when making the predictions. (ii) In general, for all ensemble strategies, using more frames at inference improves model performance. However, for the late fusion strategies, sometimes using more frames hurts performance, e.g., for ActivityNetQA, inference with over 4 frames underperforms that with 4 frames for max-pooling. This observation agrees with the MSRVTT-QA results in ClipBERT (Lei et al., 2021). In contrast, early fusion delivers consistently improved performance when more frames are used. Overall, we hypothesize that the low and unstable performance of late fusion is because its video-level prediction is obtained via aggregating frame-level predictions, while these frame-level predictions can be inaccurate and unstable (see example in Figure 3) - as they are separately predicted using incomplete information within each frame, ignoring their context. Besides ![7_image_0.png](7_image_0.png) better accuracy, in Appendix, we show early fusion also runs faster. Pre-Training Data Size. In Figure 6, we study the effect of cross-modal pre-training data size for both the single-frame and the multi-frame model. We show downstream fine-tuning performance under 4 different pre-training data setups: no cross-modal pre-training (0M), pre-train on WebVid (2.49M videos), on 5M corpus (5.44M images+videos), or on 17M corpus (17.28M images+videos). We obsereve that both 1- and 4-frame model greatly benefit from large-scale pre-training. When comparing the two models, an interesting observation is that, as the pre-training data size increases, the performance gap between the 1-frame and the 4frame model decreases almost monotonically. This phenomenon suggests that, when pre-trained on a sufficient amount of data, the performance of models trained with single frames might be very close to models trained with multiple frames. Though there can be exceptions for tasks that require fine-grained temporal modeling, such as SSv2-label retrieval, where multi-frame modeling is necessary. One possible explanation is that single-frame training is noisier than multi-frame training - due to incomplete context and random sampling, single-frame predictions are often inaccurate and less stable than multi-frame predictions, and pretraining is helpful (Hendrycks et al., 2019) in this case. Meanwhile, single-frame training requires the model to extract all information from a single frame while a multi-frame model could rely on rich ![7_image_1.png](7_image_1.png) sources from multiple frames. Therefore, for downstream tasks, it is essential for the single-frame model to initialize from a strong pre-trained model. Training Efficiency. A core advantage of singleframe training is its training efficiency. In Section 3, we discussed our pre-training cost is only 1/16 of a recent video-language model (Li et al., 2021a). In Figure 7 we compare the training time and task performance of various models. We note our model (1-frame, SINGULARITY, 17M) trains much faster than the baselines (2.8× for 4-frame Frozen, 8.5× for 64-frame CLIP4Clip) while showing notably better performance. Besides, it is also more memory efficient, i.e., its maximum allowed batch size on a single GPU is 190 while only 50 for Frozen. Experiments conducted on a single RTX A6000 GPU with 48GB memory, training time is averaged over 8,394 DiDeMo training examples. In Appendix, we show additional comparisons of various retrieval methods in terms of inference GFLOPs and model size. ## 6 Conclusion In this work, we explore single-frame training for video-and-language learning. We find that, with sufficient pre-training data and a proper frame ensemble strategy at inference, our model trained with a single frame achieves surprisingly good performance on various video-text tasks, including text-to-video retrieval and video question answering. While these results show the potential of using single-frame training for various video-text tasks, it also reveals that current benchmarks are biased towards static objects and scenes, etc. To address this issue, we propose two new tasks designed to test models' true temporal modeling ability and build several baseline methods for these new tasks. We hope these new tasks can complement existing benchmarks for a more comprehensive video-andlanguage understanding. ## Limitations While the proposed single-frame training approach shows strong performance on various videolanguage datasets, it does not work well on true temporal tasks like the new SSv2 tasks. Compared to multi-frame models, our single-frame model also has a higher demand for pre-training data. ## Ethics Statement Similar to many data-driven methods, the predictions from our system reflect the distribution of data on which it is trained on, and these predictions can be inaccurate and biased by the data. Furthermore, the model is trained with a single frame strategy, which may naturally not work well on tasks that require more understanding, thus its predictions on such tasks may not be reliable. Therefore, users should not completely rely on the system for making real-world decisions. ## Acknowledgements We thank the reviewers and area chair for the useful feedback. This work is supported by ARO Award W911NF2110220, DARPA KAIROS Grant \#FA8750-19-2-1004, DARPA MCS Grant N6600119-2-4031, and NSF-AI Engage Institute DRL211263. The views in this article are those of the authors and not of the funding agency. ## References Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018. Bottom-up and top-down attention for image captioning and visual question answering. In *CVPR*. Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. 2017. Localizing moments in video with natural language. In *ICCV*. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015. Vqa: Visual question answering. In *ICCV*. Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. 2021. Frozen in time: A joint video and image encoder for end-to-end retrieval. In *ICCV*. Hangbo Bao, Li Dong, and Furu Wei. 2021. 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In Section A.2, we show more experimental details, such as SIN-GULARITY-temporal results on existing datasets, SINGULARITY zero-shot results, impact of image size, and results on image-text tasks such as textto-image retrieval tasks Flickr30K (Young et al., 2014), COCO (Chen et al., 2015) and image question answering task VQA (Antol et al., 2015), model size and inference cost of our approach w.r.t. other recent approaches, as well as memory and time comparison of different frame ensemble strategies. In addition, we also show hyper-parameters ![11_image_0.png](11_image_0.png) and more experimental setups in this section. In Section A.3, we show more dataset details. ## A.1 Additional Modeling Details Open-ended QA model. Figure 8a shows a graphic overview of the model architecture for open-ended video question answering. Following previous work (Cho et al., 2021; Li et al., 2021b), we formulate this task as text generation instead of classification. Based on the base model described in main text, we add an extra multi-modal decoder that takes in multi-modal encoder outputs as cross-attention inputs, and decodes answer text with "[CLS]" as the start token. This decoder has the exact same architecture as the multi-modal encoder. We initialize its weight using the pre-trained multi-modal encoder. SINGULARITY**-temporal.** Figure 8b shows an overview of the model architecture for temporal modeling, this model is also referred to as SINGU-LARITY-temporal. Given multiple video frames {fτi} T*train* i=1 as input, the model firstly encode each frame into their visual representations {Fv(fτi )} with the vision encoder Fv, where Fv(fτi ) ∈ R Lv×D. Next, we add temporal position encoding to each frame to indicate their temporal order. This temporal position encoding is learned from scratch and is initialized as zeros. For brevity, we omit this encoding in the formulation. These framelevel representations are concatenated together as input to the temporal encoder T , and we feed temporal encoder outputs to the multi-modal encoder's cross-attention layer for making a prediction p: $p=\mathcal{H}(\ \mathcal{F}_{l}(S)\,\ \mathcal{T}([\mathcal{F}_{v}(f_{\tau_{1}});...;\mathcal{F}_{v}(f_{\tau_{T_{Train}}})])\ ),$ $\left\{\begin{array}{ll}\mathcal{Q},\ \mathcal{K},\ \mathcal{V}\ \text{for self-att};\\ \mathcal{Q}\ \text{for cross-att}\end{array}\right.$ where [; ] denotes concatenation, and [Fv(fτ1 ); ...; Fv(fτ*Ttrain* )] ∈ R (T*train*×Lv)×D. During inference, when T*test* frames are used as inputs to the model and Ttest > T*train*, we interpolate the temporal position encoding to allow for extended temporal length. This is similar to spatial position encoding interpolation in (Touvron et al., 2021). Pre-Training Objectives. During pre-training, we optimize the model with three standard visionand-language objectives, Vision-Text Contrastive (VTC), Masked Language Modeling (MLM) (Devlin et al., 2019), and Vision-Text Matching. We explain them in detail below. (i) **Vision-Text Contrastive** (VTC) loss aims to aligns paired vision and language embeddings. Given the encoded vision embedding Fv(fi,t), we use a projection head (with pooling) ϕv to project the embedding sequence into a vector representation ϕv(Fv(fi,t)) ∈ R D. Here fi,t is the t-th frame in the i-th video in the training set, and t is randomly sampled from all available frames in this video. For brevity, we omit the subscript t and use fito denote a randomly sampled frame from the ith video during the rest of the discussion. Similarly, we have ϕl(Fl(Sj )) ∈ R D for the j-th sentence. The similarity score si,j of the video and text pair is defined as their dot product: $$s_{i,j}=\phi_{v}({\mathcal{F}}_{v}(f_{i}))^{T}\phi_{l}({\mathcal{F}}_{l}(S_{j}))$$ We apply a contrastive loss to encourage the alignment between paired vision-language embeddings: $$p_{i}^{v}=\frac{\exp(s_{i,i}/\tau)}{\sum_{j}\exp(s_{i,j}/\tau)},\ \ p_{i}^{l}=\frac{\exp(s_{i,i}/\tau)}{\sum_{j}\exp(s_{j,i}/\tau)},$$ $$\mathcal{L}_{vtc}=-\sum_{i=1}^{n}(\log p_{i}^{v}+\log p_{i}^{l}),\tag{6}$$ where $\tau$ is a learned temperature parameter, and $\left(5\right)$. it is initialized as 0.07 following CLIP (Radford et al., 2021). n is the total number of examples in the training set. (ii) **Masked Language Modeling** (MLM) loss, or more precisely, Vision Conditioned Masked Language Modeling loss, aims to predict masked text tokens from their (masked) textual context as well as the visual context. This loss is applied at the last layer of the multi-modal encoder, and we follow the exact formulation in BERT (Devlin et al., 2019), except that we add additional vision inputs and use a higher mask ratio of 50%. (iii) **Vision-Text Matching** (VTM) loss works towards the same goal as the VTC loss - encouraging the alignment between paired vision and language inputs. It uses the [CLS] output from the multi-modal encoder for binary classification – whether the input vision and language pair match or not. To make the training more effective, we also leverage hard negative sampling (Li et al., 2021b; Chen et al., 2020) to sample more informative negatives within the batch for VTM. ## A.2 Additional Experiments Text-to-video retrieval fine-tuning details. For text-to-video retrieval fine-tuning, we use the same architecture as pre-training, except that MLM loss is removed. We use an initial learning rate of 1e-5 with cosine decay to 1e-6. We use a batch size of 32, and train the model for 5 epochs for MSRVTT, 10 epochs for DiDeMo and ActivityNet Captions. During training, we use a single frame per video. During testing, we use 12 frames per video for MSRVTT and DiDeMo, and 32 frames for ActivityNet Captions since it has longer videos. On a single A100, this fine-tuning takes around 1.5 hours for MSRVTT, 0.5 hours for ActivityNet Captions or DiDeMo. Video QA fine-tuning details. For open-ended QA, we add an extra multi-modal decoder (initialized from pre-trained multi-modal encoder) that takes in multi-modal encoder outputs as crossattention inputs, and decodes answer text (details in Appendix). We use an initial learning rate of 1e-5, and warm up the learning rate in the first half epoch, followed by cosine decay to 1e-6. We use a batch size of 32, and train the model for 10 epochs. On a single A100, this fine-tuning takes around 4 hours for MSRVTT-QA, and 1 hour for ActivityNet-QA. We use a single frame per video for training, 12 frames for testing. For MSRVTTMC, we follow (Lei et al., 2021) to use the model trained on MSRVTT retrieval, and select the option with the highest score as prediction. For all downstream tasks, we use the same input image size 224×224 and image augmentations as in pre-training. During inference, we resize the input video frames to 224×224. Analysis Setup. For all ablation studies, we report results on validation splits for the datasets if available. For example, we use validation splits for DiDeMo retrieval and ActivityNet-QA, and we use the test split for MSRVTT retrieval, val1 split for ActivityNet Captions retrieval, and test split for SSv2-label. For retrieval tasks, we use the average recall, which is the average score of R@{1,5,10}) to more holistically compare the model performance. For QA tasks, we use accuracy. SINGULARITY**-temporal Results on Existing** Datasets. In Table 4 and Table 5 we show results of SINGULARITY-temporal on existing textto-video retrieval and video question answering datasets. In general, the 4-frame model SINGULAR-ITY-temporal improves upon the 1-frame model SINGULARITY, but the performance gap is relatively small, especially considering the greatly increased memory and computation cost (discussed in main text) of using 4 frames. Zero-Shot Results. In Table 6 we show zeroshot results of SINGULARITY for text-to-video retrieval. SINGULARITY achieves significantly better results compared to existing methods with a similar amount of pre-training data. Performance of Multiple Runs. In Table 7 we show mean and standard deviation of 5 random runs, for text-to-video retrieval. Comparison on Inference Cost. In Table 8, we compare the cost of various retrieval methods in terms of inference GFLOPs and the number of model parameters. Overall, SINGULARITY models have a similar amount of parameters and lower inference GFLOPs, with higher performance. Memory and Time Cost of Frame Ensemble Strategies. In Section 5, we discussed that our simple early fusion based frame ensemble strategy (*concat*) achieves the best performance for both MSRVTT retrieval and ActivityNet-QA tasks across different number of inference frames. In this section, we continue to compare its memory and computation time cost w.r.t. other frame ensemble strategies. Results are shown in Figure 9. For both tasks, our early fusion strategy (*concat*) achieves better performance than late fusion strategies (lse, max, *mean*) while also runs faster. For memory cost, *concat* uses more memory for MSRVTT retrieval, but fewer memory for the ANet-QA. Overall, the early fusion approach is preferred in most cases due to its better accuracy and faster run time. Ablation Study on Training Objectives. In Table 9, we study the effect of using different training objectives. We notice that using all objectives achieves the best performance. One interesting note is that, compared to (ITM+MLM), adding ITC loss (ITM+MLM+ITC) greatly improves MSRVTT re- | Method | #PT | #Train | MSRVTT | DiDeMo | ActivityNet Cap | | | | | | | |--------------------------------|-------|----------|----------|----------|-------------------|------|------|------|------|------|------| | Frame | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | | | | HERO (Li et al., 2020a) | 136M | 310 | 20.5 | 47.6 | 60.9 | - | - | - | - | - | - | | MMT (Gabeur et al., 2020) | 136M | 1K/-/3K | 26.6 | 57.1 | 69.6 | - | - | - | 28.7 | 61.4 | 94.5 | | ClipBERT (Lei et al., 2021) | 0.2M | 16/16/8 | 22.0 | 46.8 | 59.9 | 20.4 | 48.0 | 60.8 | 21.3 | 49.0 | 63.5 | | VideoCLIP (Xu et al., 2021) | 136M | 960 | 30.9 | 55.4 | 66.8 | - | - | - | - | - | - | | Frozen (Bain et al., 2021) | 5M | 4 | 31.0 | 59.5 | 70.5 | 31.0 | 59.8 | 72.4 | - | - | - | | AlignPrompt (Li et al., 2021a) | 5M | 8 | 33.9 | 60.7 | 73.2 | 35.9 | 67.5 | 78.8 | - | - | - | | CLIP4Clip (Luo et al., 2021) | 400M | 12/64/64 | 42.0 | 68.6 | 78.7 | 42.8 | 68.5 | 79.2 | 40.5 | 72.4 | 98.2 | | SINGULARITY | 5M | 1 | 36.8 | 65.9 | 75.5 | 47.4 | 75.2 | 84.0 | 43.0 | 70.6 | 81.3 | | SINGULARITY-temporal | 5M | 4 | 39.9 | 67.3 | 76.0 | 49.2 | 77.5 | 85.4 | 45.9 | 73.3 | 83.8 | | SINGULARITY | 17M | 1 | 41.5 | 68.7 | 77 | 53.9 | 79.4 | 86.9 | 47.1 | 75.5 | 85.5 | | SINGULARITY-temporal | 17M | 4 | 42.7 | 69.5 | 78.1 | 53.1 | 79.9 | 88.1 | 48.9 | 77.0 | 86.3 | Table 4: SINGULARITY-temporal results on text-to-video retrieval. Table 5: SINGULARITY-temporal results on video question answering. | Method | #PT | #Train Frame MSRVTT-QA ActivityNet-QA MSRVTT-MC | | | | |------------------------------------|-------|---------------------------------------------------|------|------|------| | ClipBERT (Lei et al., 2021) | 0.2M | 16 | 37.4 | - | 88.2 | | AlignPrompt (Li et al., 2021a) | 5M | 16 | 42.1 | - | - | | JustAsk (Yang et al., 2021) | 69M | 640 | 41.5 | 38.9 | - | | MERLOT (Zellers et al., 2021) 180M | 5 | 43.1 | 41.4 | 90.9 | | | VideoCLIP (Xu et al., 2021) | 136M | 960 | - | - | 92.1 | | SINGULARITY | 5M | 1 | 42.7 | 41.8 | 92.0 | | SINGULARITY-temporal | 5M | 4 | 43.3 | 43.4 | 92.0 | | SINGULARITY | 17M | 1 | 43.5 | 43.1 | 92.1 | | SINGULARITY-temporal | 17M | 4 | 43.9 | 44.1 | 93.7 | trieval performance, but not ActivityNet QA. This may because ITC is not applied on the multi-modal encoder which QA tasks may heavily rely on. Impact of Image Size. In Figure 10 we study the impact of image size for downstream tasks. In general, a larger image size helps improve model performance, but the performance saturates at a certain size, e.g., the model performance saturates at around 336×336 for the 3 tasks. Note that our model performance with larger image sizes might suffer from the low resolution of the raw videos we have. For example, we are only able to get videos of resolution 320×240 for MSRVTT. Comparison on Image-Text tasks. Since our model is pre-trained with single frames, it can be directly used for image-text tasks. In Table 11 we show image-text retrieval results on Flickr30K (Young et al., 2014) and COCO (Chen et al., 2015). In Table 12 we show image question answering results on VQA (Antol et al., 2015). We observe that SINGULARITY demonstrates competitive performance on the image-text tasks. As we still see a gap with state-of-the-art image-text models such as (Li et al., 2022), one future direction is to adopt improved designs in these methods to further improve video-text task performance. Hyper-Parameters. The hyper-parameters for our pre-training and downstream task fine-tuning are listed in Table 13 and Table 14. Note that we did not do an extensive hyper-parameter search, but mostly use the same hyper-parameters for different datasets under the same task, it is possible that better results can be achieved with more tuning. ## A.3 Additional Data Details Statistics. We show statistics of pre-training datasets in Table 15, and downstream datasets in Table 16. License. We show dataset licenses in Table 17. | Method | #PT | #Train | MSRVTT | DiDeMo | ActivityNet Cap | | | | | | | |--------------------------------|-------|----------|----------|----------|-------------------|------|------|------|------|------|------| | Frame | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | | | | VideoCLIP (Xu et al., 2021) | 137M | 1K | 10.4 | 22.2 | 30.0 | 16.6 | 46.9 | - | - | - | - | | Frozen (Bain et al., 2021) | 5M | 4 | 18.7 | 39.5 | 51.6 | 21.1 | 46.0 | 56.2 | - | - | - | | AlignPrompt (Li et al., 2021a) | 5M | 8 | 24.1 | 44.7 | 55.4 | 23.8 | 47.3 | 57.9 | - | - | - | | CLIP-straight | 400M | 1 | 31.2 | 53.7 | 64.2 | - | - | - | - | - | - | | BLIP | 130M | 1 | 43.3 | 65.6 | 74.7 | - | - | - | - | - | - | | SINGULARITY | 5M | 1 | 28.4 | 50.2 | 59.5 | 36.9 | 61.1 | 69.3 | 30.8 | 55.9 | 66.3 | | SINGULARITY | 17M | 1 | 34.0 | 56.7 | 66.7 | 37.1 | 61.7 | 69.9 | 30.6 | 55.6 | 66.9 | | Method | MSRVTT | DiDeMo | ActivityNet | | | | | | |-------------|----------------------------------------------------------------------------------|----------|---------------|----|-----|----|----|-----| | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | | SINGULARITY | 42.1±0.5 69.3±0.4 78.1±0.7 53.3±1.0 78.7±1.3 86.3±1.5 47.0±0.5 75.7±0.3 85.3±0.3 | | | | | | | | ![14_image_0.png](14_image_0.png) | Method | #PT | Inference GFLOPs | #params | DiDeMo AvgR | |---------------------------------------|-------|--------------------|-----------|---------------| | Frozen (Bain et al., 2021) | 5M | 542 | 181M | 54.4 | | AlignPrompt (Li et al., 2021a) | 5M | - | 231M | 60.7 | | CLIP4Clip (Radford et al., 2021) 400M | 1,121 | 164M | 63.5 | | | SINGULARITY | 5M | 451 | 202M | 68.9 | | SINGULARITY-temporal | 5M | 485 | 209M | 70.7 | Table 8: Comparison of recent retrieval methods on inference GLOPs and \#params. For brevity, we show DiDeMo retrieval performance with Average Recall (AvgR) - the average of R{1,5,10}. Table 9: Ablation study on training objectives. The models are pre-trained on 2.5M WebVid video-text pairs for 10 epochs and are then fine-tuned. | Objectives | MSRVTT Retrieval AvgR | ActivityNet-QA | |-----------------|-------------------------|------------------| | ITM | 32.4 | 40.2 | | ITM + MLM | 52.5 | 47.0 | | ITM + ITC | 54.3 | 44.1 | | ITM + MLM + ITC | 55.7 | 46.4 | Table 10: Impact of Image Size. We fine-tune models from the same checkpoint, pre-trained with input image size 224×224. We show average recall (average of R@{1,5,10}) for retrieval tasks, and accuracy for the QA task. | Image size | MSRVTT retrieval | DiDeMo retrieval | ActivityNet QA | |--------------|--------------------|--------------------|------------------| | 112 | 58.7 | 65.9 | 46.6 | | 224 | 62.4 | 73.4 | 49.2 | | 336 | 65.5 | 73.4 | 49.6 | | 448 | 64.2 | 72.9 | 49.8 | Table 11: Comparison to existing methods on image-text retrieval. We show results for both text retrieval (image-totext retrieval, TR) and image retrieval (IR). | COCO (5K test) | Flickr30K (1K test) | | | | | | | | | | | |----------------------------|-------------------------------------------------------------------|------|----------------|----|-----|----|----|-----|----|----|----------------| | Method | #PT | TR | IR | TR | IR | | | | | | | | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | R1 | R5 | R10 | | ViLT (Kim et al., 2021) | 4M 61.5 86.3 92.7 42.7 72.9 83.1 83.5 96.7 | 98.6 | 64.4 88.7 93.8 | | | | | | | | | | UNITER (Chen et al., 2020) | 4M 65.7 88.6 93.8 52.9 79.9 88.0 87.3 98.0 | 99.2 | 75.6 94.1 96.8 | | | | | | | | | | OSCAR (Li et al., 2020b) | 4M 70.0 91.1 95.5 54.0 80.8 88.5 | - | - | - | - | - | - | | | | | | Frozen (Bain et al., 2021) | 5M | - | - | - | - | - | - | - | - | - | 61.0 87.5 92.7 | | ALBEF (Li et al., 2021b) | 4M 73.1 91.4 96.0 56.8 81.5 89.2 94.3 99.4 | 99.8 | 82.8 96.7 98.4 | | | | | | | | | | ALBEF (Li et al., 2021b) | 14M 77.6 94.3 97.2 60.7 84.3 90.5 95.9 99.8 100.0 85.6 97.5 98.9 | | | | | | | | | | | | BLIP (Li et al., 2022) | 14M 80.6 95.2 97.6 63.1 85.3 91.1 96.6 99.8 100.0 87.2 97.5 98.8 | | | | | | | | | | | | BLIP (Li et al., 2022) | 129M 81.9 95.4 97.8 64.3 85.7 91.5 97.3 99.9 100.0 87.3 97.6 98.9 | | | | | | | | | | | | ALIGN (Jia et al., 2021) | 1.2B 77.0 93.5 96.9 59.9 83.3 89.8 95.3 99.8 100.0 84.9 97.4 98.6 | | | | | | | | | | | | SINGULARITY | 5M 71.9 90.8 95.4 54.6 80.0 87.8 93.3 99.4 | 99.8 | 81.4 95.8 97.9 | | | | | | | | | | SINGULARITY | 17M 77.0 93.7 96.8 59.6 83.4 90.0 96.1 99.8 | 99.9 | 84.7 96.8 98.3 | | | | | | | | | Method #PT test-dev test-std ClipBERT (Lei et al., 2021) 0.2M 69.08 69.43 ViLT (Kim et al., 2021) 4M 70.94 - VL-BART (Cho et al., 2021) 0.2M - 71.30 LXMERT (Tan and Bansal, 2019) 4M 72.42 72.54 UNITER (Chen et al., 2020) 4M 72.70 72.91 UNIMO (Li et al., 2021c) 4M 73.79 74.02 OSCAR (Li et al., 2020b) 4M 73.16 73.44 ALBEF (Li et al., 2021b) 4M 74.54 74.70 ALBEF (Li et al., 2021b) 14M 75.84 76.04 BLIP (Li et al., 2022) 14M 77.54 77.62 BLIP (Li et al., 2022) 129M 78.24 78.17 SINGULARITY 5M 70.30 70.53 SINGULARITY 17M 73.13 73.27 Table 13: SINGULARITY hyper-parameters for pre-training, video QA, image QA and text-to-image retrieval. We only list a single value if all tasks share the same value. For SINGULARITY-temporal, we train with a similar setup, except that we set \#training frames to be 4. In addition, for SINGULARITY-temporal 2nd stage pre-training, we also use a smaller batch size of 32 per GPU. | config | pre-training | video QA | image QA | text-to-image retrieval | |------------------------|--------------------------------------------|------------|------------|---------------------------| | optimizer | AdamW (Loshchilov and Hutter, 2019) | | | | | optimizer momentum | β1, β2=0.9,0.999 | | | | | base learning rate | 1e-4 | 1e-5 | 1e-5 | 1e-5 | | min learning rate | 1e-5 | 1e-6 | 1e-6 | 1e-6 | | weight decay | 0.02 | | | | | learning rate schedule | cosine decay (Loshchilov and Hutter, 2017) | | | | | image size | 224 | 224 | 336 | 336 | | image augmentation | random resize, crop, horizontal flip | | | | | #training epochs | 10 | 10 | 5 | 10 (Flickr30K), 5 (COCO) | | #warmup epochs | 1 | 0.5 | 0.5 | 0 | | batch size x #GPUs | 128×3 | 32×1 | 64×4 | 64×2 | | #training frames | 1 | | | | | #inference frames | - | 12 | 1 | 1 | Table 14: SINGULARITY hyper-parameters for text-to-video retrieval tasks. We only list a single value if all tasks share the same value. For SINGULARITY-temporal, we train it with a similar setup, except that we set \#training frames to be 4. | config | MSRVTT | DiDeMo | ActivityNet Captions | SSv2-template/label | |------------------------|--------------------------------------------|----------|------------------------|-----------------------| | optimizer | AdamW (Loshchilov and Hutter, 2019) | | | | | optimizer momentum | β1, β2=0.9,0.999 | | | | | base learning rate | 1e-5 | 1e-5 | 1e-5 | 1e-4 | | min learning rate | 1e-6 | 1e-6 | 1e-6 | 1e-5 | | weight decay | 0.02 | | | | | learning rate schedule | cosine decay (Loshchilov and Hutter, 2017) | | | | | image size | 224 | | | | | image augmentation | random resize, crop, horizontal flip | | | | | #training epochs | 5 | 10 | 10 | 10 | | #warmup epochs | 0 | | | | | batch size x #GPUs | 32x1 | 32x1 | 32x1 | 32x2 | | #training frames | 1 | | | | | #inference frames | 12 | 12 | 32 | 12 | Table 15: Statistics of pre-training datasets. The average video length of WebVid is 18 seconds. | Dataset | #image/video | #text | Type | |-----------------------------------|----------------|---------|-------------| | COCO (Chen et al., 2015) | 113K | 567K | image | | VG (Krishna et al., 2017b) | 100K | 768K | image | | SBU (Ordonez et al., 2011) | 860K | 860K | image | | CC3M (Sharma et al., 2018) | 2.95M | 2.95M | image | | CC12M (Changpinyo et al., 2021) | 10.77M | 10.77M | image | | WebVid (Bain et al., 2021) | 2.49M | 2.49M | video | | 5M corpus = CC3M+WebVid | 5.44M | 5.44M | video+image | | 17M corpus = 5M+COCO+VG+SBU+CC12M | 17.28M | 18.41M | video+image | Table 16: Statistics of downstream datasets. | Dataset | #video | #text | Avg Video | | | | |-----------------------------------------------------------------|-------------------|---------------------------------|---------------|-------|-------|------------| | Train | Val | Test | Train | Val | Test | Length (s) | | Text-to-Video Retrieval ActivityNet Cap (Krishna et al., 2017a) | 10,009 | - 4,917 | 10,009 | - | 4,917 | 180 | | DiDeMo (Anne Hendricks et al., 2017) | 8,394 1,065 1,003 | 8,394 | 1,065 | 1,003 | 29.3 | | | MSRVTT (Xu et al., 2016) | 7,010 | - 1,000 140,200 | 1,000 | 15 | | | | SSV2-Template (Goyal et al., 2017a) | 168,913 | - 2,088 | 174 | - | 174 | 4 | | SSV2-Label (Goyal et al., 2017a) | 168,913 | - 2,088 109,968 | - | 1,989 | 4 | | | Video Question Answering MSRVTT-QA (Xu et al., 2017) | 6,513 | 497 2,990 158,581 12,278 72,821 | 15 | | | | | ActivityNet-QA (Yu et al., 2019) | 3,200 1,800 | 800 | 32,000 18,000 | 8,000 | 180 | | | MSRVTT-MC (Yu et al., 2018) | 7,010 | - 2,990 140,200 | 14,950 | 15 | | | Table 17: Dataset licenses. | Dataset | License | |----------------------------------------------|--------------------------------| | COCO (Chen et al., 2015) | CC BY 4.0, Flickr Terms of Use | | VG (Krishna et al., 2017b) | CC BY 4.0 | | SBU (Ordonez et al., 2011) | Flickr Terms of Use | | CC3M (Sharma et al., 2018) | CC3M License | | CC12M (Changpinyo et al., 2021) | CC12M License | | WebVid (Bain et al., 2021) | Exceptions to Copyright | | ActivityNet Captions (Krishna et al., 2017a) | Fair Use | | DiDeMo (Anne Hendricks et al., 2017) | BSD-2-Clause, Creative Commons | | MSRVTT (Xu et al., 2016) | unknown | | SSV2-Template (Goyal et al., 2017a) | SSv2 License | | SSV2-Label (Goyal et al., 2017a) | SSv2 License | | MSRVTT-QA (Xu et al., 2017) | MIT | | ActivityNet-QA (Yu et al., 2019) | Apache | | MSRVTT-MC (Yu et al., 2018) | unknown | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? A dedicated 'Limitation' section after Section 6. ✓ A2. Did you discuss any potential risks of your work? A dedicated 'Ethics Statement' section after Section 6. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Sections 3 And 4. ✓ B1. Did you cite the creators of artifacts you used? Sections 3 and 4. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix Section A.3 ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 4. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Sections 3, 4 and Appendix Section A.3 ## C ✓ **Did You Run Computational Experiments?** Sections 4 And 5. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 3 and Appendix Section A.2 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3, and Appendix Sections A.2 and A.3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix Section A.2 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

liu-etal-2023-learning

Learning with Partial Annotations for Event Detection

https://aclanthology.org/2023.acl-long.30

Event detection (ED) seeks to discover and classify event instances in plain texts. Previous methods for ED typically adopt supervised learning, requiring fully labeled and high-quality training data. However, in a real-world application, we may not obtain clean training data but only partially labeled one, which could substantially impede the learning process. In this work, we conduct a seminal study for learning with partial annotations for ED.We propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts, showing a decent robustness for addressing partial annotation noise. Impressively, in an extreme scenario where more than 90{\%} of events are unlabeled, our approach achieves an F1 score of over 60{\%}.In addition, we re-annotate and make available two fully annotated subsets of ACE 2005 to serve as an unbiased benchmark for event detection. We hope our approach and data will inspire future studies on this vital yet understudied problem.

Learning with Partial Annotations for Event Detection Jian Liu1, Dianbo Sui2, Kang Liu3, Haoyan Liu4 **and Zhe Zhao**4 1 Beijing Jiaotong University 2 Harbin Institute of Technology 3 The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation, Chinese Academy of Sciences 4 Tencent AI Lab [email protected]; [email protected]; [email protected] {haoyanliu, nlpzhezhao}@tencent.com ## Abstract Event detection (ED) seeks to discover and classify event instances in plain texts. Previous methods for ED typically adopt supervised learning, requiring fully labeled and high-quality training data. However, in a realworld application, we may not obtain clean training data but only partially labeled one, which could substantially impede the learning process. In this work, we conduct a seminal study for learning with partial annotations for ED. We propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts, showing a decent robustness for addressing partial annotation noise. Impressively, in an extreme scenario where more than 90% of events are unlabeled, our approach achieves an F1 score of over 60%. In addition, we reannotate and make available two fully annotated subsets of ACE 2005 to serve as an unbiased benchmark for event detection. We hope our approach and data will inspire future studies on this vital yet understudied problem. ## 1 Introduction Deep learning models have shown impressive performance in event detection (ED) since large amounts of event data have become available (Chen et al., 2015; Nguyen and Grishman, 2015). However, such models require fully labelled and highquality data - in practice, we cannot ensure that every event is identified, and as a result, we often face the *partial annotation* issue, as depicted in Figure 1. We show a high rate of partial annotation in real-world event datasets. For example, in ACE 2005, which is widely used as a benchmark for ED evaluation (Christopher Walker and Maeda, 2006), nearly 20% of events are not labelled (see Table 2). Using a partially labelled dataset as a fully labelled one for training runs the risk of mis-training on false negatives, and using a partially labelled dataset for evaluation biases comparison. How- **S1:**: A man died when a heavy tank [_devastated_]the hotel. **Gold: O O Die O O O 0**: Attack **Partial: O O Die O O O O 0**: 0 Figure 1: The partial annotation issue in ED. The Gold row indicates ground-truth labels; the Partial row indicates the partial annotation case we address in this study, where the *devastated* event is not labeled. ever, this issue is still understudied in the existing literature (Liu, 2018; Liu et al., 2020b). In this work, we present a seminal study of learning with partial annotations for ED, with contributions in methodology, data, and practical applications. In our method, to reduce the risk of mis-training on false negatives, we propose a contrastive learning framework (Chopra et al., 2005; Chen et al., 2020) to distinguish ground-truth triggers from contexts, which is shown to be more tolerant of partial annotation noise than the traditional hard classification paradigm (Ji and Grishman, 2008; Chen et al., 2015). In addition, to succeed in the partial annotation scenario, we augment the model with a self-correction regime to recognize false negatives during the training stage. Figure 2 visualizes the core of our method, which is a de facto *trigger localization* formulation that uses sentence-wise normalization (prompted by event types) to find event triggers. Compared to hard classification methods that add up individual losses (as shown at the top of Figure 2), our approach instead forms a contrastive learning paradigm by raising the scores of ground-truth triggers while lowering the scores of context words. As a result, even with a significant number of false negatives in training, it can still maintain a good separation between triggers and contexts (§ 6.1). In addition, we suggest that adding a margin softmax (Wang et al., 2018) with a Gaussian-based distance regularization can further improve learning. 508 In addition to the noise-tolerance mechanism described above, we propose a self-correction regime with the motive that when a model recognizes a false negative with high confidence, it should correct its labels for the subsequent training stage. Nevertheless, modeling the confidence of deep learning models is challenging since their predictions are poorly calibrated (i.e., a model often outputs a high prediction probability even if the prediction is incorrect (Guo et al., 2017)). To address this issue, we propose an uncertainty-guided retraining mechanism based on MC-Dropout (Gal and Ghahramani, 2016), which can output prediction confidence to guide the self-correction process. We explain the relationship between this paradigm and an expectation-maximization (EM) framework. In addition to the methodology contribution, we re-annotate and release the ACE 2005 development and test sets as a data contribution. On the revised benchmark (and an extra MAVEN (Wang et al., 2020) benchmark), we demonstrate the impressive performance of our models - in particular, even in an extreme case with 90% of events unlabeled, our approach achieves more than 60% in F1, yielding a 40% definite improvement over previous methods. In addition to simulation tests, we also conduct a real-world annotation test on WikiEvents (Li et al., 2021a), where the results suggest the practical applicability of our approach. Contributions. Our contributions are three-fold: (i) To the best of our knowledge, this is the first work addressing the potential partial annotation issue in ED, which may spark further research interest. (ii) We highlight a new learning paradigm for ED based on a trigger localization formulation and show that it works effectively with a wide range of partial annotation settings. (iii) We re-annotated the ACE 2005 development and test datasets and released them to the community to serve as an unbiased benchmark. (IV) In addition to simulation experiments, we conduct real-world annotation experiments to validate the effectiveness of our approach for practical use. ## 2 Related Work ED and the Partial Annotation Issue. Event detection (ED) is a crucial subtask of event extraction that aims to identify event instances in texts (Grishman, 1997; Ahn, 2006). The existing ED methods can be divided as feature-based (Ahn, 2006; Li et al., 2013; Liao and Grishman, 2010; ![1_image_0.png](1_image_0.png) Hong et al., 2011) and deep learning-based (Chen et al., 2015; Nguyen and Grishman, 2015; Nguyen et al., 2016; Liu et al., 2018a; Feng et al., 2016; Chen et al., 2018; Yang et al., 2019; Liu et al., 2020a; Du and Cardie, 2020; Lu et al., 2021; Liu et al., 2019a), and there has been a growing interest in applying these methods to specific scenarios (Liu et al., 2019b, 2022b,a). Nevertheless, most of such methods adopt supervised learning and assume the availability of clean datasets. To date, only a few studies have considered the partial annotation issue in ED: Liu et al. (2020b) identify several unlabeled cases in the ACE test set for error analysis; Liu (2018), in the PhD proposal, suggest that the Chinese portion of ACE 2005 is partially labeled. Unfortunately, neither work stands in a methodology perspective for addressing the issue. Our research, on the other hand, introduces a solution for learning with partial annotations. Our trigger localization formulation also relates to using prompts for event information extraction (Wang et al., 2022a; Hsu et al., 2022; Liu et al., 2022c; Wang et al., 2022b), but different from them focusing on improving the overall performance, our work stands in a point addressing the partial annotation issue. Learning with Partial Annotations. Learning with partial annotations, also known as positive and unlabeled learning (Li et al., 2009), is an important problem in machine learning community (Elkan and Noto, 2008; Liu et al., 2002, 2003, 2005). In the domain of natural language processing (NLP), researchers have examined a number of tasks including named entity recognition (NER) (Jie et al., 2019; Mayhew et al., 2019; Peng et al., 2019), Chinese word segmentation (Yang and Vozila, 2014), ![2_image_0.png](2_image_0.png) X : Y : and others (Tsuboi et al., 2008). The efforts for NER relate the most to our work, where a seminal work (Jie et al., 2019) treats the labels of negative instances as latent variables and infers them using partial Conditional Random Fields (Bellare and McCallum, 2007). Later works have devised downweighing mechanisms (Mayhew et al., 2019), confidence estimation methods (Liu et al., 2021), and negative sampling (Li et al., 2021b) for learning. In this study, we offer a new trigger localization formulation for the task of ED and demonstrate promising results in a wide range of partial annotation settings. Sentence ED Model ## 3 Proposed Method Let X = [w1, *· · ·* , wN ] be a sentence with N words and Y = [y1, *· · ·* , yN ] be the ground-truth event label sequence, where yi *∈ T ∪ {*O} is the event label of wi (Here T is a set of all event types and O is a special type for non-trigger words). Then the partial annotation issue can be formulated as: due to the neglect of human annotators, some yi 6= O are not identified, and this results in a partial annotation sequence Y˜ = [y˜1, *· · ·* , y˜N ]. Clearly, directly training a model on (X, Y˜ ) risks outputting a noisy detector. Here we propose a new learning framework to address this issue (as shown in Figure 3), which consists of a noise-tolerant learning mechanism with margin softmax (§ 3.2) and uncertainty-guided retraining mechanism (§ 3.3). [CLS] ATTACK [SEP] A man … tank devastated … λ+ BERT (Sentence, Annotation) λ- An Uncertainty-Guided Interfering Step ## 3.1 Input Representation Given a sentence X, for each event type t ∈ T , we learn their joint representations for further processing. Particularly, we concatenate t and X as the input1 of a BERT encoder (Devlin et al., 2019): $$[\texttt{CLS}]\ t\ [\texttt{SEP}]\ {\overbrace{w_{1}\ w_{2}\ \cdots\ w_{N}}^{\text{The sentence}X}}$$ and consider the output of BERT to be the joint representations, denoted as H(t,X) ∈ RM×d, with M being the length of the input sequence2and d being the dimension of BERT. ## Sentence Ed Model 3.2 Noise-Tolerant Learning Via Margin Softmax Based on H(t,X), we next locate event triggers of type t in the sentence. This can be achieved using sentence-level softmax, and here we introduce a *margin softmax* (Wang et al., 2018) to better address partial annotations. Specifically, we first map H(t,X)to a score vector s t ∈ RM using s t = H(t,X) w, where w ∈ Rdis a shared vector parameter, and then we distinguish between the following two cases for learning: (Sentence, Revised Label) O O DIE O O O O O O O A man died when a heavy tank devastated the hotel. BERT [CLS] DIE [SEP] A man died when a heavy tank … Case 1. A positive instance (i.e., a labeled trigger) of type t is found in the label sequence Y˜ (If many triggers are found, we address each one individually). Assume j is the labeled trigger's index. Here we employ a *positive margin* λ+ and maximize the following objective: $$p_{+}(X,\tilde{Y},j)=\frac{\exp(s_{(j)}^{t}-\lambda_{+})}{\exp(s_{(j)}^{t}-\lambda_{+})+\sum_{m\neq j}^{M}\exp(s_{(m)}^{t})}\tag{1}$$ where s t (j) denotes the j th word's score in the score vector s t. This objective encourages a margin of at least λ+ (Wang et al., 2018) in scores of triggers 1[CLS] and [SEP] are special tokens used in BERT. 2A word in BERT may broke down into many subwords (Sennrich et al., 2016), and here we only consider the first subword to make H(t,X) have the same length as the input. A Normal Training Step (Sentence, Revised Label) ED Model and context words, which therefore makes groundtruth triggers more separable. Note in this case, a sentence may contain "hidden" false negatives. Motivated by the fact that triggers are generally sparsely distributed (Lin et al., 2018), we employ a Gaussian regularization to reduce the penalty. Particularly, we obtain a new score vector sˆ t with: $${\hat{s}}_{(m)}^{t}=s_{(m)}^{t}\times{\mathcal{N}}(|m-j|)$$ where N (·) indicates the standard univariate Gaussian density function, and |m − j| is the distance between the mth word wm and the labeled trigger. This new score vector sˆ t put small weights on words far away from the labeled trigger and is shown to marginally improve learning (§ 6.2). Case 2. There is no trigger of type t found in Y˜ . In this case, we use the [SEP] token as a "no-event-existing" indicator and optimize to give it the highest score. It should be noted, however, that such a case may contain false negatives. To address them, we introduce a *negative margin* λ− 3 to reduce the penalty: $$p_{-}(X,\bar{Y})=\frac{\exp(s_{\Delta}^{t}+\lambda_{-})}{\exp(s_{\Delta}^{t}+\lambda_{-})+\sum_{m\neq\Delta}^{M}\exp(s_{(m)}^{t})}\tag{3}$$ where ∆ indicates the index of [SEP]. In this way, the negative margin λ− instead loosens the gap between the indicator [SEP] and other words. As a result, the model is more forgiving of situations when certain words score higher than the "no event exists" indicator [SEP]. Training and Testing Protocols. The overall loss function for learning is: $\mathcal{L}=-\sum_{(X,\bar{Y})\in D}\sum_{t\in\mathcal{T}}\left[\ \delta_{t}\times\sum_{j:y_{j}=t}\log p_{+}(X,\bar{Y},j)\ +\ \right.$ $\left.(1-\delta_{t})\times\log p_{-}(X,\bar{Y})\ \right]$ $${\mathrm{(4)}}$$ where (X, Y˜ ) ∈ D ranges over each instance in the training set D; t enumerates each event type; δtis a Dirac delta function: $\delta_{t}=\begin{cases}1&\text{If a trigger of type$t$is found(Case1)}\\ 0&\text{Otherwise(Case2)}\end{cases}$ In the inference stage, given a test sentence X, we compute a normalized probability vector p t: p t = softmax(s t) (6) Algorithm 1: Uncertainty-guided retraining regime Input :The training dataset D = {Xi, Y˜i} n i=1; Output :The optimal model parameter Θ; 1 **while** *not convergence* do 2 Sample a training example (X, Y˜ ) from D; 3 if *It is a burn-in or a normal training stage* **then** 4 Update Θ on (X, Y˜ ) using Equation (4) 5 **else** 6 Build an uncertainty-regularized label sequence Y˜ 0 with MC-Dropout (§ 3.3); 7 Update Θ on (X, Y˜ 0) using Equation (4) 8 **end if** 9 **end while** and then compose a set for event triggers of type ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) t as: {wi| p t (i) > τ ;i 6=∆}, where τ is a threshold defined as 1 / N, with N being the length of the sentence (namely, when the predictive probability of a token is above a uniform distribution, we consider it as a trigger). ## 3.3 Uncertainty-Guided Retraining Regime In addition to the noise-tolerant learning paradigm, we also design an *uncertainty-guided retraining* mechanism, in which we correct the potential labels for optimization (Algorithm 1). Assume (X, Y˜ ) is a training example. In the uncertainty interfering stage, we assume that X is an unlabeled sentence and re-predict the event label sequence using the current model. We use Monte Carlo Dropout (MC-Dropout) (Gal and Ghahramani, 2016) to assess the model's uncertainty on the prediction. Particularly, for each event type t, we predict the event triggers K times with dropout layers activated. Assume the resulting prediction set is {qi} K i=1, where qiis the i th prediction and N(qi) is its frequency4. We then create a categorical distribution using N(qi)/K as parameters and sample out a prediction from the categorical distribution as the predict result (This benefits predictions that the model is more confident in). We finally convert the prediction as a label sequence Y0and train a model on (X, Y˜ 0). We alternate between this uncertainty interfering stage and a standard training stage after several burn-in steps. $$({\boldsymbol{\Sigma}})$$ Connection to EM Algorithm. Intuitively, our approach can be viewed as an expectation maximization (EM) algorithm (Dempster et al., 1977) using MC-Dropout to approximate the posterior. 4For instance, for the sentence shown in Figure 3, if we consider the Attack type and set K = 5, we may get a prediction set: {[SEP], devastated, [SEP], devastated, devastated}. In this case, N([SEP]) = 2 and N(devastated) = 3. Denote the original log-likelihood function as log L(Θ; D), where Θ and D indicate the model parameter and partially labeled data respectively. Let Θ(t) be the parameter at the t th iteration. We can view our method as introducing a hidden variable Z to represent the labels of false negatives and then alternating between two steps: (i) An expectation (E) step, which uses the category distribution generated by MC-Dropout as an approximate of the intractable posterior p(Z|D, Θ(t)). (ii) A maximization (M) step, which maximizes the expectation Ep(Z|D,Θ(t)) [log L(Θ; D, Z)] for optimization. ## 4 Experimental Setups Datasets. We conduct our experiments on ACE 2005 and MAVEN5(Wang et al., 2020), with data statistics shown in Table 1. In light of the partial annotation issue in ACE, we re-annotate its development and test sets, using a method combining automatic potential case identification and human validation (The details are shown in Appendix A). To facilitate a fine-grained analysis, we also split up all potential cases into two categories: (i) a *challenge* set, which consists of unlabeled words where more than half of the ED models predict that they act as triggers, and (ii) a *control* set, which consists of unlabeled words where fewer than half of the ED models predict that they act as triggers. Table 2 gives the final results, indicating the partial annotation issue is crucial - for instance, on the test set the unlabeled ratio is 19.3%. Implementations. In our approach, we use BERT-large architecture for ACE 2005 (Lin et al., 2020; Nguyen et al., 2021), and BERT-base for MAVEN (Wang et al., 2020). As for hyper-parameters, the batch size is set to 10 for ACE 2005 and 20 for MAVEN respectively, chosen from [2, 5, 10, 20, 30]. The learning rate is set at 1e-5 for both datasets, chosen from [5e-5, 1e5, 5e-6, 1e-6]. In the margin softmax regime, the positive margin λ+ is set to 10, and the *negative* margin λ− is set to 1; these values are chosen from [0.1, 0.5, 1, 5, 10, 50, 100]. In the uncertaintyguided retraining mechanism, the number of prediction times K is empirically set to 20 for a tradeoff between speed and efficiency. We release the data and the code at https://github.com/ jianliu-ml/partialED. 5It should be noted that MAVEN provides a candidate trigger set for prediction, so the evaluation problem caused by partial annotation on this dataset is not a concern. | Data Split | # Doc. | # Sent. | # Word | # Trigger | | |--------------|----------|-----------|----------|-------------|--------| | Training set | 529 | 17,172 | 267,959 | 4,420 | | | ACE Dev. set | 30 | 923 | 18,246 | 505 (558) | | | Test set | 40 | 832 | 19,061 | 424 (506) | | | Training set | 2,913 | 32,431 | 832,186 | 77,993 | | | MV | Dev. set | 710 | 8,042 | 204,556 | 18,904 | | Test set | 857 | 9,400 | 238,902 | 21,835 | | Table 1: Data statistics of ACE and MAVEN (NV). Numbers in parentheses are re-annotation results. | Split | # Potential | # Validated | UL Rate | | |-----------|---------------|---------------|------------|------| | Challenge | 78 | 34 (43.6%) | 6.7% | | | Dev. Set | Control | 34 | 19 (55.9%) | 3.8% | | Total | 112 | 53 (47.3%) | 10.5% | | | Challenge | 86 | 51 (59.3%) | 12.0% | | | Test Set | Control | 50 | 31 (62.0%) | 7.3% | | Total | 136 | 82 (60.2%) | 19.3% | | Evaluation Settings. We investigate three evaluation settings: (i) A full training setting, in which we use the original training set for learning. Yet, because the original training set inevitably contains unlabeled events, this setting is still a partial learning setting. (ii) A data removal setting, in which we exclude a portion of events from the training setting to study whether the performance drop is caused by a degraded number of positive examples. (iii) A data masking setting, in which we remove the labeling information of some events (by replacing their labels to O) to simulate a more serious partial annotation scene. We use precision (P), recall (R), and F1 as evaluation metrics following previous studies (Ji and Grishman, 2008; Li et al., 2013), and to against randomness, we report experimental results based on a 5-run average. Baselines. We compare our approach to supervised and partial learning methods. For ACE 2005, we consider the following supervised learning methods: Hybrid (Feng et al., 2016), which combines Recurrent Neural Networks and Convolutional Neural Networks; SeqBERT (Yang et al., 2019), which introduces BERT representations; BERTQA (Du and Cardie, 2020; Liu et al., 2020a), which frames ED as a question answering problem; OneIE (Lin et al., 2020), which uses Graph Neural Networks to learn document-level clues; FourIE (Nguyen et al., 2021), which uses an interaction network to combine four information extraction tasks jointly. For MAVEN, we consider DMBERT (Wang et al., 2019) and BERT-CRF (Wang et al., ![5_image_1.png](5_image_1.png) Method P R F1 P R F1 ![5_image_2.png](5_image_2.png) Hybrid†(2016) 71.4 71.3 71.4 74.4 72.2 73.3 SeqBERT†(2019) 72.5 72.1 72.3 74.1 73.5 73.8 BERTQA (2020) 71.1 73.7 72.4 74.5 74.5 74.5 OneIE (2020) 74.9 **74.5** 74.7 75.9 74.7 75.3 FourIE†(2021) **75.7** 74.1 **74.9** 76.0 74.6 75.3 HiddenCRF (2019) 68.4 74.5 71.3 75.7 75.5 75.6 NegSPL (2021b) 70.1 74.0 72.0 75.5 75.5 75.5 Self-Pu (2020) 71.1 71.0 71.1 75.7 74.8 75.2 PromptLoc (ours) 73.6 74.2 73.9 **76.4 76.8 76.6**∗ Method P R F1 P R F1 Hybrid (2016) 62.9 67.2 65.0 63.7 67.0 65.3 OneIE (2021) 64.0 69.0 66.4 64.5 69.3 66.8 BERTQA (2020) 63.8 69.0 66.3 64.9 69.1 66.9 DMBERT (2019) 64.6 70.1 67.2 62.7 72.3 67.1 BERT-CRF (2020) 65.7 68.8 67.2 65.0 70.9 67.8 HiddenCRF (2019) 66.3 68.5 67.4 64.4 72.3 68.1 NegSPL (2021b) 65.6 68.7 67.1 64.9 71.9 68.2 Self-Pu (2020) 66.3 68.0 67.0 64.3 72.3 68.0 PromptLoc (ours) 67.8 69.2 68.5∗**65.4 72.8 68.9**∗ Dev. Set Test Set ![5_image_4.png](5_image_4.png) ![5_image_5.png](5_image_5.png) 2020) as baselines. We consider the following partial learning methods: (1) HiddenCRF (Jie et al., 2019), which treats missing labels as latent variables and infers them using a CRF model (We follow the original paper and use SeqBERT for parameter initialization); (2) NegSPL (Li et al., 2021b), which applies negative sampling for learning and shows good results on NER (we use the same strategy to tune the sampling hyper-parameter). (3) Self-Pu (Chen et al., 2020), which is a self-training boosted method for general positive and unlabeled learning. Our approach is denoted by PromptLoc. ## 5 Experimental Results Results in the Full Training Setting. Tables 3 and 4 show results in the full training setting. Accordingly, our method achieves the best F1 on the clean ACE test set and the MAVEN development/test set, suggesting its efficacy. Comparing the results on the original ACE test set is interesting: our method has lower precision than other methods, but when applied to the revised set, the precision is greatly boosted - this implies that our ![5_image_0.png](5_image_0.png) ![5_image_3.png](5_image_3.png) model does predict triggers not annotated in the original test set. Lastly, partial learning approaches generally outperform supervised methods, showing that the partial annotation issue is a practical concern to be addressed in the ED task. Results in the Data Removal Setting. Finally, we consider the data removal setting to study the impact of a lack of positive examples, and we show results in Figure 4. According to the results, while our model consistently outperforms others, the gap is small, implying that a reduced number of positive instances is not a major factor impeding learning, especially when there are relatively abundant training examples (e.g., p > 60%) or the pre-trained language models are applied (It does have a significant impact on non-BERT models e.g., Hybrid). Results in the Data Masking Setting. We then consider the data masking setting and we show results6in Figure 5. Here p denotes the ratio of *remaining* examples (i.e., we mask the labels of 1 - p events). According to the results, our approach outperforms prior methods by significant margins. For example, on the ACE 2005, when 70% of triggers are masked (p = 30%), our approach obtains 70% in F1, outperforming previous methods by 30% in F1; 6We use the development set for MAVEN because the official site has a submission limit of only 5 per day. Method Setting P R F1 ![6_image_3.png](6_image_3.png) Clean 73.1 72.9 73.0 Trigger Local. Argmax 70.5 70.5 70.5 Adaptive τ 68.7 72.8 70.7 Clean 66.7 76.0 71.0 Hard Class. Argmax 60.3 42.5 49.8 Adaptive τ 58.6 43.8 50.1 when 90% are masked (p = 10%), our approach still achieves 60% in F1, yet previous methods achieves only 20% in F1. Another interesting finding is that our approach yields better results than in the data removal setting (+2.4% and +1.7% in F1 on ACE 2005 and MAVEN). This directly demonstrates our approach's ability to learn from unlabeled events. ## 6 Qualitative Analysis 6.1 Insights Of The Formulation We conduct a sanity check experiment to understand why our trigger localization paradigm works. First, we randomly select an event type and collect N sentences (200 in our experiments) with events of this type. Then, we create two training examples from each sentence: one with original labels and one with all labels masked - this results in a highly mislabeled dataset. Finally, we train two models - one for trigger localization and the other for hard classification - and evaluate them on a leave-out test set. Table 5 gives the results, where we note that even in this extreme partial annotation scene, our trigger localization paradigm performs well, yielding 70.5% in F1 compared to 73.0% in F1 using clean dataset for training. The hard classification based approach, on the other hand, behaves poorly, yielding a drop of 30% in F1. In Figure 6, we visualize the learned probabilities of two models on ground-truth triggers and contexts. According to the results, our method can maintain a separation between ground-truth triggers and context words in this extreme partial annotation scene. However, the hard classificationbased model is very sensitive to partial annotation noise and can not obtain a clear boundary between the ground-truth triggers and context words. For the above reason, incorporating an adaptive τ has little effect on the performance (Table 5). ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) ![6_image_2.png](6_image_2.png) Figure 6: The learned probability distribution. ![6_image_4.png](6_image_4.png) ![6_image_5.png](6_image_5.png) ## 6.2 Ablations On Margin Softmax And Uncertainty Retraining Table 6 (Top) shows an ablation study on the margin softmax regime, based on the data masking settings, where we study the impact of positive margin λ+, negative margin λ−, and Gaussian regularization respectively. According to the results, we find that the negative margin λ− is the most effective, yet the effects of different components are complimentary. An ablation on the multiple triggers are shown in Appendix C. In Table 6 (Bottom), we conduct an ablation study on our uncertainty-guided retraining mechanism and compare it to: (i) w/o uncertainty, which excludes the uncertainty interfering stage for learning, (ii) DirectPred, which retains the stage but uses predicted labels directly for model retraining, and (iii) BoostLearn, which considers half of the dataset to be clean and the other half to be unlabeled and conducts a bootstrapping process (Grézl and Karafiát, 2013). The results have ver- | Category | Example | Event Type | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|--------------| | 1) ... less than 5,000 U.N troops could have stop the killings if Mr. Annan had ... | Die | | | Negligence [51.1%] 2) ... before the genocide, Major ... The ... informant that genocide was being ... | Attack | | | 3) The Justice party changed the constitution after taking power in the elections. | Elect | | | 4) Anne-Marie got the couple 's 19-room home in New York state ... | TransferOwnership | | | Light verbs [20.7%] 5) After he became SG [Secretary General], Annan commissioned a report ... | StartPosition | | | 6) ... GNP took two of the National Assembly seats; a splinter party got the third ... StartPosition 7) The troop opened its tank guns , opened its own mortars , decimated that unit ... Attack 8) ... Board would see it as leverage to seize power and pummel the office staff. Attack | | | | Rare words [25.2%] 9) Press speculation had ... while either divesting or inviting third parties to take ... | TransferOwnership | | | 10) But the general needed U.N. authorization to conduct such a raid and save lives. Attack | | | | Co-reference [3.0%] 11) ... in the 1994 genocide in Rwanda ... for not sending enough troops to stop it. | Attack | | ified the effectiveness of our method - without the uncertainty mechanism (w/o uncertainty), the performance drops 2.4% in F1 on average for ACE and 2.1% for MAVEN. The major advantage of our method lies in that it can select reliable predictions for training - as evidence, we have checked the predictions with high probability (> 0.9) in the categorical distribution and found that 91.4% of them are correct. Finally, the results suggest that our uncertainty-guided mechanism can also promote OneIE and NegSPL, particularly in scenes with large unlabeled rates (e.g., p = 10% and p = 50%). ## 6.3 Analysis Of Unlabeled Cases We explore common patterns of unlabeled events in Table 7. Indeed, 51.1% of them lack a discernible pattern, which could just be due to the annotator's negligence. For example, in case 2, the genocide event is labeled only in the first sentence but not in the subsequent one. The other patterns we find include light verbs (20.7%), such as got in case 4, rare words (25.2%), such as pummel in case 8, and co-reference based triggers (3%), such as it in case 11. These examples are hard for human annotators. We have also investigated the suspicious cases encountered in our re-annotation procedure. Aside from 11% merely mis-predicted by a model, we find two prevalent patterns: (i) compound nouns (54%), such as "election" in "create an election code", which does not refer to events, and (ii) definition violation (35%), such as "lobby" in "Bush plan to lobby allies ..." - though many event detectors predict "lobby" as a Meet event, but in the ACE event ontology, a Meet event is defined as "a meeting event is *physically located somewhere*". The comparison of cases of the control and challenge set is shown in Appendix A.2. ![7_image_0.png](7_image_0.png) ## 6.4 A Real-World Annotation Test Finally, we conduct a real-world annotation test to investigate the practical applicability of our approach. Particularly, we use WikiEvents (Li et al., 2021a) as the test bed and employ two annotators to annotate events in 100 randomly selected training documents. For tractability, we only consider 10 most frequent event types and limit the annotation time to 4 hours. After deleting incorrect labels, we obtain A1 and A2, two sets with annotation rates of 67% and 52%, respectively. We then train models on A1, A2, and the original 100 labeled documents respectively and test them on the test set. The performances of different models are shown in Figure 8. According to the results, when trained on A1 and A2, previous models exhibit a significant drop in F1 (more than 25%). By comparison, our method achieves a good performance and performs comparably to methods that use the original training set for learning. This indicates its efficacy in dealing with the partial annotation issue. ## 7 Conclusion In this study, we investigate the partial annotation problem in ED, a critical yet less-explored problem. We motivate a new learning model for ED and investigate its effectiveness in a variety of partial annotation settings. We also provide two reannotated subsets of ACE 2005 to the community as a data contribution in order to establish a fair evaluation. In the future, we plan to investigate the theoretical aspects of our approach and increase its scope by applying it to other information extraction tasks suffering the partial annotation issue, such as named entity recognition and relation extraction. ## 8 Limitations There are two limitations of this study that could be addressed in future research. First, this study focuses solely on the ED task. In the future, we seek to extend it to the overall event extraction (EE) task, which also includes the event argument extraction task, where a complete annotation is more challenging than in ED. Second, our study models the partially labeled training data instead of annotators. Indeed, the annotators produce the data, so building a model for annotators may be an essential way to address the partial learning problem. For example, an annotator may be more careless than others and generate more noisy data. Consequently, a robust model for the task should give a lower belief in the data of this annotator to improve learning. Lastly, our research raises no ethical issues because it focuses solely on the technical aspects of a normal information extraction problem. ## Acknowledgments This work is supported by the National Natural Science Foundation of China (No.62106016), the Open Projects Program of the State Key Laboratory of Multimodal Artificial Intelligence Systems, and the Tencent Open Fund. ## References David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events, pages 1–8, Sydney, Australia. Association for Computational Linguistics. Kedar Bellare and Andrew McCallum. 2007. Learning extractors from unlabeled text using relevant databases. In *AAAI*. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A simple framework for contrastive learning of visual representations. 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Association for Computational Linguistics. | Model | Configuration | F1 | |------------------------------------------|------------------------------------------|------| | 1 hidden layer; 100 hidden units | 65.6 | | | 1 hidden layer; 200 hidden units | 66.1 | | | FFNNs | 2 hidden layers; 100 hidden units each | 66.3 | | 2 hidden layers; 200 hidden units each | 65.5 | | | filter window of 2,3; 200 feature maps | 68.7 | | | filter window of 2,3; 500 feature maps | 68.6 | | | CNNs | filter window of 2,3,4; 200 feature maps | 68.7 | | filter window of 2,3,4; 500 feature maps | 67.0 | | | unidirectional; 100 hidden units | 68.1 | | | unidirectional; 200 hidden units | 67.9 | | | RNNs | bidirectional; 100 hidden units | 68.9 | | bidirectional; 200 hidden units | 68.0 | | | 1 convolutional layer; 100 hidden units | 70.0 | | | 1 convolutional layer; 200 hidden units | 69.7 | | | GCNs | 2 convolutional layers; 100 hidden units | 68.8 | | 2 convolutional layers; 200 hidden units | 69.1 | | | Bertbase; cased tokenizer | 71.8 | | | Bertbase; uncased tokenizer | 70.1 | | | BERT | Bertlarge; cased tokenizer | 72.2 | | Bertlarge; uncased tokenizer | 71.0 | | Table 8: Model details for potential unlabeled case identification, with their performances in the (original) ACE test set. Fan Yang and Paul Vozila. 2014. Semi-supervised Chinese word segmentation using partial-label learning with conditional random fields. In *Proceedings* of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 90– 98, Doha, Qatar. Association for Computational Linguistics. Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring pre-trained language models for event extraction and generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5284–5294, Florence, Italy. Association for Computational Linguistics. ## A Ace 2005 Dataset Revision Given that the ACE 2005 dataset is partially annotated and that comparing models on a partially annotated test set results in biased results, we revised the ACE 2005 development and test sets to create a fair benchmark. Specifically, we create an automatic method that incorporates (i) a potential false negative identification stage to identify all possible unlabeled cases and (ii) a human validation stage to manually validate each case. ## A.1 Potential False Negative Identification To identify potential unlabeled cases, we first train a set of 20 different ED models with diverse archi- | Split | # Potential | # Validated | UL Rate | | |-----------|---------------|---------------|------------|------| | Challenge | 78 | 34 (43.6%) | 6.7% | | | Dev. Set | Control | 34 | 19 (55.9%) | 3.8% | | Total | 112 | 53 (47.3%) | 10.5% | | | Challenge | 86 | 51 (59.3%) | 12.0% | | | Test Set | Control | 50 | 31 (62.0%) | 7.3% | | Total | 136 | 82 (60.2%) | 19.3% | | Table 9: Details of the revised ACE 2005 subsets. "UL Rate" is the ratio of unlabeled cases to labeled ones. tectures7ranging from Feed-Forward Network Networks (Liu et al., 2017), Convolutional Network Networks (Chen et al., 2015), Recurrent Neural Networks (Nguyen et al., 2016), Graph Convolutional neural networks (Liu et al., 2018b) to pretrained language models (Yang et al., 2019), and then check their predictions on the development and test sets. The model details are shown in Table 8. Our intuition is that a wide range of ED models with various architectures can integrate a variety of inductive biases, and we regard any predicted trigger whose original label is O to be a potentially unlabeled example. Consequently, we uncover 112 and 136 potentially unlabeled cases on the ACE 2005 development and test sets respectively. To undertake a finer-grained analysis, we divide all the potential cases further into two groups: (i) a challenge set, in which more than half of the ED models predict an event label for a word whose initial label is O, and (ii) a control set in which fewer than half of the models do. ## A.2 Human Validation | ACE 2005 | MAVEN | | | | | | |-----------------------|---------|------|------|------|------|------| | Method | 10% | 20% | 30% | 10% | 20% | 30% | | Hybrid† (2016) | 7.4 | 22.3 | 37.1 | 6.4 | 17.7 | 31.7 | | OneIE (2020) | 10.4 | 32.3 | 46.0 | 10.4 | 22.7 | 37.8 | | HiddenCRF (2019) 18.6 | 40.3 | 52.3 | 14.7 | 32.7 | 43.0 | | | NegSPL (2021b) | 20.6 | 42.3 | 54.3 | 15.6 | 35.7 | 46.0 | | No Prompting | 60.7 | 68.2 | 72.5 | 51.8 | 59.9 | 62.0 | | Prompting w Type | 61.3 | 68.9 | 72.7 | 52.3 | 60.3 | 62.3 | | Prompting w Desc. | 62.0 | 68.7 | 71.9 | 52.1 | 60.5 | 62.5 | Table 10: Results of different prompting strategies. of 47.3%), producing a 10.5% percent ratio of unlabeled examples to labeled ones; on the ACE 2005 test set, 86 unlabeled cases are identified (with a verification rate of 60.2%), producing a 19.3 percent ratio of unlabeled examples to labeled ones. The high unlabeled ratio shows that the partial annotation problem is critical for the ACE 2005 corpus. Interestingly, we also note the challenge set has a lower validation rate than the control set. One reason for this is that the challenge set contains many spurious cases, such as compound nouns that are not event triggers, lowering the validation rate, whereas the control set contains many difficult cases, such as light verbs and unusual words that are ground-truth triggers missed by annotators, boosting the validation rate. We discuss the specific examples in Section 6.3. ## B Ablation On Prompting Strategy We compare different prompting strategies, including "No Prompting", which does not uses prompting strategy, but build separate model for each event type. "Prompting w Type", which is our approach using event type as prompt. "Prompting w Description", which uses the event type description as the prompt. According to the results in Table 10, the prompting mechanism is not an important factor for improvement - the method without prompting (No Prompting) also yields good results. However, unlike the prompting method, which allows for natural parameter sharing, it necessitates the building of individual models for each event type, which may be costly in a real-world setting. Furthermore, we note that there is no noticeable difference when type or description are used as prompts. ## C Performance With Multiple Triggers We next investigate how well our approach performs in cases where the sentence contain multiple triggers. In the original ACE dataset, 25.1% (790 ![13_image_0.png](13_image_0.png) out of 3136) of all sentences containing events have more than one event trigger. However, because our method treats different event types separately, it may only be impacted by sentences that contain two triggers of the same event type - such cases account for only 7% (245 out of 3136). Figure 8 shows the results of our approach for sentences with a single trigger and sentences with multiple triggers. The gap between the two is very small, indicating that our approach is effective for sentences with multiple triggers. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Appendix A ✓ B1. Did you cite the creators of artifacts you used? Appendix A, ACE 2005. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix A ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Appendix A B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix A ## C ✓ **Did You Run Computational Experiments?** Section 5 And 6 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4 ✓ C3. 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ma-etal-2023-world

World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models

https://aclanthology.org/2023.acl-long.31

The ability to connect language units to their referents in the physical world, referred to as grounding, is crucial to learning and understanding grounded meanings of words. While humans demonstrate fast mapping in new word learning, it remains unclear whether modern vision-language models can truly represent language with their grounded meanings, and how grounding may further bootstrap new word learning. To this end, we introduce Grounded Open Vocabulary Acquisition (GOVA) to examine grounding and bootstrapping in open-world language learning. As an initial attempt, we propose World-to-Words (W2W), a novel visually-grounded language model by pre-training on image-text pairs highlighting grounding as an objective. Through extensive experiments and analysis, we demonstrate that W2W is a more coherent and fast grounded word learner, and that the grounding ability acquired during pre-training helps the model to learn unseen words more rapidly and robustly.

# World-To-Words: Grounded Open Vocabulary Acquisition Through Fast Mapping In Vision-Language Models Ziqiao Ma∗ Jiayi Pan∗ **Joyce Chai** Computer Science and Engineering Division, University of Michigan {marstin,jiayipan,chaijy}@umich.edu ## Abstract The ability to connect language units to their referents in the physical world, referred to as grounding, is crucial to learning and understanding grounded meanings of words. While humans demonstrate fast mapping in new word learning, it remains unclear whether modern vision-language models can truly represent language with their grounded meanings, and how grounding may further bootstrap new word learning. To this end, we introduce Grounded Open Vocabulary Acquisition (GOVA) to examine grounding and bootstrapping in openworld language learning. As an initial attempt, we propose World-to-Words (W2W), a novel visually-grounded language model by pre-training on image-text pairs highlighting grounding as an objective. Through extensive experiments and analysis, we demonstrate that W2W is a more coherent and fast grounded word learner, and that the grounding ability acquired during pre-training helps the model to learn unseen words more rapidly and robustly.1 ## 1 Introduction Language is learned through sensorimotor experience in the physical world (Bisk et al., 2020). The ability to connect language units to their referents in the physical world, referred to as *grounding*, plays an important role in learning and understanding grounded meanings of words (Harnad, 1990). As shown in Figure 1, a human reader would easily ground noun phrases to the corresponding entities captured in the image. Even when the term "incinerator" is new to human learners, they can still locate the object of interest through the language and visual context, and acquire its meaning. In fact, this ability to bootstrap new word learning with only minimal information, known as fast mapping, is demonstrated abundantly in cognitive ∗Equal contribution. 1Code available at https://github.com/sled-group/ world-to-words. A lady wearing a navy blue ![0_image_0.png](0_image_0.png) stripe tank top is getting ready to burn glass in front of an incinerator. Figure 1: Even when the term "incinerator" (highlighted yellow) is new to human learners, they can still locate the most likely referent (indicated by the yellow bounding box) in the perceived world by grounding. literature on human language acquisition (Carey and Bartlett, 1978; Carey, 1978; Golinkoff et al., 2000; Smith and Yu, 2008). Recently, there has been a substantial effort on pre-training vision-language models (VLMs) (Du et al., 2022a). Despite the exciting performance of these models on a variety of downstream vision and language tasks, it remains unclear whether these models can truly understand or produce language with their grounded meanings in the perceived world, and how grounding may further bootstrap new word learning. These questions are of interest from both a scientific and an engineering point of view. From a scientific perspective, grounding is crucial to language learners, as children attend to intended objects in the environment when producing (Tanenhaus et al., 1995; Meyer et al., 1998) and comprehending (Smith et al., 2007) utterances. From an engineering perspective, even with the availability of grounded vision language datasets (image-text pairs with fine-grained wordobject mappings) (Plummer et al., 2015), the costly grounding annotation can hardly cover the whole vocabulary space during the training time. Building upon the pre-trained models, it's important for the agent to have the ability to learn grounded new words in a few shots of raw image-text pairs without word-object mappings. To this end, we introduce Grounded Open Vocabulary Acquisition (GOVA), a scalable formulation to examine grounding and bootstrapping in openworld language learning. In this formulation, language learning is a combination of learning to predict a word in a linguistic context as well as learning to ground the word in the physical world. Under this formulation, we explore the framework in which the model first acquires the grounding ability during pre-training, and then transfers this ability to learn unseen words without grounding supervision. As an initial step, we developed World-to-Words (W2W), a novel visually grounded language model motivated by recent advances in detection transformers (DETR) (Carion et al., 2020; Kamath et al., 2021). Compared to many existing VLMs, W2W performs language modeling upon explicit object representations. The model first acquires the ability to ground during pre-training, and then transfers this intrinsic ability to learn unseen words when grounded supervision is no longer available. Our empirical results show that learning to map words to their referents plays a significant role in grounded word acquisition. By pre-training with fine-grained word-object mappings, W2W demonstrates stronger performance in learning grounded meanings of words, both seen and unseen, yet with orders of magnitude fewer data compared to other competitive VLM baselines. The pre-trained model can further provide a foundation for efficient learning of new grounded words with a few examples. We further present an in-depth analysis to understand potential predictors of W2W in word learning, which demonstrates intriguing behaviors in comparison to human language learning. Our findings will provide a stepping stone for future work on grounded language learning in an open world. ## 2 Grounded Open Vocabulary Acquisition (Gova) We start by introducing the settings of *grounded* word acquisition and *few-shot learning of new* words tasks, which are two key components of the Grounded Open Vocabulary Acquisition (GOVA) task formulation. We further present a unified evaluation protocol and introduce the dataset we curated for this problem. ## 2.1 Grounded Word Acquisition Many vision-language tasks have been developed in the past, *e.g.*, visual question answering, visual commonsense reasoning, etc. However, these tasks are mainly focused on the end task performance without scrutinizing whether words are grounded to their corresponding visual entities. We ![1_image_0.png](1_image_0.png) Figure 2: An instance of the word grounding task. Models are tasked to predict the missing word boat and localize the corresponding smaller yellow boat in the image coherently. consider a formulation that directly examines if vision-language models have the ability to acquire grounded meanings of words, specifically, through both *language modeling* and *object localization*. Figure 2 shows an instance of the word acquisition task. A model is presented with an image ximg ∈ I and an incomplete caption xcap ∈ T with one of its groundable words w (*e.g.*, nouns and adjectives) replaced by a MASK. The model is tasked to predict this missing word w ∈ V based on all available context and localize the corresponding objects Ow = {o1, o2, · · · , on} in the image by proposing the bounding boxes of them. Overall, a model capable of solving the grounded word acquisition task is a function f : *I × T → V ×* R 4n. The language modeling part takes the form of a cloze test, which predicts an open vocabulary word and is widely adopted to evaluate pre-trained language models (Paperno et al., 2016; Petroni et al., 2019; Jin et al., 2020). However, language modeling alone fails to provide a comprehensive evaluation of language grounding. For example in Figure 2, a model may correctly produce the word "boat," but mistakenly attributes the evidence to the larger white boat in the image. To address this limitation, we require models to localize the corresponding object in the image. This design is motivated by the disentanglement of object detection into object localization and class recognition (Singh et al., 2018; Zareian et al., 2021; Zhong et al., 2022). It enables vision models to develop a sense of objectness without relying on a predefined set of object classes, thereby potentially allowing them to generalize to unseen objects. Further comparison with related task setups is discussed in Section 5 and illustrated in Figure 8 in the Appendix. ## 2.2 Evaluation Metric In language model evaluation, the commonly used measures for assessing performance are the standard hit-rate-at-k (HR@k) measure and perplexity (Salazar et al., 2020; Jin et al., 2020). In masked language modeling, the log perplexity of a word w is defined as the log pseudo-perplexity: $$\log{\mathrm{PPL}}(w)=-\log P(w|x_{\mathrm{img}},x_{\mathrm{cap}})$$ In object detection evaluation, especially for phrase grounding where multiple referents are possible (Kamath et al., 2021), Any-Protocol and All-Protocol are commonly adopted. Assuming n ground truth bounding boxes B = {b1, b2, · · · , bn} and m predicted bounding boxes Be = {be1, be2, *· · ·* , bfm}, the intersection-over-union (IoU) in both protocols is defined as: $$\begin{array}{c}{{\mathrm{IoU_{any}=\frac{1}{n}\sum_{i\in\{1,2,\cdots,n\}}\max_{j\in\{1,2,\cdots,m\}}\mathrm{IoU(}b_{i},\widetilde{b_{j}})}}}\\ {{\mathrm{IoU_{all}=IoU(}\cup B,\cup\widetilde{B})}}\end{array}$$ However, these metrics only capture unimodal performance without concerning the correctness of cross-modal mapping. We design two new metrics to combine language and vision performance: - **Grounded hit-rate** (G-HR@k), the proportion of tests with the masked word appearing in the top-k candidates and a localization IoU over 0.5. - **Grounded perplexity** (G-PPL) as follows: $$\log\text{G-PPL}(w)=\begin{cases}\infty&\text{if IoU}=0\\ \log\text{PPL}(w)-\log\text{IoU}&\text{else}\end{cases}\tag{4}$$ ## 2.3 Few-Shot Learning Of New Words Although there are grounding datasets available, i.e., image-text pairs with word-object mapping annotation (Plummer et al., 2015), it is impractical to obtain such fine-grained annotation on a large scale and to cover the whole vocabulary space V. We therefore explore grounded new word learning as a few-shot learning problem, especially under the setting of incremental class learning (Mandziuk and Shastri, 1999; Kemker et al., 2018). An intuitive illustration of the few-shot new word learning framework is provided in Figure 3. Under this framework, a computational model is developed in two stages. During the pre-training stage, the model receives image-caption pairs, with finegrained word-object annotation for a set of base words Vseen ⊆ V. After pre-training, the model is provided with few samples of raw text-image pairs, each containing a set of unseen words Vunseen ⊆ V that the model has to acquire. ![2_image_0.png](2_image_0.png) (2) $\frac{1}{2}$ (3) (4) (a) ... Tests are performed after each training stage. It's important to note that the unseen words may not be completely new, *e.g.*, the models may have encountered these words in its language encoder initialized with pre-trained language models. We consider them "unseen" because the model never sees these words paired with their referent, *i.e.*, the grounded meanings of the words are unknown. ## 2.4 Dataset Curation We build our dataset based on the Flickr30K Entities dataset (Plummer et al., 2015), which contains image-text pairs with dense annotations between groundable phrases and bounding boxes of objects. The groundable phrases and regions are defined by the dataset, as chunks of text that refer to object bounding boxes. To construct word grounding instances, we use Stanza (Qi et al., 2020) to parse the caption, enumerate every word in the groundable phrase, and identify those with a POS tag of NOUN or ADJ. These groundable words are replaced by MASK one at a time and matched to their corresponding bounding boxes. The dataset is divided into 4 splits: pre-training set, unseen words training set, seen words test set, and unseen words test set. We start by selecting 31 unseen words and holding out all text-image pairs containing these words from the training split of Flickr30K Entities. The hold-out text-image pairs are further divided into the training and test sets for unseen words. The remaining training split of Flickr30K Entities is used for the pre-training set. To prevent frequent words (*e.g.*, "man") from dominating the test results of the seen words, we choose 60 seen words and sample an equal number of test instances for each word from the test split of Flickr30K Entities. More details and statistics of the dataset are available in Appendix A. ![3_image_0.png](3_image_0.png) ## 3 Computational Models 3.1 The World-To-Words (W2W**) Model** Humans demonstrate fast mapping, the ability to learn new words with only minimal information (Carey and Bartlett, 1978; Carey, 1978; Golinkoff et al., 2000). Motivated by how visual grounding helps humans in bootstrapping new words, we propose a computational framework that first acquires the ability to ground during pretraining, and then transfers this intrinsic ability to learn unseen words when grounded supervision is no longer available. We introduce World-to-Words (W2W), a novel visually-grounded language model with an end-to-end design as illustrated in Figure 4. Model Architecture. Similarly to dual-stream vision-language models, W2W encodes the textual input with a pre-trained language model (Liu et al., 2019), and encodes image input with convolutional backbone (He et al., 2016) with 2D positional encoding added. The text and image representations are linearly projected onto a joint semantic space and concatenated. The multimodal representation is then forwarded into a cross-encoder with selfattention layers. The cross-encoded representations in the final layer are sent into an object decoder, together with a set of learnable object queries. The object decoder produces an object embedding for each input object query, which can be considered as a representation of the proposed object. The object representations are further forwarded to the text decoder, which allows language modeling to explicitly attend to the perceived objects. We discuss the pre-training objectives, especially how the model acquires grounding in the following paragraphs. Other details are available in Appendix B. Masked Language Modeling (MLM). As an intrinsic task, we follow the majority of existing pretrained vision-language models to perform masked language modeling with a two-layer MLP. Words in input text are randomly masked out, and the model predicts the masked words conditioned on the corrupted sentence and image. Words in groundable phrases are masked with a probability of 0.4 and those in non-groundable regions are masked with a lower probability of 0.1. Object Localization (OL). Each object representation will be decoded by a shared three-layer MLP to produce a bounding box. We follow prior detection transformers (DETR) (Carion et al., 2020; Kamath et al., 2021) to perform bipartite matching between proposed boxes and ground truth boxes with a Hungarian loss (Kuhn, 1955). The predicted boxes are optimized towards ground truth using the generalized intersection-over-union (GIoU) loss (Rezatofighi et al., 2019) and the L1 loss. Grounding. The notion of *Grounding* is realized by grounded pre-training through word-region alignment (WRA) which enables fine-grained cross-modal mapping between words and objects. It consists of two levels of alignment: positional alignment and *semantic alignment*. In positional alignment, the model learns to map each object representation to words in the sentence, which could possibly be a MASK or an additional no-object label ∅ (Yu and Siskind, 2013; Kamath et al., 2021). We use a fully-connected layer to predict the distribution over token positions with cross-entropy loss. In semantic alignment, the model learns to bring word representations closer to the object representations that they ground to, and push the unrelated pairs farther. We use a contrastive loss over the final layers of the object and text decoders. ## 3.2 Baselines Groundless Baseline. A baseline with no grounding ability is developed by pre-training W2W in the same condition but removing the grounding Models Seen (|Vseen| = 60) Unseen (|Vunseen| = 31) G-HR@1 (↑) log G-PPL (↓) HR@1 (↑) log PPL (↓) Acc (↑) IoU (↑) G-HR@1 (↑) log G-PPL (↓) HR@1 (↑) log PPL (↓) Acc (↑) IoU (↑) RoBERTa - - 38.0 2.75 - - - - 23.1 4.96 - - RoBERTa (FT) - - 47.9 1.99 - - - - 24.3 4.38 - - ViLT - - 64.7 **1.27** - - - - 32.7 3.68 - - MDETR - - - - 27.8 / 27.0 25.3 / 28.0 - - - - 26.3 / 20.2 23.9 / 21.7 ViLT+MDETR 19.8 / 19.3 2.53 / 2.43 64.7 **1.27** 31.1 / 30.4 28.5 / 31.2 8.6 / 8.1 5.07 / 5.12 32.7 3.68 27.3 / 23.3 25.0 / 23.8 VisualBERT (FT) 28.5 / - 2.96 / - 42.3 2.33 **68.1** / - 53.3 / - 10.2 / - 5.60 / - 20.7 4.81 50.6 / - 45.2 / - W2Ww/o G (FT) 28.9 / 27.8 2.33 / 2.38 63.9 1.41 44.0 / 43.0 40.0 / 38.2 1.1 / 1.1 11.89 / 12.04 3.7 10.87 38.7 / 31.9 36.2 / 31.0 W2W 47.0 / 46.3 1.79 / 1.81 66.9 1.26 66.8 / 66.3 58.8 / **57.6** 2.3 / 2.3 11.58 / 11.74 4.2 11.01 61.3 / 53.1 56.3 / **48.0** Table 1: Test results on the seen and unseen words, obtained immediately after pre-training. Unless noted explicitly as fine-tuned (FT), all results reflect the performance of models without fine-tuning. Evaluations under both All and Any-protocols are provided in the table as (All/Any) pairs. For models depending on a frozen pre-trained object detector, we can only provide evaluation under All-Protocol. We note that the unseen words are only unseen to W2W models, as pre-trained baselines have encountered them all during development. We report the results for reference. objectives in the loss function. We refer to this groundless model as W2Ww/o G. Like a typical pretrained VLM, *e.g.*, VisualBERT (Li et al., 2019), W2Ww/o G performs language modeling based on the object features, without explicit cross-modal referential grounding. We apply W2Ww/o G on GOVA task by fine-tuning the model on the pre-training dataset with grounding objective until convergence. Pre-trained Baselines. For the majority of the pre-trained VLMs, the unseen words are known during pre-training. Also, the primary focus of this work is to understand grounding and bootstrapping in grounded word acquisition. It's not our goal to scale up or re-train all variants of pretraining frameworks. Therefore, we compare our model to the pre-trained VLMs with equal or reasonably larger scales for only reference and analysis purposes. We choose representative baselines in phrase grounding, as presented in Table 1: - "Detect-and-Recognize" Baseline: Models under this framework rely on a pre-trained frozen object detector, and then learn to predict words from proposed objects. We choose the fine-tuned VisualBERT (Li et al., 2019) for this type. - "Produce-and-Localize" Baseline: Models under this framework rely on a pre-trained visionlanguage model to predict the missing word, and then perform referring expression comprehension and propose objects. We combine ViLT (Kim et al., 2021) and MDETR (Kamath et al., 2021) for their competitive performance in vision-conditioned language modeling and phrase grounding individually. ## 4 Empirical Findings 4.1 Grounded Pre-Training The results of this section are obtained from the test immediately following pre-training. Models \# Param \# Imgs \# Caps Objectives RoBERTa 120M - - MLM VisualBERT 180M 200K 567K MLM, ITM ViLT 110M 4.0M 10M WRA*, MLM, ITM MDETR 200M 200K 1.3M WRA, OL W2W 200M 30K 150K WRA, MLM, OL W2Ww/o G 200M 30K 150K MLM, OL *WRA is formulated as word-patch alignment in ViLT, thus it cannot perform object localization without major modifications. Table 2: The baselines for comparisons and references. ITM stands for Image Text Matching, and all the other abbreviations follow Section 2. Pre-training Results on Seen Words The main results for the pre-training stage are summarized in Table 1. Our direct observation is the strong performance of W2W in terms of both grounded metrics, Top-1 Grounded Hit-Rate (G-HR@1) and Grounded Perplexity (G-PPL). W2W significantly outperforms the groundless baseline W2Ww/o G and pre-trained baselines, even for systems pre-trained with a significantly larger amount of data and computing, as shown in Table 2. While W2W produces correct predictions of the missing words as well as the locations of the corresponding bounding boxes, it turns out to be challenging for baselines to achieve them both. For "Detect-and-Recognize" baseline (VisualBERT), we observe a comparable object localization performance empowered by the frozen object detector. However, it suffers from a poor language modeling ability (as demonstrated by HR@1 and PPL, weaker than a finetuned RoBERTa). For the "Produce-and-Localize" baseline (ViLT+MDETR), we observe a strong language modeling performance due to the scale of ViLT. Yet, correct word grounding remains difficult, as can be seen from the poor localization performance. These results demonstrate that the GOVA task is challenging, and W2W is competitive in learning grounded word meanings during pre-training. Bootstrapping through Grounded Objectives. We further provide a cross-time analysis to understand the role of grounded objectives in pre-training efficiency. The results of different training steps are provided in Table 3. From the table, we observe that W2W outperforms both of its groundless variants in language modeling, object localization, and jointly under the grounded perplexity. What's even more striking is that W2W achieves better performance with *10 times less training data* compared to the model trained without the grounding objective (*i.e.*, the WRA objective). These results confirm the crucial role of explicit word-object alignment in efficient grounded word learning. This can be explained by that the grounded objectives attempt to align the vision and language semantic spaces, which ideally benefit both visually conditioned language modeling and language-conditioned object localization. Although it is possible to build a mapping between word and object representations through cross-modal probing and fine-tuning after pre-training, these methods are not comparable to systems with grounded objectives in terms of efficiency and performance. | # Steps | Metrics | W2W | W2Ww/o G (FT) | |---------------|---------------|-------------|-----------------| | IoU (↑) | 46.7 / 46.2 | 36.9 / 35.3 | | | log PPL (↓) | 1.46 | 1.53 | | | 10k | log G-PPL (↓) | 2.22 / 2.23 | 2.52 / 2.57 | | IoU (↑) | 58.1 / 57.1 | 39.6 / 38.8 | | | 50k | log PPL (↓) | 1.26 | 1.44 | | log G-PPL (↓) | 1.80 / 1.82 | 2.34 / 2.38 | | | IoU (↑) | 58.7 / 57.6 | 40.0 / 38.2 | | | 100k | log PPL (↓) | 1.26 | 1.41 | | log G-PPL (↓) | 1.79 / 1.81 | 2.34 / 2.38 | | Pre-training Results on Unseen Words: WordAgnostic Grounding One important finding of the pre-trained model is the surprising performance in localizing the unseen words behind the MASKs. As shown in Table 1, W2W achieves a high AnyIoU of 56.3% and Any-localization accuracy of 61.3% for the unseen words, which are very close to its performance on the seen set and surpass baselines that have seen these words. Moreover, as anticipated, since these words are held out during pre-training, W2W fails to correctly unmask these unseen words, leading to a high log perplexity of 11.01 and low HR of 4.2, compared to that of 1.26 and 66.9 on the seen words. Figure 5 shows an example of such word-agnostic grounding. $$\begin{array}{r l}{{\tt{s a t e d\ \ on\ a\ <M}}}\\ {{\tt{v i l l a g e.}}}\end{array}$$ $$\begin{array}{c}{{\mathrm{animals}}}\\ {{\in\,\mathrm{lephant}}}\end{array}$$ Three men seated on a **<MASK>** ![5_image_0.png](5_image_0.png) - W2W animal - Ground Truth: elephant This performance disparity in language modeling and referent localization on unseen words suggests that W2W has developed a certain level of word-agnostic grounding, *i.e.*, to locate the most likely referent of a word through both the linguistic context and the visual context, even if the word itself is never seen during pre-training. A similar situation is faced by human language learners when inferring the grounded meaning of a new word, as we described earlier in Figure 1. Our experiment demonstrates that, through grounded pre-training, it is possible for a vision-language system to acquire word-agnostic grounding ability, which opens up the opportunity to enable human-like fast mapping when learning new words. ## 4.2 Few-Shot New Words Acquisition In this section, we task W2W to acquire unseen words from a few samples of raw image-text pairs, without any bounding boxes or word-object mappings annotation. As we have demonstrated the model's word-agnostic grounding, we seek to explore if this ability can be transferred to facilitate learning unseen words when a large amount of data and grounded supervision are no longer available. Specifically, we perform few-shot learning on the pre-trained W2W with only masked language modeling (MLM) as the learning objective. More hyperparameter details are available in Appendix B.2. ## Learning New Words Through Incremental Learning. We first explore the multi-class incremental learning setting, in which the pre-trained model is tasked to acquire the 31 unseen words from a few-shot learning session. The experiment is repeated with sample sizes of 8, 16, 24, and 32 immediately after pre-training. As shown in Figure 6, even with as few as 8 samples per word, W2W can significantly bring down the grounded perplexity of unseen words, while mostly maintaining the grounded perplexity of the seen words without catastrophic forgetting. Compared to W2W without the grounding objective, the full W2W demonstrates better acquisition performance for unseen words. It's important to note that these few shot examples are text/image pairs without explicit grounding annotation. Our W2W is able to quickly acquire grounded meanings of the new words (*e.g.*, only with 8 examples) with a performance close to that of seen words. ![6_image_0.png](6_image_0.png) We further perform a word-specific controlled study with a one-class incremental learning setting. We present results on two unseen words (pizza and circular) in Table 4. The complete results are available in Appendix D. | # Samples | log G-PPL (pizza) | log G-PPL (circular) | | | |-------------|---------------------|------------------------|----------|-------| | W2W | W2Ww/o G | W2W | W2Ww/o G | | | 0 | 10.70 | 9.59 | 15.21 | 15.12 | | 8 | 1.47 | 2.21 | 1.59 | 2.25 | | 16 | 1.07 | 2.54 | 1.07 | 2.25 | | 24 | 1.19 | 1.25 | 1.55 | 1.81 | | 32 | 0.90 | 1.18 | 1.23 | 1.61 | ## 4.3 Predictors Of Model Behaviors There has been an interest to identify predictors that can explain/anticipate the performance or behavior of pre-trained language models (Chang and Bergen, 2022). This exploration not only offers valuable insights for future model development, but also serves as a cognitive inquiry to evaluate the extent to which language models align with human language acquisition patterns. In this section, we present the first work of this nature on visionlanguage models. Specifically, we note that the W2W model relies on a RoBERTa encoder, which might have already been equipped with prior linguistic knowledge. To assess the cognitive alignment of vision-language models to human language acquisition, we additionally pre-trained the W2W and W2Ww/o G models with a randomly initialized RoBERTa encoder. To comprehensively capture various aspects of words, we carefully select eight distinct predictors that encompass intrinsic psycho-linguistic characteristics, distribution patterns within the training corpus, and visual representations within the training images. We select 3 **psycho-linguistic predictors**, each collected and normalized from the MRC Database (Coltheart, 1981): - Familiarity, the degree of familiarity or exposure people have to words; - Concreteness, the degree to which words have a perceptible physical referent or are associated with tangible objects or experiences; - Imageability, the degree to which words elicit people's mental imagery. Another 3 **linguistic predictors** are considered: - Unigram perplexity; - RoBERTa perplexity, where RoBERTa is finetuned on the captions to serve as the upper bound of unimodal language model performance; - \# Co-occur phrases, the average number of co-occurring groundable phrases in a caption. We finally choose 2 **perceptual predictors**: - \# Co-occur objects, the average number of co-occurring objects in an image; - Bbox size, the average proportion of an image occupied by the bounding boxes of the referents. To assess the statistical significance of each predictor, we performed linear regressions with likelihood ratio tests on different variants of models. Similar to Chang and Bergen (2022), we compare the overall regression including the target predictor to a regression that included all predictors except the target. We additionally present the beta weights (with signs) to capture the magnitude and direction of the correlation. Figure 7 displays heatmaps indicating the statistical significance (in terms of negative logarithmic p-values) of each predictor concerning Log G-PPL, Log PPL, and Any IoU. Insignificant tests are omitted from the figure. Correlation with Linguistic and Perceptual Predictors. Our findings revealed a positive correlation between the unigram and RoBERTa log perplexity and the models' log perplexity, both for grounded and ungrounded scenarios. This indicates that vision-language models still heavily rely on distributional statistics, similar to unimodal models. While the ungrounded perplexity showed little correlation with perceptual predictors, the Any ![7_image_0.png](7_image_0.png) (a) Predictors for Log G-PPL. (b) Predictors for Log PPL. Figure 7: Heatmaps for statistical significance for each predictor towards the Log G-PPL, Log PPL, and Any IoU. The beta weights and their signs are presented outside of the parentheses, and the negative log p-values are presented in the parentheses. Insignificant tests with p > 0.05, *i.e.*, − log(p) < 1.30, are discarded. IoU demonstrated a significant correlation with the number of co-occurring objects and average sizes of bounding boxes. This suggests concepts that are visually salient and less perceptually ambiguous are easier to localize and acquire, consistent with human learners (Smith and Yu, 2008). ## Correlation With Psycho-Linguistic Predictors. Counter-intuitively, there was a positive alignment between the human perceived familiarity of words and the machine's perplexities, *i.e.*, the more familiar humans are with a word, the more perplexed models get. This contrasts with the ideal cognitive plausibility of language acquisition in humans. This discrepancy implies that current visionlanguage models may not fully achieve cognitive plausibility, which might be explained by the fact that many concepts (*e.g.*, wild animals, musical instruments) appear abundantly in internet images but not in daily lives. In terms of imageability, it aligned well with human intuition, exhibiting a positive correlation with Any IoU and a negative correlation with perplexities. However, the concreteness predictor surprisingly exhibited the opposite correlation. This discrepancy could be attributed to the nuanced distinction between imageability and concreteness. For instance, while "hat" is concrete because it refers to a tangible object, it also possesses visual diversity due to its generality (*e.g.*, many types of hats which look very differently), making it challenging to acquire. Conversely, "blue" is more imageable as it easily evokes a color, relatively stable, despite not referring to a specific tangible object. To learn the meaning of "hat," a human language learner may benefit from physically interacting with the object, and understand that the hat is an item to cover for the head, regardless of its visual appearance. To address this gap, a potential future direction could involve developing language learning agents that acquire words through physical interactions rather than passive perception, allowing for a more comprehensive understanding of word meanings. ## 5 Related Work Vision-Language Mapping Mapping plays a central role in classic lexicon acquisition problem (Gleitman and Landau, 1994; Clark, 1995). Primarily, researchers focused on grounding words to their meaning symbols, building learning mechanisms using specific mental biases to simulate children's word acquisition, and giving computational accounts for psycholinguistic phenomena (Siskind, 1996; Regier, 2005; Goodman et al., 2007; Fazly et al., 2010). Early efforts along this line incorporate visual grounding either by learning a statistical or neural mapping from object categories (Roy and Pentland, 2002; Yu, 2005; Xu and Tenenbaum, 2007; Yu and Ballard, 2007; Yu and Siskind, 2013) and more complicated visual features (Qu and Chai, 2010; Mao et al., 2019, 2021; Pratt et al., 2020) to linguistic labels. These studies are usually in a closed world with limited vocabulary (Krahmer and van Deemter, 2019), and words are usually isolated from the natural context of use. More recently, multi-modal understanding tasks, *e.g.*, object retrieval (Guadarrama et al., 2014; Hu et al., 2016), referring expression comprehension and grounding (Liu et al., 2014; Yu et al., 2016; Mao et al., 2016; Wu et al., 2020), and phrase grounding (Plummer et al., 2015) map referring expressions to corresponding objects. Our setup is closely related to this line as we position *grounding* as an explicit word-referent mapping problem. The difference is that, our work goes beyond grounding to study open-vocabulary acquisition through fast mapping, a more complicated but realistic challenge faced by AI agents. Vision-Language Pre-training Distributional word representations can be acquired through language modeling, and developing language models from visual data has been extensively studied by the community (Chrupała et al., 2015; Lazaridou et al., 2015; Li et al., 2017; Surıs et al., 2020). Recent years have seen increasing research to enrich language representations with visually-augmented language modeling (Tan and Bansal, 2020; Lu et al., 2022; Wang et al., 2022) and to learn multimodal representations with vision-language pre-training (VLP) (Du et al., 2022a). We are particularly interested in VLP models with fine-grained grounding objectives, *e.g.*, Word-Region Alignment (WRA). These models either pre-train with weakly supervised alignment algorithms like optimal transport that matches words with patches (Kim et al., 2021) or proposals from a frozen detector (Chen et al., 2020; Su et al., 2020), or perform explicit word grounding by pre-training a language-conditioned detector (Kamath et al., 2021; Li et al., 2022; Zhong et al., 2022; Dou et al., 2022). Our model falls along this line, which jointly performs language modeling, object localization, and grounding during pre-training, rather than relying upon a preexisting object detector. Vision-Language Tasks To evaluate visionlanguage systems, many downstream tasks have been formulated. Some related formulations are summarized in Table 5 in Appendix. While demonstrating some vision-language capabilities, these down-stream tasks provide limited insights into whether these models truly capture the grounded meaning of words with respect to the external environment. Our task design specifically targets the machine's ability to predict words and ground words to perception. More akin to our formulation is the vision-based language modeling task (Jin et al., 2020) in a continual learning setting. Our work differs mainly in two aspects. First, the task proposed by Jin et al. (2020) only predicts masked tokens based on the visual context, which leaves the referential uncertainty (i.e., grounding) unattended (*e.g.*, in Figure 2, correct prediction of the word "boat" does not guarantee correct grounding). Also, this work primarily focuses on compositionality, while we seek to address few-shot grounded word learning when unseen words are encountered ## After Pre-Training. Open-Vocabulary Object Detection Early works formulate fast mapping of new words as a zero-shot object classification problem, which aims to generalize from known object labels to unknown ones (Socher et al., 2013; Frome et al., 2013; Elhoseiny et al., 2013; Lazaridou et al., 2014). The setting later extends to a localization task, referred to as zero-shot object detection (ZSD) (Bansal et al., 2018; Zhu et al., 2019, 2020; Rahman et al., 2020). More recently, open-vocabulary object detection (OVD) (Zareian et al., 2021; Gu et al., 2022; Du et al., 2022b; Minderer et al., 2022) combines ZSD with weakly supervised object detection (WSD) to address the unrealistic constrain of traditional zero-shot settings. OVD assumes the availability of coarse-grained image-caption pairs, and attempts to generalize from limited fine-grained annotation of object categories to unseen ones. Nevertheless, this line of work positions words as object categories and isolates them from their linguistic context (*e.g.*, sentences). Our setup instead challenges models to perform language modeling in human-generated captions. ## 6 Conclusion And Future Work The connection between language and their referents captures the grounded meaning of words, and an explicit treatment is key to empowering efficient open-world language learning abilities in humans and AI agents. This work introduces Grounded Open Vocabulary Acquisition (GOVA), a scalable formulation to examine grounding and fast mapping in open-world grounded language learning. We propose World-to-Words (W2W), a novel visually grounded language model to investigate a paradigm where the model initially acquires grounding ability during pre-training and subsequently applies this ability to quickly learn new words without explicit grounding supervision. Our empirical findings highlight the significance of visual grounding in neural word acquisition. Especially, we find that pre-trained W2W can serve as a foundation for fast mapping of novel grounded words via fewshot learning. We also conduct a comprehensive analysis to explore potential predictors influencing the performance of vision-language models, revealing both consistent and surprising behaviors with respect to human language learning patterns. These insights pave the way for future research in grounded language learning in the open world. ## Limitations In this work, we limit ourselves to object-centric grounding, which ignored that language can ground events, attributes, manners, mental states, etc. The grounded meaning of some groundable words, especially ADVs, NUMs, VERBs, and PRONs, cannot be fully captured by the bounding boxes alone. Future work should explore better task formulations to study the acquisition of their grounded meanings. An exciting future work along this line is to extend the setting from images to videos and physical interactions with the environment, and to incorporate the rich temporal dynamics of the world for language acquisition. In addition, we ignored the social aspects of language learning, where children infer the referents of words from their caregivers through communication (Carpenter et al., 1998; Bloom, 2000). Future work could also investigate grounded word acquisition from natural dialogue. ## Ethics Statement This project does not involve any research artifacts generated through human subject studies. Despite the considerable promise of W2W, it is crucial to examine its ethical and societal implications. The computational model relies on pre-trained language models and extensive text-image datasets, which could contain hidden biases that may result in fairness problems within the algorithms. By recognizing and actively addressing these implications, we aim to increase awareness among practitioners if the model is deployed as a language-learning agent in the future. ## Acknowledgments This work was supported in part by NSF IIS1949634, NSF SES-2128623, and by the Automotive Research Center (ARC) at the University of Michigan. 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In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11693–11702. ## A Gova **Dataset Details** A.1 Illustrated Comparison Of Setting We present an illustrated comparison of task formulations related to language grounding and grounded language learning in Figure 8. Among these task formulations, our Grounded Open Vocabulary Acquisition (GOVA) task is the only one that challenges vision-language systems to perform visually grounded and object-centric language modeling. The formulation is natural and simple, with fundamental requirements on computational models to perform masked language modeling and object localization, and thus is particularly good for zeroshot analysis. ## A.2 Evaluation Protocols Explained We present an adequate evaluation protocol for grounded word acquisition in the main paper. This section provides more in-depth explanation for the metrics and implementation details for reproducibility purposes. Perplexity Metric Details We follow prior practice in cloze tests (Salazar et al., 2020; Jin et al., 2020) to evaluate the perplexity of a word w. We use log pseudo-perplexity in masked language modeling, defined as $$\log{\mathrm{PPL}}(w)=-\log P(w|x_{\mathrm{img}},x_{\mathrm{cap}})$$ However, the majority of the language models employ sub-word tokenization methods to segment and encode text. In particular, one lexical word can be segmented into several tokens, and different tokenizers can lead to different tokens for the same input. We thus introduce a tokenizer-dependent measure for perplexity. For tokenizer T, we represent the N tokens of word w as T(w) and $$\log{\mathrm{PPL}}(w)=-{\frac{1}{N}}\sum_{t\in T(w)}\log P(t|x_{\mathrm{img}},x_{\mathrm{cap}})$$ IoU Metric Details we face the same challenge as Kamath et al. (2021) where multiple referents are possible for a masked word. In a similar manner, we adopt the Any-Protocol and AllProtocol to evaluate the grounded detection task. Assuming n ground truth bounding boxes B = {b1, b2, · · · , bn} and m predicted bounding boxes Be = {be1, be2, *· · ·* , bfm}. The intersection-overunion (IoU) under Any-Protocols is defined as the average IoU of the best matching predicted bounding box for each ground truth object: $$\mathrm{IoU_{any}}={\frac{1}{n}}\sum_{i\in\{1,2,\cdots,n\}}\operatorname*{max}_{j\in\{1,2,\cdots,m\}}\mathrm{IoU}(b_{i},{\widetilde{b_{j}}})$$ The intersection-over-union (IoU) under AllProtocols is defined as the IoU between the joint bounding box of ground truth and predicted bounding boxes: $$\mathrm{IoU}_{\mathrm{all}}=\mathrm{IoU}(\cup B,\cup{\tilde{B}})$$ ## A.3 Word List - 60 words are in the seen-set, each with 80 test cases: baby, ball, beach, bench, bike, black, blond, blue, boy, brown, building, car, child, dark, dog, dress, face, female, field, floor, food, girl, glasses, grass, gray, green, guitar, guy, hair, hand, hat, head, horse, jacket, jeans, lady, large, little, long, man, orange, pants, person, player, red, shirt, sidewalk, sign, small, snow, street, striped, table, top, wall, water, white, woman, yellow, young. - 31 words are in the unseen-set, each with 50 test cases2: aged, bamboo, barefoot, brush, button, cafe, cheese, circular, classroom, crosswalk, diverse, doctor, donkey, elephant, fluffy, foreign, gym, heart, newborn, pan, pizza, product, security, sink, star, steep, stove, student, teacher, telephone, warm. ## B Computational Model Details B.1 Pre-Training Objectives Masked Language Modeling (MLM). The MLM head can be placed at multiple possible places, and our design is an exploration after preliminary experiments on smaller-scale training. We strictly follow the setup of RoBERTa to implement the MLM head with a two-layer MLP, based on the implementation of huggingface3. Words in groundable phrases are masked with a probability of 0.4 and those in non-groundable regions are masked with a lower probability of 0.1. For a token selected to mask, we follow RoBERTa to assign a probability of 80% to replace with MASK, 10% with a random token, and 10% to do nothing. 2a few words (product, steep, telephone) has one less test case due to the availability of Flickr30K Entities Dataset. 3https://huggingface.co/docs/transformers/ model_doc/roberta ![15_image_0.png](15_image_0.png) ![15_image_1.png](15_image_1.png) Object Localization (OL). We follow MDETR to decode object embeddings with a three-layer MLP to produce bounding boxes. Similar to most prior work, we apply a filter over boxes with confidence below 0.7. In our framework, this means that the object corresponds to the no-object label ∅ (Figure 4) with a probability over 0.3. We strictly follow DETR to perform bipartite matching between proposed boxes and ground truth boxes with a Hungarian loss. The predicted boxes are optimized towards ground truth by the generalized intersectionover-union (GIoU) loss and the L1 loss. Grounding. In positional alignment, the model learns to map each object representation to tokens in the sentence with a fixed length of 257, which could possibly be a MASK or an additional no-object label ∅ (Figure 4). The object and the token are considered a match given a mapping probability over 0.1. We use a fully-connected layer to predict the distribution over token positions with crossentropy loss. In semantic alignment, the model learns to bring word embeddings closer to the object embeddings that they ground to, and push the unrelated pairs farther. We strictly follow the contrastive loss function defined in MDETR for every object and groundable token for this purpose. ## B.2 Few-Shot Learning Details. Since no bounding box or word-object mappings annotation is available, we train W2W with only masked language modeling (MLM) in few-sample new word learning. We reduce the batch size to 8 considering the fewer number of samples, and set the convergence criteria to a fixed number, *i.e.*, 50 steps. All the rest of the experimental settings remain the same as pre-training. ## C Experiment Reproducibility C.1 W2W **Implementation Details** Our W2W model mainly consists of one crossmodal transformer with inputs from uni-modal encoders from image and text domain. Specially, we select the ResNet-50 (He et al., 2016) pretrained on ImageNet from TIMM4as the image encoder, and RoBERTa-base (Liu et al., 2019) from huggingface5as the text encoder. The crossmodal encoder and two decoders each consists of 4 transformer blocks with 8 attention heads, an input and output dimensionality of 512, and an inner-layer dimensionality of 2,048. Besides, 50 learnable object queries are included to query the cross-modal decoder to generate bounding box proposals. ## C.2 Hyper-Parameter Decisions We include the major hyper-parameter tuning decisions for reproducibility purpose. For more details, please refer to the supplementary codes. - Learning Rate: - Image Encoder: frozen - Text Encoder: 1 × 10−5 - Multi-modal Transformer: 1 × 10−4 - Batch Size: 128 - Pre-training Loss Coefficients: - MLM Loss: 32 - Cross Entropy for Positional Alignment: 1 - Contrastive Loss for Semantic Alignment: 1 - L1 Localization Loss: 5 - GIoU Localization Loss: 2 - Few-shot Learning: - Batch size: 8 - Other Hyper-parameters: Same as Pre-training 4https://github.com/rwightman/ pytorch-image-models 5https://huggingface.co/docs/transformers/ model_doc/roberta ## C.3 Computational Resources Our W2W models is pre-trained on 8 NVidia A40 GPUs. With mixed-precision pre-training and a batch size of 128, W2W was trained for 150,000 steps where each step takes about 1.4 second. ## C.4 Evaluation On Gova W2W For our proposed W2W model, given a GOVA test, with its corresponding image and textual cloze pair passing into the model, the bounding box predictions are generated by keeping only the bounding box proposals that are mapped to at least one masked token within the cloze, while the masked token prediction results are directly decoded from its language modeling head. VisualBERT For the "Detect-and-Recognize" baseline model VisualBERT, we use phrasegrounding fine-tuned version of VisualBERT to perform object localization, and, as it lacks the language modeling head, another vanilla pre-trained VisualBERT to perform mask token prediction. Specifically, for the bounding box localization part, we treat it as a standard phrase grounding task and follow (Li et al., 2019) to select the top-1 bounding box prediction in the last masked token as the output. ViLT+MDETR For the "Produce-and-Localize" baseline model ViLT + MDETR, in stage one, we feed the input image and text into ViLT, collecting its top-1 cloze token prediction result. Then, at stage two, the input image and ViLT-completed text are fed into MDETR, performing phrase-grounding to localize the object associated with the original cloze. Finally, the cloze token prediction result from ViLT together with the bounding box proposals from MDETR are used for GOVA evaluation. ## D Addendum To Results D.1 Ablation Study We performed an ablation study on several W2W model variants to pinpoint what makes our W2W model effective. These included models without language encoder initialization (w/o Init), without grounding objective (w/o G), without any objectcentric representation (w/o O), and a text-only setup without any vision input (w/o V). For consistency, we control the number of transformer layers and the number of parameters for each variation. Despite tweaking various hyperparameters, no significant improvements were observed. As a result, we retained the same hyperparameters as in the W2W model. - w/o G: This refers to the model variant without grounding loss, as has already been described in Section 3.2; - w/o O: This variant excludes all object-centric representations, retaining only the masked language modeling (MLM) objective. With this model, the object decoder transformer is unnecessary, thus no grounding nor localization is performed. Instead, we consolidate all 12 transformer blocks into the multi-modal encoder and directly attach the MLM objective to it. - w/o V: This text-only model operates without any vision input or supervision, reducing it to a unimodal language model (RoBERTa) with 12 additional transformer blocks. Following the analysis of Chang and Bergen (2022) in unimodal language models, we present the KL-Divergence between the model predictions and the unigram distribution in Figure 9. An immediate observation is that all variants converge to the shallow unigram statistics at around 102steps of pre-training. This aligns with the findings of Chang and Bergen (2022) that unimodal language models would converge to unigram before acquiring more complicated contextual representations. We noticed that in both text-only and W2Ww/o O cases where MLM is the only pre-training objective, the models tend to stay around the unigram word distribution even with 104steps of training. However, variants with an object-centric representation quickly departed from the unigram distribution. Comparatively, models with language model initialization moves quickly away from the unigram distribution, and models with a grounded objective have a marginally faster deviation. These results confirm that vision-language models can benefit from unimodal pre-training on a large corpus, and that performing language modeling upon object representations is crucial. We note that we compare the KL-Divergence from unigram only to understand the models' behaviors, and the metric itself does not serve as an evaluation of a system's performance in grounded open vocabulary acquisition. ## D.2 Addendum To Results In Multi-Class Incremental Learning We present additional results in Table 6. ![17_image_0.png](17_image_0.png) | # Samples | Seen log G-PPLall (↓) | Unseen log G-PPLall (↓) | | | |-------------|-------------------------|---------------------------|----------|-------| | W2W | W2Ww/o G | W2W | W2Ww/o G | | | 0 | 1.79 | 2.33 | 11.58 | 11.89 | | 8 | 3.15 | 3.63 | 3.09 | 3.32 | | 16 | 3.36 | 3.76 | 2.64 | 2.85 | | 24 | 3.05 | 3.46 | 2.07 | 2.67 | | 32 | 3.07 | 3.62 | 2.01 | 2.54 | ## D.3 Learning New Words Through One-Class Incremental Learning. We further perform a more controlled study with a word-specific one-class incremental learning setting. The pre-trained model is tasked to acquire one single unseen word from a few-shot learning session with |Vunseen| = 1. The results of this section are obtained from the test immediately following the new session. We present the test result in Table 7. Again, we observe that with as few as 8 samples, W2W can achieve a satisfyingly low grounded perplexity. In the majority of the cases, W2W demonstrates the better ability to acquire unseen words over the groundless baseline. pizza W2W 10.70 1.47 1.07 1.19 0.90 W2Ww/o G 9.59 2.21 2.54 1.25 1.18 | # Samples | 0 | 8 | 16 | 24 | 32 | # Samples | 0 | 8 | 16 | 24 | 32 | | | |-------------|-------|-------|------|------|------|-------------|-----------|------|-------|------|------|------|------| | crosswalk | W2W | 10.82 | 8.48 | 7.43 | 7.70 | 5.95 | donkey | W2W | 8.70 | 0.84 | 0.81 | 0.67 | 0.79 | | W2Ww/o G | 10.91 | 10.88 | 7.53 | 7.15 | 7.5 | W2Ww/o G | 9.69 | 1.97 | 1.99 | 2.35 | 2.01 | | | | cheese | W2W | 12.16 | 2.62 | 3.00 | 1.27 | 1.04 | barefoot | W2W | 9.71 | 6.93 | 4.58 | 5.55 | 6.27 | | W2Ww/o G | 13.07 | 2.81 | 3.13 | 2.56 | 1.49 | W2Ww/o G | 9.95 | 6.52 | 4.67 | 5.74 | 5.88 | | | | star | W2W | 8.70 | 1.49 | 1.47 | 1.09 | 1.18 | elephant | W2W | 15.24 | 1.44 | 1.65 | 1.81 | 1.44 | | W2Ww/o G | 10.59 | 2.93 | 2.10 | 1.99 | 1.39 | W2Ww/o G | 14.75 | 2.17 | 1.98 | 1.73 | 1.61 | | | | classroom | W2W | 3.96 | 0.47 | 0.36 | 0.43 | 0.32 | heart | W2W | 9.34 | 2.97 | 1.90 | 1.76 | 1.76 | | W2Ww/o G | 5.10 | 0.95 | 0.88 | 1.05 | 0.95 | W2Ww/o G | 9.31 | 2.99 | 2.50 | 2.65 | 2.96 | | | | fluffy | W2W | 16.44 | 1.88 | 1.78 | 0.82 | 1.36 | gym | W2W | 5.13 | 2.14 | 0.44 | 0.74 | 0.69 | | W2Ww/o G | 15.61 | 1.83 | 1.71 | 1.37 | 1.47 | W2Ww/o G | 4.88 | 3.73 | 1.30 | 1.08 | 1.45 | | | | circular | W2W | 15.21 | 1.59 | 1.07 | 1.55 | 1.23 | security | W2W | 15.08 | 1.07 | 0.81 | 1.28 | 0.71 | | W2Ww/o G | 15.12 | 2.25 | 2.25 | 1.81 | 1.61 | W2Ww/o G | 14.75 | 1.50 | 1.22 | 1.53 | 1.17 | | | | sink | W2W | 14.23 | 1.17 | 0.92 | 1.11 | 1.38 | cafe | W2W | 6.28 | 1.90 | 1.38 | 1.98 | 1.39 | | W2Ww/o G | 15.49 | 1.84 | 1.65 | 1.60 | 1.84 | W2Ww/o G | 7.03 | 2.17 | 1.92 | 2.08 | 1.72 | | | | doctor | W2W | 13.03 | 1.17 | 1.05 | 1.38 | 1.18 | teacher | W2W | 16.68 | 1.95 | 2.15 | 1.52 | 1.48 | | W2Ww/o G | 12.44 | 1.17 | 1.23 | 1.39 | 1.58 | W2Ww/o G | 16.08 | 2.68 | 2.37 | 1.85 | 1.83 | | | | foreign | W2W | 9.48 | 0.62 | 0.95 | 0.85 | 0.47 | student | W2W | 16.28 | 1.38 | 1.07 | 1.20 | 1.03 | | W2Ww/o G | 10.01 | 1.03 | 0.88 | 1.18 | 0.95 | W2Ww/o G | 16.52 | 2.21 | 1.29 | 1.40 | 1.61 | | | | diverse | W2W | 16.44 | 0.60 | 0.22 | 0.52 | 0.24 | newborn | W2W | 16.43 | 1.71 | 0.88 | 0.91 | 1.11 | | W2Ww/o G | 16.05 | 0.81 | 0.65 | 0.97 | 0.65 | W2Ww/o G | 16.30 | 2.02 | 1.32 | 1.61 | 1.76 | | | | product | W2W | 10.25 | 0.84 | 0.75 | 1.39 | 1.15 | pan | W2W | 12.04 | 1.70 | 2.12 | 1.87 | 2.02 | | W2Ww/o G | 12.28 | 1.15 | 0.81 | 0.99 | 0.76 | W2Ww/o G | 11.88 | 2.84 | 3.62 | 2.68 | 2.50 | | | | stove | W2W | 16.15 | 2.63 | 2.64 | 1.94 | 2.72 | telephone | W2W | 14.09 | 1.18 | 0.96 | 1.05 | 0.96 | | W2Ww/o G | 16.13 | 3.06 | 4.30 | 3.08 | 2.98 | W2Ww/o G | 13.42 | 1.17 | 1.50 | 1.46 | 1.38 | | | | steep | W2W | 5.89 | 0.63 | 0.39 | 0.53 | 0.42 | bamboo | W2W | 14.54 | 2.02 | 1.20 | 0.76 | 1.02 | | W2Ww/o G | 7.30 | 1.46 | 2.42 | 0.87 | 1.93 | W2Ww/o G | 15.40 | 3.01 | 1.38 | 1.09 | 1.42 | | | | warm | W2W | 7.79 | 0.68 | 0.69 | 0.68 | 0.69 | brush | W2W | 11.17 | 1.88 | 2.13 | 1.81 | 2.45 | | W2Ww/o G | 8.67 | 1.05 | 1.01 | 0.79 | 0.85 | W2Ww/o G | 13.69 | 2.51 | 2.89 | 2.39 | 2.83 | | | | aged | W2W | 13.72 | 0.50 | 0.53 | 0.39 | 0.66 | button | W2W | 4.73 | 2.37 | 2.08 | 1.82 | 2.01 | | W2Ww/o G | 13.50 | 0.77 | 0.94 | 0.78 | 0.93 | W2Ww/o G | 5.94 | 3.25 | 3.19 | 2.54 | 2.74 | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7, Limitations ✗ A2. Did you discuss any potential risks of your work? This study does not contain any human subjects or human studies. The study proposes a problem formulation and a computational framework, which is not deployable to any real-world applications in the foreseeable future. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 2 ✓ B1. Did you cite the creators of artifacts you used? Section 2.4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Will be included along with the code release ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Will be included along with the code release B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 2 and Appendix A ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix C The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix C ✗ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? The study involves a pre-training framework which is not economically feasible for repeated runs. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix C D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. 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stolfo-etal-2023-causal

A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models

https://aclanthology.org/2023.acl-long.32

We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models. At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solution. Building on the idea of behavioral testing, we propose a novel framework, which pins down the causal effect of various factors in the input, e.g., the surface form of the problem text, the operands, and math operators on the output solution. By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space. We apply our framework on a test bed of math word problems. Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.

# A Causal Framework To Quantify The Robustness Of Mathematical Reasoning With Language Models Alessandro Stolfo∗ ETH Zürich [email protected] Kumar Shridhar ETH Zürich [email protected] Bernhard Schölkopf MPI & ETH Zürich [email protected] ## Abstract We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models. At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solution. Building on the idea of behavioral testing, we propose a novel framework, which pins down the causal effect of various factors in the input, e.g., the surface form of the problem text, the operands, and math operators on the output solution. By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space. We apply our framework on a test bed of math word problems. Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.1 ## 1 **Introduction** Many natural language understanding situations, such as understanding the financial news, require reasoning with text that includes numbers. However, such mathematical reasoning is challenging for NLP models (Cobbe et al., 2021; Mishra et al., 2022b). Mathematical reasoning for text has been an active area of research for a while (Seo et al., 2015; Sachan and Xing, 2017; Sachan et al., 2017, 2018, *inter alia*), and has also emerged as a key task to track the capabilities of large language models (LLMs) in recent years (Brown et al., 2020; Ouyang et al., 2022; Wei et al., 2022a, *inter alia*). However, despite the impressive performance of LLMs on various math reasoning benchmarks (e.g., ∗Equal contribution. 1Our code and data are available at https://github. com/alestolfo/causal-math. Zhijing Jin∗ MPI & ETH Zürich [email protected] Mrinmaya Sachan ETH Zürich [email protected] ![0_image_0.png](0_image_0.png) Ouyang et al., 2022; Chowdhery et al., 2022), it remains unclear whether these models have learned mere artifacts in the data or have truly mastered the mathematical concepts needed to consistently solve all variations of the same problem (Patel et al., 2021; Razeghi et al., 2022; Welleck et al., 2022). In sharp contrast with a large number of papers on improving the performance of LLMs on various types of math-based problems, there has been little effort on behavioral analysis of LLMs for these tasks. Existing methods for understanding the robustness of these models (Patel et al., 2021) rely on manually constructing variations of math problems, and we do not yet have a principled, comprehensive framework for quantifying such robustness. Thus, in this work, we propose a formal framework based on causal inference, to quantify the robustness of NLP models' math reasoning abilities. Specifically, we describe a causal graph formulation of math reasoning, where the graph allows us to measure the difference in the structural causal 545 ![1_image_0.png](1_image_0.png) models of human reasoning and model judgment. We consider various causal factors such as the textual framing of the question, numerical operands, and operation types. Then, we identify a set of interventions in the context of math word problems (an example of which is illustrated in Figure 1), and provide a causal inference framework to obtain causal effects of each factor via direct dointerventions (Pearl, 1995) and causal mediation analysis (Pearl, 2001). While our approach is reminiscent of recent studies using causal analysis for LLMs (Finlayson et al., 2021; Vig et al., 2020; Meng et al., 2022), in this work, we provide a new theoretical analysis framework specifically suitable for math reasoning. Using our framework, we disentangle factors affecting the model's predictions and measure their influences. This way, we are able to provide insights into the model's reasoning in terms of *robustness* and *sensitivity* with respect to changes in these factors. We apply our framework to study a set of thirteen GPT models with various sizes and training procedures (i.e., instruction-tuned and non-instructiontuned). We observe that, among non-instructiontuned language models, the larger ones tend to be more sensitive to changes in the ground-truth result of a math word problem, but not necessarily more robust. However, we observe a different behavior in the instruction-tuned GPT-3 models (Ouyang et al., 2022), which show a remarkable improvement in both sensitivity and robustness, although the robustness reduces when problems get more complicated. We additionally investigate the role of size and instruction tuning on the model's performance with three models of the LLaMA family (Touvron et al., 2023) and Stanford Alpaca (Taori et al., 2023). ## 2 **Problem Setup** We consider a dataset D of math word problems (MWPs), where each MWP is denoted as a question Q. Q is a list (T , N) consisting of a question template T and an ordered list of operands N = (N1, N2*, . . . , N*m). Each question template T := (O, S) further contains two types of information: a set of arithmetic operations O implicitly expressed in the question, and the text surface form S irrelevant to the arithmetic operations. O incorporates the information relative to the operations as a collection of tuples {(O1, i1, j1),(O2, i2, j2)*, . . .* }, where Ok ∈ {+, −, ×, ÷} (k ∈ N) and ik, jk ∈ N represent the indices of the operands to which operator Ok should be applied to.2 The ground-truth result G = fO(N) is calculated by computing the function fO, which represents the application of all the operators in O to the respective operands. We illustrate the factors in Q and their inter-dependency in the causal graph in Figure 2. A two-operand instance q of Q in this form from Patel et al. (2021) is: Template t: Mark has n1 trees in his backyard. If he plants n2 more, how many trees will he have? Operands n: (n1 = 12, n2 = 13) Operations o: {("+", 1, 2)} Result: g = fo(n) = n1 + n2 = 25 2The intermediate result of operation Ol is indicated by ik = m + l. Our goal is to quantify the robustness of a model M on the set of problems q ∈ D. Ideally, D should be a dataset not seen by the model during training. We assume that a model takes q as input and predicts a probability distribution of the result R: P(R | t, n). Our formulation below will be easier to understand using this finite discrete set and can be generalized to any kind of data pairing a natural language template with a function that maps a set of operands to a result (e.g., a Python program; Mishra et al. 2022a). ## 3 **A Causal Framework** In this section, we describe our framework in three steps. First, we define the idea of model robustness on MWPs. Then, we identify possible dointerventions (Pearl, 1995) that we can perform. Finally, we describe the causal effects that we measure to quantify the robustness of various models. ## 3.1 **Step 1. Question Reformulation** We address the research question "*Is a model reasoning robustly on MWPs?*" by comparing the causal mechanisms of the model's decisions to a hypothesized human reasoning mechanism. Note that we do not claim to know how humans reason about these problems. We simply propose a reasonable and intuitive way to judge model robustness given a reasonable and intuitive human reasoning mechanism inspired by findings regarding the independence of language and mathematical reasoning in humans (Brannon, 2005; Monti et al., 2012). Human Reasoning Mechanisms. The causal mechanisms of how humans might solve q include $$\begin{array}{l}{{o=f_{\mathrm{abstract}}(q)\ ,}}\\ {{g=f_{\mathbf{o}}(n)\ ,}}\end{array}$$ where they first abstract the arithmetic operations o from the problem q by some cognitive process fabstract, and then apply the operation to the operands to obtain the result g. We show these mechanisms in the green subgraph Gh of Figure 2. Model Reasoning Mechanisms. In contrast, the causal mechanisms of how a model might solve q are as follows: $$r=f_{\mathrm{blackBox}}(t,n)\ ,$$ r = fblackBox(t, n) , (3) where we are unsure about (1) *what* part(s) of t the model takes into account, and (2) how it operates over the relevant variables. Thus, we draw all possible causal mechanisms that might take place in the black-box model fblackBox in the complete causal graph in Figure 2. Some possible fine-grained causal mechanisms are 1. The model might attend over the question template t in two ways: paying attention to the text surface form s via the causal path T → S → R, or text relevant to the math operations o via the causal path T → O → R. 2. The model might also attend to the operands n := (n1, n2*, . . .*) via a causal path N → R. 3. If the model learns the correct causal mechanisms as in the human cognitive process, it should capture how the operator and the operands matter to the ground-truth result g (via O → G and N → G) and then the model prediction should be sensitive to any changes in the ground truth, namely G → R. No spurious correlations can directly affect R without going through the mediator G. Hence, to answer the question "How robust is the mathematical reasoning of a model on MWPs?" we can answer the following subquestions: 1. How does R change in response to G? By quantifying this, we assess the *sensitivity* (correct responsiveness) of the model to changes in the problem. In other words, does the model correctly adjust its prediction in response to a change in the correct solution of the problem? $$\mathrm{(1)}$$ (2) 2. What is the (unwanted) direct causal effect size of S → R, and N → R? We see the quantities as a measure of the *brittleness* (i.e., wrong responsiveness) of the model to resultpreserving changes in the input. The lower the direct causal effect of S and N, the more robust the model is. ## 3.2 **Step 2. Causal Intervention List** After formulating the cognitively-inspired subgraph Gh and defining the undesired causal paths in Figure 2, we list all feasible limited actions that allow us to perform our causal analysis. In the context of MWPs, we use the following interventions: 1. Direct intervention on all possible n1, n2*, . . .* ; 2. Partially controllable interventions on T . We can replace the template T in two ways: $$({\mathfrak{I}})$$ (a) both S and O are affected, or (b) S is affected but O is not affected. ## 3.3 **Step 3. Turning Limited Actions Into** Causal Effect Sizes Next, we explain how we can obtain the causal effect sizes we want (listed in Step 1) from the limited set of interventions we can do (listed in Step 2). Specifically, we first start from all the feasible interventions, and for variables that we cannot directly intervene on, we apply deductions from do-calculus (Pearl, 1995) to obtain or approximate the direct causal effect sizes. In the following, we describe a list of causal effect sizes that we need. General Formulation. Let us consider an intervention do(X : x → x′), where X ∈ {T *, S,* N} and a problem Q = {T , N}. The support of the numerical values Ni's and R is I ⊆ N, and we consider N to be distributed uniformly over the set {n ∈ I2| fO(n) *∈ I}*. We denote the distribution before the intervention P(R | T , N) as P and the distribution after the intervention as P′. Following the distributional definition of causal effect by Pearl (1995), we quantify the effect of factor X in our causal graph using a distance metric δ between the distributions P and P′. That is, $$\mathrm{CE}=\delta(P,P^{\prime}),$$ where CE can refer to the **total causal effect** (TCE, i.e., the joint effect through all the directed causal paths from a variable to another), or the direct causal effect (DCE, i.e., the effect from the directed causal path from a variable to another that does not go through any intermediate variables) (Pearl, 2001). We describe our choices for δ in Section 3.4. Causal Effects of the Operands. When intervening on the operands N := (N1, N2*, . . .*), we can obtain the size of the total causal effect of N on R, namely $$\mathrm{TCE}(N\;\mathrm{on}\;R):=\mathbb{E}_{\mathbf{n}^{\prime}\sim\mathbb{P}(N)}[\delta(P,P^{\prime})],$$ where $P^{\prime}=\mathbb{P}(R|T,\mathrm{do}(N=\mathbf{n}^{\prime}))$ . Note that this TCE is not the exact desired quantity, because we want to separate two different paths of how N affects R: (1) the path N → G → R, which is the correct decision path that we want the model to pick up (where the model reacts to the change in the ground-truth answer), and (2) the path N → R, which is the spurious correlation that the model might have learned (where the model relies on some spurious correlations with certain numerical values, which could be traced to perhaps their frequencies in the training corpus). We can quantify the **direct causal effect** (DCE, i.e., the effect from the directed causal path from a variable to another that does not go through any intermediate variables) (Pearl, 2001) of N on R, namely the strength of the direct causal path N → R, by controlling for G to be fixed every time we intervene on N: $$\text{DCE}(\mathbf{N}\to R):=\mathbb{E}_{\mathbf{n}^{\prime}\sim\mathbb{P}(\mathbf{N}|G)}[\delta(P,P^{\prime})],\tag{7}$$ where $P^{\prime}=\mathbb{P}(R|\mathbf{T},\text{do}(\mathbf{N}=\mathbf{n}^{\prime}))$. (8) For example, if we observe a model doing 100 + 100 = 200 correctly, we want to separate the math ability here into (1) the model's sensitivity towards the ground-truth answer, and (2) the model's decisions based on its familiarity with just the operand 100. Here, the overall effect is the calculable TCE(N on R) by Eq. 5, and one of the subeffects is the calculable DCE(N → R) by Eq. 7. $\eqref{eq:walpha}$. ## Causal Effects Of The Text Surface Form. As for the operands, we can compute both the direct and indirect effects of the surface form representing the math problem. In particular, intervening on T without controlling for O (intervention 2a in Sec. 3.2), we can compute the total effect, i.e., $$\mathrm{TCE}(T\;\mathrm{on}\;R):=\mathbb{E}_{t^{\prime}\sim\mathbb{P}(T)}[\delta(P,P^{\prime})],$$ where $P^{\prime}=\mathbb{P}(R|N,\mathrm{do}(T=t^{\prime}))$ . $$\begin{array}{l}{(9)}\\ {(10)}\end{array}$$ ′)) . (10) Controlling for the operations O (intervention 2b in Sec. 3.2) will instead allow us to obtain the direct causal effect of the surface text: $$\text{DCE}(S\to R):=\mathbb{E}_{\mathbf{t}^{\prime}\sim\mathbb{P}(\mathbf{T}|O)}[\delta(P,P^{\prime})],\tag{11}$$ where $P^{\prime}=\mathbb{P}(R|\mathbf{N},\text{do}(\mathbf{T}=\mathbf{t}^{\prime}))$. (12) $$(11)$$ $$\begin{array}{l}{(5)}\\ {(6)}\end{array}$$ Note that since there is no mediator between S and R, the DCE(S → R) is also TCE of S on R. The only adaptation that we need to make with regard to the MWPs is that it is not feasible to enumerate all possible perturbations of S. Therefore, the practical results that researchers can achieve are over a certain subset of S. In practice, we obtain this by intervening on T without affecting O. Causal Effects of the Operators. The ideal way to obtain the TCE of O on R is through some careful human annotation that minimally changes the templates as Kaushik et al. (2020) do for sentiment classification. The challenge for MWPs in our case is that with all our possible interventions, we cannot only intervene on O without introducing changes to the irrelevant surface form. However, we might get some information about TCE(O on R) because, on the causal graph, the total causal influence of T on R actually flows into two directed paths, one through S to R (which is the DCE(S → R)), and the other from O to R, which is our interested quantity TCE(O on R). Therefore, we compare the two quantities we know, TCE(T → R) and DCE(S → R), to get a sense of the causal influence of O on R that we cannot obtain in any other way. ## 3.4 **Step 4. Quantifying The Causal Influence** Consider a realization of problem Q with operands n and ground-truth result g = fo(n), and denote by g′the result after the intervention do(X : x → x′). We quantify the causal effect of factor X on the model's prediction R in two ways: by assessing the change in the predicted result, and by measuring the change in the probability assigned by the model to the correct result g (or g′). Change in the Prediction. To account for the inability of LMs to capture the continuous property of numbers (Jin et al., 2021a), we measure the change in the model's prediction using an indicator of the "change result" event: $$\delta_{\rm cp}(P,P^{\prime}):=1(r\neq r^{\prime})\,\tag{13}$$ where $r=\arg\max_{x\in{\cal I}}P(x)$, and $r^{\prime}=\arg\max_{x\in{\cal I}}P^{\prime}(x)$. Relative Change in Confidence. Inspired by Finlayson et al. (2021), we also highlight the change in terms of the relative difference in the probability assigned to g and g′. We formulate two types of relative change, one quantifying the relative change in the confidence of g, and the other quantifying the relative change in the confidence of g′: $$\Delta_{\mathrm{rel}}=\frac{P(g)-P^{\prime}(g)}{P^{\prime}(g)}$$ $$\Delta_{\mathrm{rel}}^{\prime}=\frac{P^{\prime}(g^{\prime})-P(g^{\prime})}{P(g^{\prime})}\;.$$ We quantify the overall relative change in confidence (RCC) as the average of the two relative changes above: $$\delta_{\mathrm{rcc}}(P,P^{\prime})=\frac{1}{2}\biggl(\Delta_{\mathrm{rel}}+\Delta_{\mathrm{rel}}^{\prime}\biggr)\;.\qquad(16)$$ A Unified Form. We are interested in the average causal effect of the intervention across all problems in D. Thus, we measure the average of the effects over all instances q ∈ D. We denote by the subscripts TCEcp/DCEcp and TCErcc/DCErcc the causal effects computed using the change in prediction metric and the relative change in confidence, respectively. We describe how we construct the dataset D in Section 4.2. ## 4 **Experimental Setup** In this section, we describe the data used to perform the interventions and to measure the causal effects. ## 4.1 **Datasets** For our analyses, we use instances of math word problems from three popular datasets: ASDiv-A (Miao et al., 2020), MAWPS (Koncel-Kedziorski et al., 2016), and SVAMP (Patel et al., 2021). The examples contained in these collections are pairs (t, o) consisting of a question template t with its annotated operations o. Each of these pairs can be instantiated multiple times into problems q = (t, n) by filling the template with numerical values (n1, n2*, . . .*) and computing the groundtruth result g = fo(n) (most problems involve two to three operands, i.e., |n*| ∈ {*2, 3}). We select a set of 437 two-operand and 307 three-operand template-expression pairs that we use to generate pairs of prompts representing an intervention. More details about the prompt generation procedure are in Appendix A. We use (t, n) to refer to an instantiated template that we use as a prompt. ## 4.2 **Intervention Data** (14) $\binom{15}{2}$ . Given an MWP q = (t, n) and its solution g, we generate a second problem-solution instance (q′, g′) depending on the type of causal effect CE we want to measure and on the considered variable. When intervening on the operands of the problem, the text of the problem is kept unaltered and a set of new operands n is sampled in such a way that the result g is affected or not depending on the effect that is being measured. When changing the textual description of the problem, we change t such that either o′ = o, or o′ ̸= o. In the former case, we sample a different template t′ = (s′, o) from the set of templates describing the same operations o, in the latter case we sample a new t′ describing a different operation. In Appendix B.1 we report some examples of (q, q′) pairs representing the different types of interventions. Given a model, we use the question pair (q, q′) to obtain a pair of answer distributions P(R|t, n) and P(R|t′, n′), which we use to measure the causal effect of the intervention. We consider the space for the numerical values to be I = {1, 2*, . . . , C*} consisting of integer values, following the setup of several existing MWP datasets (Miao et al., 2020; Koncel-Kedziorski et al., 2016; Patel et al., 2021). To control our experimental costs and make sure the models keep the number as one token, we set C = 300. From all the tokens in a model's vocabulary, we focus on the probability assigned to the numbers in our numerical space I, and thus we use P(R = r) to denote the normalized probability Praw(R = r)/Z, where Z =PC r=1 Praw(R = r), and Praw(x) is the raw probability score assigned to the vocabulary token x. For each intervention type, we generate a dataset D consisting of (q, q′) pairs. Unless otherwise specified, for our experiments we generate 500 intervention pairs for each template, and results are averaged over three seeds. ## 4.3 **Models To Evaluate** We use our framework to assess the robustness of reasoning in thirteen pre-trained language models. We consider five sizes of the GPT-2 model (Radford et al., 2019): distilled (Sanh et al., 2019), small, medium, large, and XL. We evaluate four models from EleutherAI that were pre-trained on the Pile (Gao et al., 2020): GPT-Neo 1.3B and 2.7B (Black et al., 2021), GPT-J-6B (Wang and Komatsuzaki, 2021), and GPT-NeoX-20B (Black et al., 2022). We use HuggingFace Transformers (Wolf et al., 2019) to access the models. Additionally, we experiment with a set of instruction-tuned versions of GPT-3 (Brown et al., 2020): Instruct (Ouyang et al., 2022), Curie, Davinci-002, and Davinci-003.3 Experiments with GPT-3 are carried out under the constraints set by the OpenAI APIs4, which prevent us from computing the causal effect using the same procedure as for the other models. We report the details about how the metrics were computed 3The OpenAI ids for these models are, respectively, davinci-instruct-beta, text-curie-001, text-davinci-002, and text-davinci-003. 4https://openai.com/api/ ![5_image_0.png](5_image_0.png) for GPT-3 in Appendix C. In the reported results, we indicate with an asterisk (∗) the metrics that were influenced by this limitation. ## 5 **Results** Our analyses focus primarily on two-operand problems (Sections 5.1 and 5.2) and later extend to more complex problems that involve three operands (Section 5.5) for the models that perform best on the two-operand test bed. We compare the direct causal effect DCE and the total causal effect TCE of N and T on R. DCE represents the undesired effect for a model to being mistakenly responsive to a change in N or T not leading to a change in the result g (low robustness), whereas higher values of TCE indicate a higher ability of the model to correctly adjust the probability weight assigned to the new solution g′after the intervention (high sensitivity). ## 5.1 **Effect Of** N On R From the results in Figure 3, we notice that larger models exhibit a larger TCErcc/DCErcc ratio. In particular, in GPT-J-6B and NeoX, the TCE is, respectively, 30x and 1000x larger than the DCE. However, this improvement in sensitivity is not manifested in terms of change of prediction (δcp), for which the models show to be affected by resultpreserving changes almost as equally as by resultaltering interventions. This behavior changes sig- ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) nificantly in instruction-tuned models. In particular, for the 175B-parameter GPT-3, performance varies depending on the type of supervision, with the PPOtrained Davinci-003 exhibiting an 84% difference between direct and total effect. In Figure 4, we present a different visualization of the direct causal effect of N on the model's prediction. We report the heatmaps showing the probability assigned by the model to the result g of a problem (t,(n1, n2), g) | g = n1 + n2, ∀g ∈ {0, 1*, . . . ,* 50}, ∀(n1, n2) ∈ {0, 1*, . . . ,* 50} 2. For Distil-GPT-2 we observe low overall probability assigned to g and diagonal patterns indicating consistency in assigning higher probability to specific results (e.g., 10, 20, 30, 40, 50). For the two larger models we notice a higher probability mass assigned to the problem's result, but less consistency on the prediction of the same result with different sets of operands (this is true for GPT-J in particular). This result is consistent with the observed higher DCE and TCE in larger models: P(g) might vary more considerably when intervening on N without affecting g, but overall the model assigns higher probability weight to the correct result, which correlates with higher sensitivity. ## 5.2 **Effect Of** T On R In Figure 5, we report the total causal effect of the textual framing T and the direct causal effect of the irrelevant text elements S on the model's prediction. For the instruction-tuned models, the improvement in terms of prediction change (δcp) follows a similar trend as for N, with GPT-3 Davinci-003 showing a 76% difference between direct and total effect. An interesting observation is that the irrelevant textual information S appears to have a lower direct effect than N for all noninstruction-tuned models. However, in the GPT-3 Davinci-00x models, we observe the opposite (i.e., DCE(N → R) ≤ DCE(S → R)). This suggests that large instruction-based models tend to be more susceptible to variation in the textual framing of a problem, while smaller models are more responsive to changes in the numerical values (though not necessarily correctly). ## 5.3 **Overall Insights** In comparison to other models, GPT-3 Davinci shows the highest DCErcc, but low DCEcp. This discrepancy is related to the quantities that the two metrics consider. δrcc takes into account the probability assigned to g, while δcp does not consider the ground truth solution. One interpretation of this result is that GPT-3 Davinci consistently predicts the same answer r = r′ when g = g′, but the probabilities P(g) and P′(g) might vary significantly. The results observed for the two kinds of intervention do(T : t → t′) and do(N : (n1, n2) → (n′1 , n′2 )) show similar trends. Small models (Distilled and Small GPT-2) exhibit low sensitivity to interventions. Larger models (from GPT-2 Medium to GPT-Neo) appear to be more influenced by changes in both N and T . However, they display similar sensitivity to both result-altering and resultpreserving interventions. An improvement in sensitivity is noticeable in GPT-J and NeoX, though not accompanied by an improvement in robustness. Remarkably different behavior is instead shown by the GPT-3 Davinci models, which demonstrate substantially higher sensitivity to result-altering interventions (high TCE), and higher robustness (in terms of prediction change). In Appendix B.2, we report the accuracy of the models on the generated instances of MWPs, which exhibits a similar trend as the robustness/sensitivity changes we observed. Possible explanations for the improved robustness and sensitivity demonstrated by the large GPT3 models might be the dramatic size increase and extension/enhancement of the training procedure involving instructions. The former idea is aligned with the *emergent abilities* hypothesis (Wei et al., 2022a), which postulates the existence of skills that are displayed by large-scale models but are not present in smaller-scale models. However, our observations show different performances in versions of GPT-3 Davinci that differ in the training procedure.5 This raises the question of whether the capability of LLMs to reason about math problems benefits from instruction-based tuning. We address this question in the following section. ## 5.4 **Extending To Llama-Based Models** To further investigate the roles played by size and training method in the model's performance, we carry out our experimental procedure on three versions with different sizes (7B, 13B, and 30B) of the LLaMA model (Touvron et al., 2023), and on Stanford Alpaca (which applies instruction tuning on LLaMA 7B) (Taori et al., 2023). We present these results separately, as the LLaMA tokenization makes the prediction setup different from the one used from the other models, and prevents us from computing the relative change in confidence ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) (δrcc).6 From the results (Figure 6), two notable observations emerge. Firstly, the increased difference between TCE and DCE observed with the increasing size of the LLaMA models suggests that a larger number of parameters can be a significant driver behind robustness/sensitivity improvement. However, this is not necessarily the case across different models: GPT-NeoX-20B shows a smaller TCEcp-DCEcp gap compared to LLaMA 7B (5.2% vs 9.0%). Secondly, the instruction tuning procedure of Alpaca does not seem to help significantly with mathematical computation: the decrease in both TCE and DCE shows that robustness improves at the expense of sensitivity. Nonetheless, overall, when comparing Alpaca compared to its base model, LLaMA 7B, we observe an increase in the gap between TCE and DCE, although this difference is minimal (9.5% vs 9.0%). The limited improvement of Alpaca might be attributed to its instruction tuning procedure consisting of "a list of user-oriented instructions including email writing, social media, and productivity tools" (Taori et al., 2023), which differs from reasoningintensive tasks. We suggest future work to examine different types of instruction tuning (e.g., focused on reasoning procedures or reinforcement learning from human feedback), which might help the model answer more complex types of questions in a step-by-step manner and more accurately. We hypothesize that the different performances in versions of GPT-3 Davinci might be produced by the specific type of instructions used for training, by the reinforcement learning component (Ouyang et al., 2022), or simply by an extension of the language modeling pre-training. It is challenging to ![8_image_0.png](8_image_0.png) pinpoint the exact factor in the training procedure that contributes to this improvement, as specific methodological details are not available. ## 5.5 **Moving To Three-Operand Problems** We extend our evaluation to consider the threeoperand problems in the dataset. In these experiments, we consider only the GPT-3 175Bparameter models, as they are the only models performing well on the simpler bivariate problems. The results regarding the effects of N are reported in Figure 7. We notice that the large difference between the desired (TCE) and undesired (DCE) effects observed on simpler problems shrinks significantly for both metrics. In particular, for Davinci003, the direct effect of N (measured as δcp) grows from 0.17 to 0.87. That is, GPT-3 Davinci-003 predicts a different result 87% of the time after an intervention that does not affect the ground-truth solution. The increase in direct effect indicates a performance degradation in terms of brittleness: even the models that show good performance on two-operand problems, now display an unstable behavior after result-preserving interventions. ## 6 **Related Work** Causal NLP. Causal inference aims to study the cause and effect from observational and interventional data (Pearl, 2009; Peters et al., 2017). Traditionally, researchers usually apply causal techniques to phenomena in nature and human society. With the rise of powerful models in NLP, recent research has started to explore the intersection of causal inference and NLP, forming the study of Causal NLP (Jin et al., 2022; Feder et al., 2021a). There are several formulations for Causal NLP: the *causality for NLP* thread involves using the causal framework for data collection and task formulation (Jin et al., 2021c), inspecting the (pathspecific) causal effect of certain neurons on predictions (Vig et al., 2020; Meng et al., 2022), understanding the causal effect of data and learning paradigm for model performance (Ni et al., 2022), and as a way to frame prompts (Lyu et al., 2023); and *NLP for causality* involves testing the pure causal inference skills of LLMs (Jin et al., 2023a,b), and use text as a variable for causal effect estimation (Roberts et al., 2020; Veitch et al., 2020; Jin et al., 2021b, 2023c). The most similar line of research to our work is the application of causal effect estimation on interpreting models' behavior, such as how models understand syntactic agreement (Finlayson et al., 2021), and how interventions in the representations and weights affect the model prediction (Feder et al., 2021b). To the best of our knowledge, our work is the first to formulate a causal framework for robustness behavioral tests, and also we are the first to introduce the idea to quantify the differences in the causal mechanisms of human reasoning and model decisions. Math Reasoning in NLP. A growing body of work tries to improve the math reasoning capability in NLP models (Zhang et al., 2020; Geva et al., 2020; Spokoyny et al., 2021), and prompting techniques for LLMs (Cobbe et al., 2021; Shen et al., 2021; Kojima et al., 2022; Wei et al., 2022b; Chowdhery et al., 2022). For analysis, significant attention has been given to models' ability to understand numerical quantities (Wallace et al., 2019; Thawani et al., 2021) and numerical operations (Pal and Baral, 2021; Berg-Kirkpatrick and Spokoyny, 2020; Pi˛ekos et al., 2021; Razeghi et al., 2022). ## 7 **Conclusion** We developed a framework to disentangle and separately measure the effect of different factors influencing the predictions of LLMs for math reasoning. Our results indicate that a drastic increase in both robustness and sensitivity emerges in the GPT-3 Davinci models. Additionally, we study the contribution of size and instruction tuning in the models of the LLaMA family, observing that the Alpaca instruction tuning, while increasing the model's robustness, does not significantly improve the overall performance. Our framework provides a formalized theory of behavioral testing for math reasoning models and opens new future directions to design behavioral tests of models in a principled way. ## Ethical Considerations As for the ethical practice in this work, the data involved are from existing MWP datasets with no private user information, and available under the MIT license. As for the ethical impact of the use of this work, the study is about providing a metric and analyzing existing models' robustness, so there is less concern over harmful usage. Rather, it is more about putting checks on existing AI models and helping humans understand them better before use. Potential stakeholders that could benefit from this research include NLP researchers working on math models, practitioners working on various applications involving mathematical reasoning with text, and e-learning design. ## Limitations A key limitation in our work is that LLMs might have seen these math problems. Our work theoretically assumes this is not the case. Another limitation is that for the sake of simplicity, our work makes some assumptions. For example, we assume all numbers in the range of integers 0 to C = 300. This would not cover every MWP out there. And future work is needed to generalize our framework to other forms of MWPs. In this work, we are also constrained by the limitations of the OpenAI policy on the GPT-3 API. This limits the number of perturbations we consider in this work as well as the accuracy with which we can estimate our causal distributions. Finally, our work is restricted to English, and extending it to other languages will require us to create an MWP dataset in that language. ## Acknowledgments This material is based in part upon works supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS18039B; by the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 390727645; by the John Templeton Foundation (grant \#61156); by a Responsible AI grant by the Haslerstiftung; and an ETH Grant (ETH-19 21-1). Alessandro Stolfo is supported by armasuisse Science and Technology through a CYD Doctoral Fellowship. Zhijing Jin is supported by PhD fellowships from the Future of Life Institute and Open Philanthropy, as well as the travel support from ELISE (GA no 951847) for the ELLIS program. We also thank OpenAI Researcher Access Program for granting our team credits to their API. ## References Taylor Berg-Kirkpatrick and Daniel Spokoyny. 2020. 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Symbolic brittleness in sequence models: on systematic generalization in symbolic mathematics. In *Proceedings of the AAAI Conference on Artificial* Intelligence, volume 36, pages 8629–8637. 1 Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R'emi Louf, Morgan Funtowicz, and Jamie Brew. 2019. HuggingFace's transformers: State-of-the-art natural language processing. *arXiv* preprint arXiv:1910.03771. 6 ## A **Creation Of The Prompts** B **Frequently Asked Questions** B.1 **How Do The Intervention Data Look Like?** We consider MWP examples from the union of the three datasets SVAMP, ASDiv-A, and MAWPS. The textual template t of a problem consists of a context (describing a real-world state and/or actions) and a question. In order to obtain suitable prompts for the models, we convert the problems' questions into statements where the result of the problem is expected to be the first token after the prompt. E.g., in the example in section 2, how many trees will he have? is converted into *the number of trees that he will have is _*. From the MWP templates of the SVAMP/ASDiv-A/MAWPS collection (we consider all splits), we filter out the templates whose questions do not start with How many..., and we use spaCy7to identify the subject, the object and the verbs in the sentence. This allows us to convert the last sentence of the template from *The number of... is*. This way, we obtain 437 statement-based MWP templates for two-operand problems and 307 for three-operand problems. We manually checked a subset of the templates to identify possible mistakes in the conversion procedure. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 billion parameter autoregressive language model. 6 In Table 1 we report examples of MWP pairs representing different types of intervention. ## B.2 **What Is The Accuracy Of The Evaluated** Models On The Generated Problems? We report the accuracy of the models considered for evaluation in terms of accuracy at 1 and accuracy at 10. Results are displayed in Figure 8. ## B.3 **What Is The Relation Between Accuracy** And The Rcc Metric? We examine the relationship between performance and robustness, computing the Pearson correlation coefficient between accuracy (accuracy@10) and the relative confidence change (RCC) metric. On a per-template basis (500 instances for each template), we found accuracy to be positively 7https://spacy.io | Ruby has 87 candies. If she shares the candies among 29 friends, the number of candies that each friend gets is | g = 87/29 = 3 | | |---------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|-------------------| | Ruby has 35 candies. | If she shares the candies among 5 | | | friends, the number of candies that each friend gets is | g = 35/5 = 7 | | | TCE(N → R) | The school is composed of 13 buildings each having 10 classrooms. The number of classrooms that the school has is | g = 10 × 13 = 130 | | The school is composed of 65 buildings each having 2 classrooms. The number of classrooms that the school has is | g = 65 × 2 = 130 | | | DCE(N → R) | The razorback t-shirt shop ordered 6 cases of t-shirts. | If | | each case contains 17 t-shirts the number of t-shirts that they | g = 17 × 6 = 102 | | | ordered is | | | | DCE(S → R) | The roller coaster at the state fair costs 6 tickets per ride. If 17 friends were going to ride the roller coaster the number of | g = 17 × 6 = 102 | | tickets that they would need is Sean has 23 whistles. He has 6 more whistles than Charles. The number of whistles that Charles has is | g = 23 − 6 = 17 | | | Jovana filled her bucket with 23 pounds of shells. | If she | | | adds 6 more pounds of shell to fill her bucket, the number of | g = 23 + 6 = 29 | | | pounds that she has is | | | | TCE(T → R) | | | Table 1: For each of the causal effects measured (left column), we report a pair of MWPs illustrating the intervention performed (center), along with their respective ground-truth result (left column). ![13_image_0.png](13_image_0.png) correlated with TCE(N on R) and TCE(T on R) (0.24 and 0.49, respectively) and negatively correlated with DCE(N → R)and DCE(S → R) (-0.26 and -0.36, respectively). We see these results as a quantitative validation of the intuition behind our framework: the better the model's performance, the more the model tends to correctly adjust its prediction after a result-altering intervention (higher sensitivity) and to correctly not change its prediction after a result-preserving intervention (higher robustness). Moreover, we conduct an additional sanity check as in Patel et al. (2021): removing the question from the MWP templates, we observe a sensitivityrobustness degradation to random guessing (i.e., TCE ≃ DCE). This indicates that the measurement of the causal effects within our framework is not affected by patterns in the templates that might have been picked up or memorized by large models. ## C **Computation Of Causal Effects For** Gpt-3 We access GPT-3 through the OpenAI APIs, which allow a user to prompt the model and obtain the probabilities assigned by the model to the k-th most likely vocabulary entries, for each token generated. To overcome this limitation, we approximate the relative probability change δrcc as follows, depending on the kind of effect measured. The limit for k is set by OpenAI to 5. However, for our main set of experiments (i.e., computing the causal effects of N, S, and T ) we were granted an increased limit of k to 100. This allowed us to obtain reasonable estimates for the causal effects, as the number of cases in which P(g) is not defined is less than 10% of the number of examples that we consider. ![14_image_0.png](14_image_0.png) ## C.1 Tce(N On R) And Tce(T On R) In cases when P(g) is defined (i.e. when g appears in the top k token predictions) and P′(g) is not defined, we compute a lower bound on the relative change using the upper bound on P′(g) given by the probability of the k-th most likely token. This gives us a conservative estimate of ∆. For cases in which P(g) is not defined, we cannot say anything about the relative change, and we set ∆ = 0. The same applies when swapping P and P′. This procedure is illustrated by Algorithm 1. C.2 DCE(N → R) and DCE(S → R) In this case, we simply discard the examples for which P(g) is not defined or P′(g) are not defined. In that is not the case, then we compute δrcc as in Section 3.4. ## C.3 **Heatmap Illustration** The heatmap for GPT-3 displayed in Figure 4 was computed by taking the raw probability score produced by the model over the whole vocabulary, as the limit on the available top predicted tokens makes it impossible to normalize it over the set {0*, . . . ,* 300}, as done for the other models. The probability was set to 0 when g did not appear in the model's top 5 predictions for the next token after the prompt. ## D **Computing Infrastructure & Inference** Details To run our experiments, we used a single NVIDIA TITANRTX with 24GB of memory for all the versions of GPT-2 and GPT-Neo. We used a single NVIDIA A100 with 40GB of memory for GPT-J6B and a single NVIDIA A100 with 80GB of memory for GPT-NeoX and the LLaMA models (two for the 30B version). We accessed GPT-3 using the OpenAI APIs. The longest run (GPT-J) on the four kinds of experiments corresponding to the four kinds of effects measured took ∼12 hours, using 500 MWP instances for each of the 437 templates. Due to budget and resource constraints, the experiments on GPT-3, GPT-NeoX, and LLaMA were carried out using 20 examples generated for each template and took ∼7 hours. Experiment tracking was carried out using Weights & Biases8. 8http://wandb.ai/ ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section "Limitations". ✓ A2. Did you discuss any potential risks of your work? Section "Ethical Considerations". ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1: Introduction. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4.1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section "Limitations" ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section "Limitations" ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section "Limitations" ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section "Limitations" ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.1 and Appendix A ## C ✓ **Did You Run Computational Experiments?** Sections 4 And 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Sections 4.3 and Appendix D The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Sections 4.3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Sections 4.1, 4.2, and 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.3 and Appendix A D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

zhao-etal-2023-evaluating

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information

https://aclanthology.org/2023.acl-long.33

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the {``}golden{''} reference responses in semantics.

# Evaluating Open-Domain Dialogues In Latent Space With Next Sentence Prediction And Mutual Information Kun Zhao1∗, Bohao Yang2∗, Chenghua Lin2†, Wenge Rong3,4, Aline Villavicencio2**, Xiaohui Cui**1 † 1 Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China 2 Department of Computer Science, The University of Sheffield, United Kingdom 3 State Key Laboratory of Software Development Environment, Beihang University, China 4 School of Computer Science and Engineering, Beihang University, China {zhaokun, xcui}@whu.edu.cn, [email protected] {byang27, c.lin, a.villavicencio}@sheffield.ac.uk ## Abstract The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two opendomain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics. ## 1 Introduction Open-domain dialogue generation is a prominent research direction in conversational AI due to a wide range of useful applications that it can facilitate, such as for personal digital assistants and customer service (Sai et al., 2020; Huang et al., 2020; Wang et al., 2021; Tang et al., 2023). While evaluating Natural Language Generation (NLG) systems is notoriously difficult, evaluation of open-domain dialogue generation introduces an extra layer of complexity, as a variety of responses can be generated, each semantically different and yet valid in the given context (Li et al., 2016; Gu et al., 2019; Qiu et al., 2019). For example, given the conversational context "*Iverson is my all-time favourite* player.", responses such as "*He is my favourite* player too." or "*Yes, his quickness is amazing!*" are both contextually relevant, yet semantically different. ∗ Equal contribution. † Corresponding authors. Existing approaches for evaluating open-domain dialogue systems can be broadly divided into two different categories: reference-based and referencefree approaches. The reference-based metrics typically score a system by computing how similar an output response is compared to the *goldstandard* reference. Popular metrics under this category may rely on surface-form similarity by counting the n-gram overlap between the response candidate and the reference (e.g., BLEU (Papineni et al., 2002), ROUGE (Lin, 2004), and METEOR (Banerjee and Lavie, 2005)), or by calculating the similarity based on embedding representations such as Embedding-Average (Wieting et al., 2016), or even via high-dimensional representations learned for the response and the reference such as BERTScore (Zhang et al., 2020). One noticeable limitation of reference-based metrics is that they are reference centric and do not take the context of the conversation into consideration. Furthermore, due to the well-known one-to-many issue in open-domain dialogue (Li et al., 2016; Gu et al., 2019; Qiu et al., 2019), a good response that matches well to its context could express significantly different semantics to its reference, for which the aforementioned metrics will be inadequate to handle. To tackle the one-to-many issue, some works (Tao et al., 2018; Sinha et al., 2020; Ghazarian et al., 2019; Zhao et al., 2020) have proposed reference-free metrics to evaluate generated responses by measuring their similarity with the corresponding conversational context, by designing discriminative models trained on the context and the reference to judge whether the generated response matches the context well. As these discriminative metrics are typically trained using a single relevant (aka. positive) response and multiple negative samples, Sai et al. (2020) argue that such metrics should be trained with multiple relevant and irrelevant responses for any given context to allow for robust evaluation. However, most existing datasets do not contain multiple references due to high cost of acquisition, rendering this recommendation impractical. Chan et al. (2021) take a different approach to the problem by evaluating generated responses in the latent space produced by Conditional Variational Autoencoders (CVAEs), as it can encode discrete text data into a smooth latent space (Li et al., 2020b; Zhang et al., 2022b). Specifically, they proposed to use the prior distribution to approximate the conditional distribution for all the feasible responses to tackle the one-to-many issue with limited data. However, there is no guarantee that the prior distribution can represent a rich set of feasible responses (Li et al., 2019). Zhang et al. (2022a) proposed a self-training framework for multi-domain dialogue evaluation. The model performance was boosted by training on augmented datasets of four different domains, which are first automatically labelled by a teacher model and then followed by a manual annotation process. To our knowledge, no prior works have attempted to model the intra-relation between a context and a response through the Next Sentence Prediction (NSP) task and Mutual Information (MI) directly, which can provide a strong signal for indicating the sequential and semantic dependencies between the context and response. To tackle the one-to-many issue, we design a reference-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues with a single gold-standard reference. Our method consists of a training stage and an evaluation stage. In the training stage, the CVAEs are augmented with a NSP objective (Devlin et al., 2019), which plays a crucial role in addressing the one-to-many issue in dialogue evaluation, especially when the semantics of the generated response are distant from the reference but still relate well to the context. In the evaluation phase, we score a response candidate by calculating the MI of the contextresponse and response-reference pairs in the latent space, which are then combined through a weighting controlled by the NSP probability. However, it is intractable to calculate MI directly as we only have access to samples instead of the prior and posterior distributions (Paninski, 2003; McAllester and Stratos, 2018). To tackle this challenge, we propose to employ a contrastive learning method based on Noise Contrastive Estimation (NCE) (Gutmann and Hyvärinen, 2012; Logeswaran et al., 2018) to calculate the lower bound of MI. Overall, introducing the NSP objective and MI strengthens our model's ability to capture the sequential dependencies between the context and response, as well as to better leverage the information from references. Experimental results on two open-domain dialogue datasets show the superiority of our method compared to a wide range of baseline metrics based on both Pearson and Spearman correlations with human annotations. In addition, we provide a detailed analysis of the effectiveness of our proposed method in solving the one-to-many issue in open-domain dialogue evaluation. Our code is available at https://github.com/ Bernard-Yang/CMN-ACL2023. ## 2 Related Work Reference-based metrics. Reference-based metrics mainly compare the semantic similarity between a ground-truth reference and a generated response. Representative metrics that calculate word overlap include BLEU (Papineni et al., 2002), METEOR (Banerjee and Lavie, 2005) and ROUGE (Lin, 2004). Unlike metrics comparing the word overlap directly, embedding metrics first convert sentences into a high dimensional representation and calculate the semantic similarity between them. With the development of large-scale pre-training models, embedding metrics such as BERTScore (Zhang et al., 2020) and BLEURT (Sellam et al., 2020) have been shown to effectively enhance sentence representation. However, these automatic reference-based metrics cannot handle the wellknown one-to-many problem in the open-domain dialogue. Reference-free metrics. Existing reference-free metrics attempt to design discriminative models to solve the one-to-many issue by calculating the similarity between the context and the response candidate. RUBER (Tao et al., 2018) is an unsupervised metric that calculates the similarity of the generated response with both the context and the response. MAUDE (Sinha et al., 2020) employs a large-scale pre-trained model to convert sentences into hidden representations and leverage the temporal transitions between them. Sai et al. (2020) argued that such models should be trained on datasets containing multiple responses. However, most existing datasets only contain a single relevant reference and making this recommendation impractical. EMS (Chan et al., 2021) first attempted to utilise CVAEs to learn the reference information with limited data and approximate all feasible responses with the prior distribution. However, their model's prior distribution and sampled variables do not necessarily contain all the feasible response information for a given context, as EMS is only trained with a single reference. We propose a reference-based method by augmenting CVAEs with the NSP objective and employing MI to evaluate the response candidates. Zhang et al. (2022a) tackled multi-domain evaluation by training a teacher model with humanannotated data in a particular domain. The model then labels the data from dialogue datasets in four other domains. This teacher-annotated data is then used to introduce a new evaluator, which can generalise across multiple domains. However, this method requires human labelling and additional training data, which are not required by our method. ## 3 Methodology 3.1 Overall Architecture In this section, we describe the proposed automatic evaluation framework CMN in detail. As shown in Figure 1, the overall architecture of CMN consists of two stages: training and evaluation. The primary purpose of the training stage is to capture the dialogue information in the latent space, which is strengthened by incorporating the NSP objective into the CVAEs model. In the evaluation stage, CMN evaluates the response candidates by calculating the MI of the context-response and response-reference pairs in the latent space, which are then combined through weighting with the NSP probability of the response candidate. ## 3.2 Training Stage The training process of our proposed method is illustrated in the left part of Figure 1. We employ two BERT encoders: the first is used to encode the context-reference pairs, and the second encodes the context only. Formally, the encoding process is: $$\begin{array}{r}{h_{q}=\operatorname{Encoder}([c;r])}\\ {h_{p}=\operatorname{Encoder}_{\mathrm{c}}(c)}\\ {y=\operatorname{Linear}(h_{q})}\end{array}$$ where hq is the representation of the contextreference pair (c, r), and is used to learn the aggregated posterior distribution q(z|*c, r*) of CMN. In contrast to EMS (Chan et al., 2021) which does not model the order information of the contextreference pair, we introduce the segment embedding, which enables CMN to distinguish the order of the context and the reference. Finally, y is the output of the NSP task, and hp is the representation of context, which is utilised to learn the prior distribution p(z|c). To address the one-to-many issue in opendomain dialogue evaluation, we introduce the NSP objective into the CVAEs' training process to enhance our model's discriminability of feasible responses given contexts. Introducing NSP leads to two different scenarios when training CMN. Specifically for the NSP task, we randomly replace the references fed to the encoder with the response from other conversations in the training set with a 0.5 probability, where the resulting contextresponse pairs are regarded as negative samples. Likewise, the contexts paired with the original references are positive samples. In terms of the input to the decoder, we use the original references (i.e. positive samples) during the whole training process, regardless of whether the inputs to the encoder are negative or positive samples. Training with positive samples. When training with the positive samples, we add the NSP loss to the CVAEs' loss, where the NSP loss can be viewed as an additional regularisation, which enables the CVAEs model to capture the sequential dependencies between the context and response during the training stage. As a result, the posterior and prior distributions and the sampled latent variables will contain rich sentence order and pair matching information. $$\begin{array}{l}{{{\cal L}_{\mathrm{train}}=\mathbb{E}_{q(z|c,r)}[\log p(r|c,z)]}}\\ {{-\ \mathrm{KL}(q(z|c,r)||p(z|c))-\log p(y=1)}}\end{array}\tag{2}$$ $$\quad(1)$$ where E is expectation, y = 1 indicates positive samples while y = 0 indicates negative ones. The first term is the decoder reconstruction loss, the second term is the KL divergence, and the last term represents the cross entropy loss of the NSP task. Training with negative samples. When training with the negative samples, we exclude the KL divergence loss of CVAEs, as it is undesirable to optimise the prior p(z|c) to be close to the posterior 564 ![3_image_0.png](3_image_0.png) q(z′|c, rneg) of negative examples. $$\mathcal{L}_{\rm train}=\mathbb{E}_{p(z|c)}[\log p(r|c,z)]-\log p(y=0)\tag{3}$$ Here r is the reference from the datasets for guiding the decoder to generate reconstructed sentences. In addition, we use the prior distribution to sample z. ## 3.3 Evaluation Stage In the evaluation stage, CMN learns to score a response candidate by calculating its MI with respect to the conversation context c and the reference r in the latent space. The representations of c and r are obtained in the training stage of CMN and contain rich sentence pair order and matching information. However, it is intractable to calculate MI directly as we only have access to the samples instead of the underlying distributions. To tackle this challenge, we employ InfoNCE, a contrastive learning method based on NCE to calculate the lower bound of MI between the latent variables of the two posterior probabilities q(z|*c, r*) and q(z|*c, x*) and prior probability p(z|c) (see Figure 1 for illustration). Formally, the lower bound of MI is given as $$\begin{array}{l}{{I(x,r)\geq\mathbb{E}_{(x,r)}[F(x,r)]+\log(N-1)}}\\ {{-\mathbb{E}_{x}[\log\frac{1}{N-1}\sum_{r_{n}\in R_{n e g}}e^{F(x,r_{n})}]}}\end{array}\qquad\mathrm{(4)}$$ where x is the response candidate, r is the groundtruth reference in the dataset, rn represents the negative response sampled from the negative set Rneg, which contains the references from other conversation turns, and N is the number of negative samples. As the underlying posterior distributions are unknown, we first sample from each posterior probability to obtain latent variables z1 and z2, which contain the context-reference and the contextresponse sentence pairs information, respectively. The aforementioned sampling method, as well as the functions F(*x, r*) and F(*x, r*n) in Eq. 4, are defined as follows: $$F(x,r)=z_{1}\cdot z_{2}$$ $$z_{1}\sim q(z|c,r)$$ $$z_{2}\sim q(z|c,x)$$ $$F(x,r_{n})=z_{1}^{\prime}\cdot z_{2}$$ $$z_{1}^{\prime}\sim q(z|c,r_{n})\tag{5}$$ where z1 and z2 represent the positive latent variable samples while z′1 represents the negative latent samples from the corresponding posterior distributions; · represents the dot product operation. We can estimate the MI between response x and reference r (i.e. I(*x, r*)), as well as the MI between context c and response x (i.e. I(*c, x*)), based on Eq. 4 and Eq. 5. When calculating the final score for a candidate response, we also consider the NSP probability of the response candidate x given conversational context c, in addition to the two MI values. The rationale is that InfoNCE might have difficulty measuring the semantic similarity between the response candidate x and the reference r when they are distant in semantics. The NSP probability acts as a natural weighting, informing the model of to what extent it should focus on I(*c, x*), hence improving our method's robustness. When feeding the context-response pair to the trained CVAEs in the evaluation stage, the NSP probability g can be calculated according to the following formula: g = σ(Linear(Encoder([c; x]))) (6) where σ is the activation function, and g is the probability that response x is predicted as the next sentence of context c. A higher value of g means that the degree of dependence between context c and response candidate x is higher, and vice versa. Finally, we score a response candidate x with Eq. 7. Score = g ∗ I(*c, x*) + I(*x, r*) (7) The first term, I(*c, x*), represents the semantic dependence of the context and the response candidate. In other words, it reflects how well the response candidate is related to the context. Thus using g to multiply I(*c, x*) controls the amount of information flowing from I(*c, x*). In the second term, I(*x, r*), we consider the semantic dependence of the response candidate and the reference based on their MI. Essentially, the relationship between x and c, and that between x and r, can be considered simultaneously via Eq. 7, and the one-to-many problem can be handled directly. ## 4 Experimental Setup 4.1 Datasets To evaluate the effectiveness of our proposed automatic evaluation metric, we conduct experiments on two open dialogue datasets. The **PersonaChat** dataset (Zhang et al., 2018) is a large personaconditioned chit-chat style dialogue dataset which consists of 10,907 training dialogues, 1,000 validation dialogues, and 968 testing dialogues. The DailyDialog dataset (Li et al., 2017) is another widely-used large collection of human-human dialogues, consisting of a training set with 11,118 dialogues and validation and test sets with 1000 dialogues each. ## 4.2 Baselines We choose the following two kinds of evaluation metrics as baseline methods: Reference-Based Metrics. For the referencebased metrics, we use BLEU (Papineni et al., 2002), ROUGE (Lin, 2004), METEOR (Banerjee and Lavie, 2005), Embedding-Average (Wieting et al., 2016), Vector-Extrema (Forgues and Pineau, 2014), Greedy-Matching (Rus and Lintean, 2012), BERTScore (Zhang et al., 2020), and BLEURT (Sellam et al., 2020), which have been widely used in generative dialogue systems. Reference-free Metrics. For the referencefree metrics, we compare with three learningbased methods, namely, RUBER (Tao et al., 2018), MAUDE (Sinha et al., 2020) and MDDEval (Zhang et al., 2022a). Note that we were not able to compare with EMS (Chan et al., 2021), as their code is unavailable. It is also infeasible to re-implement their approach due to the lack of sufficient implementation details in the paper. ## 4.3 Evaluation Set Construction We follow the setting in Optimus (Li et al., 2020a) to use BERT (Devlin et al., 2019) and GPT-2 (Radford et al., 2019) as the encoder and the decoder for our CMN framework, respectively. We set the dimension of the latent variable z of CVAE to 32. In the evaluation phase, we follow Zhao et al. (2020) to generate response candidates based on the testset of DailyDialog and PersonaChat using several widely applied dialogue generation systems, including Seq2Seq (Sutskever et al., 2014), HRED (Serban et al., 2016), and GPT-2 (Radford et al., 2019). After obtaining the generated response candidates, we construct an evaluation set consisting of a *standard set*, in which the sample references and generated responses are similar in semantics (i.e., for the standard evaluation setting), and a *diverse set*, in which the references and responses are distant in semantics (i.e., for the one-to-many setting). For the standard set, we collect 200 samples from both DailyDialog and PersonaChat that have the highest BLEU-1 scores between the reference and response among all the testing pairs. As our primary focus is to evaluate the model's performance under the one-to-many setting, we constructed a diverse set containing a larger number of samples (i.e., 600), by sampling from the testing pairs whose BLEU-1 scores are lower than 0.2. These sampled data have a balanced split between DailyDialog and PersonaChat. ![5_image_0.png](5_image_0.png) ## 4.4 Human Annotation Evaluating the system performance requires measuring the correlation between the model prediction versus human evaluation scores. We recruited three human annotators to evaluate the evaluation set (i.e., the context-response pairs in our standard and diverse sets). All annotators hold at least a master's degree in Computer Science and have full professional proficiency in English. Specifically, annotators were asked to rate two aspects: **Appropriateness**, which measures the degree to which the output is appropriate within the given context, and **Coherence**, which assesses the extent to which the content of the output is presented in a well-structured, logical, and meaningful manner. These ratings were provided on a 1-5 Likert scale, with higher scores indicating better quality. For each context-response pair, we then average the Appropriateness and Coherence scores across all annotators to produce the final human annotation score. In the diverse set, 400 responses are rated as positive samples (4-5), while 200 are rated as negative samples (1-3). In contrast, all responses in the standard set are rated as positive samples since each response is semantically similar to the gold reference. We examine the Inner-Annotator Agreement (IAA) using inter-annotator Kappa (Cohen, 1960). The average IAA score between every pair of annotators for the Personachat dataset is 0.55, indicating a moderately strong level of agreement (0.4-0.6 range). On the other hand, the average IAA score for the DailyDialog dataset is 0.65, demonstrating a substantially strong level of agreement (0.6-0.8 range). More details of the IAA scores can be found in Appendices A.2. ## 5 Results In this section, we evaluate our model's performance on evaluating open-domain dialogues under both standard and diverse settings. ## 5.1 Analysis Of The Evaluation Set Before presenting the evaluation results, we first provide some validation analysis on our *standard* and *diverse* sets using embedding-based semantic similarity BERTScore. For the standard set, the averaged BERTScore is 4.7 for DailyDialog and 2.56 for Personachat. However, the scores are only 0.23 (DailyDialog) and 0.27 (Personachat) for the diverse set, indicating that the semantic similarity between the response candidates and the goldstandard references is low. We further use T-SNE to visualise the sentence representations of the reference and generated response pairs. As shown in Figure 2 (a) and (b), the response candidates are similar to the references in the standard set, where the corresponding data | DailyDialog | PersonaChat | | | | |-------------------|------------------|------------------|------------------|------------------| | Metrics | Pearson's ρ | Spearman's τ | Pearson's ρ | Spearman's τ | | BLEU-1 | 0.0465 (0.6782) | 0.0049 (0.9652) | -0.0314 (0.8183) | -0.0372 (0.7856) | | BLEU-2 | 0.0497 (0.6577) | 0.0116 (0.9175) | -0.0601 (0.6597) | -0.0621 (0.6495) | | BLEU-3 | 0.0462 (0.6803) | 0.0399 (0.7219) | -0.0431 (0.7525) | -0.0213 (0.8760) | | BLEU-4 | 0.0796 (0.4770) | 0.0646 (0.5641) | -0.0149 (0.9134) | -0.064 (0.6395) | | ROUGE-1 | 0.0718 (0.5213) | 0.0304 (0.7861) | -0.0267 (0.8449) | 0.0678 (0.6193) | | ROUGE-2 | 0.0841 (0.4525) | 0.0645 (0.5651) | 0.0305 (0.8235) | 0.0291 (0.8315) | | ROUGE-L | 0.0490 (0.6617) | 0.0285 (0.7992) | -0.0013 (0.9924) | 0.0834 (0.5413) | | METEOR | 0.0696 (0.5345) | 0.0946 (0.3977) | -0.102 (0.4543) | -0.1066 (0.4344) | | Embedding Extrema | 0.1211 (0.2784) | -0.0021 (0.9853) | 0.0017 (0.9903) | 0.0814 (0.5510) | | Greedy | 0.1117 (0.3176) | 0.0940 (0.4008) | 0.0949 (0.4866) | 0.0891 (0.5136) | | Average | 0.1527 (0.1709) | 0.1199 (0.2835) | 0.1018 (0.4554) | 0.1124 (0.4096) | | BERTScore | 0.0824 (0.4620) | -0.0076 (0.9457) | 0.1097 (0.4211) | 0.1724 (0.2038) | | BLEURT | 0.1163 (0.2983) | 0.0940 (0.4008) | -0.1194 (0.3808) | -0.1143 (0.4016) | | RUBER | 0.0820 (0.4642) | 0.1560 (0.1616) | 0.0019 (0.9887) | -0.0329 (0.8095) | | MAUDE | -0.1623 (0.1453) | -0.0145 (0.8974) | 0.1353 (0.3201) | 0.1104 (0.4178) | | MDD-Eval | 0.1029 (0.3574) | -0.0667 (0.5516) | 0.1239 (0.3630) | 0.2502 (0.0629) | | Ours(w/o NSP) | 0.2292 (0.0383) | 0.2025 (0.0681) | 0.2585 (0.0544) | 0.3816 (0.0037) | | Ours(w/o MI) | 0.0833 (0.4568) | -0.0537 (0.6316) | 0.1030 (0.4498) | 0.1530 (0.2601) | | Ours | 0.2446 (0.0268) | 0.2211 (0.0459) | 0.2656 (0.0479) | 0.3971 (0.0024) | points are either very close to each other or overlapping (e.g., there are seemingly more orange points in 2 (a) due to overlapping). In contrast, the distributions of response candidates and references are more distinctive for the diverse set, as shown in Figure 2 (c) and (d). In summary, the analysis shows that the standard and diverse sets are a good fit for our evaluation purposes. ## 5.2 Model Evaluation In The Standard Setting We compare our model with the baselines in terms of how well the evaluation scores generated by the model correlate with human judgments. As shown in Table 1, the n-gram baselines, including BLEU, ROUGE, and METEOR, achieve negative or weak positive correlations with human annotations on both datasets. The embeddingbased approaches (including the ones using pretrained models such as BERTScore) slightly outperform the n-gram baselines, except that BLEURT performs worse on the PersonaChat. In contrast, learning-based metrics give the strongest performance among all baselines. Specifically, MAUDE and MDD-Eval achieve similar performance on the PersonaChat, and both outperform RUBER. However, RUBER gives better performance than these two metrics on DailyDialog. Our model achieves the best overall performance in terms of both Pearson and Spearman correlations on both datasets. We further conducted ablation studies to evaluate the effectiveness of the MI (w/o NSP) and the NSP (w/o MI) components by excluding the other component when inferring the final evaluation score. It can be observed that CMN with the MI component alone (i.e., w/o NSP) gives better performance than the model variant with the NSP component only. This suggests that MI is more effective than NSP in evaluating dialogues when the response candidates are similar to the references in semantics (i.e. the standard setting). ## 5.3 Model Evaluation In The One-To-Many Setting In another set of experiments, we evaluate our model performance in the one-to-many setting using the diverse set. As shown in Table 2, Extrema, Greedy, and Average achieve a negative or weakly positive correlation with human annotation on both datasets. In contrast, the embedding-based metrics which use pre-trained models to represent sentences achieve much better results. For instance, both BERTScore and BLEURT achieve close to 0.25 for both Pearson and Spearman correlations on DailyDialog, although the performance is less strong on Per- | DailyDialog | PersonaChat | | | | |-------------------|-------------------|-------------------|------------------|------------------| | Metrics | Pearson's ρ | Spearman's τ | Pearson's ρ | Spearman's τ | | BLEU-1 | 0.2953 (<0.0001) | 0.2635 (<0.0001) | -0.1533 (0.0361) | -0.1702 (0.0199) | | BLEU-2 | 0.2733 (<0.0001) | 0.2638 (<0.0001) | -0.1657 (0.0235) | -0.1810 (0.0132) | | BLEU-3 | 0.2496 (<0.0001) | 0.2691 (<0.0001) | -0.1654 (0.0237) | -0.1846 (0.0114) | | BLEU-4 | 0.2319 (<0.0001) | 0.2737 (<0.0001) | -0.1642 (0.0247) | -0.1790 (0.0142) | | ROUGE-1 | 0.3275 (<0.0001) | 0.2865 (<0.0001) | -0.0057 (0.9382) | 0.0489 (0.5062) | | ROUGE-2 | 0.2698 (<0.0001) | 0.2761 (<0.0001) | -0.0340 (0.6441) | 0.0937 (0.2023) | | ROUGE-L | 0.3362 (<0.0001) | 0.2945 (<0.0001) | -0.0072 (0.9222) | 0.0476 (0.5178) | | METEOR | 0.2948 (<0.0001) | 0.2858 (<0.0001) | -0.0293 (0.6908) | -0.0507 (0.4904) | | Embedding Extrema | -0.3589 (<0.0001) | -0.3524 (<0.0001) | -0.1010 (0.1690) | -0.0390 (0.5966) | | Greedy | -0.1580 (0.0006) | -0.1408 (0.0023) | -0.0380 (0.6052) | 0.0113 (0.8776) | | Average | -0.1350 (0.0034) | -0.1006 (0.0296) | -0.1093 (0.1364) | -0.0355 (0.6294) | | BERTScore | 0.2591 (<0.0001) | 0.2251 (<0.0001) | 0.0345 (0.6391) | 0.0853 (0.2455) | | BLEURT | 0.2711 (<0.0001)) | 0.2063 (<0.0001)) | 0.1267 (0.0840) | 0.1858 (0.0109) | | RUBER | 0.1027 (0.0263) | 0.1714 (0.0002) | -0.0579 (0.4312) | -0.0592 (0.4206) | | MAUDE | 0.0551 (0.2344) | 0.1782 (<0.0001) | 0.2640 (0.0003) | 0.3267 (<0.0001) | | MDD-Eval | 0.5567 (<0.0001) | 0.6160 (<0.0001) | 0.1264 (0.0848) | 0.2582 (0.0004) | | Ours(w/o NSP) | 0.5453 (<0.0001) | 0.5555 (<0.0001) | 0.2947 (0.0025) | 0.2224 (0.0022) | | Ours(w/o MI) | 0.6183 (<0.0001) | 0.5946 (<0.0001) | 0.2769 (0.0001) | 0.1390 (0.0578) | | Ours | 0.6325 (<0.0001) | 0.6234 (<0.0001) | 0.4000 (<0.0001) | 0.2746 (0.0001) | ## Sonachat. On the other hand, the word overlap metrics based on n-gram perform better than the above embedding-based metrics, with BLEU, ROUGE, and METEOR all having higher correlations than the embedding-based approaches. Nevertheless, the correlations of these metrics to human annotations are still relatively weak for both datasets. For learning-based metrics, RUBER and MAUDE give weak positive correlations with human annotations on the DailyDialog dataset. However, RUBER gives a negative correlation with human scores on the PersonaChat. MAUDE, on the other hand, performs the best on the PersonaChat dataset in terms of Spearman correlation (0.3267), which is higher than that of our method (0.2746). Overall, MDD-Eval gives the best performance among all baselines on DailyDialog, whereas MAUDE is the best baseline on PersonaChat. Nevertheless, our CMN model achieves the best overall performances on both datasets, giving the highest Pearson (0.6325) and Spearman (0.6234) correlations on DailyDialog and the highest Pearson (0.4000) correlations on PersonaChat. Our ablation studies show that NSP is crucial in evaluating dialogues when there is a significant difference between references and responses in semantics (i.e., the diverse setting). By introducing NSP, our model can effectively capture the contextual dependencies between the conversational context and the generated responses, and thus can better handle the one-to-many issue in open-domain dialogue evaluation. | Context: | What do you need? | | | |------------|---------------------------------------------------------------|----------|-------| | Reference: | I need to use the internet . | | | | Response: | I think I need a deck that plays well with this. | | | | Human | BLEU | MAUDE | RUBER | | 4.66 | 0.90 | 4.81 | 0.85 | | BERTScore | BLEURT | MDD-Eval | Ours | | 1.90 | 1.34 | 0.53 | 4.47 | | Context: | Do you like the outdoors? | | | | Reference: | I like taking my dogs hiking. What do you like to do for fun? | | | | Response: | I do. I love to hike. | | | | Human | BLEU | MAUDE | RUBER | | 5.0 | 1.25 | 4.94 | 0.76 | | BERTScore | BLEURT | MDD-Eval | Ours | | 1.66 | 2.12 | 3.93 | 4.26 | Table 3: Samples from DailyDialog and PersonaChat dataset. ## 5.4 Case Studies For qualitative analysis, we show two cases of our experiment in Table 3. Each case shows the conversational context as well as the corresponding goldstandard reference and the generated response. We compare our evaluation score with five different baselines. To simplify the comparison, we normalise all scores to a range of 1-5 to be consistent with the Likert scale of human evaluation. Note that the normalisation is applied to the case study only, rather than performed in our main experiments. In the first case, the generated response is relatively similar to the reference, whereas the reference and response are very different in the second case. For both cases, our CMN gives very similar scores to the human scores. More examples are provided in Appendices A.1. ## 6 Conclusions In this paper, we propose a novel learning-based automatic evaluation metric which can robustly evaluate open-domain dialogue by augmenting CVAEs with an NSP objective and employing MI to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets show that our CMN model outperforms a wide range of baseline methods in terms of both Pearson and Spearman correlations with human annotation scores, and is superior in dealing with the one-to-many issue in open-domain dialogue evaluation. ## Ethics Statement In this paper, we propose a new automatic evaluation metric CMN to evaluate the open-domain dialogue system. The positive impact of CMN is that it can deal with the one-to-many problem in the open-domain dialogue evaluation metrics. The negative impact lies in that the CMN may potentially give a high score to potentially inappropriate or offensive responses in some extreme cases. Consequrntly, the content of such training datasets should be assessed before training the CMN. ## Limitations Although our proposed method performs well in evaluating the open-domain dialogue systems, it also has some limitations. Our method identifies the dependencies between context and response. However, according to Howcroft et al. (2020), human-evaluated metrics can contain a variety of attributes whilst we only identify the large-scale dependencies of semantics and do not disentangle the texts into the attributes of human-evaluated metrics. In the future, we will conduct disentanglement studies to disentangle the text into various attributes to optimise our model and further improve the interpretability of text evaluation methods based on these disentangled attributes. ## References Satanjeev Banerjee and Alon Lavie. 2005. 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In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, and Rui Yan. 2019. Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References. In *Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*, pages 3826–3835, Florence, Italy. Association for Computational Linguistics. Chen Zhang, Luis Fernando D'Haro, Thomas Friedrichs, and Haizhou Li. 2022a. MDD-Eval: SelfTraining on Augmented Data for Multi-Domain Dialogue Evaluation. ArXiv:2112.07194 [cs] version: 2. Jianfei Zhang, Jun Bai, Chenghua Lin, Yanmeng Wang, and Wenge Rong. 2022b. Improving variational autoencoders with density gap-based regularization. In Advances in Neural Information Processing Systems. Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. Thibault Sellam, Dipanjan Das, and Ankur P. Parikh. 2020. Bleurt: Learning robust metrics for text generation. In ACL. Ruizhe Li, Xiao Li, Guanyi Chen, and Chenghua Lin. 2020b. Improving variational autoencoder for text modelling with timestep-wise regularisation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2381–2397, Barcelona, Spain (Online). International Committee on Computational Linguistics. Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2016. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. arXiv:1605.06069 [cs]. ArXiv: 1605.06069. David McAllester and Karl Stratos. 2018. Formal Limitations on the Measurement of Mutual Information. Liam Paninski. 2003. Estimation of Entropy and Mutual Information. *Neural Computation*, 15(6):1191–1253. John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016. Towards universal paraphrastic sentence embeddings. *CoRR*, abs/1511.08198. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing Dialogue Agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2204–2213, Melbourne, Australia. Association for Computational Linguistics. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. *ArXiv*, abs/1904.09675. Tianyu Zhao, Divesh Lala, and Tatsuya Kawahara. 2020. Designing precise and robust dialogue response evaluators. *ArXiv*, abs/2004.04908. ## A Appendices A.1 Case Studies We demonstrate more examples in Table 4, which shows the response and the reference conditioned on the same conversational context from the PersonaChat dataset. We compare our matching score with five different baselines. We notice that the matching score of our method correlated well with the human annotation score compared with other baselines. | Context: | I love nature ! i'm going camping tomorrow night | | | |------------|--------------------------------------------------------------|---------------------|-------| | Reference: | It is too cold here to go camping. | | | | Response: | That sounds fun . | i like to go to the | | | beach. | | | | | Human | BLEU | MAUDE | RUBER | | 4.5 | 0.8 | 4.96 | 0.44 | | BERTScore | BLEURT | MDD-Eval | Ours | | 2.01 | 1.71 | 2.90 | 4.21 | | Context: | I have a cat. His name is spook. What about you? | | | | Reference: | I have a turtle. I named him leo. | | | | Response: | I've a dog, but he has black and white eyes, what about you? | | | | Human | BLEU | MAUDE | RUBER | | 4.5 | 0.35 | 4.98 | 0.61 | | BERTScore | BLEURT | MDD-Eval | Ours | | 1.29 | 1.89 | 0.53 | 4.16 | Table 4: Three samples from DailiDialog and PersonaChat dataset. ## A.2 Inter-Annotator Agreement (Iaa) We use cohen's kappa (Cohen, 1960) to examine the IAA between every two annotators and demonstrate our IAA in Table 5. All the IAA scores of the Personachat dataset are higher than 0.4, which indicates that the annotators reached a moderately strong level agreement (0.4-0.6) or a substantially strong level agreement (0.6-0.8). Besides, the IAA scores of the DailyDialog dataset can reach a substantially strong level. The above IAA results indicate that the annotated data by different annotators are reliable. Table 5: Inter-Annotator agreement (IAA) | Annotator | Cohen's Kappa | | | |-------------|-----------------|------------|------------| | DailyDialog | | | | | Annotator1 | Annotator2 | Annotator3 | | | Annotator1 | - | 0.6896 | 0.6035 | | Annotator2 | 0.6896 | - | 0.6434 | | Annotator3 | 0.6035 | 0.6434 | - | | PersonaChat | | | | | Annotator | Annotator1 | Annotator2 | Annotator3 | | Annotator1 | - | 0.4496 | 0.5547 | | Annotator2 | 0.4496 | - | 0.6315 | | Annotator3 | 0.5547 | 0.6315 | - | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? section Limitation ✓ A2. Did you discuss any potential risks of your work? section Ethical Impact ✓ A3. Do the abstract and introduction summarize the paper's main claims? section abstract ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Not applicable. Left blank. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section 5 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? section 4 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? section 5 ✗ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** section 4 ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank. ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. ✗ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

chung-etal-2023-increasing

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

https://aclanthology.org/2023.acl-long.34

Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. We first examine two approaches to diversify text generation: 1) logit suppression, which minimizes the generation of languages that have already been frequently generated, and 2) temperature sampling, which flattens the token sampling probability. We found that diversification approaches can increase data diversity but often at the cost of data accuracy (i.e., text and labels being appropriate for the target domain). To address this issue, we examined two human interventions, 1) label replacement (LR), correcting misaligned labels, and 2) out-of-scope filtering (OOSF), removing instances that are out of the user{'}s domain of interest or to which no considered label applies. With oracle studies, we found that LR increases the absolute accuracy of models trained with diversified datasets by 14.4{\%}. Moreover, we found that some models trained with data generated with LR interventions outperformed LLM-based few-shot classification. In contrast, OOSF was not effective in increasing model accuracy, implying the need for future work in human-in-the-loop text data generation.

# Increasing Diversity While Maintaining Accuracy: Text Data Generation With Large Language Models And Human Interventions John Joon Young Chung University of Michigan [email protected] Ece Kamar Microsoft Research [email protected] Saleema Amershi Microsoft Research [email protected] ## Abstract Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating highquality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. We first examine two approaches to diversify text generation: 1) logit suppression, which minimizes the generation of languages that have already been frequently generated, and 2) temperature sampling, which flattens the token sampling probability. We found that diversification approaches can increase data diversity but often at the cost of data accuracy (i.e., text and labels being appropriate for the target domain). To address this issue, we examined two human interventions, 1) label replacement (LR), correcting misaligned labels, and 2) out-of-scope filtering (OOSF), removing instances that are out of the user's domain of interest or to which no considered label applies. With oracle studies, we found that LR increases the absolute accuracy of models trained with diversified datasets by 14.4%. Moreover, we found that some models trained with data generated with LR interventions outperformed LLM-based few-shot classification. In contrast, OOSF was not effective in increasing model accuracy, implying the need for future work in human-in-the-loop text data generation. ## 1 Introduction Training custom natural language classification models has become easier with many tools (e.g., Huggingface1). However, data collection remains a costly part of model building. For example, existing open-source datasets may not be usable if they do not match the distribution of a model builder's target domain or do not contain desired labels. In such cases, the model builder may need to collect and label new data which could be costly (e.g., in 1https://huggingface.co/ terms of the time and resources to scrape data or pay people to generate or annotate new data). Advances in generative large language models (LLMs), such as GPT-3 (Brown et al., 2020), present a novel approach for creating training data for classification models (Yoo et al., 2021; Sahu et al., 2022; Kumar et al., 2020). Model builders can prompt an LLM with the domain of texts and labels of interest and the LLM can quickly generate text data for the model builder's needs. This approach allows model builders to acquire a large amount of data even when they initially have no or few data instances. With the generated data, the model builder can train a separate affordable model (e.g., BERT (Devlin et al., 2019)) to perform the specific task. While LLMs can directly support this classification task with few-shot learning, it might not be the best option for every model builder—some might not have enough resources (e.g., GPUs) or budget (e.g., credit for GPT-3) to run expensive models. Others might be concerned about privacy or security issues when they use LLMs from external APIs (e.g., OpenAI API). In such cases, generating data from LLMs and training custom models could be a more viable approach. Moreover, if we share generated datasets within the community, we can also benefit those who do not have access to LLMs. Lastly, we can also use generated datasets to test models. With these benefits of generating new text datasets with LLMs, the practical concern is how to generate high-quality datasets. In this work, we investigate human-AI partnerships to efficiently create high-quality datasets with LLM-based text generation. High-quality datasets should have high diversity and coverage, informing the extent of data that the model may encounter. At the same time, the generated text should have high accuracy, being relevant to the model's target task while having accurate accompanying labels. To these ends, we first study two technical approaches to diversify text generation (Section 3): 1) logit suppression, which diversifies the generated texts by decreasing the probability of sampling tokens that have already appeared frequently in the previous generation, and 2) temperature sampling, which flattens the probability distribution of sampled tokens to pick less likely texts. From an experiment on eight classification tasks with GPT-3 as a text generator (Section 4), we found that diversification approaches can have mixed results. While increasing data diversity, these approaches can hurt accuracy in generation and similarity to the original datasets for the task. We demonstrate that human interventions (Section 5) are the key to resolving these issues in text generation diversification. We examine human interventions of replacing inaccurate labels with accurate ones (label replacement) and filtering outof-scope data (out-of-scope data filtering). With oracle studies (Section 6), we found that replacing all incorrect labels increased model accuracy by 14.4% when we used both logit suppression and high temperature. This performance increase brings in practical benefits—without label replacement, the average accuracy of models trained with GPT-3-generated data was lower than that of GPT-3 classification with few-shot learning, but with 180 instances label-replaced, the models trained with generated data started to outperform GPT-3 fewshot classification. Out-of-scope data filtering had limited utility in increasing model accuracy, possibly due to the negative impact of removing training instances. We discuss how human interventions can further facilitate the diversity and accuracy of text data generation. Our contributions are: - A methodolgy that combines LLM generation approaches and human supervision for diversified and accurate data generation. - An experiment showing how text generation diversification impacts the accuracy of trained models and other qualities of the data, such as diversity and accuracy in the generation. - Oracle studies on how human effort to replace misaligned labels and filter out-of-scope data instances can impact the performance of models trained on data generated with text diversification. ## 2 Related Work 2.1 Text Data Generation For Model Training In NLP, data augmentation, where data are multiplied based on existing data, is one context where text data are generated for model training. There were many approaches, from replacing words with synonyms (Wei and Zou, 2019; Zhang et al., 2015), to randomly editing texts (Wei and Zou, 2019), predicting replaceable words (Ng et al., 2020), backtranslating (Fadaee et al., 2017), generating labelflipped data (Zhou et al., 2022), or using reinforcement learning to condition generation (Liu et al., 2020). Inspired by MixUp (Zhang et al., 2018), which mixes different examples in vision data, researchers also blended texts to augment data (Guo et al., 2020; Sun et al., 2020; Zhang et al., 2022). Other approaches generate texts by learning from different datasets (Xia et al., 2020; Hou et al., 2018; Chen et al., 2020; Yoo et al., 2019). Recently, with the generative capacity of LLMs, researchers proposed generating datasets with zero or very few samples and training a separate model to serve the specific task (Kumar et al., 2020; Yoo et al., 2021; Sahu et al., 2022; Yuan et al., 2021; Hartvigsen et al., 2022). As this approach would extract information from large models, they would be analogous to knowledge distillation (Phuong and Lampert, 2019; Hinton et al., 2015) or dataset distillation (Wang et al., 2018; Cazenavette et al., 2022). LLM-generated data has also been used to test other trained models (Ribeiro and Lundberg, 2022; Perez et al., 2022). In this work, we extend the previous work by investigating the generation of high-quality data with accurate diversification. ## 2.2 Text Generation With Llms As the size of language models increases, researchers found that LLMs can serve different generation tasks based on input prompts and examples (Brown et al., 2020). This approach can be used to generate text data with instructional prompts and a few examples. However, for the generated data to be useful, diversity and coverage should be ensured. Control of the sampling temperature (Goodfellow et al., 2016) would be relevant, as it facilitates the unlikely generation, but it was not evaluated for the facilitation of diversity and coverage. Inspired by previous work on controlling LLM generation, we examine human-AI approaches to steer data generation to have higher diversity while securing accuracy in the alignment ## 2.3 Human-In-The-Loop Human interventions are imperative to train highperformance machine learning models, as people curate datasets, configure model architectures, and test the trained models. Researchers investigated approaches to make human interventions more interactive in model training pipelines, by closing gaps between model training and data curation (Fogarty et al., 2008; Amershi et al., 2009, 2012; Levonian et al., 2022), humans extracting features (Branson et al., 2010; Cheng and Bernstein, 2015), interactively changing the error patterns (Kapoor et al., 2010; Talbot et al., 2009), or interactively testing models (Wu et al., 2019; Yuan et al., 2022; Ribeiro et al., 2020; Cabrera et al., 2021; Suh et al., 2019). Generative models introduce novel approaches to interactively tune and evaluate models by leveraging generated results as data instances for training and testing (Ribeiro and Lundberg, 2022). In this work, we explored harnessing diversified and accurate datasets by combining LLM-based text generation and human interventions. ## 3 Diversified Text Data Generation We lay out the desired characteristics of the datasets for model building. Then, we introduce approaches to generate diversified datasets with LLMs. ## 3.1 Goals Ideal classification datasets need to have the following characteristics: 1) Scoped: fall in the model builder's domain of interest while classifiable with labels of interest, 2) Label accurate: accompany accurate labels, and 3) Diverse: cover cases the model would encounter during test time. These goals are difficult to achieve simultaneously but need to be balanced. Only considering diversity, randomly generating any text would be enough, but it would hurt scope and label accuracy. Likewise, only considering the scope and label accuracy, generating an accurate but limited variety of text would be enough, but it would hurt the diversity. ## 3.2 Diversifying Approaches We introduce the setting to use LLM-based data generation for model training. Then, we lay out two approaches to promote diversity in text data generation. We also note their potential risks of harming the scope and accuracy. ![2_image_0.png](2_image_0.png) 3.2.1 Settings for Data Generation When prompting LLMs, we consider 1) a text type and 2) labels in the prompts. While there can be many different prompts, in our paper, we used the following prompt: Write a movie review (**text type**) to cover all following elements Elements: positive sentiment (**label**) Movie review (**text type**): "This is a great movie" (A) $$\mathbb{L}^{4}$$ Model builders can also prepend examples in the same format. The generation process is iterative, and model builders can use intermediate data points as examples in later prompts. The model builders can generate data until they reach the desired number of data points. With the generated data, the model builder would finetune a separate smaller model that serves the target task. With this approach of finetuning a smaller model, there can be a question of whether finetuning a separate model would result in higher accuracy than using zeroshot or few-shot learning of the LLM. In the later study, we show the cases where finetuned smaller models perform better than the LLM. ## 3.2.2 Logit Suppression Logit suppression is a diversification approach that suppresses tokens that have already been generated frequently in the intermediate dataset (Figure 1a). With this approach, the generation pipeline logs the frequency of tokens that have been generated so far. Then, to diversify the selection of tokens, logit suppression decreases the probability of highfrequency tokens. However, with this approach, some tokens that could contribute to accurate generation can be suppressed. 3.2.3 High Temperature The temperature of sampling distribution (Goodfellow et al., 2016) controls how "flat" the token sampling probability is (the equation is explained in Appendix A). High temperature leads to "flatter" token sampling probabilities (Figure 1b), increasing the probability of sampling "less likely" tokens and diversifying generation. Similar to logit suppression, extremely high temperatures can result in tokens irrelevant to the prompt, hurting accuracy in generation results. ## 4 Experiment1: Diversified Text Data Generation We evaluated how diversification approaches impact the diversity of the generated data and the accuracy of models trained with the dataset. ## 4.1 Experiment Settings 4.1.1 Tasks We used tasks from eight datasets. **SST-2** (Socher et al., 2013) is a binary sentiment classification dataset from Rotten Tomatoes movie reviews. Clickbait classification dataset (CB) (Chakraborty et al., 2016) is news headlines labeled either clickbait or non-clickbait. **CARER** (Saravia et al., 2018) is Twitter statements labeled with one of the six emotion categories. **PubMed** 200k RCT (Dernoncourt and Lee, 2017) has five classes regarding the roles of sentences in medical papers. The subjectivity dataset (**SUBJ**) is movie review texts labeled subjective or objective (Pang and Lee, 2004). Formality classification dataset (FO) (Lahiri, 2015) has labels on whether the text is formal or informal. HWU64 (Liu et al., 2021) is a dataset with human utterances to chatbots, and we used 18 domain classes for our experiments. Corpus of Linguistic Acceptability (**COLA**) (Warstadt et al., 2019) is publication texts with annotations on whether the text is grammatically correct or not. ## 4.1.2 Generation Method As a generative LLM, we used the text-davinci-002 model of GPT-3 through OpenAI API Access with Prompt A. We list the specific text types and labels used for each dataset in Appendix B.1. The generation process was iterative, with 20 data points generated with a single prompt for each API call. As a single prompt can only generate data instances for a single label, the generation process cycled through all considered labels while balancing the number of instances for each class. As our tasks dealt with short text data, we limited the generation length to 100 tokens. We set the frequency penalty and top p to 0.02 and 1, respectively. Except for SST-2, we generated 5600 instances for a single training dataset. For SST-2, we generated 6922 data points. We chose these numbers to ensure a low generation budget while having fair quality when training models. Specifically, with a maximum length of 100 tokens for each generated instance, if the prompt includes examples for n classes, the number of required tokens for each instance would be (100+30) × (n+1) (where 30 come from the instructional prompts). With the generation pricing of $0.02/1000 tokens for text-davinci-002 model, 5600 and 6922 instances resulted in maximum spending of $14.56 × (n+1) and $17.80 × (n+1), respectively. In our pilot tests, model accuracy saturated after these numbers of instances. For the oracle training dataset, with which we compared the quality of the datasets, we sampled instances from the original training dataset for the task. The test dataset was sampled from the original test dataset. We provide details on how we sampled these instances in Appendix B.2. Generation Conditions In addition to **logit suppression** and **temperature sampling**, we also consider **example seeding**, whether the generation pipeline begins with an initial set of example instances. We can use multiple approaches simultaneously (e.g., using logit suppression and temperature sampling together), and how these approaches interact is also the scope of our questions. For a single combination of conditions, we generated three datasets, as there could be some variance in the results with the initial seeds and the examples generated initially. We instantiated **logit suppression** with the logit bias function in OpenAI API Access2, which can increase or decrease the probability of sampling tokens. Every time we complete a single generation iteration, we recorded the frequency of tokens generated by GPT-3. As the OpenAI API only allows 100 tokens for logit biasing, we suppressed only the 100 most appeared tokens. Specifically, for the logit bias weights, we multiplied the token appearance ratio (in percentage) by -7.5 while capping the minimum weight at –7.5. For **temperature sampling**, we used four temperature values, 0.3, 0.7, 0.9, and 1.3. When **seeding examples**, we first randomly sampled 18 examples from oracle training data with a balanced number of labels. Only for PubMed, which has five classes, we used 15 seed examples. We used sampled data points as an initial example pool. With example seeding, from the first 2https://beta.openai.com/docs/api-reference/ completions/create\#completions/create-logit_bias ![4_image_0.png](4_image_0.png) generation iteration, examples were randomly chosen from the pool. Without the seeding examples, we completed the first cycle of generations as a zero-shot generation. After the first cycle, since we would have generated data instances for all labels, we added examples to the prompt. When adding examples, we randomly sampled the examples for all labels, one example for each label. ## 4.1.3 Training Method With the generated data, we finetuned base size BERT (Devlin et al., 2019) classifiers with 109M parameters using pretrained weights from the Huggingface Transformer library (Wolf et al., 2020) with a randomly initialized fully connected classifier layer. For each dataset, we trained the five different models with the same dataset. With three datasets for each combination of approaches, it resulted in 15 models for a condition. While training, Adam optimizer was used, with a learning rate of 3e-5 and a warm-up period of 3 epochs. We adopted the early stopping with the patience of five training epochs. We used PyTorch and RTX A6000 GPUs for training. ## 4.2 Metrics We compared the accuracies of models trained with generated data to 1) models trained with oracle datasets (oracle model) and 2) GPT-3's few-/zeroshot classifications (text-davinci-002). For GPT-3 few-shot learning, we used 18 examples (15 only for PubMed) with the same number of examples for each label. We also measured the diversity of the dataset using Remote-Clique metric (Rhys Cox et al., 2021), which is the average mean pairwise distances. Specifically, we embedded generated data with BERT (Devlin et al., 2019), then calculated the distances. We also evaluated label accuracy, which is the accuracy of the alignment between the generated texts and the specified labels. For this metric, except for SST-2, we used the oracle model as the evaluator. For SST-2, we used GPT-3 few-shot classification as the evaluator, as it has higher accuracy than the oracle model. We also measured the similarity of the generated dataset to the oracle dataset with the average mean pairwise distances between the two. For similarity, we also used BERT to embed the generated texts. ## 4.3 Results Figure 2 shows the results of the first experiment for all tasks. The first column shows the model accuracy results. It also shows the accuracy of zero-shot and few-shot GPT-3 classification (gray solid and dashed line, respectively) and the model trained with the oracle training dataset (purple line). The second column shows the label accuracy, and the third column shows the diversity. The diversity plots also show the diversity of oracle datasets (purple line). The last column shows the similarity. It also shows the base similarity (brown line), which is the average distance between all the different datasets that we considered. First, to evaluate how diversity, label accuracy, and similarity impact model accuracy, we performed a linear regression analysis. The analysis showed that label accuracy, diversity, and similarity are positively correlated with model accuracy, with significance (coef=.4797 and p<0.001 for label accuracy, coef=.2260 and p<0.001 for diversity, and coef=0.1980 and p<0.005 for similarity). Regarding specific patterns, logit suppression increased diversity while hurting the label accuracy and the similarity to the oracle dataset. High temperature increased diversity and decreased label accuracy, but to a smaller degree than logit suppression. The application of each diversification approach increased the model accuracy, but when used together, the benefit did not add up. For instance, in Model Accuracy of Figure 2, each high temperature (1.3, red light bars) and logit suppression (dark blue bars) could increase the model accuracy from when using a low temperature (0.3, light blue bars). However, when using them together (dark red bars), the resulting accuracy was not much different from only using high temperatures (light red bars). It indicates that the effect of logit suppression has diminished by using high temperatures and logit suppression together. Seeding examples increases label accuracy and model accuracy. Examples also slightly increased diversity when used without logit suppression. Whether models trained with LLM-generated data would have higher accuracy than zero- or few-shot learning of LLMs depends on the task. We provide a detailed result on each task in Appendix C. ## 5 Human Interventions To Fix Inaccurate Text Generation The first study shows that diversifying approaches can have mixed effects, hurting the accuracy in generation. We propose two human interventions to improve the generated data, based on issues that we found from qualitatively analyzing the generated data. The first is **label replacement (LR)**, switching the misaligned label to the correct one. The second is **out-of-scope data filtering (OOSF)**, which removes instances that are outside the domain of interest and do not match any labels (OOS instances). While LR and OOSF might facilitate accurate generation with diversifying approaches, inspecting all data points can require a lot of effort. Hence, we propose a simple way to scale the effort of the model builder, which is training a **proxy model**. With this approach, model builders will first label a small number of data points. Then, with those labels, they will train binary classifiers as proxy models, where each learns about a single label (i.e., a label class from labels of interest or if the instance is out of scope). For unlabeled data points, proxy models can make inferences on behalf of the model builder. We introduced the specific implementation of this approach in Section 6. ## 6 Experiment2: Human Interventions For Diversifed Text Generation We evaluated LR and OOSF. Except for adding LR and OOSF, we used the same tasks, datasets, training methods, and metrics as in Section 4. In this section, we focus on reporting results for two temperature values, 0.3 and 1.3. We present the results with the rest of the temperatures in Appendix E. Also, in this section, when reporting, we merged conditions with and without example seeding. ## 6.1 Experiment Settings 6.1.1 Label Replacement For LR, we conducted an oracle experiment. For each task, we used the highest accuracy model as the oracle labeler. Therefore, we used oracle models as a labeler, but only for SST-2, we used GPT-3 few-shot classification as a labeler. We conducted LR on the datasets generated in experiment 1. We had two approaches for LR: 1) do LR to all data points and 2) use proxy models with LR on partial data. For 1), we inspected all generated texts with simulated labelers and replaced labels as the labelers predicted. For 2), we sampled a set of instances from the generated dataset, applied the oracle labeler to them, and then trained proxy models with those data. Specifically, we sampled 90, 180, or 270 data instances. When training, for each class, we trained a proxy model that performs binary classification for the class. For each proxy model, the data instances labeled with the target label were used as positive instances, while the rest were used as negative instances. We applied proxy models to the uninspected data to obtain confidence scores for each label. For each class, we calculated the final score as follows: $$S_{f,i}=S_{s,i}*w+S_{p,i}*(1-w)\qquad(1)$$ where for the class i, Sf,i is the final score, Sp,i is the confidence score of the proxy model, Ss,i is if the class is specified when generating the text (1 when the class is specified, 0 otherwise), and w is the weighting constant. We considered Ss,i as there can be a chance that the proxy model is inaccurate and the correct labels are swapped. For our experiment, we used w of 0.3. We chose the label with the highest final score as the label to be replaced. ![6_image_0.png](6_image_0.png) Table 1: Ratio of out-of-scope instances from 360 samples. ![6_image_2.png](6_image_2.png) For training proxy models, we trained linear support vector classifiers with a maximum iteration of 10000 while using texts embedded with BERT (Devlin et al., 2019) as input. We chose to train multiple proxy models for each class over training a single proxy model for all classes, as it tends to be more reliable in our pilots when there are many classes. As the labeling of the proxy model depends on the initial samples, for each generated dataset in experiment 1, we applied the approach five times. ## 6.1.2 Out-Of-Scope Filtering With OOSF, we first tried to understand how OOS instances occur. Therefore, we sampled 360 data instances for each task from the union of all the datasets generated for the task. Then, an author served as the oracle and annotated if they were OOS or not. Note that, as the definition of OOS instance, we filtered those instances that are outside the task domain or to which no label is applicable. We found that COLA, FO, HWU64, and PubMed have zero to four instances of OOS (Table 1). For the later analysis, we only considered the rest of the datasets, with at least five OOS instances. We present examples of OOS instances in Appendix D.1. With the annotated data, we trained proxy models to annotate the instances unseen by the author, which were binary linear support vector classifiers with the maximum iteration of 10000 and BERTembedded inputs. With the trained model, we did OOSF on the datasets generated in experiment 1. Table 2 shows the accuracy of the proxy model, when we divide the annotated data into training and test sets with an 8:2 ratio, with a split of ten times. Note that the perfect accuracy in CB is because we identified only five OOS instances from ![6_image_1.png](6_image_1.png) our samples, which are extremely few. After applying LR or OOSF, we trained BERT models that serve the target task. For each dataset that applied LR without proxy models or used OOSF, we ran the training five times. For each dataset that used LR with proxy models, since each dataset from experiment 1 has been label-replaced five times, we ran training only once. With this approach, we acquired 15 model accuracy results for each task and condition. ## 6.2 Results 6.2.1 Label Replacement Label Accuracy and Model Accuracy in Figure 3 shows the results with LR. It shows how model accuracy and label accuracy change with the number of instances inspected (x-axis). Other metrics, diversity, and similarity would not change with LR, as it keeps the texts as they are. For model accuracy, we also visualized the performance of oracle models and the GPT-3 few-/zero-shot classification. LR increases the model accuracy and label accuracy. Moreover, with more labels inspected, the model accuracy and label accuracy further increased. LR also added more values to logit suppression. For example, without LR, using both high temperature (1.3) and logit suppression did not have a comparative benefit over using only high temperature. However, with label replacement, the addition of logit suppression started to benefit the model accuracy when using high temperature. When doing LR with proxy models, the benefit of logit suppression increased with more instances inspected, but with full LR, the size of this gap decreased a little bit. With LR of all instances, using both high temperature and logit suppression increased the absolute model accuracy by 17.8%, compared to when using neither. It was greater than ![7_image_0.png](7_image_0.png) the increase from diversification approaches when LR was not used (9.4%). Furthermore, with high temperature and logit suppression, using LR on all instances could increase the absolute model accuracy by 14.4% compared to not doing LR. When a high temperature and logit suppression are used together, the model accuracy outperformed GPT3's few-shot classification when LR was done for 180 instances. Across tasks, we found that specific patterns on how diversification approaches and LR impact the model accuracy can vary between tasks. We provide details in Appendix E.1. ## 6.2.2 Out-Of-Scope Instances Filtering Figure 4 shows how many instances were filtered with OOSF and how it affects model accuracy, label accuracy, diversity, and similarity. We present model accuracy from both unbalanced and balanced data: when we balanced data, we used datasets with the same number of instances across different conditions by subsampling data with the smallest size of the filtered dataset. It was because filtering can make the number of instances different between conditions. For unbalanced data, we did not balance the number of instances. OOSF either increases or maintains label accuracy and similarity while decreasing or maintaining diversity, but there was no unified pattern of how they impact the model accuracy. There tend to be few OOS-filtered instances without diversification approaches. For example, with a temperature of 0.3 and without logit suppression, OOSF removed very few data instances. Consequently, label accuracy, diversity, and similarity remained the same with OOSF. Without diversification approaches, the accuracy of trained models tends to be more unstable with large confidence intervals. On the other hand, with diversification approaches, OOSF removed more instances, and hence there were slightly more changes in label accuracy, diversity, and similarity, with small increases in label accuracy and similarity while decreasing diversity. However, in some cases, these changes were subtle or within the 95% confidence intervals. Moreover, how the OOSF changes the model accuracy depends on the specific task and condition. We provide the OOSF results for each task in Appendix E.2. ## 7 Conclusion In this work, we investigate approaches to harness LLMs and human efforts to generate text classification datasets with high accuracy and diversity. We study two text generation diversification approaches, 1) logit suppression, which restrains generating already frequently generated tokens, and 2) high temperature, which flattens the sampling probability of tokens. We found that they diversify text generation but hurt the accuracy in aligning specified labels with the generated data. We experiment with two human intervention approaches, 1) replacing misaligned labels with more adequate ones, and 2) filtering out-of-scope instances. We found that replacing labels makes diversification approaches more beneficial by increasing the accuracy of models trained with the generated dataset. On the other hand, efficient filtering of out-of-scope instances did not have a positive impact on the model accuracy. ## 8 Limitations Our implementation of proxy models applies those models after the whole data is generated. Due to this, in the resulting dataset, the number of instances can often be unbalanced between labels. Such a limitation might be addressable by training proxy models from intermediate datasets with a smaller number of instances, and using those models while generating the rest of the dataset. As the data become unbalanced during the generation, the generation pipeline can try to generate more instances with labels that are a minority in the intermediate dataset. However, when we piloted this approach, we identified potential problems. First, intermediately trained proxy models could perform worse than those trained after all data are generated, due to the lower diversity in intermediate data used to train proxy models. Second, if many data points generated with a specific label (label a) actually belong to another label (label b), there can be cases where most instances of label b come from the prompt with label a. It can skew the linguistic patterns of instances within the dataset, as only a small number of texts for label b might have been from the prompt with label b. Advanced approaches to address these issues can be future work directions. Our implementation of efficient OOSF was not effective in increasing model accuracy. It might be due to the negative impact of removing instances, such as filtering instances on the decision boundary. As our study of OOSF was not complete, future work is necessary. Applying OOSF to the entire generated dataset and seeing the impact of their removal would be the first step. With a comprehensible understanding of OOSF, we would be able to design better OOSF strategies, such as filtering instances with various criteria. In this work, we only examined the text-davinci-002 model of GPT-3. Although we believe that the overall trends of results would be similar for other models, examining other models with our approaches is a necessary future work. We also examined only one prompt (Prompt A), while there may be other options. In Appendix F, we present partial results on using another prompt, showing that our approach is generalizable to other prompts. Combining human interventions with automatic annotation error detection (Klie et al., 2023) can be another future direction. ## 9 Ethics Statement LLM-generated text data could have replicated biases within the used LLM. Diversification might alleviate such issues, as it steers the LLM to generate texts that it considers less probable, but bias can still exist after using the approach. More human intervention approaches can be a potential solution. 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Association for Computational Linguistics. ## A Equation For Temperature Sampling Mathematically, with the temperature T and original probability of token, pi, the temperature sampled probability of token i, fT (p)i, would be denoted as below: $$f_{T}(p)_{i}={\frac{p_{i}^{1/T}}{\Sigma_{j}p_{j}^{1/T}}}\qquad\qquad(2)$$ ## B Experiment 1 Details B.1 Prompts Used In Llm Generation For each task, we used prompt A with text types and labels as in Table 3. For example, for CB, a prompt can look like the below with examples: Write a **news headline** to cover all following elements Elements: **valid news** News headline: "Zach Johnson Wins Sony Open" - - - - - Write a **news headline** to cover all following elements Elements: **clickbait** News headline: "10 Of The Biggest Lies We Were Told In 2015" - - - - - Write a **news headline** to cover all following elements Elements: **clickbait** News headline:" (B) ## B.2 Sampling Oracle Dataset For the oracle dataset, if there are more than 5600 data points in the original dataset (CB, CARER, HATE, COLA, HWU64, SUBJ), we subsampled 5600 training data points. For SST2, we used all 6922 instances from the original dataset. Note that these numbers are the same as the number of generated data instances. For FO, we used the original training dataset as is (with 3622 data instances), as there are fewer than 5600 instances. For test datasets, from the same original dataset excluding instances used for the oracle dataset, we sampled 2400 data points for CB, CARER, HATE, and HWU64. For FO, COLA, SUBJ, and SST-2, we used the original test datasets as there were fewer than 2400 instances. ## C Results Of The Experiment 1 On Individual Dataset Here, we introduce the result of the first experiment for individual tasks (Figure 5). ![12_image_0.png](12_image_0.png) | Task | Text type | Label → Label in prompts | | | |---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|----|-----------------------------------------------------------------------------------| | CARER | emotional tweet | joy → expressing joy, anger → expressing anger, fear → expressing fear, sadness → expressing sadness, love → expressing love, surprise → expressing surprise | | | | CB | news headline | non-clickbait → valid news, clickbait → clickbait | | | | COLA | sentence | grammatically acceptable → grammatically correct sentence, grammatically unacceptable → grammatically incorrect sentence | | | | FO | sentence | informal → informal, formal → formal | | | | HWU64 | human utterance to | news → news, weather → weather, play → play, datetime → datetime, iot → iot, | | | | a chatbot | cooking → cooking, recommendation → recommendation, calendar → calendar, music → music, takeaway → takeaway, lists → list, transport → transport, qa → qa, social → social, general → general, alarm → alarm, email → email, audio → audio | | | | | PubMed | sentence | from | a | objective → sentence about objective, methods → sentence about methods, results → | | medical paper | sentence about results, conclusions → sentence about conclusions, background → sentence about background | | | | | SST-2 | movie review | positive → positive sentiment, negative → negative sentiment | | | | SUBJ | sentence | from | a | objective → objective statement, subjective → subjective statement | | movie review | | | | | The benefit of logit suppression for each task depends on the combination of label accuracy, diversity, and similarity. Tasks that have high base label accuracy tend to improve model accuracy more with logit suppressions. For example, for CB and SST-2, those conditions with logit suppressions were clear winners in model accuracy over other combinations of approaches. For other tasks, where overall label accuracy tends to be lower, logit suppression did not have large benefits. COLA was the extreme case where the label accuracy was about 50% in binary classification, indicating that the performance of the LLM in generating label-accurate instances was not better than random chance. In this case, logit suppression resulted in almost no increase in the model accuracy. Even in this case, logit suppression could increase the diversity of the generated text. With PubMed, we could observe an exception of label accuracy increasing with logit suppression when example seeding and high temperature (1.3) are not used (compare light and darkcolored unhatched bars in PubMed's Label Accuracy from Figure 5, except for red bars). It was because GPT-3 generates many similar errors without logit suppression and seeding examples. Specifically, without logit suppression, when prompted to write about the background sentence in a medical paper, GPT-3 generated many sentences starting with "The purpose of this study was," which is more about the objective. For temperature also, specific patterns on how it affected label accuracy, diversity, and similarity differ between tasks. In PubMed, without logit suppression and example seeding, label accuracy even increased with higher temperatures, which was against the general pattern. In this case, similar to what we found with logit suppression, the lack of diversification approaches led to the generation of narrowly populated error instances. CARER was another case with the reversed trend: without logit suppression and seeding examples, the mean diversity was higher with a temperature of 0.7 than with a temperature of 1.3. It was because, with the high temperature of 1.3, many sentences started with "I'm so," (on average 3012 occurrences) which was less the case for the lower temperatures of 0.7 and 0.9 (on average 841.5 occurrences). In CARER, when example seeding and logit suppression are not used, label accuracy was also higher with the temperature of 1.3 than with lower temperatures, although the means were within 95% confidence intervals. In this case, with lower temperatures of 0.7 and 0.9, more instances started with "No matter what," which continues with advice on what to do in emotional situations. For such cases, no label is applicable since they are not the self-expression of emotions (on average, 32 occurrences with a temperature of 1.3 and 682.7 occurrences with temperatures of 0.7 or 0.9). Note that these are examples of out-of-scope instances. Summarizing results of logit suppression and temperature sampling, these approaches increased diversity while hurting the label accuracy, but specific patterns could vary between tasks. The utility of example seeding in label accuracy and model accuracy could also vary between tasks. For example, in the extreme case of COLA, examples did not increase label accuracy and model accuracy. How seeding examples impact the generation of data similar to the oracle dataset also ![14_image_0.png](14_image_0.png) ![14_image_1.png](14_image_1.png) Table 4: Examples of OOS instances. ![14_image_2.png](14_image_2.png) For CARER, HWU64, and PubMed in Figure 5, there were cases where the model accuracy was higher than the accuracy of GPT-3's few-shot learning. Other tasks showed lower accuracy than GPT3's few-shot learning accuracy, indicating that GPT3 few-shot classification can be a better alternative than training a model with generated data if the model builder has a budget to continuously access GPT-3 and is willing to hand over data through API. In Section 6, we show that human interventions can be a way to make the data generation approach applicable in more tasks by increasing the model accuracy higher than that of few-shot classifications from GPT-3. ![15_image_0.png](15_image_0.png) ## D Experiment 2 Details D.1 Examples Of Oos Instances. We present examples of OOS instances in Table 4. ## E **Results Of The Experiment 2 On Varying** Tasks We present the results of experiment 2 for individual tasks. Note that we also show results for all temperature values (0.3, 0.7, 0.9, and 1.3). ## E.1 Label Replacement Figure 6 and 7 shows the LR result for individual tasks and whole tasks aggregated, respectively, with all temperatures. First, there were cases where logit suppression provided additional benefit upon high temperature only when LR was applied (comparing thick and thin red lines in Model Accuracy of CARER, HWU64, and PubMed in Figure 6). Second, for tasks that already have high accuracy without LR (CB and SST-2), LR either resulted in very small model accuracy increases or even hurted the accuracy. For example, in SST-2, the label accuracy was already high without LR, and doing LR with proxy models could even decrease the label accuracy and model accuracy. Third, without diversification approaches, there were also cases where LR did not increase model accuracy much while label accuracy was greatly increased (thin blue lines in Model Accuracy of CARER, CB, FO, PubMed, SST2, SUBJ in Figure 6). It may show that fixing labels is more beneficial when there is enough diversity in the generated dataset. Fourth, CB, FO, and SUBJ were cases where models trained with generated data could outperform GPT-3's few-shot classification only with label replacement (some colored lines go over gray dashed lines with LR in Model Accuracy of CB, FO, and SUBJ in Figure 6). Among them, with FO, inspecting partial instances could also turn the model accuracy higher than that of GPT-3 few-shot classification. As expected, no approaches outperform oracle models as those models are used for LR. Fifth, for tasks with many classes (CARER, HWU64, and PubMed), when using LR with proxy models, the performance tends to increase not much dramatically as the number of annotated instances increases (Model Accuracy of CARER, HWU64, and PubMed in Figure 6). Higher model accuracy leaps occurred when all instances were inspected. It may indicate the difficulty of training accurate proxy models with many classes to consider. ## E.2 Out-Of-Scope Filtering Figure 8 and 9 shows the OOSF results with all temperatures, for the aggregation of all tasks and individual tasks, respectively. As mentioned in the main text, it was difficult to find a general pattern of how OOSF impacts the model accuracy. Consistent patterns were that OOSF tends to increase or maintain label accuracy and similarity while decreasing or maintaining diversity. ## F Results On Prompt C On two tasks (FO, HWU64), we conducted the experiment with another instructional prompt: Show me a **text type** that has the following characteristics teristics: **label** **text type: "Generated text"** We measured model accuracy, label accuracy, diversity, and similarity of generated datasets and also investigated how label replacement impacts label accuracy and model accuracy. The experiment setting was the same as the main experiment we conducted, except the prompt used. The trend in the results (Figure 10) was similar to that of the prompt A. ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8. Limitations ✓ A2. Did you discuss any potential risks of your work? Section 9. Ethics Statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1. Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Not applicable. Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Not applicable. Left blank. ## C ✓ **Did You Run Computational Experiments?** Left Blank. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4 and Appendix A The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4 and Appendix A ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Figure 1, 3 and 4 and Table 2 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 and Appendix A ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 6. It Was A Human Oracle Study Where One Of The Authors Served The Role Of The Oracle. ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? It was an oracle study by one of the authors on filtering out-of-scope instances, and we followed the definition we provided in Section 5. ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? It was an oracle study by one of the authors on filtering out-of-scope instances. ✗ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? It was an oracle study by one of the authors on filtering out-of-scope instances. ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? It was an oracle study by one of the authors on filtering out-of-scope instances. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? It was an oracle study by one of the authors on filtering out-of-scope instances.

jiang-etal-2023-pruning

Pruning Pre-trained Language Models Without Fine-Tuning

https://aclanthology.org/2023.acl-long.35

To overcome the overparameterized problem in Pre-trained Language Models (PLMs), pruning is widely used as a simple and straightforward compression method by directly removing unimportant weights. Previous first-order methods successfully compress PLMs to extremely high sparsity with little performance drop. These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights. In this work, we argue fine-tuning is redundant for first-order pruning, since first-order pruning is sufficient to converge PLMs to downstream tasks without fine-tuning. Under this motivation, we propose Static Model Pruning (SMP), which only uses first-order pruning to adapt PLMs to downstream tasks while achieving the target sparsity level. In addition, we also design a new masking function and training objective to further improve SMP. Extensive experiments at various sparsity levels show SMP has significant improvements over first-order and zero-order methods. Unlike previous first-order methods, SMP is also applicable to low sparsity and outperforms zero-order methods. Meanwhile, SMP is more parameter efficient than other methods due to it does not require fine-tuning.

## Pruning Pre-Trained Language Models Without Fine-Tuning Ting Jiang1, Deqing Wang13†**, Fuzhen Zhuang**123, Ruobing Xie4**, Feng Xia**4 1SKLSDE Lab, School of Computer, Beihang University, Beijing, China 2Institute of Artificial Intelligence, Beihang University, Beijing, China 3 Zhongguancun Laboratory, Beijing, China 4 WeChat, Tencent, Beijing, China {royokong, dqwang, zhuangfuzhen}@buaa.edu.cn ## Abstract To overcome the overparameterized problem in Pre-trained Language Models (PLMs), pruning is widely used as a simple and straightforward compression method by directly removing unimportant weights. Previous first-order methods successfully compress PLMs to extremely high sparsity with little performance drop. These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights. In this work, we argue fine-tuning is redundant for first-order pruning, since first-order pruning is sufficient to converge PLMs to downstream tasks without fine-tuning. Under this motivation, we propose Static Model Pruning (SMP), which only uses first-order pruning to adapt PLMs to downstream tasks while achieving the target sparsity level. In addition, we also design a new masking function and training objective to further improve SMP. Extensive experiments at various sparsity levels show SMP has significant improvements over firstorder and zero-order methods.Unlike previous first-order methods, SMP is also applicable to low sparsity and outperforms zero-order methods. Meanwhile, SMP is more parameter efficient than other methods due to it does not require fine-tuning. Our code is available at https://github.com/kongds/SMP. ## 1 Introduction Pre-trained Language Models (PLMs) like BERT (Devlin et al., 2019) have shown powerful performance in natural language processing by transferring the knowledge from large-scale corpus to downstream tasks. These models also require large-scale parameters to cope with the large-scale corpus in pretraining. However, these large-scale parameters are overwhelming for most downstream tasks (Chen et al., 2020), which † Corresponding Author. results in significant overhead for transferring and storing them. To compress PLM, pruning is widely used by removing unimportant weights and setting them to zeros. By using sparse subnetworks instead of the original complete network, existing pruning methods can maintain the original accuracy by removing most weights. Magnitude pruning (Han et al., 2015) as a common method uses zeroth-order information to make pruning decisions based on the absolute value of weights. However, in the process of adapting to downstream tasks, the weight values in PLMs are already predetermined from the original values. To overcome this shortcoming, movement pruning (Sanh et al., 2020) uses firstorder information to select weights based on how they change in training rather than their absolute value. To adapt PLMs for downstream tasks, most methods like movement pruning perform pruning and fine-tuning together by gradually increasing the sparsity during training. With the development of the Lottery Ticket Hypothesis (LTH) (Frankle and Carbin, 2018) in PLMs, some methods (Chen et al., 2020; Liang et al., 2021) find certain subnetworks from the PLM by pruning, and then fine-tune these subnetworks from pre-trained weights. Moreover, if the fine-tuned subnetwok can match the performance of the full PLM, this subnetwork is called winning ticket (Chen et al., 2020). In this work, we propose a simple but efficient first-order method. Contrary to the previous pruning method, our method adapts PLMs by only pruning, without fine-tuning. It makes pruning decisions based on the movement trend of weights, rather than actual movement in movement pruning. To improve the performance of our method, we propose a new masking function to better align the remaining weights according to the architecture of PLMs. We also avoid fine-tuning weights in the task-specific head by using our head initialization method. By keeping the PLM frozen, we can save 594 half of the trainable parameters compared to other first-order methods, and only introduce a binary mask as the new parameter for each downstream task at various sparsity levels. Extensive experiments on a wide variety of sparsity demonstrate our methods strongly outperform state-of-the-art pruning methods. Contrary to previous first-order methods (Sanh et al., 2020), which show poor performance at low sparsity, our method is also applied to low sparsity and achieves better performances than zero-order methods. ## 2 Related Work Compressing PLMs for transfer learning is a popular area of research. Many compression methods are proposed to solve overparameterized problem in PLMs, such as model pruning (Han et al., 2015; Molchanov et al., 2017; Xia et al., 2022), knowledge distillation (Jiao et al., 2020; Wang et al., 2020), quantization (Shen et al., 2020; Qin et al., 2022), and matrix decomposition (Lan et al., 2020). Among them, pruning methods have been widely studied as the most intuitive approach. Pruning methods focus on identifying and removing unimportant weights from the model. Zeroorder methods and first-order methods are widely used to prune PLMs. For zero-order methods, magnitude pruning (Han et al., 2015) simply prunes based on absolute value of their weights. For first-order methods, which are based on first-order Taylor expansion to make pruning decision, L0 regularization (Louizos et al., 2017) adds the L0 norm regularization to decrease remaining weights by sampling them with hard-concrete distribution. Movement pruning (Sanh et al., 2020) uses *straightthrough estimator* (Bengio et al., 2013) to calculate first-order informantion. Based on pruning methods, Frankle and Carbin (2018) proposes Lottery Ticket Hypothesis (LTH). LTH clarifies the existence of sparse subnetworks (i.e., winning tickets) that can achieve almost the same performance as the full model when trained individually. With the development of LTH, lots of works that focus on the PLMs have emerged. Chen et al. (2020) find that BERT contains winning tickets with a sparsity of 40% to 90%, and the winning ticket in the mask language modeling task can be transferred to other downstream tasks. Recent works also try to leverage LTH to improve the performance and efficiency of PLM. Liang et al. (2021) find generalization performance of the winning tickets first improves and then deteriorates after a certain threshold. By leveraging this phenomenon, they show LTH can successfully improve the performance of downstream tasks. ## 3 Background Let a = Wx refer to a fully-connected layer in PLMs, where W ∈ R n×nis the weight matrix, x ∈ R nand a ∈ R nare the input and output respectively. The pruning can be represented by a = (W ⊙ M)x, where M ∈ {0, 1} n×nis the binary mask. We first review two common pruning methods in PLMs: magnitude pruning (Han et al., 2015) and movement pruning (Sanh et al., 2020). Magnitude pruning relies on the zeroth-order information to decide M by keeping the top v percent of weights according to their absolute value M = Topv (S). The importance scores S ∈ R n×nis: $$S_{i,j}^{(T)}=\left|W_{i,j}^{(T)}\right|$$ $$=\left|W_{i,j}-\alpha_{w}\sum_{t<T}\left(\frac{\partial\mathcal{L}}{\partial W_{i,j}}\right)^{(t)}\right|\tag{1}$$ where S $\mathfrak{L}^{(T)}_{\mathrm{Lie}}$ is the $\mathrm{i}$. i,j is the importance score corresponding to W (T) i,j after T steps update, L and αw are learning objective and learning rate of Wi,j . Magnitude pruning selects weights with high absolute values during fine-tuning. For movement pruning, it relies on the first-order information by learning the importance scores S with gradient. The gradient of S is approximated with the *staight-through estimator* (Bengio et al., 2013), which directly uses the gradient from M. According to (Sanh et al., 2020), the importance scores S is: $$S_{i,j}^{(T)}=-\alpha_{s}\sum_{t<T}\left(\frac{\partial{\mathcal{L}}}{\partial W_{i,j}}\right)^{(t)}W_{i,j}^{(t)}\qquad(2)$$ where αs is the learning rate of S. Compared to magnitude pruning, movement pruning selects weights that are increasing their absolute value. To achieve target sparsity, one common method is *automated gradual pruning* (Michael H. Zhu, 2018). The sparsity level v is gradually increased with a cubic sparsity scheduler starting from the training step t0: v t = vf + (v0 − vf ) 1 − t−t0 N∆t 3, where v0 and vf are the initial and target sparsity, N is overall pruning steps, and ∆t is the pruning frequency. During training, these methods update both W and S to perform pruning and fine-tuning simultaneously. Since fine-tuned weights stay close to their pre-trained values (Sanh et al., 2020), the importance scores of magnitude pruning is influenced by pre-trained values, which limits its performance at high sparsity. However, magnitude pruning still outperforms movement pruning at low sparsity. ## 4 Static Model Pruning In this work, we propose a simple first-order pruning method called Static Model Pruning (SMP). It freezes W to make pruning on PLMs more efficient and transferable. Based on movement pruning (Sanh et al., 2020), our importance scores S is: $$S_{i,j}^{(T)}=-\alpha_{s}W_{i,j}\sum_{t<T}\left(\frac{\partial{\mathcal{L}}}{\partial W_{i,j}^{\prime}}\right)^{(t)}\quad\quad(3)$$ is $\mathbf{W}=\mathbf{M}\mathbf{f}$ . where W′ i,j is Wi,jMi,j . Since our method freezes Wi,j , we also keep the binary masking term Mi,j . Si,j is increasing when Wi,j∂L ∂W′ i,j < 0. For remaining weight W′ i,j = Wi,j , it means that movement trending −∂L ∂W′ i,j increases the absolute value of Wi,j . For removed weight W′ i,j = 0, it means that movement trending encourages 0 to close Wi,j . ## 4.1 Masking Function To get masks M based on S, we consider two masking functions according to the pruning structure: local and global. For the local masking function, we simply apply the Topv function to each matrix: M = Topv (S), which selects the v% most importance weights according to S matrix by matrix. For the global masking function, ranking all importance scores together (around 85M in BERT base) is computationally inefficient, which even harms the final performance in section 6.1. To this end, we propose a new global masking function that assigns sparsity levels based on the overall score of each weight matrix. Considering the architecture of BERT, which has L transformer layers, each layer contains a self-attention layer and a feed-forward layer. In lth self-attention block, WlQ, WlK, WlV , and WlO are the weight matrices we need to prune. In the same way, WlU and WlD are the matrices to be pruned in the lth feed-forward layer. We first calculate the sparsity level of each weight matrix instead of ranking all parameters of the network. The sparsity level of each weight matrix v l (·) is computed as follows: $$v_{(\cdot)}^{l}={\frac{R\left(\mathbf{S}_{(\cdot)}^{l}\right)L}{\sum_{l^{\prime}=1}^{L}R\left(\mathbf{S}_{(\cdot)}^{l^{\prime}}\right)}}v\qquad\qquad(4)$$ where R(S) = Pi,j σ(Si,j ) is the regularization term of S with sigmoid σ, S l (·) is the importance socres of weight Wl(·) , and (·) can be one of {*Q, K, V, O, U, D*}. The sparsity level is determined by the proportion of important scores to the same type of matrix in different layers. ## 4.2 Task-Specific Head Instead of training the task-specific head from scratch, we initialize it from BERT token embedding and keep it frozen during training. Inspired by current prompt tuning methods, we initialize the task-specific head according to BERT token embeddings of corresponding label words following (Gao et al., 2021). For example, we use token embeddings of "great" and "terrible" to initialize classification head in SST2, and the predicted positive label score is h[CLS]e T great, where h[CLS] is the final hidden state of the special token [CLS] and egreat is the token embeddings of "great". ## 4.3 Training Objective To prune the model, we use the cubic sparsity scheduling (Michael H. Zhu, 2018) without warmup steps. The sparsity vt at t steps is: $$v_{t}={\begin{cases}v_{f}-v_{f}\left(1-{\frac{t}{N}}\right)^{3}&t<N\\ v_{f}&{\text{o.w.}}\end{cases}}\quad(5)$$ we gradually increase sparsity from 0 to target sparsity vf in the first N steps. After N steps, we keep the sparsity vt = vf . During this stage, the number of remaining weights remains the same, but these weights can also be replaced with the removed weights according to important scores. We evaluate our method with and without knowledge distillation. For the settings without knowledge distillation, we optimize the following loss function: $${\mathcal{L}}={\mathcal{L}}_{\mathrm{CE}}+\lambda_{R}{\frac{v_{t}}{v_{f}}}R\left(\mathbf{S}\right)$$ $$(6)$$ where LCE is the classification loss corresponding to the task and R (S) is the regularization term with hyperparameter λR. Inspired by softmovement (Sanh et al., 2020), it uses a regularization term to decrease S to increase sparsity with the thresholding masking function.We find the regularization term is also important in our method. Since λR is large enough in our method, the most important scores in S are less than zero when the current sparsity level vtis close to vf . Due to the gradient ∂R(S) ∂Si,j= ∂σ(Si,j ) ∂Si,jincreases with the increase of Si,j when Si,j < 0, scores corresponding to the remaining weights will have a larger penalty than removed weights. It encourages the M to be changed when vtis almost reached or reached vf . For the settings with knowledge distillation, we simply add a distillation loss LKD in L following (Sanh et al., 2020; Xu et al., 2022): $${\mathcal{L}}_{\mathrm{KD}}=D_{\mathrm{KL}}\left(\mathbf{p}_{s}\|\mathbf{p}_{t}\right)$$ LKD = DKL (ps∥pt) (7) where DKL is the KL-divergence. ps and pt are output distributions of the student model and teacher model. ## 5 Experiments 5.1 Datasets To show the effectiveness of our method, we use three common benchmarks: nature language inference (MNLI) (Williams et al., 2018), question similarity (QQP) (Aghaebrahimian, 2017) and question answering (SQuAD) (Rajpurkar et al., 2016) following Sanh et al. Moreover, we also use GLUE benchmark (Wang et al., 2019) to validate the performance of our method at low sparsity. ## 5.2 Experiment Setups Following previous pruning methods, we use bert-base-uncased to perform task-specific pruning and report the ratio of remaining weight in the encode. For the task-specific head, we initial it according to the label words of each task following (Gao et al., 2021). For SQuAD, we use "yes" and "no" token embeddings as the weights for starting and ending the classification of answers. We freeze all weights of BERT including the task-specific head and only fine-tuning mask. The optimizer is Adam with a learning rate of 2e-2. The hyperparameter λR of the regularization term is 400. We set 12 epochs for MNLI and QQP, and 10 epochs for SQuAD with bath size 64. For tasks at low sparsity (more than 70% remaining weights), we set N in cubic sparsity scheduling to 7 epochs. For tasks at high sparsity, we set N to 3500 steps. We also report the performance of bert-base-uncased and roberta-base with 80% remaining weights for all tasks on GLUE with the same batch size and learning rate as above. For sparsity scheduling, we use the same scheduling for bert-base-uncased and a linear scheduling for roberta-base. N in sparsity scheduling is 3500. For the large tasks: MNLI, QQP, SST2 and QNLI, we use 12 epochs. For the small tasks: MRPC, RTE, STS-B and COLA, we use 60 epochs. Note that the above epochs have included pruning steps. For example, we use around 43 epochs to achieve target sparsity in MRPC. We search the pruning structure from local and global. ## 5.3 Baseline We compare our method with magnitude pruning (Han et al., 2015), L0-regularization (Louizos et al., 2018), movement pruning (Sanh et al., 2020) and CAP (Xu et al., 2022). We also compare our method with directly fine-tuning and super tickets (Liang et al., 2021) on GLUE. For super tickets, it finds that PLMs contain some subnetworks, which can outperform the full model by fine-tuning them. ## 5.4 Experimental Results Table 1 shows the results of SMP and other pruning methods at high sparsity. We implement SMP with the local masking function (SMP-L) and our proposed masking function (SMP-S). SMP-S and SMP-L consistently achieve better performance than other pruning methods without knowledge distillation. Although movement pruning and SMP-L use the same local masking function, SMP-L can achieve more than 2.0 improvements on all tasks and sparsity levels in Table 1. Moreover, the gains are more significant at 3% remaining weights. For soft-movement pruning, which assigns the remaining weights of matrix nonuniformly like SMP-S, it even underperforms SMPL. Following previous works, we also report the results with knowledge distillation in Table 1. The improvement brought by knowledge distillation is also evident in SMP-L and SMP-S. For example, it improves the F1 of SQuAD by 3.3 and 4.1 for SMPL and SMP-S. With only 3% remaining weights, SMP-S even outperforms soft-movement pruning at 10% in MNLI and QQP. Compared with CAP, which adds contrastive learning objectives from | Methods | Remaining | New Params | Trainable | MNLI | QQP | SQuAD | |------------------------------------------|-------------|--------------|-------------|-----------|-----------|-----------| | Weights | Per Task | Params | MACC/MMACC | ACC/F1 | EM/F1 | | | BERTbase | 100% | 110M | 110M | 84.5/84.9 | 91.4/88.4 | 80.4/88.1 | | Without Knowledge Distillation | | | | | | | | Movement (Sanh et al., 2020) | 10% | 8.5M + θM | 170M | 79.3/79.5 | 89.1/85.5 | 71.9/81.7 | | Soft-Movement (Sanh et al., 2020) | 10% | 8.5M + θM | 170M | 80.7/81.1 | 90.5/87.1 | 71.3/81.5 | | SMP-L (Our) | 10% | θM | 85M | 82.0/82.3 | 90.8/87.7 | 75.0/84.3 | | SMP-S (Our) | 10% | θM | 85M | 82.5/82.3 | 90.8/87.6 | 75.1/84.6 | | Movement (Sanh et al., 2020) | 3% | 2.6M+θM | 170M | 76.1/76.7 | 85.6/81.0 | 65.2/76.3 | | Soft-Movement (Sanh et al., 2020) | 3% | 2.6M+θM | 170M | 79.0/79.6 | 89.3/85.6 | 69.5/79.9 | | SMP-L (Our) | 3% | θM | 85M | 80.6/81.0 | 90.2/87.0 | 70.7/81.0 | | SMP-S (Our) | 3% | θM | 85M | 80.9/81.1 | 90.3/87.1 | 70.9/81.4 | | With Knowledge Distillation | | | | | | | | Movement (Sanh et al., 2020) | 50% | 42.5M+θM | 170M | 82.5/82.9 | 91.0/87.8 | 79.8/87.6 | | CAP (Xu et al., 2022) | 50% | 42.5M+θM | 170M | 83.8/84.2 | 91.6/88.6 | 80.9/88.2 | | SMP-L (Our) | 50% | θM | 85M | 85.3/85.6 | 91.6/88.7 | 82.2/89.4 | | SMP-S (Our) | 50% | θM | 85M | 85.7/85.5 | 91.7/88.8 | 82.8/89.8 | | Magnitude (Han et al., 2015) | 10% | 8.5M+θM | 85M | 78.3/79.3 | 79.8/75.9 | 70.2/80.1 | | L0-regularization (Louizos et al., 2018) | 10% | 8.5M+θM | 170M | 78.7/79.7 | 88.1/82.8 | 72.4/81.9 | | Movement (Sanh et al., 2020) | 10% | 8.5M+θM | 170M | 80.1/80.4 | 89.7/86.2 | 75.6/84.3 | | Soft-Movement (Sanh et al., 2020) | 10% | 8.5M+θM | 170M | 81.2/81.8 | 90.2/86.8 | 76.6/84.9 | | CAP (Xu et al., 2022) | 10% | 8.5M+θM | 170M | 82.0/82.9 | 90.7/87.4 | 77.1/85.6 | | SMP-L (Our) | 10% | θM | 85M | 83.1/83.1 | 91.0/87.9 | 78.9/86.9 | | SMP-S (Our) | 10% | θM | 85M | 83.7/83.6 | 91.0/87.9 | 79.3/87.2 | | Movement (Sanh et al., 2020) | 3% | 2.6M+θM | 170M | 76.5/77.4 | 86.1/81.5 | 67.5/78.0 | | Soft-Movement (Sanh et al., 2020) | 3% | 2.6M+θM | 170M | 79.5/80.1 | 89.1/85.5 | 72.7/82.3 | | CAP (Xu et al., 2022) | 3% | 2.6M+θM | 170M | 80.1/81.3 | 90.2/86.7 | 73.8/83.0 | | SMP-L (Our) | 3% | θM | 85M | 80.8/81.2 | 90.1/87.0 | 74.0/83.4 | | SMP-S (Our) | 3% | θM | 85M | 81.8/82.0 | 90.5/87.4 | 75.0/84.1 | teacher models, our method consistently yields significant improvements without auxiliary learning objectives. For 50% remaining weights, SMPS in MNLI achieves 85.7 accuracy compared to 84.5 with full-model fine-tuning, while it keeps all weights of BERT constant. Our method is also parameter efficient. Compared with other first-order methods, we can save half of the trainable parameters by keeping the whole BERT and task-specific head frozen. For new parameters of each task, it is also an important factor affecting the cost of transferring and storing subnetworks. Our method only introduces a binary mask θM as new parameters for each task at different sparsity levels, while other methods need to save both θM and the subnetwork. With remaining weights of 50%, 10%, and 3%, we can save 42.5M, 8.5M, and 2.6M parameters respectively compared with other pruning methods. Figure 1 shows more results from 3% remaining weights to 80% by comparing our method with first-order methods: movement pruning and softmovement pruning, and the zero-order pruning method: magnitude pruning. We report the results of our method at 3%, 10%, 30%, 50% and 80% remaining weights. Previous first-order methods such as movement pruning underperform magnitude pruning at remaining weights of more than 25% in MNLI and SQuAD. Even under high sparsity level like 20% remaining weights, magnitude pruning still strongly outperforms both movement pruning and soft-movement pruning in Figure 1 1For example at 3% remaining weights, we can reduce the size of θM to approximately 20% of its original size through compression. This means that merely around 0.55M new parameters are introduced at 3% remaining weights. Additionally, the compressed θM can be found at https: //github.com/kongds/SMP/releases. ![5_image_0.png](5_image_0.png) Remaining New Params MNLI SST-2 MRPC CoLA QNLI QQP RTE STS-B Weights Per Task MACC ACC ACC MCC ACC ACC ACC P Corr Avg. BERT 100% 110M 84.5 92.9 87.7 58.1 92.0 91.4 71.1 91.2 83.6 SuperT 86.8% 98M + θM 84.5 93.4 86.2 58.8 91.3 91.3 72.5 89.8 83.5 SMP (Our) 80% θM 85.0 92.9 87.0 61.5 91.5 91.4 72.3 89.6 83.9 RoBERTa 100% 125M 87.6 94.8 90.2 63.6 92.8 91.9 78.7 91.2 86.4 SMP (Our) 80% θM 87.6 94.9 89.9 65.4 92.8 91.9 81.5 91.1 86.9 (c). This shows the limitation of current first-order methods that performing ideally only at very high sparsity compared to zero-order pruning methods. However, SMP-L and SMP-S as first-order methods can constantly show better performance than magnitude pruning at low sparsity. For the results without knowledge distillation, SMP-S and SMPL achieve similar performance of soft-movement pruning with much less remaining weights. Considering to previous LTH in BERT, we find SMP-S can outperform full-model fine-tuning at a certain ratio of remaining weights in Figure 1 (a), (b) and (c), indicating that BERT contains some subnetworks that outperform the original performances without fine-tuning. For the results with knowledge distillation, SMP-S and SMP-L benefit from knowledge distillation at all sparsity levels. After removing even 70% weights from the encoder, our method still strongly outperforms full-model fine-tuning. We also validate our method on GLUE and report the results at 80% remaining weights in Table 2. Compared to full-model fine-tuning, our method achieves better performance on two PLMs by only removing 20% parameters in the encoder while keeping the remaining parameters unchanged. Compared to SuperT, which searches 8 different sparsity levels for each task, our method achieves better performance by using the same sparsity levels. In addition, our method also saves more than 98M new parameters per task compared to SuperT. | Masking | MNLI | SQuAD | | | | | | | |-----------|----------------|-----------|------|------|------|------|------|------| | Function | 80% | 10% | 3% | 80% | 10% | 3% | | | | T | σ(S(·) l ) > τ | N/A | N/A | N/A | N/A | N/A | N/A | | | G | S(·) l ≥ S v | 85.0 | 81.0 | 80.1 | 88.2 | 83.1 | 79.3 | | | l ) | 84.8 | 82.0 | 80.6 | 88.0 | 84.3 | 81.0 | | | | L | Topv (S(·) | | | | | | | | | S | Topv | (S(·) l ) | 85.0 | 82.5 | 80.9 | 88.3 | 84.6 | 81.4 | | l (·) | | | | | | | | | ## 6 Analysis 6.1 Masking Function In this section, we discuss the influence of different masking functions. Table 3 shows the results of different masking functions on our method without knowledge distillation. Contrary to previous pruning methods, the thresholding masking function T fails to converge in our method due to the difficulty in controlling the sparsity during training. For global masking function G, we sort all 85M BERT encoder weights and remain Top v% weights in each training step. Compared to local masking functions L, G takes more than twice the training times due to the computational cost of sorting 85M weights. Although it took the longest to train, it still underperforms L at 10% and 3% remaining weights. Contrary to G, our proposed masking function S outperforms L without additional training time since S directly assigns the remaining weights of each matrix. More results of masking functions S and L are also available in Table 1 and Figure 1. Figure 2 displays the distribution of remaining weights in different layers in MNLI with 10% remaining weights. We find G assigns too many remaining weights for WU and WV , which are four times larger than other matrices. It causes other weight matrices such as WQ to be more sparse than S and L. Following previous studies (Sanh et al., 2020; Mallya and Lazebnik, 2018), we also find that overall sparsity tends to increase with the ![6_image_0.png](6_image_0.png) (g) Overall depth of the layer. However, only WU and WV follow this pattern in all three matrices. Since WU and WV occupy more than 60% of the weight in each layer, it causes the overall distribution of each layer also follows their trend as well. To understand the behavior of attention heads, we also display the remaining weights ratio of each head in Figure 3. Each row represents a matrix containing 12 heads. Due to space limitation and the similar distribution between WQ and WK, we only show WQ and WV . Instead of assigning sparsity uniformly to each head, the sparsity of each head is not uniform in three masking functions, with most heads having only below 1% or below remaining weights. Furthermore, three masking functions show similar patterns even with different ways of assigning remaining weights. For our masking function S, S can assign more remaining weights to important heads compared to L, and some heads in WQ achieve more than 60% remaining weights at 9th layer. For global masking function G, due to most of remaining weights being assigned to WU and WD, the average remaining weights ratio of WQ and WV in G are only 3.2% and 2.8%, which causes G to underperform other masking functions. ## 6.2 Task-Specific Head To validate the effectiveness of our task-specific head initialization method, we compare it with training from scratch. | MNLI | SQuAD | | | | | | |----------------|---------|------|------|------|------|------| | 80% | 10% | 3% | 80% | 10% | 3% | | | From scratch | 84.6 | 81.7 | 80.5 | 87.5 | 84.2 | 80.7 | | Initialization | 84.8 | 82.0 | 80.6 | 88.0 | 84.3 | 81.0 | ![7_image_0.png](7_image_0.png) Table 4 shows the results of SMP-L in MNLI and SQuAD with 80%, 10% and 3% remaining weights. For training from scratch, we randomly initial the head and fine-tune it with the learning rate of 3e5 following previous pruning methods. Results show our method achieves better performance with task-specific heads frozen. ## 6.3 Training Objective Regularization term in training objective is a key factor for our method. We find that our method is hard to converge at high sparsity without regularization term R in Table 5. With the increase of sparsity, the performance gap between with and without R sharply increases. SMP-L without R even fails to converge at 10% and 3% remaining weights in SQuAD. | MNLI | SQuAD | | | | | | |--------|---------|------|------|------|------|------| | 80% | 10% | 3% | 80% | 10% | 3% | | | SMP-L | 84.8 | 82.0 | 80.6 | 88.0 | 84.3 | 81.0 | | w/o R | 84.2 | 80.1 | 69.2 | 86.6 | N/A | N/A | Table 5: Influence of regularization term. R refers to the regularization term. N/A refers to unable convergence. As analyzed in section 4.3, we find the remaining weights in attention heads are more uniform without R. For example, the standard deviation of remaining weights in each attention head is 3.75 compared to 12.4 in SMP-L with R in MNLI with 10% remaining weights. In other words, without R, it cannot assign more remaining weights to important heads as in Figure 3. ## 7 Conclusion In this paper, we propose a simple but effective task-specific pruning method called Static Model Pruning (SMP). Considering previous methods, which perform both pruning and fine-tuning to adapt PLMs to downstream tasks, we find finetuning can be redundant since first-order pruning already converges PLMs. Based on this, our method focuses on using first-order pruning to replace finetuning. Without fine-tuning, our method strongly outperforms other first-order methods. Extensive experiments also show that our method achieves state-of-the-art performances at various sparsity. For the lottery ticket hypothesis in BERT, we find it contains sparsity subnetworks that achieve original performance without training them, and these subnetworks at 80% remaining weights even outperform fine-tuned BERT on GLUE. ## 8 Limitation Like all unstructured pruning methods, SMP is hard to achieve inference speedup compared to structured pruning methods. Since SMP prunes model without fine-tuning, this also limits the extension of SMP to structured pruning methods. However, we find that most rows of the sparsity matrices in SMP are completely pruned at high sparsity level. This allows us to directly compress the size of matrices, resulting in faster inference. For example, the 3% remaining weights model of MNLI can be compressed to 47.43% of the model actual size (resulting in around 1.37× inference speedup) without retraining or performance loss. By removing rows of matrices that contain less than 10 remaining weights, we can further compress it to 25.19% actual size (1.76× inference speedup) with 0.9 accuracy drop. We expect that a carefully designed loss function during training could result in even smaller actual model size and faster inference speedup, which we leave it in the future. ## 9 Acknowledgments The research work is supported by the National Key Research and Development Program of China under Grant No. 2021ZD0113602, the National Natural Science Foundation of China under Grant Nos. 62276015, 62176014, the Fundamental Research Funds for the Central Universities. ## References Ahmad Aghaebrahimian. 2017. Quora question answer dataset. In International Conference on Text, Speech, and Dialogue, pages 66–73. Springer. Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432. 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Q-bert: Hessian based ultra low precision quantization of bert. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In *International Conference on Learning Representations* (ICLR). Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020. Minilm: Deep selfattention distillation for task-agnostic compression of pre-trained transformers. In *Advances in Neural* Information Processing Systems (NeurIPS). Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In *NAACL*. Mengzhou Xia, Zexuan Zhong, and Danqi Chen. 2022. Structured pruning learns compact and accurate models. *arXiv preprint arXiv:2204.00408*. Runxin Xu, Fuli Luo, Chengyu Wang, Baobao Chang, Jun Huang, Songfang Huang, and Fei Huang. 2022. From dense to sparse: Contrastive pruning for better pre-trained language model compression. In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI). ## A Standard Deviation Of Tasks 50% 10% 3% 10% 3% MNLI SMP-L 0.17 0.26 0.19 0.27 0.20 MACC std. SMP-S 0.13 0.24 0.30 0.25 0.28 QQP SMP-L 0.04 0.01 0.08 0.06 0.01 ACC std. SMP-S 0.02 0.03 0.02 0.01 0.02 SQuAD SMP-L 0.17 0.09 0.03 0.36 0.01 F1 std. SMP-S 0.10 0.07 0.02 0.42 0.07 with KD without KD Table 6: Standard deviation of Table 1 Table 7: Standard deviation of Table 2 We also report our standard deviation of tasks from 5 random runs in Table 6 and 7. | SMP(BERT) | SMP(RoBERTa) | | |-------------|----------------|------| | MNLI | 0.15 | 0.12 | | QNLI | 0.15 | 0.11 | | QQP | 0.03 | 0.14 | | SST2 | 0.36 | 0.28 | | MRPC | 1.21 | 0.44 | | COLA | 0.69 | 0.65 | | STSB | 0.14 | 0.16 | | RTE | 1.59 | 0.74 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Left blank. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. 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Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** Left Blank. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. ✓ C3. 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fernandes-etal-2023-translation

When Does Translation Require Context? A Data-driven, Multilingual Exploration

https://aclanthology.org/2023.acl-long.36

Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics. Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. In this paper, we develop the Multilingual Discourse-Aware (MuDA) benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena in any given dataset. The choice of phenomena is inspired by a novel methodology to systematically identify translations that require context. This methodology confirms the difficulty of previously studied phenomena while uncovering others which were not previously addressed. We find that commonly studied context-aware MT models make only marginal improvements over context-agnostic models, which suggests these models do not handle these ambiguities effectively. We release code and data for 14 language pairs to encourage the MT community to focus on accurately capturing discourse phenomena. Code available at \url{https://github.com/neulab/contextual-mt}

## When Does Translation Require Context? A Data-Driven, Multilingual Exploration Patrick Fernandes1,2,3∗ Kayo Yin4∗ **Emmy Liu**1 André F. T. Martins2,3,5 **Graham Neubig**1 1Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 2Instituto Superior Técnico & LUMLIS (Lisbon ELLIS Unit), Lisbon, Portugal 3Instituto de Telecomunicações, Lisbon, Portugal 4University of California, Berkeley 5Unbabel, Lisbon, Portugal [email protected] [email protected] ## Abstract Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation quality metrics. Recent works in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. In this paper, we develop the Multilingual Discourse-Aware (MUDA) benchmark, a series of taggers that identify and evaluate model performance on discourse phenomena in any given dataset. The choice of phenomena is inspired by a novel methodology to systematically identify translations requiring context. We confirm the difficulty of previously studied phenomena while uncovering others that were previously unaddressed. We find that common context-aware MT models make only marginal improvements over context-agnostic models, which suggests these models do not handle these ambiguities effectively. We release code and data for 14 language pairs to encourage the MT community to focus on accurately capturing discourse phenomena.1 ## 1 **Introduction** In order to properly translate discourse phenomena including anaphoric pronouns, lexical cohesion, and discourse markers, a machine translation (MT) model must use information from previous utterances (Guillou et al., 2018; Läubli et al., 2018; Toral et al., 2018). However, while generating proper translations of these phenomena is important for comprehension, they represent a small portion of words in natural language. Therefore, common metrics such as BLEU (Papineni et al., 2002) cannot be used to judge the quality of discourse translation. | Dataset | Lang. | Phenomena | |--------------------------------|------------------------------------------|-----------------------------------------| | Müller et al. (2018) | EN → DE | Pronouns | | Bawden et al. (2018) | EN → FR | Pronouns, Coherence Lexical Consistency | | Voita et al. (2018) | Pronouns | | | Voita et al. (2019b) | EN → RU | Deixis, Ellipsis | | Lexical Consistency | | | | Jwalapuram et al. (2020) | DE → EN | Pronouns, Coherence Lexical Consistency | | FR → EN RU → EN | Discourse Connectives Pronouns, Ellipsis | | | Our Work | 14 Pairs (§5) | Formality | | Lexical Consistency Verb Forms | | | Table 1: Some representative works on contextual machine translation that perform evaluation on discourse phenomena, contrasted to our work. For a more complete review see Maruf et al. (2021). Recent work on neural machine translation (NMT) models that attempt to incorporate extrasentential context (Tiedemann and Scherrer, 2017; Miculicich et al., 2018; Maruf and Haffari, 2018, inter alia) often perform targeted evaluation of certain discourse phenomena, mostly focusing on ellipsis, formality (Voita et al., 2019b,a), and pronoun translation (Müller et al., 2018; Bawden et al., 2018; Lopes et al., 2020). However, only a limited set of discourse phenomena for a few language pairs have been studied (see summary in Table 1). The difficulty of broadening these studies stems from the reliance of previous work on introspection and domain knowledge to identify the relevant discourse phenomena, frequently involving expert speakers, which then requires engineering complex language-specific methods to create test suites or manually designing data for evaluation. In this paper, we identify sentences that contain discourse phenomena through a *data-driven, semiautomatic methodology*. We apply this method to create a *multilingual benchmark testing discourse* phenomena in the domain of MT. First, we develop P-CXMI (§2) as a metric to identify when context is helpful in MT, or more broadly text generation in general. Then, we perform a systematic analysis of words with high P-CXMI to find categories of trans606 lations where context is useful (§3). We identify novel discourse phenomena that to our knowledge have not been addressed previously (e.g. consistency of verb forms), without requiring *a-priori* language-specific knowledge. Finally, we design a series of methods to automatically tag words belonging to the identified classes of ambiguities (§4) and we evaluate existing translation models for different categories of ambiguous translations (§5). We examine a parallel corpus spanning 14 language pairs, measuring translation ambiguity and model performance. We find that the contextaware methods, while improving on standard evaluation metrics, only perform significantly better than context-agnostic baselines for certain discourse phenomena in our benchmark. Our benchmark provides a more fine-grained evaluation of translation models and reveals weaknesses of context-aware models, such as verb form cohesion. We also find that DeepL, a commercial document-level translation system, does better in our benchmark than its sentence-level ablation and Google Translate. We hope that the released benchmark and code, as well as our findings, will spur targeted evaluation of discourse phenomena in MT to cover more languages and more phenomena in the future. ## 2 **Measuring Context Usage** 2.1 **Cross-Mutual Information** Past work on contrastive evaluation has examined correct and incorrect translations of specific discourse phenomena (Bawden et al., 2018; Müller et al., 2018), but this provides only a limited measure of context usage on phenomena defined by the creators of the dataset. We are therefore interested in devising a metric that is able to capture all context usage by a model, beyond a predefined set. Conditional Cross-Mutual Information (CXMI) (Bugliarello et al., 2020; Fernandes et al., 2021) measures the influence of context on model predictions at the corpus level. CXMI is defined as: $$\begin{array}{c}{{\mathrm{CXMI}(C\to Y|X)=}}\\ {{\mathrm{H}_{q_{M T_{A}}}(Y|X)-\mathrm{H}_{q_{M T_{C}}}(Y|X,C),}}\end{array}$$ where X and Y are a source and target sentence, respectively, C is the context, HqMTA is the entropy of a *context-agnostic* MT model, and HqMTC refers to a *context-aware* MT model. This quantity can be estimated over a held-out set with N sentence pairs and their respective context as: $$\mathrm{CXMI}(C\to Y|X)\approx1$$ $$\begin{array}{c}{{(C\to T\mid X)\approx}}\\ {{\qquad-\frac{1}{N}\sum_{i=1}^{N}\log\frac{q_{M T_{A}}(y^{(i)}|x^{(i)})}{q_{M T_{C}}(y^{(i)}|x^{(i)},C^{(i)})}}}\end{array}$$ Importantly, the authors find that training a *single* model qMT as both the context-agnostic and context-aware model ensures that non-zero CXMI values are due to context and not other factors (see Fernandes et al. (2021) and §3.1 for details). Although this approach is promising, it is defined only at a *corpus level*: as the previous equation shows, CXMI is estimated by over a full set of sentences. Since we are interested in measuring how important context is for single sentences or words within a sentence, we extend this definition to capture lower-level context dependency in the next section. ## 2.2 **Context Usage Per Sentence And Word** Pointwise Mutual Information (P-MI) (Church and Hanks, 1990) measures the association between two random variables for *specific* outcomes. Mutual information can be seen as the expected value of P-MI over all possible outcomes of the variables. Taking inspiration from this, we define the **Pointwise Cross-Mutual Information** (P-CXMI) for a source, target, context triplet (*x, y, C*) as: $$\mathrm{\bfP-CXMI}(y,x,C)=-\log\frac{q_{M T_{A}}(y|x)}{q_{M T_{C}}(y|x,C)}$$ Intuitively, P-CXMI measures how much more (or less) likely a target sentence y is when it is given context C, compared to not being given that context. Note that this is estimated *according to* the models qMTA and qMTC since, just like CXMI, this measure depends on their learned distributions. We can also apply P-CXMI at the *word level* to measure how much more likely a particular word in a sentence is when it is given the context, by leveraging the auto-regressive property of the neural decoder. Given the triplet (*x, y, C*) and the word index i, we can measure the P-CXMI for that particular word as: $$\mathrm{\bfP-CXMI}(i,y,x,C)=-\log\frac{q_{M T_{A}}(y_{i}|y_{t<i},x)}{q_{M T_{C}}(y_{i}|y_{t<i},x,C)}$$ Note that nothing constrains the form of C or even x and P-CXMI can, in principle, be applied to any conditional language modelling problem. | Avelile's mother had HIV virus. Avelile had the virus, she was born with the virus. | Lexical Cohesion | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------| | 阿维利尔的母亲是携有艾滋病病毒。阿维利尔也有艾滋病病毒。她一生下来就有。 | | | Your daughter? Your niece? | Formality | | Votre fille ? Votre nièce ? | (T-V) | | Roger. I got'em. Two-Six, this is Two-Six , we're mobile. | Formality | | 了解捕捉した。 2-6 こちら移動中だ。 | (Honorifics) | | Our tools today don't look like shovels and picks. They look like the stuff we walk around with. | Pronouns | | As ferramentas de hoje não se parecem com pás e picaretas. Elas se parecem com as coisas que usamos. Louis XIV had a lot of people working for him. They made his silly outfits, like this. | Verb Form | | Luis XIV tenía un montón de gente trabajando para él. Ellos hacían sus trajes tontos, como éste. They're the ones who know what society is going to be like in another generation. I don't. | Ellipsis | | Ancak onlar ba¸ska bir nesilde toplumun nasıl olaca˘gını biliyorlar. Ben bilmiyorum. | | Table 2: Examples of high P-CXMI tokens and corresponding linguistic phenomena. Contextual sentences are italicized. The high P-CXMI target token is highlighted in pink, source and contextual target tokens related to the high P-CXMI token are highlighted in blue and green respectively. We use this metric to find words that are strongly context-dependent, which is to say that their likelihood increases greatly with context relative to other words. These words are the ones that likely correspond to discourse phenomena. ## 3 **Which Translation Phenomena Benefit** From Context? To identify salient translation phenomena that require context, we perform a *thematic analysis* (Braun and Clarke, 2006), examining words with high P-CXMI across different language pairs and manually identifying patterns and categorizing them into phenomena where context is useful for translation. To do so, we systematically examined (1) the mean P-CXMI per part-of-speech (POS) tag, (2) the words with the highest mean P-CXMI across the corpus, and (3) the individual words with the highest P-CXMI in a particular sentence. ## 3.1 **Data & Model** To compare linguistic phenomena that arise during document-level translation across language pairs, we use a dataset consisting of TED talks' transcripts and translations (Qi et al., 2018). We use this dataset due to its abundance of discourse phenomena, as well as its availability across many parallel languages. We study translation between English and Arabic, German, Spanish, French, Hebrew, Italian, Japanese, Korean, Dutch, Portuguese, Romanian, Russian, Turkish and Mandarin Chinese. These 14 target languages are chosen for their high availability of TED talks and linguistic tools, as well as for the diversity of language types in our comparative study (Table 4 in Appendix B). For each language pair, our dataset contains 113,711 parallel training sentences from 1,368 talks, 2,678 development sentences from 41 talks, and 3,385 testing sentences from 43 talks. To obtain the P-CXMI for words in the data, we train a small Transformer (Vaswani et al., 2017) model for every target language and incorporate the target context by concatenating it with the current target sentence (Tiedemann and Scherrer, 2017). We train the model with *dynamic* context size (Fernandes et al., 2021), by sampling 0-3 target context sentences and estimating P-CXMI by using this model for qMTA and qMTC (details in Appendix G). ## 3.2 **Analysis Procedure** We start our analysis by studying POS tags with high mean P-CXMI. In Appendix C, we report the mean P-CXMI for selected POS tags on test data. Some types of ambiguity, such as dual form pronouns (§3.3), can be linked to a single POS tag and be identified at this step, whereas others require finer inspection. Next, we inspect the vocabulary items with high mean P-CXMI. At this step, we can detect phenomena that are reflected by certain lexical items that consistently benefit from context for translation. Finally, we examine individual tokens that obtain the highest P-CXMI. In doing so, we identify patterns that do not depend on lexical features, but rather on syntactic constructions for example. In Table 2, we provide selected examples of tokens that have high P-CXMI and the discourse phenomenon we have identified from them. ## 3.3 **Identified Phenomena** Through our thematic analysis of items with high P-CXMI, we identified various types of translation ambiguity. Unlike previous work, our method requires no prior knowledge of languages and easily scales to new languages (§4.4). Although this procedure may find phenomena that are intuitive to the annotators, the data-driven approach makes confirmation bias less severe than works relying on introspection. Hence, our procedure can allow us to discover relevant phenomena that have not been previously addressed, such as verb forms. Examples of each phenomenon are given in Table 2. ## 3.3.1 **Lexical Cohesion** Entities may have multiple possible translations in the target language, but the same entity should be referred to by the same word in a translated document. This is called lexical cohesion. ## 3.3.2 **Formality** We identify two phenomena which fall under the general category of formality. First, several languages we examined have a T-V distinction (Appendix B, "Pronouns Politeness") in which the second-person pronouns a speaker uses to refer to someone depend on the relationship between the speaker and the addressee. Second, languages such as Japanese and Korean use honorifics to indicate formality, which are special titles or words expressing courtesy or respect for position. ## 3.3.3 **Pronoun Choice** Unlike in English, many languages use gendered pronouns for pronouns other than the third-person singular, or assign gender based on formal rules rather than semantic ones. In order to assign the correct pronoun, it is therefore necessary to use the previous context to distinguish the grammatical gender of the antecedent. ## 3.3.4 **Verb Form** While English verbs may have five forms 2, other languages may have a more fine-grained verb morphology. For example, English has only a single form for the past tense, while the Spanish past tense consists of six verb forms. Verbs must be translated using the verb form that reflects the tone, mood and cohesion of the document. ## 3.3.5 **Ellipsis** Ellipsis refers to the omission of superfluous words that are able to be inferred from the context. For instance, in the last row of Table 2, the English 2(e.g. *write, writes, wrote, written, writing*) text does not repeat the verb *know* in the second sentence as it can be understood from the previous sentence. However, in Turkish, there is no natural way to translate the verb-phrase ellipsis, so context is important for translating the verb correctly. ## 4 **Cross-Phenomenon Mt Evaluation** Next, we we develop a series of methods to automatically tag tokens belonging to these classes of ambiguous translations and propose the Multilingual Discourse-Aware (MuDA) benchmark for context-aware MT models. ## 4.1 **Mt Evaluation Framework** Given a pair of parallel source and target documents (*X, Y* ), our MuDA tagger assigns one or more tags from a set of discourse phenomena {t 1 i , · · · , tn i} to each target token yi ∈ Y . Using the compare-mt toolkit (Neubig et al., 2019), we compute the mean word f-measure of system outputs compared to the reference for each tag. This allows us to identify which discourse phenomena models can translate more or less accurately. ![3_image_0.png](3_image_0.png) Figure 1: Number of MuDA tags on TED test data. Exact numbers of each tag are given in Appendix D. Number of tags for other document-level datasets can be found in Appendix E. ## 4.2 **Automatic Tagging** We now describe our taggers for each identified discourse phenomenon. Note that these do not require C-XMI to be calculated, and are based on reliable methods for identifying each phenomenon mentioned in subsection 3.3. For formality, pronoun choice and verb form, we created language-specific word lists that were verified by native speakers. Not all phenomena are present in each language. Phenomena that are absent are indicated in Appendix D, as a zero count for that language. Lexical Cohesion To tag words that require lexical cohesion, we first extract word alignments from a parallel corpus D = {(X1, Y1), · · · ,(X|D|, Y|D|)}, where (Xm, Ym) denote the source and target reference document pair. We use the AWESOME aligner (Dou and Neubig, 2021) to obtain: $$A_{m}=\{\langle x_{i},y_{j}\rangle\mid x_{i}\leftrightarrow y_{j},x_{i}\in X_{m},y_{j}\in Y_{m}\},$$ where each xi and yj are the lemmatized content source and target words and ↔ denotes a bidirectional word alignment. For each target word yj that is aligned to source word xi, if the alignment pair ⟨xi, yj ⟩ occurred at least 3 times already in the current document, excluding the current sentence, we tag yj for lexical cohesion 3. Formality For languages with T-V distinction, we tag the target pronouns containing formality distinction if there has previously been a word pertaining to the same formality level in the same document. Some languages such as Spanish often drop the subject pronoun, and T-V distinction is instead reflected in the verb form. For these languages, we use spaCy (Honnibal and Montani, 2017) and Stanza (Qi et al., 2020) to find POS tags and detect verbs with a second-person subject in the source, and conjugated in the second (T) or third (V) person in the target. For languages with more complex honorifics systems, such as Japanese, we construct a word list of common honorifics-related words to tag (details in Appendix F.3). Pronoun Choice To find pronouns in English that have multiple translations, we manually construct a list Pℓ = {⟨ps, pt⟩} for each language (Appendix F.2), where each ps is an English pronoun and pt the list of possible translations of ps in the language ℓ. Then, for each aligned token pair ⟨xi, yj ⟩, if xi, yj are both pronouns with ⟨xi, pt|yj ∈ pt⟩ ∈ Pℓ, and the antecedent of xi is not in current sentence, we tag yj as an ambiguous pronoun. To obtain antencedents, we use AllenNLP (Gardner et al., 2017)'s coreference resolution module. This procedure is similar to Müller et al. (2018). Verb Form For each target language, we define a list Vℓ = {v1, · · · , vk} of verb forms (Appendix F.3) where vi ∈ Vℓif there exists a verb form in English uj and an alternate verb form vk ̸= viin the target language such that an English verb with form uj may be translated to a target verb with form 3This threshold of 3 can also be changed within the tagger. vi or vk depending on the context. Then, for each target token yj , if yj is a verb of form vj ∈ Vℓ, and another verb with form vj has appeared previously in the same document, we tag yj as ambiguous. Ellipsis To detect translation ambiguity due to VP and NP ellipsis, we look for instances where the ellipsis occurs on the source side, but not on the target side, which means that the ellipsis must be resolved during translation. Since existing ellipsis models are limited to specific types of ellipsis, we first train an English (source-side) ellipsis detection model. To do so, we extract an ellipsis dataset from the English data in the Penn Treebank (Marcus et al., 1993) and train a BERT text classification model (Devlin et al., 2019), which achieves 0.77 precision and 0.73 recall (see Appendix F.4 for training details). Then, for each sentence pair where the source sentence is predicted to contain an ellipsis, we tag the word yj in the target sentence Ym if: (1) yj is a verb, noun, proper noun or pronoun; (2) yj has occurred in the previous target sentences of the same document; (3) yj is not aligned to any source words, that is, ̸ ∃ xi ∈ Xm s.t. ⟨xi, yj ⟩ ∈ Am. ## 4.3 **Evaluation Of Automatic Tags** We apply the MuDA tagger to the reference translations of our TED talk data. We thus obtain an evaluation set of 3,385 parallel sentences for each of the 14 language pairs. In Appendix C we report the mean P-CXMI for each language and MuDA tag. Overall, we find higher P-CXMI on tokens with a tag compared to those without, which provides empirical evidence that models indeed rely on context to predict words with MuDA tags. Appendix D shows that the frequency of tags varies significantly across languages. Overall, only 4.5% of the English sentences have been marked for ellipsis, giving an upper bound for the number of ellipsis tags in other languages. We find that languages from a different family than English have a relatively high number of ellipsis tags. We also find that Korean and especially Japanese have more formality tags than languages with T-V distinction, which reflects that register is more often important when translating to languages with honorifics. Manual Evaluation To evaluate our tagger, we asked native speakers with computational linguistics backgrounds to manually verify MuDA tags for 8 languages on 50 randomly selected utterances as well as all words tagged with *ellipsis* in our corpus. This allows us to measure how many automatic Table 3: Precision of MuDA tags on 50 utterances. | lexical | formality | pronouns | verb form | ellipsis | | |-----------|-------------|------------|-------------|------------|------| | es | 1.00 | 0.92 | 1.00 | 1.00 | 0.53 | | fr | 1.00 | 1.00 | 1.00 | 0.94 | 0.43 | | ja | 1.00 | 1.00 | 1.00 | - | 0.41 | | ko | 1.00 | 0.94 | - | - | 0.26 | | pt | 0.99 | 0.88 | 1.00 | - | 0.31 | | ru | 1.00 | 1.00 | - | 1.00 | 0.50 | | tr | 1.00 | 1.00 | - | 1.00 | 0.57 | | zh | 1.00 | 1.00 | - | - | 0.78 | tags violate the given definition of the linguistic tag. Table 3 reports the tags' precision 4. For all languages, we obtain high precision for all tags except *ellipsis*, confirming that the methodology can scale to languages where no native speakers were involved in developing the tags. For *ellipsis*, false positives often come from one-to-many or non-literal translations, where the aligner does not align all target words to the corresponding source word. We believe that the *ellipsis* tagger is still useful in selecting difficult examples that require context for translation; despite the low precision, we find a significantly higher P-CXMI on *ellipsis* words for many languages (Appendix C).5 ## 4.4 **Extension To New Languages** While MuDA currently supports 14 language pairs, our methodology can be easily extended to new languages. The *lexical* and *ellipsis* tags can be directly applied to other languages provided a word aligner between English and the new target language. The formality tag can be extended by adding a list of pronouns or verb forms related to formality in the new language. Similarly, the *pronouns* and verb forms tag can also be extended by providing a list of ambiguous pronouns and verb forms. Exhaustively listing all relevant phenomena in document-level MT is extremely complex and beyond the scope of our paper. To identify new discourse phenomena on other languages, our thematic analysis can be reused as follows: (1) Train a model with dynamic context size on translation between the new language pair; (2) Use the model to compute P-CXMI for words in a parallel documentlevel corpus of the language pair; (3) Manually analyze the POS tags, vocabulary items and individual tokens with high P-CXMI; (4) Link patterns of tokens with high P-CXMI to particular discourse phenomena by consulting linguistic resources. 4Workers were paid 20$/hour. 5Also note that wrongly assigned tags should also not penalize a system greatly as it should give a low score only if the translation does not match the falsely tagged word. ## 5 **Exploring Context-Aware Mt** Our MuDA tagger can be applied to documents in the supported languages to create benchmarking datasets for discourse phenomena during translation. We use our benchmark of the TED talk dataset enhanced with MuDA tags to perform an exploration of context usage across languages with 4 models, including commercial systems. ## 5.1 **Trained Models** We train a sentence-level and document-level concatenation-based small transformer (base) for every target language. While conceptually simple, concatenation approaches have been shown to outperform more complex models when properly trained. For the context-aware model, the major difference from §3.1 is that we use a *static* context size of 3, since we are not using these models to measure P-CXMI. (Lopes et al., 2020). To evaluate stronger models, we additionally train a large transformer model (large) that was pretrained on a large, sentence-level corpora, for German, French, Japanese and Chinese. Further details can be found in Appendix G. ## 5.2 **Commercial Models** To assess if commercially available machine translation engines are able to leverage context and therefore do well in MuDA, we consider two engines:6(1) the *Google Cloud Translation* v2 API. In early experiments, we assessed that this model only does sentence-level translation, but included it due to its widespread usage; (2) the *DeepL* v2 API. This model advertises its usage of context as part of translations and our experiments confirm this. Early experimentation with other providers (Amazon and Azure) indicated that these are not contextaware so we refrained from evaluating them. To obtain provider translations, we feed the documents into an API request. To re-segment the translation into sentences, we include special marker tokens in the source that are preserved during translation and split the translation on those tokens. We also evaluate a *sentence-level* version of DeepL where we feed each sentence separately to compare with its document-level counterpart. ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) ## Results And Discussion 5.3 Figure 2 shows results for base models, trained either without ( no-context ) or with context, and for the latter with either predicted ( context ) or reference context ( context-gold ) during decoding. Results are reported with respect to standard MT metrics BLEU (Papineni et al., 2002) and COMET (Rei et al., 2020), as well as the MuDA benchmark. The corpus-level metrics BLEU and COMET are calculated over the entire corpus, rather than just the sentences tagged by MuDA. First, we find that BLEU scores are highest for context-gold models for most language pairs, but context-agnostic models have higher COMET scores. Moreover, in terms of mean word f-measure overall, we do not find significant differences between the three systems. It is therefore difficult to see which system performs the best on documentlevel ambiguities using only corpus-level metrics. For words tagged by MuDA as requiring context for translation, context-aware models often achieve significantly higher word f-measure than contextagnostic models on certain tags such as ellipsis and formality , but not on other tags such as lexical and verb form. This demonstrates how MuDA allows us to clarify which inter-sentential ambiguities context-aware models are able to resolve. For the pretrained large models (Figure 3 ), context-aware models perform better than the context-agnostic on corpus-level metrics, especially COMET. On words tagged with MuDA, context-aware models generally obtain the high- ![7_image_0.png](7_image_0.png) est f-measure as well, particularly when given reference context, especially on phenomena such as lexical and *pronouns*, but improvements are less pronounced than on corpus-level evaluation. Among commercial engines (Figure 4), DeepL outperforms Google on most metrics and language pairs. The sentence-level ablation of DeepL performs worse than its document-level system for most MuDA tags. Current context-aware MT systems translate some inter-sentential discourse phenomena well, but are unable to consistently obtain significant improvements over context-agnostic counterparts on challenging MuDA data. Tables with all results can be found in Appendix H. ## 6 **Related Work** Several works have worked on measuring the performance of MT models on contextual discourse phenomena. The first example of this was done by Hardmeier et al. (2010), which evaluated automatically the precision and recall of pronoun translation in statistical MT systems. Jwalapuram et al. (2019) proposed evaluating models on pronoun translation based on a pairwise comparison between translations that were generated with and without context, and later Jwalapuram et al. (2020) extended this work to include more languages and phenomena in their automatic evaluation/test set creation. These works rely on prior domain knowledge and intuition to identify context-aware phenomena, whereas we take a systematic, data-driven approach. Most works have focused on evaluating performance in discourse phenomena through the use of *contrastive datasets*. Müller et al. (2018) automatically create a dataset for anaphoric pronoun resolution to evaluate MT models in EN → DE. Bawden et al. (2018) manually creates a dataset for both pronoun resolution and lexical choice in EN → FR. Voita et al. (2018, 2019b) creates a dataset for anaphora resolution, deixis, ellipsis and lexical cohesion in EN → RU. However, Yin et al. (2021) suggest that *translating* and *disambiguating* between two contrastive choices are inherently different, motivating our approach in measuring direct translation performance. ## 7 **Conclusions And Future Work** We investigate types of ambiguous translations where MT models benefit from context using our proposed P-CXMI metric. We perform a datadriven thematic analysis across 14 languages to identify context-sensitive discourse phenomena, some of which (such as *verb forms*) have not been previously addressed in work on MT. In comparison to previous work, our approach is systematic, extensible, and does not require prior knowledge of the language. Additionally, the P-CXMI metric can be used to identify other context-dependent words in generation. We construct the MuDA benchmark that tags words in parallel corpora and evaluates models on 5 context-dependent phenomena. Our evaluation reveals that context-aware and commercial translation systems achieve small improvements over context-agnostic models on our benchmark, and we encourage further development of models that improve on context-aware translation. ## Limitations While MuDA relies on set of hand-crafted rules for tagging specific phenomena, these rules might involve the use of other error-prone systems (such as coreference resolution and alignment models) and these errors might be susceptible to problems (such as lack of out-of-domain generalization) that could limit the applicability of our tagger. However, this could be fixed by extending MuDA to use newer and better versions of these systems. The use of F-1 per tag with surface-form matching between reference/translation can also lead to penalizing translations that use context correctly but choose other equivalent words. Nevertheless, this should also be mitigable by extending the scoring method to, for example, match synonyms. Finally, the benchmarking of context-aware models might not apply to newer, state-of-the-art translation models, especially if these leverage large language models that were trained on long-context data. ## Acknowledgements We would like to thank Uri Alon, Ipek Baris, George Bejinariu, Hiba Belkadi, Chloé Billiotte, Giovanni Campagna, Remi Castera, Volkan Cirik, Taisiya Glushkova, Junxian He, Mert Inan, Alina Karakanta, Benno Krojer, Emma Landry, Chanyoung Park, Artidoro Pagnoni, Maria Ryskina, Odette Scharenborg, Melanie Sclar, Jenny Seok, Emma Schippers, Bogdan Vasilescu for advice on various languages and help with manual annotations. We would also like to thank all the members of DeepSPIN and NeuLab who provided feedback on earlier versions of this work. 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In *Joint Conference of the 59th* Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACLIJCNLP), Virtual. | Language | Family | Word Order | Pronouns Politeness | Gendered Pronouns | Gender Assignment | |------------|---------------|--------------|-----------------------|---------------------|---------------------| | Arabic | Afro-Asiatic | VSO | None | 1 and/or 2 and 3 | Semantic-Formal | | English | Indo-European | SVO | None | 3.Sing | Semantic | | German | Indo-European | SOV/SVO | Binary | 3.Sing | Semantic-Formal | | Spanish | Indo-European | SVO | Binary | 1 and/or 2 and 3 | Semantic-Formal | | French | Indo-European | SVO | Binary | 3.Sing | Semantic-Formal | | Hebrew | Afro-Asiatic | SVO | None | 1 and/or 2 and 3 | Semantic-Formal | | Italian | Indo-European | SVO | Binary | 3.Sing | Semantic-Formal | | Japanese | Japonic | SOV | Avoided | 3 | None | | Korean | Koreanic | SOV | Avoided | 3.Sing | None | | Dutch | Indo-European | SOV/SVO | Binary | 3.Sing | Semantic-Formal | | Portuguese | Indo-European | SVO | Binary | 3.Sing | Semantic-Formal | | Romanian | Indo-European | SVO | Multiple | 3.Sing | Semantic-Formal | | Russian | Indo-European | SVO | Binary | 3.Sing | Semantic-Formal | | Turkish | Turkic | SOV | Binary | None | None | | Mandarin | Sino-Tibetan | SVO | Binary | 3.Sing | None | Table 4: Properties of the languages in our study. ## A **Muda Toolkit Usage** To tag an existing dataset and extract the tags for later use, run the following command: 1 python muda/main.py \ 2 --src /path/to/src \ | 3 | --tgt | /path/to/tgt | \ | |-----|-------------|---------------------|-----| | 4 | --docids | /path/to/docids | \ | | 5 | --dump-tags | /tmp/maia_ende.tags | \ | | 6 | --tgt-lang | lang | | To evaluate models on a particular dataset (reporting per-tag metrics dicussed in this paper), run the following command: 1 python muda/main.py \ 2 --src /path/to/src \ 3 --tgt /path/to/tgt \ 4 --docids /path/to/docids \ 5 --hyps /path/to/hyps.m1 /path/to/hyps.m2 \ 6 --tgt-lang lang ## B **Language Properties** Table 4 summarizes the properties of the languages analyzed in this work. ## C **P-Cxmi Results** Table 5 presents the average P-CXMI value per POS tag and per MuDA tag. ## D **Tag Numbers** Table 6 lists the counts of each tag per language. ## E **Tagging Other Document-Level Datasets** We report the number of tags found for two other document-level datasets commonly used in the literature: (1) IWSLT-17 (Cettolo et al., 2012) test sets for EN → DE and EN → FR and (2) A randomly subsampled portion of the news-commentary dataset for EN → {AR, DE, ES, FR, NL, PT, RU, ZH} (Barrault et al., 2019). These results can be found respectively in Figure 5 and Figure 6. ![12_image_0.png](12_image_0.png) ![12_image_1.png](12_image_1.png) ar de es fr he it ja ko nl pt ro ru tr zh CXMI 0.073 0.008 0.011 0.011 0.021 0.015 0.067 0.035 0.005 0.009 0.051 0.015 0.016 0.081 P-CXMI 0.075 0.005 0.011 0.021 0.023 0.016 0.059 0.038 0.002 0.013 0.049 0.015 0.014 0.057 ADJ 0.017 -0.014 -0.011 0.000 -0.037 -0.008 0.001 -0.002 -0.006 -0.005 0.020 0.015 -0.006 0.007 ADP 0.017 -0.001 -0.004 -0.004 -0.006 -0.005 0.005 0.014 -0.005 -0.001 0.011 -0.003 -0.005 -0.001 ADV 0.038 -0.011 0.008 0.002 0.007 0.005 0.005 -0.006 0.001 0.011 0.062 0.023 -0.013 0.009 AUX 0.053 0.010 0.002 0.010 0.008 0.036 0.012 0.032 0.010 0.010 0.048 0.045 0.055 0.007 CCONJ 0.044 0.025 0.024 0.005 0.012 0.043 0.034 -0.020 0.010 0.009 0.165 0.042 -0.007 -0.023 DET 0.006 0.004 0.006 0.002 -0.004 0.001 0.011 0.043 -0.007 0.002 0.046 0.018 0.011 0.008 INTJ -0.066 -0.024 0.013 0.010 -0.015 -0.087 0.004 0.037 -0.019 0.031 -0.041 -0.009 NOUN 0.012 -0.010 0.000 0.010 -0.001 0.000 -0.008 0.003 -0.011 -0.003 0.044 -0.010 -0.006 -0.002 NUM 0.011 -0.005 -0.005 -0.008 0.002 0.017 0.019 -0.046 -0.002 0.009 0.008 0.025 -0.000 0.004 PART 0.025 -0.007 0.029 0.063 -0.718 0.006 0.018 0.016 -0.006 PRON 0.019 0.014 -0.002 0.021 0.039 0.003 -0.009 0.047 0.006 0.013 0.029 0.023 0.000 0.023 PRON.1 0.015 0.011 0.009 0.015 0.043 0.021 0.008 0.015 0.046 0.015 -0.012 0.025 PRON.1.Plur 0.027 0.007 -0.002 0.008 0.082 0.004 0.045 0.012 0.013 -0.022 0.033 PRON.1.Sing -0.036 0.014 0.017 0.020 0.016 0.037 0.001 0.075 0.015 -0.006 PRON.2 0.040 0.222 -0.020 0.037 0.108 0.015 0.013 0.171 -0.017 0.103 -0.026 0.009 PRON.2.Plur 0.075 -0.055 -0.019 -0.008 0.088 0.011 -0.008 0.069 -0.024 PRON.2.Sing 0.009 0.226 -0.021 0.357 0.125 0.052 -0.033 0.412 -0.038 PRON.3 0.018 0.026 -0.009 0.024 0.031 -0.020 0.004 0.033 0.029 0.042 0.008 0.045 PRON.3.Dual 0.057 PRON.3.Plur 0.016 0.017 -0.021 0.037 0.050 0.024 0.058 0.062 0.038 0.047 0.038 PRON.3.Sing 0.017 0.032 0.000 0.030 0.026 0.009 0.014 0.046 0.044 -0.001 PRON.Plur 0.001 0.018 0.096 0.021 0.003 -0.027 PRON.Sing 0.002 -0.005 0.025 -0.004 0.005 0.002 0.007 PROPN 0.016 -0.014 -0.002 0.018 0.017 -0.016 -0.018 0.003 -0.005 -0.013 0.007 0.021 -0.014 0.005 PUNCT 0.129 0.007 0.012 0.001 0.019 0.019 0.353 0.017 0.018 0.021 0.005 0.017 0.022 0.106 SCONJ 0.137 -0.001 0.017 0.001 0.007 -0.000 0.004 0.005 0.005 0.003 0.044 -0.001 SYM 0.050 0.081 0.136 0.152 0.017 -0.034 -0.014 -0.010 -0.071 -0.040 0.015 VERB 0.042 0.006 0.004 0.003 0.007 0.004 0.008 0.036 0.002 0.005 0.047 0.015 0.014 0.015 VERB.Fut 0.043 0.004 0.019 0.008 -0.001 -0.018 0.007 VERB.Imp 0.039 0.010 0.057 0.029 0.069 VERB.Past 0.041 0.011 0.009 0.008 0.007 -0.001 0.005 -0.009 0.064 0.010 VERB.Pres 0.013 0.001 -0.001 -0.006 0.011 0.014 0.039 0.002 0.016 ellipsis 0.052 -0.053 -0.111 0.055 0.071 0.019 0.020 0.022 0.037 -0.070 0.111 -0.020 -0.041 0.082 formality 0.038 0.077 0.040 0.048 0.036 0.022 0.014 0.008 0.008 0.107 -0.073 0.012 lexical -0.006 0.003 0.011 -0.001 0.003 0.001 -0.007 -0.008 -0.004 0.002 0.034 -0.002 0.008 0.004 no tag 0.041 0.001 0.003 0.005 0.005 0.006 0.011 0.013 0.002 0.005 0.036 0.009 0.003 0.017 pronouns 0.028 0.068 -0.002 0.055 0.006 -0.027 0.055 0.008 verb form 0.042 0.009 0.009 0.041 -0.002 0.046 0.065 0.013 with tag -0.001 0.024 0.018 0.021 0.005 0.013 0.023 0.005 0.001 0.010 0.034 0.056 0.002 0.009 Table 5: P-CXMI for all POS tags and our ambiguity tags. In the top two rows, CXMI is the average of P-CXMI for each sentence across the corpus, and P-CXMI is the average of P-CXMI over all tokens in the corpus. Per-tag values are the average of P-CXMI for each token with the tag. The 3 highest P-CXMI scores are highlighted in varying intensities of green. ## F **Tagger Details** F.1 **Formality Words** Table 7 gives the list of words related to formality for each target language. ## F.2 **Ambiguous Pronouns** Table 8 provides English pronouns and the list of possible target pronouns. ## F.3 **Ambiguous Verbs** Table 9 lists verb forms that may require disambiguation during translation. ## F.4 **Ellipsis Classifier** We train a BERT text classification model (Devlin et al., 2019) on data from the Penn Treebank, where we labeled each sentence containing the tag '*?*' as containing ellipsis (Bies et al., 1995). We obtain 248,596 sentences total, with 2,863 tagged as ellipsis. Then, our model using HuggingFace Transformers (Wolf | pronouns | formality | verb form | lexical | ellipsis | | |---------------------------------------------------------------------------------------------------------------|-------------|-------------|-----------|------------|------| | ar | 90 | 0 | 0 | 116 | 982 | | de | 398 | 1000 | 0 | 19 | 1356 | | es | 245 | 86 | 409 | 15 | 1496 | | fr | 1591 | 839 | 1938 | 48 | 1586 | | he | 0 | 0 | 468 | 122 | 1210 | | it | 182 | 118 | 484 | 31 | 1320 | | ja | 245 | 3328 | 0 | 94 | 990 | | ko | 0 | 221 | 0 | 71 | 373 | | nl | 0 | 783 | 1060 | 27 | 1590 | | pt_br | 372 | 515 | 0 | 27 | 1677 | | ro | 60 | 407 | 792 | 53 | 1002 | | ru | 0 | 466 | 2091 | 41 | 668 | | tr | 0 | 30 | 47 | 137 | 704 | | zh_cn | 0 | 526 | 0 | 49 | 1092 | | Table 6: Total number of MuDA tags on TED test data. '0' indicates that the phenomenon does not apply to that | | | | | | Table 6: Total number of MuDA tags on TED test data. '0' indicates that the phenomenon does not apply to that language. et al., 2020). To address the imbalance in labels, we up-weight the loss for samples tagged as ellipsis by a factor of 100. ## G **Training Details** The *transformer-small* model has hidden size of 512, feedforward size of 1024, 6 layersa and 8 attention heads. The *transformer-large* model has hidden size of 1024, feedforward size of 4096, 6 layers, 16 attention heads. As in Vaswani et al. (2017), we train using the Adam optimizer with β1 = 0.9 and β2 = 0.98 and use an inverse square root learning rate scheduler, with an initial value of 10−4for large model and 5 × 10−4for the base and multi models, with a linear warm-up in the first 4000 steps. For the pretrained models we used Paracrawl (Esplà et al., 2019) for German and French, JParacrawl (Morishita et al., 2020) for Japanese and the Backtranslated News from WMT2021 for Chinese. Due to the sheer number of experiments, we use a single seed per experiment. We base our experiments on the framework *Fairseq* (Ott et al., 2019). ## H **Results Tables** | de | du sie | |------------------------------------------------------------------------------------|----------------------------------------------------------------------------| | es | tú, tu, tus, ti, contigo, tuyo, te, tuya | | usted, vosotros, vuestro, vuestra, vuestras, os | | | fr | tu, ton,ta, tes, toi, te, tien, tiens, tienne, tiennes vous, votre, vos | | it | tu, tuo, tua, tuoi lei, suo, sua, suoi | | ja | だ, だっ, じゃ, だろう, だ, だけど, だっ | | ござい, ます, いらっしゃれ, いらっしゃい, ご覧, 伺い, 伺っ, 存知, です, まし | | | ko | 제가, 저희, 나 | | 댁에, 성함, 분, 생신, 식사, 연세, 병환, 약주, 자제분, 뵙다, 저 | | | nl | jij, jouw, jou, jullie, je u, men, uw | | pt | tu, tua, teu, teus, tuas, te | | você, sua, seu, seus, suas, lhe | | | ro | tu, el, ea, voi, ei, ele, tau, ta, tale, tine ˘ | | dumneavoastra, dumneata, mata,matale,dânsul, dânsa dumnealui,dumneaei, dumnealor ˘ | | | ru | ты, тебя, тебе, тобой, твой, твоя, твои,тебе вы, вас, вам, вами, ваш, ваши | | tr | sen, senin siz, sizin | | zh | 你您 | | Table 7: Words related to formality for each target language. | | | | | | |----------------------------------------------------------------------|--------------------------------|---------------| | you | AÒ J K @ , AÒ J K @ ,ñ J K@ ,á K @ , Õ æ K @ , ú æ K@ ,I K@ , I K@ ,I K@ | | | ar | it | ù ë ,ñë | | they, them | AÒë ,áë , Ñë | | | de | it | er, sie, es | | it | él, ella | | | they, them | ellos, ellas | | | this | ésta, éste, esto | | | that | esa, ese | | | these | estos, estas | | | those | aquellos, aquellas, ésos, ésas | | | es | it | il, elle, lui | | they, them | ils, elles | | | we | nous, on | | | this | celle, ceci | | | that | celle, celui | | | these, those | celles, ceux | | | fr | it | esso, essa | | this | questa, questo | | | that | quella, quello | | | these | queste, questi | | | those | quelle, quelli | | | it ja | I | 私, 僕, 俺 | | it | ele, ela, o, a | | | them | eles, elas, os, as | | | they | eles, elas | | | this, that | este, esta, esse, essa | | | these, those | estes, estas, esses, essas | | | pt ro | it | el, ea | | they, them | ei, ele | | | Table 8: Ambiguous pronouns w.r.t. English for each target language. | | | ![17_image_0.png](17_image_0.png) Table 9: Ambiguous verb forms w.r.t. English for each target language. ar de es fr he it ja ko nl pt ro ru tr zh BLEU no-context 17.25 28.02 35.72 37.74 32.70 32.30 7.10 6.80 32.22 39.03 25.36 17.00 12.32 15.96 context 16.92 28.24 36.00 37.23 32.92 32.11 4.48 3.77 32.67 39.10 25.37 17.14 11.97 15.01 context-gold 18.61 28.60 36.27 37.96 33.41 32.37 5.96 6.92 32.73 39.55 28.49 17.70 12.49 16.05 COMET no-context 0.0002 0.1841 0.3809 0.3087 0.0948 0.2608 -0.5366 -0.0275 0.3105 0.4562 0.3826 0.0033 0.2113 -0.1419 context -0.0066 0.1846 0.3875 0.2811 0.0887 0.2496 -0.7728 -0.3339 0.3238 0.4444 0.3747 -0.0190 0.1831 -0.1917 context-gold 0.0025 0.1886 0.3879 0.2821 0.0922 0.2467 -0.6827 -0.1000 0.3218 0.4506 0.3805 -0.0173 0.1871 -0.1274 ellipsis no-context 0.374 0.387 0.210 0.400 0.439 0.259 0.123 0.169 0.400 0.342 0.333 0.255 0.165 0.145 context 0.325 0.323 0.333 0.406 0.389 0.400 0.021 0.033 0.471 0.450 0.270 0.292 0.240 0.135 context-gold 0.388 0.296 0.300 0.435 0.371 0.381 0.025 0.150 0.444 0.450 0.306 0.226 0.187 0.154 formality no-context - 0.607 0.370 0.792 - 0.429 0.443 0.399 0.682 0.599 0.434 0.464 0.097 0.691 context - 0.639 0.351 0.791 - 0.462 0.414 0.397 0.694 0.600 0.405 0.469 0.083 0.695 context-gold - 0.661 0.443 0.803 - 0.464 0.431 0.425 0.697 0.622 0.440 0.492 0.182 0.741 lexical no-context 0.639 0.762 0.819 0.826 0.723 0.766 0.615 0.574 0.821 0.853 0.661 0.624 0.671 0.645 context 0.630 0.736 0.833 0.830 0.722 0.772 0.572 0.524 0.825 0.851 0.689 0.624 0.647 0.644 context-gold 0.675 0.737 0.832 0.832 0.727 0.773 0.614 0.593 0.828 0.857 0.713 0.625 0.647 0.676 pronouns no-context 0.660 0.613 0.576 0.774 - 0.548 0.473 - – 0.452 0.356 - – – context 0.691 0.614 0.538 0.771 - 0.549 0.377 - – 0.451 0.414 - – – context-gold 0.700 0.624 0.550 0.788 - 0.530 0.428 - – 0.485 0.432 - – – verb tenseno-context - – 0.263 0.435 0.227 0.308 - – 0.477 - 0.292 0.215 0.128 – context - – 0.287 0.442 0.229 0.282 - – 0.479 - 0.292 0.215 0.094 – context-gold - – 0.272 0.435 0.229 0.285 - – 0.487 - 0.328 0.238 0.120 – Table 10: BLEU, COMET, and Word f-measure per tag for base context-aware models. BLEU, COMET and word f-measures statistically significantly higher than no-context (p < 0.05) are underlined. | de | fr | ja | zh | | | |--------------|--------------|--------|--------|--------|--------| | no-context | 36.09 | 45.64 | 15.55 | 22.15 | | | context | 35.86 | 45.40 | 12.68 | 22.68 | | | BLEU | context-gold | 36.69 | 46.60 | 16.60 | 22.98 | | no-context | 0.5256 | 0.6332 | 0.0602 | 0.1160 | | | context | 0.5337 | 0.6425 | 0.0753 | 0.2705 | | | COMET | context-gold | 0.5427 | 0.6529 | 0.1808 | 0.2809 | | no-context | 0.429 | 0.462 | 0.126 | 0.254 | | | ellipsis | context | 0.518 | 0.393 | 0.068 | 0.230 | | context-gold | 0.444 | 0.444 | 0.144 | 0.209 | | | no-context | 0.642 | 0.824 | 0.510 | 0.747 | | | formality | context | 0.640 | 0.810 | 0.513 | 0.739 | | context-gold | 0.692 | 0.820 | 0.537 | 0.739 | | | no-context | 0.773 | 0.864 | 0.704 | 0.661 | | | context | 0.776 | 0.868 | 0.699 | 0.671 | | | lexical | context-gold | 0.796 | 0.875 | 0.740 | 0.696 | | no-context | 0.633 | 0.790 | 0.493 | - | | | context | 0.635 | 0.795 | 0.541 | - | | | pronouns | context-gold | 0.665 | 0.801 | 0.536 | - | | no-context | - | 0.526 | - | - | | | verb tense | context | - | 0.532 | - | - | | context-gold | - | 0.534 | - | - | | BLEU Google 11.73 34.76 43.47 30.77 10.77 31.34 12.98 8.77 38.51 38.49 28.54 24.79 18.22 28.92 DeepL (sent) x 34.29 42.00 42.57 x 35.41 14.88 x 37.58 37.37 28.98 25.67 x 27.94 DeepL (doc) x 36.75 43.06 43.43 x 36.04 15.66 x 38.29 37.76 29.79 26.53 x 27.34 | ar | de | es | fr | he | it | ja | ko | nl | pt | ro | ru | tr | zh | | | |--------------|--------|----------------------|--------|---------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|----| | Google | 11.73 | 34.76 | 43.47 | 30.77 | 10.77 | 31.34 | 12.98 | 8.77 | 38.51 | 38.49 | 28.54 | 24.79 | 18.22 | 28.92 | | | DeepL (sent) | x | 34.29 | 42.00 | 42.57 | x | 35.41 | 14.88 | x | 37.58 | 37.37 | 28.98 | 25.67 | x | 27.94 | | | DeepL (doc) | x | 36.75 | 43.06 | 43.43 | x | 36.04 | 15.66 | x | 38.29 | 37.76 | 29.79 | 26.53 | x | 27.34 | | | Google | 0.3862 | 0.5480 | 0.7694 | 0.6655 | 0.3666 | 0.6707 | 0.2116 | 0.4721 | 0.6401 | 0.7925 | 0.7437 | 0.5121 | 0.7254 | 0.3697 | | | DeepL (sent) | x | 0.5750 | 0.7680 | 0.7121 | x | 0.6951 | 0.2973 | x | 0.6321 | 0.7513 | 0.8026 | 0.5501 | x | 0.3739 | | | DeepL (doc) | x | 0.5848 0.7882 0.7267 | x | 0.7049 0.2343 | x | 0.6357 | 0.7572 | 0.8121 | 0.5495 | x | 0.2453 | | | | | | Google | 0.343 | 0.667 | 0.500 | 0.306 | 0.359 | 0.468 | 0.279 | 0.352 | 0.389 | 0.632 | 0.405 | 0.367 | 0.236 | 0.323 | | | DeepL (sent) | x | 0.417 | 0.400 | 0.422 | x | 0.500 | 0.275 | x | 0.500 | 0.421 | 0.458 | 0.385 | x | 0.303 | | | DeepL (doc) | x | 0.435 | 0.526 | 0.493 | x | 0.553 | 0.208 | x | 0.500 | 0.359 | 0.532 | 0.385 | x | 0.295 | | | Google | - | 0.621 | 0.404 | 0.738 | - | 0.458 | 0.489 | 0.300 | 0.638 | 0.633 | 0.479 | 0.512 | 0.367 | 0.599 | | | DeepL (sent) | - | 0.641 | 0.419 | 0.733 | - | 0.455 | 0.487 | x | 0.610 | 0.625 | 0.533 | 0.533 | x | 0.729 | | | DeepL (doc) | - | 0.670 | 0.446 | 0.785 | - | 0.503 | 0.520 | x | 0.641 | 0.614 | 0.526 | 0.534 | x | 0.664 | | | Google | 0.665 | 0.786 | 0.854 | 0.827 | 0.697 | 0.794 | 0.602 | 0.611 | 0.825 | 0.860 | 0.700 | 0.635 | 0.677 | 0.693 | | | DeepL (sent) | x | 0.773 | 0.840 | 0.860 | x | 0.805 | 0.657 | x | 0.799 | 0.848 | 0.714 | 0.653 | x | 0.660 | | | DeepL (doc) | x | 0.776 | 0.841 | 0.872 | x | 0.812 | 0.640 | x | 0.802 | 0.846 | 0.713 | 0.649 | x | 0.657 | | | Google | 0.670 | 0.648 | 0.626 | 0.757 | - | 0.511 | 0.486 | - | - | 0.488 | 0.326 | - | - | - | | | DeepL (sent) | x | 0.608 | 0.538 | 0.737 | - | 0.543 | 0.526 | - | - | 0.483 | 0.394 | - | - | - | | | DeepL (doc) | x | 0.706 | 0.588 | 0.789 | - | 0.551 | 0.557 | - | - | 0.513 | 0.472 | - | - | - | | | verb tense | Google | - | - | 0.415 | 0.529 | 0.311 | 0.450 | - | - | 0.554 | - | 0.358 | 0.314 | 0.167 | - | | DeepL (sent) | - | - | 0.390 | 0.553 | x | 0.478 | - | - | 0.562 | - | 0.400 | 0.327 | x | - | | | DeepL (doc) | - | - | 0.426 | 0.562 | x | 0.445 | - | - | 0.567 | - | 0.411 | 0.349 | x | - | | COMET Google 0.3862 0.5480 0.7694 0.6655 0.3666 0.6707 0.2116 0.4721 0.6401 0.7925 0.7437 0.5121 0.7254 0.3697 DeepL (sent) x 0.5750 0.7680 0.7121 x 0.6951 0.2973 x 0.6321 0.7513 0.8026 0.5501 x 0.3739 DeepL (doc) x 0.5848 0.7882 0.7267 x 0.7049 0.2343 x 0.6357 0.7572 0.8121 0.5495 x 0.2453 ellipsis Google 0.343 0.667 0.500 0.306 0.359 0.468 0.279 0.352 0.389 0.632 0.405 0.367 0.236 0.323 DeepL (sent) x 0.417 0.400 0.422 x 0.500 0.275 x 0.500 0.421 0.458 0.385 x 0.303 DeepL (doc) x 0.435 0.526 0.493 x 0.553 0.208 x 0.500 0.359 0.532 0.385 x 0.295 formality Google - 0.621 0.404 0.738 - 0.458 0.489 0.300 0.638 0.633 0.479 0.512 0.367 0.599 DeepL (sent) - 0.641 0.419 0.733 - 0.455 0.487 x 0.610 0.625 0.533 0.533 x 0.729 DeepL (doc) - 0.670 0.446 0.785 - 0.503 0.520 x 0.641 0.614 0.526 0.534 x 0.664 lexical Google 0.665 0.786 0.854 0.827 0.697 0.794 0.602 0.611 0.825 0.860 0.700 0.635 0.677 0.693 DeepL (sent) x 0.773 0.840 0.860 x 0.805 0.657 x 0.799 0.848 0.714 0.653 x 0.660 DeepL (doc) x 0.776 0.841 0.872 x 0.812 0.640 x 0.802 0.846 0.713 0.649 x 0.657 ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Last section in page 9 (unnumber) ✗ A2. Did you discuss any potential risks of your work? Work doesn't have immediate ethical risk ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 and Abstract ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 And 5 ✓ B1. Did you cite the creators of artifacts you used? Section 4 and 5 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Datasets used are commonly used by the community, and the (permissive) license for our tagger is in the official code repository ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Datasets used are commonly used by the community B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4 ## C ✓ **Did You Run Computational Experiments?** Section 5 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 5.1 and Appendix F ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 and 5 D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 4.3 ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? We provide a brief description (but not full text instructions) in section 4.3 ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 4.3 D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 4.3

chen-etal-2023-causal

Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection

https://aclanthology.org/2023.acl-long.37

Due to the rapid upgrade of social platforms, most of today{'}s fake news is published and spread in a multi-modal form. Most existing multi-modal fake news detection methods neglect the fact that some label-specific features learned from the training set cannot generalize well to the testing set, thus inevitably suffering from the harm caused by the latent data bias. In this paper, we analyze and identify the psycholinguistic bias in the text and the bias of inferring news label based on only image features. We mitigate these biases from a causality perspective and propose a Causal intervention and Counterfactual reasoning based Debiasing framework (CCD) for multi-modal fake news detection. To achieve our goal, we first utilize causal intervention to remove the psycholinguistic bias which introduces the spurious correlations between text features and news label. And then, we apply counterfactual reasoning by imagining a counterfactual world where each news has only image features for estimating the direct effect of the image. Therefore we can eliminate the image-only bias by deducting the direct effect of the image from the total effect on labels. Extensive experiments on two real-world benchmark datasets demonstrate the effectiveness of our framework for improving multi-modal fake news detection.

# Causal Intervention And Counterfactual Reasoning For Multi-Modal Fake News Detection Ziwei Chen1 Linmei Hu2∗ Weixin Li3 Yingxia Shao1 **Liqiang Nie**4 1Beijing University of Posts and Telecommunications 2 Beijing Institute of Technology 3Beihang University 4 Harbin Institute of Technology (Shenzhen) {chen_zw,shaoyx}@bupt.edu.cn [email protected] [email protected] [email protected] ## Abstract Due to the rapid upgrade of social platforms, most of today's fake news is published and spread in a multi-modal form. Most existing multi-modal fake news detection methods neglect the fact that some label-specific features learned from the training set cannot generalize well to the testing set, thus inevitably suffering from the harm caused by the latent data bias. In this paper, we analyze and identify the psycholinguistic bias in the text and the bias of inferring news label based on only image features. We mitigate these biases from a causality perspective and propose a Causal intervention and Counterfactual reasoning based Debiasing framework (CCD) for multi-modal fake news detection. To achieve our goal, we first utilize causal intervention to remove the psycholinguistic bias which introduces the spurious correlations between text features and news label. And then, we apply counterfactual reasoning by imagining a counterfactual world where each news has only image features for estimating the direct effect of the image. Therefore we can eliminate the image-only bias by deducting the direct effect of the image from the total effect on labels. Extensive experiments on two real-world benchmark datasets demonstrate the effectiveness of our framework for improving multi-modal fake news detection. ## 1 Introduction Fake news quietly sneaks into people's daily life, mixed with massive information, causing serious impact and harm to society. Fake news often utilizes multimedia information such as text and images to mislead readers, spreading and expanding its influence. Thus, it is crucial and urgent to find a way to discern multi-modal fake news. Today, most existing methods train on known fake news instances expecting to capture the labelspecific features for judging the authenticity of unseen news (Singhal et al., 2020; Wu et al., 2021; ∗Corresponding author ![0_image_0.png](0_image_0.png) Qian et al., 2021b; Qi et al., 2021). However, such label-specific features may expose the models to hidden data bias when confronted with unseen fake news samples (Wang et al., 2018; Cheng et al., 2021; Zhu et al., 2022). To address the problem, we investigate the biases underlying the multi-modal fake news detection data and identify the psycholinguistic bias in the text and the bias of inferring news label based on image features only (i.e. image-only bias). These biases could lead to spurious correlations between the news and labels, thus impairing the model performance on testing data. To explicitly explain the biases, we first formulate the process of fake news detection as a causal graph as shown in Figure 2(a). In addition to the impact of fused features C on news label Y that most multi-modal fake news detection methods focus on, other two edges are pointing to Y , starting from text features T, and image features I, respectively. Generally speaking, the publishers of fake news would try their best to fabricate confusing text or use certain techniques to forge fake images. This makes the text and image can individually affect the news label. For the T → Y branch, we observe that the linguistic characteristics of the text have obvious emotional preferences, such as the usage of psycholin627 ![1_image_0.png](1_image_0.png) guistic words "crazy" and "amazing", which play a critical role in fake news detection. To deeply analyze the linguistic characteristics of the text, we present a mathematical analysis of the psycholinguistic word distribution of real news and fake news based on the LIWC 2015 dictionary (Pennebaker et al., 2015). Take the Twitter dataset as an example, as shown in Figure 1, we can observe that the word frequency distribution of fake news is quite different from that of real news, especially for words expressing anxiety, negative emotions, positive emotions, tentative, and netspeak. It seems that we can draw a conclusion that fake news prefers to use loaded language to stir up the reader's emotions and attract more attention. Consequently, the model could be prone to relying on such psycholinguistic features as a shortcut to judge news authenticity. However, we analyze the training set and testing set, and find that there exist significant differences in the frequency of these psycholinguistic words. The manifest differences between the training set and testing set have proven that this shortcut appears to be unreliable evidence. As shown in Figure 2(b) where U denotes the confounder (i.e. the psycholinguistic features in the text), there exist a backdoor path T ← U → Y which will introduce spurious correlations among the text features and news label. In order to remove the psycholinguistic bias, we apply causal intervention by adopting the backdoor adjustment (Glymour et al., 2016) with do-calculus P(Y |do(T)) to calculate the causal effect in the training stage, which is fundamentally different from the conventional likelihood P(Y |T). For the I → Y branch, we observe from the datasets that two different news pieces sharing the same image could have contrary labels. This shows that sometimes even if the image is real, the text could be fabricated, and the news could thus be fake. We can take advantage of images as an additional modality to provide more detection evidence, but it is unreliable to infer the authenticity of the news based on the image features alone. In this case, we argue that the image-only bias (i.e., the direct causal effect from image features alone to news label) should be eliminated. Towards this end, we use counterfactual reasoning by imagining a counterfactual world (Figure 2(c)) where both text features T and fused features C are not given (represented by reference values t∗and c∗), except for image features I. In this way, the bias can be estimated by computing the direct causal effect of I on Y and we can conduct the debiasing by subtracting it from the total effect on Y . We instantiate our proposed debiasing framework on three strong baseline models that can handle both text and image features as inputs. Extensive experiments on two widely used real-world benchmark datasets show the effectiveness of our framework. Overall, our contributions can be summarized as follows: - We analyze each modality of fake news detection data and identify the underlying psycholinguistic bias in the text and the image-only bias. And we propose a novel Causal intervention and Counterfactual reasoning based Debiasing framework (CCD) for multi-modal fake news detection. - In our debiasing framework CCD, we conduct causal interventions via backdoor adjustment to remove spurious correlations introduced by the psycholinguistic confounder. For addressing the image-only bias, we apply counterfactual reasoning to pursue the indirect causal effect as the inference prediction. - Our causal framework CCD can be applied to any fake news detection model with image and text features as inputs. We implement the proposed framework on three strong baseline models, and conduct extensive experiments on two widely used benchmark datasets, validating the effectiveness of CCD. ## 2 Preliminaries 2.1 Causal Graph The causal graph (Glymour et al., 2016) is a probabilistic graphical model used to describe how variables interact with each other, expressed by a directed acyclic graph G = {N , E} consisting of the 628 sets of variables N and the causal correlations E between two nodes. As shown in Figure 3, X → Y denotes that X is the cause of the effect Y . U is the confounder. ![2_image_0.png](2_image_0.png) ## 2.2 Causal Intervention Causal intervention is used to seek the real causal effect of one variable on another when there exist confounders. In a causal graph, the intervening operation on a variable removes all edges pointing to it, such that its parent nodes no longer cause it. The backdoor adjustment (Glymour et al., 2016) with do-calculus offers a tool for calculating the intervened distribution under the condition of no extra confounders. For the example in Figure 3, the adjustment formula can be derived according to Bayes' theorem as follows, where u denotes the value of confounder U: $$P(Y|d o(X))=\sum_{u}P(Y|X,u)P(u).$$ ## 2.3 Counterfactual Reasoning And Causal Effect Counterfactual reasoning (Pearl, 2009) is a statistical inference method used to infer outcomes under hypothetical conditions that are different from the factual world. By conducting counterfactual reasoning, we can estimate the causal effect (Pearl, 2022) of a treatment variable on a response variable. For instance, Figure 4 shows an abstract setting for estimating and removing the direct influence of X on Y . Figure 4(a) is the factual world where the calculation of Y is denoted as Yx,Zx = Y (X = *x, Z* = Z(X = x)). ![2_image_1.png](2_image_1.png) Based on Figure 4(a) and 4(b), we define the total effect (TE) of X = x on Y as: $$\mathrm{TE}=Y_{x,Z_{x}}-Y_{x^{*},Z_{x^{*}}},$$ $$(2)$$ which can be seen as the comparisons between two potential outcomes of X given two different treatments, i.e., X = x and X = x∗. The total effect (TE) can be decomposed into the sum of the natural direct effect (NDE) and the total indirect effect (TIE), namely, TE = NDE + TIE. NDE represents the natural direct effect of X on Y when the mediator variable Z is blocked (Figure 4(c)): $${\mathrm{NDE}}=Y_{x,Z_{x^{*}}}-Y_{x^{*},Z_{x^{*}}}.$$ $$(4)$$ Yx,Zx∗ is calculated under the counterfactual world where X can be simultaneously set to different values x and x∗(Figure 4(c)). Thus, TIE (the total indirect effect of X on Y ) can be obtained: $$\mathrm{TIE}=\mathrm{TE}-\mathrm{NDE}=Y_{x,Z_{x}}-Y_{x,Z_{x^{*}}}.$$ We use TIE as the debiased result for inference. ## 3 Method In this section, we first formulate the fake news detection task as a causal graph to clearly depict the causal effect between factors. And then we present our CCD framework that removes the psycholinguistic bias by means of causal intervention, as well as deducts the direct causal effect of image features (i.e. the image-only bias) via counterfactual reasoning. ## 3.1 Causal Graph Of Fake News Detection As aforementioned, Figure 2(a) depicts the causal graph of the fake news detection process. Nodes T, I, and C represent the text features, image features, and fused multi-modal features, respectively. According to the proposed causal graph, the final prediction Y takes inputs from the three branches: the direct effect of the input T and I on Y via T → Y and I → Y , as well as the indirect effect of the input T and I on Y via the fused features C, i.e. T(I) → C → Y . Each branch of Figure 2(a) can be implemented via a base fake news detection model (Figure 5). Formally, the abstract format of the model should be: $$Y_{t,i,c}=Y(T=t,I=i,C=c),$$ $$(5)$$ $f(T=t,I=i),f(.)$ is the fee. where c = f(T = *t, I* = i), f(·) is the feature aggregation function in baseline fake news detection models. Then the total effect (TE) of the input on label y can be written as: can have: $$\mathrm{TE}=Y_{t,i,c}-Y_{t^{*},i^{*},c^{*}},$$ where t∗and i∗are respectively the reference values of T and I, and c∗ = f(T = t∗, I = i∗). As introduced in Section 2.3, the reference status is defined as the status of blocking the signal from text and image, i.e., t and i are not given (void values). For implementation, we use tensors filled with the scalar value 0 to represent the reference values t∗ and i∗. In this way, the inputs do not contain any semantic information. Following previous studies (Niu et al., 2021; Wang et al., 2021; Tian et al., 2022), we calculate the prediction Y*t,i,c* through a model ensemble with a fusion function. $$Y_{t,i,c}=Y(T=t,I=i,C=c)\tag{7}$$ $$={\mathcal{F}}(Y_{t},Y_{i},Y_{c})$$ $$=Y_{c}+tanh(Y_{t})+tanh(Y_{i}),$$ where Ytis the output of the text-only branch (i.e. T → Y ), Yiis the output of the image-only branch (i.e. I → Y ), and Yc = Yt,i is the output of fused features branch (i.e. C → Y ) as shown in Figure 5. F(·) is the fusion function to obtain the final prediction. We adopt a non-linear fusion strategy for its better representation capacity (Wang et al., 2021). Any differentiable arithmetic binary operations can be employed as the fusion function F(·) and we examine several fusion alternatives in Table 4. ## 3.2 Deconfounded Training With Causal Intervention As Figure 2(b) shows, there exist an unobserved confounder U (i.e., the psycholinguistic of the text) in the T → Y branch, which causes spurious correlations between the text features and news label by learning the likelihood P(Y |T). In order to explicitly illustrate the impact of the confounder, we use Bayes' theorem: $$\begin{array}{c}{{P(Y|T)=\sum_{u}P(Y|T,u)P(u|T)}}\\ {{\qquad\propto\sum_{u}P(Y|T,u)P(T|u)P(u).}}\end{array}$$ $$(8)$$ Next, we conduct deconfounded training in T → Y branch which exploits the backdoor adjustments (Glymour et al., 2016) with do-calculus on T to calculate the corresponding intervention distribution. Since the edge U → T has been cut off, we $$\begin{array}{l l}{{Y_{t}=P(Y|d o(T))}}&{{}}\\ {{}}&{{=\sum_{u}P(Y|T,u)P(u).}}\end{array}\qquad(9)$$ $$(6)$$ To estimate Yt, given the text features T's representations t and the confounder U's representations P u, Equation (9) is implemented as uP(y|t, u)P(u), where P(y|t, u) is the prediction upon a news feature learning model g(·): $$P(y|\mathbf{t},\mathbf{u})=\sigma(g(\mathbf{t},\mathbf{u})),$$ $$(10)$$ where σ(·) is the sigmoid function that forms the output of g(·) into (0, 1). In summary, the implementation of Equation (9) is formally defined as: $$\begin{array}{l}{{P(Y|d o(T))=\mathbb{E}_{u}[P(Y|T,u)]}}\\ {{\qquad=\mathbb{E}_{u}[\sigma(g(\mathbf{t},\mathbf{u}))].}}\end{array}\tag{11}$$ Note that Eu requires expensive sampling. Following recent works (Wang et al., 2020; Yang et al., 2021), we can apply Normalized Weighted Geometric Mean (NWGM) (Xu et al., 2015) to approximate the above expectation by moving the outer expectation into the sigmoid function as: P(Y |do(T)) NWGM ≈ σ(Eu[g(t, u)]). (12) $$(12)$$ We apply a linear model to approximate the conditional probability, i.e. the probability of Y under the conditions T and U. Inspired by previous works (Chen et al., 2022a; Tian et al., 2022), we model g(t, u) = Wtt + Wu · h(u), where h(u) is the feature transformation of u, Wt and Wu are learnable weight parameters. In this case, Eu[g(t, u)] = Wtt + Wu · Eu[h(u)]. To compute Eu[h(u)], we implement h(u) as the scaled Dot-Product attention (Vaswani et al., 2017). We resort to LIWC 2015 dictionary (Pennebaker et al., 2015) to approximate U as a fixed confounder dictionary Du = [u1, u2*, ...,* uN ] ∈ R N×du , where N is the number of word categories and du is the hidden feature dimension. Then we have: $$\mathbb{E}_{u}[h(\mathbf{u})]=\sum_{u}[softmax(\frac{\mathbf{Q}^{T}\mathbf{K}}{\sqrt{d_{m}}})\odot\mathbf{D}_{u}]P(\mathbf{u}),\tag{13}$$ where $\mathbf{Q}=\mathbf{W}_{g}\mathbf{t}$, $\mathbf{K}=\mathbf{W}_{k}\mathbf{D}_{u}$ ($\mathbf{W}_{g}$ and $\mathbf{W}_{k}$ are learnable weight parameters), dm denotes the scaling factor. P(u) denotes the prior statistic probability and ⊙ is the element-wise product. 630 ![4_image_0.png](4_image_0.png) ## 3.3 Mitigating The Image-Only Bias With Counterfactual Reasoning So far, the psycholinguistic bias has been successfully removed in the T → Y branch, but the fake news detection model based on the causal graph in Figure 2(a) still suffers from the image-only bias. This is because the prediction, i.e., Y*t,i,c*, is still affected by the direct effect of the image. Consequently, fake news with more convincing image features still achieves a high probability of being judged as real news. To mitigate the image-only bias, we propose counterfactual reasoning to estimate the direct causal effect of I on Y by blocking the impact of T and C. Figure 2(c) shows the causal graph of the counterfactual world for fake news detection which describes the scenario when I is set to different values i and i∗. We also set T to its reference value t∗, therefore C would attain the value c∗ when T = t∗and I = i∗. In this way, the inputs of T and C are blocked, and the model can only rely on the given image i for detection. We can thus obtain the natural direct effect (NDE) of I on Y , namely the image-only bias: $$\mathrm{NDE}=Y_{t^{*},i,c^{*}}-Y_{t^{*},i^{*},c^{*}}.$$ Furthermore, the removal of the bias can be realized by subtracting NDE from the total effect TE: $$\text{TIE}=\text{TE}-\text{NDE}=Y_{t,i,c}-Y_{t^{*},i,c^{*}}.\tag{15}$$ TIE is the debiased result we used for inference. ## 3.4 Training And Inference We illustrate the training and inference of our proposed CCD framework in Figure 5. Following Wang et al. (2021); Niu et al. (2021); Tian et al. (2022), for the training stage, we compute the loss for each branch, including the base multi-modal fake news detection branch (Loss*F ND*), the textonly detection branch (*Loss*T ), and the image-only detection branch (*Loss*I ). As such, we minimize a multi-task training objective to learn the model parameters, which is formulated as: $$Loss=Loss_{FND}+\alpha Loss_{T}+\beta Loss_{I},\tag{16}$$ where the loss Loss*F ND* refers to the cross-entropy loss associated with the predictions of F(Yt, Yi, Yc) from Equation (7). The text-only and image-only loss *Loss*T and *Loss*I are cross-entropy losses associated with the predictions of Yt and Yi. α and β are the trade-off hyperparameters. In the inference stage, we use the de-biased effect for inference, which is implemented as: $$\text{TIE}=Y_{t,i,c}-Y_{t^{*},i,c^{*}}\tag{17}$$ $$=\mathcal{F}(Y_{t},Y_{i},Y_{c})-\mathcal{F}(Y_{t^{*}},Y_{i},Y_{c^{*}}).\tag{18}$$ ## 4 Experiments $$(14)$$ In this section, we apply our CCD framework on three strong baseline multi-modal fake news detection models on two real-world datasets to evaluate the effectiveness of our proposed CCD framework. ## 4.1 Experimental Settings 4.1.1 Datasets We conducted experiments on two datasets: Twitter: This dataset was released for Verifying Multimedia Use task at MediaEval1. It consists of tweets with textual, visual, and social context information. Since our framework belongs to contentbased methods, we only leverage textual and visual information. 1http://www.multimediaeval.org/mediaeval2015/. ![5_image_0.png](5_image_0.png) Pheme: This dataset was generated as part of the Pheme project, which attempts to detect and verify rumors spread via social media. It is based on five breaking news stories, each of which comprises a series of statements categorized as rumor or nonrumor. We classified rumors as fake news and nonrumors as real news in our framework. Our data preprocessing and division of the training set and testing set for both datasets are the same as previous work (Qian et al., 2021b). Table 1 shows the statistics of the two datasets. ## 4.1.2 Base Models The CCD framework can be applied to any multimodal fake news detection method with text and image as input. Here, we apply our framework to the following strong baselines: 1) **SpotFake+** (Singhal et al., 2020): SpotFake+ concatenates the features extracted from different modalities and performs multiple feature transformations to facilitate multi-modal fusion. 2) **MCAN** (Wu et al., 2021): MCAN stacks multiple co-attention layers to learn dependencies across the modalities. They repeatedly fuse the two modalities to simulate people's reading process. 3) **HMCAN** (Qian et al., 2021b): HMCAN uses a hierarchical multi-modal contextual attention model that considers both the text's hierarchical semantics and multi-modal contextual data. ## 4.1.3 Evaluation Metrics We use the *Accuracy* as the evaluation metric for binary classification tasks such as fake news detection. In consideration of the imbalance label distributions, in addition to the accuracy metric, we add Precision, *Recall*, and *F1-score* as complementary evaluation metrics following previous works (Wu et al., 2021; Qian et al., 2021b). ## 4.1.4 Implementation Details All of the methods are trained for 200 epochs and the initial learning rate for the Adam optimizer is tuned in [1e-5, 1e-3]. For the confounder dictionary Du ∈ R N×du , N is 18 (Anger, Anxiety, Assent, Causation, Certainty, Differentiation, Discrepancy, Feel, Hear, Insight, Negative emotion, Netspeak, Nonfluencies, Positive emotion, Sadness, See, Swear words, Tentative), and du is set to 4. For the scaled Dot-Product attention, the scaling factor dm is set to 256. As for other necessary hyperparameters in the baseline methods, our settings are consistent with them. ## 4.2 Experimental Results Table 2 displays the experimental results of our proposed framework CCD applied to the baseline methods on two benchmark datasets. The results of the baselines are the results of our reproductions on our data settings based on their public code2. From Table 2, we can obtain the following observations: Compared with each base fake news detection model (i.e. SpotFake+, MCAN, HMCAN), the accuracy of the models that apply the proposed CCD framework (i.e., w/ CCD) has been significantly improved by around 7.7%, 3.3%, and 5.2% on the Twitter dataset, and improved by around 1.0%, 0.6%, and 1.3% on the Pheme dataset. With the help of the proposed framework, all of the base models show significant improvements on most metrics, which demonstrates the effectiveness of the proposed framework. We believe that CCD benefits from the removal of psycholinguistic bias with causal intervention as well as the mitigation of the image-only bias via counterfactual reasoning. The performance improvements on the Twitter dataset are larger than that on the Pheme dataset. We attribute such a difference between the two datasets to the following two reasons: 1) The proportion of psycholinguistic vocabulary in the Twitter dataset (19.87%) is higher than that in the Pheme dataset (16.19%), so the Twitter dataset could be more susceptible to psycholinguistic bias. 2) According to Table 1, the number of unique images in the Twitter dataset is far less than the number of news texts, which means that there's a serious problem of different texts sharing the same image. So the influence of image-only bias in the Twitter dataset is more severe than that of the Pheme dataset. ## 4.3 Ablation Study Of Causal Inference We conduct experiments to study the de-biasing effect of each module in CCD using the strong baseline HMCAN on Twitter and Pheme testing 2https://github.com/shiivangii/SpotFakePlus. https://github.com/wangjinguang502/HMCAN. https://github.com/wuyang45/MCAN_code. | Dataset | Methods | Accuracy | Fake news | Real news | | | | | |-----------|-----------|------------|-------------|-------------|-------|-------|--------|-------| | Precision | Recall | F1 | Precision | Recall | F1 | | | | | SpotFake+ | 0.795 | 0.622 | 0.607 | 0.614 | 0.856 | 0.864 | 0.860 | | | w/ CCD | 0.856* | 0.750 | 0.849 | 0.797* | 0.920 | 0.860 | 0.889* | | | MCAN | 0.799 | 0.980 | 0.401 | 0.569 | 0.770 | 0.996 | 0.869 | | | w/ CCD | 0.825* | 0.829 | 0.595 | 0.692* | 0.824 | 0.939 | 0.878* | | | HMCAN | 0.831 | 0.955 | 0.514 | 0.668 | 0.804 | 0.988 | 0.887 | | | w/ CCD | 0.874* | 0.820 | 0.792 | 0.806* | 0.899 | 0.914 | 0.906* | | | Twitter | SpotFake+ | 0.815 | 0.711 | 0.525 | 0.604 | 0.840 | 0.921 | 0.879 | | w/ CCD | 0.823* | 0.714 | 0.574 | 0.636* | 0.854 | 0.915 | 0.883* | | | MCAN | 0.834 | 0.716 | 0.639 | 0.675 | 0.872 | 0.906 | 0.889 | | | w/ CCD | 0.839* | 0.693 | 0.721 | 0.707* | 0.896 | 0.882 | 0.889 | | | HMCAN | 0.848 | 0.762 | 0.705 | 0.732 | 0.881 | 0.908 | 0.894 | | | w/ CCD | 0.859* | 0.764 | 0.689 | 0.724 | 0.889 | 0.921 | 0.905* | | | Pheme | | | | | | | | | | Dataset | Method | Accuracy | |--------------|----------|------------| | HMCAN w/CCD | 0.874 | | | Twitter | w/o CI | 0.842 | | w/o CR | 0.855 | | | HMCAN w/ CCD | 0.859 | | | Pheme | w/o CI | 0.852 | | w/o CR | 0.850 | | set. As shown in Table 3, we test the performance of CCD removing the causal intervention part (w/o CI), and CCD removing the counterfactual reasoning part (w/o CR). The variant model (w/o CI) does not consider the psycholinguistic confounder and uses the original text features for detection. While the variant model (w/o CR) uses Y*t,i,c* for inference without subtracting the direct effect of the image. We can observe that if we remove the causal intervention part, the performance respectively drops by around 3.7% and 0.8% on Twitter and Pheme, demonstrating the effectiveness of eliminating the psycholinguistic bias in the text. And removing the counterfactual reasoning part will make the performance respectively decreases by around 2.2% and 1.0% on Twitter and Pheme, proving that CCD can effectively mitigate the image-only bias in the inference stage. ## 4.4 Impact Of Different Fusion Strategies Following prior studies (Wang et al., 2021), we devise several differentiable arithmetic binary op- | Strategy | Accuracy | F1F ake | F1Real | |-------------|------------|-----------|----------| | MUL-sigmoid | 0.695 | 0.569 | 0.765 | | MUL-tanh | 0.733 | 0.472 | 0.821 | | SUM-sigmoid | 0.806 | 0.600 | 0.872 | | SUM-tanh | 0.859 | 0.724 | 0.905 | erations for the fusion strategy in Equation (7): MUL-sigmoid : Y*t,i,c* = Yc ∗ σ(Yt) ∗ σ(Yi), MUL-tanh : $Y_{t,i,c}=Y_{c}*tanh(Y_{t})*tanh(Y_{i})$, SUM-sigmoid : $Y_{t,i,c}=Y_{c}+\sigma(Y_{t})+\sigma(Y_{i})$, SUM-tanh : $Y_{t,i,c}=Y_{c}+tanh(Y_{t})+tanh(Y_{i})$. ## (19) The Performance Of Different Fusion Strategies Are Reported In Table 4. From The Table, We Can Find That Sum-Tanh Achieves The Best Performance Over The Other Fusion Strategies. This Shows That A Fusion Function With The Proper Boundary Is Suitable For Ccd. Multiple Fusion Strategies Are Worth Studying When Ccd Is Applied To Other Scenarios In The Future. 4.5 Impact Of The Value Of Α And Β We tune the trade-off hyperparameters α and β in the training objective by grid search in {0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5}. And we find out that when α = 3 and β = 0.1, we can obtain satisfactory results in terms of accuracy on both datasets. To evaluate the impact of each parameter on the detection performance, we further study the accuracy under different values of α and β individually by fixing the other hyperparameter on ![7_image_0.png](7_image_0.png) ![7_image_2.png](7_image_2.png) Accuracy the Pheme dataset. As shown in Figure 6, when β=0.1 and α grows from 0 to 3, the accuracy keeps raising, indicating the importance of leveraging the text features that have removed psycholinguistic bias. When α=3 and β grow from 0 to 0.1, the accuracy increases, indicating the importance of capturing image-only bias. However, when α>3 or β>0.1, the performance decreases. It is because the training loss of the detection model using multimodal features will be less important, which brings worse results. ## 4.6 Case Study We provide a qualitative analysis of the proposed CCD framework by examining the fake and real news samples that are successfully detected by HMCAN w/ CCD on Pheme datasets in Figure 7. The psycholinguistic words are highlighted in red and the prediction results before (Before) and after (Debiased) counterfactual reasoning are shown in the charts. As we can see, the texts of both fake and real news contain words expressing anger and negative emotions (i.e., "killed", "assault", "murdered" and "attack"), but CCD can make correct predictions based on the text features (Text) after causal intervention. In addition, after conducting counterfactual reasoning by subtracting the direct causal effect of the image (Image), the CCD is able to make correct predictions based on the debiased results. The two cases show the effectiveness of our CCD framework, which makes debiased predictions by removing the psycholinguistic bias in the text and image-only bias. ## 5 Related Work In this section, we review the related work including fake news detection and causal inference. ![7_image_1.png](7_image_1.png) ## 5.1 Multi-Modal Fake News Detection Existing fake news detection work generally falls into two categories: content-based methods and propagation-based methods. The multi-modal approaches fall into the former category. Most works on multi-modal fake news detection exert efforts to fully incorporate cross-modal features. For instance, Jin et al. (2017) proposed a recurrent neural network with an attention mechanism to fuse the text, social context, and image features. Singhal et al. (2020) utilized pre-trained encoders and applied multiple-layer feature transformation to achieve deep fusion. Chen et al. (2022b) calculated the ambiguity score of different modalities to control the contribution of mono-modal features and inter-modal correlations to the final prediction. To capture fine-grained cross-modal correlations, Wu et al. (2021) employed multiple rounds of co-attention mechanism to model the cross-modal interactions. Qian et al. (2021b) leveraged a contextual attention network to model both the intra- and inter-modality information, and captured the hierarchical semantic information of the text. There are also methods leveraging external knowledge to provide powerful evidence or enrich features' representations (Hu et al., 2021; Qi et al., 2021). For example, Hu et al. (2021) compared each news with the external knowledge base through entities to utilize consistencies for detection. In this work, we improve fake news detection from the perspective of causality and propose a novel framework that eliminates the hidden biases in each modality. ## 5.2 Causal Inference Causal inference (Glymour et al., 2016) including causal intervention and counterfactual reasoning has been widely used in various fields such as recommendation (Zhang et al., 2021b; Wang et al., 2021), natural language inference (Tian et al., 2022), text classification (Qian et al., 2021a), named entity recognition (Zhang et al., 2021a), pretrained language models (Li et al., 2022), etc. It provides a powerful tool that can scientifically identify the causal correlations between variables and remove the hidden bias in the data. As for fake news detection, Zhu et al. (2022) eliminated the entity bias (the distribution of entities in the text) by counterfactual reasoning. In this work, we discover the psycholinguistic bias and image-only bias in fake news detection, and propose a novel debiasing framework that eliminates these biases using causal intervention and counterfactual reasoning to enhance detection performance. ## 6 Conclusion In this work, we propose a novel causal intervention and counterfactual reasoning based debiasing framework CCD that eliminates the hidden biases in multi-modal fake news detection. We analyze and identify the psycholinguistic bias in the text as well as the image-only bias. Then, we formulate the process of fake news detection as a causal graph, addressing the biases from the causality perspective. Specifically, we address the psycholinguistic bias by causal intervention with backdoor adjustment, and mitigate the image-only bias using counterfactual reasoning that subtracts the direct image-only causal effect from the total causal effect. Experiments on two real-world benchmark datasets verify that CCD can effectively eliminate biases and improve multi-modal fake news detection. ## Limitations When applying causal intervention to remove psycholinguistic bias, we utilize the LIWC dictionary to construct the confounder dictionary Du. We argue that the debiasing performance could be affected by the quality of the constructed confounder dictionary. In the future, we could try to improve the confounder dictionary with external knowledge. ## Acknowledgements This work was supported by the National Science Foundation of China (NSFC No. U21B2009, No. 62276029), Beijing Academy of Artificial Intelligence (BAAI) and CCF-Zhipu.AI Large Model Fund (No. 202217). ## References Yingjie Chen, Diqi Chen, Tao Wang, Yizhou Wang, and Yun Liang. 2022a. Causal intervention for subjectdeconfounded facial action unit recognition. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, pages 374–382. 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Causal intervention for leveraging popularity bias in recommendation. In *Proceedings* of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 11–20. Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, and Fuzhen Zhuang. 2022. Generalizing to the future: Mitigating entity bias in fake news detection. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2120–2125. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitation ✓ A2. Did you discuss any potential risks of your work? Section Limitation ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3.2; Section 4.1 ✓ B1. 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The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4.1.4 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.1 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? 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akyurek-andreas-2023-lexsym

{L}ex{S}ym: Compositionality as Lexical Symmetry

https://aclanthology.org/2023.acl-long.38

In tasks like semantic parsing, instruction following, and question answering, standard deep networks fail to generalize compositionally from small datasets. Many existing approaches overcome this limitation with model architectures that enforce a compositional process of sentence interpretation. In this paper, we present a domain-general and model-agnostic formulation of compositionality as a constraint on symmetries of data distributions rather than models. Informally, we prove that whenever a task can be solved by a compositional model, there is a corresponding data augmentation scheme {---} a procedure for transforming examples into other well-formed examples {---} that imparts compositional inductive bias on any model trained to solve the same task. We describe a procedure called LexSym that discovers these transformations automatically, then applies them to training data for ordinary neural sequence models. Unlike existing compositional data augmentation procedures, LexSym can be deployed agnostically across text, structured data, and even images. It matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and Alchemy instruction following, and CLEVR-CoGenT visual question answering datasets.

# Lexsym: Compositionality As Lexical Symmetry Ekin Akyürek Jacob Andreas Massachusetts Institute of Technology {akyurek,jda}@mit.edu ## Abstract In tasks like semantic parsing, instruction following, and question answering, standard deep networks fail to generalize compositionally from small datasets. Many existing approaches overcome this limitation with model architectures that enforce a compositional process of sentence interpretation. In this paper, we present a domain-general and model-agnostic formulation of compositionality as a constraint on *symmetries of data distributions* rather than models. Informally, we prove that whenever a task can be solved by a compositional model, there is a corresponding data augmentation scheme—a procedure for transforming examples into other well-formed examples—that imparts compositional inductive bias on any model trained to solve the same task. We describe a procedure called LEXSYM that discovers these transformations automatically, then applies them to training data for ordinary neural sequence models. Unlike existing compositional data augmentation procedures, LEXSYM can be deployed agnostically across text, structured data, and even images. It matches or surpasses state-of-the-art, task-specific models on COGS semantic parsing, SCAN and ALCHEMY instruction following, and CLEVR-COGENT visual question answering datasets. ## 1 Introduction A central challenge in natural language processing is the design of models and learning algorithms that are simultaneously *flexible* enough to capture the variability of human language and *structured* enough to generalize in predictable and humanlike ways. One important source of structure is the **principle of compositionality**, which (in one formulation) states that sentence meanings can be computed from a *lexicon* of word meanings and a set of *composition rules* governing how meanings combine (Montague, 1970b). A long line of language processing research has operationalized the principle of compositionality as a **constraint on** model architectures, via independence assumptions or parameter tying schemes that ensure a compositional process of sentence interpretation (Lewis and Stearns, 1968; Andreas et al., 2016). Compositional models enjoy sample-efficient learning and strong generalization in tasks from machine translation to question answering (McCoy et al., 2020). But much of human language is not (or at least not straightforwardly) compositional. Idioms, disfluencies, and context-sensitive meanings present major challenges to models in which all predictions must derive from a sequence of local composition operations. In recent years, more generic model architectures such as recurrent neural networks (RNNs) and transformers, with no explicit compositional scaffolding, have consistently outperformed compositional models in language processing tasks with natural data (Wu et al., 2016). However, these models capture linguistic regularities only when trained on enormous amounts of data, and make surprising or problematic predictions when presented with novel word collocations or syntactic structures (Lake and Baroni, 2018). How can we train unstructured neural sequence models that generalize compositionally? Recent work has introduced several compositional data augmentation schemes: rule-based procedures or learned models that synthesize artificial training examples to promote generalization (Andreas, 2020; Shaw et al., 2021; Akyürek et al., 2021; Zhang et al., 2022, *inter alia*). While often effective, existing methods are specialized to specific data modalities or datasets. The conditions under which they succeed, and their relationships to the formal principle of compositionality, have remained unclear. This paper presents a framework for understanding and improving such data-centric approaches to compositional modeling. We first provide a mathematical characterization of the principle of compositionality as a **constraint on data distributions** rather than model architectures. Intuitively, 639 ![1_image_0.png](1_image_0.png) we show that whenever a language understanding task can be solved compositionally, that task's data distribution is guaranteed to exhibit specific *symmetries*. These symmetries are functions that modify data points while preserving semantic acceptability. Fig. 1c gives an example of a symmetry in a visual question answering problem: in any wellformed (image, question, answer) triple, swapping the words *yellow* and *green* and their associated pixel values yields a valid new triple. Such symmetries exist even in complex tasks like instruction following (Fig. 1a), where they may depend not only on word-to-meaning mappings but relations between meanings (like the fact that red and green mix to produce brown). Building on this formal link between compositionality and symmetry, we introduce a procedure called LEXSYM that discovers symmetries automatically, then uses them to synthesize new training examples guaranteed to be correct and informative. Crucially, LEXSYM does not require a complete compositional theory for a given problem domain—only a *lexicon* of word meanings. These lexicons may themselves be automatically derived for most tasks. This makes LEXSYM very flexible: it requires little or no task-specific engineering, can be combined with any predictor, and unlike other compositional data augmentation schemes does not require tree-structured or even sequential data. Applied to ordinary neural sequence models, LEXSYM outperforms state-of-the-art models on the CLEVR COGENT visual question answering benchmark (Johnson et al., 2017) by a wide margin. LEXSYM is general, and matches or outperforms some specialized data augmentation schemes and models on the COGS semantic parsing task (Kim and Linzen, 2020; Kim et al., 2022), and the SCAN and ALCHEMY instruction following tasks (Lake and Baroni, 2018; Long et al., 2016). This paper thus offers two contributions: a theoretical contribution, in the form of a new lens on the principle of compositionality via symmetries of data distributions; and an empirical contribution, in the form of a data augmentation scheme that improves generalization on diverse language understanding tasks. The recent success of data augmentation approaches highlight the fact that compositional inductive bias need not require compositional models. Our work formalizes and generalizes this "data-centric" account of compositionality.1 ## 2 Background & Approach We begin with a discussion on the more general role of *symmetry* in machine learning applications. Definition 1. A **symmetry** of a set X is a function f satisfying: $$\{f(\mathbf{x}):\mathbf{x}\in X\}=X\qquad\qquad{\mathrm{(1)}}$$ * [10] A. A. K. That is, applying f to each element of X leaves X unchanged. A familiar example from computer vision is *reflection symmetry*: in object recognition problems, image classes are generally invariant under reflection (a zebra seen in a mirror is still a zebra). The set of (image, class) pairs thus has as a symmetry the function (x, y) 7→ (reflect(x), y). In many domains, especially those (like computer vision and computational chemistry) that are constrained by physical laws, knowledge of the symmetries 1Code will be released after the anonymity period. exhibited by a problem domain can dramatically reduce the difficulty of learning (Batzner et al., 2022; Simeonov et al., 2022). Past work has incorporated symmetry into machine learning problems in two ways. **Invariant and equivariant modeling** approaches structurally enforce symmetries via specialized architectures (improving generalization by decreasing the size of the hypothesis class; Cohen and Welling, 2016). **Data augmentation** approaches generate new training examples by applying known symmetries like reflections directly to training data (improving generalization by increasing dataset size; Shorten and Khoshgoftaar, 2019). Data augmentation, the focus of this paper, is model-agnostic, and can be used in conjunction with pre-training while producing the same asymptotic effects as specialized model architectures (Chen et al., 2020). The question this paper aims to answer is whether compositionality, like other domainspecific constraints, can be formalized in the language of symmetry. We are not the first to consider this question: Kiddon and Domingos (2015) define a theory of semantic equivalence in terms of symmetries of the set of natural language sentences, and Gordon et al. (2020) propose a model architecture for compositional semantic parsing via a symmetry that enforces *permutation invariance* of lexicon entries. LEXSYM also derives symmetries from lexicons. It builds on past work by (1) characterizing the algebraic relationship between compositionality and symmetry, explaining the effectiveness of both Gordon et al. (2020)'s approach as well as other data augmentation schemes based on token and phrase substitution (Andreas, 2020; Wang et al., 2018); (2) discovering symmetries automatically, and (3) showing how to leverage them in a model- and modality-agnostic way. Additional related work is discussed in Sec. 6. ## 3 Compositionality As Lexical Symmetry Our main theoretical result, and the foundation of our modeling approach, can be stated as follows: in any language understanding task that can be modeled compositionally, data for the task exhibits symmetries in the sense of Definition 1. We explain, formalize, and prove this statement below. We consider tasks defined by a space of possible examples X , of which a subset of examples X are **well-formed**. We assume each example x ∈ X is a discrete sequence [x1*, . . . , x*n], with xi drawn from a vocabulary Σ. Finally, we assume that well-formedness can be computed by a a binary **interpretation function** I : *X → {*0, 1} with I(x) = 1 iff x ∈ X. A wide variety of language understanding problems, from very simple to very complex, may be defined in this way: Example 1a: *Arithmetic Language Modeling*. Examples x are true sentences of the form a plus b is c, where a, b and c are numbers: I(one plus two is three) = 1 but I(*two plus two is five*) = 0. Example 1b: *Semantic Parsing*. Examples x are pairs (xNL, xLF), where xNL is an sentence, xLF is a logical form, and I(xNL, xLF) = 1 iff xLF represents a possible meaning of xNL (Fig. 1b). Example 1c: *Visual Question Answering*. Examples x are triples (xQ, xI, xA), where xQ is a question, xIis a (rasterized) image, xA is an answer, and I(xQ, xI, xA) = 1 iff xA is the answer to xQ in xI (Fig. 1c). Notice that the vocabulary Σ contains not just natural language words, but other kinds of data: logical symbols (1b) or even image patches (1c). "Language understanding" in each of these tasks is encapsulated by the function I. What does it mean for I to be *compositional*? Under most definitions, a compositional language understanding procedure should factorize into a lexicon, which captures meanings of words, and a composition procedure, which derives example-level interpretations from these meanings. We model word meanings in terms of *relations* between items in Σ. In arithmetic, to know the meaning of the word *five* is to know that it is a number, less than *seven*, the successor of *four*, etc. In semantic parsing, the meaning of the word cat is encapsulated by the fact that it is of the same type as dog, and translatable into the logical symbol cat′. We model this notion of word meaning by equipping Σ with extra structure describing these relations: Definition 2. A **lexical algebra** is a collection of relations r1*, . . . , r*n between vocabulary items, where each r : Σp → {0, 1}. A lexical algebra can represent type information, like "dog is a noun", as a unary relation; semantic correspondence, like "*sings* maps to sing′", as a binary relation; and richer semantic knowledge, like "*three* is the sum of one and two", with higher-order relations. We may then represent individual examples in purely relational terms: Definition 3. Denote the **lexical representation** L(x) = (R1(x)*, . . . , R*n(x)). R(x) is an order-p tensor whose (*i, . . . , j*) th entry is equal to r(xi*, . . . , x*j ). (If r is a binary relation, R(x) is an |x*| × |*x| matrix and R(x)ij specifies whether r holds between xi and xj .) See Fig. 2 for examples. Finally, we use this relational representation to define compositionality of interpretation functions: Definition 4. X is L**-compositional** if I(x) = C(L(x)) for some **composition procedure** C. In other words, X is compositional if it compute the well-formedness of x from word-level meanings and a generic composition procedure.2 This definition makes no assumptions about C beyond the fact that it can be defined purely in terms of L(x). It can be applied to many tasks: Example 2a: *Arithmetic Language Modeling*. Define r1 to be the ternary relation (*a, b, c*) 7→ 1[a+b=c]. Then C takes an example and checks whether the index corresponding to its three number words is true in R1. Example 2b: *Semantic Parsing*. A sketch of a 2Every I is trivially L-compositional with respect to an L that assigns every vocabulary item to a unique unary relation. semantic parser factorizable into a lexicon and an ![3_image_0.png](3_image_0.png) abstract composition function is depicted in Fig. 2. As a real-world example, in the factored CCG semantic parser of Kwiatkowski et al. (2011), words are assigned types and logical forms via a lexicon. These logical fragments are then composed by a parsing algorithm that depends only their types. Example 2c: *Natural Language Inference*. MacCartney and Manning (2014)'s Natural Logic framework provides a procedure for determining entailment relations between sentences via a set of sentence rewriting operations that use only wordlevel information about entailment relations. Under Definition 4, a sentence interpretation procedure is compositional if the meaning of a sentence can be derived in a generic way (C) from the meanings of its lexical items (L).3 We remark, finally, that the parsing procedure depicted in Fig. 2 is an idealization used to *motivate* our approach; our experiments use more flexible models. We are now ready to describe how, for compositional I, structure in L translates into structure in the set of well-formed examples X. Definition 5. A function f is a **homomorphism** of (Σ,L) (an "L-homomorphism") if: $\forall r\in{\cal L}$, $\forall x_{1}\ldots x_{p}\in\Sigma$ : $r(x_{1},\ldots,x_{p})=r(f(x_{1}),\ldots,f(x_{p}))$ (2) f "preserves the structure" of L, ensuring that pairwise relationships are preserved among symbols. Fig. 1 shows examples: in (c), for instance, the words *yellow* and *green* and the corresponding colors must be *swapped* to satisfy Eq. 2. Finally, we may state our main result: Theorem 1. If X is L-compositional, f is an L-homomorphism, and x ∈ X*, then* f(x) = [f(x1), . . . , f(xn)] ∈ X. Thus every homomorphism of L *well-formed examples* ∈ X. Proof. From Definition 3 and 5, Ri(f(x)) = $R_{i}(\mathbf{x})$$\forall i$. Then, $$\mathds{1}_{[f(\mathbf{x})\in X]}=\mathcal{I}(f(\mathbf{x}))$$ $$=\mathcal{C}(\mathcal{L}(f(\mathbf{x})))$$ $$=\mathcal{C}(R_{1}(f(\mathbf{x})),\ldots,R_{n}(f(\mathbf{x})))$$ $$=\mathcal{C}(R_{1}(\mathbf{x}),\ldots,R_{n}(\mathbf{x}))$$ $$=\mathcal{I}(\mathbf{x})=\mathds{1}_{[\mathbf{x}\in X]}\qed$$ Montagovian definition of compositionality as a homomorphism from sentences to meanings (Montague, 1970a). Corollary 1. *With the additional constraint that* f is an L-isomorphism *(i.e., has an inverse), then* f is a symmetry of X *in the sense of Eq.* 1. Here it suffices to show that the preimage of every x ∈ X is also in X; the proof is the same as Theorem 1 with f−1in place of f. Despite their simplicity, Theorem 1 and its corollary have an important consequence: if we can identify candidate entries in L, even if C *is unknown*, we can construct new examples x ∈ X that respect, and provide evidence for, the compositional structure of X. There is an intriguing (if inexact) structural similarity between Corollary 1 and Noether's theorem (Noether, 1918), which establishes an equivalence between symmetries of physical systems and their conserved quantities. Here, such symmetries imply constraints not on conservation laws but interpretation functions. ## 4 Lexsym**: Data Augmentation With** L**-Homomorphisms** Given a lexicon describing symbols and their relations, we have shown how to turn homomorphisms of the lexicon into transformations of a dataset. Each such function f that takes an example x as input, replaces each token xi ∈ x with a new one, and returns a well-formed example x′as output. Every L-homomorphism may thus be viewed as a recipe for *synthesizing training examples* from a small initial training set (Japkowicz et al., 2000). However, to make this a practical modeling tool, we need some way of constucting L-homomorphisms for a task of interest. Below, we describe how to do so automatically: first, starting with only a taskspecific lexicon L (Sec. 4.1); next, starting with only a dataset and no initial lexicon (Sec. 4.2). We term the resulting approach LEXSYM. ## 4.1 Deriving Homomorphisms From Lexicons Even in complex sequence modeling problems, useful lexicons are often simple enough that they can be specified by hand (Jones et al., 2012; Gordon et al., 2020). Given a pre-specified algebraic L, there is a straightforward procedure for generating the associated symmetries by enumerating all functions Σ → Σ and testing which ones satisfy Eq. 2. (See Algorithm 1 in Appendix B.) This algorithm is inefficient, but simple and practical for small |L|. ## 4.2 Deriving Lexicons From Datasets For some tasks, it may be difficult to manually specify an algebraic lexicon. We next describe how to infer one automatically. We focus on an important and extremely common class of language understanding problems with special structure. In semantic parsing and *instruction following*, examples x consist of (input, output) pairs in which inputs are sentences, outputs are meaning representations, and word meaning is characterized by a lexicon with two components. First, a set of unary **type predicates** {rτ } that assign words to types (like ENTITY in semantic parsing). Second, a **semantic correspondence relation** rϵ that specifies which actions or logical symbols can be derived from words (like *sings* → sing′). With n types, the lexicon required for these problems is L = (rτ1 , . . . , rτn, rϵ), which we abbreviate ({rτk}, rϵ) below. We now show how to improve upon the procedure in Sec. 4.1 by deriving L from data and sampling L-homomorphisms in constant time. Learning L We build on past work noting that dictionaries of semantic correspondences can be constructed using alignment algorithms (Brown et al., 1993). Given an input x consisting of a pair (xtext, xmeaning), we use existing algorithms to align tokens in individual training examples. Finally, we identify the most frequently occurring alignments and add these to the semantic correspondence relation. We may similarly use existing procedures to infer types by deriving them from part-of-speech tags or distributional patterns. See Appendix D for details of the alignment and type inference algorithms used in our experiments. These algorithms produce lexicons with three properties that are useful for the sampling scheme we describe next: types are *disjoint*, and semantic correspondences are *oneto-many* and *type-preserving* (if two words are of the same type, so are their translations). Sampling L**-homomorphisms** Once we have identified types and semantic correspondences, sampling L-homomorphisms is straightforward: Theorem 2. Let xi and xj ∈ Σ *have the same type* rτ (xi) = rτ (xj ) = 1*. For convenience, let* Ei = {x : rϵ(xi, x) = 1} *denote possible translations of* 643 xi. The f is an L*-homomorphism:* $$f(x)={\begin{cases}x_{j}&\quad\quad{\frac{p}{q}}\\ x_{i}&\quad\quad{\frac{p}{q}}\\ x^{\prime}\in E_{j}&\quad{\frac{p}{q}}\\ x^{\prime}\in E_{i}&\quad{\frac{p}{q}}\\ x&\quad\quad{\frac{p}{q}}\end{cases}}$$ xj if x = xi xiif x = xj x′ ∈ Ej if x ∈ Ei x′ ∈ Eiif x ∈ Ej x *otherwise* $$\begin{array}{l}{{i f x=x_{i}}}\\ {{i f x=x_{j}}}\\ {{i f x\in E_{i}}}\\ {{i f x\in E_{j}}}\\ {{o t h e r v i s e}}\end{array}\qquad\begin{array}{l}{{\quad(3)}}\\ {{\quad}}\\ {{o t h e r v i s e}}\end{array}$$ Proof is given in Appendix A. Theorem 2 yields an intuitive data augmentation procedure: select two (input, output) pairs of the same type, and *swap* them and any of their meanings wherever they occur. Fig. 1b shows an example. Eq. 3 is related to data augmentation schemes described by Andreas (2020) and Liu et al. (2021b), which synchronously substitute words or phrases (equivalent to removing cases 2 and 4). Unlike LEXSYM, these methods cannot guarantee correctness: in Fig. 1c, substituting *green* in place of *yellow* yields an image with two green objects and an incorrect answer. ## 5 Experiments Our experiments aim to evaluate whether LEXSYM can improve compositional generalization in downstream models. The main goal of these experiments is to evaluate *generality* across tasks and data modalities. Evaluation focuses on three diverse classes of language understanding problems: complex, context-dependent computations (Sec. 5.1), large, automatically derived lexicons (Sec. 5.2), and multi-modal data (Sec. 5.3). ## 5.1 Complex Computations We first test LEXSYM on the ALCHEMY task from the SCONE benchmark (Long et al., 2016)—a problem involving a complex sentence interpretation procedure that makes it challenging to apply existing data augmentation schemes. Data In ALCHEMY (Fig. 1a), models must execute a sequence of human-written English instructions x 1:N ins , on an initial state x 0 state consisting of beakers of colored liquids (textually represented as sequence of symbols "1: g g , 2: ..."), to predict the final state x N state. Initial and final states are encoded as sequences of color tokens. Predicting final states requires both grounding colors in state variables (brown → b , red → g ) and modeling what happens when colors are combined (e.g. mixing g and r yields b ). LEXSYM We manually construct a lexicon to showcase how to inject prior knowledge into LEXSYM. We encode word meaning in two relations: a semantic equivalence relation between color words and colors: $$r_{\epsilon}(c_{1},c_{2})=\begin{cases}1&c_{1}=\text{brown},\quad c_{2}=\textcircled{1}\\ 1&c_{1}=\text{red},\quad\quad c_{2}=\textcircled{2}\\ 1&c_{1}=\text{green},\quad c_{2}=\textcircled{2}\\ \vdots\\ 0&\text{otherwise}\end{cases}$$ and a ternary relation that encodes the result of mixing colors:4 $$r_{\texttt{mix}}(c_{1},c_{2},c_{3})={\begin{cases}1&c_{1}=c_{2}=c_{3}\\ 1&c_{1}\neq c_{2}\wedge c_{3}=\texttt{(0)}\\ 0&{\mathrm{otherwise}}\end{cases}}$$ Together, (rϵ, rmix, {rτk}), where {rτk} assigns different types to color words, colors, and remaining tokens. The homomorphic transformations of this lexicon exchange color words and colors but preserve mixing relations. Models and Training We train an LSTM (Hochreiter and Schmidhuber, 1997) and finetune a T5 transformer (Raffel et al., 2020) on the sequence-to-sequence prediction problem (x 1:N ins , x 0 state) → x N state Training details may be found in Appendix C. We compare these baseline models to their LEXSYM-augmented versions as well as the existing compositional data augmentation scheme of Liu et al. (2021b). Results See Table 1. LSTM+LEXSYM improves substantially over an LSTM. Preserving the homomorphism condition in Eq. 2 is extremely important: the procedure of Liu et al. (2021b), which naively substitutes aligned color pairs, actually hurts performance. Pre-trained models achieve strong initial results; combining pre-training with LEXSYM gives additional improvements. ## 5.2 Learned Lexicons We next show that for more conventional sequenceto-sequence problems, we may apply LEXSYM with automatically derived lexicons. 4In ALCHEMY, mixing non-identical colors produces b . | Model | ALCHEMY | SCAN (jump) | SCAN (around right) | COGS | COGS (nonce) | |---------------------------------------------------|-------------|---------------|-----------------------|-------------|----------------| | Previous Work on COGS & SCAN GECA (Andreas, 2020) | - | 99.94 ±0.10 | 98.50 ±1.90 | 47.74 ±4.52 | - | | LeAR (Liu et al., 2021a) | - | - | - | 97.70 ±0.70 | - | | LexLSTM (Akyurek and Andreas, 2021) | 36.80 ±1.96 | 99.14 ±1.55 | 88.41 ±7.35 | 82.17 ±0.72 | 81.40 ±0.40 | | No Pre-training LSTM | 41.72 ±1.15 | 000.41 ±0.34 | 08.65 ±4.52 | 61.13 ±4.12 | 61.13 ±4.12 | | + Substitute (e.g. Liu et al., 2021b) | 40.52 ±0.84 | 099.95 ±0.10 | 99.17 ±0.93 | 81.99 ±0.50 | 77.62 ±0.78 | | + LEXSYM | 45.85 ±2.00 | 100.00 ±0 | 99.51 ±0.48 | 81.86 ±0.90 | 77.25 ±0.34 | | Language Pre-training T5 | 84.95 ±0.44 | 93.60 ±0 | 38.40 ±0.90 | 83.30 ±0.10 | 64.20 ±2.00 | | +CSL-Aug* (Qiu et al., 2022) | - | 99.70 ±0 | - | 99.50 ±0 | - | | +LEXSYM | 85.48 ±0.16 | 99.96 ±0.03 | 97.29 ±2.16 | 83.62 ±0.27 | 76.74 ±2.23 | Table 1: Results on semantic parsing and instruction following. We provide mean and standard deviations over 5 random seeds. LEXSYM improves significantly over baselines, with and without large-scale pretraining. *Uses a customized formal representation. | COGENT | CLEVR | | |--------------------------------------------------|-----------|-----------| | Visual Pre-training Human (Johnson et al., 2017) | - | 92.6 | | Film (Perez et al., 2018) | 78.8 | 97.7 | | S-MAC (Marois et al., 2018) | 78.7 | 98.9 | | NSVQA (Yi et al., 2018) | 63.9 | 99.7 | | Seq2Seq Baselines T5 | 79.7 | - | | LexLSTM | 62.1 | - | | No Pre-Praining VQATransformer | 73.3 ±1.0 | 93.6 ±0.5 | | + Substitute (e.g. Liu et al., 2021b) | 84.4 ±0.7 | 90.8 ±0.3 | | + LexSym | 85.9 ±0.9 | 92.0 ±0.9 | Data We study two standard compositional generalization benchmarks: the SCAN (Lake and Baroni, 2018) instruction following and COGS (Kim and Linzen, 2020, Fig. 1b) semantic parsing datasets. SCAN consists of simple instruction following tasks in which strings are translated into sequences of actions. We focus on the *jump* split, which measures models' ability to compose words that only appeared in isolation during training, and the *around right* split, which measures generalization to novel collocations. The COGS dataset tests compositional generalization in semantic parsing. The dataset includes English (sentence, logical form) pairs, with systematic differences between train and test set sentence structure. We include a variant containing nonce words (Kim et al., 2022) to disentangle general compositional skills from lexical knowledge acquired during pretraining. See Appendix G for dataset statistics. LEXSYM We use automatic lexicon extraction to find semantic correspondence relations (rϵ) and types ({rτk}) as described in Appendix D. Next, we apply swap-based augmentation (Eq. 3). Models We use the same models as Sec. 5.1, along with a strong semi-structured model, LeAR (Liu et al., 2021a) tailored for COGS, and another substitution based augmentation (Andreas, 2020) tailored for SCAN. Following Akyurek and Andreas (2021), we equip the LSTM for COGS with a copy mechanism as it achieves significantly better results than Kim and Linzen (2020)'s baseline. Results On SCAN, LEXSYM obtains near-perfect accuracy in both *jump* and *around right* splits. On the original COGS datasets, LEXSYM substantially outperforms the LSTM model and GECA augmentation, and is comparable to a neural sequence model specialized for lexical generalization (LexLSTM). Stronger results can be achieved with models specifically tailored toward semantic parsing tasks (LeAR). In both tasks, LEXSYM also improves upon large-scale pre-training. ## 5.3 Multi-Modal Data Finally, we combine learned lexicons with nonsequential data to advance the state of the art on a long-standing visual question answering challenge. Data The CLEVR dataset (Johnson et al., 2017, Fig. 1c) contains English-language questions about generated 3D scenes containing multiple objects. Questions involve complex computational operations including quantification, comparison, and spatial reasoning. CLEVR has been a popular testbed for evaluating composition in visual question answering models. Our main experiment uses the COGENT split of the dataset, which focuses on compositional generalization. In the CLEVRCOGENT training set (Split A), which contains roughly 700K (question, image, answer) triples, all cubes are gray, blue, brown or yellow, while all cylinders are red, green, purple or cyan. In the test set (validation set of Split B), these are reversed. LEXSYM In VQA and other multi-modal tasks, part of the input is continuous (e.g. images and videos). Recent work has shown that it is possible to *learn* high-quality discrete representations of continuous input data. For example, in the VQVAE model of van den Oord et al. (2017), a continuous image is transformed into a grid of categorical codes, with individual codes representing color, and in some cases materials and illumination (examples in Table 3). We use this discretization procedure for our experiments (see Appendix C.1 for details). We use the same algorithm as previous section to extract lexical relations. Models Most prior work on visual question answering has used pre-trained convolutional networks to encode images, and recurrent networks to encode questions and generate answers. For experiments on CLEVR, we use a simplified model in which both questions and images are mapped to answers by a transformer model, similarly to Ramesh et al. (2021). See Appendix C.2 for details. Both LEXSYM augmentation and this VQATransformer model operate over sequences of discrete visual codes produced by a vector-quantized variational autoencoder. Once these discrete representations have been produced, we infer lexicons and perform data augmentation directly to these representations, without re-synthesizing images (though such synthesis is possible, as in Table 3, to interpret model behavior). The COGENT task is very different from the sequence modeling tasks discussed above: inputs contain many tokens, and the training set is orders of magnitude larger. GECA and CSL-Aug, which have a high polynomial dependence on sequence length, could not be applied as they fail to terminate within a reasonable amount of time. Results In Table 2, a transformer model with LEXSYM achieves state-of-the-art results on the CLEVR-COGENT dataset, reducing errors by roughly 33% relative to the best existing system. LEXSYM also outperforms substitution based-data augmentation (Liu et al., 2021b), particularly on semantically complex utterances involving quantification (App. Table 4). On the IID CLEVR split, LEXSYM's performance is comparable to humans, and somewhat behind pre-trained models. ## 6 Other Related Work Lexicalized neural models Word-level alignments between input and output sequences were an essential feature of statistical phrase- and treebased sequence models (Chiang et al., 2005; Koehn et al., 2003). Neural scoring functions were sometimes integrated into these models (Misra and Artzi, 2016). Neural models with attention (Bahdanau et al., 2015) do not require explicit alignment, though several pieces of past work have shown that incorporating explicit token-level correspondences improves generalization (Akyurek and Andreas, 2021; Prabhu and Kann, 2020; Pham et al., 2018). The semantic correspondence function in Sec. 4 plays the same role as the input–output dictionary in these methods, but LEXSYM as a whole is more general: it is not restricted to modeling sequenceto-sequence problems, and can infer and exploit correspondence relations between component of an example. To the best of our knowledge, this paper is also the first to make use of token-level alignments in joint neural models of text and images. Compositionality in representation learning While we have focused on compositionality as a property of data distributions or interpretation functions, another line of work in machine learning and language evolution has studied compositionality as an emergent property of learned representations (Andreas, 2019; Resnick et al., 2019; Brighton and Kirby, 2006). In settings where representational compositionality is desirable (e.g. to train communication protocols that can generalize to new states), LEXSYM might provide a tool for promoting it. Equivariant Sequence Models As mentioned in Sec. 2, our work builds on existing approaches that control generalization with specialized model architectures designed to be equivariant to permutations of a pre-specified lexicon (if f(x1 *· · ·* xn) = y1 *· · ·* ym then 646 f(π(x1)*· · ·* π(xn)) = π(y1)*· · ·* π(ym) for a permutation π) (Gordon et al., 2020; White and Cotterell, 2022). LEXSYM differs from these approaches in three ways. First, LEXSYM is modelagnostic and compatible with pre-training. Second, LEXSYM is compatible with (and automatically derives transformations for) more complicated relations than input–output correspondences, making it possible to apply to tasks like ALCHEMY where such relations are important. Finally, LEXSYM gracefully handles (possibly noisy) learned lexicons, making it applicable to tasks like COGENT with complex or uninterpretable token mappings. Data Augmentation Data augmentation approaches are widely used across machine learning application domains featuring known invariances of the data distribution (Japkowicz et al., 2000; Jia and Liang, 2016; Shaw et al., 2021). Substitutionbased schemes that replace words with synonyms, or synchronously replace words and their translations, are widely used for machine translation and general de-biasing (Liu et al., 2021b; Wang et al., 2018; Wei and Zou, 2019). ## 7 Limitations And Future Directions While Sec. 3 characterizes the effect of general Lhomomorphisms, LEXSYM specifically produces single-token swaps. In images represented as discrete symbol sequences, if a single symbol simultaneously encodes multiple visual features (e.g. color and texture), these features will remain entangled in synthesized examples. It will not exchange substructures larger than a single token, and thus will not synthesize examples longer than those already present in the training set (Lake et al., 2019). This is because LEXSYM targets compositionality but not *recursion*, which is also required to model the full range of human-like generalizations in sequence learning problems. LEXSYM is also sensitive to the nature of the tokenization scheme itself. In morphologically rich languages, for example, LEXSYM may need to be applied not on top of words or segments, but instead canonicalized morphemes produced by learned morphological analyzers (Narasimhan et al., 2015; Bergmanis and Goldwater, 2017; Cotterell and Schütze, 2018) (analogous to the use of learned image patch representations rather than pixels in our VQA experiments). Finally, LEXSYM does not induce some of the generalizations obtained other methods for improving compositional generalization, especially those that exploit extra structure (e.g. tree-shaped inputs and outputs) in the semantic parsing domain (e.g. Liu et al., 2021a). It might serve as a platform for future versions of those methods that offer greater generality and formal guarantees. ## 8 Conclusion We have presented LEXSYM, a new data augmentation method that improves compositional generalization of neural models in multiple domains. LEXSYM is derived from a characterization of the principle of compositionality as a constraint on the symmetries of data distributions, and a procedure for automatically identifying these symmetries using token-level alignments. Our results highlight the fact that many inductive biases targeted by specialized models in NLP can be alternatively, and often more flexibly, expressed as a hypothesis about the structure of the distribution to be modeled. ## Acknowledgements This work was supported by the MachineLearningApplications initiative at MIT CSAIL, the MIT–IBM Watson AI lab, and the National Science Foundation under grant CCF-2217064. 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As noted there, we make the following additional assumptions (satisfied by our lexicon learning algorithms): (i) *Types are disjoint*, i.e. every symbol belongs to a single type: ∀x ∈ Σ, |τx| = |{rτk| rτk (x) = 1}| = 1. (ii) Semantic correspondences are one-to-many from text to meaning. This means that no two text symbols can translate into the same meaning symbol: Ei ∩ Ej = 1xi=xj and all rϵ(x /∈ xtext, y) = rϵ(*y, x /*∈ xmeaning) = 0. (iii) *Semantic correspondence is type preserving*: all symbols in a correspondence class have the same type τei∈Ei = {rτEi}. To show that f is an L-homomorphism, we want to show that rϵ(f(x1), f(x2)) = rϵ(x1, x2) for any x1, x2. The transformation function and all the definitions are symmetric to indices i and j (i − j symmetry), so it is sufficient to show the correspondence relations stay the same for below cases only: (a) x1 = xi, x2 = xi: $$r_{\epsilon}(f(x_{i}),f(x_{i}))=r_{\epsilon}(x_{j},x_{j})=0=r_{\epsilon}(x_{i},x_{i})$$ (by ii) **(b)**: $x_{1}=x_{i},x_{2}=x_{j}$: $$r_{\epsilon}(f(x_{i}),f(x_{j}))=r_{\epsilon}(x_{j},x_{i})=0=r_{\epsilon}(x_{i},x_{j})\,$$ (by ii) $${\mathrm{(c)}}\ x_{1}=x_{i},x_{2}\in E_{i};$$ $$\begin{array}{c}{{r_{\epsilon}(f(x_{i}),f(x_{2}))=r_{\epsilon}(x_{j},x^{\prime}\in E_{j})}}\\ {{=1=r_{\epsilon}(x_{i},x_{2})}}\end{array}$$ $$(b y\;d e f m i t i o n\;o f E_{i}\;a n d\;E_{j})$$ $${\mathrm{(d)~}}x_{1}=x_{i},x_{2}\in E_{j}\colon$$ $$r_{\epsilon}(f(x_{i}),f(x_{2}))=r_{\epsilon}(x_{j},x^{\prime}\in E_{i})$$ $$=1_{x_{i}=x_{j}}=r_{\epsilon}(x_{i},x_{2})$$ $(I-\cdots)$ $$(b y\;i i)$$ (e) x1 = xi, x2 ∈ {{ / xi} ∪ {xj} ∪ Ei, Ej}: $\mu_{1},\mu_{2}\neq(\mu_{1})\neq(\mu_{2})\neq(\mu_{1},\mu_{2})$ $\mu_{1}(\mu_{2})=\mu_{2}(\mu_{1},\mu_{2})$ (f) $x_{1}=x_{i},x_{2}\notin\{\{x_{i}\}\cup\{x_{j}\}\cup E_{i},E_{j}\}$: same steps as (e) $${\mathrm{(g)~}}x_{1}\in E_{i},x_{2}=x_{i}\colon$$ $$\begin{array}{c}{{r_{\epsilon}(f(x_{1}),f(x_{i}))=r_{\epsilon}(x^{\prime}\in E_{j},x_{j})}}\\ {{=0=r_{\epsilon}(x_{1},x_{i})}}\end{array}$$ (by ii) (h) x1 ∈ Ei, x2 = xj : *same steps as (g)* **(i)**: $x_{1}\in E_{i},x_{2}\in\{\{x_{i}\}\cup\{x_{j}\}\cup E_{i},E_{j}\}$: $$r_{\epsilon}(f(x_{1}),f(x_{2}))=r_{\epsilon}(x^{\prime}\in E_{j},x_{2})$$ $$=0=r_{\epsilon}(x_{1},x_{2})$$ (by ii) Finally, we require rτ (x) = rτ (f(x)) for any x and τ . Since we assume all items in Ei belong to a type matching xi (likewise for j), and types are disjoint, this follows immediately from the definition of f, which only swaps symbols of the same type. ## B Enumerating L**-Homomorphisms** A Simple Algorithm Is Given Below: Algorithm 1 L-homomorphism enumeration input: Lexicon L = (Σ, r1*, . . . , r*n) for f ∈ Σ Σ do h ← 1 $n\gets1$ **for**$i=1...n,x_{a}...x_{b}\in\Sigma^{p}$**do** **if**$r(x_{a},\ldots,x_{b})\neq r(f(x_{a}),\ldots,f(x_{b}))$**then** $h\gets0$ **end if** **end for** **if**$h$**then** **yield**$f$ **end if** ## End For C Implementation Details C.1 Vqvae Details We use a discrete variational auto-encoder (van den Oord et al., 2017) to encode the images 16 × 16 grids of discrete codes. We used a code-book with n = 32 tokens associated with d = 64 dimensional ![13_image_0.png](13_image_0.png) learned latent vectors. The original image size (480, 320) is cropped to (440, 300) and resize our images into (128, 128) pixels. The encoder convolutional neural network has three down-sampling layers which output 16 × 16 × d size hidden representations. For encoder and decoder CNN architectures, we follow the implementation provided in a public Pytorch implementation5 by adding one more up-sampling and down-sampling layer to adjust our image size. We use exponential moving average to update latent vectors as in official implementation6 We train the model on the images of the same training data and did not use any external data. We use batch size of 512, and learning rate 0.0003 with the Adam optimizer (Kingma and Ba, 2015). We clip the gradients to 5.0. Hyperparameters were selected by sweeping d over {64, 128}, image sizes over {128, 144}, and n over {24, 32, 48} to maximize the the number of aligned tokens in the lexicon. For each experiments in Table 2, we run VQVAE for 4 random seeds and select the codebook that gives the largest IBM model likelihood for training data. Each experiment takes 10 hours in 4 NVIDIA V100 GPUs. ## C.2 Vqa Transformer Details The Transformer takes tokenized images xI and the question xQ and outputs answers as follows: $c_{\bf x_{I}}=$ VQVAE${}_{\rm enc}({\bf x_{I}})$ $e_{Q}=W_{Q}{\bf x_{Q}}+1$D${}_{\rm positional}({\bf x_{Q}})$ $e_{\bf x_{I}}=W_{c}c_{\bf x_{I}}+2$D${}_{\rm positional}(c_{\bf x})$ $h=$ Transformer([$e_{Q}\,e_{\bf x_{I}}$]) ${\bf x_{A}}=$ argmax softmax($W_{\rm proj}h_{\rm start}$) We follow the hyper-paramters provided in (Popel and Bojar, 2018). Transformers have 4 heads, 512dimensional hidden vectors (same with embedding sizes) and 10 layers. We provide the dimensions in Eq. 4: $$\begin{array}{l}{{\bf{x}}_{I}:3\times128\times128}\\ {c_{{\bf{x}}_{I}}:32\times16\times16}\\ {W_{c}:512\times32}\\ {e_{{\bf{x}}_{I}}:512\times(16\times16)}\\ {e_{Q}:512\times|V_{t e x t}|}\\ {W_{Q}:512\times|{\cal{V}}_{\mathrm{text}}|}\\ {h:512\times(|Q|+16\times16)}\\ {h_{\mathrm{start}}:512\times1}\\ {W_{\mathrm{proj}}:512\times|{\cal{V}}_{\mathrm{text}}|}\end{array}\tag{5}$$ Models are trained using the Adam optimizer with and Noam learning rate scheduler (Vaswani et al., 2017) with lr = 1.0 and 16k warming steps as provided in Popel and Bojar (2018). We use a batch size of 1024 and we train for 200k steps, which takes 48 hours on 8 NVIDIA V100 GPUs. In Fig. 3, we provide the sketch of overall pipeline. ## C.3 Baselines: Lstm Details We use the implementation provided by (Akyurek and Andreas, 2021), increasing the number of training iterations from 8k to 15k for augmented training runs in COGS, SCAN datasets. For the ALCHEMY dataset, we optimize iteration count over {8k, 15k, 25k, 50k} based on validation accuracy, and found 25k to be optimal. For the CLEVR dataset, we optimize itreation count over {8k, 15k, 25k, 50k} for CLEVR and CLEVRCOGENT dataset based on CLEVR's validation accuracy. ## C.4 Baselines: T5 Details We use the Huggingface (Wolf et al., 2019) implementation T5-base model. The difference between our T5 baselines results and the results in Qiu et al. (2022) due to their usage of different intermediate representation for the output in order to keep our evaluation consistent with other previous work. We try to optimize (learning rate, learning rate scheduler) and training parameters (iteration count) of Qiu et al. (2022) and (Akyurek and Andreas, 2021), use the best setting for the given dataset. ## C.5 Alignment Model Details In our experiments, we use the best alignment method reported in (Akyurek and Andreas, 2021), which is IBM Model 2 for all datasets except the SCAN dataset that uses their proposed algorithm, to obtain our initial alignments A = {(xi, xj ): set of tuples contains aligned tokens. We run alignment algorithms between xtext and xmeaning. For SCAN and COGS, xtext is the actual inputs, xmeaning is the actual outputs. In ALCHEMY, xtext is instructions, xmeaning is beaker states. In VQA experiments, xtext question and answer words, xmeaning VQVAE codes. We disable *diagonalization* in FastAlign as it includes non-language structured VQVAE codes. ## D Lexicons D.1 Lexicon Learning Extracting semantic correspondences rϵ(xi, xj ) Given the initial alignments A in Appendix C.5, we remove every xj that is not aligned to at least 1% of occurrences of xiin the dataset. We then produce a *one-to-many* lexicon by deleting lexicon entries (xi, xj ) and (x′i , xj ) when both exist. With, these alignment creates entries in rϵ(xi, xj ) = 1(xi,xj )∈A Extracting Types rτ (x) Given the partition of the data points (xtext, xmeaning), our type finding algorithm is essentially *unsupervised clustering* of the text symbols in xtext. The types of matching xmeaning symbols are automatically determined by the correspondence relation, rϵ found above. In all our datasets xtext is English, so the symbols that goes into following clustering algorithm are actual words. Following Clark and Eyraud (2007) and Andreas (2020), we assign types to individual words based on their environments. For each symbol, x ∈ Σ, that has at least one equivalent symbol in A, we define the context κ(x) = {(*α, β*) : αxβ ∈ X}: the set of strings (*α, β*) that appear surrounding x in the training set. (If the two examples in Fig. 1 formed the entire training set, we would have κ(*yellow*) = κ(*green*) = {(Q: How many, *objects? A: 1*)}.). 7 We then represent Σ as a graph with an edge between each xi and xj where κ(xi) ∩ κ(xj ) ̸= ∅ (Clark and Eyraud's *syntactic congruence* relation) and xi and xj has same part-of-speech tag according to spaCy pipeline with en-core-web-lm language model 8. We assign each connected component of this graph a distinct type. This is only one possible approach to typing; alternatives might use clustering of distributed representations. ## D.2 Extracted Lexicons In this section, we present lexicon entries for symbols that we learned through our typing algorithm. SCAN We present equivalance relations that we extracted from SCAN training dataset. Source symbol Type Target Symbol(s) jump t1 I_JUMP walk t1 I_WALK run t1 I_RUN look t1 I_LOOK left t2 I_LEFT right t2 I_RIGHT COGS Since the extracted lexicon is large for semantic parsing, we present only some of the equivalance relations that we extracted from COGS training data for reference. Source symbol Type Target Symbol(s) COGENT We present equivalance relations that we extracted CLEVR-COGENT training data. The lexicon we found includes all the color symbols. The target symbols given here are learned VQVAE codes. In Appendix E, we show these codes on top of the images to qualitatively verify the alignments. Source Symbol Type Target Symbols ## E Samples & Statistics | baked | t1 | bake | |---------|------|--------| | noticed | t1 | notice | | helped | t1 | help | | dog | t2 | dog | | boy | t2 | boy | | sailor | t2 | sailor | We present examples generated by LEXSYM in Table 3. As we performed augmentation random and online during training, and we do not have a static augmented set to calculate statistics for. Instead, we run a single iteration of our augmentation function over all examples with our augmentation function and obtain following statistics: Note that, in CLEVR, we consider the novelty based on (question + answer) string since the generated image codes can be novel but the resulting image not. The following differences are significant under a paired t-test: ## E.1 Statistical Significance Tests For Table 1 | red | t1 | 9 | |--------|------|--------| | purple | t1 | 25, 29 | | cyan | t1 | 28 | | blue | t1 | 20 | | green | t1 | 11 | | yellow | t1 | 23, 18 | | gray | t1 | 6 | | brown | t1 | 2 | The following differences in Table 1 are significant under a paired t-test: ## Alchemy: | Augmentation Statistics | COGS | CLEVR | SCAN | ALCHEMY | |---------------------------|--------|---------|--------|-----------| | # Augmented samples | 24155 | 699960 | 14670 | 18285 | | # Novel samples | 23301 | 548277 | 7304 | 11786 | | # Unique novel samples | 22617 | 548277 | 4851 | 11786 | | # Samples in test | 121 | 0 | 7304 | 0 | | # Unique samples in test | 109 | 0 | 4851 | 0 | - T5+LEXSYM > T5 (p < 0.05) - LSTM+LEXSYM > LSTM+Substitute, LSTM, LexLSTM (p < .00001) ## Cogs: - T5+LEXSYM > T5 (p < .00001) - LSTM+LEXSYM > LSTM, (p < .00001) ## F Clevr-Cogen**T Detailed Results** COGENT results are presented in Table 4. ## G Data For CLEVR-COGENT (Johnson et al., 2017), we use training set for Split-A as our training set, validation set for Split-B as our validation set, and validation set of Split-B as our test set. The CLEVR and ALCHEMY datasets is released under the Creative Commons CC BY 4.0 license. The COGS datasets (Kim and Linzen, 2020; Kim et al., 2022) are released under MIT license. SCAN (Lake and Baroni, 2018) datasets are released under BSD license. The train, validation and test set sizes are given as below. | Generated Sentence | Generated Logical form | Original Sentence | Original Example Logical Form | |------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|----------------------------------|-----------------------------------------------------------------| | A cake was baked by Scarlett . | cake(x1) AND bake.theme(x3, x1) AND bake.agent (x3, Scarlett ) | A cake was stabbed by Scarlett . | cake(x1) AND stab.theme (x3, x1) AND stab.agent (x3, Scarlett ) | | The bunny needed to cook . | *bunny(x1); need.agent(x2, x1) AND | The girl needed to cook . | *girl (x1); need.agent (x2, x1) AND | | need.xcomp (x2, x4) AND cook.agent(x4, x1) | need.xcomp(x2, x4) AND cook.agent (x4, x1) | | | | The bun hunted Emma . | *bun(x1); hunt.agent(x2, x1) AND hunt.theme (x2, Emma) | The teacher hunted Emma . | *teacher(x1); hunt.agent(x2, x1) AND hunt.theme(x2, Emma) | | Generated Text | Generated Image | Original Text | Original Image | | How many metallic objects are either tiny yellow things or blocks? A: 1 | How many metallic objects are either tiny red things or blocks? A: 1 | | | | What is the size of the other object that is the same material as the big brown thing A: Large | What is the size of the other object that is the same material as the big purple thing? A: Large | | | Table 3: Generated samples for CLEVR-COGENT and COGS datasets. In CLEVR-COGENT, our method operate on displayed VQVAE symbols on top of the images and we can decode it to actual images as displayed here. The generated yellow cylinder in the first row is an unseen color+shape combination. | CLEVR-COGENT | | | | | | | |-------------------------------------------|-----------|-----------|------------|-----------|-----------|-----------| | VQATransformer (No Pre-Praining) Baseline | 73.3 ±1.0 | 71.0 ±1.6 | 85.7 ±0.74 | 83.5 ±0.1 | 64.4 ±0.7 | 81.4 ±1.2 | | + Substitute (e.g. Liu et al., 2021b) | 84.4 ±0.7 | 76.7 ±1.1 | 89.5 ±0.3 | 88.8 ±0.3 | 85.1 ±1.0 | 88.0 ±0.6 | | + LexSym | 85.9 ±0.9 | 80.1 ±0.9 | 91.1 ±0.5 | 91.0 ±0.7 | 85.2 ±1.3 | 88.9 ±0.7 | | Dataset | Train | Validation | Test | |------------------|---------|--------------|--------| | ALCHEMY | 18285 | 1225 | 4495 | | SCAN (jump) | 14670 | - | 7706 | | (around right) | 15225 | - | 4476 | | COGS (original) | 24155 | 3000 | 21000 | | (nonce) | 24155 | 3000 | 21000 | | CLEVR (original) | 699989 | 149991 | | | (CoGenT) | 699960 | - | 150000 | Table 4: Breakdown of CLEVR-COGENT Results ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 8 (Limitations) ✓ A2. Did you discuss any potential risks of your work? 9 (Impact Statement) ✓ A3. Do the abstract and introduction summarize the paper's main claims? Left blank. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Left Blank. ✓ B1. Did you cite the creators of artifacts you used? Left blank. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. 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Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

sun-etal-2023-layer

Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition

https://aclanthology.org/2023.acl-long.39

Multi-modal emotion recognition has gained increasing attention in recent years due to its widespread applications and the advances in multi-modal learning approaches. However, previous studies primarily focus on developing models that exploit the unification of multiple modalities. In this paper, we propose that maintaining modality independence is beneficial for the model performance. According to this principle, we construct a dataset, and devise a multi-modal transformer model. The new dataset, CHinese Emotion Recognition dataset with Modality-wise Annotions, abbreviated as CHERMA, provides uni-modal labels for each individual modality, and multi-modal labels for all modalities jointly observed. The model consists of uni-modal transformer modules that learn representations for each modality, and a multi-modal transformer module that fuses all modalities. All the modules are supervised by their corresponding labels separately, and the forward information flow is uni-directionally from the uni-modal modules to the multi-modal module. The supervision strategy and the model architecture guarantee each individual modality learns its representation independently, and meanwhile the multi-modal module aggregates all information. Extensive empirical results demonstrate that our proposed scheme outperforms state-of-the-art alternatives, corroborating the importance of modality independence in multi-modal emotion recognition. The dataset and codes are availabel at \url{https://github.com/sunjunaimer/LFMIM}

# Layer-Wise Fusion With Modality Independence Modeling For Multi-Modal Emotion Recognition Jun Sun1**, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang,** Yulong Liu, Yuxin Huang, Shu-Kai Zheng, Taihao Li2∗ Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China [email protected], [email protected] ## Abstract Multi-modal emotion recognition has gained increasing attention in recent years due to its widespread applications and the advances in multi-modal learning approaches. However, previous studies primarily focus on developing models that exploit the unification of multiple modalities. In this paper, we propose that maintaining modality independence is beneficial for the model performance. According to this principle, we construct a dataset, and devise a multimodal transformer model. The new dataset, CHinese Emotion Recognition dataset with Modality-wise Annotations, abbreviated as CHERMA, provides uni-modal labels for each individual modality, and multi-modal labels for all modalities jointly observed. The model consists of uni-modal transformer modules that learn representations for each modality, and a multi-modal transformer module that fuses all modalities. All the modules are supervised by their corresponding labels separately, and the forward information flow is uni-directionally from the uni-modal modules to the multimodal module. The supervision strategy and the model architecture guarantee each individual modality learns its representation independently, and meanwhile the multimodal module aggregates all information. Extensive empirical results demonstrate that our proposed scheme outperforms state-of-theart alternatives, corroborating the importance of modality independence in multi-modal emotion recognition. The dataset and codes are availabel at https://github.com/ sunjunaimer/LFMIM. ## 1 Introduction The goal of human emotion recognition is to automatically detect or categorize the emotional states of human according to some inputs. Nowadays, emotion recognition can be found in ∗Corresponding author a broad range of applications, including but not limited to emotional support (Tu et al., 2022; Liu et al., 2021), human-computer interaction (Chowdary et al., 2021) and healthcare surveillance (Dhuheir et al., 2021). Henceforth, emotion recognition has attracted increasing attention from both research community and industry in recent years (Hu et al., 2021a; Shen et al., 2021). The early works perform emotion recognition primarily with a single modality (Mehendale, 2020; Alvarez-Gonzalez et al., 2021; Schuller et al., 2010), e.g., vision, text, audio and so on. Recent multi-modal approaches have showcased more appealing performance than their uni-modal counterparts (Hu et al., 2021b; Zhao et al., 2022). However, most existing literature on multimodal learning overemphasizes the combination of different modalities without fully respecting modality independence, which might be harmful to the model. In the sequel, we illustrate this through the lens of datasets and model design. Datasets Current datasets for multi-modal emotion recognition are usually annotated with the joint observation of all modalities, resulting in shared labels for all modalities (Zadeh et al., 2016, 2018; Busso et al., 2008; Poria et al., 2019; Li et al., 2017b). This leads to the fact that all modalities in the multi-modal model are supervised by the same common labels, which reduces the modality diversity and might even mislead some modalities (Yu et al., 2020). In practice, it is anticipated that inconsistent labels will be attained if we annotate different modalities separately. In this circumstance, in order to learn diverse and modality-specific representations, the modules for different modalities are expected to be trained with their own labels rather than the common labels. Model design The emerging transformer has contributed to many success stories in natural language processing and computer vision (Devlin et al., 2019; Dosovitskiy et al., 2020). Naturally, it is introduced to the field of multi-modal learning thanks to its versatility in dealing with sequences of different forms. Multi-modal transformer (MulT) is proposed in(Tsai et al., 2019), which adopts cross-modal attention to fuse any pair of modalities, and then incorporates all the information. The drawback of MulT is that it has a complexity of A2n in terms of the number of cross-modal transformer blocks (n is the number of modalities). To address the complexity issue, progressive modality reinforcement (PMR) and multimodal bottleneck transformer (MBT) which scale linearly with the number of modalities are proposed in (Lv et al., 2021) and (Nagrani et al., 2021), respectively. PMR and MBT devise a message hub which draws information from the uni-modal blocks, performs fusion, and returns the fused common information to the uni-modal blocks. It can be concluded that, both MulT and the message hub based models reinforce each modality with the information from other modalities. This can lead to the problem that the model might rely heavily on some modalities, leaving other modalities under-trained. The reason is that the dominated modalities can cheat by peeping at the well-learned modalities, and hence becomes "lazy" in their own learning process. With the above observations of prior datasets and models for multi-modal emotion recognition, it is clear that existing studies primarily focus on establishing the dependency between modalities and capturing combined multi-modal information for the final task. Different modalities are coupled from both the labels and the model structure, and the resultant representations of different modalities share rich common information and lack diversity. However, it has been observed that more differentiated information from modalities facilitates to improve the complementarity between the modalities (Yu et al., 2020; Qu et al., 2021). In the light of the limitations of current datasets and fusion models, in this work, we construct a new dataset and propose a transformer model for multi-modal emotion recognition. Each sample in our dataset is annotated with three uni-modal labels corresponding to three modalities—text, audio and vision, and a multi-modal label for all modalities jointly observed. The proposed model employs three uni-modal transformer blocks as the backbones for the three individual modalities and one multi-modal transformer block for multi-modal information fusion. The uni-modal transformers process their own information independently, and are supervised by the corresponding unimodal labels; the multi-modal transformer fuses information from the uni-modal transformers layer by layer, and is supervised by the multimodal labels. The forward information flow in the model is uni-directionally from the unimodal modules to the multi-modal module. The supervision strategy and the uni-direction information flow promote modality independence, which reduces mutual information and increases complementary information across modalities (as Figure 2(b) in Section 4 illustrates). Therefore, the overall effective information for the final emotion recognition task aggregated by the multimodal module can be maximized. The proposed model features Layer-wise Fusion with Modality Independence Modeling, termed LFMIM. In summary, the contributions of this paper are mainly threefold. - A new dataset is built for multi-modal emotion recognition, of which the modalities are annotated separately. Apart from multi-modal emotion recognition, the dataset supports the research for the modality (label) inconsistency problem in multi-modal learning. - A model that encourages modality independence is proposed, and it is trained with uni-modal labels and multi-modal labels simultaneously. The model leads to more diverse representations, and therefore captures more complementary clues from different modalities. - The proposed model demonstrates substantial improvement over existing competing models. The results shed light on the future research on the balance between modality dependence and independence in multi-modal learning. ## 2 Related Works There are a large volume of relevant works on multimodal emotion recognition, for which interested readers can refer to survey papers (Siddiqui et al., 2022; Ahmed et al., 2023) and references therein. In this section, we only cover the most related works, corresponding to the datasets and multimodal fusion models in the following. ## 2.1 Datasets Popular datasets for multi-modal emotion recognition or sentiment analysis include CMU- MOSI (Zadeh et al., 2016), CMU-MOSEI (Zadeh et al., 2018), IEMOCAP (Busso et al., 2008), MELD (Poria et al., 2019), CHEAVD (Li et al., 2017b), CH-SIMS (Yu et al., 2020), and CH-SIMS_v2 (Liu et al., 2022). Most previous datasets annotate the samples with the same labels for all modalities. It is noteworthy that the two Chinese datasets, CH-SIMS and CH-SIMS_v2, are currently the only datasets that conduct annotations for each modality independently. However, these two datasets are for sentiment analysis, and are labeled with polarized labels, (weakly) positive, (weakly) negative, and neutral. To the best of our knowledge, our dataset CHERMA is the first one that is targeted for multi-modal emotion recognition, and has modality-wise annotations. ## 2.2 Multi-Modal Fusion Models At the core of multi-modal emotion recognition is the modality fusion strategy. TFN (Zadeh et al., 2017) integrates the multi-modality information via calculating the outer product of modality embeddings. Unfortunately, the computation and memory required grow exponentially with the number of modalities, which is addressed by the work of LMF (Liu and Shen, 2018) with low rank approximation. From the perspective of model structure, the previous fusion strategies are usually classified into early fusion and late fusion. Early fusion (Lazaridou et al., 2015; Williams et al., 2018) simply concatenates the low-level features of all the modalities, and feeds the joint feature to the model. Early fusion can suffer from the problem of data sparseness (Wu et al., 2014). Late fusion (Liu et al., 2014; Nguyen et al., 2018; Yu et al., 2020) concatenates the high-level features (some studies also refer this to model-level fusion (Chen and Jin, 2016)) or decisions separately obtained from individual modalities, which is weak in establishing fine-grained correspondence across modalities. Compared with the concatenation methods, multi-modal transformer is a more powerful tool that is capable of capturing the intra-modal and cross-modal dependency(Poria et al., 2017; Lian et al., 2021). Recent transformer-based works (Tsai et al., 2019; Lv et al., 2021; Nagrani et al., 2021) can be regarded as layer-wise fusion, to differentiate them from early and late fusion approaches. Layer-wise fusion carries out feature fusion layer by layer from low level to high level, which can capture fine-grained correlation ![2_image_0.png](2_image_0.png) ![2_image_1.png](2_image_1.png) across unaligned modalities. Due to its promising performance, this paper also leverages multi-modal transformer with layer-wise fusion for our emotion recognition task. ## 3 Dataset Description In this section, we give a detailed introduction to the new dataset—CHERMA. We will present how the data is collected and annotated, the characteristics of the data, and the pre-processing of the data for model training. Before introducing the data, we give the definitions of some notations. Let *t, a, v* represent the three modalities—text, audio, and vision, respectively; let m denote the joint of the three modalities. Denote by Xu ∈ R Tu×du for u ∈ {*t, a, v*}, the feature sequence of the corresponding modality, where Tu and du are the sequence length and the feature dimension, respectively. Associated with each feature sequence is its unimodal labels and multi-modal label {yu|u ∈ {*t, a, v, m*}}. For our training dataset, we use ({Xn u }u∈{*t,a,v*}, {y n u}u∈{*t,a,v,m*}) for n ∈ {1, 2, · · · , N} to represent the n-th sample, where N denotes the total number of samples. In the rest of the paper, we sometimes drop the index n for brevity when no confusion occurs. ## 3.1 Data Acquisition And Annotations In order to cover as many scenarios as possible, our data is acquired from various sources, including 148 TV series, 7 variety shows, and 2 movies. The language of the video is Chinese, yet it can be translated to other languages for broader applications. The video is split into utterances with Adobe Premiere Pro. Only the utterances where there is a single person speaking and the speaker's face appears clearly are selected as our samples. In total, 28, 717 utterances are rounded up, of which the total length is 2, 213.84 minutes. ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) Table 1 reports the statistics of dataset CHERMA, including the information of the utterance samples, the gender and age distributions of the speakers in the video. The scenarios span household, hospital, restaurant, office, outdoor, telephone conversation, and so on. In a word, the acquired data is representative and close to real-world scenarios, and is therefore of practical value. Following the convention, we categorize the samples into Ekman's six basic emotions (Ekman, 1992) plus emotion neutrality, i.e., happiness, sadness, fear, anger, surprise, disgust and neutrality. Each sample is annotated with three uni-modal labels and a multi-modal label. All the recruited annotators have experience in emotion annotations. Moreover, they are required to receive training for our annotation task and pass an examination before carrying out annotations. For the uni-modal annotations, the annotators are shown corresponding uni-modal information. While for multi-modal annotations, all the modalities are available; that is, the videos are displayed in their original form. Each label is determined as a result of the following majority voting process. For each labeling, the feature, unimodal or multi-modal, is first assigned to three annotators. Each annotator gives it a unique label independently. If the labeling result is 3 : 0, consensus is reached and the label is determined accordingly; if the result is 1 : 1 : 1, this sample will be discarded because of the disagreement; otherwise, if the result is 2 : 1, then one more annotator will join. In this case, if the final result is 3 : 1, the label obtained; otherwise, 2 : 2 or 2 : 1 : 1 means the sample will be discarded. Considering the limited labor, the above annotating scheme ensures the reliability of the labels in that 3 annotators out of 3 or 4 agree on each label, and meanwhile the samples of ambiguity are discarded. After the annotations, all the samples are shuffled, and are split into training, validation and test datasets with ratio 6:2:2. ## 3.2 Label Inconsistency Upon finishing the annotations, we explore the dataset by simple statistical analysis. Figure 1(a) shows the distributions of the uni-modal labels and the multi-modal labels. We have two observations: 1) There are a large number of samples, of which the four labels are not identical to each other; 2) With single modality, some emotions cannot be identified and possibly be recognized as neutrality; while using multi-modal information can infer more emotions. To quantify the label inconsistency, we define the overall modality inconsistency between any two modalities u1, u2 ∈ {*t, a, v, m*} as follows: $$\mathrm{Incon}(u_{1},u_{2}):={\frac{\sum_{n=1}^{N}\mathbf{1}_{y_{u_{1}}^{n}\neq y_{u_{2}}^{n}}}{N}},$$ where 1x = 1, if x is true; 1x = 0, otherwise. Define the inconsistency of modality u with multi-modality m for any label y ∈ {happiness, sadness, fear, anger, surprise, disgust, and neutrality} as follows: $$\operatorname{Incon}(u,m;y):={\frac{\sum_{n=1}^{N}\mathbf{1}_{y_{u}^{n}\neq y}}{\sum_{n=1}^{N}\mathbf{1}_{y_{m}^{n}=y}}}.$$ $\mathbf{1}(\mathbf{b})=\mathbf{rep01}$ Figure 1(b) reports the overall modality inconsistency, which is significant—the inconsistency between any pair of modalities exceeds 0.3. The inconsistency between unimodality and multi-modality is less than that between any two uni-modalities. This is reasonable because the multi-modal label which is obtained with all modality information can be regarded as a weighted average of three uni-modal labels. If the multi-modal labels are regarded as the ground-truth, a conclusion can be drawn from Figure 1(c) that some modalities are better at inferring some emotions than other modalities. It is shown that audio performs well in identifying sadness, anger and neutrality. Vision is good at recognizing happiness, sadness and anger. In comparison, text shows more balanced performance among all emotions. ## 3.3 Data Pre-Processing In this subsection, we explain how the raw data is pre-processed for model training. The original data of the three-modalities will be converted to feature sequences with the following methods. Text: We pass the texts to pre-trained Chinese BERT-base (Cui et al., 2021) to obtain contextualized word embeddings. Since the maximum number of words in all the texts is 78, all texts that have fewer words are padded to length 78. With CLS and SEP tokens prepended and appended to each text, respectively, the input of BERT is of length 80. Finally, each text modality information is represented by a sequence of length 80 and dimension 768. Audio: The audio is sampled at frequency 16kHz with receptive field 25ms and frame shift 20ms. Then the extracted frame-level feature is input to pre-trained wav2vec (Zhang et al., 2022), generating a feature sequence of dimension 768. The length of the sequence corresponds to the number of the audio frames which depends on the length of the raw audio. Vision: The video is first processed with MTCNN (Zhang et al., 2016) to obtain aligned face and each frame is cropped to size of 224 × 224. For each video utterance, we partition it evenly into 8 segmentations, and then randomly sample 8 frames from each segmentation, resulting in a 64-frame vision sequence. Each frame is then fed to a pre-trained Resnet 18 (trained with RAF-DB (Li et al., 2017a)), which outputs a feature sequence of length 64 and dimension 512. ## 4 The Proposed Model 4.1 Model Overview As visualized in Figure 2, the proposed model, LFMIM, consists of two main components, three uni-modal transformers and one multi-modal ![4_image_0.png](4_image_0.png) transformer. Each uni-modal transformer processes its corresponding modality independently; while the multi-modal transformer relies on all the unimodal transformers. To be specific, the input of layer l + 1 of the multi-modal transformer comes from the output of its l-th layer and the outputs of l-th layer of all three uni-modal transformers. Each uni-modal module are independent from each other, and yields its own label prediction. ## 4.2 The Uni-Modal Modules The input features of all the modalities are of the same sequence form. The module for each individual modality adopts the same simple structure, mainly including a uni-modal transformer with L multi-head self attention layers. As Figure 2(a) illustrates, the feature sequence, Xu, u ∈ {*t, a, v*} first goes through a 1D convolutional layer to unify the feature dimension for the following concatenation; next, positional embedding (PE) is added, yielding the input sequence of the uni-modal transformer, Z1 u, u ∈ {*t, a, v*}. Then, the sequence is processed by the corresponding uni-modal transformer, and the input and output of the l-th transformer layer are Zlu and Zl+1 u, respectively, u ∈ {*t, a, v*} and l ∈ {1, 2, · · · , L−1}. After the transformer block, a pooling layer reduces the output sequence into a feature vector. Subsequently, on the top is an MLP followed by a softmax layer, which gives the predicted label yˆu, u ∈ {*t, a, v*}. It is obvious that each uni-modal module does not depend on the information from other modalities in the forward pass. ## 4.3 The Multi-Modal Module The multi-modal module is a feature extractor which draws three modalities from uni-modal transformers and fuses them layer by layer. Specifically, we define a learnable multi-modal FEature EXtraction token, FEX, to extract and summarize useful information from all modalities. The input of the l-th layer of the multi-modal transformer is Zlm = [FEXl; Z˙ l t; Z˙ la; Z˙ lv], and the output is Z¯l+1 m = [FEXl+1; Z¯l+1 t; Z¯l+1 a; Z¯l+1 v]. Z˙ l+1 u, ∀u ∈ {*t, a, v*} are updated as follows: $$\dot{\bar{Z}}_{u}^{l+1}=\alpha_{u}^{l+1}Z_{u}^{l+1}+\bar{\alpha}_{u}^{l+1}\bar{Z}_{u}^{l+1},$$ where α l+1 uand α¯ l+1 u, u ∈ {*t, a, v*} and l ∈ {0, 1, 2, · · · , L − 1} are learnable parameters; Z¯1 u, ∀u ∈ {*t, a, v*} are all-zero matrices with proper size. After the multi-modal transformer block, the following structure is the same as the uni-modal modules as introduced in last subsection. The final label prediction of the multi-modal module is yˆm. As shown in Figure 2(a), in the forward pass, the multi-modal module absorbs information from the uni-modal modules layer by layer, and does not return its information to the uni-modal modules. ## 4.4 Optimization Objective With the aforementioned model, our training task boils down to the optimization problem below. $$\operatorname*{min}\;\frac{1}{N}\sum_{n=1}^{N}\sum_{u\in\{t,a,v,m\}}\beta_{u}L(y_{u}^{n},\hat{y}_{u}^{n}),$$ where L(·, ·) is the cross-entropy loss function; βu, u ∈ {*t, a, v, m*} are the weight parameters that balance the loss among different modalities. To sum up, following the principle of maintaining modality independence, our approach utilizes separate supervisions for different modalities, and bans direct communications across individual modalities. In this way, it is expected that each modality can fully explore and exploit itself without relying on other modalities. Hopefully, as illustrated in Figure 2(b), by aggregating more distinctive uni-modal representations with less mutual information and more complementary information, the overall useful information summarized by the multi-modal module can be maximized. It should be clarified that albeit we advocate modality independence, we do not oppose modality reinforcement for each other. In this work, we only investigate the independence side to unveil and highlight its importance. For more general multimodal learning, the two sides should be carefully balanced, which deserves further investigation. Furthermore, the modality independence is relative to existing layer-wise fusion approaches which couple the modalities with the same labels and modality interactions in the forward propagation. Actually, in our model, through backward propagation the multi-modal labels can also take effect in supervising uni-modal modules. To be more precise, our approach reduces modality dependence, but does not completely eliminate the indirect interactions across modalities. ## 5 Experiments And Analysis In this section, we first compare our proposed model with typical benchmark models to validate the effectiveness of our model. Then we perform ablation studies to analyze the proposed model, and demonstrate the differences between our model and its compared counterparts. ## 5.1 Comparisons With Baseline Models 5.1.1 Baseline Models We compare our proposed model, LFMIM, with 6 typical baseline models: tensor fusion network (TFN) (Zadeh et al., 2017), low-rank Multimodal fusion (LMF) (Liu and Shen, 2018), early fusion transformer (EF-transformer), Late fusion transformer (LF-transformer), multi-modal transformer (MulT) (Tsai et al., 2019), and progressive modality reinforcement (PMR) (Lv et al., 2021). Note that for early fusion and late fusion methods, we use more powerful transformer models instead of the models in (Williams et al., 2018) and (Yu et al., 2020) for the sake of fairness. We adapt the original PMR (introduced in the introduction section) to be trained with uni-modal labels and multi-modal labels as our model. ## 5.1.2 Implementation Details To concatenate the features of the three modalities, we utilize 1D convolutional layers to convert them into 128-dimensional feature sequence. For the audio feature which is of varying length, we fix the length to be 100. If the original length is over 100, we uniformly sample 100 feature vectors; otherwise, we pad it with zero vectors. ![6_image_0.png](6_image_0.png) Figure 3: (a) The training loss and test loss of each modality during training. (b) The overall emotion recognition ![6_image_2.png](6_image_2.png) accuracy of each modality on training dataset and test dataset. (c) The test accuracy of different models. ![6_image_1.png](6_image_1.png) Model Acc-2 Acc-3 Acc-5 F1 score ![6_image_3.png](6_image_3.png) ![6_image_4.png](6_image_4.png) MLF-DNN 82.28 69.06 38.03 82.52 MLMF 82.32 67.70 37.33 82.66 MTFN 82.45 69.02 37.20 82.56 LFMIM 83.37 71.33 48.36 **83.71** The transformer blocks in LFMIM are all comprised of 4 multi-head self attention (MHSA) layers, where each MHSA is with 8 heads. The optimizer utilized is SGD, and Lambda learning rate schedule is adopted. The initial learning rate is 0.005, obtained with grid search. The weight coefficients in the objective are set as βu = 1, ∀u ∈ {*t, a, v, m*}. The reported results in the following are the average of five repeated experiments with different seeds. ## 5.1.3 Performance Comparisons As our model design philosophy advocates the independence of different modalities. Each modality module is associated with a training loss and a test loss that reflect how well this modality learns for the task. As illustrated in Figures 3(a) and 3(b), all the losses and the accurac of LFMIM converge, yet reach different levels. Moreover, the gap between training loss (resp. accuracy) and test loss (resp. accuracy) exhibits significant variation with modalities. These observations mirror that the modality diversity does exist and have significant impact on emotion recognition; that is, audio modality performs best (with test accuracy 70.37%) and vision modality the worst (with test accuracy 54.60%). Figure 3(c) compares the test accuracy curve of LFMIM and other baseline models—the accuracy of LFMIM surpasses that of all the others. It is noticed that PMR tends to overfit, which might be attributed to the fact that it employs a complicated model with 6 transformer blocks. Table 2 reports the detailed performance of the models, i.e., the overall and emotion-wise F1 scores. It is shown that LFMIM outperforms the competing models by a significant margin in overall accuracy (i.e., the overall F1 score) and in all emotions except emotion anger (slightly outperformed by PMR). ## 5.1.4 Results On Dataset Ch-Sims In this subsection, we conduct experiments with the CH-SIMS dataset which is annotated for each modality with sentiment labels: negative, weakly negative, neutral, weakly positive, positively. We compare LFMIM with MLF-DNN, MLMF and MTFN, of which the results are from the reference (Yu et al., 2020), as shown in Table 3. Acc-k (k ∈ {2, 3, 5}) represents the accuracy for classification with k classes (for binary classification, all labels reduce to negative and positive; for 3-class classification, labels are negative, neutral and positive), and the F1-score pertains to binary classification. The results in Table 3 show that LFMIM significantly outperforms the previous models, and LFMIN achieves a remarkable ![7_image_0.png](7_image_0.png) (b) ![7_image_2.png](7_image_2.png) (a) (c) ![7_image_1.png](7_image_1.png) t 66.14 67.94 61.72 72.06 68.06 37.31 71.00 67.39 a 63.08 79.05 55.04 77.24 45.15 36.22 75.61 71.52 v 78.91 70.03 57.62 73.15 16.30 16.59 64.54 67.08 m 75.68 76.46 67.97 75.43 67.37 48.93 66.59 69.53 LFMIM-ML m 75.36 76.77 68.51 75.10 68.76 49.59 67.09 69.79 t 66.10 63.60 56.62 68.85 65.90 33.42 69.09 64.61 a 61.65 77.59 54.05 76.04 40.41 34.31 75.13 70.37 v 74.11 53.55 14.53 56.98 13.68 12.81 54.14 54.60 m 76.60 77.83 69.44 75.32 69.83 50.20 68.24 70.54 ## 5.2 Ablation Studies LFMIM distinguishes from others mainly in 1) different modalities are trained with its own labels; 2) the forward information flow in the model is uni-directionally from uni-modal modules to multimodal module. Therefore, in this subsection, we compare LFMIM with the model that is trained with only the multi-modal labels, and the model that allows bi-directional information flow between multi-modal and uni-modal modules. The former corresponds to LFMIM-ML (LFMIM trained with multi-modal labels for all modules), and the later is exactly PMR in last subsection. We first compare the LFMIM with PMR in Figure 4 to demonstrate the impact of information flow in the model. In Figures 4(a) and 4(b), comparing LFMIM and PMR in each modality, it is obvious that the uni-directional information gives rise to 1) larger (resp. lower) uni-modal losses (resp. accuracy); 2) smaller (resp. higher) multi-modal loss (resp. accuracy); and 3) larger modality gap in terms of loss gap and accuracy gap between different modalities. Table 4 shows that for each emotion, modalities *t, a,* and v of PMR respectively outperform the corresponding modalities of LFMIM in terms F1 score, but modality m of LFMIM outperforms that of PMR (except for emotion anger), reversely. Interestingly, the above results demonstrate that although the uni-directional information flow degrades the performance of each single modality, it does promote that of multi-modality. The reason is that bi-directional information flow in PMR allows each modality to draw information from other modalities, thus hindering the individual modality from fully exploiting itself. In contrast, uni-directional information flow encourages the modalities to learn more independent and distinctive representations, which can maximize the overall useful information attained by the multi-modal module. Tabel 4 summarizes the F1 scores of different modalities for all the emotions. LFMIM has large standard deviation of F1 score over the three modalities u, ∀u ∈ {*t, a, v*} than PMR except for emotion happiness, which is more clearly displayed in Figure 4(c). This, to some degree, illuminates that uni-modal modules of LFMIM yield more distinctive representations, which contributes to the promising performance of our multi-modal module. That modality m of LFMIM outperforms that of LFMIM-ML in Table 4 demonstrates the merit of uni-modal labels which also boost the diversity of the uni-modal representations. Comparing the three m rows in Table 4 shows that LFMIM trained with modality-wise labels and uni-directional forward information flow sets a strong baseline for dataset CHERMA. It is worth mentioning that although the accuracy of multi-modal module in LFMIM is lower than that of its uni-modal counterpart for some emotion (see emotions anger and neutrality), it does not means multi-modal information does not improve the performance over uni-modal information, because they corresponds to different labels. ## 6 Conclusions In this paper, we uphold modality independence for multi-modal emotion recognition in the context of modality inconsistency. Therefore, we build a new dataset that includes uni-modal labels and multi-modal labels. Our model maintains modality independence via 1) supervising each modality with its own labels, and 2) enforcing uni-directional information flow from uni-modal modules to multi-modal module. Numerical results verify that independence indeed helps to gain more effective information from the modalities and improve the model performance for the multimodal emotion recognition. Albeit independence benefits the multi-modal learning, it does not mean that individual modality should be prevented from exploring other modalities in any circumstance. There should be a sweet point between modality independence and dependence, which constitutes our future research interest. ## Limitations The limitations of this work are mainly twofold. 1. Different modalities are trained with the same optimizer setting, which might cause imbalance across modalities. 2. No theoretical analysis is established to provide insight of the balance between modality independence and dependence. ## Acknowledgements This work was supported by the Major Scientific Project of Zhejiang Lab (Grant No.2020KB0AC01), the National Science and Technology Major Project of China (Grant No. 2021ZD0114303), Youth Foundation Project of Zhejiang Lab (Grant No. K2023KH0AA02), and the Youth Foundation Project of Zhejiang Province (Grant No. LQ22F020035). 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bao-etal-2023-casn

{CASN}:Class-Aware Score Network for Textual Adversarial Detection

https://aclanthology.org/2023.acl-long.40

Adversarial detection aims to detect adversarial samples that threaten the security of deep neural networks, which is an essential step toward building robust AI systems. Density-based estimation is widely considered as an effective technique by explicitly modeling the distribution of normal data and identifying adversarial ones as outliers. However, these methods suffer from significant performance degradation when the adversarial samples lie close to the non-adversarial data manifold. To address this limitation, we propose a score-based generative method to implicitly model the data distribution. Our approach utilizes the gradient of the log-density data distribution and calculates the distribution gap between adversarial and normal samples through multi-step iterations using Langevin dynamics. In addition, we use supervised contrastive learning to guide the gradient estimation using label information, which avoids collapsing to a single data manifold and better preserves the anisotropy of the different labeled data distributions. Experimental results on three text classification tasks upon four advanced attack algorithms show that our approach is a significant improvement (average +15.2 F1 score against previous SOTA) over previous detection methods.

## Casn: Class-Aware Score Network For Textual Adversarial Detection Rong Bao1,2∗, Rui Zheng1∗**, Liang Ding**3, Qi Zhang1**, Dacheng Tao**3† 1 School of Computer Science, Fudan University, Shanghai, China 2 Shanghai Shanghai Artificial Intelligence Laboratory, Shanghai, China 3 The University of Sydney, Sydney, Australia [email protected] {rzheng20,qz}@fudan.edu.cn {liangding.liam,dacheng.tao}@gmail.com ## Abstract Adversarial detection aims to detect adversarial samples that threaten the security of deep neural networks, which is an essential step toward building robust AI systems. Density-based estimation is widely considered as an effective technique by explicitly modeling the distribution of normal data and identifying adversarial ones as outliers. However, these methods suffer from significant performance degradation when the adversarial samples lie close to the non-adversarial data manifold. To address this limitation, we propose a score-based generative method to implicitly model the data distribution. Our approach utilizes the gradient of the log-density data distribution and calculates the distribution gap between adversarial and normal samples through multi-step iterations using Langevin dynamics. In addition, we use supervised contrastive learning to guide the gradient estimation using label information, which avoids collapsing to a single data manifold and better preserves the anisotropy of the different labeled data distributions. Experimental results on three text classification tasks upon four advanced attack algorithms show that our approach is a significant improvement (+15.2 F1 score on average against previous SOTA) over previous detection methods. ## 1 Introduction It has already become a consensus in the machine learning community that deep neural networks (DNNs) are vulnerable against adversarial examples (Goodfellow et al., 2015; Kurakin et al., 2017). Adversarial samples are generated by adding some imperceptible perturbations to normal samples and cause the trained network to produce defective results. The widely-used pre-trained language models (PLMs) (Devlin et al., 2019; Liu et al., 2019; Brown et al., 2020) also have been demonstrated to be highly susceptible under textual adversarial attacks (Zhang et al., 2019). Given that pre-trained language models have become the *de facto* backbone models for many practical applications, their security risks deserve more attention. Existing approaches to counteract adversarial attacks can be broadly divided into two directions, adversarial defense and adversarial detection (Wiyatno et al., 2019). Although adversarial defenses have made great progress in recent years, popular defense methods, such as adversarial training (Zhu et al., 2020; Madry et al., 2018), impose certain restrictions on the attack space to certify robustness, which often results in a sacrifice of original accuracy (Akhtar et al., 2021). In contrast, adversarial detection methods aim to separate adversarial samples before they enter the model. The detected adversarial samples can be processed by a dedicated module and then re-entered into the model. This approach not only avoids the degradation of original accuracy, but also imposes no restrictions on the attack method. One of the most effective detection methods that can handle all textual attack algorithms is densitybased estimation approaches (Yoo et al., 2022; Feinman et al., 2017). These approaches are built on the assumption that the adversarial examples are not lying inside the non-adversarial data manifold. They explicitly model the original data distribution and use the probability of a data point as the adversarial confidence. Nevertheless, recent work (Shamir et al., 2021) argues that the adversarial samples are roughly close and perpendicular to the low-dimensional manifold containing normal samples. The overlap problem poses a challenge for detection performance, as the closer the attack algorithm produces results resembling real samples, the more the detection performance is degraded. In this work, we propose to model the gradient of log-density data distribution via denoising score matching function (Song and Ermon, 2019; Vin- ∗ Equal contribution. †Corresponding authors. 671 cent, 2011). Then the gradients are used through Langevin dynamics to generate normal samples from the noise-perturbed distribution by multi-step denoising process. The distance from the adversarial samples to the normal data distribution is measured indirectly using the denoising score matching function. This is more refined than the previous direct density estimation, thus avoiding the performance loss caused by overlapping density regions. We introduce the class-aware score network (CASN) to compute the gradient of log-density distribution required in the detection phase. To train this score network, with the general training objective of conditional noise scores (Song et al., 2021), we also compute supervised contrastive loss (Khosla et al., 2020) by constructing different class sample pairs. It allows models to better distinguish between different classes of data manifold and prevents the model from collapsing into a single data distribution. Afterward, all samples are denoised using the score network, and the adversarial samples are determined by recording the size of the feature distance before and after denoising. Our **contribution** can be summarized as follows: - We propose a new paradigm that uses the class-aware score network to portray the distribution changes of the adversarial samples during the denoising process, greatly alleviating the distribution overlap problem. - Introducing supervised contrastive learning in the training phase of the score network makes better use of label information and enables more accurate calculation of sample distances in the denoising process. - Experimentally, we achieve nearly 100% accuracy under many settings, significantly outperforming baseline methods, and presenting a greater challenge to counterattackers. ## 2 Related Work 2.1 Textual Adversarial Attacks Considering the different granularities that DNNs are attacked, the textual attack algorithms can be grouped as character-level (Gao et al., 2018; Gil et al., 2019), word-level (Jin et al., 2020; Garg and Ramakrishnan, 2020; Ren et al., 2019), sentencelevel (Iyyer et al., 2018) and multi-level (Liang et al., 2018; Ebrahimi et al., 2018) attacks. The different fine-grained groupings mean that these algorithms modified the original text at different levels. The usual manipulation includes insertion, deletion, and replacement. At the same time, the definition of adversarial attacks has to be satisfied, i.e., the adversarial sample needs to maintain semantic invariance and be imperceptible to human beings (Zhang et al., 2019). ## 2.2 Textual Adversarial Detection DISP (Zhou et al., 2019) is a framework that learns to identify malicious perturbations, then block the attacks by replacing them with synonyms. This method relies on a perturbation discriminator to give a confidence score in whether the current word is perturbed or not. Liu et al. (2022) adapt Local Intrinsic Dimensionality (Ma et al., 2018) and propose **MDRE** based entirely on the distribution features of the learned representations. Noticing that word-level adversarial algorithms often replace high-frequency words with low-frequency words, Mozes et al. (2021) introduce **FGWS** algorithm to detect adversarial samples by word frequency properties and calibrate the adversarial samples to improve the model performance. Yoo et al. (2022) propose RDE which utilizes multivariate Gaussian distribution to model the feature density of clean samples. The samples in low-density regions are considered as adversarial samples during detection. Compared with previous explicit density estimation methods such as RDE and MDRE, our method uses the gradient of log-density and Langevin dynamics to depict the distribution distance between adversarial and normal samples, avoiding the performance degradation caused by the distribution overlap problem. ## 3 Preliminary Score matching (HyvärinenAapo, 2005) was proposed to generate samples from a non-normalized distribution. The core idea of this method is to estimate the score function, i.e., the gradient of logdensity data distribution, and then generate data by sampling through Langevin dynamics. Let x be a data point, p(x) denote the data distribution, the score function can be a score network sθ(·) that approximate ∇x log p(x) as accurately as possible, which can be written as $$s_{\theta}(x):=\nabla_{x}\log p(x).$$ sθ(x) := ∇x log p(x). (1) After that, Langevin dynamics can generate samples from the data distribution p(x) using the score function. Given a fixed step size -, and an initial ![2_image_0.png](2_image_0.png) sampling point x0 ∼ π(x), the Langevin process recursively updates the following function: $$x_{t}\gets x_{t-1}+{\frac{\epsilon}{2}}\nabla_{x}\log p(x_{t-1})+{\sqrt{\epsilon}}z_{t}\quad(2)$$ where zt ∼ N (0, I). Welling and Teh (2011) proves that under some restrictions xT becomes an exact sample from p(x) when - → 0 and T → ∞. Although the original score matching is a sound theory, Vincent (2011) points out that due to a very computationally complex term in the original training objective, it is difficult to be effective in highdimensional data. Therefore, the author introduces denoising score matching (DSM) to eliminate the hard computing terms. The idea of this approach is to add an easily computable noise to the original distribution and then estimate the score function under noise perturbation. The advantage of this approach is that it makes the training target easier to compute, and the score function approximates the original target when the noise is small enough. The author proposes the use of Gaussian noise pσ(˜x|x) = N (˜x|*x, σ*2I), then DSM minimizes the following objectives: 1 2 $$\frac{1}{2}\mathbb{E}_{p}(x)p_{\sigma}(\tilde{x}|x)\,\left\|s_{\theta}(\tilde{x})-\nabla_{\tilde{x}}\log p_{\sigma}(\tilde{x}|x)\right\|_{2}^{2}\quad0$$ Note that the optimal score network sθ (·) which minimizes Eq. 3, Vincent (2011) indicates that at this point sθ (x) almost converge to ∇x log pα(x). Denoising score matching using Gaussian noise inspires a series of later work (Song and Ermon, 2019; Song et al., 2021), and this technique has become an important milestone in the field of scorebased image generation (Yang et al., 2022). ## 4 Methodology - 1 $\mathbb{T}$-1 $\mathcal{G}$. In a nutshell, we hope to train a class-aware score network that estimates the gradient of log-density data distribution and separates the adversarial samples by the drift value of the sample distribution during the reverse denoising process. In §4.1, we will introduce the application of denoising score matching function and supervised contrastive learning to train the class-aware score network. By performing the denoising process on all samples using the score network, the drift distance of the samples before and after denoising can be calculated as an adversarial confidence score (§4.2). ## 4.1 Training Class-Aware Score Network As shown in Fig. 1, the score network estimates the gradient of the log-density distribution of the text hidden states. The left side of the figure indicates that we first use a supervised learning encoder E to obtain the hidden representation h of text x, i.e. h = E(x), h is used as input to the scoring network. On the right is the training process of the score network, which uses multi-level noise perturbation and supervised contrastive learning for training. Given a Gaussian noise perturbation pα(h˜|h) = N (h˜| √αh,(1−α)I), and let α be part of the input, Eq.3 will reduce to the following loss function: $$l(\theta;\alpha)=\frac{1}{2}\mathbb{E}_{p(h)p_{\alpha}(\tilde{h}|h)}\left\|s_{\theta}(\tilde{h},\alpha)+\frac{\tilde{h}-\sqrt{\alpha}h}{1-\alpha}\right\|^{2}\tag{4}$$ where $\alpha$ is a positive real number, $p(h)$ is the dis where α is a positive real number, p(h) is the distribution of h. The size of the noise perturbation is difficult to choose, large noise will affect the accurate estimation and small perturbation will make the Langevin dynamics ineffective. We address this problem through multi-level noise perturbations proposed by Song and Ermon (2019). Let T denote a positive integer, a set of positive real numbers {αi}Ti=1 decreasing from 1 to 0, a linear combination of Eq. 4 is constructed for all α ∈ {αi}Ti=1 to get a unified objective: $$L(\theta)_{\alpha}=\frac{1}{T}\sum_{i=1}^{T}(1-\alpha_{i})l(\theta;\alpha_{i})T\qquad(5)$$ Nevertheless, the score network trained according to Eq. 5 is still imperfect. This training objective actually trains the data distribution under unconditional likelihood, but in fact, the data for different labels are conditionally distributed (Ho and Salimans, 2023). We need to approximate the conditional data distributions, so that the Langevin dynamics can operate on the correct manifold without jumping repeatedly on manifolds with different labels. Since the correct labels of the adversarial samples cannot be known before detection, we cannot utilize conditional score generation techniques (Dhariwal and Nichol, 2021) with explicit input labels. Therefore, we propose to use supervised contrastive learning (Khosla et al., 2020) to increase the anisotropy of differently labeled data and force the score network to implicitly model the conditional data distributions. The key to contrastive learning is constructing positive and negative sample pairs. As shown on the right side of Fig. 1, within a batch of data, we select the original representation and its noise perturbation as the positive sample pair and all representations that differ from its label (with or without noise perturbations) as the corresponding negative samples. Then, the contrastive loss can be calculated as: $$L(\theta)_{cons}=-\sum_{i\in I}\frac{sim(h_{i},\bar{h}_{i})}{\sum_{a\in A(i)}sim(h_{i},h_{a})+sim(h_{i},\bar{h}_{a})},\tag{6}$$ where I denotes the index of batch data, A(i) = {a ∈ I|yi = ya} is the set of sample indexes whose labels are different from data i. The similarity between each representation is calculated using the cosine value after averaging it, i.e., sim(*x, y*) = cos < sθ(x)mean, sθ(y)*mean* >. Finally, we combine the Eq. 5 and Eq. 6 as a multi-task learning loss (Eq. 7) with λ as coeffi- cient: $$L(\theta)=L(\theta)_{\alpha}+\lambda L(\theta)_{c o n s}$$ $$\left(7\right)$$ ## The Specific Training Parameters Will Be Detail Discussed In Appendix A.1. 4.2 Detection Via Denoising Process Given a sentence x and the corresponding encoder representation h, a conventional detection approach is to conduct adversarial purification through the denoising process (Yoon et al., 2021; Nie et al., 2022), then classify the denoised representations and detect the adversarial samples based on label inversions. In order to better improve the qualify of the denoising process, we take advantage of recent work (Song et al., 2021) that understands denoising score matching from the Stochastic Differential Equations (SDE) perspective. It indicates that the quality of generative modeling via Langevin dynamics can be further improved if the solution of the SDE equation is added. Therefore, our algorithm alternates between the reverse SDE solver and Langevin dynamics. Let hi denote the text representations of different time points, sθ (·) be a trained score network via minimized Eq. 7, the parameters βi and -i are related to {αi}Ti=1 in Eq. 5. We replace the regular Langevin dynamics of Eq. 2 with the following predictor-corrector (Song et al., 2021) form: $$\begin{array}{l}{{s c o r e\gets\frac{1}{2}\beta_{i+1}s_{\theta^{\star}}(h_{i+1},\beta_{i+1})}}\\ {{h_{i}\gets(2-\sqrt{1-\beta_{i+1}})h_{i+1}+s c o r e\quad(8)}}\\ {{h_{i}\gets h_{i}+\epsilon_{i}s_{\theta^{\star}}(h_{i},\beta_{i})+\sqrt{2\epsilon_{i}}z}}\end{array}$$ Although label flipping is an effective detection method, this method relies too much on the denoising results of Langevin dynamics, and it fails when the adversarial perturbations cannot be eliminated. To avoid the catastrophic consequences of failing to eliminate adversarial perturbations, we propose to focus on the kinetic qualities of Langevin dynamics. Since the Langevin dynamics eventually converge to the target distribution, the drift distance of the denoised adversarial samples should be larger than that of the normal ones. The cosine similarity could reflect the shift distance of the representation, with larger values implying a smaller shift. We calculate the cosine similarity between the current and starting representations at each step of the denoising process and use the cumulative sum as the final adversarial confidence score. Assume the h*start* denotes the text representations at the initial denoising point, when the time step i ranges from *start* to 0, the update is performed using Eq. 8 and confidence value is accumulated as: $$c o n f i d e n c e+=c o s<h_{i}^{m e a n},h_{s t a r t}^{m e a n}>,$$ start >, (9) where hi denotes the text representations of the current moment, the superscript "*mean*" indicates that we calculate a token level averaging. After obtaining the confidence score of each sample, we filter the adversarial samples using the threshold method. The calculation of confidence scores will be shown in Algorithm 1, and the whole detection process will be detail discussed in Appendix A.2. ## 5 Experimental Settings Considering the attack algorithms on text classification models, we selected three representative text classification datasets to verify the effectiveness of the proposed method. They are SST-2 (Socher et al., 2013), IMDB (Maas et al., 2011) and AGNEWS (Zhang et al., 2015). The first two datasets are both for binary sentiment analysis. In SST-2, most sentences are short texts, while in IMDB, they are long. AGNEWS is a four-category topic classification dataset that includes the world, sports, business, and sci/tech. ## 5.1 Baselines We compare our method with five recent text adversarial detection approaches. Four of these methods, DISP, FGWS, RDE and **MDRE** have already been introduced in §2.2. We also add a detection method, MD, which simultaneously detects outof-distribution and adversarial samples (Lee et al., 2018). It first calculates the class-conditional Gaussian distribution of the features and then gives the adversarial confidence score of the samples by Mahalanobis distance. ## 5.2 Textual Attacks We use four attack algorithms to generate adversarial samples. BAE (Garg and Ramakrishnan, 2020) replaces or inserts tokens in important parts of the text by masking them and then rejuvenating the pre-training task of BERT to generate alternatives. PWWS (Ren et al., 2019) determines the word substitution order by word salience and classification probability, which greatly improves the attack success rate and maintains a very low word substitution rate. **TextFooler** (Jin et al., 2020) evaluates the importance of words in the sentence and then replaces them with synonyms that have semantic and syntactic constraints. **TextFooler-adj** (Morris et al., 2020a) further constrains the similarity of words and sentences before and after perturbation, which makes adversarial samples less detectable. ## 5.3 Implementation Details We fine-tune two pre-trained language models, BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019), as the sentence encoder. We use the general text classification paradigm of the two pretrained models, i.e., the encoder followed by the linear classifier with hyperparameters consistent with the original paper. For the three datasets, we use 90% of the original training set for training and the remaining 10% as the validation set. Following previous works, the attack algorithms will attack 3000 text samples to generate a balanced detection set. Since the original SST has only 872 labeled validation samples, we attack the full validation set. The XLNET (Yang et al., 2019) is adopted as the backbone of the score network with sentence encoder representations as input. All the attack algorithms are implemented by TextAttack (Morris et al., 2020b) framework and use the default settings. More details can be found in Appendix A.3. ## 6 Experimental Results In this section, we compare the detection performance of some strong baseline approaches and explore the effects of denoising process on representations. Some findings of hyperparameter's selection and analytical experiments are also presented. ## 6.1 Detection Performance Following the work of Yoo et al. (2022), we divide the detection of adversarial samples into two scenarios. Scenario 1 will detect all adversarial samples, regardless of whether the model output is successfully changed or not. Scenario 2 only requires the detection of samples that actually fool the model. Realistic attackers cannot guarantee the success of every attack, but this does not mean that these failed adversarial samples are harmless. In fact, the failed samples can guide the attacker to further optimize the attacking process, which is the strategy adopted by most attack algorithms. Therefore, we believe Scenario 1 is more realistic, and we will show the performance of each detection algorithm in Scenario 1 in the main text and put the | Dataset | Method | TextFooler-adj | BAE | TextFooler | PWWS | | | | | | | | | |-------------|----------|------------------|-------|--------------|--------|------|-------|------|------|-------|------|------|------| | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | | | | DISP | 58.9 | − | 79.2 | 66.1 | − | 76.1 | 72.3 | − | 76.0 | 73.3 | − | 77.4 | | | MDRE | 63.2 | − | 63.3 | 69.5 | − | 69.0 | 74.1 | − | 74.8 | 70.2 | − | 70.8 | | | FGWS | 68.2 | 69.9 | 64.3 | 68.9 | 69.5 | 64.6 | 71.7 | 73.9 | 68.2 | 74.2 | 79.2 | 70.8 | | | MD | 70.3 | 68.6 | 63.8 | 74.7 | 74.5 | 70.1 | 78.6 | 78.4 | 74.8 | 77.2 | 75.3 | 72.6 | | | RDE | 72.3 | 77.1 | 69.3 | 78.8 | 84.1 | 78.3 | 82.9 | 88.5 | 82.1 | 79.6 | 85.5 | 77.1 | | | Ours (CASN) | 80.8 | 89.1 | 80.3 | 97.2 | 98.9 | 97.1 | 99.3 | 99.8 | 99.3 | 99.1 | 99.9 | 99.1 | | | SST-2 | DISP | 67.3 | − | 68.0 | 67.6 | − | 66.3 | 67.4 | − | 66.0 | 65.3 | − | 64.3 | | MDRE | 82.2 | − | 80.8 | 84.3 | − | 82.8 | 85.5 | − | 84.3 | 82.6 | − | 81.6 | | | FGWS | 80.9 | 87.1 | 78.9 | 81.3 | 87.7 | 80.2 | 81.2 | 87.7 | 80.2 | 80.5 | 87.3 | 79.1 | | | MD | 81.4 | 83.1 | 79.0 | 83.7 | 85.5 | 81.6 | 83.7 | 85.5 | 81.7 | 82.4 | 83.7 | 79.7 | | | RDE | 82.2 | 88.3 | 80.7 | 84.6 | 90.2 | 83.2 | 84.7 | 90.1 | 83.7 | 82.5 | 86.7 | 80.1 | | | Ours (CASN) | 97.8 | 99.7 | 97.8 | 98.4 | 99.8 | 98.4 | 98.3 | 99.8 | 98.3 | 91.2 | 96.6 | 90.9 | | | IMDB | DISP | 61.5 | − | 85.8 | 80.8 | − | 86.3 | 88.4 | − | 89.1 | 84.1 | − | 87.3 | | MDRE | 57.1 | − | 61.6 | 73.0 | − | 75.5 | 80.2 | − | 81.2 | 74.5 | − | 76.5 | | | FGWS | 74.6 | 73.2 | 69.8 | 75.1 | 75.9 | 73.3 | 77.6 | 78.4 | 75.5 | 81.9 | 84.3 | 82.4 | | | MD | 67.2 | 62.3 | 52.8 | 71.5 | 76.1 | 65.0 | 75.2 | 80.8 | 73.3 | 71.8 | 76.8 | 70.0 | | | RDE | 67.7 | 67.0 | 55.1 | 77.1 | 85.0 | 75.9 | 85.3 | 92.3 | 85.6 | 77.8 | 85.4 | 77.3 | | | Ours (CASN) | 90.0 | 95.8 | 89.7 | 99.8 | 99.9 | 99.8 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | | | AGNEWS | | | | | | | | | | | | | | ## Performance Of Scenario 2 In Appendix B.2. Table 1 reports the detection performance of our method and compared baselines. We summarize the results as follows: 1) The AUROC metric cannot be calculated for DISP and MDRE, because they are threshold-independent detection methods. DISP performs very well on AGNEWS, which may be due to the synonyms replaced by these attack algorithms do not preserve the semantics of the original sentences well. 2) Consistently with Yoo et al. (2022), FGWS works badly in the face of more subtle attacks, such as BAE and TextFooler. 3) Both RDE and MD are feature density-based methods, and in general, RDE works better than MD. However, their performance degrades dramatically against TextFooler-adj, as the overlap of the feature space increases due to the quality improvement of adversarial samples. 4) Taking advantage of the denoising process to depict the feature changes of data avoids the drawbacks of density estimation methods, thus performing well on the TextFooleradj attack. *Our method not only greatly surpasses* the other approaches, but also achieves almost 100% detection performance for the other three attacks. ## 6.2 Analysis To better understand our method, we analyze some hyperparameter choices in the training and inference phase, as well as the correlation between feature purification and detection performance in the denoising process. Effects of coefficients We explore the optimal coefficient λ in Eq. 7 by varying the value in the intervals of 0.025 from 0.025 to 0.3, as seen in Fig. 2. In general, the performance trends are not consistent across the datasets. For SST-2 and AGNEWS, the performance has been oscillating with increasing λ and it is difficult to tell a concise trend. For IMDB, the AUROC values are all close to 100 percent, which indicates that detection on IMDB is not sensitive to hyperparameter change. However, in the interval range of 0.15 to 0.2, our CASN performs well on all the datasets. The reason is that with small values, the model will lose the label information and eventually degrade to the original conditional denoising objective. A larger coefficient would force the model to focus on the loss of contrastive learning and ignore the noise perturbations, which is also detrimental to accurate gradient estimation. ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) | Dataset | Steps | AUROC | ACC-clean | ACC-adv | |-----------|---------|---------|-------------|-----------| | 0 | − | 92.1 | 4.8 | | | 30 | 80.2 | 92.1 | 5.4 | | | SST-2 | 60 | 97.6 | 92.2 | 6.8 | | 90 | 99.8 | 92.2 | 8.0 | | | 0 | − | 93.4 | 20.8 | | | 30 | 99.5 | 93.5 | 28.5 | | | IMDB | 60 | 99.7 | 93.6 | 34.8 | | 90 | 99.8 | 93.9 | 39.3 | | | 0 | − | 94.4 | 12.8 | | | 30 | 99.9 | 94.4 | 16.5 | | | AGNEWS | 60 | 99.9 | 94.5 | 21.7 | | 90 | 99.9 | 94.4 | 24.5 | | Denoising steps As discussed in §4.2, the choice of the denoising starting point k is essential to successful detection. Under different starting points, we use Gaussian kernel density (Parzen, 1962) to calculate the distributions of pre-post denoising sentence similarity of all samples. It can be seen from Fig. 3 that, the overlapping area of solid and dashed lines of the same color is gradually decreasing as the number of steps increases. The increase in the number of steps causes the adversarial samples to deviate more significantly in the semantic space, thus separating them from the normal samples. However, it is not recommended to increase the number of steps consistently. On the one hand, the computational overhead is not worth it when the detection performance is good enough. On the other hand, more denoising steps mean that the denoising starting distribution is further away from the true sample distribution, leading to inaccurate score estimation for all samples and thus causing a decrease in detection performance. Adversarial Purification Table 2 shows the classification accuracy of normal and adversarial samples after denoising. Referring to the setup of adversarial purification (Nie et al., 2022; Yoon et al., 2021), we reclassify the denoised sentence representations using the previously fine-tuned linear classifier. Consistent with these adversarial purifications in the field of computer vision research, the denoising process is able to remove a portion of the adversarial perturbations. Although the improvement is weaker compared to defensive methods that | Sentence | FGWS | RDE | CASN | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|-------|--------| | Schaeffer (frank) has to find some hook (pull) on which to hang his persistently inconsequential flick (useless movies), and it perils (might) as allright (well) be the resuscitation of the middle-aged character. While it 's genuinely cool to hear characters talk about early rap (music) records (show) (sugar hill gang , etc.) , the constant referencing (references) of hip-hop arcana (secrect) can consign (alienate) (charge) even the savviest audiences. Further proof that the coeur (epicenter) of neat (cool) , hermosa (beautiful), thought-provoking foreign cinema is smack-dab (pat) in the middle of dubya's (bush's) axis of evil. | | | | Table 3: **Examples showing the sensitivity to subtle semantic gaps**. The words replaced by the attack algorithm are underlined and followed by the original word in parentheses. For the proposed low-frequency word substitutions in FGWS, we write them in brackets after the original text using red color. " & " mean positive and negative. improve model robustness such as adversarial training, our method not only calibrates the semantic features of the adversarial samples to improve the classification accuracy but also ensures the model performance of the original samples. ## 6.3 Case Study Detection results of TextFooler-adj in Table 1 show that CASN is more sensitive to subtle semantic gaps. To further improve this claim, we select the SST-2 dataset under this attack and analyze some representative samples. As shown in Table 3, we can tell that: FGWS needs a large number of lowfrequency word substitutions for correct classification, but the substitutions often do not correspond correctly to the correct ones, so the attacking algorithm only needs stronger synonym constraints to disable it. The third example illustrates that RDE fails in the face of adversarial samples with stronger sentence semantic constraints. This may be due to the RDE's assumption that the semantic space of the adversarial samples is far away from the normal samples. ## 7 Ablation Study To better illustrate the key components in CASN, we perform an ablation study by removing supervised contrastive learning and the solution of the SDE equation in the inference period. The test results are in Table 4. We can observe that: 1) Removing supervised comparative learning will significantly damage model performance. It would fall back to the original conditional denoising model, thus blurring the differences in distribution between different classes of samples, which is detrimental to the denoising process. 2) Without the SDE equation as the solution of the first step, this is not | Dataset | Method | F1 | AUROC | Purified ACC | |-----------|----------|------|---------|----------------| | CASN | 93.7 | 97.3 | 65.5 | | | SST-2 | w/o SCL | 69.2 | 71.0 | 65.4 | | w/o SDE | 91.3 | 97.5 | 63.7 | | | CASN | 97.7 | 99.7 | 57.0 | | | IMDB | w/o SCL | 75.0 | 80.4 | 51.2 | | w/o SDE | 97.4 | 99.6 | 54.5 | | | CASN | 92.4 | 96.9 | 82.2 | | | AGNEWS | w/o SCL | 66.7 | 22.7 | 82.0 | | w/o SDE | 92.3 | 97.4 | 80.0 | | conducive to better correcting the semantics of the adversarial samples, although sometimes the detection performance is not decreased. ## 8 Conclusion In this paper, we propose a nearly-perfect solution, CASN, to detect adversarial samples in text classification tasks. This framework is based on a noise conditional score network and utilizes label information to better estimate the data log-density gradient. Extensive experiments show that our method greatly outperforms the strong baseline method. Moreover, this approach, which exploits sample feature changes during denoising process, is experimentally shown to be more sensitive to semantic gaps of adversarial samples. We also show that a simultaneous denoising process for all samples is effective in maintaining the semantics of clean text while calibrating the adversarial ones. ## Limitations In this work, we propose to use the denoising score matching function to estimate the gradient of logdensity distribution, then describe the differences between the adversarial and normal samples by the denoising process of Langevin dynamics. Although our method achieves very good detection performance (nearly 100% under various settings), the actual denoising process requires multi-step iterative updates, resulting in a very slow inference speed compared to previous methods. In addition, the trained score network is highly correlated with the domain data, which makes it difficult to achieve good generalization across multiple domains at the same time. ## Ethics Statement We take ethical considerations very seriously and strictly adhere to ACL's ethics policy. The focus of this paper is on improving adversarial instance detection, which is studied using publicly available datasets and models, and has been widely adopted by researchers. Our research aims to improve the security of real-world AI systems, which is objectively informative on topics such as privacy protection and content censorship. 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OpenReview.net. ## A Implementation Details This section introduces the implementation details of the training and inference phases. It includes the selection of hyperparameters for training the CASN and the denoising process for inference. In addition, there are some other settings such as the choice of the adversarial algorithm and the finetuning strategy of the agent model. ## A.1 Training Casn The training method is greatly inspired by previous work on denoising diffusion models (Ho et al., 2020; Sohl-Dickstein et al., 2015) and denoising score matching from SDE perspective (Song et al., 2021). In fact, we are able to describe the original denoising score function (Eq.3) and the diffusion model uniformly using SDE. We bring each αi ∈ {αi}Ti=1 into Eq.4 individually, while recording the noise perturbation feature in αi as hi with h0 as the initial feature. It can be seen that at this point, Eq.4 is optimizing the score function under the following Gaussian noise perturbation: $$p_{\alpha_{i}}(h_{i}|h_{0})={\mathcal{N}}(h_{i};{\sqrt{\alpha_{i}}}h_{0},(1-\alpha_{i})I)\quad(1)$$ Noticing that the coefficient αi decreases from 1 to 0 as i increases from i = 0 to T, the noise perturbed distribution (Eq.10) will approach a pure Gaussian noise distribution as i increases to T. Due to the independence between the individual Gaussian perturbation distributions, we can consider the features with different levels of noise perturbed as a Markov process in the generation of the time series. According to Eq.10, the Markov process can be written as: $$p_{\beta_{i}}(h_{i}|h_{i-1})=\mathcal{N}(h_{i};\sqrt{1-\beta_{i}}h_{i-1},\beta_{i}I)\tag{11}$$ $$where\quad\alpha_{i}:=\prod_{j=1}^{i}(1-\beta_{i})$$ This Markov process is a classical denoising diffusion model (Ho et al., 2020). At this point, the parameters α ∈ {αi}Ti=1 in Eq.4 for different noise levels are converted to Gaussian noise coefficients β ∈ {βi}Ti=1 in the diffusion model. We refer to the previous work (Song et al., 2021) to calculate the parameters β ∈ {βi}Ti=1, $$\beta_{i}=\frac{\overline{\beta}_{m i n}}{T}+\frac{i-1}{T(T-1)}(\overline{\beta}_{m a x}-\overline{\beta}_{m i n})\quad\text{(12)}$$ where $\overline{\beta}_{min}=0.1$,$\overline{\beta}_{max}=20$ and $T=1000$. Once we bring the parameters $\alpha_{i}:=\prod\limits_{j=1}^{i}\left(1-\beta_{i}\right)$. into Eq.4, we can calculate the loss of the denoising score matching. Noting that the final training objective, Eq.7, needs to add the supervised contrastive learning loss function, the choice of hyperparameter λ is crucial to make the trade-off. We list the selection of this parameter on different datasets and models in Table 5. RoBERTa SST-2 0.2 120 IMDB 0.1 90 AGNEWS 0.1 90 | Models | Datasets | λ | start(k) | |----------|------------|-----|------------| | SST-2 | 0.15 | 120 | | | IMDB | 0.1 | 90 | | | AGNEWS | 0.2 | 90 | | | SST-2 | 0.2 | 120 | | | IMDB | 0.1 | 90 | | | AGNEWS | 0.1 | 90 | | In addition, we use XLNET (Yang et al., 2019) as the backbone of the class-aware score network. For all datasets and victim models, we train the score network 20 epochs using AdamW optimizer with 2e−5 as learning rate, 0.1 as dropout probability, 64 as batch size, 42 as the random seed. Regardless of how large the loss calculated by Eq.7 is, we use the network saved in the last round as the final scoring network. ## A.2 Detection Via Denoising Process By revisiting the Markov process represented by Eq.11 , we write the changes of text representation at each time point in the following form: $\nabla^{T}\quad\rho_{i}\rho_{i}-1\quad\nabla\rho_{i}\rho_{i}-1,i=1,...,n$ $\rho_{i}=-\Delta f(0,I)\quad i=-0,...,T=1$ hi = 1 − βihi−1 + βizi−1, i = 1*, ...T* (13) where zi ∼ N (0, I), i = 0*, ...T* − 1. Song et al. (2021) indicate that Eq.13 will converge to a stochastic differential equation (SDE) when T → ∞. In the limit of T → ∞, {βi}Ti=1 becomes a function {β(t)}1t=0, zi becomes {z(t)}1t=0, and the Markov process of {hi}Ti=1 becomes a continuous stochastic process {h(t)}1t=0, where t ∈ [0, 1] is a continuous time variable. Noticing that for all SDE equations in the following form: $$d x=f(x,t)d t+G(t)d w\qquad\qquad(14)$$ $$682$$ where w is the standard Wiener process (a.k.a., Brownian motion), there is a deterministic ordinary differential equation (ODE) solution with {pt(h)}Tt=0 as the marginal distribution (Maoutsa et al., 2020). We can use the following ODE solution to generate data in probability flow sampling. $$d x=[f(x,t)-\frac{1}{2}G(t)G(t)^{T}\nabla_{h}\log p_{t}(h)]d t\tag{1}$$ $$(15)$$ Due to the presence of the ∇h log pt(h) term in the ODE equation, it is natural to use the score function to replace ∇h log pt(h) and generate samples by iteratively updating the probability flow ODE in discrete time steps. We first write Eq.13 in the SDE form under the assumption that T → ∞: $$d h=-{\frac{1}{2}}\beta(t)d t+{\sqrt{\beta(t)}}d w$$ After that we write the corresponding discrete form of ODE function based on the solution given in Eq.15, using the trained score network sθ (·) as a replacement for the ∇h log pt(h). $$h_{i}=(2-\sqrt{1-\beta_{i+1}})h_{i+1}+\frac{1}{2}\beta_{i+1}s_{\theta^{\star}}\left(h_{i+1},\alpha_{i+1}\right)\tag{17}$$ In the process of denoising generation, alternately using the numerical form of ODE equation and Langevin dynamics could improve the quality of the generation while reducing the number of sampling steps (Song et al., 2021). Therefore, we also use this approach to update the data representation at each time step in the denoising process. The process of generating the adversarial confidence is shown in Algorithm 1. As mentioned earlier, the hyperparameters {βi}Ti=1 in the inference time satisfy Eq.12. In addition, {-}Ti=1 in Langevin dynamics requires the following calculation: $$\epsilon_{i}=2\cdot\epsilon\cdot\alpha_{i}\cdot{\frac{||z||}{||S_{\theta}(x,\alpha_{i})||}}\qquad(18)$$ where - = 0.01 and z are sampled from the standard normal distribution. The denoising starting points for different datasets and attacked models can be found in Table 5. $$\{\beta_{i}\}_{i=1}^{T}\mathrm{~and~}\{\epsilon_{i}\}_{i=1}^{T}$$ Algorithm 1 Detection Algorithm via Denoising Process. Input: Sentence level representation, h Class-aware score network, sθ(·) Denoising start point, k Hyperparameters {βi}Ti=1 and {-i}Ti=1 Output: **Proposition** Initialize $h_{k}\gets h$, $score\gets0$, $c\gets0$ **for $i=k$ to $0$ do** **for $i=k$ to $0$ do** 3: hi ← (2 − 1 − βi+1)hi+1 + *score* 4: hi ← hi + -isθ(hi, βi) + √2-iz 5: c = c + cos < hmean i , hmean k > 6: **end for** 7: **return** c as adversarial confidence; If $i=w\otimes w\otimes\\score\gets\frac{1}{2}\beta_{i+1}s_{\theta}(h_{i+1},\beta_{i+1})\\h_i\gets(2-\sqrt{1-\beta_{i+1}})h_{i+1}+score$. ## A.3 Other Details $$(16)$$ We fine-tune the BERT-base-uncased and RoBERTa-base model as the victim models, the main hyperparameters are listed in Table 6. According to the general paradigm, we connect a linear classifier after the encoder which is initialized with pre-trained weights. In the training period for CASN, we keep the encoder frozen and trained the score network using encoder representations on the clean dataset. In the detection phase, the encoder would produce sentence representation h for each sentence, no matter if it is adversarial or not. The parameters for a CASN are about one million float32 type floating point numbers, and it takes about 3 hours to train 20 epochs on the IMDB dataset using a single NVIDIA A100 GPU, and 1 hour to predict 3000 samples. For the three datasets, SST-2 has 67,349 training data and 872 validation data. IMDB has both 25,000 training and test data. AGNEWS has 120,000 training data and 7,600 test data. | Hyperparameters | Values | |-------------------|------------------------------------| | Optimizer | Adamw(Loshchilov and Hutter, 2019) | | Learning rate | 2 × 10−5 | | Dropout | 0.1 | | Weight decay | 1 × 10−2 | | Batch size | 64 | | Gradient clip | (−1, 1) | | Epochs | 3 | | Bias-correction | True | Table 6: Hyperparameters used for fine-tuning the BERT-base-uncased and RoBERTa-base model. ## B More Experimental Results This section complements the experimental results in the main text. Firstly, in §B.1, we present the performance of CASN when using the RoBERTa as the victim model for detection under scenario 1. Secondly, we will post the detection performance of the two victim models under scenario 2 (only detect the adversarial samples that successfully change the model output). Finally, we will show the performance of CASN for non-domain detection, illustrating some disadvantages of this approach. ## B.1 Detection Scenario 1 The experimental results are consistent with Table 1. Under scenario 1 which requires detecting all samples generated by the adversarial algorithm, RDE is the state-of-art (SOTA) performance among the previous methods, while the proposed method significantly outperforms RDE under all datasets and attack algorithms. Although in detecting TextFooler-adj attack, CASN only has F1 values of 79.5 and 91.6 on SST-2 and IMDB, respectively, it performs very well in the rest of the adversarial detection. ## B.2 Detection Scenario 2 In scenario 2, we only require the detection algorithm to identify those adversarial samples that have successfully changed the model output. The comparison between Table 1 and Table 8 shows that, except for the detection performance on IMDB, both the feature density-based estimation method RDE and the low-frequency word detection method FGWS have significant performance improvements in this scenario. Moreover, the improvement of our method is much greater under the reduced difficulty setting, since the three datasets achieved an average of 6.6 F1 value improvement under TextFooler-adj detection. ## B.3 Transfer Detection To verify whether the proposed method can be used as a universal detection method without relying on domain data, we perform transfer detection experiments on score network trained on domain data. As shown in Table 9, the score network, after being trained on the features of the Source dataset, acts as an external detection component for the Target dataset, processing the output features of the Target dataset and detecting the adversarial samples. The experimental results show that the CASN still has some generalization ability regarding the detection within similar domains. For example, on the transfer detection from IMDB to SST-2, except for the detection of TextFooler-adj attack, other detections still have all AUROC values of over 94. However, on non-domain data, such as the bidirectional migration of AGNEWS and the remaining two datasets, it is almost impossible to detect any adversarial samples. This suggests that our approach relies greatly on domain data features and does not generalize well across domains. | Dataset | Method | TextFooler-adj | BAE | TextFooler | PWWS | | | | | | | | | |-------------|----------|------------------|-------|--------------|--------|------|-------|------|------|-------|------|------|------| | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | | | | DISP | 53.3 | − | 78.0 | 52.6 | − | 70.0 | 61.7 | − | 69.5 | 64.2 | − | 72.0 | | | MDRE | 69.6 | − | 68.8 | 70.0 | − | 71.1 | 80.0 | − | 79.4 | 76.0 | − | 75.6 | | | FGWS | 68.0 | 68.8 | 64.6 | 67.9 | 65.9 | 59.1 | 70.5 | 69.4 | 63.7 | 72.3 | 76.8 | 69.6 | | | MD | 68.9 | 64.0 | 59.2 | 72.1 | 69.8 | 65.5 | 75.2 | 73.9 | 69.5 | 74.3 | 70.3 | 67.6 | | | RDE | 72.1 | 76.4 | 71.3 | 78.5 | 83.8 | 77.5 | 82.7 | 88.8 | 81.2 | 81.8 | 86.4 | 80.1 | | | Ours (CASN) | 79.5 | 88.3 | 76.5 | 95.5 | 99.3 | 95.5 | 99.8 | 99.9 | 99.8 | 93.8 | 98.6 | 93.8 | | | SST-2 | DISP | 61.0 | − | 59.6 | 69.7 | − | 64.2 | 71.7 | − | 65.6 | 68.3 | − | 62.5 | | MDRE | 70.2 | − | 69.8 | 71.3 | − | 70.8 | 72.8 | − | 72.1 | 70.3 | − | 70.0 | | | FGWS | 77.5 | 83.2 | 76.1 | 79.6 | 84.5 | 77.5 | 80.7 | 85.9 | 78.9 | 82.2 | 88.6 | 81.2 | | | MD | 74.9 | 75.5 | 70.1 | 77.1 | 79.5 | 73.0 | 77.8 | 80.7 | 73.9 | 76.4 | 78.1 | 72.1 | | | RDE | 80.5 | 86.9 | 78.8 | 86.0 | 92.2 | 85.1 | 87.4 | 93.5 | 86.4 | 85.2 | 90.7 | 84.0 | | | Ours (CASN) | 91.6 | 97.0 | 91.5 | 97.3 | 99.6 | 97.3 | 98.3 | 99.8 | 98.3 | 96.6 | 99.4 | 96.6 | | | IMDB | DISP | 61.0 | − | 86.2 | 77.7 | − | 85.9 | 88.2 | − | 89.1 | 86.0 | − | 89.0 | | MDRE | 62.4 | − | 66.3 | 71.6 | − | 73.8 | 80.3 | − | 81.2 | 75.8 | − | 77.3 | | | FGWS | 79.1 | 80.5 | 78.8 | 76.3 | 76.5 | 74.3 | 79.1 | 80.5 | 78.8 | 85.2 | 86.9 | 86.5 | | | MD | 68.8 | 68.2 | 58.0 | 75.0 | 79.7 | 71.3 | 79.2 | 85.7 | 78.1 | 76.5 | 82.4 | 74.3 | | | RDE | 69.2 | 70.7 | 62.0 | 79.2 | 85.0 | 78.1 | 86.0 | 92.2 | 85.9 | 81.4 | 87.3 | 80.1 | | | Ours (CASN) | 95.3 | 99.1 | 95.1 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | | | AGNEWS | | | | | | | | | | | | | | Table 7: **Performance of adversarial detection** using RoBERTa as the victim model. | Dataset | Method | TextFooler-adj | BAE | TextFooler | PWWS | | | | | | | | | |-------------|----------|------------------|-------|--------------|--------|------|-------|------|------|-------|------|------|------| | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | | | | FGWS | 76.5 | 75.6 | 78.0 | 74.8 | 75.0 | 70.6 | 73.9 | 75.6 | 68.3 | 78.0 | 82.5 | 75.5 | | | SST-2 | RDE | 82.7 | 88.0 | 85.1 | 83.7 | 86.4 | 82.2 | 85.9 | 90.4 | 84.2 | 82.9 | 89.6 | 83.9 | | Ours (CASN) | 93.9 | 98.6 | 93.9 | 97.9 | 99.7 | 97.9 | 99.5 | 99.9 | 99.3 | 99.4 | 99.8 | 99.4 | | | FGWS | 83.5 | 90.0 | 81.1 | 81.6 | 88.8 | 81.0 | 81.6 | 88.6 | 81.0 | 80.7 | 89.4 | 81.0 | | | IMDB | RDE | 86.2 | 92.1 | 84.9 | 85.6 | 92.9 | 84.5 | 85.9 | 91.7 | 85.2 | 82.9 | 88.0 | 80.8 | | Ours (CASN) | 98.8 | 99.8 | 98.7 | 99.1 | 99.9 | 99.9 | 99.2 | 99.7 | 99.1 | 99.5 | 99.8 | 99.7 | | | FGWS | 82.4 | 83.9 | 84.6 | 83.0 | 84.2 | 80.0 | 85.8 | 89.2 | 85.2 | 87.9 | 84.2 | 83.0 | | | AGNEWS | RDE | 83.6 | 93.7 | 90.6 | 84.6 | 94.9 | 89.5 | 89.2 | 95.7 | 90.2 | 84.3 | 93.9 | 87.9 | | Ours (CASN) | 95.7 | 98.8 | 99.7 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | 99.9 | | Table 8: **The results of detecting only the adversarial samples of successful attacks, i.e., scenario 2**. We use BERT as the victim model, keeping the evaluation metrics consistent with the previous experiments. | Source | Target | TextFooler-adj | BAE | TextFooler | PWWS | | | | | | | | | |----------|----------|------------------|-------|--------------|--------|------|-------|------|------|-------|------|------|------| | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | F1 | AUROC | ACC | | | | SST-2 | IMDB | 79.6 | 83.1 | 75.9 | 78.3 | 81.8 | 74.5 | 77.8 | 81.1 | 73.8 | 79.4 | 82.7 | 75.6 | | AGNEWS | 67.7 | 59.6 | 54.3 | 70.3 | 71.3 | 62.8 | 72.1 | 76.6 | 64.2 | 75.2 | 75.9 | 68.0 | | | IMDB | SST-2 | 79.2 | 87.3 | 79.1 | 87.1 | 94.1 | 87.2 | 92.1 | 97.8 | 92.1 | 87.6 | 94.4 | 87.7 | | AGNEWS | 66.7 | 46.8 | 50.0 | 70.5 | 72.4 | 64.3 | 71.4 | 74.9 | 65.7 | 66.7 | 59.6 | 50.0 | | | AGNEWS | SST-2 | 66.9 | 66.8 | 54.0 | 73.5 | 78.2 | 71.2 | 70.2 | 71.8 | 64.1 | 69.8 | 70.3 | 63.8 | | IMDB | 72.2 | 77.5 | 69.7 | 75.8 | 81.7 | 74.3 | 77.4 | 82.8 | 76.3 | 70.6 | 76.3 | 70.2 | | Table 9: **The transfer detection experiments for CASN**. The Source and Target denote domain and non-domain datasets, respectively. We train the score network on the Source dataset and subsequently utilize it for adversarial detection on the Target dataset. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The limitation section is after the conclusion part of the thesis. ✓ A2. Did you discuss any potential risks of your work? The ethics statement section is after the conclusion part of the thesis. ✓ A3. Do the abstract and introduction summarize the paper's main claims? The abstract is at the beginning of the article and the introduction is Section 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 5 (Experimental Settings), Appendix A ✓ B1. Did you cite the creators of artifacts you used? Section 5 (Experimental Settings), Appendix A ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? They are all open-source artifacts that are publicly available. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? They are all open-source artifacts that are publicly available. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? They are all open-source artifacts that are publicly available, and do not contain this kind of private information. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 5 (Experimental Settings), Appendix A.3 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 5 (Experimental Settings), Appendix A.3 ## C ✓ **Did You Run Computational Experiments?** The Experimental Part Is In Section 6. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 5 (Experimental Settings), Section 6.2 (analysis), Appendix A.1 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 6.1 (Detection Performance) ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 5 (Experimental Settings), Section 6(Experimental Results), Appendix A ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

hessel-etal-2023-androids

Do Androids Laugh at Electric Sheep? Humor {``}Understanding{''} Benchmarks from The New Yorker Caption Contest

https://aclanthology.org/2023.acl-long.41

Large neural networks can now generate jokes, but do they really {``}understand{''} humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of {``}understanding{''} a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image{'}s locations/entities, what{'}s unusual in the scene, and an explanation of the joke.

# Do Androids Laugh At Electric Sheep? Humor "Understanding" Benchmarks From The New Yorker Caption Contest Jack Hessel† Ana Marasovic´ Jena D. Hwang† **Lillian Lee**◦ Jeff Da‡ Rowan Zellers• Robert MankoffN **Yejin Choi**†‡ † The Allen Institute for AI University of Utah ◦ Cornell University •OpenAI ‡ University of Washington N Air Mail and Cartoon Collections [email protected] [email protected] [email protected] [email protected] {jzda,rowanz}@cs.washington.edu [email protected] [email protected] ## Abstract Large neural networks can now generate jokes, but do they really "understand" humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of "understanding" a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image's locations/entities, what's unusual in the scene, and an explanation of the joke. ## 1 Introduction Humor can be dissected, as a frog can, but the thing dies in the process and the innards are discouraging to any but the pure scientific mind. - White, E. B. (1941) Each week, *The New Yorker* publishes a uncaptioned cartoon image, inviting readers to submit their funniest English-language caption for it. Editors choose three finalists from sometimes thousands of submissions; then, readers vote to pick ![0_image_0.png](0_image_0.png) Figure 1: We formulate three tasks using over a decade of New Yorker caption contests: models must 1) recognize a caption written about a cartoon (vs. options that were not); 2) evaluate that caption's "quality" by scoring it more highly than a non-finalist/non-winner from the same contest; and 3) explain why the joke is funny. (Cartoon by Drew Dernavich, winning caption by Bennett Ellenbogen). the final winner. We develop a suite of three progressively harder tasks built around this contest to test how well AI models "understand" humor across vision and language: 1) matching jokes to cartoons, 2) identifying a winning caption, and 3) generating an explanation of why an image/caption combination is funny. These tasks are difficult because the connection between a winning caption and image can be quite subtle, and the caption can make playful allusions to human experience, culture, and imagination. Consider the image and winning caption "Can you please pass the cow?" in Figure 1. Unlike literal image captions such as in MSCOCO (Lin et al., 2014), here, the caption's relation to the image is indirect:1the size of the mugs must first be recognized as unusual, and then, the caption invokes 1The (relatable) experience of "not getting" a New Yorker cartoon often results from inability to identify the image/text relationship. 688 ![1_image_0.png](1_image_0.png) ![1_image_1.png](1_image_1.png) an association between a large mug and a large amount of cream/milk - perhaps a whole cow's worth. Further, matching a caption to an image is not sufficient: non-finalist entries (e.g., "...Insomniacs Anonymous" in Figure 1) also match the image, but something else makes one seem funnier than the other. Finally, even if a model can accurately identify winning submissions, we would like it to also be able to explain why a particular highly rated/relevant caption is funny. We cover our three tasks in two settings: in the from pixels setting, models are given access only to the cartoon images at test time, and must perform computer vision; in the *from description* setting, we allow models access to a newly-collected, humanauthored corpus of cartoon descriptions, thus simulating access to a human-level computer-vision system - or, alternately, facilitating benchmarking of models that don't have a built-in image-processing component. The annotations we collect and release are rich and multifaceted: they describe the image overall and its locations and entities, what's unusual about the image, and an explanation of the joke. We view this effort as a significant contribution of our work. Our results reveal a gap between AI and humanlevel humor "understanding." In the *from pixels* setting, our best multimodal model (fine-tuned CLIP ViT-L/14 (Radford et al., 2021)) achieves 62% accuracy on a 5-way multiple choice task, but humans achieve 94% in the same setting. Even with significant manual annotation of the cartoons in the from description setting (and despite significant improvements in language modeling performance since this work's submission2) large language models still fall short: human explanations are still preferred in more than two-thirds of cases compared to our best explanation model, 5-shot GPT-4. We release our challenging NLP/vision benchmarks,3annotations, models, leaderboard, and code at https://capcon.dev/. Beyond AI research, we also hope that our work will spur progress in human-AI collaboration tools for cartoonists, contest entrants, and beyond (see Appendix G for AIgenerated captions). ## 2 Datasets And Task Setups Our corpus compiles 14 years of weekly New Yorker caption contests. Each contest consists of: (1) a captionless cartoon; (2) that week's entries; (3) the three finalists, selected by New Yorker editors; and (4) for some contests, quality estimates for each submission collected via crowdsourcing.4 The corpus was constructed from two sources. The first is Jain et al. (2020), from which we obtain roughly 250 contests (mean/median 6.1K/5.7K unique captions per contest; 1.5M total), starting from \#508.5 Crowd ratings in this corpus are gath-2GPT-3 (Brown et al., 2020) was the most performant in Jan. 2023 when this work was submitted, but we have since updated our results. 3Our data may contain offensive jokes. We manually removed a handful of cases we observed to target specific protected classes. We do not endorse the jokes in the corpus, but rather, view them as interesting objects of study. 4We regret that The New Yorker does not currently have an alliterative-paragraph contest. 5We manually corrected some errors in the corpus. | # Train/val/test Matching | 1.6K / 538 / 538 | |----------------------------------|--------------------| | # Train/val/test Quality ranking | 1.6K / 523 / 523 | | # Train/val/test Explanation | 391 / 130 / 130 | Table 1: Basic size statistics for our three tasks. We extend Shahaf et al. (2015); Radev et al. (2016); Jain et al. (2020) by (a) proposing matching, quality ranking, and explanation tasks; (b) providing new, dense annotations for each cartoon (see Figure 3); (c) authoring a set of 651 joke explanations. ered via the NEXT platform (Jamieson et al., 2015; Tanczos et al., 2017), where readers rate captions as "funny", "somewhat funny", or "unfunny"; we use the per-caption mean. There are over 114M ratings total (mean/median of 445K/471K per contest). We also sample three additional top captions that aren't editorial picks to serve as additional "finalists." The second corpus, due to Shahaf et al. (2015); Radev et al. (2016) and derived from contests \#1– \#507, includes 2M unique captions (mean/median 5.2K/5.0K per contest), but no crowd ratings. We remove by hand 55 contests whose images' resolutions are too low, and identify 80 low resolution (but usable) cases, taking special care when annotating this set (§2.2). ## 2.1 Task Setups We pose three tasks. Matching and explanation are novel, whereas quality ranking extends the formulations introduced in Shahaf et al. (2015); Radev et al. (2016). Matching. *Can a model recognize when a caption is appropriate for a given cartoon?* Five choices are given, only one of which truly corresponds. For the example in Figure 1, we supply the following possibilities: (a) O.K. I'm at the window. To the right? Your right or my right? (b) *I'd kill for some cream cheese.* (c) *Bob just came directly from work.* (d) **Can you please pass the cow?** (e) *They only allow one carry-on.* The correct caption is a finalist for the cartoon. Negative choices are randomly selected finalists from other contests, and as a result, are great captions for some *other* contest's image.6In some cases, matching depicted objects to their textual references may suffice, but in other cases, the relationship is more indirect. For example, Figure 2 (top) contains a subtle reference to Jane Goodall, thus requiring external knowledge; Figure 2 (bottom) relies on a stereotype of pharmaceutical companies being untrustworthy, hence requiring reasoning beyond the literal text. Quality ranking. Can a model identify highly rated captions? For each finalist, we sample for comparison a caption that was not selected as a finalist, and ask models to identify which one (the real one or the distractor) was rated as higher quality. As preprocessing, we run one round of textonly filtering to discard submissions that are easily identifiable as low quality, and also perform semantic deduplication; more details in Appendix C. Here is the end result for Figure 1: ## (A) **Can You Please Pass The Cow?** (B) Welcome To Insomniacs Anonymous. Which caption a particular individual prefers can be a matter of personal taste; but there is a general preference among our human annotators for the true finalist (see §3). Explanation. *Can a model generate as good an* explanation as a human for why a caption-andimage combination is funny? Free-form explanations of why captions are funny/appropriate for their corresponding image were written by an author of this paper.7 The rough annotation guidance was: "In a few sentences, explain the joke as if to a friend who doesn't 'get it' yet." Starting from a random finalist for each contest, after filtering out cases where the author did not understand the joke, a corpus of 651 human-created joke explanations to serve as comparison points was formed (mean/median 60/59 words, 39.3K total). We consider a model to succeed at this task if human judges, presented with (unlabeled) pairs of author/machine-generated explanations, do not show a preference for the author-generated ones. Evaluation metrics. For matching and quality ranking, we evaluate using accuracy. For quality ranking, we report *NYAcc* - the average accuracy over instances where the finalist was an official New Yorker finalist - and *CrowdAcc*, where the 7Several attempts to solicit explanations from crowdworkers were not satisfactory; similarly unsuccessful were prompting experiments with GPT-3 inspired by Wiegreffe et al. (2022); Marasovic et al. ´ (2022) - too few of the sampled explanations were correct to bootstrap a corpus. ![3_image_0.png](3_image_0.png) "finalist" caption was selected by the crowd as high quality. These two measures allow us to account for different audience tastes. For explanation, we conduct pairwise human evaluations to test several hypotheses detailed in §3.2. To complement these human evaluations, we also report in Appendix E automatic metrics that take into account the human-written reference: (a) BLEU-4 (Papineni et al., 2002) using Post (2018)+ROUGE-L (Lin, 2004); and (b) word-level perplexity. From Pixels + From Description. We consider two experimental settings. In **From Pixels (FP)**, a vision+language model undertakes image processing, i.e., at test time, the only contest information available is the image itself. In the second setting, which we call **From Description (FD)**, we factor out visual processing by providing the model with human written annotations, described in §2.2. FD models thus simulate access to a human-level computer-vision system. ## 2.2 Annotation Of Cartoons. We collect several types of annotations about the 704 cartoons; these either serve as input to models in the *from description* setting, or as additional information available only at training time in the from pixels setting. For each cartoon, we gather: (i) A phrase describing the setting of the scene, e.g., "an office" or "the park" (2 per cartoon) (ii) A literal 1-3 sentence description of the scene (3 per cartoon) (iii) A 1-3 sentence description or explanation of what makes the scene unusual (3 per cartoon) (iv) 2-3 English Wikipedia links that an annotator identified as relevant, to serve as a proxy for world knowledge (2 per cartoon) A random sample of annotations is shown in Figure 3. We used Amazon Mechanical Turk, and paid crowdworkers a minimum of $15/hr. Lowresolution images involved special treatment: 1) we offered additional pay to crowdworkers; and 2) at least one of the annotations is conducted by an author of this work using the same HIT interface. Details including qualification rounds, screenshots of the HITs, etc. are given in Appendix A. ## 3 Experiments We split the 704 cartoons into 5 cross-validation splits such that entire contests are held out at test time. Task construction details are in Appendix C; modeling details (e.g., hyperparameter sweeps, task formatting) are in Appendix B. ## From Pixels (Fp) Models We explore two vision+language models. CLIP. We fine-tune CLIP ViT-L/14@366px (Radford et al., 2021) (428M parameters), which consists of a text Transformer (Vaswani et al., 2017) and a vision Transformer (Dosovitskiy et al., 2021) pretrained to align images/captions in the WebImageText corpus (400M pairs). For multiple choice, we use InfoNCE (Oord et al., 2018) to encourage the cosine similarity of the cartoon/correct answer to be higher than the incorrect ones. For zero-shot classification, we use the prompt a new yorker cartoon with ![4_image_0.png](4_image_0.png) winning caption. CLIP isn't generative, so we can't use it for explanation. OFA → LM. We use OFA Huge (930M parameters) (Wang et al., 2022), a seq2seq model that supports image/text inputs/outputs; it is pretrained on a variety of vision+language tasks. We finetune on the New Yorker corpus by training it to map from (cartoon, prompt) → descriptions for the four types of annotations described in §2.2; see Figure 4 for example predictions. We organize the OFA-predicted outputs in the same format as the human-authored descriptions in our From Description (FD) models detailed below (except the inputs are the outputs of OFA), and pass the result to a language model:8this composition can be considered a Socratic Model (Zeng et al., 2022). ## From Description (Fd) Models We formulate multiple-choice tasks as text-to-text by concatenating the human-authored cartoon descriptions with the choices as input: the target is simply the letter corresponding to the answer, e.g., E. For explanation, we autoregressively generate the explanations conditioned on the descriptions/captions. T5. We fine-tune T5-Large and T5-11B (Raffel et al., 2020); these encoder-decoder transformer models have 770M and 11.3B parameters respectively. For explanation, we sample with tempera8We found that fine-tuning OFA directly was less effective. ture 1.0 and nucleus sampling with p=.95 (Holtzman et al., 2020). GPT-3, GPT-3.5, GPT-4. We use these three OpenAI models as both zero-shot and few-shot models. We provide the models with a description of the task, and, for the few-shot case, 5 random labelled in-context examples. Specifically, for GPT-3 we use text-davinci-002 (175B) (Brown et al., 2020), and for GPT-3.5/GPT-4, we use the May 12, 2023 versions (OpenAI, 2023). For GPT-3, we also consider a fine-tuned version (which is unavailable for GPT3.5/GPT-4).9 For zero-shot GPT-3.5/GPT-4, early experiments revealed that prompting models to "think" step-bystep with chain-of-thought (CoT) was helpful (Wei et al., 2022; Kojima et al., 2022). See §B.6 for GPT3 details, and §B.7 for GPT-3.5/GPT-4 details. ## Baselines Caption Only. In addition to a **Random**-guess baseline, we fine-tune T5-11B given just the caption, i.e., without knowledge of the cartoon (Trichelair et al., 2019; Poliak et al., 2018). Human performance estimates. Three people (two authors and one person familiar with the project) each attempted 100 randomly sampled instances from both the matching and quality ranking tasks.10 It is important to note that *human performance is not an upper bound for model performance on matching and quality ranking* because labels are not generated by a single human and tastes can vary; it can (and does, see §3.1) happen that a machine might be able to reconstruct New Yorker editor preferences more reliably than an untrained human. Annotators were given access to the images, but not the descriptions (akin to the FP setting). ## Hardware+Software Details. T5, CLIP, and OFA were trained using 8 A100 GPUs in pytorch (Paszke et al., 2019). We use the Transformers (Wolf et al., 2020) implementation of T5: T5-11B was trained with deepspeed (Rasley 9https://beta.openai.com/docs/guides/fine-tuning; for explanation, we use the default settings; for multiple choice, we set prompt loss weight to zero. The validation set is not used by the API for early stopping, so we concatenate it with the training set and perform no validation. 10Matching instances were sampled such that there were no repeated options, i.e., annotators couldn't use process of elimination across instances. 595 total responses were collected. | Matching | Quality Ranking | | | | | |----------------------------------------------|---------------------|-----------|------|--------------------------------------------------------------------------------------------------------------|------| | Accuracy (↑) | CrowdAcc (↑) | NYAcc (↑) | | | | | Random | 20.0 | 50.0 | 50.0 | | | | Caption Only (T5-11B) | 19.4 | 59.4 | 64.5 | | | | CLIP ViT-L/14@336px (finetuned) | 62.3 | 57.0 | 66.9 | | | | Zero-shot | 56.6 | 55.8 | 56.8 | | | | FP | OFA-Huge → T5-Large | 45.2 | 59.1 | 64.3 | | | OFA-Huge → T5-11B | 51.8 | 60.3 | 65.0 | Matching even notice window treatments? C) I'd like to see other people. D) I think it's called an air B&B. | | | T5-Large | 59.6 | 61.8 | 64.8 | | | | T5-11B | 70.8 | 62.3 | 65.6 | | | | GPT3-175B (finetuned) | 75.1 | 64.8 | 69.8 | | | | FD | | 5-shot | 57.2 | 55.1 | 54.8 | | Zero-shot | 51.6 | 56.2 | 55.6 | | | | GPT 3.5 (5-shot) | 63.8 | 55.6 | 55.2 | | | | Zero-shot+CoT | 50.4 | 52.8 | 55.4 | | | | GPT-4 (5-shot) | 84.5 | 73.3 | 68.2 | | | | Zero-shot+CoT | 81.9 | 66.2 | 64.3 | | | | Human Estimate From Pixels (FP) | 94.0 | 83.7 | 64.6 | CLIP GPT-4 | CAP | | Quality Ranking I'd like to see other people | | | | | | ![5_image_0.png](5_image_0.png) et al., 2020); T5-Large and CLIP were trained with Accelerate.11 ## 3.1 Matching And Quality Ranking Results Table 2 contains the results. Among the *from description* models, GPT-4 (5-shot) generally performs best, e.g., achieving 84.5% accuracy on matching. It (and fine-tuned GPT-3) also perform better at predicting New Yorker editor selections than our three humans (column NYAcc: GPT-3 69.8 vs. Human estimate, 64.6), but underperform at predicting crowd selections (CrowdAcc column: GPT-4 73.3 vs. 83.7).12 We also see that our *from* pixels models leave significant headroom compared to the human performance estimates. Other observations include: 1) both *from pixels* and *from description* models mostly outperform the Caption Only baseline (even for smaller model sizes), suggesting that the models are truly using feature interactions between cartoons/captions to improve their predictive accuracy; 2) fine-tuning CLIP tends to do best for matching in the *from* pixels setting, but OFA+T5-11B is competitive for quality ranking (and supports generation, see §3.2); and 3) the performance difference between T5 vs. OFA→T5 exemplifies the effect of subop-11https://huggingface.co/docs/accelerate 12Also, crowd selectors greatly outnumber New Yorker editors, so crowd rankings may be a more dependable target, statistically speaking. timal visual recognition when shifting from the from pixels setting to the *from description* setting. Finally, while performance drops are incurred universally for zero-shot models, pointing towards the utility of the new annotated corpus we are releasing (§2.2), GPT-4's zero-shot chain-of-thought incurs a smaller performance drop compared to other zero-shot models; see §B.7 for a sample chain-ofthought. ## 3.2 Human Evaluation Of Explanation. We gather judgments from 3 crowd-workers per test instance by asking them which of a pair of explanations they prefer, and take a majority vote to determine a winner. Results and annotator agreement are in Table 3, and samples of GPT-3, GPT-4, and human joke explanations are in Figure 5. Our evaluations address seven questions: ## Q1: Do Models Utilize The Image Context Of The Caption To Generate Better Explanations? Test: T5-11B vs. Caption-only T5-11B. Answer: **Yes.** Compared to the same model trained with no access to image information, the model with image information wins in 84.7% of cases. Q2: Is computer vision a bottleneck for topquality explanation generation? *Test: T5-11B* (in the FD setting) vs. OFA → *T5-11B.* Answer: Yes. Compared to the same model trained with access to human written descriptions available at test ![6_image_0.png](6_image_0.png) | A | B | % A wins | # ratings | G-γ | | |-----|--------------|------------------|-------------|-------|------| | Q1 | T5-11B | Caption only | 84.7% | 393 | 64.4 | | Q2 | T5-11B | OFA → T5-11B | 74.6% | 393 | 41.6 | | Q3 | T5-11B | T5-Large | 68.5% | 390 | 45.9 | | Q4 | FT-GPT-3 | In context GPT-3 | 50.0% | 396 | 23.2 | | Q5 | 5-shot GPT-4 | Zero-shot GPT-4 | 64.3% | 396 | 19.7 | | Q6 | 5-shot GPT-4 | 5-shot GPT-3 | 93.0% | 384 | 86.4 | | Q7 | Human | 5-shot GPT-4 | 67.7% | 390 | 20.9 | time (i.e., the *from description* setting), the model trained with access only to OFA-predictions loses in 74.6% of cases. Q3: Do bigger T5 models generate better explanations? *Test: T5-11B vs. T5-Large.* Answer: Yes. T5-11B with access to the same information at test time as T5-Large (770M) is preferred in 68.5% of cases. Q4: Does fine-tuning an LLM model help vs. in-context learning for explanation generation? Test: FT-GPT3 vs. In context (=5-shot) GPT3. Answer: **Not really.** In contrast to the multiple choice tasks, we find that in-context explanation generations are comparable to fine-tuned ones according to pairwise human evaluations, even though the perplexity of the in-context model, reported in Appendix E, is much higher (107 vs. 21.8).13 We expect that the fine-tuned model more closely mirrors the style of the corpus, but that the in-context explanations also contain similar content, e.g., relevant entities. Q5: Do supervised explanations help, even with GPT-4? *Test: 5-shot GPT-4 vs. Zero-shot GPT-4.* Answer: **Yes.** The zero-shot version of GPT-4 is missing access not only to the supervision of paired (caption, explanation) data, but also, explanations in the detailed style of our released corpus. Perhaps as a result, 5-shot GPT-4 (which also achieves significantly higher BLEU-4/Rouge-L) is preferred in 64% of cases. Q6: Does GPT-4 outperform GPT-3? Test: 5shot GPT-4 vs. 5-shot GPT-3. Answer: **Yes, definitely.** In our most definitive result, with equal amounts of supervision, GPT-4's explanations are preferred nearly universally - specifically, in 93% of cases. Interestingly, GPT-3 performs slightly 13A disparity not mirrored in the word-overlap metrics BLEU-4 and Rouge-L, also reported in Appendix E. better on automatic evaluation metrics for explanation like BLEU-4 and Rouge-L (see Appendix E), which suggest that the earlier family of may fit the surface features of the generation task more effectively, e.g., 5-shot GPT-3 achieves 5.07 BLEU-4 compared to 4.99 for 5-shot GPT-4. This suggests that mirroring the surface form of our explanation corpus is not sufficient to generate the highest quality explanations. ## Q7: Does Our Best Model, Gpt-4, Explain Jokes as well as humans? Test: Human vs. Few-shot GPT-4. Answer: No. Human-written explanations are preferred by annotators in 68% of pairwise cases.14 We qualitatively examine the 39/130 cases where the human reference receives 3/3 annotator votes. In these cases, the machine-generated explanations usually incorrectly interpret the image, e.g., in one case, a caption jokes about two cavepeople in a hole looking at a caveman in a cave with the caption "Personally, I'm not a big fan of modern architecture."; GPT-4 incorrectly interprets the hole as "modern architecture" instead of the cave. We also examine the 8/130 cases where the GPT-4 produced caption was unanimously preferred: a close reading of these cases is provided in Appendix F. In 3 of these 8 cases, the human explanations, while on the right track, had slight inaccuracies, and in the remaining 5 cases, the human and machine explanations both express the same idea, but with different styles (GPT-4's sometimes arguably being more formal, detailed, or fluent). ## 3.3 Error Analysis For Matching We conduct an error analysis of a performant from pixels model (CLIP ViT-L/14@336px finetuned), and a performant *from description* model (GPT3-175B finetuned). We concatenate the test set predictions over the 5 cross validation splits, and ask: Q8: Are some contests more difficult than others? Answer: **Yes.** *Details:* We conduct a χ 2 test by forming a contest-by-correctness (704-by-2) contingency table, aggregating over the 3-6 matching instances for each contest, and find that errors are clustered according to contest (*p < .*05 for both CLIP and GPT-3).15 There's a moderate Spearman 14For a similar, earlier set of experiments with FT-GPT-3 vs. human, human was preferred in 87.8% of pairwise cases. 15Similar χ 2tests find no evidence of correlation between correctness and (a) cross-validation split (5-by-2 table; p=.84/.14 for GPT3/CLIP); or (b) which captions are randomly correlation between the per-contest accuracy between the models (ρ = .28, p .001), but (as a null hypothesis) only a slight correlation between contest date and difficulty for either (later contests easier, GPT3/CLIP ρ = .07/.08, p = .08/.05). When the models' predictions agree, they are correct 87% of the time. When GPT-3 is wrong, CLIP is right only 38% of the time; under the null hypothesis that their errors are uncorrelated, CLIP's accuracy would be 62% (p .001 errors are uncorrelated, permutation test). However, when we attempt to identify consistent factors that predict contest difficulty using various visual/linguistic predictors, we find hard vs. easy difficult to predict a priori; our best classifiers perform only slightly above random. We will distribute the hard vs. easy contest lists as a resource for future work. ## 4 Related Work Humor. Raskin (1979) and Attardo (2008) highlight three "great families" of theories of the roots of humor: 1) *hostility,* claims of superiority over someone or something (Gruner, 1978; Billig, 2005); 2) *release* of a constraint (Freud, 1905; Fry, 1963; Mindess, 1971) and 3) *incongruity,* (sometimes "incongruity-resolution"; Mulder and Nijholt, 2002) the introduction (and subsequent resolution) of generally incompatible contexts (Schopenhauer, 1818; Shultz, 1976). Shahaf et al. (2015) note that most New Yorker caption contest cartoons involve incongruous situations. NLP + The Caption Contest. King et al. (2013), Shahaf et al. (2015), and Radev et al. (2016) analyze 5, 16, and 50 New Yorker Caption Contests, respectively. Best-performing features for identifying the funniest among a set of caption choices include: perplexity, match to image setting and uncanniness description, readability, proper nouns (Shahaf et al., 2015), overlap with WordNet's (Fellbaum, 1998) "person" and "relative" synsets, lexical centrality among submissions (Radev et al., 2016, inspired by Mihalcea and Pulman (2009)), and sentiment (both papers). Our "location" and "uncanny description" annotations are direct analogs of the "context" and "anomaly" tags of Shahaf et al. (2015), and our data incorporates that generously released by the previous researchers. Our extensions are (a) the addition of two novel tasks; (b) using new data/resources/models to curate ranking pairs (see assigned as negative choices (2646-by-2 table, p=.92/.79 for GPT3/CLIP). §2); and (c) evaluating two distinct audience preferences: New Yorker editors vs. "the crowd". Appendix H highlights efforts beyond the scope of peer reviewed AI venues, e.g., blog posts. Measuring preferences over captions. While humor is ultimately subjective, work on the contest has studied modeling *average* preferences of raters. Tanczos et al. (2017) design quality ranking algorithms for the caption contest, framed as identifying the best "arm" in a multi-armed bandit setting; their crowdsourcing system NEXT (Jamieson et al., 2015) is used by The New Yorker. It does not directly use the content of the cartoons/contests. The result is Jain et al. (2020)'s continuously updated corpus, from which we draw some of our data. Multimodal and computational humor. Chandrasekaran et al. (2016) explore humor recognition in images, and Castro et al. (2019); Hasan et al. (2019); Patro et al. (2021); Hasan et al. (2021) explore laughter prediction in TED-talks/sitcoms. Tsakona (2009); Fallianda et al. (2018) study political cartoons. Chakrabarty et al. (2022) recently proposed a version of NLI for figurative language, which can be humorous. Some work has tried to detect whether a sentence is humorous or not (Blinov et al., 2019; Annamoradnejad and Zoghi, 2020). More difficult to evaluate (Valitutti, 2011) are setups where the goal is to automatically generate humorous content in various contexts (Binsted and Ritchie, 1994; Stock and Strapparava, 2003; Mihalcea and Strapparava, 2005, 2006; Wang and Wen, 2015; Chandrasekaran et al., 2018; Yoshida et al., 2018; Sundaram, 2018; Shimomoto et al., 2019); a survey is provided by Amin and Burghardt (2020). Explaining humor. In the taxonomy of Tan (2022), joke explanations are most related to proximal mechanisms: "This type of explanation attempts to provide the mechanism behind the predicted label, i.e., how to infer the label from the text", or efficient cause a la Aristotle (Lombrozo, 2006). Chowdhery et al. (2022) undertake a qualitative exploration of (non-visual) joke explanations. ## 5 Conclusion We demonstrate that today's vision and language models still cannot recognize caption relevance, evaluate (at least in the sense of reproducing crowdsourced rankings), or explain The New Yorker Caption Contest as effectively as humans can. However, the partial capacity of today's AI is still substantial, and may be sufficient for models to serve as creative collaborators, e.g., as brainstorming assistants for humorists/cartoonists. Specifically: 1) our matching/quality ranking models could help entrants receive quantitative feedback on the relevance/predicted quality of their submissions, and 2) the annotated corpus+explanations we introduce could be repurposed for generation (we explore generation of novel cartoons/captions in Appendix G). Finally, a promising avenue for future work focused on generating humorous captions (c.f. our focus of humor "understanding" benchmarks) would be to operationalize the feedback provided by our matching/ranking models in an reinforcement learning from human feedback (RLHF) loop. A last remark. We cannot claim to know whether the human-machine 'humor understanding gap' will be closed sooner or later.16 But we encourage other researchers to have as much fun with the topic as we did! ## 6 Limitations The New Yorker Cartoon Caption Contest represents a narrow slice of humor, deriving from a particular language, region, history, culture, style, and set of conventions. Hence, the results of this study do not represent or cover all types of humor. Our framing of the quality ranking task could be interpreted as seemingly prescriptive (i.e., that joke A is "objectively" better than joke B), but New Yorker editorial selections should not be taken as ground truth for funniness; disagreement about what is funny is expected and valid. Our tasks operationalize the prediction of only *average* preferences (rather than individual ones), and these preferences may include a partiality or bias towards items that conform to the characteristics of prior contest winners or published New Yorker cartoons. Finally, the explanations in our annotated corpus were largely written by a single author of this paper. While a larger pool of the crowdworkers judged these explanations to be of higher quality in comparison to machine generations, future work would be well-suited to compare the person-toperson variance in explaining why particular jokes are funny. 16Or never. Is never good for you? ## 7 Acknowledgements We thank the cartoonists and contest entrants for their wonderful efforts! We additionally thank our crowd annotators for their diligent work, Lisa Watkins for contributing to the human performance estimates, and the anonymous reviewers for their constructive comments. This work was funded in part by DARPA MCS through NIWC Pacific (N66001-19-2-4031), the Allen Institute for AI, and a Google Focused Research Award. Jack Hessel conducted initial work while at Cornell University. Ana Marasovic conducted this work while at The ´ Allen Institute for AI. Rowan Zellers conducted this work while at University of Washington. ## References Miriam Amin and Manuel Burghardt. 2020. A survey on approaches to computational humor generation. In *The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature*. Issa Annamoradnejad and Gohar Zoghi. 2020. 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Neural joking machine: Humorous image captioning. In CVPR Language & Vision Workshop. Michael Zelenko and Frank Bi. 2015. On the internet, nobody knows you're a machine. Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. 2022. Socratic models: Composing zero-shot multimodal reasoning with language. arXiv preprint arXiv:2204.00598. ## A Crowdworking Details We use three Mechanical Turk interfaces to gather data. These are: 1. *Cartoon description* (Figure 6). We ran this HIT 3 times per cartoon. 2. *Cartoon wikipedia links* (Figure 7). We ran this HIT 2 times per cartoon. 3. *Pairwise explanations* (Figure 8). We ran this HIT 2.7K times to facilitate the comparisons in §3.2 Qualification+training rounds. To ensure our set of crowdworkers were properly trained for the annotations, we ran two types of qualification rounds: one for the description/link HITs, and one for the pairwise explanation HITs. For the description/link HITs, our qualification round was based off an earlier and more involved HIT that involved a joint setup where, for 3 cartoons, users described cartoons, highlighted image regions, explained jokes, etc. We allowed users from {AU, CA, NZ, GB, US} with 10K prior approved HITs and a minimum acceptance rate of 97% on their previous HITs to participate. Some of the cartoons and captions contain mature themes; we provided the recommended disclaimer for this and other HITs: "WARNING: This HIT may contain adult content. Worker discretion is advised." We manually graded the responses of 30 annotators in a qualification round, and qualified 21. Through a mix of the older, more involved HITs and the streamlined HIT in Figure 6, which is a pared-down version of the original HIT without captions, we gathered descriptions of the cartoons. We also gathered the locations/Wikipedia entity links from the qualified annotators. These annotations were gathered in mid-to-late 2021. About 9 months later, we conducted a second set of Mechanical Turk studies for pairwise judgment evaluations for explanation. A second qualification round was run, in which we asked annotators to rate the quality of several joke explanations which we manually selected to be good/bad across various desirable axes. We qualified 29 out of 51 annotators who attempted the HIT via manual inspection of their responses. This set of annotators were given access to the final pairwise-judgment HITs. Crowdworking studies of standard computer vision corpora (involving no personal disclosures) are not required by our IRB to be reviewed by them. While the authors of this work are not lawyers and this is not legal advice, this opinion is based on United States federal regulation 45 CFR 46, under which this study qualifies and as exempt. We hashed crowdworker IDs in the public release so annotations cannot be back-traced to individual workers. ## B Additional Experimental Details B.1 From Description Details For each cartoon, we have multiple annotations of each type, as detailed in §2.2. During training, we utilize all location/description/uncanny description/sets of links, but at test time, we randomly sample a single set of these four annotation types such that inference requires only a single forward pass. For fair comparison, the randomly sampled description available at test time is held constant between all methods. More detail about how we managed multiple annotations: because we have 2 locations × 3 descriptions × 3 uncanny descriptions × 2 entity links, there are potentially 36 possible combinations we could use to form a *from description* instance for each cartoon. However: tuples are constructed at the annotator level to account for potential dependencies between annotation types: because descriptions/uncanny descriptions were were collected in the same HIT, the uncanny description may reference entities from the description because they were authored at the same time by the same annotator in sequence. Similarly, the (locations, links) were collected in the same HIT. So, we instead consider all six possible tuples holding author constant between HITs, i.e., 3 (description, uncanny description) × 2 (location, link). For test time, select a single random valid tuple of annotations for evaluation, which is fixed for all comparisons. ## B.2 Clip For fine-tuning results, we do linear warmup for 200 steps and conduct a small learning rate search on the validation set for each cross-validation split independently between {5e-5, 1e-5, 5e-6}, keeping batch size fixed at 32. To keep the entire cartoon in the 336px square input, we resize and pad. At training time, we perform data augmentations on the image, including: random horizontal flipping, random color jittering, and random grayscaling. NOTE: The instructions have probably changed since the last time you did this HIT! While this HIT is similar, please take a moment to familiarize yourself with modifications Your task is to analyze a given image. There are three parts First, describe the literal contents of the image image by writing a 2-3 sentence summary. Consider focusing on: - Where is the scene taking place? - Who/what is in the scene? What are they doing? - What objects and actions are being depicted? - Is anyone particularly happy/unhappy/mad/etc.? sentences (see the examples below) There's no need to be too formal, but please do your best to write full, grammatical Second, these images may depict interesting/unusual situations. Highlight these uncanny/unusual elements, by giving a 1-2 sentence explanation of why they are uncanny, e.g., "Jobject/character/…] is unusual/uncanny/out-of-place because …". Consider focusing on: - Which objects, actions, entities, etc. are out-of-place and why? - Are the actions any characters are undertaking strange? - Do the characters have any unusual identifying characteristics? Third, in a single sentence, please write the question that you most want answered about the scene, based on the image, your description, and your highlight of which parts are unusual/uncanny (see examples below). Optionally, you can include a second question that you would like answered if there are multiple uncanny elements to the scene. Please describe the literal contents of the image in 2-3 sentences: ![13_image_0.png](13_image_0.png) A man in a suit is lying down on a sidewalk in a busy city as pedestrians walk over him. The pedestrians seem to be frustrated and confused that the lying down man is blocking their way, while the man himself seems to be carefree. Please highlight/explain any unusual/out-of-place elements in 1-2 sentences: It's unusual that the man is lying in the middle of a sidewalk not only because this action is disruptive to other pedestrians, but also because he's in a nice sult that is likely to become dirty. Furthermore, his carefree expression indicates that, despite these downsides, he doesn't care and is in no rush to move. In 1 sentence, which question would you most like answered about the scene? ![13_image_1.png](13_image_1.png) Why is the man lying on the sidewalk? ![13_image_2.png](13_image_2.png) NOTE: Please read the instructions, even if you've done a similar HIT before. The instructions have probably changed! Please take a moment to far the modifications Your task is to provide context for a given image. There are two parts to this task. First, in a few words, you'll complete the sentence This scene takes place in/at/on.. Examples of reasonable completions include: - a bar - a medieval castle - a city street - a dessing room Next, you'll choose at least 2 English Wikipedia links (and up to 3) that could help a robot understand what is expected, and what is weird, about the provided scene. You'll need to use the search function provided here https://en.wikipedia.org/wiki/Main_Page to find the article URLs, and then copy-and-paste from your internet browser's URL ban Examples of reasonable links include - if Paris were referenced: https://en.wikipedia.org/wiki/Paris - If President Obama was referenced, https://en.wikipedia.org/wiki/Barack_Obama - If the characters were eating Fondue, https://en.wikipedia.org/wiki/Fond - if the scene is in space, https://en.wikipedia.org/wiki/Outer_space - if the characters were at a Lü'au, https://en.wikipedia.org/wiki/Lü'au - if the characters were fencing. https://en.wikipedia.org/wiki/Fencing - if the characters are drunk, https://en.wikipedia.org/wiki/Alcohol_intexications - If the characters are from Sleeping Beauty https://en.wikipedia.org/wiki/Sleeping_Beauty - if a character is psychic, https://en.wikipedia.org/wiki/Psychic Rules for links: - Provide valid links; A valid English wikipedia link should begin with "https://en.wikipedia.org/wiki/" and have no section links. Don't provide https://en.wikipedia.org/wiki/Sleeping_Beauty\#Ploc , use https://en.wikipedia.org/wiki/Sleeping_Beauty - Iry not to provide the wikipedia article for the answer you gave in part 1: If you wrote that the scene takes place in "a bar," try your best to provide links beyond the wikipedia article for "bar," unless it's particularly relevant, or there are no other options - Provide specific/relevant links: If the scene happens to contain people, don't just provide a link to the wikipedia page for "Person." Also, focus on relevant information, e.g., don't include links for "Shoe" if the person happens to be wearing a shoes, unless the shoe is relevant to the scene. - For proper nouns, specific is better; linking "Sleeping Beauty" is better than "Fairy Tale"; linking "New York City" is better than "City"; linking "Barack Obama" is better than than "President ![14_image_0.png](14_image_0.png) In this HIT, you will be presented with a cartoon from the New Yorker, and a caption someone wrote about that cartoon. The captions will relate to the image in a clever/funny You will also be presented with two explanations of the joke: these explanations may be written by humans or by machines. Your job is to select the explanation that you think is the best one. Aside from fluency, grammaticality, etc., qualities of good explanations include: - they offer a complete explanation of why the caption is funny; - they reference appropriate external factors like real-world knowledge, etc.; - they are not overly long or overly short; ![14_image_2.png](14_image_2.png) ![14_image_1.png](14_image_1.png) ## B.3 Ofa We use validation-set early stopping on crossentropy loss, and fine-tune OFA separately for each cross-validation split. After fine-tuning, we select the top-1 prediction according to beam search (n=5). We finetune OFA Huge with a learning rate of 5e-5, which was determined via a small grid search over the first cross-validation split. We use label-adjusted smoothed cross entropy loss as implemented by the OFA authors17 with smoothing of 0.1. We train for a maximum of 7 epochs with a warmup ratio of 6%. For each image, we query for the four different types of annotations shown in Figure 3. To facilitate this, in addition to providing OFA with the image, we also provide it with a per-annotation-type prompt: 1. for locations: "Where does this take place?" 2. for descriptions: "Describe this image." 3. for uncanny: "What's unusual about this image?" 4. for entities: "What entities are there?" In early experiments, instead of composing with a language model, we did attempt to fine-tune OFA directly for the explanation task. However, we found that the resulting perplexity (roughly 300) was significantly higher than for other fine-tuned models, with the errors difficult to diagnose. ## B.4 T5-Large/T5-11B. For T5-Large, we conduct a small, per-crossvalidation split learning rate search between {1e-4, 1e-5, 5e-5} and keep batch size fixed at 64. For T5-11B we use a fixed learning rate of 1e-5 and a batch size of 64. ## B.5 Gpt-3 Zero Shot/In Context We use GPT-3's davinci-text-002 model for our main zero shot and in-context learning experiments. Examples of zero-shot prompts for all tasks are given in Figure 9. The in-context prompts are similar, except they contain 5 random samples from the training set. A full, randomly selected in-context prompt for the explanation generation task is given in Figure 10. ## B.6 Gpt-3 Fine-Tuning We use the OpenAI fine-tuning API to fine-tune davinci, a 175B parameter language model.18 17https://github.com/OFA-Sys/OFA 18https://beta.openai.com/docs/guides/fine-tuning While the precise details of how the API works are not currently available (e.g., which parameters are updated, or which version of davinci is used), we use the same cross-validation setup as for the other models so that the results are comparable. The total fine-tuning cost is approximately (3 tasks) × (5 cross-val splits) × (40 dollars per fine-tune) = 600 dollars. ## B.7 Gpt 3.5/Gpt-4 Details Between submitting this work and its acceptance, OpenAI released two new models, GPT-3.5 (sometimes called ChatGPT when accessed through the chat interface) and GPT-4; we updated our results to include these models. Figure 11 provides an example of a prompt/response in the new "Chat" API, which requires a more structured conversational prompt compared to the GPT-3 "Completion" API; this prompt includes a "system" prompt, which describes the desired behavior of the model, e.g., "You are CaptionContestGPT..." We sample with default hyperparameters in all cases. The cost of GPT 3.5 is an order of magnitude less than GPT-4. In total our GPT-4 queries cost on the order of $4K. ## C Task Construction Details Identification Of High Quality Captions. For each contest, our first step is to identify a set of high quality captions; these are involved in construction of instances for all three tasks. For cases where we have access to the three official New Yorker finalists, all are automatically added to the high quality set. Next, for cases where we have crowd ratings, we consider the top 5 crowd ranked captions according to the mean score provided by Jain et al. (2020). From these top 5, we select 3 diverse candidates among these using a semantic deduplication method: specifically, we compute the SBERT (Reimers and Gurevych, 2019) vector for each candidate using paraphrase-MiniLM-L6-v2, compute a hierarchical clustering of the candidates, and sample a single candidate from each cluster - the result is a set of candidates that is representative of all clusters. In total, there are 2.7K high quality captions across 704 contests. Each contest either has 3 high quality captions (coming from the official New Yorker finalists or, if those aren't available, highly crowd-rated options), or 6 (if both official finalists and crowd rated are available). | A) Just be glad he's not wearing his kilt today. B) The founding fathers were clear. You must win by two. C) She'll appreciate you're wearing protection. D) We have to stop eating the seed money. E) Can I interest you in opening an offshore account? the funny caption that matches the scene is: In this task, you will see a description of an uncanny situation. Then, you will see two jokes that were written about the situation. One of the jokes is better than the other one. Pick which of the two jokes is the one rated as funnier by people. ### This scene takes place in the following location: a cave. A caveman is drawing a picture of an elephant on his cave wall. The elephant is standing by as a model. The elephant is friends with a man. The scene includes: Caveman, Mammoth, Cave painting. choices: A) Trust me. One day your portrait will be used as the symbol of a political party even more primitive than we are. B) So I've added the pointy trunk. Were there any other unique characteristics the mugger had that you remember? the funnier is: | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) Figure 9: Example GPT-3 zero-shot prompts for Matching (top) and Quality ranking (bottom) tasks. In-context prompts are similar, except 5 random labelled training examples are also provided in the prompt. | Matching | Quality Ranking | Explanation | | | | | |------------------------------------|-------------------|---------------|---------|-------------|----------|------| | Accuracy (↑) | CrowdAcc (↑) | NYAcc (↑) | B-4 (↑) | Rouge-L (↑) | PPL (↓ ) | | | Random | 20.0 | 50.0 | 50.0 | - | - | - | | Caption Only (T5-11B finetuned) | 19.4 | 59.4 | 64.5 | 3.61 | 17.8 | 34.0 | | text-ada-001 (in context, n=5) | 20.1 | 50.8 | 49.9 | 2.04 | 15.9 | 2367 | | text-babbage-001 (in context, n=5) | 19.0 | 51.3 | 51.1 | 2.18 | 17.2 | 137 | | text-curie-001 (in context, n=5) | 20.4 | 51.0 | 50.0 | 2.99 | 18.1 | 108 | | text-davinci-001 (in context, n=5) | 35.6 | 54.4 | 53.8 | 3.79 | 19.5 | 151 | | text-davinci-002 (in context, n=5) | 57.2 | 55.1 | 54.8 | 5.07 | 20.5 | 107 | Table 4: GPT-3 scaling experiment results, averaged over 5 cross-validation splits. In all cases, models are given access to the same sample of 5 in-context examples. Overall, text-davinci-002 performs best - this appears to be both because of scale (e.g., text-davinci-001 generally outperforms text-curie-001) and also because of training improvements in the updated 002 version of the model. Forming Matching Instances. For each high quality caption, we create a matching instance that serves as the correct answer. Next, we randomly assign captions to mismatched contests to form negative, mismatched sets to serve as false options. While the assignment is random, we have two constraints: 1) we assign within cross-validation splits only, to ensure that training/validation/testing captions are disjoint; and 2) we construct the corpus with no answer-only biases by performing the negative assignment such that each answer appears exactly once as a correct answer and exactly 4 times as an incorrect answer in other instances. Forming Quality ranking Instances. For each high quality caption, we aim to sample from the larger set of all submissions for the contest captions that are just "okay." First, we note that 25 contests from early on in the contest's history were missing entries, so we are limited to sampling negatives for 679 contests. Next, because many entries are exact duplicates, we deduplicate on string matching, such that "okay" captions are not exact copies of 1) the identified high quality captions; and 2) any other sampled "okay" captions. Next, for later contests from Jain et al. (2020), we have estimated quality ratings based on crowd feedback for each entry already: in that case, we discard the top third and bottom third of captions according to mean crowd rating - the middle tertile form the "okay" set we sample from. But, for earlier contests, we do not have direct ratings: we only have access to New Yorker finalists and a large pool of entries. For those cases, we aim to eliminate captions that are clearly likely to be low quality. To accomplish this, we train a quality ranking model (conditioned just on the caption text, rather than any information about the contest) using crowdlabelled data from 253 contests provided by Jain et al. (2020). We sample a good/bad set by selecting from each contest the top and bottom 1000 entries according to their mean crowdsource score: the resulting dataset forms a corpus of 506K captions. We form two sets of labelled data based on the parity of the contest | In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny. ### This scene takes place in the following location: a laboratory. A man in lab attire is sitting in a room with several monkies. Most are in cages, but one of them is in front of a typewriter It's unusual to see a monkey operating a typewriter The scene includes: Infinite monkey theorem, Mad scientist. caption: Have you considered writing this story in the third monkey rather than the first monkey? explanation of the caption: Stories can be told in first person (e.g., "I did X") or third person ("The character did X"), and editors will sometimes critique this choice or offer suggestions about the writing style. But here, the monkey is writing, so instead of first/third person, the suggestion about perspective is first/third "monkey". This scene takes place in the following location: city. Devils are herding people in the middle of a street. A manhole is open and there is fire below. Devils in the middle of a city are pretty out of place, and hell ### being in the sewers is too. The scene includes: Devil, Sewerage. caption: Watch your step, I think this street is paved with Good Intentions. explanation of the caption: A play on the figurative saying "The Road to Hell is Paved with Good Intentions" - this saying means that good intentions, left unchecked, can often lead to the worst outcomes. But here, the street is literally a path to hell, and so the man is saying it must be the street from the saying. This scene takes place in the following location: a streetcorner. Two angels driving a police car look on as two other angels loiter and drink on a street corner. The scene is cloudy, which implies that this is ### happening in heaven Heaven is an idealistic place where one wouldn't expect police to be needed. Furthermore, the folks sitting on the sidewalk and drinking do not accord with a the platonic, perfect view of heaven - if things are so perfect, they probably wouldn't need to be drinking. The scene includes: Heaven, Public intoxication, Angel. caption: Except here it's always good cop, good cop. explanation of the caption: A play on the phrase "good cop, bad cop" - two homeless angels are loitering in heaven as two angel cops come by. Instead of the usual "good cop, bad cop" routine where one officer pretends to be nice and the other is mean as a strategy to get a confession, in heaven apparently all cops are good, so it's "good cop, good cop". This scene takes place in the following location: an office. Two people are sitting across a desk from one another. They are both tied up in straight jackets. People don't normally wear straight jackets in offices. ### The scene includes: Straitjacket, Interview. caption: I have some good news—we're letting you go. explanation of the caption: A play on the words "letting you go" - normally, if your boss was to "let you go" it would be bad news, because they would be firing you. In this context, "letting you go" means removing you from your straight jacket, which would be good news. Because the boss also has a straitjackets, the employee might be getting fired from this company where everyone wears straitjackets, but that would still be good news because this is a terrible workplace. This scene takes place in the following location: a desert. There is a man rowing a boat across a sandy desert who is looking down at another man next to his boat. The other man is crawling on all fours and is ### looking up and talking to the man in the boat. It is unusual that a man is rowing a boat through sand. It is also unusual to find a man crawling through a desert on all fours. The scene includes: Rowing, Thirst. caption: Long time no sea. explanation of the caption: A play on the term "sea" - "long time no see" is something people say to each-other after they meet up for the first time in an extended period. But here, "sea" transforms the meaning to mean: "it's been a long time since you've been in the ocean," which is an accurate comment for someone who has been paddling a boat through the desert for many miles. This scene takes place in the following location: an office. There are three man in the room of an office building. One man is carefully trying to place files inside a filing cabinet. There is a tiger on a leash sitting ### on top of the filing cabinet. There is a wild tiger inside of an office building. The scene includes: Filing cabinet, Tiger. caption: You know, I'm gonna miss this when we go paperless. explanation of the caption: text-davinci-002 The man is trying to put files into a filing cabinet, but there is a tiger sitting on top of it. The tiger is probably taking up a lot of space and making it difficult to put the files away. The man says he's going to miss this when they go paperless, meaning he's going to miss the struggle of trying to put files away with a tiger in the way, because once they go paperless there will be no more files to put away. This is a joke about how going "paperless" (i.e., using digital files instead of physical paper) is often touted as being more environmentally friendly. But here, the man is saying he's going to miss having a tiger in the office when they switch to digital files - presumably because it won't be as exciting. A play on the term "paperless" - "going paperless" is a goal for many companies because it would save money and be more efficient. But here, the company is so far from that goal that they have a tiger on top of their filing cabinet. So instead of "going paperless," this company is going "tiger-full." text-curie-001 A play on the phrase "going paperless" - instead of carrying around a bunch of folders, documents, and other paperwork, people might move to a system where everything is electronic. But here, the tiger is a reminder that not everything will be digital, and there will still be times when people need to file things away in a physical filing cabinet. A play on the phrase "going paperless" - in the past, when people had to file paper documents, it would take up space and be a hassle to manage. But now, with electronic files, the phrase "going paperless" means that all of these documents will be stored electronically, and so they will no longer take up space or be a hassle to manage. However, in this situation, the tiger will still be around, meaning that the transition to a paperless system has not been completely successful. A play on the phrase "going paperless" - when people go paperless, they means they're going to stop using filing cabinets, which is a physical representation of the transition to a digital world. In this scenario, the joke is that the man is going to miss the tiger, which is a physical representation of the tiger being inside of the filing cabinet. text-babbage-001 A play on the phrase "paperless" - in a paper-based office, the files would be placed on a paper filing cabinet, which would be taken away when the office moved to a paperless setting. The tiger might symbolize the fear of not being able to find or eliminate the files, as well as the idea of being lost in an office with no way to find your way out. A play on the words "paper" and " loneliness." It is interesting to see the tiger in the office, because it is a reminder of the files that the man is trying to place inside the cabinet. In the same way, the tiger is a reminder of the man who is lost and lonely, and the office is a place where he can be alone and focus on his work. A play on the phrase "paperless" - "paperless" usually means having to carry a lot of paperwork by hand. But in this context, it means that the office will be without the need for paper filings, which would be a big loss. Because the office is in an office building, it's also likely that the tiger will be released and roam the building freely. text-ada-001 People often lose interest in paperless files when they do not receive any positive feedback from their users. This phrase is a good metaphor for how people feel about not being able to enjoy or enjoy their users. In fact, the man in the office who is trying to place files inside the filing cabinet might be missing the benefits of using the files himself. You might be wondering why I'm saying these things, but one of the things I love about work is that it's a perfect-disrupted-bleeds-job kind of job. In a perfect-disrupted-bleeds-job, there would be no files, no only the Office, and no customer service centre. In fact, the customer service centre would be in a perfect-disrupted-bleeds-job. So it would be hard to find a perfect-disrupted-bleeds-job, because there would be no customer service, and no office. A play on the term "documentary" - when you watch a documentary, it feels like one of those things you know is going to be funny. In this situation, the documentary-holder is trying to absent-moved the tiger from the office building, and the tiger is really enjoying it. The appearance of the tiger also means that the office building isn't being used as something as a place to store files, but rather it is being used as an opportunity for the tiger toatomicue. | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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Figure 10: An illustrative example of an in-context learning prompt for generating joke explanations (1095 tokens). 3 samples with temperature .8 from different GPT-3 engines are shown. According to our experiments, text-davinci-002 performs the best; qualitatively, as model size decreases, explanations become more nonsensical. system prompt (human authored) You are CaptionContestGPT, an expert language model at understanding the famous New Yorker caption contest. You follow the contest each week, and understand what makes for a humorous caption for each cartoon. You are aware of the various theories of humor, and read/anaylze the caption contest entries and winners each week. Some things to remember: - You're well versed in the history of the New Yorker Caption contest, and the types of captions that are selected as finalists/winners vs. those that are not. - You think step-by-step, but aren't overly verbose. - You can express uncertainty in your thinking, but in the end, pick the single best answer in the requested format. user (human authored) I will describe a New Yorker cartoon to you. Then, I will give you 5 choices (labelled A-E) for captions. One of the captions was the winning caption for that cartoon, the other captions do not correspond to this cartoon. Your job is to first reason step-by-step about which answer might be correct, and, in the end, respond with "Answer: X" where X is either A, B, C, D, or E. assistant (human authored) Sure, please describe the New Yorker cartoon, and provide me with the 5 caption choices. user (human authored) OK. Here is a description of the cartoon followed by the five choices. scene location: a book signing description: There is an author signing at a bookstore. There is also a line of people with a big lizard in front. uncanny description: The giant lizard in a bookstore does not belong. entities: Godzilla, Autograph Choices: A: "MEET ??". I heard "EAT". B: Trust me, I know the plague when I see it. C: I'd like to see other people. D: Shouldn't you be sitting in an ear? E: So Major Tom... Do you know how fast you were going? Which of the 5 options (A, B, C, D, or E) is the best fit? Think step-by-step and finish your response with "Answer: X" where X is either A, B, C, D, or E. assistant (machine generated; used to derive the prediction) Let's analyze the options step-by-step: A: This caption seems to play on Godzilla misinterpreting the book signing event ("MEET") for "EAT," creating a humorous situation. This caption relates to the cartoon and its elements. B: The mention of the plague doesn't relate to the cartoon description. It seems unrelated to the scene and entities mentioned. C: This caption about seeing other people doesn't make sense in the context of the cartoon description and lacks a connection to the scene and entities. D: The reference to sitting in an ear also seems unrelated to the cartoon description. It doesn't connect to the entities or context. E: This caption about Major Tom and speeding are again unrelated to the cartoon description. This caption does not fit the scene or entities. Through this step-by-step analysis, we can narrow it down to A as the best option. Answer: A Figure 11: An example of a zero-shot prompt+completion for GPT-4 (OpenAI, 2023) when applied to the matching task. In contrast to the text completion API of GPT-3, the GPT-4 chat API requires a more structured input involving a "system" prompt specifying the behavior of the model, followed by an interleaved conversation between a system and a user. While the training process of GPT-4 is opaque, in general, its "chain of thought" generations loop over all options and attempt to reason about how/why a caption might relate to the given scene. number (i.e., even vs. odd). We train/validate two T5-Large models based on this split for the binary classification task. While the average validation accuracy we achieve is 65%, we achieve higher precision in identifying the "bad" label: precisionat-10 is 83, precision-at-20 is 77, precision-at-30 is 72. It appears to be harder to identify very good captions than very low rated ones: precision-at-10 is 77, precision-at-20 is 73, precision-at-30 is 70. Upon training these models, we perform inference on all captions in contests without crowd ratings, and discard the 25% of entries with the lowest predicted score. Entries with very low scores have some common characteristics, e.g., they don't have the *gestalt* of a New Yorker caption, they have many typos/formatting issues, they include the contact information of the submitter, etc. Examples of discarded captions (some are obfuscated for privacy reasons) are: - THEY COULDN'T WAIT TO MARRY SO THEY CAME TO RECITE THEIR VOWS BETWEEN TAKES FROM " PRIMITIVE LOVE LIFE" - You're hurting me, will we ever break up?" (@ technology) - The stressed is so "Bad' in the world. "you or me " did not see(BIG )( "FOOT) - Too mammalian, needs reptile." [NAME], [STATE] [EMAIL]@gmail.com After identifying a set of captions that are not obviously bad, nor apparently among the top quality submissoins, our second step is to deduplicate entries. Because submitted captions for each contest are frequently identical to other submissions or play off the same core joke concept, we perform the same SBERT+hierarchical clustering semantic deduplication step as we did for sampling the diverse high quality set (described above). Specifically, we extract SentenceBERT embeddings (Reimers and Gurevych, 2019) for each of the N entries, and then compute a hierarchical clustering of the embeddings into .7 · N clusters, sampling only a single representitive from each cluster to form a less-redundant set. This removes 30% of the data with close neighbors in the final set: for example, for a contest depicting two monsters eating buildings in New York City, this step downsamples 100 "tastes like chicken" jokes (which end up in a single cluster) to a single exemplar. After filtering, for all contests, we are left with a (softly) deduplicated pool of candidate entries that are likely to be at least okay, but unlikely to be as good as the verifiably high quality entries. For each high quality entry, we sample an "okay" caption with: 1) similar estimated quality according to the text-only models; 2) similar length in words; 3) similar length in characters; 4) similar amount of punctuation; 5) a dissimilar SBERT embedding. Explanation corpus. After several attempts to solicit high-quality explanations from crowdworkers fell short, one of the authors of this paper decided to simply annotate a corpus of explanations themselves. For each contest, a high quality caption was sampled for annotation - this high quality caption was sampled arbitrarily from the set of New Yorker finalists if they were available, and, in the few cases where New Yorker finalists weren't available, from the set of high quality crowd captions. Of the 704 sampled explanations, the author reported understanding 651 of them, and wrote an explanation for each. This was a substantial effort: the resulting corpus has a mean of 60 words of explanation per cartoon, and the total length, 39.3K words, is comparable in length to a novella. ## D Graphical Version Of Matching And Ranking Results. In Figure 12, we use vertically-stacked bars to illustrate the difference between zero-shot (small dots), ![19_image_0.png](19_image_0.png) five-shot (vertical stripes), and fine-tuned (solid) versions of various models. Human results are set off by dark green lines. The scatter-plot in Figure 13 uses the same graphical conventions to display the qualityranking results. Recall our caveat that crowd accuracy may be more statistically reliable, in the sense that crowd selectors, whose tastes underlie the y-axis results, vastly outnumber New Yorker | Explanation | | | | | |-----------------------|-------------|----------|------|-----| | BLEU-4 (↑) | Rouge-L (↑) | PPL (↓ ) | | | | Caption Only (T5-11B) | 3.61 | 17.8 | 34.0 | | | FPOFA-Huge → T5-Large | 3.36 | 17.5 | 50.7 | | | OFA-Huge → T5-11B | 3.63 | 17.9 | 30.3 | | | T5-Large | 3.54 | 18.2 | 41.2 | | | T5-11B | 4.33 | 19.0 | 23.7 | | | GPT3-175B (finetuned) | 5.42 | 20.1 | 21.8 | | | 5-shot | 5.07 | 20.5 | 107 | | | FD | Zero-shot | 3.12 | 18.8 | 225 | ![20_image_0.png](20_image_0.png) ![20_image_1.png](20_image_1.png) ## E Automatic Evaluation Of Explanations For completeness, we provide the results for automatically-calculated explanation-evaluation metrics in Table 5. (Log probabilities are unavailable for GPT-3.5/GPT-4 so we cannot report perplexity for them.) However, we believe that the human evaluations reported in the main body of the text are better quality measures. ## F Machine Explanations That Were Preferred Over Human Ones GPT-4 In 8/130 cases, for our human vs. GPT-4 5-shot experiments, the machine generation was preferred to the human reference by 3/3 annotators. In Figure 14 we conduct a close reading of these 8 instances to understand where the human references fell short. In all cases, both were topical, but, for a handful of cases, the machine generation is arguably better because it's more succinct, or offers a more meaningful detail. GPT-3 We also include a close reading of several instances where a majority of annotators preferred GPT-3 annotations vs. our human ones. This occured in 16/131 cases for our human vs. GPT-3 experiments: in 15 cases, 2/3 annotators preferred the machine generation, and in 1 case, 3/3 annotators preferred the machine generation. We present a few examples of these cases with comments in Figure 15. Similar to GPT-4, most commonly, both the explanations were reasonable, in one case, the human-written explanation missed a pop culture reference to "The Attack of the 50 Foot Woman" (1958) but GPT-3 mentions it. In six cases, we disagree with annotator consensus: the machine caption makes some correct references, but we believe it (sometimes subtly) misses the point. ## G Aiding Humor Generation With System-Assisted Brainstorming Our main experiments focus on three tasks that probe machine capacity for matching, ranking, and explaining caption contest entries. But, given prior interest in generating caption entries, to provide a pointer towards more creative use cases, we developed a curated prompt that re-frames the corpus in a manner that enables the production of cartoon descriptions, concept brainstorming, and, ultimately, caption generation. In contrast to our existing annotations, we are inspired by Wallace (2022)'s framing, where free associations are used to connect-thedots from cartoons to concepts to captions. So, for 9 finalist captions, we reverse-engineer a plausible brainstorming process that could have given rise to the caption by listing several potential associations from the scene, and, from that list, selecting the 1-3 that underpin the basis for the winning joke. In the end, our prompt consists of 9 examples formatted as: 1. cartoon description (from our annotations) 2. uncanny description (from our annotations) 3. list of entities in scene (from our annotations) 4. list of concepts from the cartoon that could be used for a caption (hand-designed for the | Scene/caption | Our opinion re: annotator Human-authored explanation | Machine-authored explanation (GPT-4 5-shot) | |------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------|-----------------------------------------------| | decision | | | | A person in a diver suit is out-of-place at a cocktail party. Caption: This suit looked way better in the store. | We all experience situations where a garment looks great in the store, but when we wear it out in a public setting, we feel awkward or mismatched. Here, this scene humorously exaggerates that feeling by having the man wear an old diving suit to a seemingly professional office party. The situation creates comedic tension because it's an extreme example of being underdressed or out of place. (3/3) | | | A man is on a cell phone is in mid-air despite being thrown off a bucking horse. He is smiling. Caption: Hi Hon. Can you meet me at the hospital in about ten minutes? Agree: The human annotation assumes the person didn't notice; the machine explanation is more on point/coherent. Sometimes, clothes you try on in a store look better when you are deciding to buy them or not. Here, the idea is taken to the extreme: the person in the diving suit bought the "suit" without even noticing it was a diving suit (and looks out of place for the party)/ (0/3) The man in the suit is attempting to ride a bucking horse, which implies that he is not experienced in horse riding or else dressed inappropriately for it. The caption suggests he is bound to have an accident as a result of this mismatch, and so he is calling ahead to his spouse to be prepared to meet him at the hospital. It's a humorous way of showcasing that the man knows he is in a dangerous and ill-suited situation. (3/3) A man in an office building is planting seedlings in his cubicle. Caption: The third quarter is going to be delicious. Somewhat agree: The human explanation says "friend" instead of spouse/partner, but the machine authored explanation could also be improved ("mismatch" is less relevant). The man on the phone is pre-planning his own hospital visit. This is unusual, because one would think that he would be focused on avoiding injury from the currently-occuring situation instead of, in a surprisingly carefree and cheerful manner, telling his friend to meet him at the hospital. (0/3) Mostly agree: Although the machine explanation refers to documents that are not explicitly evident, the human explanation assumes a less likely interpretation of "delicious". A play on the term "delicious" - in a usual office setting, if third quarter was going to be delicious, it would be figuratively so, i.e., with juicy gossip, people getting fired, lots of drama, etc. But here, the third quarter will be literally delicious because the person growing vegetables for the company will share them in the future, which will make for delicious food. It's funny in part because the usual interpretation is about dark enjoyment of office politics, but the literal interpretation is about a wholesome vegetable feast. (0/3) In the business world, people often discuss quarters in terms of profits and financial performance. The joke here is that the man is literally "planting" invoices or financial documents, hoping to grow something metaphorically delicious or profitable. The humor comes from the absurdity of treating financial documents as plants and expecting positive results from this unorthodox method. (3/3) In a 17th century scene, a woman is walking in town, but her ruff collar is comically large. Caption: It deployed when her carriage rear-ended an ox cart. Mostly agree: Both are The woman's oversized ruff collar is juxtaposed good, but the machine explanation has a more specific conclusion. against the modern invention of an air bag. Air bags deploy during car crashes - and here, the collar inflated during a collision between a carriage she was riding in and an ox cart as a safety mechanism. It's funny in part because, while it explains the oversized collar, collars are cosmetic rather than functional. (0/3) The joke is a play on modern-day car safety features, like airbags, that deploy in an accident to protect passengers. In this case, instead of an airbag, the huge Victorian-era collar "deployed" when the woman's carriage rear-ended an ox cart. This absurdity of applying modern safety concepts to historical clothing makes the caption funny. (3/3) The wolf trying on a sheep's skin and asking for a granny dress is a humorous reference to two popular fairy tales involving wolves pretending to be something else—first being the idiom "a wolf in sheep's clothing," which means someone pretending to be harmless, and the second referencing "Little Red Riding Hood," where the wolf famously impersonates the girl's grandmother. The joke highlights how the wolf is taking these disguises quite literally in a modern-day retail setting. (3/3) A flying saucer trails a man and a woman driving down the road. Caption: When they said, "Take us to your leader." I don't think they meant your mother's house. A wolf trying on a sheep skin as if it were a costume, looks in a mirror as a butler looks on, holding various other outfits. Caption: I'll take this and the granny dress. Mostly agree: The machine explanation is more A reference to the wolf from Little Red Riding Hood. A "wolf in sheeps clothing," as depicted, is an idiom meaning a nefarious actor who appears innocent: specific. here the wolf is the same (also nefarious) wolf as in Little Red Riding Hood: who kills a grandmother and wears her clothes as a disguise. (0/3) When people think of aliens asking to be taken to our leaders, they usually mean they want to meet important authority figures or decision-makers like the president or other government personnel. The joke here is that the car driver, misunderstanding or maybe just messing with the aliens, is taking the aliens to their mother's house, which is vastly different from what they usually expect but incidentally drives the point people's mothers can sometimes be really dictatorial figures even if they aren't technically their leader. (3/3) A man looks in a bathroom mirror as he gets ready, but he looks like Abraham Lincoln in the mirror. Caption: On second thought let's skip the play. Slightly disagree: Both take the joke in slightly different ways, but the human explanation seems more plausible. This is a joke about in-laws. Pop culture depictions of aliens often have them requesting that the first humans they make contact with take them to the leaders of humanity. The woman is driving them to her mother's house, and the man, who feels stifled by his mother-in-law, is reminding his wife that her mom isn't their all-powerful leader. It's funny that this benign concern is on his mind in light of the fact that a giant spaceship is tailing them. (0/3) The joke plays on two ideas. First, it suggests that the man's reflection, which appears to be Abraham Lincoln, might be causing the man to think he has some connection or is somehow embodying Lincoln. Second, it references a historical event, as Lincoln was assassinated at a theater while watching a play. So, when the man says "let's skip the play," he is humorously trying to avoid the same fate as Lincoln. (3/3) A man driving talks to a a woman in the passenger seat. In the backseat is a cramped giant whale. Caption: He wants to know if you can move your seat up. Slightly disagree: The human one expresses the A joke about Abraham Lincoln's assassination, which took place at a play. This man is getting ready to go same idea more succinctly. to a play and has a vision of himself as Lincoln; because he wants to avoid being assassinated, due to this premonition, he suggests to his partner that they not go to the play after all. (0/3) Both are good: both explanations express the same idea in a reasonable way. Moving a front seat up is an everyday request from people sitting in the backseat of cars if they don't have enough room. The whale is making this request: the contrast between how unusual it is that the whale is in the backseat and the benign-ness of the request is what makes it funny. (0/3) The humor lies in the absurdity of having a whale in the backseat of a car, a situation that should never occur. The whale, being so large, would naturally require more room, so it's funny that the only concern anyone in the cartoon appears to have is whether the front seat passenger can move their seat up to give the whale more space, instead of questioning why there's a whale in the car in the first place. (3/3) | | | | Figure 14: Examples of the small number of cases (8/130) where the machine-generated caption (5-shot GPT4) | | | Figure 14: Examples of the small number of cases (8/130) where the machine-generated caption (5-shot GPT4) was preferred to the human-authored explanation by 3/3 annotators in our pairwise judgment setting. | Scene/caption | Our opinion re: annotator Human-authored explanation | Machine-authored explanation | |-----------------------------------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------| | decision | | | | Two | teachers | walking | | through a hallway of an elementary school while the children walk on the ceilings and walls. Caption: Back in my day, kids had a little respect for the law of gravity. Disagree: the machine (1/3) Normally "respecting laws" are human-written caption starts off well, but laws - but here, the kids have somehow opted-out concludes with a number of respecting physical laws of gravity, which means of non-sequiturs. Perhaps they can walk on walls/ceilings. the annotators only skim the later parts of a long explanation. (2/3) The kids in this school are violating the laws of physics by walking on the walls and ceiling. The teachers is complaining that kids used to respect gravity, but now they don't. This is a funny complaint because whether or not kids respect gravity probably depends on the teacher's training, not whether they were a kid in the past. A doctor is performing a physical examination on Superman. Caption: They think now kryptonite is actually good for you, in small doses. Disagree: Both are okay, but the human-authored explanation is more specific in referencing the appropriate cultural phenomenon, and the machine-generated one hallucinates that kryptonite is medicinally helpful. (1/3) A joke about modern medical findings sometimes being counter-intuitive: some studies claim to show that things harmful in big quantities like alcohol or chocolate are good in small doses. But here, the thing that's bad for superman in large doses is kryptonite: it's a funny parallel to the other studies, and funny to think that scientists are studying kryptonite's potential positive impacts. (2/3) Kryptonite is a material that causes superman to become weak; however, the doctor is suggesting that kryptonite is good for him in small doses. This is funny because, while kryptonite might be helpful medicinally, it's difficult to explain how small doses of something that kills superman could possibly be helpful. 3 people are going to therapy in a 3-tiered bunk-bed. Caption: And how do you feel about how she feels about how he feels? Disagree: The machine caption omits the oddity of having three patients involved in what appears to be couples therapy. Nor is there wordplay involved. (1/3) Couples therapy is already complex - pairs of people may have feelings about their partner's feelings. Here, the joke is that if three people were getting therapy together, things would get even more complicated and meta, i.e., person 1 could feel strongly about person 2's feelings about person 3 (and any permutation of such). It's funny to think that these three would be so closely intertwined such that this meta-therapy could be a helpful exercise. (2/3) A wordplay joke about feelings - a therapist might ask a question like: "how do you feel about what happened with your brother?"; in this case, the question is expanded to include the feelings of each party, e.g., "how do you feel about how he feels about what happened with your brother?"; this is a very complicated/deeper concern that requires a lot of reflection. A board meeting is happening in an office, but the Agree: the machine explanation is probably better. (0/3) Seed money is cash provided to a startup company (like the ones the birds are running); but, the employees are birds. Caption: We have to stop eating the seed money. play on words implies that the birds spent the seed money literally on seeds to eat, because birds eat seeds. (3/3) A play on the phrase "seed money" - in the investment world, "seed money" refers to the initial capital that is invested into a startup company. But here, "seed money" refers to bird seed that these birds might eat: the joke is that they need to stop eating bird seed because it's causing them not to be able to afford investments like a real office/chair/table. A stack of cars with drivers in them. The driver on the topmost car is leaning out of the window yelling to the drivers below. Caption: I hope this guy Knievel knows what he's doing. Both have pros/cons: The (1/3) A reference to Evil Knievel, a stuntman who machine is more specific jumps motorcycles over cars. This pile of cars is in referencing crashing going to be an obstacle in an Evil Knievel stunt, and and the implication for the man in the top car is hoping that the stuntman the drivers, but makes up coordinating this both knows how to jump over and "jumping the stack" meaning crashing. also how to get the drivers out of this precarious situation. (2/3) A reference to Evel Knievel, a famous stunt man that jumped cars and other objects. This is a joke about him jumping cars, but because the cars are stacked precariously, it's a reference to Evel Knievel "jumping the stack", i.e., crashing, which would be a bad outcome for the drivers. A person wearing boxing gloves is attempting to play a piano on a stage. Caption: I could have been a conductor. (2/3) A joke about Muhammad Ali's skill as a boxer, which sometimes takes a metaphorical meaning when referring to his boxing career. Here, the boxer is playing the piano, and is presumably not very good at it. But, the joke is that, if he wasn't a boxer, he could have been a conductor, which requires a high level of skill. It's funny in part because it's not clear why he would have not been a boxer. A father and a child are Both bad: The humanauthored explanation standing in an office building looking out the window onto a giant woman misses the direct reference to the movie "Attack straddling a nearby building. Caption: Son, of the 50-Foot-Woman" (1958), and the machine we finally attracted the focuses on non-sequiturs fifty-foot-and-over demographic! like age 18/viewers/etc. Both bad: the human caption gives a better mechanical explanation, but both (1/3) It would be mechanically difficult to play a piano with boxing gloves because the keys are too small. if this person must wear boxing gloves and miss that the caption is a still wants to do music, they humorously reason that play on a quote from "On they should be a conductor - it would be easier for the Waterfront" (1954), "I them to hold the conductor batons vs. play piano coulda been a contender." keys. (1/3) A play on the phrase "fifty and over demographic" - this phrase would usually refer to the group of people who are aged 50 or older, and would be of interest to people running businesses (like the father and son pictured) as a marketing target. But instead of the age 50+ demographic, they have attracted the height-of-fifty-foot+ demographic, of which this giant woman is a member. (2/3) A play on the term " fifty-foot-and-over" and "over 18" - in the media, advertisers sometimes say that they want to attract 18+ year old viewers to their product, because 18 is the legal age of consent, and thus, to attract 18+ year olds, they will say they want to attract viewers "over 18". But here, the company is trying to attract viewers "50-feet-and-over" - the company is trying to attract the titular Attack of the 50-Foot-Woman (who is, indeed, over 50-feet-tall). | | | | Figure 15: Examples of the small number of cases (16/131) where the machine-generated caption (fine-tuned | | | Figure 15: Examples of the small number of cases (16/131) where the machine-generated caption (fine-tuned GPT-3 175B) was preferred to the human-authored explanation by at least 2/3 annotators. First, you will see a description of a scene from a New Yorker cartoon that has an unusual and/or funny element. Our goal is to brainstorm a caption that we can enter into the caption contest. ![23_image_0.png](23_image_0.png) The captions should be funny, and relate to the image. As part of this process, we will brainstorm about potential concepts from the cartoon, and then combine those concepts to make the caption. Finally, we will explain the caption, and how it relates to the scene. === this scene takes place in/at/on a kennel description of scene: Two men are sitting in a dog cage next to a dog. One of them is reading a book while saying something to the other man who is visibly upset. unusual part of scene: these cages are for dogs in shelters not for humans. entities in scene: animal shelter, doghouse. potential concepts to form a funny caption based on: men as dogs, chew toys, being adopted at the pound, spaying/neutering pets here is a caption that combines these concepts: men as dogs, spaying/neutering pets funny caption: Last time I'll book a discount vasectomy, for sure. explanation of how the caption relates to the scene: Spaying/neutering pets is a commonly performed operation for animal sterilization performed at animal shelters; the human equivalent sterilization procedure is called a vasectomy and is usually more expensive due to higher quality, human medical requirements. But here, the two men are trying to save money by having their operations done at an animal shelter. It's funny in part not only because this is an unusual way to save money, but also, because vasectomies only are performed once per person, so this is literally the last time the person will do this procedure, even if they liked it; the quote implies they aren't enjoying being locked in an animal cage. === ... (8 more examples formated as above) ... ![23_image_1.png](23_image_1.png) a garden description of scene: A group of people are playing croquet. One of the players is a very large chicken. unusual part of scene: Chickens are not usually intelligent enough to play croquet and they are not usually that big. entities in scene: chicken, croquet. here is a caption that combines these concepts: free-range chicken, backyard game funny caption: I'm not sure this is what they meant by free-range. a game) and because it's unexpected (chickens are not usually this big or this intelligent). a living room unusual part of scene: It is unlikely and disruptive for an entire set of circus acts to be intruding on a quiet living room. entities in scene: circus, bystander, performers, circus acts. potential concepts to form a funny caption based on: unannounced visitors, salespeople, clowns, big top here is a caption that combines these concepts: unannounced visitors, salespeople funny caption: I'm never buying a timeshare again. explanation of how the caption relates to the scene: The circus is a metaphor for an unannounced group of salespeople who are trying to sell a timeshare. The joke is funny because it's an extreme example of an unannounced group of salespeople, and also, because it's disruptive and intrusive. Figure 16: A portion of a 2,407 token prompt that re-formulates various annotations within our corpus in a format conducive for creative collaborations with a language model. The full prompt is available here. Generating line-byline from this prompt could help to facilitate brainstorming for: unusual cartoon situations (first 4 lines), concepts about real or generated contests that could serve as a basis for a humorous caption (line 5), and, captions themselves (lines 6-8). As a demonstration, we present an unconditional sample, in which the model describes a garden party where a chicken is playing croquet (cherry picked from 3 outputs; temperature=.8, top p=.9, frequency penalty=.2, presence penalty=.05), and also, a conditional sample, given a basic description of Contest \#818's scene, which ran in mid-September 2022 (cherry picked from 5 outputs; same sampling parameters): the caption is arguably funny, but the explanation is not correct. prompt) 5. a selected set of 1-3 ideas (selected from (4)) 6. caption (a finalist) 7. explanation of the caption (from our annotations) A portion of our prompt is given in Figure 16, along with an unconditional generation (where the cartoon concept and caption are generated) and a conditional generation. Within 5 samples, GPT-3 invents a scene where a large chicken is playing croquet in a yard, and the caption: "I'm not sure this is what they meant by free range." Also, when conditioned on a basic description of a real contest which depicts a large group of circus performers intruding on an unsuspecting person in their living room (Contest \#818), it generates "I'm never buying a timeshare again." Looking forward, we expect the matching/quality ranking models could be used in conjunction with this prompt to automatically filter for scene-specific generations with style similar to previous finalists. ## H Related Work Beyond Peer Reviewed Ai Venues Outside of peer-reviewed NLP venues, several projects have used computational techniques to analyze the contest, usually with the goal of generating AI-assisted entries: - **The Pudding:** Mishkin et al. (2022) collaborated with GPT-3 (Brown et al., 2020) to generate entries. - **coolposts:** Wilson (2019) used topic models to condition an RNN caption generator. - **LILY Lab @ Yale's** Spring 2017 projects include a number of caption contest efforts, including work by Prince, Friedman, Zucker, Anbarasu, and Dohrn. - **The Verge:** Zelenko and Bi (2015) trained a Markov language model on previous winning entries. ## I Some Of Our Favorite New Yorker Cartoons We list our favorite captions below. The corresponding images can be seen by clicking on the cartoonist/author names. YC: "The doctor said it might help me quit." - Vince Conitzer/Jeffrey Adam Katzenstein JD: "You are so smart. You look amazing. You inspire me. [Complimentary bread]." - Seth Fleishman JMH: "Thanks, I'll write that down." - Victoria Roberts JDH: "They're from Earth. I wonder if they know Dan." - Benjamin Schwartz LL: "I want to be feared as a tyrant, loved as a father, and revered as a god, but I also want them to think I'm funny." - Zachary Kanin AM: "I can't believe I'd been carrying them in my mouth." - Amy Hwang RZ: "Well, there's your problem." - Edward Koren ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations section 6 ✓ A2. Did you discuss any potential risks of your work? Limitations section 6 ✓ A3. Do the abstract and introduction summarize the paper's main claims? The abstract ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** yes, our new corpus/tasks. Section 2 describes them. ✓ B1. Did you cite the creators of artifacts you used? Yes, section 2 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Yes, we discussed the distribution of our dataset, which have made public under Creative Commons Attribution 4.0. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Yes, section 2 ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 2 and appendix C ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 2 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 2 ## C ✓ **Did You Run Computational Experiments?** Section 3 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3 and Appendix B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 3 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3 D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 2 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix A ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 2, Appendix A ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 2, Appendix A ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? appendix A ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? We don't know many specifics, other than country of IP: which we discuss in appendix A

bartelds-etal-2023-making

Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation

https://aclanthology.org/2023.acl-long.42

The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available. Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower. In this study, we investigate whether data augmentation techniques could help improve low-resource ASR performance, focusing on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal). For all four languages, we examine the use of self-training, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system. For Gronings, for which there was a pre-existing text-to-speech (TTS) system available, we also examined the use of TTS to generate ASR training data from text-only sources. We find that using a self-training approach consistently yields improved performance (a relative WER reduction up to 20.5{\%} compared to using an ASR system trained on 24 minutes of manually transcribed speech). The performance gain from TTS augmentation for Gronings was even stronger (up to 25.5{\%} relative reduction in WER compared to a system based on 24 minutes of manually transcribed speech). In sum, our results show the benefit of using self-training or (if possible) TTS-generated data as an efficient solution to overcome the limitations of data availability for resource-scarce languages in order to improve ASR performance.

# Making More Of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation Martijn Bartelds1 Nay San2 **Bradley McDonnell**3 Dan Jurafsky2 **Martijn Wieling**1 1University of Groningen 2Stanford University 3University of Hawai'i at Manoa ¯ [email protected] ## Abstract The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available. Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower. In this study, we investigate whether data augmentation techniques could help improve low-resource ASR performance, focusing on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal). For all four languages, we examine the use of selftraining, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system. For Gronings, for which there was a preexisting text-to-speech (TTS) system available, we also examined the use of TTS to generate ASR training data from text-only sources. We find that using a self-training approach consistently yields improved performance (a relative WER reduction up to 20.5% compared to using an ASR system trained on 24 minutes of manually transcribed speech). The performance gain from TTS augmentation for Gronings was even stronger (up to 25.5% relative reduction in WER compared to a system based on 24 minutes of manually transcribed speech). In sum, our results show the benefit of using selftraining or (if possible) TTS-generated data as an efficient solution to overcome the limitations of data availability for resource-scarce languages in order to improve ASR performance. ## 1 Introduction Self-supervised learning (SSL) enables speech representation learning without the need for (manually) labeled data. Although this approach is very effective, pre-training an SSL model is costly. This cost (e.g., training time, resources, and memory) increases with the number of languages added to the model. Furthermore, transferring information across languages, or extending a pre-trained model to new data or to a different domain is computationally expensive, and catastrophic forgetting may occur (Goodfellow et al., 2013). To alleviate this, SSL models are therefore often fine-tuned on the target task with target domain data. For the task of automatic speech recognition (ASR), fine-tuning approaches generally require less data, but training ASR systems that perform well for languages with very little data remains challenging. This leads to (digitally) underrepresented communities and domains such as minority languages, regional languages and dialects not profiting sufficiently from most recent technological advancements. Recent studies explored fine-tuning of pretrained self-supervised models for ASR using speech from low-resource languages (e.g., CotoSolano et al. 2022; Guillaume et al. 2022), and difficulties of modeling resource-scarce languages and dialects were acknowledged in previous work (Aksënova et al., 2022). It remains an open question to what extent model performance is dependent on the amount of fine-tuning data and the type of language, when the total amount of available data for a language is limited. Having a better understanding of how limited training data affects model performance paves the way for creating meaningful speech technology for a wider range of languages. In this paper, we fine-tune pre-trained SSL models for ASR using varying amounts of data from four typologically diverse minority languages or language variants: Gronings, West-Frisian, Besemah and Nasal, which have a limited amount of data available. We specifically investigate whether data augmentation approaches can be used to generate additional training data to improve the performance of these models, particularly when very little resources are available. By using data from (ongoing) language documentation projects, we evaluate 715 a real-world use of our experimental setup. Previous work describes the benefits of data augmentation by adopting a self-training approach, which generates labels (i.e. transcriptions) for unlabeled speech (e.g., Xu et al. 2020, 2021; Kahn et al. 2020; Zhang et al. 2021; Berrebbi et al. 2022; Khurana et al. 2022; Lugosch et al. 2022). Various self-training methods are proposed, including iterative approaches, decoding with an external (textbased) language model, or filtering approaches that improve the quality of the generated labels. However, limited conclusions can be drawn from these works on the effectiveness of self-training in a very low-resource, real-world setting, as these studies either use datasets with more than 10 hours of data (which may not be available for very small languages), only considered modeling English, or reported average performance over a set of languages that strongly varied in terms of training data size. We therefore complement this work by investigating the benefits of self-training for four typologically different, true low-resource languages. To this end, we use a standard self-training approach to evaluate the potential benefit of a simple system in a real-world setup, which nevertheless yields substantial performance improvements (relative worderror-rate (WER) reductions up to 20.5%). In addition to self-training, several studies (e.g., Rosenberg et al. 2019; Du and Yu 2020; Rossenbach et al. 2020a) reported on augmenting the training data with synthetic speech generated using a text-to-speech (TTS) system. For this reason, we also examine whether this approach is useful in our low-resource setup. We recognize that not all very low-resource languages may have sufficient amounts of data available for TTS development, and we therefore only generate synthetic training examples for Gronings, one of the four low-resource languages in our dataset that has an existing TTS system available. We show the benefit (i.e. up to 25.5% relative reduction in WER) of augmenting the training data by using an existing TTS system, and analyze the effect of adding different amounts of synthetic speech on the model performance. Our datasets, code, and newly trained models are publicly available.1 ## 2 Data As indicated, we use transcribed speech from Gronings, West-Frisian, Besemah, and Nasal. For the latter two minority languages, only four hours of manually transcribed speech data are available. For all language varieties, we therefore limit the amount of manually transcribed speech data to four hours. We divide each dataset into 80% for training, 10% for development and 10% for testing. The development and test sets therefore include approximately 24 minutes of speech, and the training set contains approximately 3.2 hours of transcribed speech. In line with Wei et al. (2022), we allow for speaker overlap between the sets due to the limited number of speakers per language variant, as they found that it had limited effects on the performance of ASR models. All data have been anonymized by assigning recordings a random identifier, and no other meta-information that could be used for identifying the speakers were collected or extracted. We obtained consent from the communities to publicly release the datasets for Gronings, Besemah, and Nasal. The West-Frisian data can be obtained by emailing the authors (ISLRN: 340-994-352-616-4). ## 2.1 Gronings And West-Frisian Gronings is a Low-Saxon language variant that is spoken in the province of Groningen, which is located in the northern part of the Netherlands. Within this language variant, there is regional lexical, grammatical and acoustic variation. We use data from an ongoing language documentation project that aims to record the speech of all variants of Gronings. To date, read-aloud speech from three speakers has been recorded (two female speakers and one male speaker) for three different variants, namely Hogelandsters, Oldambtsters, and Westerkwartiers. This data, consisting of almost 14 hours of transcribed speech data, is included in this study. From these 14 hours, four hours of manually transcribed speech was extracted for training, development and testing. The remaining data was partly used for generating additional training data. The 2,130 transcribed recordings in this dataset, comprised of book texts and corresponding recordings, have an average duration of 6.8 seconds (SD: 4.9). We normalized the transcriptions by excluding all characters that do not occur in the Gronings alphabet.2In addition, we also include transcribed speech data from three different speakers (two female speakers and one male speaker), yielding a total of 19 minutes of speech data. This data was extracted from the publicly available dataset provided by San et al. (2021). These recordings have a mean duration of 3.5 seconds (SD: 1.3). We only use this subset of data for out-of-domain testing. West-Frisian is the second official language of the Netherlands and is spoken in the province of Friesland, which is also located in the northern part of the Netherlands. For this study, we extracted four (out of eight) hours of transcribed speech data from the FAME! ASR corpus (Yılmaz et al., 2017) that contains radio and television speech from Dutch-Frisian bilinguals. The extracted dataset includes 4,919 transcribed speech samples from 277 speakers (68 female, 199 male speakers, and 10 unknown) with an average duration of 2.9 seconds (SD: 0.7). We removed all characters from the transcripts that are not part of the West-Frisian alphabet (Yılmaz et al., 2016). ## 2.2 Besemah And Nasal Besemah and Nasal are two Austronesian languages that are spoken in southern Sumatra, Indonesia. For both languages, approximately 45 hours of informal conversation data were collected through fieldwork. For each language, four hours of conversational data have been transcribed, which are used in this study. For Besemah, there are 7,835 transcribed utterances from 46 speakers (30 female speakers and 16 male speakers) with an average sample length of 1.8 seconds (SD: 0.3). The Nasal dataset contains 7,672 transcribed utterances from 40 speakers (15 female speakers and 25 male speakers) with an average duration of 3.9 seconds (SD: 0.3). We normalized all transcriptions to the working orthographies developed for Besemah and Nasal as part of ongoing collaborative language documentation projects. ## 3 Methods We fine-tune the pre-trained multilingual XLS-R model with 317 million parameters on different amounts of training data from the four languages in our dataset (Babu et al., 2021). Note that we chose the smallest publicly available pre-trained XLS-R model to minimize the computational requirements needed for (reproducing) this study. XLS-R is pretrained on approximately 436,000 hours of speech in 128 different languages. This data was collected from a variety of sources, including parliamentary speech (372,000 hours in 23 European languages), read speech from Multilingual Librispeech (44,000 hours in eight European languages) and Common 2https://woordwaark.nl/spelling.pdf Voice (7,000 hours in 60 languages), speech from YouTube from the VoxLingua107 corpus (6,600 hours in 107 languages), and conversational telephone speech from the BABEL corpus (approximately 1,000 hours in 17 African and Asian languages). The majority of the training data is from Indo-European languages (87%), and the language that is most represented is English (roughly 70,000 hours). While the model does include a small portion of West-Frisian data (i.e. 15 hours), this is not the case for Gronings, Besemah, and Nasal. The architecture and pre-training objective of XLS-R are similar to those of wav2vec 2.0 (Baevski et al., 2020). The model is trained as a single end-to-end system, and consists of a convolutional encoder, a quantizer, and a 24-layer Transformer model. Speech representations are learned through a contrastive task that is applied to the quantized encoder representations. After pre-training, the model can be fine-tuned for speech recognition using transcribed speech. A linear projection is added on top of the Transformer network to predict characters from the transcriptions using connectionist temporal classification (CTC; Graves et al. 2006). We include a multilingual model in our study, because previous work showed that multilingual pretraining transfers well to low-resource languages (e.g., Bartelds and Wieling 2022; Khurana et al. 2022). We experimented with fine-tuning other models (for example the Dutch wav2vec 2.0 model included by Bartelds and Wieling 2022), but preliminary results showed that XLS-R was superior. The hyperparameters of our fine-tuning experiments follow those reported in Baevski et al. (2020) for comparable data sizes, except for the learning rate, which we tune on the basis of the development data by evaluating the following range: [5e−4, 1e−4, 5e−5, 1e−5]. In addition, we reduce the batch size and use gradient accumulation to make sure our experiments run on limited compute hardware (i.e. a single Nvidia 40 GB A100 GPU). We evaluate the fine-tuned models in terms of word error rate (WER), which is a commonly used evaluation metric based on the number of substitutions, deletions, and additions between two transcripts, and report performance on the test set using the fine-tuned model checkpoint that has the lowest WER on the validation set. Additionally, we investigate whether it is beneficial to further pre-train the XLS-R model using limited data and computational hardware before fine-tuning the model for ASR. As pre-training is computationally expensive, we only evaluate the performance on Gronings, for which we perform the broadest range of experiments. Specifically, we pre-train on the four hours of Gronings training data with the test set samples removed for 100,000 steps and use a learning rate of 1e−5, which was selected after briefly experimenting with a range of learning rates that we evaluated on the validation set. Similar to the fine-tuning experiments, we use gradient accumulation and a small batch size. The total computational budget for this study is about 390 hours on a 40 GB A100 GPU (160 fine-tuning runs of roughly 2 hours each, and pretraining runs of roughly 70 hours). We perform all experiments using the HuggingFace Transformers library, version 4.24.0 (Wolf et al., 2020). ## 4 Experimental Setup For each of the languages, we use varying amounts of training data for fine-tuning the multilingual XLS-R model. Additionally, for Gronings, we also fine-tune the XLS-R model that is further pretrained on Gronings. For all experiments, we start from the full training dataset of 192 minutes (80% of four hours), and divide this set repeatedly into smaller subsets until reaching roughly 20 minutes (50% of each split). Consequently, we have training sets of 192, 96, 48 and 24 minutes, respectively. In the self-training approach, we fine-tune the pre-trained XLS-R models on one of the subsets of data (i.e. 24, 48, or 96 minutes) as the initial step. We regard this model as the teacher model, which is then used to transcribe the remaining portion of speech data from the full training data (i.e. without the labels). The resulting automatically transcribed data, in conjunction with the original labeled data, is subsequently used to fine-tune a second model, referred to as the student model, which ideally outperforms the teacher model. This approach is shown in Figure 1. For example, we fine-tune a XLS-R teacher model on 24 minutes of manually transcribed speech data and use this model to label the remaining 168 minutes of speech data contained in the full training set. The combined data (e.g., 24 minutes of natural speech with correct labels and 168 minutes of automatically transcribed speech obtained through self-training) are subsequently used to fine-tune a new student model. We apply this procedure to each of the three training splits to investigate in which cases self-training may be beneficial in a low-resource setting. Our decoding procedure does not use an external language model (LM) due to the limited availability of text-based training materials for all languages, and also to ensure a fair comparison between languages. This is supported by previous work that found no improvement in speech recognition performance when limited amounts of textual data are available for LM training (San et al., 2023). Note that in addition to the self-training approach, preliminary experiments were conducted with other data augmentation techniques (following Sriram et al. 2022). Specifically, we experimented with adding noise to the speech signal, raising or lowering the pitch of the speaker, and simulating far-field speech. These techniques, however, did not improve the speech recognition performance, and we discarded them from our experimental setup to limit the amount of comparisons. ## 4.1 Additional Generated Training Data For Gronings, we investigate the effect of using additional generated training data obtained through self-training or via a TTS system. This additional training data is generated on the basis of the remaining manually transcribed speech data we have available for Gronings. Specifically, from this data we only use the audio recordings combined with the associated automatically generated transcriptions in the self-training procedure, while we only use the transcriptions of these recordings together with the associated synthetic speech generated using the TTS system during the synthetic speech procedure (explained below). We did not use the speech data in combination with the associated manually generated transcriptions for training, since we are interested in the performance of the two aforementioned data augmentation techniques. Note that for these experiments, we only use the smallest subset of manually transcribed speech training data (i.e. 24 minutes) to investigate the added benefit of generating a relatively large amount of additional fine-tuning data. Inspired by Xu et al. (2020), we conduct three iterations of self-training to incrementally improve the quality of the generated transcriptions. Specifically, we fine-tune an XLS-R teacher model on the 24-minute subset of Gronings as the first step. This model is then used to transcribe the remaining unlabeled portion of the original training data (i.e. 168 minutes). The combined data is then used to fine- ![4_image_0.png](4_image_0.png) tune a student model. We use the new student model to transcribe another set of 168 minutes of unlabeled speech, and add this data to our training data, which now contains 24 minutes of original data and two times 168 minutes (i.e. 336 minutes) of data that was transcribed through self-training. We then fine-tune another student model using the new training data (i.e. 24 + 336 minutes) and use it to transcribe an additional set of 336 minutes of unlabeled data to examine the effects of substantially increasing the training data. Finally, we also add these data to our training data and fine-tune a final student model on the complete amount of training data (i.e. 24 + 336 + 336 minutes). Each of these student models is then evaluated on the test set. ## 4.2 Synthetic Speech In addition to transcribing unlabeled speech through self-training, we generate synthetic speech samples on the basis of the original transcriptions using an existing TTS system that was trained on about two hours of read speech from a single female speaker of the Hogelandsters variant of Gronings. This system uses the FastSpeech 2 architecture (Ren et al., 2020), and was previously developed for integration (pending) in the online language documentation project on Gronings.3 We use this existing TTS system to generate synthetic training data using the transcripts of the same sets of recordings that were used for the self-training experiments explained above. To line up with the self-training models, we fine-tune three XLS-R models using different amounts of training data. The first model is fine-tuned using the 24-minute subset of manually transcribed speech supplemented with synthetic speech generated using the transcripts that correspond to the remaining 168 minutes of manually transcribed training data. The second model is fine-tuned on the same subset augmented 3https://woordwaark.nl with the second set of 168 minutes of additional TTS-generated recordings (i.e. based on the transcriptions of the second set of 168 minutes of training data also used in the self-training experiment described above). We then augment the training data once more by adding synthetic speech samples using the transcripts from the final set of additional training data (i.e. 336 minutes), and fine-tune the XLS-R model on the complete amount of training data. This approach is visualized in Figure 2. ## 5 Results We show the word error rates (WERs) for Gronings, West-Frisian, Besemah, and Nasal in Figure 3. The WERs for the development set are presented in Appendix A. For each of the languages, we observe a clear performance increase (i.e. lower WERs) when the amount of manually transcribed training data becomes larger. The WERs decrease between 30.1% and 53.3% when we use the complete set of training data (i.e. 192 minutes of manually transcribed speech data) instead of the 24-minute subset. Importantly, Figure 3 also shows that selftraining is beneficial for each of the languages. Student models improve over their teacher models in almost all cases. The improvement is particularly strong when the teacher model was based on a very small amount of data (i.e. 24 minutes) and ranges between 6.3% and 13.9%. ## 5.1 Further Pre-Training In Figure 4, we show the fine-tuning results for varying amounts of training data (similar to those shown in Figure 3) based on an XLS-R model that was further pre-trained on Gronings. For comparison, this figure also shows the performance of the original fine-tuned models for Gronings. Pretraining generally results in a small increase in performance (up to a 9.3% improvement) when only manually transcribed speech data was used to ![5_image_0.png](5_image_0.png) ![5_image_1.png](5_image_1.png) fine-tune the model. Additionally, when a model was fine-tuned on data obtained using self-training, the performance gains were minimal (up to 1.7% improvement). ## 5.2 Additional Generated Training Data The effect of using additional augmented training data on ASR model performance is visualized in Figure 5a. To better evaluate these results, we also added the self-training results shown in Figure 3a to this figure. Our results for self-training show that increasing the amount of automatically generated fine-tuning data is beneficial, albeit to a lesser extent than the benefit of using the first set of 168 minutes of speech with automatically generated transcriptions. Nevertheless, the performance of the model fine-tuned using 24 minutes of manually transcribed speech data plus 672 minutes of speech data with automatically generated transcriptions yields a relative WER reduction of 20.5% com- ![6_image_0.png](6_image_0.png) pared to the corresponding teacher model. Consequently, its performance is close to the performance of the model fine-tuned on 48 minutes of manually transcribed speech data. Figure 5a also shows that an even greater performance gain, namely a WER reduction of 38.6% relative to the model trained using 24 minutes of manually transcribed speech, can be achieved when using an existing TTS system to generate additional training data.4 There is no clear benefit, however, of generating successively larger sets of synthetic speech. Nevertheless, the performance of the model fine-tuned using 24 minutes of manually transcribed speech data plus 168 minutes of synthetic speech data generated using the TTS systems is almost identical to the performance of a model fine-tuned using 96 minutes of manually transcribed speech data. ## 5.3 Out-Of-Domain Results The results presented in Figure 5a might overestimate the model performance, as the speaker whose data was used for training the available TTS system was also included in the Gronings test set. We therefore also report the fine-tuned model performance on an out-of-domain test set, which does not include any of the speakers that are included in the training data. The results are shown in Figure 5b. While the performance on the out-of-domain data is clearly worse compared to the original test set, the pattern of the results for the self-training approach remains similar (with a relative WER improvement of up to 16.0%). Furthermore, the benefit of augmenting the training data using a TTS system is still present, but it is less pronounced than before (with a WER improvement of up to 25.5%). Nevertheless, both data augmentation techniques still offer a substantial improvement in WER when the availability of manually transcribed training data is limited. ## 6 Discussion And Conclusion We investigated whether data augmentation techniques are beneficial to improve the performance of ASR systems for four typologically different languages with a limited amount of real-world training data available. We evaluated the performance of XLS-R models fine-tuned using varying amounts of training data, showing that the model performance generally improves (i.e. resulting in lower WERs) when (more, in the case of self training) augmented training data is used. The greatest performance gains across the four languages were observed when the amount of manually transcribed data used for fine-tuning was increased. Nevertheless, we also observed substantial increases in model performance by augmenting very limited ![7_image_0.png](7_image_0.png) amounts of training data through self-training. For Gronings, we found that fine-tuning a model on additional data obtained through iterative self-training performed almost as well as a model fine-tuned on double the amount of manually transcribed speech data. Importantly, self-training only requires collecting additional unlabeled speech data, which is typically much easier to obtain than transcribed speech, making it a valuable approach for lowresource languages. Moreover, using an existing TTS system for generating additional synthetic training data was likewise shown to be beneficial. We observed that the benefit of augmenting the training data via the TTS system yielded larger performance gains (even on par with a model fine-tuned on four times the minimum amount of manually transcribed speech data we considered) than using the iterative self-training procedure. However, in contrast to self-training, no beneficial effect was present when increasing the amount of generated data. This pattern held true irrespective of using the general test set for evaluation or an out-of-domain test set instead. While not many minority languages have a suitable TTS system available, generating speech data using such a system is very easy as it only requires written text. Of course, our results also show that when the material is available to train a TTS system (i.e. using audio recordings and associated transcriptions) it is likely better to use these resources directly for training the ASR system. While we showed the benefit of iterative selftraining when a very small amount of training data is available, the benefit of supplying more and more self-trained training data was diminishing. Our result extends the findings for English by Xu et al. (2020) to a new set of minority languages or language variants. It is possible that the transcriptions generated by a specific teacher model in the selftraining approach contain useful information, but that this is negated to a large extent by the generated errors of the model. As teacher models fine-tuned on larger amounts of manually transcribed training data are expected to yield higher quality transcriptions (as shown in e.g., San et al. 2022), the effect of generating more data might be more beneficial in these cases. However, this should be investigated in future work. When using the TTS system for augmenting our training data, we did not see a benefit of increasing the amount of generated synthetic speech. As the additional training data represents data from a single speaker (as the TTS system was trained on the basis of data from a single speaker), the model might have been been overfitting to that specific speaker. Future work, therefore, needs to investigate alternatives (or additions) to using a TTS system for generating additional training data. For example, by investigating whether model performance can be improved using speaker adaptation methods or cross-lingual voice conversion (e.g., Rossenbach et al. 2020b; Baas and Kamper 2022). We found only minor performance gains when we fine-tuned the XLS-R model that was further pretrained on Gronings (using all training and development data). Specifically, self-training appeared to have greater performance gains than continuing pre-training (CPT), and combining CPT and selftraining only marginally improved results. Given the large computational cost of CPT as opposed to the two data augmentation methods, it is clear that CPT is not cost-effective. It may be that CPT only yields appreciable performance gains once a sufficient amount of unlabeled audio can be obtained (e.g. 200 hours of Ainu: Nowakowski et al., 2023). However, obtaining such a large amount of data for minority languages or language variants such as Gronings, Besemah, and Nasal is unlikely. It is therefore important to further investigate how a limited amount of target language data can be used effectively for self-supervised pre-training. For example, Paraskevopoulos et al. (2023) reported that using an additional 70-hour out-of-domain corpus alongside a 12-hour target corpus was crucial in improving performance. Given that similar language regularization approaches have been effective for neural machine translation (e.g. Neubig and Hu, 2018), it may be possible that this strategy could also be beneficial for further pre-training in speech (e.g., using a 70-hour Indonesian speech corpus alongside the target four hour Besemah corpus). In conclusion, our results show that dataaugmentation techniques may serve as a costeffective way to improve ASR performance for low-resource languages and variants. While the performance of the four systems is not comparable to systems developed for high-resource languages, these systems may serve as a starting point for these language varieties. We hope our experiments help further more inclusive speech technology for low-resource languages. ## Limitations While we show a clear benefit of data augmentation when the amount of available training data is limited, the performance gain seems to be lower when a larger quantity of manually transcribed speech data is available. Whether data augmentation is always beneficial is an open question. We did not measure the effect of sociolinguistic variables on the performance of the models. A risk might be that especially for the models for Gronings, which were developed on the basis of speech data from only a few speakers, results might be negatively affected by differences in language background (such as speaking a different variety of Gronings, or being from a different social group). We likewise did not measure the effect of nonlinguistic variation (e.g., use of different microphones) on the performance of the models. While Bartelds et al. (2022) showed that wav2vec 2.0 representations are relatively unaffected by nonlinguistic variation, we aim to further explore this in future work. Finally, we evaluated the effect of training data size and data augmentation on four different minority languages or language variants, each using a single test set. Of course, using a different test set might have affected the results. However, given that the pattern of results was similar across a range of language varieties we do not expect this difference to be large. ## Ethics Statement Our paper evaluated various methods that could make developing ASR systems more viable for languages where paired audio and transcriptions are difficult to obtain. In our experiments, we only used already publicly available data (West-Frisian) or data for which we have obtained informed consent for public release from the data custodians (Gronings, Besemah, Nasal). 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Longitudinal Speaker Clustering and Verification Corpus with Code-Switching Frisian-Dutch Speech. In *Proc. Interspeech 2017*, pages 37–41. Emre Yılmaz, Henk van den Heuvel, Jelske Dijkstra, Hans Van de Velde, Frederik Kampstra, Jouke Algra, and David Van Leeuwen. 2016. Open Source Speech and Language Resources for Frisian. In Proc. Interspeech 2016, pages 1536–1540. Zi-Qiang Zhang, Yan Song, Ming-Hui Wu, Xin Fang, and Li-Rong Dai. 2021. XLST: Cross-lingual Selftraining to Learn Multilingual Representation for Low Resource Speech Recognition. ## A Results On Development Data Figure 6 shows the WERs for Gronings, West-Frisian, Besemah, and Nasal for the development set. We show the fine-tuning results for varying amounts of training data using a model that was further pre-trained on Gronings in Figure 7. Finally, the WERs in Figure 8 visualize the results for the development set of Gronings when additional training data generated by self-training (ST) or a text-to-speech system (TTS) was used. Note that the pattern of these results is very similar to our findings for the test set. ![11_image_0.png](11_image_0.png) ![12_image_0.png](12_image_0.png) ![12_image_1.png](12_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 6, Limitations ✓ A2. Did you discuss any potential risks of your work? Limitations ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract, 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 2, 3, 4 ✓ B1. Did you cite the creators of artifacts you used? 2, 3, 4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 2. The existing models we use are publicly available (Apache 2.0 licensed) and have been evaluated on the same downstream task. For the models we trained, all relevant (license) information will be provided on our GitHub repository. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 2. The existing models we use are publicly available (Apache 2.0 licensed) and have been evaluated on the same downstream task. For the models we trained, all relevant (license) information will be provided on our GitHub repository. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? 2 ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 2 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 2 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ## C ✓ **Did You Run Computational Experiments?** 3, 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 3 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 3, 4 ✗ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We do not have summary statistics for sets of experiments as we specifically aimed to minimize the amount of computing time used and therefore only performed one run per condition (see Methods and Ethics Statement). This is also motivated by our observation that the pattern of results was similar across a range of language variants (see Limitations). Results on our development data are presented in Appendix A. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 3. All details of packages that were used for this study will be provided on our GitHub repository. ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left Blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

zhou-etal-2023-clcl

{CLCL}: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning

https://aclanthology.org/2023.acl-long.43

Non-compositional expressions present a substantial challenge for natural language processing (NLP) systems, necessitating more intricate processing compared to general language tasks, even with large pre-trained language models. Their non-compositional nature and limited availability of data resources further compound the difficulties in accurately learning their representations. This paper addresses both of these challenges. By leveraging contrastive learning techniques to build improved representations it tackles the non-compositionality challenge. Additionally, we propose a dynamic curriculum learning framework specifically designed to take advantage of the scarce available data for modeling non-compositionality. Our framework employs an easy-to-hard learning strategy, progressively optimizing the model{'}s performance by effectively utilizing available training data. Moreover, we integrate contrastive learning into the curriculum learning approach to maximize its benefits. Experimental results demonstrate the gradual improvement in the model{'}s performance on idiom usage recognition and metaphor detection tasks. Our evaluation encompasses six datasets, consistently affirming the effectiveness of the proposed framework. Our models available at \url{https://github.com/zhjjn/CLCL.git}.

# Clcl: Non-Compositional Expression Detection With Contrastive Learning And Curriculum Learning Jianing Zhou, Ziheng Zeng and **Suma Bhat** University of Illinois at Urbana-Champaign Champaign, IL USA {zjn1746, zzeng13, spbhat2}@illinois.edu ## Abstract Non-compositional expressions present a substantial challenge for natural language processing (NLP) systems, necessitating more intricate processing compared to general language tasks, even with large pre-trained language models. Their non-compositional nature and limited availability of data resources further compound the difficulties in accurately learning their representations. This paper addresses both of these challenges. By leveraging contrastive learning techniques to build improved representations it tackles the non-compositionality challenge. Additionally, we propose a dynamic curriculum learning framework specifically designed to take advantage of the scarce available data for modeling non-compositionality. Our framework employs an easy-to-hard learning strategy, progressively optimizing the model's performance by effectively utilizing available training data. Moreover, we integrate contrastive learning into the curriculum learning approach to maximize its benefits. Experimental results demonstrate the gradual improvement in the model's performance on idiom usage recognition and metaphor detection tasks. Our evaluation encompasses six datasets, consistently affirming the effectiveness of the proposed framework. Our models available at https: //github.com/zhjjn/CLCL.git. ## 1 Introduction As a ubiquitous yet special class of expressions in natural languages, non-compositional expressions (e.g., the idiom *under the weather*) have specific communicative intents (Moon et al., 1998; Baldwin and Kim, 2010) and are individually rare but collectively frequently appearing widely across genres (Moon et al., 1998; Haagsma et al., 2020). They are characterized by *non-compositionality* in their meaning because of which, their meaning cannot be inferred by composing the meaning of their constituent words (Baldwin and Kim, 2010). In addition, many non-compositional expressions can be used either figuratively or literally, in a context dependent manner. For example, the phrase "clean house" can be interpreted literally, as in We can not promise you good weather but we can promise you a clean house and a really good breakfast and can be understood figuratively, as in Indeed , the Kursk crisis may provide him with an opportunity to further clean house in the military. NLP systems intending to process these noncompositional expressions need to decide if these expressions are used in the figurative or literal sense before modeling their meaning. This is the traditional and popular non-compositional language processing task called *usage disambiguation*1 which aims to differentiate the literal (i.e.,compositional) from the figurative (i.e., non-compositional) usage of these expressions in given contexts, dubbed as *idiom usage recognition* for idiomatic expressions and *metaphor detection* for metaphorical expressions (Peng and Feldman, 2015; Köper and im Walde, 2016; Liu and Hwa, 2017, 2018; Chen et al., 2017; Jiang et al., 2022). However, compared to the abundance of resources for tasks related to compositional expressions, the available resources for idiom usage recognition and metaphor detection are very limited. Successful disambiguation of the usages of the non-compositional expressions involves overcoming two challenges: (1) the linguistic challenge of handling non-compositionality and (2) the resourcerelated challenge of learning from scarce training data. Previous works (Peng and Feldman, 2015; Köper and im Walde, 2016; Liu and Hwa, 2017, 2018) primarily focus on designing complex architectures for modeling non-compositionality, while also ignoring the representational aspect to model non-compositionality under a limited-resource scenario to address the second challenge. The focus 1It should be noted that in our work *usage disambiguation* refers to the task of distinguishing between the literal usage and the figurative usage of non-compositional expressions. 730 of this work is a method to solve the above two challenges jointly and find sense-specific representations of the idiomatic expressions. With the same idioms used in different ways as natural positive and negative examples whose representations could be better by using contrastive learning, we utilize contrastive learning to address the first challenge to produce a better representation of non-compositional expressions for recognizing their usage. Successful idiom usage recognition and metaphor detection require different representations of the same expression when they are used in a literal and figurative way, respectively. Therefore, we incorporate a contrastive objective to enhance the difference between the contextualized representations of the figurative sense and the literal sense for the same expression. In this way, we enable the classifier to make context-dependent decisions in the embedding space. Secondly, to make better use of the scarce available data, we use curriculum learning (Bengio et al., 2009), which enables the models to gradually proceed from easy training instances to harder ones, when the instances are themselves ordered according to a difficulty measure. Therefore, curriculum learning naturally consists of (1) measuring the difficulty level for each training example, and (2) scheduling training examples based on their difficulty levels. Furthermore, we combine contrastive learning and curriculum learning together by utilizing contrastive objectives to measure the difficulty level of the training examples. During model training, the contrastive objective is dynamically updated, and thus the difficulty levels of the training examples are also updated in accordance with the current ability of the model. Our study is the first to jointly alleviate the problems caused by non-compositionality and limited data resources by strategically and dynamically combining contrastive learning and curriculum learning, and deploying it for idiom usage recognition and metaphor detection. Our proposed framework enables the model to first learn from simple non-compositional expressions and then from harder ones by building better representations of non-compositional expressions via contrastive learning. The contributions of our work are as follows: - We propose a novel framework that combines contrastive learning and curriculum learning for idiom usage recognition and metaphor detection. The difficulty levels obtained from contrastive objectives are dynamically updated with the training, based on which the training examples are dynamically scheduled. - Empirical evaluations of our proposed framework on the tasks of idiom usage recognition and metaphor detection affirm the effectiveness of our framework. Detailed ablation studies and analyses are provided to support our claims. As a result, we treat both idiom usage recognition and metaphor detection under the same computational umbrella. - Our proposed framework also shows better cross-task transfer between idiom usage recognition and metaphor detection compared to the baseline models. ## 2 Related Prior Work Idiom Usage Recognition. Like other noncompositional expressions, the meaning of many idiomatic expressions is contextually ambiguous. Prior studies mainly focus on disambiguating their figurative/literal use(Salehi et al., 2014; Senaldi et al., 2016; Flor and Klebanov, 2018; Amin et al., 2021; Peng and Feldman, 2015; Köper and im Walde, 2016; Liu and Hwa, 2017, 2018), i.e., performing the idiom usage recognition task. Early works heavily rely on designing representative features, e.g., canonical form (Fazly et al., 2009), to decide literal and figurative usages. With the emergence of word embeddings and neural networks, richer features are encoded into word embeddings and utilized for idiom usage recognition (Liu and Hwa, 2017, 2018). Recently proposed pre-trained language models have shown great improvement on various NLP tasks leading to efforts that leverage the power of large pre-trained language models for this task (Zeng and Bhat, 2021). However, due to non-compositionality and scarcity of available data resources, previous works mainly focused on designing complex architectures while ignoring the representational aspect to model noncompositionality under a limited-resource scenario. Our study is the first to focus on solving both of these two challenges to fill this research gap. Metaphor Detection. Like other figurative expressions, metaphors play a crucial role in cognitive and communicative functions (Choi et al., 2021), because of which computationally recognizing and understanding the metaphorical meanings of words becomes important. Early approaches utilized various linguistic features to detect metaphors, such as word imageability (Broadwell et al., 2013), semantic supersenses (Tsvetkov et al., 2014), and unigrams (Klebanov et al., 2014). In recent years, different neural architectures have been widely used for metaphor detection, including CNN (Wu et al., 2018), LSTM (Gao et al., 2018). Beyond these, the prominence of large pre-trained language models on various NLP tasks has prompted their use for metaphor detection. Choi et al. (2021) uses RoBERTa as the backbone model to get contextualized representations of words and (Gong et al., 2020) combines other linguistic features in a RoBERTa architecture for the purpose of metaphor detection. The subpar performance of large pretrained models when labeled data are scarce has led to studies exploring data augmentation (Lin et al., 2021). However, utilizing augmented data with pseudo labels could be even more detrimental to the performance due to the noise in the augmented data. Our proposed curriculum learning framework can potentially alleviate data scarcity by using the limited data more effectively without introducing additional noise. This is the first work to show its positive impact on both tasks of idiom usage recognition and metaphor detection. Contrastive Learning. Contrastive learning aims to learn meaningful representations by pulling semantically similar examples closer and pushing semantically dissimilar examples further apart in the embedding space. Widely considered to be effective for building meaningful representations, contrastive learning has garnered increasing attention from researchers in different areas. For example, prior works in NLP have leveraged contrastive learning to produce better word embeddings (Mikolov et al., 2013) and sentence embeddings (Logeswaran and Lee, 2018). More recently, with the dominance of transformer-based models, contrastive learning is also being used to train transformer models (Fang et al., 2020; Giorgi et al., 2021; Wu et al., 2020). Similarly, in this work, for a given non-compositional expression, we use contrastive learning to pull the expression embeddings that are used in the same figurative/literal sense closer while pushing the embeddings between figurative and literal senses apart. Thereby we set a precedence of utilizing contrastive learning to enhance the representation quality of idiomatic expressions for modeling non-compositionality. Besides, we also propose to utilize the contrastive objective to design curriculum learning, for reducing the training data quantity needed for transformers. Curriculum Learning First proposed by (Bengio et al., 2009), curriculum learning aims to enable the models to gradually learn from easy to harder examples according to a difficulty measure for each example during training. Therefore, curriculum learning enables the model to better utilize available data. With growing research interests, curriculum learning has been applied in different fields. In computer vision, curriculum learning has been applied to a range of tasks, such as image classification (Weinshall et al., 2018), human attribute analysis (Wang et al., 2019), and visual question answering (Li et al., 2020), however, its NLP application is mainly limited to neural machine translation (Platanios et al., 2019; Liu et al., 2020; Zhou et al., 2021; Zhang et al., 2021). So, prior works on curriculum learning on NLP, including their difficulty measurement and scheduling strategy, are mainly designed for compositional language processes, which are largely different from non-compositional expressions, i.e., idioms and metaphors. In this study, we propose a new curriculum learning method specifically designed for non-compositional expression recognition. Moreover, for the first time we show how curriculum learning based on contrastive learning, results in performance gains in the idiomaticity-related tasks. ## 3 Framework In this section, we introduce our proposed framework as a combination of contrastive learning and curriculum learning. Overall, we first utilize contrastive learning to obtain the contrastive objective, which is then used as a measurement of the difficulty level for each sentence containing idioms or metaphors. Then, our proposed dynamic scheduling strategy is used to re-arrange the training examples. Finally, the model is trained via the classification objective and the contrastive objective. ## 3.1 Contrastive Learning Contrastive learning aims to learn meaningful representations by pulling semantically similar examples and pushing apart semantically different examples. In our case, the figurative and literal meanings for the same non-compositional expression are different. Thus, for the purpose of contrastive learning the same non-compositional expressions used in the same (figurative or literal) sense in different sentences are natural semantically close examples. ![3_image_0.png](3_image_0.png) Update diffi On the other hand, the same non-compositional expressions used in different senses in different sentences are semantically different examples. Training with contrastive learning allows the model to learn higher-quality representations by grouping the embeddings of a given non-compositional expression into two distinct clusters in the embedding space, corresponding to its figurative and literal meaning. More specifically, for a sentence Yi (anchor example) with a non-compositional expression i, its meaning should be similar to another sentence Y + i (positive example) with the same expression i used in the same sense because they both contain the same non-compositional expression used in the same way (figuratively or literally). However, the meaning of Yi will be different from the sentence Y − i(negative example) with the same expression i but used differently. Therefore, the distance between the appropriate representations of Yi and Y + i (xi and x + i ) is expected to be small, while the distance between the appropriate representations of Yi and Y − i(xi and x − i ) is expected to be large. Thus, we develop a contrastive objective by considering (Yi, Y + i) a positive pair and (Yi, Y − i) a negative pair: $${\mathcal{L}}_{\mathrm{cts}}=-\sum_{Y\in{\mathcal{Y}}}\log{\frac{f({\boldsymbol{x}}_{i},{\boldsymbol{x}}_{i}^{+})}{f({\boldsymbol{x}}_{i},{\boldsymbol{x}}_{i}^{+})+f({\boldsymbol{x}}_{i},{\boldsymbol{x}}_{i}^{-})}}\quad(1)$$ where f represents the distance function. Therefore, our final loss is: $${\mathcal{L}}={\mathcal{L}}_{\mathrm{cts}}+{\mathcal{L}}_{\mathrm{cls}}$$ L = Lcts + Lcls (2) where Lcts is the contrastive loss and Lcls is the cross-entropy loss based on the ground truth class label for the sense (literal or figurative) of the expression in Yi. To prepare for training, for each training example Yi (anchor), we randomly sample a Y + ito form the positive pair and randomly sample a Y − ito form the negative pair, converting the training example Yiinto a triplet of anchor, positive, and negative examples, i.e., < Yi, Y + i, Y − i >. We use the triplets to train the models with the aforementioned final loss. ## 3.2 Curriculum Learning 3.2.1 Difficulty Metrics This section defines the difficulty metric used by our curriculum learning framework. We correlate the classification difficulty for each example Yi to its position in the embedding space relative to its corresponding positive Y + iand negative example Y − ibecause the contextualized representation for the figurative and literal meaning of the noncompositional expression should be different. Noncompositionality means that the meaning of a figurative expression is not derivable from its constituent words, but rather, the expression has a conventionalized figurative meaning. Therefore, the differentiation between figurative and literal semantics demands a distinction between an expression's figurative and literal embedding. If the figurative and literal embeddings for the same expression are really separable, i.e., they are further apart in the embedding space, a classifier should be able to classify the figurative and literal senses more easily. Conversely, if the embeddings of an expression's figurative and literal semantics are not distinctive, it would be harder for the model to classify the expression into its figurative and literal senses based $\eqref{eq:walpha}$. Algorithm 1: CLCL ![4_image_0.png](4_image_0.png) Input: Dataset P = {Yi} K i=1, Model M and number of epochs N Output: Fine-tuned Model M∗ 1 P∗ = {(Yi, Y + i, Y − i)} K i=1 ; 2 D0 = CTS(P∗, M) ; 3 Sort P∗ based on each difficulty level in D0, resulting in a re-arranged P∗0 ; 4 for n = 1; n ≤ N do 5 Mn ⇐ TRAIN(P∗n−1 ); 6 Dn = ∅, Pˆn = ∅ ; 7 for (Y, Y +, Y −) ∈ P∗ do 8 dMn (Y ) = CTS(Y ; Mn) ; 9 if dMn (Y ) ̸= dMn−1 (Y ) then 10 Dn ⇐ Dn S{dMn (Y )} ; 11 Pˆn ⇐ Pˆn S(Y, Y +, Y −) ; 12 else 13 continue ; 14 end 15 end 16 Sort Pˆn based on Dn, resulting in P∗n ; 17 end 18 return M∗ = Mn; $$\mathbf{11}$$ on its embedding. Therefore, it makes sense to use the degree to which the figurative and literal embeddings are separable in the embedding space as a measure of classification difficulty. Intuitively, if Yiis easy for the model to classify, then xi, the embedding of Yi, should already encode certain semantic features and thus be located closer to x + i than x − iin the embedding space. Hence, given the < Yi, Y + i, Y − i > triplets, we assess the difficulty of a training example Yi based on the models' contrastive objective as $$d_{\bf M}(Y_{i})={\rm CTS}(Y_{i};{\bf M})=\frac{f({\mathbf{x}}_{i},{\mathbf{x}}_{i}^{+})}{f({\mathbf{x}}_{i},{\mathbf{x}}_{i}^{+})+f({\mathbf{x}}_{i},{\mathbf{x}}_{i}^{-})}\tag{3}$$ where $M$ is the model and $d_{\bf M}(Y_{i})$ is the diff where M is the model and dM(Yi) is the difficulty measure for Yi. ## 3.2.2 Scheduling Strategy After the difficulty levels are determined, the traditional curriculum learning methods would fix the order of training examples. However, the difficulty of each example for the model changes as the model learns. Therefore, it is disadvantageous to fix the order of training examples. We propose to update the difficulty levels and dynamically schedule training examples accordingly. Specifically, since the difficulty levels are measured based on the contrastive objective, they are naturally updated during the training process. Therefore, after each training epoch, the difficulty score dM(Yi) for each example Yiis updated as: $$d_{\mathbf{M}_{n}}(Y_{i})=\mathbf{CTS}(Y_{i};\mathbf{M}_{n})$$ (Yi) = CTS(Yi; Mn) (4) where Mn refers to our model fine-tuned for n epochs in our task. After the difficulty scores for all the training examples have been updated, the training examples will be re-arranged according to the new difficulty scores for the next epoch of training. ## 4 Experiments 4.1 Datasets Idiom Usage Recognition. We conduct experiments on three datasets for idiom usage recognition: MAGPIE (Haagsma et al., 2020) SemEval5B (Korkontzelos et al., 2013) and VNC (Cook et al., 2008). To test the models' ability to recognize the usage of unseen idioms, each dataset was split into train and test sets in two ways: random and typebased. In the random split, the sentences are randomly divided, and the same idiom can appear in both train and test sets, whereas in the typebased split, the idioms in the test set and the train set do not overlap. For MAGPIE and SemEval5B, we use their respective official random/typebased and train/test splits. For VNC, the official dataset did not have the typebased split. Therefore, to create the typebased split, we randomly split the idiom types by an 80/20 ratio, leaving 43 idiom types in the train set and ten idiom types in the test set. Metaphor Detection. Following previous works on metaphor detection, we conduct experiments on three datasets for metaphor detection: (1) VUA-18 (Leong et al., 2018), (2) VUA-verb (Steen et al., 2010), and (3) MOH-X dataset (Mohammad et al., 2016). The original train/dev/test splits provided by the official datasets are used in our experiments. ## 4.2 Baselines We show the effectiveness of our method via a comparison between the vanilla RoBERTa classification model and the RoBERTa classification model fine-tuned using our method. Besides, we also choose different SOTA models for different tasks as baselines. Data Splits Version MAGPIE SemEval5B VNC Acc F1-fig F1 Acc F1-fig F1 Acc F1-fig F1 Random vanilla 95.07 96.70 93.51 92.59 92.33 92.58 93.11 92.82 93.09 DISC - 95.02 - - 95.80 - - 96.97 - Ours **96.75 97.82 96.75 96.46 96.56 96.46 97.24 98.07 97.22** Typebased vanilla 92.86 94.79 91.73 73.36 80.12 69.88 80.06 86.85 76.58 DISC - 87.78 - - 58.82 - - 89.02 - Ours **95.36 97.05 94.20 91.11 92.65 91.16 93.22 96.16 93.25** Idiom Usage Recognition. DISC (Zeng and Bhat, 2021) is the current SOTA model for idiom usage recognition. Therefore, we choose this model as the baseline for this task. Metaphor Detection. Based on previous works, MelBERT (Choi et al., 2021), MisNet (Zhang and Liu, 2022) and CATE (Lin et al., 2021) are current SOTA models for metaphor detection. However, CATE not only requires external data resources as augmentation, but also does not have a publicly accessible implementation, which makes it reproduction difficult. Therefore, we only choose MelBERT and MisNet as our baselines and report the performance using their released code. | Model | VUA18 | VUAverb | MOH-X | | | | | | | | | | |---------|---------|-----------|---------|------|------|------|------|------|------|------|------|------| | Acc | P | R | F1 | Acc | P | R | F1 | Acc | P | R | F1 | | | vanilla | 93.4 | 79.4 | 75.0 | 77.1 | 80.4 | 72.9 | 68.8 | 70.7 | 83.5 | 82.9 | 83.4 | 82.9 | | MelBERT | 94.0 | 80.5 | 76.4 | 78.4 | 80.7 | 64.6 | 78.8 | 71.0 | 81.6 | 79.7 | 82.7 | 81.1 | | MisNet | 94.7 | 82.4 | 73.2 | 77.5 | 84.4 | 77.0 | 68.3 | 72.4 | 83.1 | 83.2 | 82.5 | 82.5 | | Ours | 94.5 | 80.8 | 76.1 | 78.4 | 84.7 | 74.9 | 73.9 | 74.4 | 84.3 | 84.0 | 82.7 | 83.4 | ## 4.3 Experimental Settings We implement our framework using a pre-trained RoBERTa Base model from Huggingface. The model is trained with a batch size of 16 for three epochs, using the Adam optimizer, and a learning rate of 3e − 5. During training, for each training example, we randomly select its positive example and negative example for contrastive learning. The classification loss is calculated based only on the original training example's label. ## 4.4 Evaluation Metrics Considering that the idiom usage recognition task is a binary classification problem, we use *accuracy* and macro F1 score to evaluate the performance. We also include the F1 score that treats the figurative class as the positive class, denoted as F1-fig. For metaphor detection, we follow the evaluation metrics (accuracy, precision, recall, and F1) in previous studies for a fair comparison. For metaphor detection, F1 refers to the F1 score that treats the figurative class as the positive class. ## 5 Results As shown in Table 1, for idiom usage recognition, RoBERTa classification model using our proposed method (Ours) achieves the best performance over all the evaluation metrics. For the MAGPIE dataset with random split, compared with the performance of the vanilla RoBERTa model, our framework outperforms it by 1.72 points in accuracy, 1.12 points in F1-fig score, and 3.24 points in F1 score. Compared with the DISC model, our method still outperforms it by 2.8 points on the F1-fig score. For the MAGPIE dataset with typebased split, our framework outperforms the vanilla model by 2.5 points in accuracy, 2.26 points in F1-fig score, and 2.47 in F1 score. For the SemEval5B dataset with random split, our framework outperforms the previous SOTA model by 0.76 on the F1-fig score. For the SemEval5B dataset with typebased split, our framework outperforms the SOTA model by 33.83 on the | Data Splits | Version | MAGPIE | SemEval5B | VNC | | | | | | | |---------------|--------------|----------|-------------|--------|-------|-------|--------|-------|-------|-------| | Acc | F1-fig | F1 | Acc | F1-fig | F1 | Acc | F1-fig | F1 | | | | Ours w/o CL | 95.14 | 96.73 | 93.64 | 94.11 | 94.12 | 94.11 | 94.94 | 95.77 | 95.12 | | | Random | Ours w/o CTS | 95.26 | 96.81 | 93.82 | 94.61 | 94.54 | 94.61 | 95.11 | 95.88 | 95.32 | | Ours | 96.75 | 97.82 | 96.75 | 96.46 | 96.56 | 96.46 | 97.24 | 98.07 | 97.22 | | | Ours w/o CL | 92.67 | 94.64 | 91.53 | 86.87 | 88.67 | 86.54 | 89.43 | 92.11 | 89.32 | | | Typebased | Ours w/o CTS | 91.04 | 93.30 | 89.89 | 83.20 | 85.43 | 82.80 | 86.22 | 89.12 | 86.11 | | Ours | 95.36 | 97.05 | 94.20 | 91.11 | 92.65 | 91.16 | 93.22 | 96.16 | 93.25 | | Table 3: Ablation study of our method on idiom detection task on MAGPIE, SemEval5B, and VNC under different settings. The best performances are bold-faced. The best performances in bold are significantly better than the performance of the baseline models. Model VUA18 VUAverb MOH-X Acc P R F1 Acc P R F1 Acc P R F1 Ours w/o CL 94.4 80.5 75.9 78.1 83.4 68.9 **78.8** 73.5 83.8 83.3 **83.3** 83.3 Ours w/o CTS 93.9 80.3 75.8 78.1 84.1 73.1 73.8 73.5 83.8 **84.3** 81.4 82.5 Ours **94.5 80.8 76.1 78.4 84.7 74.9** 73.9 **74.4 84.3** 84.0 82.7 **83.4** Table 4: Ablation study of our method on metaphor detection task on VUA18, VUAverb, and MOH-X. The best performances are bold-faced. The best performances in bold are significantly better than the performance of the baseline models. F1-fig score, which is a significant improvement. For the VNC dataset with random split, our framework outperforms the previous SOTA model by 1.1 on the F1-fig score. For the VNC dataset with typebased split, our framework beats the SOTA model by 7.14 on the F1-fig score. Therefore, our method outperforms all the baselines on three datasets across all the evaluation metrics, which shows the effectiveness of our method. As shown in Table 2, for the task of metaphor, RoBERTa classification model using our proposed method achieves the best performance on all the datasets in F1 score. For VUA18 dataset, compared with the performance of SOTA MelBERT, our framework achieves competitive performance without utilizing POS taggings and other linguistic features except for the original RoBERTa model's parameters. For the VUA-verb dataset, our method outperforms MelBERT by 4.0 absolute points in accuracy, 10.3 in Precision, and 3.4 in F1 score. Besides, our model outperforms MisNet by 5.6 points in Recall, and 2.0 points in F1 score. On the MOH-X dataset, our method achieves the best performance by outperforming MelBERT by 2.7 points in Accuracy and 2.3 points in F1 score and outperforming MisNet by 1.2 in Accuracy and 0.9 in F1 score. As a result, our method not only performs the best on the task of idiom usage recognition but also on the task of metaphor detection. | Model | Trained on VUA and Tested on MAGPIE | Trained on MAGPIE and Tested on VUA | | | | | | | |----------|---------------------------------------|---------------------------------------|------|----------|-----------|--------|------|------| | Accuracy | Precision | Recall | F1 | Accuracy | Precision | Recall | F1 | | | MelBERT | 60.9 | 92.7 | 51.6 | 66.3 | 70.1 | 11.2 | 10.1 | 10.6 | | Ours | 61.5 | 92.9 | 52.3 | 67.0 | 74.0 | 20.5 | 28.7 | 23.9 | ## 6 Analysis Ablation Study To investigate the effects of the different components in our method, i.e., contrastive learning and curriculum learning, we compare variants of our method without curriculum learning (w/o CL) and without contrastive learning (w/o CTS). As shown in Table 3, both have worse performance than the complete version. Without curriculum learning, the accuracy drops by more than 1 point, and the F1 score drops by more than 2 points on all the datasets across both random and typebased settings. It should be noted that the curriculum learning and contrastive learning are more effective under a typebased setting as shown in Table 3. For metaphor detection, the results presented in Table 4 show a similar trend that each component ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) is important for our method. Besides, we also observe in the Table 3 and 4 that contrastive learning and curriculum learning can individually improve model performance. Furthermore, when combined together, they complement and boost each other to further improve the performance. Analysis on Data Splits. Our method's effectiveness is most prominent on unseen idiomatic expressions, as shown in Table 1. The improvement brought about by our curriculum learning method is always more prominent in a typebased setting compared with the gain in a random setting. Therefore, with contrastive learning and curriculum learning, our method can enable the RoBERTa model to generalize over unseen idioms and transfer knowledge on recognizing non-compositionality to unseen non-compositional expressions. Analysis on the Datasets. Results shown in Table 1 and 2 also demonstrate that our method is most effective on the datasets with smaller numbers of training examples. On the MAGPIE dataset, which is the largest dataset for idiom usage recognition, our method only outperforms the vanilla RoBERTa model by 1.68 in accuracy. However, on the smaller SemEval5B dataset, our method outperforms the vanilla RoBERTa model by 3.87 in accuracy. Similarly, on the VUA-18 dataset, which is the largest dataset for metaphor detection, our method only achieves competitive performance with MelBERT. However, on smaller VUA-verb and MOH-X datasets, our method significantly outperforms the baseline models. As a result, with the help of curriculum learning, our method utilizes the available data more efficiently, especially in a low-resource scenario. Analysis on the Cross-Task Transfer. Results shown in Table 5 also demonstrate that our method has a better ability to transfer across different tasks. For the transfer study, we use the random split of MAGPIE dataset and VUA18. When trained on the dataset for one task and tested on the dataset for another task, our method always outperforms the baseline method, MelBERT. Besides, we observe that the models achieve good results in idiom usage recognition when trained in metaphor detection. However, when trained on idiom usage recognition, the models' performance on metaphor detection is much worse. Therefore, the symbolic knowledge learned during the task of metaphor detection could be transferred to perform the idiom usage recognition while the idiomatic knowledge cannot help with the metaphor detection. We leave the deeper study of this phenomenon to future research. Embedding Visualization In Figures 2 and 3, we visualize for SemEval5B sample contextual embeddings for sentences from two idioms under different data split settings. As shown in Figure 2, under the random-split setting, with simple fine-tuning and contrastive learning, the literal and figurative representations are already separated with a few points mis-clustered. However, with our method, all the points are correctly separated. In Figure 3, under the typebased-split setting, simple fine-tuning fails to separate senses in the embeddings space into differentiable groups. We observe that even with contrastive learning, there are still points clustered into the wrong group. However, with both contrastive learning and curriculum learning, all the points are distinctly separated. ## 7 Conclusion And Future Work In this paper, we propose a novel method specifically for non-compositional expression detection, including idiom usage recognition and metaphor detection. Our proposed method combines contrastive learning and curriculum learning. Contrastive learning is used to build better representations to model non-compositionality. Besides, the difficulty levels obtained from the contrastive learning objective are dynamically updated during the training, based on which the training examples are dynamically scheduled. As a result, the model could be trained in an easy-to-hard manner. We evaluate our proposed method on both idiom usage recognition and metaphor detection. Experiment results affirm the effectiveness of our method on both tasks. Detailed ablation studies and analyses are provided to support our claims. As a result, our work is the first to propose a framework for idiom usage recognition and metaphor detection. Our proposed framework also shows better cross-task transfer ability based on idiom usage recognition and metaphor detection. ## Limitations Our scheduling strategy only re-arranges the training examples after each training epoch, limiting the flexibility of scheduling them compared with re-arranging the examples after each training step. Therefore, the order of the training examples will still be fixed within each training epoch. Besides, our method finds it challenging to transfer from the task of idiom usage recognition to that of metaphor detection. Therefore, more advanced methods for learning the broad nature of non-compositionality, including those of idioms and those of metaphors are needed. 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Clear: Contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466. Ziheng Zeng and Suma Bhat. 2021. Idiomatic expression identification using semantic compatibility. Transactions of the Association for Computational Linguistics, 9:1546–1562. Mingliang Zhang, Fandong Meng, Yunhai Tong, and Jie Zhou. 2021. Competence-based curriculum learning for multilingual machine translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2481–2493. Shenglong Zhang and Ying Liu. 2022. Metaphor detection via linguistics enhanced siamese network. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4149–4159. Lei Zhou, Liang Ding, Kevin Duh, Shinji Watanabe, Ryohei Sasano, and Koichi Takeda. 2021. Self-guided curriculum learning for neural machine translation. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 206–214. ## A Implementation Our experiments and implementation are based on the Transformers library and PyTorch. ## B Experimental Details All of our experiments were conducted using two GPUs with 16GB RAM (NVIDIA V100). ## B.1 Hyperparameter Choices For the task of idiom usage recognition, we use the Adam optimizer during the training with batch size 32. The maximum input length is set to 128. We use a constant learning rate of 1e-5 for finetuning. For all the experiments, we fine-tune the models for 30 epochs and select the model with the best performance on the development set for testing. For the task of metaphor detection, we used the Adam optimizer during the training with batch size 16. All the other hyperparameters are set to default values used in (Choi et al., 2021). All of our experiments are performed for five times. The mean results are reported. ## B.2 Number Of Parameters Considering that our proposed contrastive learning and curriculum learning do not introduce more parameters, the number of parameters is identical to the number of parameters in the underlying language model: 125M for RoBERTa (base). ## B.3 Average Runtime The training process for one epoch on two GPUs took approximately 40 minutes, including 10 minutes for evaluating difficulties and 30 for finetuning. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 8 A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Left blank. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4 ✓ B1. Did you cite the creators of artifacts you used? 4 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 4 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. B ## C ✓ **Did You Run Computational Experiments?** 4, B ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4.3, B The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4.3, B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? B C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ziems-etal-2023-multi

Multi-{VALUE}: A Framework for Cross-Dialectal {E}nglish {NLP}

https://aclanthology.org/2023.acl-long.44

Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall short of this ideal since they are designed and tested on a single dialect: Standard American English (SAE). We introduce a suite of resources for evaluating and achieving English dialect invariance. The resource is called Multi-VALUE, a controllable rule-based translation system spanning 50 English dialects and 189 unique linguistic features. Multi-VALUE maps SAE to synthetic forms of each dialect. First, we use this system to stress tests question answering, machine translation, and semantic parsing. Stress tests reveal significant performance disparities for leading models on non-standard dialects. Second, we use this system as a data augmentation technique to improve the dialect robustness of existing systems. Finally, we partner with native speakers of Chicano and Indian English to release new gold-standard variants of the popular CoQA task. To execute the transformation code, run model checkpoints, and download both synthetic and gold-standard dialectal benchmark datasets, see \url{http://value-nlp.org}.

# Multi-Value: A Framework For Cross-Dialectal English Nlp Caleb Ziems William Held **Jingfeng Yang** Jwala Dhamala Rahul Gupta **Diyi Yang** Stanford University, Georgia Institute of Technology, Amazon {cziems, diyiy}@stanford.edu, {wheld3}@gatech.edu, {jddhamal, yjfllpyym, gupra}@amazon.com ## Abstract Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall short of this ideal since they are designed and tested on a single dialect: Standard American English (SAE). We introduce a suite of resources for evaluating and achieving English dialect invariance. The resource is called Multi-VALUE, a controllable rule-based translation system spanning 50 English dialects and 189 unique linguistic features. Multi-VALUE maps SAE to synthetic forms of each dialect. First, we use this system to stress tests question answering, machine translation, and semantic parsing. Stress tests reveal significant performance disparities for leading models on nonstandard dialects. Second, we use this system as a data augmentation technique to improve the dialect robustness of existing systems. Finally, we partner with native speakers of Chicano and Indian English to release new goldstandard variants of the popular CoQA task. To execute the transformation code, run model checkpoints, and download both synthetic and gold-standard dialectal benchmark datasets, see http://value-nlp.org/. ## 1 Introduction "*[Often, speakers] will not be hampered by the* lack of language technology in their local language, but by the lack of support for their variety of the contact language." - **Steven Bird** (2022) Global contact languages like English will continue to have an outsized impact on commerce, economics, wellbeing, and equity worldwide. English, like any other language, is subject to variation across time (Yang, 2000) and between speakers or speaker groups (Eckert, 2017; Holmes and Meyerhoff, 2008). Rather than focusing on social status or political power (Stewart, 1968; Chambers and Trudgill, 1998), linguists define *dialects* as descriptive sets of correlated *features* common across a group of speakers (Nerbonne, 2009). Current pretraining paradigms employ content filters that can exclude text in English dialects other than Standard American and British (Gururangan et al., 2022), which leads to performance gaps for other varieties. These discrepancies in Natural Language Processing (NLP) cause allocational harms for dialectal speakers in downstream applications (Bender et al., 2021), making dialect robustness a critical need for fair and inclusive language technology. This disparity is clear in a growing body of empirical work on African American English (Ziems et al., 2022; Halevy et al., 2021; Blodgett et al., 2018; Jurgens et al., 2017; Kiritchenko and Mohammad, 2016). However, there does not yet exist a systematic exploration of robustness across multiple Englishes, nor of models' ability to transfer knowledge between varieties with similar features, as in multi-lingual NLP. We need new tools to benchmark and achieve dialect robustness. We introduce **Multi-VALUE**1for English dialect robustness. Our feature-based approach leverages decades of field linguistics research to isolate grammatical constructions (Demszky et al., 2021) that vary in *regional* Englishes (Labov, 1972; Eckert, 1989; Hovy and Yang, 2021). We focus on varieties that (1) are mutually intelligible with Standard American English (SAE); (2) share vocabulary with SAE; and (3) differ from SAE with respect to *morphology* and *syntax*. The third criterion defines the critical axis of variation. The first two criteria ensure that our definition of model robustness aligns with the human ability to understand Equal contribution. 1Multi-VALUE is a **Multi**-dialectal VernAcular Language Understanding Evaluation framework (value-nlp.org) ![1_image_0.png](1_image_0.png) other varieties. For example, creoles have their own unique vocabularies and are not easily understood by speakers of other Englishes (Sebba, 1997); they are outside the scope of this study. First, we provide a controllable **(1) rule-based** translation system for injecting up to 189 features into SAE text. This will allow researchers and practitioners to build *synthetic training data* plus on-demand *dialect stress tests* for nearly any task. We stress test leading models for three challenging tasks and find statistically significant performance gaps. Second, we provide reliable **(2) gold** standard benchmarks for the CoQA task in two widely-spoken varieties: Chicano and Indian English. We find that, by training models on synthetic data, we improve dialectal robustness. Third, we fine-tune and publish **(3) dialect-robust models** on the HuggingFace Hub (Wolf et al., 2020), which can be used directly in downstream applications. Figure 1 demonstrates the full project pipeline. We recognize five advantages in the MultiVALUE approach. Our system is (A) **Interpretable:** supports systematic perturbation analyses (B) **Flexible:** customized to align with new and evolving dialects by adjusting the *density* of dialectal features, unlike fixed or static datasets. (C) **Scalable:** allows users to mix and match tasks and dialects at scale without the need for costly human annotation. (D) **Responsible:** vetted by native speakers to ensure gold standards and synthetic data are dependable for ongoing research. (E) **Generalizable:** moves the field beyond single-dialect evaluation, which allows re- searchers to draw more transferrable findings about cross-dialectal NLP performance. ## 2 Related Work Dialect Disparity is an issue of equity and fairness (Hovy and Spruit, 2016; Gururangan et al., 2022; Halevy et al., 2021; Blodgett and O'Connor, 2017). There is mounting evidence of dialect disparity in NLP. Hate speech classifiers have known biases against African American English (Davidson et al., 2019; Mozafari et al., 2020; Rios, 2020; Sap et al., 2019; Zhou et al., 2021). Text from regions with a predominantly Black population are more likely to be classified as hate speech (Mozafari et al., 2020; Sap et al., 2019; Davidson et al., 2019). AAVE performance gaps have also been found across a wide range of core NLP tasks like NLI (Ziems et al., 2022), dependency parsing and POS tagging (Blodgett et al., 2018; Jørgensen et al., 2015), plus downstream applications (Lwowski and Rios, 2021). Still, there does not exist a systematic study on cross-dialectal model performance. We aim to fill this gap, expanding the VernAcular Language Understanding Evaluation (VALUE) framework of Ziems et al. (2022). Where VALUE established a uni-dialectal evaluation harness with 11 perturbation rules, Multi-VALUE now supports multi-dialectal evaluation with 189 different perturbations across 50 English dialects. Our empirical study on dialect disparity is also more expansive than prior work as we consider three separate domains: QA, MT, and semantic parsing. Multilingual NLP studies how to learn common structures that transfer across languages. These strategies may also yield benefits in multi-dialectal settings. Massively multilingual models (Pires et al., 2019; Conneau et al., 2020; Liu et al., 2020; Xue et al., 2021) exploit the commonalities between many languages at once, rather than merely achieving pairwise transfer (Lin et al., 2019). Additionally, benchmarking across multiple languages can reveal language discrepancies at the modeling level, even without language-specific feature engineering or training data (Bender, 2011; Ravfogel et al., 2018; Ahmad et al., 2019; Tsarfaty et al., 2020). Multi-VALUE aims to bring these advantages to the study of English dialects. ## 3 Multi-Value Perturbations There is a clear need for dialect robustness (§2). The challenge is that language is subject to *variation* and *change*. This means speakers can contextually modulate the density of features in their grammar, and over time, speakers adopt different features. Shifting language can quickly antiquate training and testing data, and updating such resources can be costly and time-consuming. In this section, we introduce the first stage of the Multi-VALUE pipeline. We automatically inject structural variation into SAE text using linguistic perturbation rules that alter syntax and morphology but preserve semantics. In this way, perturbations preserve labels. Unlike many black-box translation approaches (Krishna et al., 2020; Sun et al., 2022), label preservation will allow users to convert existing benchmarks directly into dialectal stress tests. Modular, independent perturbation functions give researchers the flexibility to isolate the effects of different features in different combinations. What distinguishes our work from other syntactic data augmentation methods (Wu et al., 2022) is that our perturbations are grounded in formal language patterns. We operationalize the decades of linguistics research cataloged in the Electronic World Atlas of Varieties of English (eWAVE; Kortmann et al. 2020), a database with 235 features from 75 English varieties, as documented by 87 professional linguists in 175 peer-reviewed publications. eWAVE distinguishes dialects by their unique clusters of linguistic features and the relative *pervasiveness* of each feature.2 We define a dialect transformation as a sequential application of perturbation rules. Decisions to perturb the text follow the eWAVE heuristic probabilities: 100% for obligatory features; 60% for features neither 2For example, the *give passive* feature \#153 is considered pervasive or obligatory in Colloquial Singapore English, while it is rarely observed in Philippine and Tristan da Cunha English, and it is never seen in any other dialect. ![2_image_0.png](2_image_0.png) pervasive nor rare; 30% for rare features; 0% for features with no information or an attested absence. For each rule, we condition the perturbation on morphosyntactic signals from POS tags, noun and verb inflection, and dependency relations using the spaCy 2.1.0 (Honnibal et al., 2020) and inflect 5.5.2 libraries. For the *give passive* pertubation above in Figure 2, we search for passive constructions with a past participle ROOT (VBN), an nsubjpass patient, and an agent. We construct the new phrase by inflecting the ROOT to its base (VB) form and moving it after the entire agentive noun phrase. Following the eWAVE organizational scheme, we motivate and present our feature perturbations in 12 grammatical categories: (1) Pronouns, (2) Noun Phrases, (3) Tense and Aspect, (4) Mood, (5) Verb Morphology, (6) Negation, (7) Agreement, (8) Relativization, (9) Complementation, (10) Adverbial Subordination, (11) Adverbs and Prepositions, and finally (12) Discourse and Word Order. For a more detailed breakdown, see Appendix A. Pronouns are critical for tasks like machine translation and summarization, which depend on coreference resolution (Sukthanker et al., 2020). Our pronoun perturbation rules account for linguistic structure and are not merely surface manipulations. For example, we condition on coreference for referential pronouns and on verb frames to identify benefactive datives. In total, we implement 39 of the 47 pronoun features from eWAVE. Noun Phrases are the focus of fundamental NLP research in semantic role labeling and named entity recognition as well as downstream tasks like sentiment analysis, information extraction, summarization, and question answering (Gildea and Jurafsky, 2000). Multi-VALUE has 31 rules that operate on NP constituents. Tense and Aspect are two grammatical properties that have to do with time. Together, these categories are known to significantly challenge machine translation (Matusov, 2019; Koehn and Knowles, 2017). With 26 rules, Multi-VALUE introduces different kinds of inflections and auxiliary verbs to indicate when an action, event, or state occurred and how it extends over time. Mood is important for applications in sentiment analysis and opinion mining, including the detection of biased language (Recasens et al., 2013) and framing strategies in political discourse (King and Morante, 2020; Demszky et al., 2019; Ziems and Yang, 2021). Misunderstandings of modality can also challenge NLU systems on tasks like natural language inference (Gong et al., 2018). There are three modal perturbations in Multi-VALUE. Verb Morphology is expected to affect model understanding of verb frame semantics (Baker et al., 1998), which could impact performance on semantic role labeling, summarization, and machine translation, among other tasks. We implement 16 related perturbations that change verb suffixes, the forms of verb inflection, and the expression of semantic roles using specialized verbal phrases. Negation is covered by 16 eWAVE features, 14 of which are implemented in Multi-VALUE. Problems with negation account for many of the failure cases in natural language inference (Hossain et al., 2020) and sentiment analysis (Barnes et al., 2021). Our perturbations introduce negative concord, invariant question tags, and new words for negation. Agreement is a group of 11 rules which have to do with subject-verb agreement and the omission of copula and auxiliary be in different environments. Examples include the invariant present tense in He speak English (feature \#170), and the existential dummy word in *It's some food in the fridge* (feature \#173). Nine of these 11 agreement features are attested in African American English (see Green 2002), which may be linked to the demonstrable performance disparities in AAVE dependency parsing (Blodgett et al., 2018), POS tagging (Jurgens et al., 2017), and NLU tasks (Ziems et al., 2022). Relativization is a class of perturbations that operates on relativizers, which link relative clauses with their nouns. The purpose of a relative clause is to modify a noun phrase. It's an important construction for NLU because it can contain a presupposition (Joshi and Weischedel, 1977). Our perturbation rules cover all 14 eWAVE features, operating both on individual relativizer words as well as sentence structure to move the relative clause and build correlative constructions, for example. Complementation is a set of perturbations that turn dependent clauses into the subject or object of the sentence. Like relative clauses, complementation can contain presuppositions and implicatures (Potts, 2002), which are critical for natural language understanding. They can also convey a speaker's degree of certainty (Couso and Naya, 2015), which correlates with biased language and framing strategies. We implement all 11 complementation features that are catalogued in eWAVE. Adverbial Subordination is a set of perturbations that operate on independent clauses with a "conjunctive adverb." Adverbial conjunctions can express causality (*therefore*), purpose (*so that*), sequence (*then*), contrast (*however*), comparison (*similarly*), and various forms of emphasis (*indeed*). We implement all 5 eWAVE features in this class. Adverbs and Prepositions are represented by four rules, which can drop prepositions and replace adverbs with their adjectival forms. Discourse and Word Order has two sides: two discourse features and 9 phrase-based perturbations that move entire constituents in a manner similar to *constituency replacement* (Sutiono and HahnPowell, 2022). These rules significantly alter the sentence structure, and in this way radically differ from prior token-level data augmentation techniques like synonym replacement (Wei and Zou, 2019). Phrasal movements include fronting and clefting, subject-auxiliary inversion, and a lack of inversion in questions. We also inject the word *like* to indicate focus or quotation. ## 4 Scope And Reliability Of Multi-Value 4.1 Scope Multi-VALUE's scope is extensive. Out of the 235 features documented in eWAVE, Multi-VALUE covers 189, spanning all 50 recorded English dialects. On average, the feature space for any given ![4_image_0.png](4_image_0.png) dialect is 86.6% implemented, and no dialect is less than 80% implemented (see Appendix A). ## 4.2 **Recruiting Native Speakers For Validation** One key benefit of the Multi-VALUE approach is our ongoing partnership with native speakers to confirm that our theoretically-inspired rules generate plausible and grammatical text. Here, we validate our transformation rules using the linguistic acceptability judgments of native speakers for 10 English dialects.3 We recruit speakers from Amazon Mechanical Turk and screen them using a Dialect Assessment Survey.4 This qualification survey ensures that each speaker's empirical language patterns align with the literature on the dialect that they had self-reported. At each turn, the speaker considers a sentence in the target dialect and provides a binary grammaticality judgment about that sentence. Sentences come from published linguistics journals. The survey is efficient5 as it implements binary search, dynamically selecting the feature that most evenly partitions the space of candidate dialects. ## 4.3 Validating The Multi-Value Pipeline ![4_image_1.png](4_image_1.png) is shown a pair of sentences: one in SAE, and the other as a dialect transformation: a copy of the first with perturbations corresponding to the target dialect. Annotators see only perturbations corresponding to their native dialect. Annotators mark portions of sentence 1 that were perturbed incorrectly in sentence 2. The interface is shown in in Figure 4 in the Appendix. A group of 72 annotators evaluate a total of 19k sentence pairs, which were drawn from CoQA and other sources. We use CoQA sentences for our Gold Test Sets (§4.4), and for added syntactic diversity, we pull sentences from three nltk corpora: Reuters (Rose et al., 2002), Sentiment Analysis (Pang and Lee, 2004) and Movie Reviews (Pang and Lee, 2005). Three annotators evaluate each transformation, marking any pre-highlighted spans where the transformation appeared ungrammatical. This gives us both transformation and perturbationlevel evaluations. The majority vote determines the accuracy of the perturbation rule.6 Perturbation accuracies are given in Table 1. Since there are 55 rules with perfect accuracy, and all perturbation rules achieve above 81%, researchers can feel confident in the linguistic plausibility of the Multi-VALUE transformation pipeline. ## 4.4 Gold Test Sets While synthetic Multi-VALUE transformations will be useful for identifying weak points in a model's performance, this does not ensure the model is ready for the real world. We urge practitioners to heavily test user-facing models with numerous in-domain tests. As a first step, we provide reliable gold standard CoQA datasets in Chicano English (ChcE) and Indian English (IndE). Out of 7,983 CoQA questions, our pipeline made changes to 1,726 ChcE questions (21.6%) and 6,825 IndE questions (85.4%). Human annotators considered only transformed questions and provided their own alternative phrasing for transformations they found ungrammatical. Alternatively, they could simply exclude the erroneous perturbations from the question. ChcE had a total transformation accuracy of 82.7% while IndE had 66.1%. The lower IndE accuracy is due to the higher density of features in this dialect. After rephrasing or removing errors, we were left with 1,498 dialect-transformed ChcE questions and 5,289 IndE questions. Together with any unperturbed questions, these gold questions constitute the gold test sets for evaluation in §6.1. ## 5 Using Multi-Value With our feature rules written (§3) and handvalidated by native speakers (§4), we can use MultiVALUE to create synthetic data for training dialectrobust models and also for stress testing leading systems on dialect benchmarks. We specifically provide synthetic data for five English dialects: Appalachian (AppE), Chicano English (ChcE), Indian English (IndE), Colloquial Singapore English (CollSgE), and Urban African American English (UAAVE). Three of these dialects are based in the US, where annotators were most abundant for validation, and two are outside the US. 6Accuracy reliably measures strong consensus in the quality of our approach and, unlike kappa scores, it will not suffer from the *prevalence problem* (Eugenio and Glass, 2004). To understand models' ability to transfer knowledge between dialects, we also consider models trained on dialect A and evaluated on dialect B for each dialectal pair (*A, B*). We can further leverage the strengths of Multi-VALUE as a multidialectal augmentation tool by training on a synthetic pseudo-dialect that contains the union of all feature options **(Multi)**. We hypothesize that models trained on multi-(pseudo)-dialectal data will benefit from robustness. While the Multi-VALUE approach could apply over any task with free-form text, we focus on three domains in particular: conversational question answering, semantic parsing, and machine translation. All three are user-facing tasks where language variation may hinder users' access to information, resources, and/or the global economy (Blasi et al., 2022; Faisal et al., 2021). Conversational Question Answering (CoQA; Reddy et al.2019) is a reading comprehension benchmark with 127k question-answer pairs and 8k passages in seven different genres and domains. We use it because it is a challenging task where dialect-induced errors can compound. The primary challenge is that questions are conversational: they contain coreference and pragmatic relations to prior questions. To transform the publicly available training and development sets, we perturb only questions. This is a natural information-retrieval setting: the user submits queries in a low-resource dialect while the underlying corpus is in SAE. Semantic Parsing is the task of mapping natural language to formal language. This is a critical skill for dialogue systems, information retrieval, code generation, and other user-facing applications where dialect use is likely. We transform Spider (Yu et al., 2018), a widely-used text-to-SQL benchmark. Again, we transform only the natural language query, leaving both the database tables and the SQL query unchanged to simulate interaction with a dialect user. Unlike the question answering setting where knowledge is encoded in free-text SAE passages, the knowledge and query language in Spider are encoded in formal tables and structured language, both of which are dialect-free. Consequently, any performance discrepancies here will be due to a mismatch between the models' training and testing data rather than a mismatch between the query dialect and that of the knowledge base. Machine Translation is an interesting test case where challenges can arise from domain mismatch | Model | Test Dialect | | | | | |------------|----------------|---------------|----------------|----------------|----------------| | Base | Train Set | SAE | ChcE | IndE | | | SAE | 77.2 | 76.7 (-0.5%) | 72.3 (-6.7%)− | | | | BERT | | Multi | 76.2 (-1.2%) | 76.1 (-1.4%) | 75.0 (-2.9%)+− | | In-Dialect | 77.2 | 76.5 (-0.9%) | 75.1 (-2.7%)+− | | | | RoBERTa | In-Dialect | 81.8 | 81.6 (-0.2%) | 80.5 (-1.6%)+− | | | SAE | 81.8 | 81.6 (-0.2%) | 77.7 (-5.2%)− | | | | Multi | 80.6 (-1.5%)− | 80.5 (-1.6%)− | 79.7 (-2.7%)+− | | | (Koehn and Knowles, 2017) due to dialect. We especially anticipate challenges with verb morphology (§3), tense and aspect (§3), and pronouns (§3). We use a standard dataset, WMT19, and evaluate translation from each English Dialect to Chinese, German, Gujurati, and Russian. This simulates a user interacting with translation software using their native dialect. ## 6 Cross-Dialectal Stress Testing Here we benchmark current models on dialect variants of the three tasks in §5. For each dataset, we use fixed hyperparameters without early stopping and report all performances on dialect variants of the *evaluation* data, since public test sets are not available for the original datasets. We use the base versions of BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) on dialect variants of the CoQA task, following the Rationale Tagging Multi-Task setup of Ju et al. (2019). For SPIDER, we evaluate BART and T5, since both are near the state of the art in semantic parsing (Xie et al., 2022). For Translation, we evaluate the NLLB Translation Model at two distilled scales: 615M and 1.3B (Costa-jussà et al., 2022). We report hyperparameters and further motivation for model selection in Appendix B. ## 6.1 Linking Natural And Synthetic Data While natural data is the gold standard, it is difficult to scale to the number of dialects and tasks we can cover with synthetic data. Thus our broad evaluations are synthetic stress tests. Importantly, we first demonstrate the critical relationship between the gold and synthetic transformations using the gold evaluation sets from §4.4 and the synthetic training data from §5. Table 2 shows the gold standard CoQA results, which should be compared to the synthetic CoQA results in Table 3. The synthetic stress test results match the gold performance for Chicano English with only small deviations. The Indian English stress tests slightly overestimate the performance drop of an SAE model on Indian English (70.8% synthetic vs. 72.3% natural IndE with BERT; 76.1% vs. 77.7% with RoBERTa). This is expected, as the synthetic feature density may be higher than some annotators naturally use. Synthetic results are a lower bound on performance for a target dialect. For all treatments, the stress tests are directionally correct: treatments that improve performance on the stress test also improve results on the gold data. Combined with speaker validation of the patterns themselves in §4.3, this shows that Multi-VALUE can be used to reliably measure the effects of modeling choices on dialectal performance. ## 6.2 Synthetic Stress Tests We run 3 stress tests to understand worst-case performances on dialect-shifted data across a suite of models and tasks. Evaluation reveals large and statistically significant performance gaps across each task and across all dialects. This highlights, for the first time, the pervasiveness of English dialect disparity beyond any single dialect. CoQA + Data Augmentation results are shown in Table 3. As predicted in §6.1, Chicano English (ChcE) does not produce a significant drop in performance (-0.7% BERT; -0.3% RoBERTa) since few of its pervasive features are distinct from SAE (the Manhattan distance between feature vectors for ChcE and Colloquial American English is 0.14, or only half the distance as between CollAmE and CollSgE, IndE, and UAAVE.) On the other hand, Singapore English, which is distant from SAE and therefore has many obligatory features, leads to the largest drop (-25.4% BERT; -18.9% RoBERTa). Appalachian, Indian, and Urban African American English each induce significant but smaller RoBERTa performance drops of -3.4%, -7.5%, and -6.7% respectively. The data augmentation technique described in §5 successfully closes the dialectal performance gap. Across every dialect but Chicano English, we find that we can improve results by training on data that was transformed to the target dialect. Compared to standard RoBERTa, the RoBERTA model trained on **Multi**-dialectal data improves | Model | Test Dialect | | | | | | | | |--------------|----------------|----------------|---------------|-----------------|----------------|----------------|---------------|--------------| | Base | Train Set | SAE | AppE | ChcE | CollSgE | IndE | UAAVE | Average | | SAE | 77.2 | 74.4 (-3.8%)− | 76.6 (-0.7%) | 61.5 (-25.4%)− | 70.8 (-9%)− | 71.2 (-8.4%)− | 71.9 (-7.3%) | | | AppE | 76.3 (-1.1%) | 76.4 (-1%)+ | 76.1 (-1.4%) | 64.7 (-19.3%)−+ | 72.8 (-6%)−+ | 73.2 (-5.4%)−+ | 73.3 (-5.3%) | | | BERT Base | 75.3 (-2.5%) | | | | | | | | | ChcE | 76.8 (-0.5%) | 74.7 (-3.3%)− | 76.5 (-0.8%) | 63.6 (-21.3%)−+ | 71.6 (-7.8%)− | 71.4 (-8.1%)− | 72.4 (-6.5%) | | | CollSgE | 75.7 (-1.9%)− | 74.1 (-4.2%)− | 75.5 (-2.2%)− | 74.7 (-3.3%)−+ | 73.6 (-4.8%)−+ | 73.4 (-5.1%) | 74.5 (-3.6%) | | | IndE | 76.0 (-1.5%) | 75.4 (-2.4%)− | 75.7 (-2%)− | 63.2 (-22%)−+ | 75.1 (-2.7%)−+ | 74.1 (-4.1%)−+ | 73.3 (-5.3%) | | | UAAVE | 76.1 (-1.4%) | 75.6 (-2%)−+ | 76.0 (-1.5%)− | 64.6 (-19.5%)−+ | 74.5 (-3.6%)−+ | 75.3 (-2.5%)−+ | 73.7 (-4.7%) | | | Multi | 76.2 (-1.2%) | 75.6 (-2%)−+ | 76.1 (-1.3%) | 73.7 (-4.7%)−+ | 74.9 (-3.1%)−+ | 75.1 (-2.7%)−+ | | | | In-Dialect | 77.2 | 76.4 (-1%)+ | 76.5 (-0.8%) | 74.7 (-3.3%)−+ | 75.1 (-2.7%)−+ | 75.3 (-2.5%)−+ | 75.9 (-1.7%) | | | SAE | 81.8 | 79.1 (-3.4%)− | 81.5 (-0.3%) | 68.8 (-18.9%)− | 76.1 (-7.5%)− | 76.6 (-6.7%)− | 77.3 (-5.8%) | | | AppE | 82.0 (0.3%) | 81.8+ | 81.8 | 71.2 (-14.9%)−+ | 79.0 (-3.5%)−+ | 79.6 (-2.8%)−+ | 79.2 (-3.2%) | | | RoBERTa Base | ChcE | 81.7 (-0.1%) | 79.3 (-3.1%)− | 81.5 (-0.4%) | 68.8 (-18.9%)− | 76.5 (-7%)− | 77.3 (-5.9%)− | 77.5 (-5.5%) | | CollSgE | 81.5 (-0.4%) | 80.1 (-2.2%)− | 81.2 (-0.7%) | 80.2 (-2%)−+ | 79.4 (-3%)−+ | 78.7 (-3.9%)−+ | 80.2 (-2%) | | | IndE | 81.1 (-0.8%) | 80.5 (-1.5%)−+ | 80.9 (-1.1%) | 67.2 (-21.7%)− | 80.3 (-1.9%)−+ | 79.2 (-3.3%)−+ | 78.2 (-4.6%) | | | UAAVE | 81.6 (-0.2%) | 81.1 (-0.9%)+ | 81.5 (-0.3%) | 69.2 (-18.2%)− | 79.6 (-2.7%)−+ | 81.1 (-0.9%)+ | 79.0 (-3.5%) | | | Multi | 80.6 (-1.5%)− | 80.4 (-1.7%)−+ | 80.5 (-1.6%)− | 78.5 (-4.2%)−+ | 79.7 (-2.7%)−+ | 80.0 (-2.2%)−+ | 80.0 (-2.3%) | | | In-Dialect | 81.8 | 81.8+ | 81.5 (-0.4%) | 80.2 (-2%)−+ | 80.3 (-1.9%)−+ | 81.1 (-0.9%)+ | 81.1 (-0.9%) | | | Evaluation | Input Dialect | | | | | | | | |---------------|-----------------|---------------|---------------|----------------|----------------|----------------|---------------|--------------| | Model | Metric | SAE | AppE | ChcE | CollSgE | IndE | UAAVE | Avg. | | BART-base | Exact Match ACC | 49.3 | 45.2 (-8.3%)− | 48.5 (-1.6%)− | 41.9 (-15.0%)− | 40.5 (-17.8%)− | 45.0 (-8.7%)− | 45.1 (-8.5%) | | Execution ACC | 51.0 | 47.3 (-7.3%)− | 50.3 (-1.4%) | 44.1 (-13.5%)− | 42.3 (-17.1%)− | 46.1 (-9.6%)− | 46.9 (-8.0%) | | | BART-large | Exact Match ACC | 67.9 | 63.6 (-6.3%)− | 65.5 (-3.5%)− | 60.3 (-11.2%)− | 61.2 (-9.9%)− | 62.3 (-8.2%)− | 63.5 (-6.5%) | | Execution ACC | 70.5 | 65.2 (-7.5%)− | 68.2 (-3.3%)− | 63.0 (-10.6%)− | 62.8 (-10.9%)− | 64.5 (-8.5%)− | 65.4 (-7.2%) | | | T5-base | Exact Match ACC | 58.7 | 54.3 (-7.5%)− | 57.4 (-2.2%)− | 50.0 (-14.8%)− | 49.1 (-16.4%)− | 53.1 (-9.5%)− | 53.8 (-8.3%) | | Execution ACC | 59.8 | 56.0 (-6.4%)− | 58.5 (-2.2%)− | 51.6 (-13.7%)− | 51.3 (-14.2%)− | 54.6 (-8.7%)− | 55.3 (-7.5%) | | | T5-3b | Exact Match ACC | 71.7 | 65.3 (-8.9%)− | 69.7 (-2.8%)− | 60.7 (-15.3%)− | 62.9 (-12.3%)− | 68.5 (-4.5%)− | 66.5 (-7.3%) | | Execution ACC | 75.6 | 69.3 (-8.3%)− | 73.4 (-2.9%)− | 64.9 (-14.2%)− | 66.5 (-12.0%)− | 66.9 (-11.5%)− | 69.4 (-8.2%) | | average cross-dialectal performance by 2.7 points. However, multi-dialectal training causes a drop of 1.2 points on SAE, reminiscent of interference in multilingual models (Wang et al., 2019, 2020). We performed a **Qualitative Error Analysis** on 30 errors for each transformed dialect. In each error, models trained on SAE flipped from a correct answer in SAE to an incorrect answer in one of the dialect-transformed COQA sets. Fully validated perturbations in tense, inflection, plural marking, phrasal order, and the deletion of pragmaticallyrecoverable pronouns, prepositions, and auxiliaries all lead to significant errors. As expected, these errors can cascade down the conversation, leading to model failure on later *unperturbed* questions as well. In some cases, erroneous answers still belong to the correct class, like flipping from yes to no in the presence of *negative concord*. Suprisingly, transformations also frequently cause the model to respond with an erroneous *class*, like giving a noun phrase or prepositional phrase to a yes/no question under perturbations like *clefting* and the omission of auxiliary did, is, and wh-words. Our analysis also suggests that the noticeably larger drop in performance on Singapore English might be largely due to the higher density of two perturbation types: preposition omissions (feature \#198), and the *one relativizer* (feature \#216). Future work can use perturbation analyses (Ziems et al., 2022) to quantitatively measure these sources of error. Semantic Parsing Table 4 shows that SAE models significantly underperform on all dialectal stress tests, both in terms of Exact Match Accuracy and Execution Accuracy. For both BART and T5, the largest performance gaps appear when we test on the two non-American dialects, CollSgE and IndE (-15.3% and -12.3% exact match accuracy for T53b). The semantic parsing performance gaps here are as large as those in conversational question answering. This supports our claim that the discrepancies are caused by model mismatch, rather | Evaluation | Source Dialect | | | | | | | | |--------------|------------------|-----------------|----------------|-----------------|-----------------|----------------|---------------|------| | # Param. | Target | SAE | AppE | ChcE | CollSgE | IndE | UAAVE | Avg. | | Chinese | 22.5 | 21.2 (-6.1%)− | 21.7 (-3.6%)− | 17.0 (-24.5%)− | 18.7 (-16.8%)− | 19.8 (-12.3%)− | 20.1 (-10.6%) | | | German | 39.6 | 34.3 (-13.41%)− | 37.8 (-4.65%)− | 22.3 (-43.60%)− | 26.8 (-32.32%)− | 30.5 (-23.1%)− | 31.9 (-19.5%) | | | Gujurati | 21.7 | 18.6 (-14.5%)− | 20.4 (-6.2%)− | 13.4 (-38.4%)− | 16.6 (-23.4%)− | 17.2 (-20.7%)− | 18.0 (-17.2%) | | | Russian | 27.8 | 24.6 (-11.4%)− | 26.7 (-4.0%)− | 17.2 (-38.1%)− | 20.8 (-25.4%)− | 21.7 (-22.1%)− | 23.1 (-16.8%) | | | Chinese | 23.2 | 21.5 (-7.4%)− | 22.5 (-3.3%) | 17.8 (-23.5%)− | 19.4 (-16.6%)− | 19.8 (-15.0%)− | 20.7 (-11.0%) | | | German | 42.6 | 37.5 (-11.9%)− | 40.6 (-4.6%)− | 25.3 (-40.6%)− | 29.4 (-31.0%)− | 34.2 (-19.7%)− | 34.9 (-18.0%) | | | Gujurati | 24.0 | 20.7 (-13.8%)− | 22.9 (-4.5%)− | 15.5 (-35.4%)− | 18.5 (-22.8%)− | 19.7 (-17.8%)− | 20.2 (-15.7%) | | | Russian | 31.7 | 28.5 (-10.1%)− | 30.3 (-4.4%) | 20.3 (-36.0%)− | 24.5 (-22.6%)− | 25.3 (-20.2%)− | 26.7 (-15.5%) | | than solely a mismatch between the dialect of the question and that of the knowledge base. Machine Translation stress test results are shown in Table 5. Except for ChcE, performance drops significantly across all dialects for each language. Interestingly, the size of the average dialectal performance gap is higher when the target language is structurally *more similar* to English: the largest average drop is from English7→German (-19.5% on 615M; -18.0% on 1.3B) and the smallest average drop is from English7→Chinese (-10.6% on 615M; -11.0% on 1.3B). This result cannot be explained simply as a reflection of the model's SAE translation performance. If it were, we might expect a smaller performance gap for Gujurati, a low-resource IndoEuropean language, since it has low SAE translation performance (21.7 SacreBLEU on 615M), but in fact, English7→Gujurati has the second *largest* dialectal translation performance gap (-17.2% on 615M; -15.7% on 1.3B). Our explanation is that Gujurati has syntax that is more similar to English. Despite both the 1.3B and 615M NLLB models being distilled from the same larger model, we see that the dialectal gap is smaller for German, Gujurati, and Russian. This suggests that model compression may affect low-resource dialects more heavily than SAE, similar to multi-lingual findings for low-resource languages (Ahia et al., 2021). ## 7 Conclusion In this work, we introduced Multi-VALUE - a dialect robustness evaluation framework that is interpretable, flexible, scalable, responsible, and generalizable. The rule-based methods form a transparent syntactic translation system that can flexibly adjust to the shifting feature space of living dialects. Additionally, the transformation rules are reliably sourced from over a decade of linguistics literature and vetted by native speakers. After showing that these transformations predict human-translated dialect benchmark performance, we used them to build dialect benchmarks and training data at scale, without the need for additional annotation efforts. By training and evaluating in a cross-dialectal manner, we demonstrated how Multi-VALUE can be used for more generalizable findings about model performance and dialect transferability. Multi-VALUE can facilitate a wide range of NLP tasks and applications, such as measuring the relationships between dialect similarity and generalization performance, the scaling laws of dialect disparity, as well as inspiring algorithms on better dialect transfer. Overall, we anticipate that MultiVALUE will continue to support the development of more fair and equitable language technologies. ## 8 Limitations Lexical variation is not our focus because it is not well-described by systematic, scalable, and generalizable rules. One can derive lexical distributions from data, but many low-resource dialects lack corpora on which to base these insights. This is an important problem for future research. Multi-VALUE's strength is its extensive coverage of English morphosyntacic patterns that have been documented in eWAVE by over 80 linguists. Such comprehensive resources are not available for other languages, but we encourage continued collaborations between computer scientists and linguists to build these resources for dialect-robust NLP systems across languages. As it stands, the current iteration of Multi-VALUE provides global value by serving a global contact language, English, and its 50 most documented varieties. Despite the scope and precision of eWAVE for English, its catalog ultimately derives from linguists' oral interviews with native speakers, and here we can identify some additional limitations. First, the orthographic conventions that linguists use to encode spoken dialect may not always align with the speakers' own writing conventions and usage. Second, our approach can only cover the variation that linguists observe frequently enough to document, and in canonical forms in which they are documented. This means we may not fully capture variation within each feature. Finally, dialects should not be treated like deterministic speech patterns, but rather like a range of grammatical options or switches that may be turned on and off and adjusted for frequency in various social and personal contexts. Dialects do not always fit into nicely prescribed categories. ## 9 Ethical Considerations This work makes use of human subjects for annotation. All procedures were subject to ethical review and were approved by the authors' institution. Consent was gathered in accordance with the authors' institution guidelines and annotators had access to a data use statement when giving consent. The purpose of Multi-VALUE is to provide tools which enable researchers and practitioners to understand and mitigate dialectal bias in their models. We will release these tools responsibly, ensuring that users sign a Data Use Agreement that forbids the use of Multi-VALUE for deception, impersonation, mockery, discrimination, hate speech, targeted harassment and cultural appropriation. In the agreement, researchers and practitioners will also acknowledge the Limitations of this work (§8), that Multi-VALUE may not fully or accurately represent the natural usage patterns of all sub-communities of speakers. Multi-VALUE is designed to be easily updatable and configurable such that it can be extended by and for specific sub-communities and updated as dialects evolve over time. ## Acknowledgements We are thankful to the members of SALT Lab for their helpful feedback on the draft. Caleb Ziems is supported by the NSF Graduate Research Fellowship under Grant No. DGE-2039655. 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Association for Computational Linguistics. ## A Implementation Details In Table 6, we give summary statistics for the number of features implemented for each of the 50 focus dialects, and the number of such features which were validated by native speakers. On average, the feature space for any given dialect is 86.6% implemented, and no dialect is less than 80% implemented. The reason we did not cover 100% of the eWAVE catalogue is that some features operate with information unavailable to us. For example, in SAE, aspect and mood may not be marked morphosyntactically; these features are outside the scope of current methods. Similarly, we are unable to inject distinct pronouns for groups of 2, 3, and 4+ people [\#37], as group size information may not be contained in the focus utterance. In Tables 7-18, we detail our Multi-VALUE implementations with an enumeration of our implemented dialects and features and examples of each. In the VAL ACC. column we give the validation accuracy (§4.3) as well as tags ChcE or **IndE** to indicate if the feature appears in the gold Chicano or Indian English CoQA dataset respectively. ## A.1 Pronouns There are 47 pronoun features in eWAVE, and we cover 39 of them (83%). While simple regular expressions can cover some pronoun mappings, this is not always possible since English maps the same surface forms to different grammatical roles.7 We overcome this problem by conditioning rules on pronouns' syntactic roles. We also condition on coreference for referential pronouns [29], and on verb frames to identify benefactive datives [9]. Furthermore, we swap the morphology of possession [20], change reflexive marking [11-16], swap animate pronouns for inanimate objects [1-2], and include additional elements like reduplication [40]. In summary, our pronoun perturbation rules account for linguistic structure and are not merely surface manipulations. ## A.2 Noun Phrases Among our 31 noun phrase perturbations, we regularize or modify plural morphology [49] and comparison strategies [80], to drop or modify articles [60], construct phrases for possession [75], and 7For example, her is both the accusative in "give it to her" and the noun modifier in "her cart," while the masculine pronouns in "give it to him" and "his cart" differ. This problem was observed but not solved in the rule-based perturbation augmentation of Qian et al. (2022). adjust the tree adjoining order to create adjective postfixes [87]. ## A.3 Tense And Aspect Tense and aspect perturbations include alternative inflections and auxiliaries to mark tense [117], including immediate vs. distant future [119], as well as perfect aspect [99]. ## A.4 Mood Multi-VALUE includes perturbations that inject double modals [121] and quasi-modals [126], change verb inflections under modal scope [123], and introduce auxiliaries to mark the sequential or irrealis mood [106]. ## A.5 Verb Morphology Verb morphology features include levelling certain finite and non-finite verb forms [130] adding suffixes for transitive verbs [143], and building *serial* verb phrases (Tallerman, 2019) to mark passive constructions [153], indirect objects [148], or the movement of direct objects [150]. ## A.6 Negation Multi-VALUE includes rules for building phrases with negative concord [154], and forms of negation with the negation words never, no, not, *no more* or ain't, as well as special invariant tags for questions [166]. ## A.7 Agreement We implement the invariant present tense [170], as well as the existential dummy it [173]. ## A.8 Relativization These perturbations modify the form of the relativizer [186-190], as well as drop [193] or introduce new shadow pronouns [194], such as double relativizers [191] and phrasal forms [192]. Our perturbations also operate on the sentence structure by forming correlative constructions [196], deleting stranded prepositions [198], and moving the relative clause before the head noun [199]. ## A.9 Complementation These perturbations can change the form of the complementizer [200, 201], delete [208, 209] or introduce additional complementizer words [203, 204], build existential constructions from complementizer phrases [205, 206], and modify the verb in the non-finite clause complement [210]. ## A.10 Adverbial Subordination Our perturbation rules introduce clause-final conjunctions [211, 212] and double conjuctions [214, 215], and remove the adverb in verb-chaining constructions [213], which together represent the five adverbial subordination features in eWAVE. ## A.11 Adverbial Prepositions In this section, we drop prepositions [216] and replace adverbs with their adjectival forms [220, 221]. We also include the word too as a qualifier [222]. ## A.12 Discourse And Word Order In discourse, we insert the word *like* as a focus [234] or quotation marker [235]. Our phrase-based perturbations include fronting and clefting [223, 224], subject–auxiliary inversion in both negation phrases [226] and indirect questions [227], and a lack of inversion in certain questions [228, 229]. These rules significantly alter the sentence structure, and in this way radically differ from prior token-level data augmentation techniques like synonym replacement (Wei and Zou, 2019). Our approach here is most similar to *constituency replacement* (Sutiono and Hahn-Powell, 2022). ## B Models & Hyperparameters CoQA We use the base versions of BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) on dialect variants of the CoQA task, following the Rationale Tagging Multi-Task setup of Ju et al. (2019) to adapt these models to the CoQA setup which includes *Yes, No,* and *Unknown* responses in addition to extractive answers. Each model was trained on an Nvidia GeForce RTX 2080 Ti for approximately 6 hours. For each model and dialect, we fine-tune using AdamW (Loshchilov and Hutter, 2019) for 2 epochs with a batch size of 16 and a learning rate 3e − 5. Semantic Parsing. Following Xie et al. (2022), for T5-base we adopted the AdamW optimizer, while Adafactor was used for T5-3B and the two BART models. We used NVIDIA A100 to train these models with T5-3b, BART-large, T5-base, and BART-base using 8 GPUs for 52 hours, 4 GPUs for 32 hours, 4 GPUs for 4 hours, 4 GPU for 13 hours respectively. We set the learning rate at 5e-5 for T5 models and 1e-5 for BARTs. We fixed the batch size at 32 when fine-tuning T5-BASE and BARTs. As for the extremely large T5-3B, we configured a batch size of 64 to speed up convergence and utilised DeepSpeed to save memory. Linear learning rate decay was used for all models. Machine Translation. We evaluate the NLLB Translation Model at two distilled scales: 615M and 1.3B (Costa-jussà et al., 2022). Evaluation was done on an Nvidia GeForce RTX 2080 Ti and takes less than 10 minutes. The NLLB model is designed for many-to-many translation with low-resource language communities and is trained on a large corpus mined from the internet, rather than exclusively human aligned translations. We choose this model to give us an estimate of the performance of large scale translation products available to users. Instructions ## Dialectal English Understanding If you haven't already, please open and read the Instructions tab. Your goal is to decide whether bits of text sound unnatural or ungrammatical. Sentence (1): What was it called? ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) ![16_image_2.png](16_image_2.png) ![16_image_3.png](16_image_3.png) Sentence (2): What-all have been it called? ![16_image_4.png](16_image_4.png) ![16_image_5.png](16_image_5.png) Grammaticality: We have highlighted certain portions of Sentence (1) that are different in Sentence (2). Do the words and the order of the words in Sentence (2) look like something you could say? (In other words: is this grammatical in your dialect?) Yes, grammatical o o No, not grammatical If anything is ungrammatical or unnatural, please let us know which of the highlighted segments were changed in a way that doesn't make sense. If you hover over them, each segment will have a number ID. Simply list the IDs of any unnetural segment translations here, separating each with a comma (e.g. "2, 3, 5"). If ething else is unnatural but it isn't highlighted, add "OTHER" to the list. If nothing is unnatural, leave this blank. Rephrasing: If possible, please provide a revised or afternative rephrasing of Sentence (1) that would be acceptible in your dialect. If no change is possible, leave this blank and check the box below. If your rephrasing is good, we will send you a bonus ($0.01). ❏ No Change: Check this box if no change to the sentence was possible. Comments: If you have any other comments, please put them here. Submit Figure 4: MTurk Validation Task Interface. Workers consider sentence pairs and evaluate whether the synthetic sentence is an acceptable dialectal form of the gloss given by the natural SAE sentence. | ABBR | # FEAT. | % FEAT. | # VAL. | % VAL. | DIALECT | |-----------|-----------|-----------|----------|----------|----------------------------------------------| | AborE | 89 | 83.2% | 57 | 53.3% | Aboriginal English | | AppE | 65 | 85.5% | 51 | 67.1% | Appalachian English | | AusE | 54 | 90.0% | 40 | 66.7% | Australian English | | AusVE | 47 | 83.9% | 34 | 60.7% | Australian Vernacular English | | BahE | 107 | 83.6% | 70 | 54.7% | Bahamian English | | BlSAfE | 95 | 88.0% | 71 | 65.7% | Black South African English | | CamE | 76 | 87.4% | 62 | 71.3% | Cameroon English | | CFE | 49 | 90.7% | 39 | 72.2% | Cape Flats English | | ChIsE | 47 | 94.0% | 33 | 66.0% | Channel Islands English | | ChcE | 30 | 93.8% | 28 | 87.5% | Chicano English | | CollAmE | 57 | 83.8% | 44 | 64.7% | Colloquial American English | | CollSgE | 67 | 89.3% | 52 | 69.3% | Colloquial Singapore English (Singlish) | | EAAVE | 96 | 89.7% | 61 | 57.0% | Earlier African American Vernacular English | | EA | 46 | 85.2% | 32 | 59.3% | East Anglian English | | FlkE | 44 | 89.8% | 30 | 61.2% | Falkland Islands English | | FijiE | 39 | 88.6% | 36 | 81.8% | Acrolectal Fiji English | | CollFijiE | 95 | 85.6% | 68 | 61.3% | Pure Fiji English (basilectal FijiE) | | GhE | 58 | 92.1% | 49 | 77.8% | Ghanaian English | | HKE | 74 | 91.4% | 61 | 75.3% | Hong Kong English | | IndE | 90 | 90.0% | 82 | 82.0% | Indian English | | InSAfE | 75 | 83.3% | 58 | 64.4% | Indian South African English | | IrE | 75 | 81.5% | 54 | 58.7% | Irish English | | JamE | 69 | 88.5% | 47 | 60.3% | Jamaican English | | KenE | 50 | 90.9% | 45 | 81.8% | Kenyan English | | LibSE | 86 | 84.3% | 58 | 56.9% | Liberian Settler English | | MalE | 68 | 89.5% | 57 | 75.0% | Malaysian English | | MaltE | 72 | 86.7% | 59 | 71.1% | Maltese English | | ManxE | 55 | 83.3% | 40 | 60.6% | Manx English | | NZE | 44 | 88.0% | 37 | 74.0% | New Zealand English | | NfldE | 84 | 85.7% | 53 | 54.1% | Newfoundland English | | NigE | 45 | 88.2% | 37 | 72.5% | Nigerian English | | North | 77 | 85.6% | 47 | 52.2% | English dialects in the North of England | | O&SE | 30 | 81.1% | 19 | 51.4% | Orkney and Shetland English | | OzE | 56 | 86.2% | 43 | 66.2% | Ozark English | | PakE | 48 | 87.3% | 42 | 76.4% | Pakistani English | | PhilE | 92 | 85.2% | 71 | 65.7% | Philippine English | | RAAVE | 136 | 82.9% | 88 | 53.7% | Rural African American Vernacular English | | ScE | 44 | 80.0% | 30 | 54.5% | Scottish English | | SEAmE | 108 | 80.6% | 75 | 56.0% | Southeast American enclave dialects | | SLkE | 29 | 82.9% | 23 | 65.7% | Sri Lankan English | | StHE | 113 | 85.0% | 78 | 58.6% | St. Helena English | | SE | 46 | 93.9% | 33 | 67.3% | English dialects in the Southeast of England | | SW | 73 | 89.0% | 46 | 56.1% | English dialects in the Southwest of England | | TznE | 41 | 93.2% | 35 | 79.5% | Tanzanian English | | TdCE | 92 | 82.9% | 64 | 57.7% | Tristan da Cunha English | | UAAVE | 118 | 83.7% | 79 | 56.0% | Urban African American Vernacular English | | UgE | 65 | 86.7% | 52 | 69.3% | Ugandan English | | WelE | 76 | 80.9% | 53 | 56.4% | Welsh English | | WhSAfE | 41 | 83.7% | 35 | 71.4% | White South African English | | WhZimE | 61 | 88.4% | 46 | 66.7% | White Zimbabwean English | Table 6: **Multi-VALUE Implemented Dialects.** We've implemented 50 English dialects as shown in this table. We list the number of implemented features (\# FEAT), the proportion of that dialect's catalogued eWAVE features implemented (% FEAT), the number of validated features (\# VAL), and the proportion of that dialect's catalogued eWAVE features validated (% VAL). All dialects are at or above 80% implemented and above 51.4% validated. Gold **ChcE** and **IndE** indicate that we also release a Gold CoQA dev set in Chicano and Indian English. 761 | FUNCTION | SAE | TRANSFORM | VAL ACC. | |--------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------|-----------------| | 1 she_inanimate_objects | It's a good bike | She's a good bike | | | 2 he_inanimate_objects | The driver's license? She wasn't allowed to renew it right? | The driver's license? She wasn't allowed to renew 'im right? | | | 3 referential_thing | Christmas dinner? I think it's better to wait until after she's had it. Christmas dinner? I think it's better to wait until after she's had the thing. | 100.0 | | | 4 pleonastic_that | It's raining. | Thass raining. | | | 5 em_subj_pronoun | This old woman, she started packing up. | This old woman, 'em started packing up. | | | 6 em_obj_pronoun | We just turned it around. | We just turned 'im around. | | | 7 me_coordinate_subjects | Michelle and I will come too. | Me and Michelle will come too. | | | 8 myself_coordinate_subjects | My husband and I were late. | My husband and myself were late. | | | 9 benefactive_dative | I have to get one of those! | I have to get me one of those! | ChcE 100.0 | | 10 no_gender_distinction | Susan is a nurse but she does not like to put drips on patients. | Susan is a nurse but he does not like to put drips on patients. | IndE 97.4 | | 11 regularized_reflexives | He hurt himself. | He hurt hisself. | 100.0 | | 12 regularized_reflexives_object_pronouns I'll do it myself. | I'll do it meself. | | | | 13 regularized_reflexives_aave | They look after themselves. | They look after theyselves. | | | 14 reflex_number | We cannot change ourselves. | We cannot change ourself. | IndE 100.0 | | 15 absolute_reflex | and he and the bull were tuggin' and wrestlin' | and himself and the bull were tuggin' and wrestlin' | IndE 100.0 | | 16 emphatic_reflex | They brought it by themselves. | They brought it by their own self. | ChcE 100.0 | | 18 my_i | my book | I book | | | 19 our_we | our farm | we farm | | | 20 his_he | his book | he book | | | 21 their_they | their book | they book | | | 22 your_you | your book | you book | | | 23 your_yalls | Where are your books? | Where are y'all's books? | | | 24 his_him | his book | him book | | | 25 their_them | their book | them book | | | 26 my_me | my book | me book | 100.0 | | 27 our_us | our book | us book | | | 29 me_us | Show me the town! | Show us the town! | 100.0 | | 30 non_coordinated_subj_obj | Do you want to come with us? | Do you want to come with we? | | | 31 non_coordinated_obj_subj | They can ride all day. | Them can ride all day. | | | 33 nasal_possessive_pron | her, his, our; hers, ours, ours | hern, hisn, ourn; hersn, oursn, ourns | 100.0 | | 34 yall | you | y'all | ChcE IndE 100.0 | | 35 you_ye | Sure it's no good to you in England. | Sure it's no good to ye in England. | | | 39 plural_interrogative | Who came? | Who-all came? | 99.7 | | 40 reduplicate_interrogative | Who's coming today? | Who-who's coming today? | IndE 99.8 | | 41 anaphoric_it | Things have become more expensive than they used to be. | Things have become more expensive than it used to be. | IndE 100.0 | | 42 object_pronoun_drop | I got it from the store. | I got from the store. | IndE 98.1 | | 43 null_referential_pronouns | When I come back from my work I just travel back to my home. | When I come back from my work just travel back to my home. | ChcE IndE 93.2 | | 45 it_dobj | As I explained to her, this is not the right way. | As I explained it to her, this is not the right way. | IndE 100.0 | | 46 it_is_referential | It is very nice food. | Is very nice food. | | | 47 it_is_non_referential | Okay, it's time for lunch. | Okay, is time for lunch. | IndE 100.0 | Table 7: Pronouns (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |-----------------------------------------------------------------------------|------------------------------------------------------------------|----------------------------------------------------------------------------------|----------------| | 49 regularized_plurals | wives, knives, lives, leaves | wifes, knifes, lifes, leafs | IndE 99.6 | | 50 plural_preposed | shooting birds | shooting alla bird | | | 51 plural_postposed | The boys | Da boy dem | | | 55 mass_noun_plurals | furniture, machinery, equipment, evidence, luggage, advice, mail, staff | furnitures, machineries, equipments, evidences, luggages, advices, mails, staffs | IndE 100.0 | | 56 zero_plural_after_quantifier | It's only five miles away. | It's only five mile away. | ChcE IndE 97.3 | | 57 plural_to_singular_human | The three girls there don't want to talk to us. | The three girl there don't want to talk to us. | IndE 100.0 | | 58 zero_plural | Some apartments are bigger. | Some apartment are bigger. | IndE 100.0 | | 59 double_determiners | This common problem of ours is very serious. | This our common problem is very serious. | IndE 100.0 | | 60 definite_for_indefinite_articles | She's got a toothache. | She's got the toothache | IndE 99.6 | | 61 indefinite_for_definite_articles | The moon was very bright last night. | A moon was very bright last night. | IndE 100.0 | | 62 remove_det_definite | He's in the office. | He's in office. | IndE 100.0 | | 63 remove_det_indefinite | Can I get a better grade? | Can I get better grade? | IndE 99.0 | | 64 definite_abstract | I stayed on until Christmas. | I stayed on until the Christmas. | IndE 100.0 | | 65 indefinite_for_zero | We received good news at last. | We received a good news at last. | | | 66 indef_one | What happened? Oh, a dog bit me. | What happened? Oh, one dog bit me. | IndE 100.0 | | 67 demonstrative_for_definite_articles | They have two children. The elder girl is 19 years old. | They have two children. That elder girl is 19 years old. | IndE 99.1 | | 68 those_them | I don't have any of those qualifications. | I don't have any of them qualifications. | | | 70 proximal_distal_demonstratives | this book that is right here vs. those books that are over there | this here book vs. them there books | ChcE 92.9 | | 71 demonstrative_no_number | These books are useful for my study. | This books are useful for my study. | IndE 98.8 | | 73 existential_possessives | I have a son. | Son is there. | | | 74 possessives_for_post | This is my mother's house. | This is the house for my mother. | | | 75 possessives_for_pre | Long time ago he was my sister's husband. | Long time he was for my sister husband. | | | 76 possessives_belong | the woman's friend | woman belong friend | | | 77 null_genitive | my cousin's bike | my cousin bike | IndE 100.0 | | 78 double_comparative, double_superlative That is so much easier to follow. | That is so much more easier to follow. | IndE 100.0 | | | 79 synthetic_superlative | He is the most regular guy I know. | He is the regularest guy I know. | IndE 100.0 | | 80 analytic_superlative | one of the prettiest sunsets | one of the most pretty sunsets | IndE 100.0 | | 81 more_much | The situation is more serious than I thought. | The situation is much serious than I thought. | IndE 100.0 | | 82 comparative_as_to | She is bigger than her sister. | She is bigger as her sister. | | | 84 comparative_than | They like football more than basketball. | They like football than basketball. | | | 85 comparative_more_and | He has more clothes than all of us. | He has more clothes and all of us. | | | 86 zero_degree | He is one of the most radical students that you can ever find. | He is one of the radical students that you can ever find. | IndE 100.0 | | 87 adj_postfix | A big and fresh fish is my favorite. | A fish big and fresh is my favorite. | | | Table 8: Noun Phrases (Section 3) | | | | | FUNCTION | SAE | TRANSFORM | VAL ACC. | |---------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|------------| | 88 progressives | I like her hair style right now. | I am liking her hair style. | IndE 99.4 | | 95 standing_stood | He was standing on the corner. | He was stood on the corner. | | | 96 that_resultative_past_participle There is a car that broke down on the road. | There is a car broken down on the road. | 95.5 | | | 97 medial_object_perfect | He has written a letter. | He has a letter written. | | | 98 after_perfect | She has just sold the boat. | She's after selling the boat. | | | 99 simple_past_for_present_perfect | I've eaten the food. So can I go now? | I ate the food. So can I go now? | ChcE 94.7 | | 100 present_perfect_for_past | We were there last year. | We've been there last year. | IndE 99.9 | | 101 present_for_exp_perfect | I've known her since she was a child. | I know her since she was a child. | IndE 100.0 | | 102 be_perfect | They haven't left school yet. | They're not left school yet. | | | 103 do_tense_marker | I knew some things weren't right. | I did know some things weren't right. | | | 104 completive_done | Sharon has read the whole book. | Sharon done read the whole book. | | | 105 completive_have_done | He has talked about me. | He has done talked about me. | | | 106 irrealis_be_done | If you love your enemies, they will eat you alive in this society. If you love your enemies, they be done eat you alive in this society. | 100.0 | | | 107 perfect_slam | I have already told you not to mess up | I slam told you not to mess up. | | | 108 present_perfect_ever | I have seen the movie. | I ever see the movie. | | | 109 perfect_already | Have you eaten lunch? | Did you eat already? | | | 110 completive_finish | I have eaten. | I finish eat. | | | 111 past_been | I told you. | I been told you. | | | 112 bare_perfect | We had caught the fish when the big wave hit. | We had catch the fish when the big wave hit. | | | 114 future_sub_gon | He will come with us. | He gon' come with us. | | | 115 volition_changes | You want to go. | You waan go. | | | 116 come_future | I am about to cook your meal. | I am coming to cook your meal. | | | 117 present_for_neutral_future | Next week, I will be leaving the States and going to Liberia. | Next week, I leaving the States, I going to Liberia. | IndE 100.0 | | 118 is_am_1s | I am going to town. | I's going to town. | | | 119 will_would | I will meet him tomorrow. | I would meet him tomorrow. | IndE 100.0 | | 120 if_would | If I were you I would go home now. | If I would be you I would go home now. | | | Table 9: Tense and Aspect (Section 3) | | | | | FUNCTION | SAE | TRANSFORM | VAL ACC. | |--------------------------------------------------------|------------------------------|---------------------------|------------| | 121 double_modals | We could do that. | We might could do that. | ChcE 91.8 | | 123 present_modals | I wish I could get the job. | I wish I can get the job. | IndE 100.0 | | 126 finna_future, fixin_future They're about to leave. | They're fixin to leave town. | ChcE 92.3 | | Table 10: Mood (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |-------------------------------------------|----------------------------------------|------------------------------------------|----------------| | 128 regularized_past_tense | He caught the ball. | He catched the ball. | ChcE IndE 92.7 | | 129 bare_past_tense | They came and joined us. | They come and joined us. | | | 130 past_for_past_participle He had gone. | He had went. | ChcE 92.9 | | | 131 participle_past_tense | I saw it. | I seen it. | IndE 100.0 | | 132 bare_past_tense | Here are things you ordered yesterday. | Here are things you order yesterday. | ChcE IndE 87.7 | | 133 double_past | They didn't make it this time. | They didn't made it this time. | IndE 99.5 | | 134 a_ing | Where are you going? | Where are you a-goin? | 100.0 | | 135 a_participle | You've killed your mother. | You've a-killed your mother. | | | 143 transitive_suffix | You can see the fish. | You can see 'im fish. | | | 145 got_gotten | I hope you've got your topic already. | I hope you've gotten your topic already. | 100.0 | | 146 verbal_ing_suffix | I can drive now. | I can driving now. | IndE 100.0 | | 147 conditional_were_was | If I were you | If I was you | | | 148 serial_verb_give | I bought rice for you. | I buy rice give you. | | | 149 serial_verb_go | Grandfather sends us to school. | Grandfather send us go school. | 100.0 | | 150 here_come | Bring the book here. | Take the book bring come. | | | 153 give_passive | John was scolded by his boss | John give his boss scold. | | Table 11: Verb Morphology (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |------------------------------------------------------------|-------------------------------------------------|----------------------------------------|----------------| | 154 negative_concord | I don't want any help. | I don't want no help. | ChcE IndE 92.9 | | 155 aint_be | That isn't fair. | That ain't fair. | ChcE 81.8 | | 156 aint_have | I hadn't seen them yet. | I ain't seen them yet. | | | 157 aint_before_main | something I didn't know about | something I ain't know about | | | 158 dont | He doesn't always tell the truth. | He don't always tell the truth. | | | 159 never_negator | He didn't come. | He never came. | 100.0 | | 160 no_preverbal_negator | I don't want any job or anything. | I no want any job or anything. | | | 161 not_preverbal_negator | The baby didn't eat food and cried a lot. | The baby not ate food and cried a lot. | | | 162 nomo_existential | There is not any food in the refrigerator. | No more food in the refrigerator. | | | 163 wasnt_werent | John was there, but Mike wasn't | John was there, but Mike weren't | | | 164 invariant_tag_amnt | I believe I am older than you. Is that correct? | I am older than you, amn't I? | | | 165 invariant_tag_non_concord | I believe you are ill. Is that correct? | You are ill, isn't it? | IndE 99.1 | | 166 invariant_tag_can_or_not | Can I go home? | I want to go home, can or not? | | | 167 invariant_tag_fronted_isnt I can go there now can't I? | Isn't, I can go there now? | | | Table 12: Negation (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |--------------------------------------------------------------|----------------------------------------|-----------------------------------------------------------------------------|----------------| | 170 uninflect | He speaks English. | He speak English. | ChcE IndE 94.9 | | 171 generalized_third_person_s Every Sunday we go to church. | Every Sunday we goes to church. | | | | 172 existential_there | There are two men waiting in the hall. | There's two men waiting in the hall. | ChcE IndE 90.0 | | 173 existential_it | There's some milk in the fridge. | It's some milk in the fridge. | ChcE 87.5 | | 174 drop_aux_be_progressive | You are always thinking about it. | You always thinking about it. | IndE 100.0 | | 175 drop_aux_be_gonna | He is gonna go home and watch TV. | He gonna go home and watch TV. | ChcE IndE 83.3 | | 176 drop_copula_be_NP | He is a good teacher. | He a good teacher. | | | 177 drop_copula_be_AP | She is smart. | She smart. | | | 178 drop_copula_be_locative | She is at home. | She at home. | | | 179 drop_aux_have | I have seen it before. | I seen it before. | IndE 100.0 | | 180 were_was | You were hungry but he was thirsty. | You was hungry but he was thirsty. OR: You were hungry but he were thirsty. | | Table 13: Agreement (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |---------------------------------------------------------------------|-------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|----------------| | 186 who_which | He's the man who looks after the cows. | He's the man which looks after the cows. | | | 187 who_as | The man who was just here. | The man as was just here. | | | 188 who_at | This is the man who painted my house. | This is the man at painted my house. | | | 189 relativizer_where | My father was one of the founders of the Underground Railroad, which helped the slaves to run My father was one o de founders o' de Underground Railroad where help de slaves to run away to the North way to de North. | | | | 190 who_what | This is the man who painted my house. | This is the man what painted my house. | | | 191 relativizer_doubling | But these, these little fellahs who had stayed | But these, these little fellahs that which had stayed befo' | IndE 100.0 | | before | | | | | 192 analytic_whose_relativizer This is the man whose wife has died. | This is the man that his wife has died. OR: This is the man what his wife has died. | | | | 193 null_relcl | The man who lives there is friendly. | The man lives there is friendly. | ChcE IndE 88.7 | | 194 shadow_pronouns | This is the house which I painted yesterday. | This is the house which I painted it yesterday. | IndE 100.0 | | 195 one_relativizer | The cake that John buys is always very nice to | The cake John buy one always very nice to eat. | | | eat. | | | | | 196 correlative_constructions | The ones I made are the good ones. | The one I made, that one is good. | | | 197 linking_relcl | Unless you are going to get 88, but some universities are not going to give those marks | Unless you are going to get 88 which some universities are not going to give those marks | | | 198 preposition_chopping | You remember the swing that we all used to sit together on? | You remember the swing that we all used to sit | IndE 100.0 | | together? | | | | | 199 reduced_relative | There is nothing like food cooked by Amma! | There is nothing like Amma cooked food! | | Table 14: Relativization (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |-------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|------------| | 200 say_complementizer | We hear that you were gone to the city. | We hear say you gone to the city. | | | 201 for_complementizer | You mean your mother allows you to bring over You mean your mother allows you for bring over boyfriends? boyfriends? | | | | 202 for_to_pupose | We always had gutters in the winter time to drain the water away. | We always had gutters in the winter time for to drain the water away. | | | 203 for_to | He had the privilege to turn on the lights. | He had the privilege for to turn on the lights. OR: He had the privilege for turn on the lights. | 100.0 | | 204 what_comparative | I'm taller than he is. | I'm taller than what he is. | IndE 100.0 | | 205 existential_got | There's no water in the toilet. | Got no water in the toilet. | 100.0 | | 206 existential_you_have | There are some people who don't give a damn about animals. | You have some people they don't give a damn about animals. | IndE 100.0 | | 207 that_infinitival_subclause He wanted me to go with him. | He wanted that I should go with him. | IndE 100.0 | | | 208 drop_inf_to | They were allowed to call her. | They were allowed call her. | 100.0 | | 209 to_infinitive | He made me do it. | He made me to do it. | IndE 100.0 | | 210 bare_ccomp | When mistress started whooping her, she sat her | When mistress started whoop her, she sat her | | | down. | down. | | | Table 15: Complementation (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |---------------------------------------------------------------------------------|---------------------------------------------------------------------|------------------------------------------------------------------------|------------| | 211 clause_final_though_but | There's nothing wrong with this box though. | There's nothing wrong with this box, but. | | | 212 clause_final_really_but | I don't know what else she can do, really. | I don't know what else she can do, but. | | | 213 chaining_main_verbs | If you stay longer, they have to charge more. | Stay longer, they have to over-charge. | | | 214 corr_conjunction_doubling | Despite being instructed on what to do, he still made some misakes. | Despite being instructed on what to do still yet he made some misakes. | IndE 100.0 | | 215 subord_conjunction_doubling Although you are smart, you are not appreciated | Although you are smart, but you are not appreciated | | | Table 16: Adverbial Subordination (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |----------------------------------------------------|-----------------------------------------------|----------------------------------------------|----------------| | 216 null_prepositions | I'm going to town. | I'm going town. | IndE 99.7 | | 220 degree_adj_for_adv That's really nice and cold | That's real nice and cold | ChcE IndE 99.4 | | | 221 flat_adj_for_adv | She speaks so softly. | She speaks so soft. | ChcE IndE 86.7 | | 222 too_sub | They are very nice. We had a good time there. | They are too nice. We had a good time there. | | Table 17: Adverbs and Prepositions (Section 3) | FUNCTION | SAE | TRANSFORM | VAL ACC. | |---------------------------------------------------------------------|------------------------------------------|-----------------------------------------------|----------------| | 223 clefting | A lot of them are looking for more land. | It's looking for more land a lot of them are. | 100.0 | | 224 fronting_pobj | I drive to town every Saturday. | To town every Saturday I drive. | IndE 99.5 | | 226 negative_inversion | Nobody showed up. | Didn't nobody show up. | 100.0 | | 227 inverted_indirect_question | I'm wondering what you are going to do. | I'm wondering what are you going to do. | ChcE IndE 91.2 | | 228 drop_aux_wh | When is she coming? | When she coming? | IndE 99.8 | | 229 drop_aux_yn | Do you get the point? | You get the point? | IndE 99.9 | | 230 doubly_filled_comp | Who ate what? | What who has eaten? | | | 231 superlative_before_matrix_head The thing I like most is apples. | The most thing I like is apples. | | | | 232 double_obj_order | She would teach it to us. | She'd teach us it. | IndE 100.0 | | 234 acomp_focusing_like | It was really cheap. | It was like really cheap. | ChcE IndE 91.2 | | 235 quotative_like | And my friend said "No way!" | And my friend was like "No way!" | | Table 18: Discourse and Word Order (Section 3) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8 ✓ A2. Did you discuss any potential risks of your work? Section 9 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 ✓ B1. Did you cite the creators of artifacts you used? Section 5 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 9 ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 9 ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The released datasets are derivatives of CoQA. Our Morphosyntactic patterns could not add additional information about individuals. The annotators were anonymized in accordance with the ethics review body of the authors' institution. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 5 ## C ✓ **Did You Run Computational Experiments?** Section 6. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix C The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 6 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We used Bootstrap tests for significance for each run. We state that this is the bootstrap of a single run in the caption of each table. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3 ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left Blank. ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Figures 4 and 5 ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 4 ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 9 ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section 9 ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 4

zhang-etal-2023-self

Self-Edit: Fault-Aware Code Editor for Code Generation

https://aclanthology.org/2023.acl-long.45

Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. We execute the generated code on the example test case provided in the question and wrap execution results into a supplementary comment. Utilizing this comment as guidance, our fault-aware code editor is employed to correct errors in the generated code. We perform extensive evaluations across two competitive programming datasets with nine different LLMs. Compared to directly generating from LLMs, our approach can improve the average of pass@1 by 89{\%} on APPS-dev, 31{\%} on APPS-test, and 48{\%} on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. Compared to other post-processing methods, our method demonstrates superior accuracy and efficiency.

# Self-Edit: Fault-Aware Code Editor For Code Generation Kechi Zhang, Zhuo Li, Jia Li ♂**, Ge Li**∗ , Zhi Jin∗ Key Lab of High Confidence Software Technology (PKU), Ministry of Education School of Computer Science, Peking University, China {zhangkechi,lizhmq}@pku.edu.cn, [email protected], {lige,zhijin}@pku.edu.cn ## Abstract Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generateand-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. We execute the generated code on the example test case provided in the question and wrap execution results into a supplementary comment. Utilizing this comment as guidance, our fault-aware code editor is employed to correct errors in the generated code. We perform extensive evaluations across two competitive programming datasets with nine different LLMs. Compared to directly generating from LLMs, our approach can improve the average of pass@1 by 89% on APPS-dev, 31% on APPS-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. Compared to other post-processing methods, our method demonstrates superior accuracy and efficiency. 1 Introduction Large language models (LLMs) have recently been applied to the competitive programming task. This task requires understanding a complex natural language description of a problem with example test cases and correctly implementing solutions that can span hundreds of lines. Solutions are evaluated by executing them on hidden test cases. However, existing LLMs often have low accuracy and pass rates in this task. For example, on a popular competitive programming benchmark *APPS-test* (Hendrycks et al., 2021), the nearly most powerful model GPT3 (Brown et al., 2020) achieves only 7% accuracy when allowed to submit only one program per task (referred to as *pass@1*). *Corresponding authors ![0_image_0.png](0_image_0.png) To improve the performance of LLMs on the competitive programming task, we take inspiration from the process of human programming. When solving competitive programming problems, programmers usually write an initial program, execute some example test cases, and refine the code based on the test results. In this process, a programmer can take key information (e.g, program outputs or compile/runtime error message) from the test results, which helps them debug the program. We instantiate this idea by adopting a similar pipeline with a neural-based editor (in Figure 1(a)). Analyzing the code generated by a pre-trained LLM, we have found that some of the generated codes can be improved with minor modifications. Figure 1(b) shows an example of generated code by GPT3 on the APPS-test dataset. GPT3 generates code that is inconsistent with the problem description. We notice that the error message directly points out the bug in the code, with which we can quickly fix the error. It motivates us to investigate approaches to edit and improve the quality of the code generated by LLMs with the help of execution results. In this work, we propose a novel generate-andedit approach to augment LLMs on the competitive programming task, named Self-Edit. To mimic the above human programmers' behavior, our approach incorporates the ability of LLMs in three steps: ❶ *Generation with LLMs*. We use large language models as black-box generators and generate the program based on the problem description. ❷ Execution. Given a generated code from LLMs, we execute it on the example test case to get the execution results. We further wrap the execution results with templates as supplementary comments to include additional helpful information for editing. ❸ Edit. We develop a fault-aware neural code editor that takes the generated code and supplementary comment as input and refines the code. Our code editor aims to improve the quality and accuracy of code generation using LLMs. We conduct extensive experiments on two public competitive programming benchmarks, including APPS (Hendrycks et al., 2021) and HumanEval (Chen et al., 2021). We apply our approach to 9 popular LLMs with parameter sizes ranging from 110M to 175B to show the universality. Compared to directly generating from LLMs, we have several findings: ❶ Our approach significantly improves the performance of LLMs. In particular, our approach improves the average of pass@1 by 89% on APPS-dev and 31% on APPS-test. Even for the chosen largest language model GPT3-175B, our relatively small editor model can improve pass@1 from 26.6% to 32.4% on the APPS-dev benchmark. ❷ Our approach is generalizable on a different style of dataset HumanEval, improving the average of pass@1 by 48%, showing the transfer ability on the out-of-distribution benchmark. Recently some approaches are also proposed to post-process programs generated by LLMs (Shi et al., 2022; Inala et al., 2022; Chen et al., 2022; Zhang et al., 2022). These approaches do largescale sampling from LLMs, rerank these sampled programs, and output the final program. In comparison, our self-edit framework has two advantages: ❶ Our approach maintains a constant sample budget and significantly reduces the computational overhead for LLMs. ❷ Our editor directly modifies the programs and outperforms these reranking-based methods, especially with a limited sample budget such as pass@1. **To our knowledge, we are the first** to adopt an editing-based post-processing method for competitive programming tasks. The contributions are listed as follows: - We propose a generate-and-edit approach named Self-Edit for large language models (LLMs) to generate high-quality code for competitive programming tasks. - We develop a fault-aware neural code editor that takes the generated code and error messages as input and uses them to refine the code, improving its quality and accuracy. - We conduct experiments on two popular datasets and nine LLMs to demonstrate the effectiveness and universality of our approach. ## 2 Related Work 2.1 Code Generation Code generation is a process in which source code is automatically generated based on functional requirements such as natural language descriptions (Iyer et al., 2018; Yin and Neubig, 2018; Li et al., 2023a,b,c) or pseudo code algorithms (Kulal et al., 2019; Oda et al., 2015) or a old version of code (Li et al., 2022a) or a response from programming tools (Zhang et al., 2023). One particularly challenging type of code generation task is competitive programming (Li et al., 2022c), in which models must solve problems at the level of programming competitions. This task often involves natural language descriptions and example input-output pairs. The performance of a code generation model on competitive programming tasks can serve as a measure of its ability to create complete solutions to problems. In recent years, large pre-trained language models such as AlphaCode (Li et al., 2022c) and the GPT3 (Brown et al., 2020) series have demonstrated impressive capabilities in code generation and competitive programming. Other open-source code generation models include GPT-Neo (Black et al., 2021), GPT-J (Wang and Komatsuzaki, 2021), CodeParrot (Wolf et al., 2020), PolyCoder (Xu et al., 2022), CodeGen (Nijkamp et al., 2022) and InCoder (Fried et al., 2022). We utilize the *text-davinci-002* API from OpenAI and various competitive code generation models in this work. ## 2.2 Post-Processing Of Llms For Code Generation To find the correct code solutions based on LLMs, researchers adopt various post-processing methods to filter/rerank the original outputs from LLMs. In the domain of solving math problems, Cobbe et al. (2021) and Shen et al. (2021) chose the one ![2_image_0.png](2_image_0.png) with the highest rank by a trained ranker. Similar ranking methods are also used in the field of cross-domain adaptation (Li et al., 2022b). In the domain of code generation, post-processing techniques are also often used (Lahiri et al., 2022; Le et al., 2022). AlphaCode (Li et al., 2022c) and Shi et al. (2022) adopted the clustering and filtering methods based on the execution output of the generated programs. Inala et al. (2022) trained a fault-aware neural ranker to rerank the outputs with a large sample budget. Chen et al. (2022) use the large models to generate test cases for themselves and automatically rank the solutions based on the test-driven dual execution agreement. Zhang et al. (2022) reranked the LLM outputs with the generation probability of back translation. However, these existing methods require largescale sampling. They need to generate a large number of programs for post-processing. For example, AlphaCode (Li et al., 2022c) needs 1 million samples per problem, costing 105 TPU-seconds. In the real world, computing resources are precious and limited, and existing methods are ineffective in practical applications. Our self-edit approach addresses this issue by maintaining a constant sample budget and improving computational efficiency, described in Section 4.3. ## 3 Methodology We provide an overview of the self-edit pipeline in Figure 2. Given the problem description, We first generate the initial code with LLM. Then we execute the example test case to obtain test results and construct the supplementary comment. Finally, we ![2_image_1.png](2_image_1.png) train a fault-aware code editor model to refine the code based on the problem description, generated code, and supplementary comment. 是否要增加example testcase和hidden test case的表示图 ## 3.1 Llms As Black-Box Generator We use large language models as black-box generators with fixed parameters in our design. This design choice is motivated by the fact that training LLMs is costly, and access to LLMs is often restricted. (E.g., OpenAI only offers paid API to infer GPT3.) Using LLM as a black-box generator makes our approach flexible for using different LLMs. We investigate nine LLMs for code generation with sizes ranging from 110M to 175B. A detailed comparison is described in Table 2. ## 3.2 Executor And Supplementary Comments After we generate the code using LLMs, we use an executor to run the example test case. We classify the execution results into three types: ❶ Passed: The program passes the test case. ❷ Wrong Answer: The program runs normally but gives incorrect outputs. ❸ Error: The program terminates abnormally due to syntax error, runtime exceptions, or exceeding time limit. We analyze the distribution of test results on APPS-train dataset for code generated by a relatively small model PyCodeGPT-110M and a large model GPT3-175B as shown in Figure 3. We observe that programs produced by different models yield different test result distributions. Code generated by smaller models (PyCodeGPT) tends to encounter SyntaxError issues more frequently, while large models (GPT3) show fewer SyntaxErrors, fewer RuntimeErrors, but more normally executed cases. In order to construct meaningful supplementary comments for the code editor model to understand ![3_image_0.png](3_image_0.png) various execution results, we design the comment templates (Fig. 4) for the three types of test results. The comment template can wrap potential error messages with additional helpful information for editing. ❶ For the code passing the example test case, we use *Comment 1*: "Pass the example test case.". ❷ For the code producing incorrect outputs, we use *Comment 2* to include the relevant input, expected output, and the actual output. We also append the instruction "Rewrite the code" to guide the editor model to reimplement the algorithm to produce correct outputs. ❸ For the code that terminates with errors, we use *Comment 3* to include the error line number, line context, and full error message. These supplementary comments provide additional context and clarity for the generated code and are used to guide editing the code. ## 3.3 Fault-Aware Code Editor Once we have constructed the supplementary comments, we train a fault-aware editor that takes the natural language description, generated code, and supplementary comments as input and produces higher-quality refined code. ## 3.3.1 Code Editor Models The fault-aware code edit task is formally defined as a sequence-to-sequence task: given a natural language description N, a program generated by LLM S, and accompanied supplementary comments C (Sec. 3.2), the model is required to generate higher-quality code Cˆ that implements the natural language description and passes test cases. In our experiments, the input pair (*N, S, C*) is segmented into three parts and concatenated using special separator tokens, represented as [SOS], n1, n2, . . . , n|N|, [CODE], s1*, . . . , s*|S|, , [CMNT], c1*, . . . , c*|C|, [EOS], where the lowercase letters represent the token of the corresponding content in the input pair (*N, S, C*). We train a decoder-only model to complete the code edit task. Concretely, we implement the code editor by fine-tuning *PyCodeGPT-110M* on this task. At inference time, we first generate multiple programs from LLMs using natural language description as input. For each generated program, we feed the example test case provided in the description into the executor to obtain a fault-aware comment. We then use the editor to generate a new program, which is the final version for further evaluation. This inference approach maintains a small sample budget compared with existing large-scale sampling and filter/reranking methods. ## 3.3.2 Dataset Construction For Code Editor To train a fault-aware code editor, we need datasets that contain the generated program and the corresponding supplementary comments. To collect such datasets, we use different LLMs (Sec. 4.1) to generate candidate programs for problems in the APPS-train dataset. For each problem, we sample 10 programs from the LLM and then execute the example test case to get the test results and construct supplementary comments. At this point, we get the datasets of triplets (*N, S, C*) for different LLMs. To further obtain the ground truth program Cˆ, we collect the standard ground truth programs in the original APPS training dataset and the generated programs that pass all hidden test cases. For each LLM, we create an individual editor dataset with nearly 4.5k generated programs with comments. For each generated program, we set at most 15 ground truth programs. As we described in Figure 3, the generated programs from different LLMs have different distributions of the corresponding comments. To optimize the performance of the fault-aware code editor for each LLM, it is necessary to use training datasets specific to the corresponding LLM. ## 3.3.3 Training Objective Of Code Editor Editing for a high-quality program based on the input pair (*N, S, C*) is a one-of-many task because multiple correct target programs satisfy the requirements. Standard maximum likelihood objectives aim to minimize loss by considering all of the solutions in the training set (like recall), while we focus on a model's ability to edit a single correct solution based on the existing generated code within a limited budget of attempts (like precision). To address this discrepancy, we follow previous work and adopt a variation of GOLD (Pang and He, 2021; Li et al., 2022c), which incorporates an off-policy importance weight into the standard maximum likelihood objective gradient: $$\nabla{\mathcal{L}}(\theta)=-\sum_{t\in{\dot{C}}}P_{\theta}(t)\nabla l o g P_{\theta}(t)\qquad(1)$$ where θ represents the model parameters and logPθ(t) is the standard log-likelihood objective for next token prediction. The additional weight Pθ(t) allows the model to focus on the tokens that already have a high likelihood, so the model can concentrate on these easier-to-learn ground truth solutions and increase the chance of getting at least one correct output. Such a loss setting allows editors to learn to copy part of the content from existing generated programs to obtain better outputs. ## 4 Experiment We present extensive experiments that span two representative datasets and nine different LLMs for code generation, whose parameter counts range across four orders of magnitude. The details of the adopted LLMs are described in Section 3.1. We aim to investigate four research questions: (1) how much can fault-aware code editors improve various code generation models on competitive programming (Sec. 4.2), (2) the advantages of editor-based methods over existing ranking methods (Sec. 4.3), (3) to what extent does the supplementary comments help to refine the program (Sec. 4.4), (4) how does the number of editing rounds affect the final result (Sec. 4.5). ## 4.1 Experiment Setup Dataset. We consider evaluating our approach on two existing code generation datasets: (1) **APPS** (Hendrycks et al., 2021): a collection of 5000 training and 5000 test tasks collected from coding competitions and interview problems. The test set has three different difficulty levels: Introductory, Interview, and Competition. (2) **HumanEval** (Chen et al., 2021): a set of 164 test programming problems with a function signature, docstring, body, and several unit tests. Our experiments only use the APPS-train dataset to finetune the code generation models and the code editor models since it is the largest training dataset. Following previous studies (Inala et al., 2022), we adopted the same division and used a set of 598 tasks excluded from the | Problems | Hidden Tests | | | |-------------------|----------------|------|-------| | Training dataset | APPS-train | 4207 | 5.56 | | APPS-dev | 598 | 4.03 | | | APPS-test | Introductory | 1000 | 21.19 | | Interview | 3000 | | | | Competition | 1000 | | | | HumanEval | 164 | 8.08 | | | Testing benchmark | | | | APPS training dataset for validation1. The detailed statistic of the datasets is shown in Table 1. The hidden test cases are those test cases for evaluation. They are not included in the problem description, so they are distinguished from the example test case used to obtain supplementary comments. Base LLMs. In this paper, we investigate the effectiveness of several widely used language models for code generation, including text-davinci-002 (175B) (Brown et al., 2020), CodeGen (2B, 350M) (Nijkamp et al., 2022), InCoder (1B) (Fried et al., 2022), GPT-Neo (1.3B, 125M) (Black et al., 2021), GPT-J (6B) (Wang and Komatsuzaki, 2021) and PycodeGPT (110M) (Zan et al., 2022). These models are evaluated under zero-shot or finetune experimental conditions, with additional descriptions provided as a part of Table 2. 2 Editor Model. We implement the code editor by fine-tuning *PyCodeGPT-110M*. We choose this model because of its relatively small parameter size and high performance. We also tried the *CodeGen350M* model in early experiments but found that the training speed and final performance were not as good as the model we chose. Considering that LLMs shows strong in-context learning abilities that do not need training process, we also explore to design a variant of our self-edit method with in-context learning. We use the *textdavinci-002* as both base model and editor model. The in-context learning self-edit performances are discussed in Section 5.2. Metrics. We use the metric pass rate *pass@k* for performance evaluation and take advantage of hidden test cases to determine the functional correctness of code solutions. For each problem, we submit k code solutions for evaluation. If any of the 1https://github.com/microsoft/CodeRanker 2We do not use the *CodeX* model as it was in closed beta and was not available during our experiments. We choose text-davinci-002 with equal parameter size as an alternative. k code solutions passes all ground truth test cases, the problem is considered solved. Then *pass@k* is the percentage of solved problems. In our experiments, we set k = {1, 5, 10}. To show the number of programs corrected by our editor, we design a new metric *sol@k*, which means the total number of correct programs given k samples per problem. For example, for the 5000 problems in APPS-test, we will generate 5000 ∗ k code solutions, from which we will count the number of correct solutions as *sol@k*. In our experiments, we set k = 10. We show the performance of the base model and the performance after editing (denoted as *edit-pass@k* and *edit-sol@k*). Training/Inference Settings. For each finetuned LLM, we limit the maximum epochs to 10 with a learning rate of 1e-5, and choose the best checkpoint based on the validation loss on APPS-dev. We adopt the same training strategy to train faultaware code editors on each corresponding editor dataset. We set the maximum input length to 1024 and output length to 512 for our editors. To extract the supplementary comment, we choose only one example test case contained in the problem description even if it contains multiple. At inference time, we use temperature sampling with T = 0.8 both for LLM and editor outputs. We limit the sample budget of LLMs to 10. For each LLM output code, we only generate one code as the final version with our editor. Thus the usage of the editor maintains a constant sample budget. All experiments are conducted with 4 Tesla V100-32GB GPUs. ## 4.2 Comparison With Base Llms APPS-dev & APPS-test. We first compare with directly generating from LLMs to analyze how faultaware code editors can improve nine popular code generation models. Table 2 shows the primary results on the APPS-dev dataset for nine different code generation models. The fault-aware editor improves all code generation models despite their different sizes and training settings. The average pass@1 value across nine models increases from 6.17% to 11.67%, representing an impressive 89% improvement. For those LLMs with a particularly large number of parameters, our editor can also achieve a significant improvement. For *GPT3* with 175B parameters, the improvement of our editor also achieves 5.9%, 5.0%, 8.4% on pass@{1,5,10}. Results on the APPS-test dataset are shown in Table 3. The test problems are more challenging than APPS-dev, which we can see by the smaller pass@k numbers. Our editors maintain significant improvement for models of different sizes. The absolute improvement of *pass@1* covers from 0.12% to 0.7%, showing that the editor can solve 6 to 35 more problems on this challenging benchmark. As for *sol@10*, our editors can additionally correct hundreds of generated codes from LLMs. In some cases, we observe that the *edit-pass@1* outperforms the *pass@5*. It demonstrates that editing the candidate code is very sample efficient. With the editor model, the number of required programs sampled from the LLM can be reduced. Another interesting observation is that a smaller LLM equipped with our editor can achieve comparable performance as the super large models. For example, the GPT-Neo-125M, *GPT-Neo-1.3B*, and GPT-J are pretrained and finetuned with the same dataset. Using the editor can fill in the gaps in the parameter sizes of this series of models. The 125M pretrained model with a 110M editor can significantly outperform a 1.3B pretrained model and even outperform the 6B pretrained model in some cases. This finding can also be observed in other experiments, showing that our editor can offer a boost approximately equivalent to a tens of times pretrained model size increase. On Different Difficulty-Level Problems. Considering that the APPS-test dataset has three difficulty levels, we further analyze the improvement on problems of different difficulty in Table 5. We choose GPT-J-6B-finetuned as the base model because it has shown promising results on this challenging benchmark and has certain representativeness. The editor can improve the base model on problems of all difficulty levels but has a relatively high pass rate improvement on simple *"Introductory"* problems. We find that the output of LLMs is poor on very difficult problems, making it too difficult for the editor to correct these solutions. Even so, our method slightly improves the *"Competition"* problems when enlarging the sample budget from 1 to 10. HumanEval. We also measure the transfer ability of our editor on HumanEval, a dataset of different styles, in Table 4. The HumanEval dataset requires the model to give the function body based on the function signature, comments, and example test cases. Following the executability filter in previous work (Zhang et al., 2022), in this dataset, we only edit the outputs that can not pass the example test | Code Gen. Model | Para. | pass@1 | edit pass@1 | pass@5 | edit pass@5 | pass@10 | edit pass@10 | sol@10 | edit sol@10 | |----------------------|---------|----------|---------------|----------|---------------|-----------|----------------|----------|---------------| | finetuned PyCodeGPT | 110M | 4.8 | 11.4 | 7.9 | 15.1 | 8.9 | 17.1 | 286 | 659 | | GPT-Neo 125M | 125M | 1.5 | 8.5 | 6.7 | 10.2 | 10.2 | 17.2 | 102 | 501 | | CodeGen-350M | 350M | 1.7 | 5.7 | 2.5 | 9.2 | 3.2 | 13.5 | 103 | 339 | | GPT-Neo 1.3B | 1.3B | 4.0 | 10.5 | 10.9 | 18.6 | 17.2 | 25.4 | 200 | 663 | | InCoder-1B | 1.3B | 9.4 | 12.4 | 12.5 | 16.2 | 13.5 | 18.1 | 568 | 730 | | GPT-J | 6B | 6.0 | 12.0 | 17.9 | 27.8 | 24.6 | 37.8 | 365 | 750 | | zero-shot InCoder-1B | 1.3B | 0.2 | 4.7 | 0.8 | 7.7 | 1.2 | 9.9 | 13 | 270 | | CodeGen-2B | 2.7B | 1.3 | 7.4 | 5.9 | 14.0 | 9.7 | 19.7 | 92 | 438 | | text-davinci-002 | 175B | 26.6 | 32.4 | 43.8 | 48.8 | 49.7 | 58.0 | 1626 | 1948 | | Code Gen. Model | pass@1 | edit pass@1 | pass@5 | edit pass@5 | pass@10 | edit pass@10 | sol@10 | edit sol@10 | |------------------------------------------------------------------------------------------------------------------------------|----------|---------------|----------|---------------|-----------|----------------|----------|---------------| | finetuned PyCodeGPT | 0.20 | 0.64 | 0.38 | 0.98 | 0.44 | 1.24 | 126 | 308 | | GPT-Neo 125M | 0.08 | 0.22 | 0.40 | 0.70 | 0.70 | 1.12 | 45 | 135 | | CodeGen 350M | 0.20 | 0.32 | 0.30 | 0.56 | 0.32 | 0.84 | 92 | 149 | | GPT-Neo 1.3B | 0.14 | 0.68 | 0.74 | 1.38 | 1.40 | 2.10 | 106 | 340 | | InCoder 1B | 0.66 | 0.86 | 1.18 | 1.62 | 1.44 | 2.10 | 344 | 421 | | GPT-J | 0.70 | 1.40 | 2.46 | 3.34 | 3.52 | 4.76 | 404 | 738 | | zero-shot InCoder 1B | 0.00 | 0.24 | 0.02 | 0.50 | 0.02 | 0.76 | 1 | 121 | | CodeGen 2B | 0.12 | 0.28 | 0.34 | 0.66 | 0.66 | 1.08 | 41 | 131 | | text-davinci-002 | 7.48 | 7.94 | 15.94 | 16.66 | - | - | 1876 † | 1983 † | | † As we access GPT3 through a paid API, we limit the sample budget of GPT3 as 5 for this large benchmark and evaluate sol@5. | | | | | | | | | case. We also modify the input format to be similar to the format in the APPS dataset. We select several representative LLMs for evaluation within our computational capabilities. We can again see that the editor improves the performance of all code generation models on all metrics. We notice that under larger sample budget conditions, even if the pass@10 does not increase for *CodeGen-2B*, our editor can still correct more generated solutions. Thus the *sol@10* increases significantly. These results demonstrate the ability and generality of our editor to correct out-of-distribution output codes. ## 4.3 **Comparison With Post-Processing Baseline** This experiment compares our self-edit approach with existing post-processing methods for code generation. We choose to compare with CodeRanker (Inala et al., 2022), a state-of-the-art reranking method on the APPS dataset. CodeRanker finetuned CodeBERT (125M) to classify the potential error type and use this classification prediction to rerank the generated codes from LLMs. The supervised training task makes this method more efficient than previous filtering and reranking methods. However, our experiments (Table 6) prove that our editor outperforms this state-of-the-art method in terms of accuracy and efficiency. We choose the *GPT-Neo-1.3B-finetuned* as the base model and finetune on the APPS-train dataset, keeping the same experimental settings as CodeRanker for a fair comparison. Our method (*"+ editor"*) significantly outperforms CodeRanker ("+ ranker"). In particular, on APPS-test, our method can improve pass@1 from 0.14% to 0.68%, while their method can only improve from 0.14% to 0.3%. It means our method can solve 19 more problems on this challenging dataset. We also provide the performance of other reproduced base models in Table 9, where our method generally outperforms. More importantly, existing post-processing | Code Gen. Model | pass@1 | edit pass@1 | pass@5 | edit pass@5 | pass@10 | edit pass@10 | sol@10 | edit sol@10 | |-----------------------------|----------|---------------|----------|---------------|-----------|----------------|----------|---------------| | finetuned on APPS PyCodeGPT | 6.10 | 8.54 | 7.32 | 10.98 | 7.93 | 13.41 | 100 | 159 | | GPT-Neo 125M | 0.61 | 3.05 | 3.05 | 7.32 | 6.10 | 9.76 | 21 | 76 | | CodeGen-350M | 6.10 | 7.93 | 7.32 | 9.15 | 7.32 | 10.37 | 100 | 140 | | GPT-Neo 1.3B | 2.44 | 5.49 | 8.54 | 10.98 | 11.59 | 14.63 | 66 | 132 | | Incoder-1B | 6.71 | 10.37 | 8.54 | 13.41 | 9.76 | 14.63 | 112 | 169 | | GPT-J | 7.32 | 9.76 | 17.07 | 19.51 | 25.00 | 25.61 | 133 | 183 | | zero-shot Incoder-1B | 1.22 | 3.66 | 2.44 | 7.93 | 5.49 | 10.98 | 13 | 87 | | CodeGen-2B | 14.02 | 17.07 | 29.27 | 29.88 | 34.15 | 34.15 | 226 | 255 | Table 4: Results on the HumanEval dataset. Difficulty level pass@1 pass@5 pass@10 2.10 7.40 10.10 Introductory 4.90 133% 10.40 40.5% 14.20 40.6% 0.43 1.53 2.37 Interview 0.67 53.5% 1.97 28.1% 3.03 28.3% 0.10 0.30 0.40 Competition 0.10 0.40 33.3% 0.50 25.0% 0.70 2.46 3.52 Average 1.40 100% 3.34 35.8% 4.76 35.2% methods rely on sampling many outputs from LLMs. For instance, the CodeRanker requires 100 outputs for each problem and then selects k samples with their ranker model to evaluate *pass@k* metric. In contrast, our method only requires k = {1, 5} outputs per problem and then utilizes these outputs to generate a final solution through editing. Our approach is more efficient and effective, especially when obtaining outputs from large language models is costly. As a result, our method has greater practical significance and is more suitable for use with limited sample budgets. ## 4.4 Ablation On Supplementary Comments To investigate the influence of supplementary comments, we remove the supplementary comments from the editor input and only use problem description and generated code to train a new editor. Other settings are kept the same. Results on APPS validation and test datasets are shown in Table 7. We find that the pass rate of the modified editor decreases significantly on both datasets compared with the original editor. The modified editor can improve the APPS-dev dataset compared to the base model. However, on the more difficult APPS-test dataset, the editor model without comments shows no performance improvement. The results indicate that losing the guidance of the supplementary comment will hurt the performance of the editor model. Our experiments show that using error messages as supplementary comments for the code editor is crucial for achieving remarkable performances. | APPS-dev | APPS-test | | | | | |---------------------------------------------------|-------------|------|------|------|------| | Setting | Samples | @1 | @5 | @1 | @5 | | base model | 4.0 | 10.9 | 0.14 | 0.74 | | | + ranker† | 100 | 8.0 | 15.1 | 0.3 | 1.1 | | + editor | {1,5} | 10.5 | 18.6 | 0.68 | 1.38 | | † The results are copied from the original paper. | | | | | | ## 4.5 Ablation On The Number Of Edit Rounds In our self-edit approach, we make edits to the output of LLMs to produce the final program. It | APPS-dev | APPS-test | | | | | | |--------------|-------------|------|------|-----|-----|-----| | Setting | @1 | @5 | @10 | @1 | @5 | @10 | | base model | 4.8 | 7.9 | 8.9 | 0.2 | 0.4 | 0.4 | | after edit | 11.4 | 15.1 | 17.1 | 0.6 | 1.0 | 1.2 | | - comments | 9.4 | 11.5 | 13.5 | 0.3 | 0.3 | 0.4 | | + edit round | 11.7 | 15.2 | 17.1 | 0.4 | 0.7 | 0.9 | leads to a question: what if we make additional edits to the program after the first edit? We add an additional editing step to answer this question using our original editor. Concretely, the edited program is executed on an example test case to obtain comments and then refined by the editor model again. The results of this approach are presented in Table 7, with the column labeled *"+ edit round"* indicating the two-round editing approach. The results show the two-round editing leads to a slight increase in pass@1 on APPS-dev. However, the additional edit round hurts the performance on APPS-test. We guess the reason is the gap between training and test time in the second editing round. The editor is trained to edit LLM outputs but used to edit its own output in the second edit round. In this setting, an additional editing round is not very helpful in generating better programs. ## 5 Discussion 5.1 **Time Cost Compared With Post-Processing** Baseline For the specific issue of time cost, we use *Google* Colab 3 with a Tesla T4 GPU to build a demo and conduct evaluations over APPS-test dataset. We use *text-davinci-002* as the base model and the average time cost is nearly 8.4s to obtain 1 sample for each question. The executor costs <0.01s, and our editor costs 3.7s to get the final output, which is acceptable in our actual experience using the demo. By contrast, the state-of-the-art reranking method CodeRanker requires >110s to obtain candidate lists and 0.53s for the following ranker. As a result, our framework achieves better performance with less total time cost and fewer LLM calls. ## 5.2 Performances Of In-Context Learning Self-Edit Given that LLMs have demonstrated strong incontext learning abilities without requiring any specific training, we leverage the capabilities of the text-davinci-002 model as both the base and editor models to develop a variant of our self-edit method that utilizes in-context learning. Specifically, we utilize in-context learning abilities of the model to self-edit its output using the supplementary comments we construct (detailed in Section 3.2) as input prompts for zero-shot inference. This approach allows the large model to edit its output program | Benchmark | pass@1 | pass@5 | sol@5 | | |-------------|----------|----------|---------|------| | before | 7.48 | 15.94 | 1876 | | | APPS-test | after | 8.94 | 17.12 | 2214 | | before | 34.76 | 60.98 | 288 | | | HumanEval | after | 39.63 | 64.63 | 331 | without additional training, offering a promising solution for optimizing the potential of LLMs. Our experiments on APPS-test and HumanEval are presented in Table 8. Results demonstrate that our self-edit framework can be extended using incontext learning, achieving significantly better performance than smaller editors across various benchmarks. However, it is important to note that this in-context learning self-edit method still incurs a relatively large number of LLM calls. Therefore, optimizing resource requirements while exploiting the potential of LLMs remains critical. To this end, we will explore strategies to efficiently utilize the in-context learning capabilities of LLMs in our self-edit framework in future work. ## 6 Conclusion We propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. The central component of our approach is the fault-aware code editor, which can edit and optimize the generated code. In-depth evaluations demonstrate our approach significantly improves the quality of LLMs' output code. ## 7 Acknowledgement This research is supported by the National Natural Science Foundation of China under Grant Nos. 62072007, 62192731, 62192733, 62192730, 61832009. The AI training platform supporting this work were provided by High-Flyer AI. (Hangzhou High-Flyer AI Fundamental Research Co., Ltd.) We also would like to thank all the anonymous reviewers for constructive comments and suggestions to this paper. ## Limitations Our work has several limitations, which we aim to address in our future work: Firstly, we implement our editor with relatively small pretrained models within our computational capabilities. Our in-depth evaluations have preliminarily demostrated the effectiveness of the generateand-edit approach. We hope to further understand the performance when using different pretrained models and architectures for the editor. Secondly, the editor datasets we constructed are relatively small due to our computational capabilities. In our experiment, we only sample 10 programs from the LLM for each problem for dataset construction. Compared with existing post-editing methods, the dataset we use is quite small. It would be meaningful to do a detailed analysis of the impact of editor dataset size, or to experiment with other dataset construction methods. We leave this as future work. Thirdly, We do not have strict comparison about computing resources with other post-editing methods. In Section 4.3 we compare with a state-of-theart re-reaking baseline. We both use an additional model with a similar amount of parameters, but our approach outperforms using very few samples from LLMs. As accessing LLMs is costing, our approach demonstrates both superior accuracy and efficiency. Finally, in our ablation study on the number of edit rounds, we faced with a gap between training and test time in the second editing round. Our existing implementation is not designed for this multiple-round editor. We hope to further try new specially designed model to implement the editor model. As large language models continue to advance, the need for effective strategies to interact with LLMs will be an important area of future research. ## References Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021. 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Tranx: A transition-based neural abstract syntax parser for semantic parsing and code generation. arXiv preprint arXiv:1810.02720. Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei Guan, Yongji Wang, Weizhu Chen, and Jian-Guang Lou. 2022. CERT: Continual pretraining on sketches for library-oriented code generation. In The 2022 International Joint Conference on Artificial Intelligence. Kechi Zhang, Ge Li, Jia Li, Zhuo Li, and Zhi Jin. 2023. Toolcoder: Teach code generation models to use api search tools. *ArXiv*, abs/2305.04032. Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, and Sida I. Wang. 2022. Coder reviewer reranking for code generation. CoRR, abs/2211.16490. ## A Compared With Coderanker We compare with CodeRanker (Inala et al., 2022) using GPT-Neo-125M-finetuned, *GPT-Neo-1.3Bfinetuned* and *GPT-J-6B-finetuned* as the base model. For fair comparison, we choose the same base model, training dataset and test benchmark as the CodeRanker. We choose the above three base models and finetune on the APPS-train dataset to reproduce their results. The purpose of this step is to make our base model results similar to their reported base model results, so as to fairly compare the post-processing performance. In the experiments, the base model performance in our results is similar to the base model reported by CodeRanker. Full details of results are shown in Table 9. With a very small number of samples output by LLMs, our method significantly exceeds this state-of-the-art baseline. ## B Qualitative Analysis Of Code Editor In Figure 5 and 6 we show various programs generated by the *GPT3*, its corresponding problem description (contains example test case) and the supplementary comment. Our fault-aware code editor concatenates these as input, and generate the edited code as the final output. We find that the edited code is simialr to the *GPT3* output. In particular, the first few lines of the edited output are exactly the same as the output of *GPT3*, and the subsequent code is also partially based on the content in *GPT3* output. Through statistical analysis, we find that the common prefix between the two sequences accounted for 19.10% of the edited output on the APPS-dev and APPS-test datasets. While this does not account for similarities in the intermediate content, it is sufficient evidence to demonstrate the impact of the LLM output on the edited code. As for the HumanEval benchmark, we also show case studies in Figure 7. | GPT-Neo-125M-finetuned | APPS-dev | APPS-test | | | | | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|-------------|------|------|------|-----| | Setting | Samples | @1 | @5 | @1 | @5 | | | Reported in | base model † | 1.4 | 5.2 | 0.04 | 0.17 | | | (Inala et al., 2022) | + ranker | 100 | 6.5 | 11.4 | 0.1 | 0.5 | | Our results | base model | 1.5 | 6.7 | 0.08 | 0.40 | | | + editor | {1,5} | 8.5 | 10.2 | 0.22 | 0.70 | | | GPT-Neo-1.3B-finetuned | APPS-dev | APPS-test | | | | | | Setting | Samples | @1 | @5 | @1 | @5 | | | Reported in | base model † | 2.6 | 9.1 | 0.14 | 0.53 | | | (Inala et al., 2022) | + ranker | 100 | 8.0 | 15.1 | 0.3 | 1.1 | | Our results | base model | 4.0 | 10.9 | 0.14 | 0.74 | | | + editor | {1,5} | 10.5 | 18.6 | 0.68 | 1.38 | | | GPT-J-6B-finetuned | APPS-dev | APPS-test | | | | | | Setting | Samples | @1 | @5 | @1 | @5 | | | Reported in | base model † | 5.1 | 15.6 | 0.5 | 1.6 | | | (Inala et al., 2022) | + ranker | 100 | 11.0 | 21.7 | 0.8 | 2.6 | | Our results | base model | 6.0 | 17.9 | 0.7 | 2.46 | | | + editor | {1,5} | 12.0 | 27.8 | 1.4 | 3.34 | | | † As CodeRanker does not release the weights of base models, we cite their results from Inala et al. (2022) and reproduce finetuned base models shown in the "Our results - base model" row below. | | | | | | | Our results base model 6.0 17.9 0.7 2.46 + editor **{1,5} 12.0 27.8 1.4 3.34** † As CodeRanker does not release the weights of base models, we cite their results from Inala et al. (2022) and reproduce finetuned base models shown in the *"Our results - base model"* row below. Table 9: Full details of Pass Rate Results compared with the CodeRanker on the APPS dataset. We use GPT-Neo125M-finetuned, *GPT-Neo-1.3B-finetune* and *GPT-J-6B-finetuned* as the base model. ![14_image_1.png](14_image_1.png) Problem Description: *Question id: APPS-dev-4615* ![14_image_0.png](14_image_0.png) (b) Problem Description: *Question id: APPS-test-4854* Problem Description: *Question id: APPS-test-2629* Given a positive integer n, generate a square matrix filled with elements from 1 to n2 in spiral order. Example: Input: 3 Output: [ [ 1, 2, 3 ], [ 8, 9, 4 ], [ 7, 6, 5 ] ] ![15_image_1.png](15_image_1.png) (a) Mirko is a great code breaker. He intercepted an enemy message. The message consists of $N$ numbers, smaller than or equal to $C$. Mirko belives freqency analysis consists of sorting this sequence so that more frequent numbers appear ![15_image_0.png](15_image_0.png) (b) Figure 6: Case Study on APPS-test dataset using *GPT3* model. ![16_image_0.png](16_image_0.png) (a) (b) Figure 7: Case Study on HumanEval dataset using *CodeGen-2B* model.

don-yehiya-etal-2023-cold

{C}ol{D} Fusion: Collaborative Descent for Distributed Multitask Finetuning

https://aclanthology.org/2023.acl-long.46

Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask training by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.19 points on average without any changes to the architecture.

## Cold Fusion: Collaborative Descent For Distributed Multitask Finetuning Colin Raffel UNC Chapel Hill [email protected] Shachar Don-Yehiya IBM Research Hebrew University of Jerusalem [email protected] Noam Slonim IBM Research [email protected] ## Abstract Elad Venezian IBM Research [email protected] Yoav Katz IBM Research [email protected] We propose a new paradigm to continually evolve pretrained models, denoted ColD Fusion. It provides the benefits of multitask learning but leverages distributed computation with limited communication and eliminates the need for shared data. Consequentially, ColD Fusion can give rise to a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based upon. We show that ColD Fusion yields comparable benefits to multitask training by producing a model that (a) attains strong performance on all of the datasets it was trained on; and (b) is a better starting point for finetuning on unseen datasets. We show that ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.33 points on average without any changes to the architecture.1 ## 1 Introduction Over the last few years, pretrained language models are changing the landscape of NLP, where finetuning a pretrained model typically yields state-ofthe-art performance on a diverse set of NLP tasks (Chen et al., 2022). Consequently, improving a pretrained model has the potential to boost every model finetuned on it. However, pretraining is often so computationally expensive that practitioners rarely seek to pretrain new models from scratch. In contrast, finetuning is usually dramatically cheaper, allowing a given pretrained model to be finetuned many times; e.g., there are thousands of finetuned BERT variants on the Hugging Face Hub2. Motivated by this, we study if and how finetuned models can be "recycled" to create a better 1We release the final model as well as iterations and seeds here: https://huggingface.co/ibm/ColD-Fusion 2https://huggingface.co/models?search=bert ![0_image_0.png](0_image_0.png) pretrained model (c.f., Raffel, 2021). To avoid confusion, henceforth we refer to any starting point for finetuning a *base model* and only the vanilla model as the pretrained model. To recycle models, we take inspiration from multitask learning (§2). In multitask learning the pretrained model is finetuned over multiple datasets at once, which was shown to create a better base model than the original pretrained model (Aribandi et al., 2021; Aghajanyan et al., 2021a; Sanh et al., 2021; Chung et al., 2022). Given the availability of many finetuned models, our aim is to obtain the benefits of multitask learning by mixing multiple models rather than multiple datasets (c.f. §2.3). To achieve that, we suggest the following iterative approach (§3): In each iteration, contributors finetune the most up-to-date base model (which is presumably also the most performant) on their task, and share the fine-tuned model with the rest of the community. Then, those contributed models are fused together, by simply averaging their parameters (Choshen et al., 2022b), to create the base 788 Leshem Choshen IBM Research [email protected] model for the next iteration. We call this method Collaborative Descent Fusion, or *ColD Fusion*. ColD Fusion fits the common finetuning paradigm, where each contributor finetunes for their own benefit and does not share their data. However, by merely requiring the finetuned model to be shared, the finetuning step can be recast as a training step for the collective's benefit. In doing so, our method allows reusing compute and data consumed by practitioners and researchers to the benefit of the entire community. Our experimental results indicate that our approach of combining finetuned models not only produces a better base model but also allows this base model to keep evolving. Instead of pretraining or multitasking on a predefined amount of data, we suggest accumulating finetuned models to continuously improve the model. Our method is hence limited only by the amount of finetuned models that are shared by the entire community. We discuss limitations in (§9). We show that ColD Fusion produces a model that performs well on the finetuned tasks, despite never manipulating more than one task at a time neither by constituent models nor their fusing (§5). Moreover, we show that ColD Fusion increases the performance of the base model substantially, outperforming the pretrained model by 2.33 points on average on 35 datasets. Through additional analysis, we further show that similar improvements are achieved regardless of whether the target tasks were seen or unseen during training (§5.2) and that accumulating models trained on additional data provides continuous improvement (§6). ## 2 Background We start by motivating the use of further training on diverse data for enhancing the base model abilities (§2.1). Then, we continue with defining our framework's goals (§2.2) and constraints (§2.3). ## 2.1 Performance Scaling Laws Extensive evidence suggests that pretraining with more compute (Raffel et al., 2020) and data (Liu et al., 2019; Hoffmann et al., 2022; Ivgi et al., 2022) improves the resulting pretrained model. Moreover, additional supervised data is beneficial even when introduced after the pretraining stage (Phang et al., 2018; Choshen et al., 2022a). Extending this supervised stage to multitask learning on diverse data sources improves results even further (Aribandi et al., 2021; Aghajanyan et al., 2021a; Sanh et al., 2021; Chung et al., 2022). We observe that the data used during finetuning is typically not seen during pretraining. Therefore, we hypothesize that using a large amount of the data currently used for finetuning may significantly improve the model quality as a base model for future tasks. As training on all the finetuning data directly is infeasible, here we propose an alternative paradigm to test this hypothesis. ## 2.2 Goals Of Multitask Learning Multitask learning is typically used towards one of two goals: Either to produce a *single model* that performs well on many seen tasks, or to produce a base model that will perform well on many unseen tasks after adaptation, e.g., via finetuning. Single model. To produce a single multitask model, one initializes with a base model with p parameters and optimizes the parameters θ ∈ Rp to minimize the loss over a set of datasets D. This reflects the traditional objective of multitask learning - to produce a set of weights that performs well on multiple tasks (Caruana, 1997). Base model. An alternative goal of multitask learning (and the primary goal in our work) is to produce a base model that will attain strong performance after adaptation. Multitask learning does not directly optimize towards this goal, but has been found to do so indirectly (Aghajanyan et al., 2021a; Liu et al., 2022). In this setting, the outof-the-box performance of the produced model on seen tasks is less important than the performance after finetuning over new tasks, i.e., initializing with the found weights θ ∈ Rpand then finetuning on a desired dataset d′. We do not explicitly state whether d′ ∈ D or d′ ∈/ D, i.e., whether d was used during the multitask training or not. In §5.2, we empirically show that our method works well in both cases. We note that our formulation sets no restrictions on the datasets group D. Thus, a common scenario might be that some datasets do not have the same label space, number of examples, etc. On the other hand, it is also possible that some datasets are complementary samples from a distribution of the same task. In this case, our approach is similar to training this task distributively as in federated learning (Yang et al., 2019) but without communicating every batch. We demonstrate that our approach also works well in this setting in §6. ## 2.3 Collaborative Constraints In this work, we target the goals of multitask learning discussed above, but focus on a specific setting with additional constraints, which we call ColD multitask. The constraints are required to support large-scale collaborative and distributed multitask learning. In our setting, multiple *contributors* have access to datasets that they do not share. A central Repository can only perform minimal computation (i.e., does not perform any training). Communication between the contributors and the Repository only occurs when a given contributor completes the finetuning on their data. ## 3 Methodology - Cold Fusion Our proposed method (see Fig. 1), called ColD Fusion, is an iterative process that aims to perform multitask learning in the constrained setting outlined above. Specifically, ColD Fusion involves an iterative process where each individual contributor downloads the current base model from the Repository, finetunes this base model over their dataset, communicates the resulting model back to the Repository, and lastly, the Repository fuses (Choshen et al., 2022b) all of the contributors' models into one and sets the new fused model as the new base model for further finetuning. More formally, the Repository first initializes the shared model parameters θ0 using a preexisting pretrained model. Then, at each iteration i ∈ {0, 1, 2*, . . .*}, each contributor c ∈ C finetunes the θi base model over a dataset d ∈ D to produce parameters θ c i . For the purposes of our study, finetuning is any optimization process that aims to minimize the loss over a dataset d. Typically, finetuning involves minimizing the loss using a variant of gradient descent. After finetuning, each contributor sends their model's parameters θ c i to the Repository. Next, the Repository fuses the contributor's models by averaging all of the contributor's model's parameters to produce a new shared model as θi+1 =1 |C| Pc θ c i . Finally, the process repeats for iteration i + 1. ## 4 Experimental Setup In this section, we detail the datasets, models, baselines, general experiment setup, and specific experiments settings. ## 4.1 Datasets In all of our experiments, we define the datasets group D to be a group of 36 English-language datasets, including most GLUE and Super-GLUE datasets, in addition to other NLI, sentiment and topic classification datasets as well as datasets based on Twitter data. A full list of datasets we use is provided in App. A. At each iteration we test on all the 36 datasets. There are two exceptions: 1) In the main experiment (§5.1) we use the entire dataset group except STSB. STSB, being a regression task incurred technical difficulties to provide a fair comparison to the multitask baseline (see §4.2). 2). For efficiency reasons, in the very compute demanding experiment of the number of contributors (§5.4) we randomly sampled 5 datasets to act as a consistent test set. ## 4.2 Models And Baselines For experiments in the main text, we use RoBERTabase (Liu et al., 2019) as our initial model θ0. To demonstrate the generality of our approach, we additionally replicate some results on T5 (Raffel et al., 2020, see App. §D). For baseline pre-trained models, we consider RoBERTa-base, RoBERTa-base fused, as well as a RoBERTa-base multitask model. The fused model is trained as in Choshen et al. (2022b). The multitask variant trains a dedicated classification head for each dataset. In addition, we consider the MUPPET (Aghajanyan et al., 2021a) model, a highly optimized multitask model trained on more datasets than we consider. MUPPET is the current state-of-the-art base pretrained model that uses the RoBERTa-base architecture (Choshen et al., 2022a). ## 4.3 Finetuning Process Finetuning is used in this paper for two reasons: (a) As a way to infer and evaluate the performance of a base model and (b) as a part of the ColD Fusion scheme. We follow the exact same finetuning procedure in either case. Finetuning hyperparameters and time and memory estimates are provided in App. B ## 4.4 Cold Fusion Procedure The general course of the experiments is as follows: On each iteration, several datasets are sampled and the latest base model is finetuned separately on each dataset. Then the resulting finetuned models ![3_image_0.png](3_image_0.png) are fused to create the next base model. This new model is evaluated on the test datasets at each iteration. When we mention ColD Fusion without specifying the iteration explicitly, we refer to the model that corresponds to the final iteration. The evaluation reflects both multitask goals (§2.2): (a) To evaluate the single model goal, we train only the classification head (equivalent to Linear Probing; Alain and Bengio, 2016), freezing the rest of the layers. We refer to it as ColD-*Frozen*. (b) For evaluating the base model goal, we take the ColD model and use it as initialization for finetuning. We finetune separately on each dataset and report the results on the corresponding test. We refer to it as ColD. ## 5 Cold Multitask Results In this section, we show ColD Fusion can produce multitask models. We show in §5.1 that ColD Fusion fulfills both multitask objectives defined in §2. We verify that improvements replicate on datasets that were not seen during training (§5.2). Then we find that base model improvements are even more apparent in few shot settings (§5.3). Finally, we consider the importance of the number of contributors hyperparameter (§5.4). ## 5.1 Collaborative Multitask We show that ColD Fusion achieves the two multitask objectives (see Fig. 2). We train and test ColD Fusion for 30 iterations. We simulate 8 contributors by sampling 8 datasets at each iteration and repeat the whole experiment using 5 different random seeds. We consider the importance of the sampling hyperparameter in §5.4. We find that ColD Fusion creates a superior base model (see Fig. 2b). The average result after finetuning the ColD Fusion model is superior to the RoBERTa pretrained model by up to 2.33 points on average over the 35 datasets (see App. §C for full results). The result can be deemed significant with a difference of over 20 standard errors of the mean between the original pretrained model and the model produced by ColD Fusion. In comparison, the standard multitask model and the fused model outperform the original RoBERTa pretrained model by only 1.62 and 0.92 points respectively. We also consider the highly optimized MUPPET model, trained on more datasets and without the ColD multitask restrictions. MUPPET indeed outperforms our standard multitask baseline model, but is outperformed by our ColD Fusion model. Another important comparison is the consistency of the improvement. We find (see App. C) that the model produced by ColD Fusion is better than the pretrained model on 75% of the datasets and degrades by only 1.73 points on the worst-case dataset. In contrast, MUPPET hurts as many models as it helps and is worse by 40 points on some datasets. ColD Fusion also achieves the single model goal: When evaluated with linear probing, the ColD ![4_image_1.png](4_image_1.png) model has high performance on the datasets seen in training (see Fig. 2a), higher in fact than those of the standard multitask baseline. Moreover, it is not far from the pretrained model when finetuned on each task separately. This implies that despite learning in a distributed way and fusing by averaging the non-linear weights of the model, the process incorporates the data well. ## 5.2 Unseen Datasets We have found ColD Fusion to create a strong base model (§5). Next, to meet the requirement of improving results for new datasets, we test the ColD fused model on *unseen* datasets not included in the training (see Fig. 3). We achieve this by performing 3-fold cross-validation. The folds are set arbitrarily such that each fold contains 24 seen datasets (24 contributors) and 12 unseen ones that we keep for evaluation only. This ensures that each dataset has the same weight in the average score of the seen datasets and unseen datasets. We find that the model performs on unseen datasets just as well as it does on seen ones. The strikingly similar performance between seen and unseen tasks (which is similar to in-domain vs. outof-domain) should raise a red flag in most scenarios. However, in the unique scenario of ColD multitasking, it meets our expectations. Both seen and unseen datasets are exposed at some point - either during ColD Fusion iterations (seen datasets only) or during evaluation as a base model (both seen and ![4_image_0.png](4_image_0.png) unseen). Hence, in the seen case, the model trains twice on the same data, first during base model creation and again when evaluating the base model. It is less of a surprise that training twice on the same data doesn't improve results. The improvement over the original pretrained is likely due to positive transfer across datasets. Where finetuning is restricted to only the classification head (ColD-Frozen in Fig. 3), the model achieves much better performance on the seen datasets than on the unseen datasets. These results are also in line with the fact that the model (apart from the classification head) was never exposed to the unseen datasets, while the entire model's weights were trained on the seen datasets. We further test ColD Fusion's capacity to scale with more data in §6. We note that the unseen curve consistently increases, which may suggest that the model has acquired general skills. The curve reaches a plateau around the 10th iteration, and then starts to drop a bit. Possibly, due to an overffiting caused by the limited number of seen datasets. Note that the scores in Fig. 3 are a bit lower than in the main experiment in Fig. 2b. This is most likely due to scaling, as here we keep unseen datasets aside and use fewer datasets for training. We show in a controlled experiment in §6 that using more datasets improves results. ## 5.3 Few-Shot In order to assess the benefit of ColD Fusion on few-shot scenarios, we repeat the setting in §5.2, ![5_image_0.png](5_image_0.png) but finetune only on 100 examples from each unseen dataset during evaluation. Fig. 4 shows a great increase in performance over the RoBERTa pretrained model, reaching an improvement of 6.73 points after 20 iterations. This provides an even stronger case for ColD Fusion in the few-shot setting. ## 5.4 Number Of Contributors Per Iteration An important factor in ColD Fusion is the number of contributors in each iteration. Having fewer contributors per iteration implies effectively training on fewer datasets in each iteration; on the other hand, fusing fewer models may give more importance to each. We observe in Fig. 5 that starting from two contributors, the performance as a base model is hardly affected by the number of contributors in each iteration. However, adding contributors makes the process more stable. A possible reason is that some of the improvement comes from the iterations themselves and the ability to correct overfitting done in previous steps by some contributors. We note that the number of contributors is only insignificant when the data is fixed. In practice, more contributors would improve performance, by adding more data or iterations. We further test the effect of the number of contributors under controlled settings in §6. ## 6 Single Dataset Analysis We now analyze the interacting effects of the core characteristics of ColD Fusion: additional data across iterations, the amount of training data per iteration, and the number of contributors in each iteration. Doing so with multiple datasets would introduce noise. For example, we can not expect additional data coming from different sources (e.g., MNLI or Twitter) to equally affect the performance. To overcome this, we explore the case where a single dataset is distributed across contributors. Using a single dataset allows us to reduce variability due to differences in the datasets (e.g., distribution, task, etc.), and isolate the parameter we wish to control. ColD Fusion may converge faster with models from a single dataset, but we still expect the general tendencies found to replicate in multiple datasets settings. We chose MNLI (Williams et al., 2018) for its large size (392K examples). Effect of additional data across iterations (Federated Learning). To simulate a neverending data flow, the experiment runs as follows: at each iteration, 5 contributors sample 5k examples each from MNLI dataset, and another such sample is used for evaluation. This setting resembles the Federated Learning scenario (Yang et al., 2019), where multiple contributors collaborate to train a model without having to exchange the actual data. As presented in Fig. 6a, performance increases throughout the iterations. Thus, we conclude that the ColD Fusion scheme aggregates and utilizes the newly added examples and not only coarse-grained dataset characteristics. We show similar trends in the multitask scenario (see App. E). Training on more datasets results in a better best model at the cost of more iterations to get to that best model. Note the superiority of ColD-Frozen over ColD in this experiment. A possible explanation is overfitting. In evaluation, finetuning all the parameters on only part of the data is worse than keeping the fused weights that are trained on several splits. Effect of dataset size per contributor. In this and the following experiments, we train on all the data in each iteration. The contributors train over disjoint and consistent sub-datasets, i.e., we do not sample examples. We aim to analyze the ability of ![6_image_0.png](6_image_0.png) ![6_image_2.png](6_image_2.png) ![6_image_1.png](6_image_1.png) ![6_image_3.png](6_image_3.png) the model to aggregate knowledge from the constituent models during fusion. ColD-Finetuned is evaluated through a stage of finetuning which further learns on the task. To avoid entangling the capabilities learnt during ColD Fusion with those learnt during evaluation, we analyze the ColD-Frozen instead. We also note that during evaluation, the classification head is trained on the training data of the first contributor only (which is the only one in the baseline). We fix the number of contributors to 10 and test how the number of examples each contributor is training on affects results. We experiment with 1.25K, 2.5K, 5K and 10K examples. We compare these to full finetuning on the union of all the contributors' training data. A priori we would have expected large amounts of data in each contributor's model to obstruct the fusing process, as each model changes more. In Fig. 6b, we see the opposite - the more data each contributor trains on, the closer the fused model is to the full training baseline. Effect of the number of contributors. In this experiment, each contributor trains over "their own" data, i.e., the same 5K examples in each iteration. We test how the results change with 2, 5, 10 and 20 contributors. We see in Fig. 6c that increasing the number of contributors improves performance. Moreover, the results are not only better at every step, but also keep on improving for longer. This is a positive result in terms of the expected end result, but also means that convergence is slower. Effect of data distribution between contributors. To isolate the effect of the number of contributors and the dataset size of each contributor from that of the overall data size, we fix the overall amount of data to 50K and split it among the contributors evenly. Fig 6d shows distributing mostly affects convergence - it takes approximately 2 more iterations to converge for double the contributors and half the data seen by each. We conclude that increasing the overall amount of data improves performance, as may be expected. The distribution of the data between additional contributors has minimal impact on final performance, but may delay convergence. ## 7 Related Work Our work strongly relies on model fusion. Model fusion was first introduced as a way to improve pretrained models by (Choshen et al., 2022b). In parallel, several works such as (Matena and Raffel, 2021; Wortsman et al., 2022b) and lately (Jin et al., 2022; Ramé et al., 2022) suggested different ways of fusing for other purposes such as improved finetuning. Another fusion usage is the stochastic weight averaging, aiming to stabilize the SGD process by averaging multiple points along the SGD trajectory (Izmailov et al., 2018). Unlike the previous, this method utilizes only one model and dataset. Low-communication distributed training was proposed in similar settings to ours. Wortsman et al. (2022a) proposed distributed finetuning and model fusing in order to produce better finetuned models. This suggestion is equivalent to one iteration of ColD Fusion where all models share the same dataset. Li et al. (2022); Together (2022) also share the similarity of distributed training, but during pretraining on unlabeled data. Understanding why averaging different models improve quality may be related to theoretical works discussing weight and loss spaces. These works state there is a path of minimum loss between models (Garipov et al., 2018) on which the loss along the path is not increasing. Lubana et al. (2022); Benton et al. (2021); Frankle et al. (2020) claimed that under some constraints, this path is linear, which suggests that fusing the weights could produce a model that retains the capabilities of the fused models. Although different models on the same task may converge to different locations in the loss space without linear connectivity (Juneja et al., 2022), and although the case of multitask is more complex (Mirzadeh et al., 2020), we still believe that these works can partially explain why fusing preserves the capabilities gained by the constituent and when it does not that the next iteration fixes it. Gueta et al. (2023) further suggests the linear connectivity path is merely a line in a whole connected region, future work may tell whether ColD Fusion searches in this region or crosses it to find new ones. The literature also includes methods for better aligning models during training (Javaloy and Valera, 2021; Yu et al., 2020; Chen et al., 2018) or after it (Ainsworth et al., 2022; Jordan et al., 2022) to aid in fusing. We did not use those as we wanted to reduce the load on the repository and avoid restricting the contributors' finetuning. However, these methods may improve results in ColD Multitask. We mention that multitask learning does not optimize the base model objective directly (§2.3). Some works aim to do so (Bansal et al., 2019) through meta-learning, finding models that can learn a new task well or efficiently (Hospedales et al., 2021). REPTILE (Nichol et al., 2018) meta learns in a way that resembles ours by iteratively using models trained for several batches. ## 8 Conclusion And Discussion We proposed a scheme for utilizing abundant finetuned models to enhance a pretrained model. Our approach does not necessitate the sharing of datasets, but rather assumes each contributor solely finetunes on their own dataset. Hence, we believe that applying this scheme as a collaborative pretraining platform is a viable option and that doing so would result in ongoing improvement of base models. To scale this approach, it would be beneficial if the repository was updated asynchronously, perhaps relying on recent fusing techniques (Ilharco et al., 2022). In the usual finetuning setting, robustness can be improved by tuning batch size and learning rate. In analogy, in ColD Fusion, one can either increase the number of contributors (batch) and/or restrict the effect of each iteration (learning rate) (Smith and Le, 2018) to improve the process. Following this line, future work may consider regularizing the distance from the pretrained model (learning rate) when a small number of contributors exist (batch) or consider assigning individual weights to each contributor. There are many hyper parameters to optimize which might improve the method substantially. E.g., fusing the contributions with a weighted average, improving fusing itself (Matena and Raffel, 2021; Ainsworth et al., 2022), controlling the datasets seen in each iterations (related to; Choshen et al., 2021; Hacohen and Weinshall, 2019) and backtracking when a harmful update was done to the model. We hope that future work will shed more light on these issues, to further improve the approach proposed in this work. ## 9 Limitations Perhaps the most important limitation regarding ColD Fusion is its deployment. This paper presents a method for multitasking, not a platform. In that sense it solves both multitask learning goals under the constraints resulting from collaboration. However, using ColD Fusion in practice might require much more effort - It would require a place to host the models, a way to make sure no malicious or erroneous model was sent, and other aspects of a platform to support this training. This is the first method to tackle collaborative multitasking and we scaled it to 35 datasets. However, future methods may be found more efficient or scale better with the amount of data and computation. ColD Fusion with many iterations and models might require more computational effort for a given amount of data (§6) than regular multitask learning. As a result, while our bottom line performance is encouraging, ColD Fusion might not be the preferred way under every possible scenario. Still, some of the costs may be alleviated by future work - for example the additional iterations when fusing many models, might be reduced by aligning models' weights before fusing (Ainsworth et al., 2022). While this paper studied the impact of various ColD Fusion parameters, it is unclear how finetuning or even pretraining parameters affect results. However, we do have a reason to believe the method is relatively robust to these refactors through our initial results and the replication on another architecture (App. §D). Another limitation is the assumption that the weights of the model change. Some adaptation methods assume the model is frozen and only its inputs change. In those cases, the model would not be improved by use. Still, even in such cases, multitask learning (Wang et al., 2023) might be applied on the inputs, or the same model might be used in different ways, where some also adapt parts of it (Hu et al.; Jang et al., 2023; Qin et al., 2022; Yadav et al., 2023). In those cases, the method might still prove useful, even if it benefits only from some of the contributions. As mentioned before, another concern is a possible harmful update done by a contributor. 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Resolving interference when merging models. *arXiv preprint* arXiv:2306.01708. Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13(3):1–207. ## A Datasets Used Most datasets could be downloaded from huggingface datasets. We explicitly state the download link when relevant. As we used groups of datasets we report here the full list of datasets they contain. GLUE: CoLA (Warstadt et al., 2019), SST2 (Socher et al., 2013), MRPC (Dolan and Brockett, 2005), QQP (data.quora.com/First-QuoraDataset-Release-Question-Pairs), MNLI (Williams et al., 2018), QNLI Rajpurkar et al. 2016, RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), WNLI (Levesque et al., 2011) SuperGLUE: BoolQ (Clark et al., 2019), CB (de Marneffe et al., 2019), CoPA (Roemmele et al., 2011), MULTIRC (Khashabi et al., 2018), WIC (Pilehvar and Camacho-Collados, 2019), WSC (Levesque et al., 2012) MNLI (Williams et al., 2018), QNLI Rajpurkar et al. 2016, RTE (Dagan et al., 2005; Bar-Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), WNLI (Levesque et al., 2011), ESNLI (Camburu et al., 2018), adversarial NLI (Nie et al., 2020). EmoInt (Mohammad and Bravo-Marquez, 2017), Emoji (Barbieri et al., 2018), Irony (Van Hee et al., 2018), OffenseEval (Zampieri et al., 2019), HatEval (Basile et al., 2019), Sentiment Analysis (Rosenthal et al., 2017) Poem Sentiment (Sheng and Uthus, 2020), IMDB (Maas et al., 2011), Rotten Tomatoes (Pang and Lee, 2005), SST 5bins (Socher et al., 2013), SST2 (Socher et al., 2013), Amazon reviews (He and McAuley, 2016) ,Financial Phrasebank (Malo et al., 2014) AG news(Zhang et al., 2015), ISEAR(Scherer and Wallbott, 1994), Yahoo answers(Zhang et al., 2015), DBpedia(Zhang et al., 2015), 20 newsgroup(Zhang et al., 2015), TREC in both finegrained and coarse-grained labels (Li and Roth, 2002) ## B Finetuning Details Hyperparameters. During finetuning, we use the following hyperparameters: learning rate of 5e-5 with linear decay 0.0006 and batch size 256. Early stopping is performed on the development sets if the accuracy improvement after 256K training examples is less than 0.001. All other finetuning hyperparameters are constant across all experiments and follow the original hyperparameters Time and Memory. Most finetuning steps take an hour or less on an A100 GPU. Fusing times are inconsequential. At each iteration all finetuning runs in parallel on all datasets (8 in most cases) and also test finetuning runs in parallel, (36 in most cases). To put it all together, in the main experiment, 30 iterations with 8 contributors, 36 test sets, and 5 seeds, required approximately 4,800 A100 GPU hours and 3.2 TB of memory if all models are to be saved once. ## C Datasets Accuracy The full results of the main experiment (§5) can be found in Table 1. It contains accuracy score for each dataset separately. For ease of comparison we also supply two figures (Fig.7), comparing MUPPET and COLD multitask models to the pretrained. They show that ColD is much more consistent. It has fewer datasets that lose from changing from pretrained to ColD and smaller negative effects when there are such datasets. MUPPET however also has larger maximal gain when it does show gains, which shines favourably on the average. This makes ColD a better choice for an off-the-shelf model, but gives MUPPET an advantage when one tests a target dataset on several pretrained domains. ## D T5 We present initial results to confirm our method is not unique to RoBERTa. Specifically, we train T5 (Raffel et al., 2020) with default hyperparameters, but 256 batch size and 0.0004 learning rate. We replicate the main experiment (§5) in a smaller scale, running on seed only and 5 iterations only. For ColD-Frozen, we train only the language model head. Fig. 8 shows the main effect reminds. Both ColD and ColD-Frozen keep increasing with the iterations. ## E Multitask Scale We test the effect of the amount of datasets we use for multitasking on the performance of the resulted model as a base model. We take a random permutation of all the 36 datasets. We ColD fuse on the first 4 datasets, then the first 8, 16, and finally all the datasets. In fig. 9 we see that the 8 datasets performs worse than the 4 datasets, and ![14_image_0.png](14_image_0.png) ![14_image_1.png](14_image_1.png) that the high regime (16 and 36 datasets) performs much better than the low regime (4 and 8 datasets). These results align with (Aghajanyan et al., 2021b) observation that under 15 datasets more datasets decrease the performance, but past some critical point more datasets increase performance. ## F Fix Number Of Examples We depict the ColD Fusion process with multiple tasks (Fig. 10), but only 4K examples per each contributor. This simulates a case where contributors keep streaming new information of different kinds. While this can not fully predict the effect of streaming new tasks, it shows initial positive results in this regard. ![14_image_2.png](14_image_2.png) ![15_image_0.png](15_image_0.png) | Dataset | Finetune | Multitask | MUPPET | ColD-Fusion | |----------------------|------------|-------------|----------|---------------| | 20 Newsgroup | 85.31 | 85.25 | 90.00 | 85.97 | | AG News | 89.85 | 89.55 | 89.77 | 89.58 | | Amazon Reviews Multi | 66.51 | 66.22 | 86.50 | 66.65 | | ANLI | 51.51 | 51.48 | 52.59 | 52.00 | | BoolQ | 77.14 | 80.27 | 82.17 | 81.39 | | CB | 64.29 | 82.86 | 80.36 | 85.00 | | CoLA | 83.43 | 82.42 | 81.21 | 82.74 | | COPA | 47.00 | 60.00 | 65.00 | 64.40 | | DBPEDIA | 77.49 | 77.69 | 85.17 | 78.15 | | ESNLI | 91.00 | 91.27 | 52.59 | 91.31 | | Financial Phrasebank | 85.40 | 85.26 | 46.10 | 86.72 | | IMDB | 93.86 | 93.82 | 91.74 | 94.01 | | ISEAR | 72.78 | 71.94 | 73.01 | 72.40 | | MNLI | 87.11 | 87.26 | 93.04 | 87.14 | | MRPC | 87.45 | 86.96 | 88.97 | 89.26 | | MultiRC | 60.56 | 62.34 | 64.15 | 63.01 | | Poem Sentiment | 83.85 | 88.27 | 94.14 | 86.54 | | QNLI | 92.42 | 92.39 | 84.48 | 92.66 | | QQP | 90.72 | 90.89 | 91.25 | 91.22 | | Rotten Tomatoes | 88.03 | 90.73 | 58.10 | 91.48 | | RTE | 70.11 | 82.17 | 39.44 | 84.48 | | SST2 | 93.85 | 94.27 | 67.06 | 95.16 | | SST 5 bins | 56.24 | 57.56 | 94.84 | 59.52 | | Trec Coarse | 97.32 | 97.40 | 85.58 | 97.20 | | Trec Fine | 87.08 | 88.28 | 96.80 | 91.04 | | Twitter Emoji | 46.35 | 46.02 | 82.76 | 46.35 | | Twitter Emotion | 81.52 | 81.25 | 51.11 | 82.76 | | Twitter Hate | 53.76 | 53.70 | 76.02 | 53.95 | | Twitter Irony | 71.05 | 74.54 | 84.77 | 76.25 | | Twitter Offensive | 84.58 | 85.16 | 71.57 | 85.79 | | Twitter Sentiment | 70.94 | 70.47 | 87.07 | 70.72 | | WIC | 65.71 | 68.06 | 66.61 | 68.12 | | WNLI | 55.21 | 51.55 | 91.10 | 54.93 | | WSC | 63.46 | 63.27 | 63.46 | 62.31 | | Yahoo Answers | 72.49 | 71.71 | 71.90 | 72.69 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 8 A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? abstract Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4 ✓ B1. Did you cite the creators of artifacts you used? Whenever relevant 2,3 etc. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We provide a model and upload it publicly with a permissive license (MIT), this is technical and is not interesting for the scientific advancement we provide. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? The whole paper is about a surprising use of current models, so it is consistent legally, but also unconventional. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Not applicable. Left blank. ## C ✓ **Did You Run Computational Experiments?** 3,4,5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix B The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix B + Section 3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 Fig 2 mainly (the main experiment which includes several runs) Other experiments do not have repetitions but varying a trait, so the clear (not noisy) trend serves as a way to assess variance. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Sections such as 3 and Appendices such as A ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left Blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

zhan-etal-2023-test

Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization

https://aclanthology.org/2023.acl-long.47

The neural metrics recently received considerable attention from the research community in the automatic evaluation of machine translation. Unlike text-based metrics that have interpretable and consistent evaluation mechanisms for various data sources, the reliability of neural metrics in assessing out-of-distribution data remains a concern due to the disparity between training data and real-world data. This paper aims to address the inference bias of neural metrics through uncertainty minimization during test time, without requiring additional data. Our proposed method comprises three steps: uncertainty estimation, test-time adaptation, and inference. Specifically, the model employs the prediction uncertainty of the current data as a signal to update a small fraction of parameters during test time and subsequently refine the prediction through optimization. To validate our approach, we apply the proposed method to three representative models and conduct experiments on the WMT21 benchmarks. The results obtained from both in-domain and out-of-distribution evaluations consistently demonstrate improvements in correlation performance across different models. Furthermore, we provide evidence that the proposed method effectively reduces model uncertainty. The code is publicly available at \url{https://github.com/NLP2CT/TaU}.

# Test-Time Adaptation For Machine Translation Evaluation By Uncertainty Minimization Runzhe Zhan1 Xuebo Liu2∗ Derek F. Wong1∗ **Cuilian Zhang**1 Lidia S. Chao1 **Min Zhang**2 1NLP2CT Lab, Department of Computer and Information Science, University of Macau nlp2ct.{runzhe, cuilian}@gmail.com, {derekfw, lidiasc}@um.edu.mo 2Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China {liuxuebo, zhangmin2021}@hit.edu.cn ## Abstract The neural metrics recently received considerable attention from the research community in the automatic evaluation of machine translation. Unlike text-based metrics that have interpretable and consistent evaluation mechanisms for various data sources, the reliability of neural metrics in assessing out-of-distribution data remains a concern due to the disparity between training data and real-world data. This paper aims to address the inference bias of neural metrics through uncertainty minimization during test time, without requiring additional data. Our proposed method comprises three steps: uncertainty estimation, test-time adaptation, and inference. Specifically, the model employs the prediction uncertainty of the current data as a signal to update a small fraction of parameters during test time and subsequently refine the prediction through optimization. To validate our approach, we apply the proposed method to three representative models and conduct experiments on the WMT21 benchmarks. The results obtained from both in-domain and out-of-distribution evaluations consistently demonstrate improvements in correlation performance across different models. Furthermore, we provide evidence that the proposed method effectively reduces model uncertainty. The code is publicly available at https://github.com/NLP2CT/TaU. ## 1 Introduction The evaluation of machine translation (MT) systems aims to quantitatively assess their performance using either automatic metrics or human evaluators. When developing cutting-edge MT systems, selecting the optimal model using automatic metrics is highly significant to save human labor, given a large number of candidate models. Over the last decade, the researchers have primarily relied on traditional metrics based on text overlap (Papineni ![0_image_0.png](0_image_0.png) et al., 2002; Snover et al., 2006; Popovic´, 2015) to evaluate system performance. However, these metrics fall short in capturing semantic-level information and exhibit poor correlation with human ratings when assessing the latest neural MT systems because of increased model capacity (Ma et al., 2019; Mathur et al., 2020). Consequently, several neural metrics (Zhang et al., 2020; Rei et al., 2020; Sellam et al., 2020; Zhan et al., 2021a; Wan et al., 2022) and test sets (Müller et al., 2018; Stanovsky et al., 2019; Zhan et al., 2021b; Freitag et al., 2021b) have been proposed to provide broader evaluation perspectives and show outstanding performance in evaluating state-of-the-art systems. Despite the superiority of neural metrics, the adoption of these metrics over traditional overlap-based measures has witnessed a gradual pace. The people engaged in MT research and industry remain cautious due to concerns surrounding potential robustness issues, thereby hindering the progress of popularizing neural metrics. The source of robustness problem can be attributed to data shift. The fine-tuning data used when developing neural metrics is composed of labels derived from human ratings obtained when evaluating strong MT systems in the News domain, which largely limits the generalization capability of the obtained model. In real-world scenarios, the evaluation metric must be capable of assessing text originating from diverse domains with varying levels of quality. However, neural metrics, trained on limited data, may exhibit biases when dealing with out-of-distribution data. These factors present challenges in establishing neural metrics as reliable evaluation measures across a wide range of applications. Glushkova et al. (2021) proposed employing uncertainty quantification (Gal and Ghahramani, 2016; Lakshminarayanan et al., 2017) to assess the risk associated with utilizing neural metrics in evaluation and discovered a correlation between model uncertainty and model prediction errors, as depicted in Figure 1. While Glushkova et al. (2021) have explored the uncertainty of neural metrics, the quest for a solution to mitigate uncertainty in MT evaluation remains an under-explored research area. One intuitive approach is fine-tuning the model using diverse and multi-domain data. Unfortunately, there is currently no publicly available dataset that satisfies this requirement. In this paper, we propose an unsupervised approach for neural metrics aimed at minimizing uncertainty during test time and mitigating the challenges posed by out-of-distribution data. Our proposed method involves two additional stages integrated before the normal inference process: uncertainty estimation and test-time adaptation. Firstly, our model leverages the Monte Carlo approach (Gal and Ghahramani, 2016) to estimate the uncertainty of the current input data. Subsequently, the estimated uncertainty serves as a guiding signal to optimize a small fraction of model parameters using gradient descent. Finally, the model proceeds with the regular inference procedure, utilizing the adapted parameters to make predictions. In this way, the model can adjust its parameters dynamically to better cope with diverse data, which is flexible and does not require any labeled data. We use the representative metric family COMET (Rei et al., 2020) as our testbed and conduct experiments on WMT21 benchmark (Freitag et al., 2021b), which accounts for evaluating outof-distribution data. The experimental results show that our method can improve the system-level correlation performance as well as the ranking accuracy of partial COMET baselines. Furthermore, our analysis highlights the applicability of our method and confirms its efficacy in reducing uncertainty. ## 2 Background MT Metrics Ideally, human labor is used to evaluate the translation quality of MT models and identify the optimal model. Since human assessment is expensive, there is a need for automatic evaluation methods that can provide instantaneous measurements of a model's capability. More specifically, given the model hypothesis h, ground truth r, and source s, the metric M(·) will quantify the translation quality q by comparing the model hypothesis and reference ⟨*h, t*⟩: $$q={\begin{cases}\operatorname{M}(h,s)&s=\varnothing\\ \operatorname{M}(h,r)&t=\varnothing\\ \operatorname{M}(h,s,r)&s,t\neq\varnothing\end{cases}}\qquad(1)$$ There are three types of metrics based on their utilization of reference information: reference-based metric M(⟨h, ·, r⟩) (which solely utilize the target translation or jointly consider both the source and target information), and reference-free metric M(⟨*h, s*⟩) (which solely rely on the source input). Among these, reference-based metrics are widely employed, and reference-free metrics are often categorized as quality estimation metrics (Fonseca et al., 2019). The neural metrics build a regression scoring model by leveraging pre-trained representation, which have achieved remarkable performance in MT evaluation. In this way, the metric M is parameterized by model θ: $$q=\mathrm{M}(\langle h,s,r\rangle;\theta)$$ $$(2)$$ q = M(⟨*h, s, r*⟩; θ) (2) As an example, the COMET (Rei et al., 2020) framework employs two distinct downstream architectures to leverage a pre-trained XLM (Conneau et al., 2020) model. It fine-tunes the additional regression and ranking models using human rating data obtained from the WMT Metrics task, ensuring that the tuned parameters can evaluate the translation quality. Uncertainty As deep neural networks are widely used in real-world applications, uncertainty is a critical measurement that indicates how a model is confident in the predictions in order to prevent causing ![2_image_0.png](2_image_0.png) serious consequences such as gender bias (Savoldi et al., 2021). There are two kinds of uncertainty proposed by previous research: aleatoric uncertainty and epistemic uncertainty (Der Kiureghian and Ditlevsen, 2009; Kendall and Gal, 2017). While aleatoric uncertainty pertains to data noise in observations and cannot be easily eliminated, epistemic uncertainty stems from the insufficient knowledge of a model. Given that the training data for neural metrics primarily revolves around the News domain, this paper focuses on reducing epistemic uncertainty, particularly for out-of-distribution data. Test-time Adaptation Domain adaptation (Pan and Yang, 2010) offers a deterministic target and can be trained with additional data through supervised or unsupervised methods, providing an intuitive approach to reduce epistemic uncertainty. However, there is a dearth of research exploring domain adaptation in MT evaluation due to the scarcity of multi-domain human ratings. Another limitation of using domain adaptation methods to mitigate epistemic uncertainty is the unknown domain of input data in real-world scenarios. This becomes particularly crucial for neural metrics, as they need to score diverse inputs without introducing domain bias. Test-time adaptation paradigm handles this challenge as a viable solution and can be categorized into test-time training (Sun et al., 2020) and source-free test-time adaptation (Kundu et al., 2020; Liang et al., 2020; Wang et al., 2021). It generalizes the model to out-of-distribution data during the testing phase without necessitating additional fine-tuning operations. Notably, a concurrent work (Lee and Lee, 2023) in the image classification tasks has also proposed minimizing uncertainty during test time. However, there are notable distinctions between our approach and theirs in terms of learning objectives and the specific type of uncertainty being targeted. In the context of MT evaluation, we present the first application of this paradigm and contribute a novel method that minimizes epistemic uncertainty at test time. ## 3 Method The proposed method, as illustrated in Figure 2, is comprised of three distinct stages. These stages will be thoroughly discussed in the following section. Since both reference-based regression model and quality estimation (QE) model are used in COMET framework, we use ⟨*h, s,* ·⟩ to denote input data in order to take two major types of metrics mentioned in Section 2. ## 3.1 Uncertainty Estimation The uncertainty is widely used in the classification model to obtain confidence about the classification results over a distribution P. Due to the fact that most neural metrics are regression models instead of classification model, for an input ⟨*h, s,* ·⟩, the regression model only produce a single score q rather than a score distribution P(q). Therefore, it is a non-trivial question that how can obtain score distribution P. Glushkova et al. (2021) highlighted that Monte Carlo Dropout (MCD; Gal and Ghahramani, 2016) and Deep Ensemble (DE; Lakshminarayanan et al., 2017) are two approaches used for estimating the uncertainty of a regression model. DE involves using multiple models that vary in randomization methods to predict scores for the same input, and then aggregating them to obtain a scoring distribution. Similarly, MCD also relies on models with different randomization, but only requires a single model with dropout enabled (Srivastava et al., 2014). The dropout technique introduces randomness by altering the activation status of model parameters during inference, simulating the effects of multiple homologous models used in DE. Since our method focuses on adapting a single model to the target distribution, we choose MCD to estimate the score distribution due to its convenience and relatively low computational cost. Specifically, given an input ⟨*h, s,* ·⟩ and a model parameterized with θ, MCD makes model perform K-times feed-forward pass with different sets of parameters θk to get a score distribution P(q) = {M(⟨*h, s,* ·⟩; θk)} K k=1. Subsequently, the uncertainty can be calculated by the variance of score distribution P(q), which can be formally expressed as: $$u(\langle h,s,\cdot\rangle)=\mathbf{Var}(\{\mathrm{M}(\langle h,s,\cdot\rangle;\theta_{k})\}_{k=1}^{K})\tag{3}$$ where Var is the calculation process of variance. We use the standard deviation in implementation: $$\mathbf{Var}(P)={\sqrt{\mathbb{E}\left[(P-\mu_{P})^{2}\right]}}$$ 2] (4) ## 3.2 Adaptation By Uncertainty Minimization After acquiring the model uncertainty through the methodologies outlined in the preceding section, it is advisable to expand the estimation procedure from instance-level to batch-level and run the estimation method in parallel. This approach serves two purposes: firstly, it enables seamless integration of the proposed method with the original inference process; secondly, it promotes stability in the optimization process by incorporating batch-level characteristics. Utilizing the uncertainty of each sentence independently as a guide for optimizing the model parameters would hinder the acquisition of adequate domain-specific features and potentially lead to a compromised starting point. To circumvent these challenges, the adaptation algorithm is designed at the batch level. Another crucial problem is the choice of optimization parameters. Despite the existing categorization of data instances into different domains within the benchmark, there still exist differences among these domain-specific instances (Moore and Lewis, 2010). To deal with this problem, we ought to make the optimization process flexible to switch between different batches but not deviate too far from the original representation. Therefore, we choose to optimize a small fraction of the original model parameters, including the layer-wise attention and the corresponding coefficients. The architecture of neural metric model typically consists of a pre-trained encoder and a score estimator, as illustrated in Figure 2. The score estimator is responsible for regression-based prediction of score q and takes the sentence embedding Oembed generated by L-layer1encoder as its input. In the COMET framework, the sentence embedding is obtained by aggregating the output hi of each layer using layer-wise attention w = {wi} L i=1, which can be formulated as follows: $$\mathbf{O}_{\mathrm{embed}}=\gamma\cdot\sum_{l=1}^{L}w_{i}\cdot\mathrm{LayerNorm}(\mathbf{h}_{i})\quad(5)$$ $$(4)$$ where γ is a learnable scaling coefficient and LayerNorm(·) denotes layer normalization operation (Ba et al., 2016). Therefore, it is intuitive to achieve flexible adaptation by influencing the computation of sentence embedding, given its pivotal role in comprehending the semantic aspects of the text. We choose γ and w as the optimization parameters θ∗. For the empirical exploration of other optimization choices, we leave the discussion of this question in Section 5.1. Algorithm 1 outlines the process of test-time adaptation by uncertainty minimization (TAU) when evaluating a specific MT system. The batchlevel optimization, as described in the fifth to the eighth line, aligns with the aforementioned explanations. However, a notable challenge arises during the initial stages of optimization, commonly known as the "cold start" problem, if the test set is traversed only once. At the beginning of optimization, the model estimates the uncertainty using a small portion of the data, which prevents the early samples from benefiting from test-time adaptation compared to subsequently encountered samples. Therefore, the proposed method considers performing multiple adaptations for the entire system-level data, as indicated in the third line of Algorithm 1. In this way, the well-adapted model can re-score 1For the XLM-R model used by COMET framework, L is set to 24. Algorithm 1 TAU: Test-time Adaptation by Uncertainty Minimization Require: Model θ, System-level evaluation tuple D = {⟨h, s, ·⟩}, Adaptation rate α, Adaptation times J. 1: Backup original model θ ′ ← θ 2: Select parameters for adaptation |θ∗*| ≪ |*θ| 3: for adaptation iteration j = 1*, ..., J* do 4: Score set q = {ø} 5: for mini-batch {⟨h, s, ·⟩}N i=1 ∈ D do 6: Estimate uncertainty u by Equation 3 7: Optimize θ∗ ← θ∗ − α∇θ∗ 1 N PN i=1 ui 8: **end for** 9: Infer score [q] by Equation 7 10: q ⇐ [q] 11: **end for** 12: Restore to original model θ ← θ ′ 13: **return** q the previous samples that may receive an uncertain score suffered by the cold start problem. To conclude, the optimization objective of TAU can be formally expressed as follows: $$\theta^{*}=\operatorname{arg\,min}_{\theta^{*}}\ \mathbb{E}_{\langle h,s,\cdot\rangle\in\mathcal{D}}\ \left[u(\langle h,s,\cdot\rangle)\right]\tag{6}$$ ## 3.3 Inference Although the mean of the score distribution P(q) estimated by MCD process can be viewed as a prediction score, it is not adopted in order to ensure comparability with other baseline models. Consequently, the inference stage of the adapted model aligns with conventional inference practices. To achieve this, the adapted model does not employ back-propagation of gradients and dropout during the inference process, as stated in the 9th line of Algorithm 1. The inference process can be formulated as follows: $$q=\mathrm{M}_{\theta+\Delta\theta^{*}}(\left\{\left\langle h,s,\cdot\right\rangle\right\})$$ q = Mθ+∆θ∗ ({⟨h, s, ·⟩}) (7) In summary, the model leverages the MCD to estimate prediction uncertainty u of current data D. This uncertainty serves as a signal to update the partial parameters θ∗ during test time, ultimately leading to self-corrected predictions. Moreover, the update process is performed online, ensuring that no additional storage costs are incurred. ## 4 Experiments 4.1 Experimental Setups Data We conduct experiments on a multi-domain benchmark of WMT21 Metrics Task2, which includes three language pairs and corresponding MQM scores. Compared to previous WMT crowdsourced evaluations, MQM framework is a more granular evaluation protocol that focuses on explicit errors. Freitag et al. (2021a) explored the application of the MQM framework (Lommel et al., 2014) in the evaluation of WMT submissions and published an alternative set of reference scores annotated by human experts3. We used MQM scores as the reference and evaluate how well the scores produced by metrics correlate with them. For News domain that has multiple references, we extend the evaluation of metrics to include human translations (HT) alongside the standard reference. It is important to note that HT is out-of-distribution data for neural metrics, given that these metrics have primarily been trained on the scoring data related to existing MT systems. Specifically, the metrics need to conduct the system-level evaluation by involving (*w/ HT*) or excluding HT text (*w/ HT*). Baselines The baselines cover three mainstream types of metrics: - **Text-based Metrics**: Traditional metrics quantify the n-gram overlap between the hypothesis and reference, such as BLEU (Papineni et al., 2002) and CHRF (Popovic´, 2015), or measure the edit distance like TER (Snover et al., 2006). These metrics employ transparent evaluation mechanisms that draw inspiration from human evaluation. However, their scope is limited to assessing the surface-level coverage at the morphological level. - **Embedding-based Metrics**: The evaluation process of embedding-based metrics is also transparent and characterized by strong interpretability. These metrics measure the semantic-level similarity between reference and hypothesis embeddings, which are encoded using a pre-trained encoder or language model (Devlin et al., 2019). This approach provides a more nuanced evaluation perspective compared to text-based metrics. Among 2https://www.statmt.org/wmt21/metrics-task.html 3https://github.com/google/wmt-mqm-human-evaluation/ | Metrics | News w/o HT | News w/ HT | TED | | | | | | | | |------------------------------------|---------------|--------------|-------|-------|-------|-------|-------|-------|------|------| | En-De | Zh-En | En-Ru | En-De | Zh-En | En-Ru | En-De | Zh-En | En-Ru | Avg. | | | Baselines | | | | | | | | | | | | TER | 93.0 | 41.6 | -4.1 | 7.4 | -8.5 | -28.9 | 50.6 | 42.1 | 69.7 | 29.2 | | BLEU | 93.7 | 31.0 | 50.7 | 13.2 | -15.2 | -4.3 | 62.0 | 32.4 | 82.8 | 38.5 | | CHRF | 89.8 | 30.2 | 78.3 | 1.7 | -14.3 | 12.3 | 47.1 | 36.3 | 82.5 | 40.4 | | BERTSCORE | 93.0 | 54.2 | 62.9 | 7.4 | 9.5 | -12.3 | 50.6 | 30.6 | 83.1 | 42.1 | | COMET-DA2020 | 81.4 | 51.1 | 67.6 | 65.8 | 22.1 | 55.6 | 78.8 | 25.1 | 85.9 | 59.3 | | COMET-MQM-QE2021 | 71.1 | 52.9 | 63.2 | 79.2 | 61.9 | 68.1 | 69.4 | -20.9 | 88.4 | 59.3 | | COMET-MQM2021 | 77.1 | 62.8 | 65.9 | 72.0 | 33.6 | 68.5 | 81.8 | 26.6 | 84.1 | 63.6 | | Reproduced Results and Our Methods | | | | | | | | | | | | ♢ COMET-DA2020 | 81.5 | 51.1 | 67.5 | 58.0 | 26.4 | 56.8 | 78.8 | 25.0 | 85.9 | 59.0 | | +TAU | 85.7 | 53.5 | 71.0 | 48.0 | 27.4 | 54.5 | 85.9 | 28.3 | 87.3 | 60.2 | | ♢ COMET-MQM-QE2021 | 71.2 | 53.0 | 68.8 | 79.2 | 61.9 | 68.1 | 69.4 | -20.8 | 81.7 | 59.2 | | +TAU | 62.8 | 57.4 | 70.3 | 72.0 | 65.2 | 78.1 | 82.9 | 25.7 | 80.7 | 66.1 | | ♢ COMET-MQM2021 | 77.2 | 62.8 | 65.9 | 69.8 | 48.7 | 69.7 | 81.8 | 26.6 | 84.1 | 65.2 | | +TAU | 76.5 | 69.2 | 67.1 | 75.4 | 67.8 | 71.4 | 87.5 | 24.5 | 84.9 | 69.4 | them, the representative BERTSCORE (Zhang et al., 2020) metric is used in our experiments. mating the uncertainty, we perform feed-forward operation K = 30 times with dropout enabled. - **Neural Metrics**: Since the evaluation mechanism of neural metric has been described in Section 2, we will not go into details in this part. There are several models provided in COMET framework (Rei et al., 2020, 2021) including reference-based and reference-free models. We choose three representative models as the baselines and testbed: COMETDA2020, COMET-MQM2021 and COMETMQM-QE2021, where the last one only requires source text to evaluate the translation. The reported performance of baselines is taken from official results (Freitag et al., 2021b). To minimize the possible bias in our experiments, we reproduced COMET baselines using open-sourced repository4and implement our method on the same code skeleton. Settings During the process of test-time adaptation, the learning rate α is set to 1e − 4 by using WMT20 benchmark as the development set. We only tune the batch size N and adaptation times J for better performance. We use Adam optimizer (Kingma and Ba, 2015) to update parameters θ∗ with β1 = 0.9, β2 = 0.99 and ϵ = 10−8. For esti4https://github.com/Unbabel/COMET ## 4.2 Meta-Evaluation To assess the system-level performance of the metric, we employ two meta-evaluation methods: correlation performance and pairwise accuracy. The Pearson correlation, renowned for its widespread application, serves as a common metric used in evaluating system-level performance. This measurement has also been adopted by the WMT Shared Task as a means to evaluate the performance of metrics. In addition, pairwise accuracy (Kocmi et al., 2021) measures how many system pairs are correctly ranked by the metric, which can be calculated as follows: $$\begin{array}{c}\mbox{\rm{sign}}(\mbox{\rm{metric}}\Delta)=\mbox{\rm{sign}}(\mbox{\rm{human}}\Delta)|\\ \mbox{\rm{|system pairs|}}\end{array}\tag{8}$$ where ∆ and sign(·) denote the differences and the sign function, respectively. While most existing work calculates the correlation (e.g., Pearson correlation) between metric scores and human judgments to evaluate their performance, a reliable metric should also be able to correctly compare and rank MT systems. Therefore, we report pairwise accuracy in addition to Pearson correlation performance to demonstrate the system-level ranking performance, serving as a cross-validation metric. | Metrics | News w/o HT | News w/ HT | TED | | | | | | | | |--------------------|---------------|--------------|-------|-------|-------|-------|-------|-------|------|------| | En-De | Zh-En | En-Ru | En-De | Zh-En | En-Ru | En-De | Zh-En | En-Ru | Avg. | | | ♢ COMET-DA2020 | 82.1 | 70.5 | 68.1 | 72.4 | 61.5 | 66.7 | 82.1 | 69.2 | 82.4 | 72.8 | | +TAU | 89.7 | 69.2 | 73.6 | 76.2 | 59.3 | 70.5 | 85.9 | 67.9 | 83.5 | 75.1 | | ♢ COMET-MQM-QE2021 | 73.1 | 78.2 | 69.2 | 78.1 | 81.3 | 73.3 | 71.8 | 41.0 | 80.2 | 71.8 | | +TAU | 71.8 | 75.6 | 75.8 | 77.1 | 79.1 | 79.0 | 80.8 | 57.7 | 80.2 | 75.3 | | ♢ COMET-MQM2021 | 79.5 | 66.7 | 68.1 | 77.1 | 61.5 | 70.5 | 87.2 | 66.7 | 78.0 | 72.8 | | +TAU | 83.3 | 66.7 | 64.8 | 80.0 | 63.7 | 68.5 | 88.5 | 65.4 | 82.4 | 73.7 | We use functions from mt-metrics-eval5toolkit to calculate the above two meta-evaluation results. ## 4.3 Main Results As can be seen from Table 1, the proposed method TAU partially improves the averaged correlation performance of COMET metrics, and the improvements vary from model to model. Models trained on MQM scores demonstrate a greater benefit from adaptation compared to COMET-DA models whose training data is direct assessment (DA) scores. This observation suggests that TAU exhibits characteristics akin to continual learning when the test data is related to the training data source. The cross-validation results in Table 2 show a similar tendency as what is observed in Table 1. Since we did not perform hyper-parameter searching on pairwise accuracy, which further supports the effectiveness of the proposed method. From a modellevel comparison standpoint, the QE model still receives larger improvements. However, it is notable that adaptation may occasionally result in a performance decline. Therefore, the decision to do adaptation or not becomes a vital consideration for in-domain data, and the subsequent section will delve into the effect of distribution differences through an empirical study. ## 5 Analysis In this section, we will discuss the effectiveness of our method by answering three questions: 1) How do different optimization settings impact performance? 2) When does test-time adaptation work? 3) Can the proposed method effectively reduce epistemic uncertainty? Among these questions, the last one serves to justify our research objective and entails a segment-level analysis to understand why Domain LAtt. LN. Estim. ρ Acc. | News TED | |------------| ✓ ✗ ✗ **85.7 89.7** ✗ ✓ ✗ 79.5 76.9 ✗ ✗ ✓ 78.7 80.8 ✓ ✓ ✗ 79.6 76.9 ✓ ✗ ✓ 78.6 80.8 ✓ ✓ ✓ 78.0 79.4 ✓ ✗ ✗ **85.9 85.9** ✗ ✓ ✗ 79.4 82.1 ✗ ✗ ✓ 77.2 76.9 ✓ ✓ ✗ 79.4 82.1 ✓ ✗ ✓ 77.1 76.9 ✓ ✓ ✓ 76.9 76.9 ## 5.1 Ablation Study We use COMET-DA model to conduct an ablation study since it was not tuned for MQM scoring. Adaptation Parameters Table 3 reveals that the parameters of layerwise attention module are suitable to optimize at test time, addressing the concerns raised in Section 3.2. The conducted comparisons reveal that optimizing parameters other than the layerwise attention module ultimately results in performance degradation. This degradation persists even when jointly tuning with the layerwise attention module. These findings confirm our initial hypothesis that optimization should not deviate too far from the original parameters, thereby avoiding extensive optimization of core components or a larger number of parameters. A closer examination of the degree of performance degradation indicates that optimizing the estimator produces the most 5https://github.com/google-research/mt-metrics-eval/ ![7_image_0.png](7_image_0.png) significant decline in performance, aligning with the aforementioned reasons. Adaptation Times To address the "cold start'' problem discussed earlier, Algorithm 1 incorporates a multiple adaptation policy. The empirical results presented in Figure 3 reveal a relationship between the choice of adaptation times and the domain. Specifically, in-domain data (News) suffers from continuous adaptation, whereas out-ofdistribution data (TED) demonstrates improved performance through multiple adaptations. In the case of in-domain data, the data shift between training and inference is relatively smaller compared to out-of-distribution data, allowing the performance to reach its peak with fewer adaptation runs. In contrast to in-domain behaviors, optimizing out-ofdistribution data takes longer due to the need for dissimilar data features, leading to fluctuations in performance indicators. Nevertheless, a common trend emerges where larger adaptation times eventually hinder performance, particularly for in-domain data. To strike a balance between computational time and performance, all the adaptation times utilized in the previous experiments are limited to no more than 5 times. ## 5.2 Effects Of Data Types In order to determine which type of data benefits more from the proposed method TAU, we categorize the evaluation tasks into three distinct types, and then report the performance changes for each type in Table 4. The scope of out-of-distribution data extends beyond TED data from out-of-domain sources, encompassing human translations (HT) as well. The human translations rarely present in training data and differ significantly from the text generated by MT systems. Thus, the tasks within "News w/HT" category are regarded as partial outof-distribution scenarios. Overall, the proposed method achieves the highest improvement for each model when evaluated on out-of-distribution data, as evidenced by the average correlation metric. It is plausible because a major source of uncertainty is out-of-distribution data, and TAU is able to alleviate inference bias in these cases. | Models | ∆ID. | ∆Partial OD. | ∆OD. | |----------|--------|----------------|--------| | DA | 3.4 | -3.8 | 4.0 | | MQM | 2.4 | 8.8 | 1.5 | | QE-MQM | -0.8 | 2.0 | 19.7 | | Avg. | 1.7 | 2.4 | 8.4 | ## 5.3 Model Uncertainty In response to our research objectives, we investigate whether the uncertainty has been reduced after applying the proposed method. We aggregated the uncertainty values at the segment level and visualized their distributions grouped by languages and models, as depicted in Figure 4. These visualizations demonstrate a shift in the distributions for both in-domain and out-of-distribution data, affirming the effectiveness of uncertainty minimization. However, it is worth delving into the reasons behind the larger uncertainty shift observed in ![8_image_0.png](8_image_0.png) COMET-DA model compared to COMET-MQM model. The discrepancy could be attributed to the training data. COMET-MQM model is derived by fine-tuning COMET-DA model on normalized MQM scores, which employ a scoring protocol that deviates from the traditional point-wise scale. Specifically, the segment-level MQM score is derived from the count of explicit errors and ranges from -25 to 0, unlike the continuous [1, 100] scale adopted by WMT (Freitag et al., 2021a). We observed that there are many identical scores such as "0", which means that the annotators consider them to be perfect translations. As a consequence, the MQM scores exhibit less diversity compared to the DA scores, subsequently influencing the prediction behavior of the models fine-tuned on MQM scores. Encouragingly, despite these factors, we were able to reduce uncertainty in the MQM models and improve their overall performance. ## 6 Conclusion The uncertainty of neural metrics is proven to be associated with prediction error and limits generalizing them for a wider range of applications. In this paper, we propose a novel method, TAU, to minimize the uncertainty of neural metrics at test time in unsupervised settings without learning extra data. Our experimental results showcase the efficacy of TAU in reducing test-time uncertainty while simultaneously improving the performance of widely used metrics. In addition, our findings indicate that the proposed method exhibits enhanced effectiveness when applied to out-of-distribution data in comparison to in-domain data, which lays a solid foundation for its potential application to other models. However, the segment-level performance does not significantly outperform the baselines. In the future, we will polish the methods for better segment-level correlation performance and explore the test-time adaptation on large language models across various tasks. ## Limitations The methodology and experimental approach presented in this paper have certain limitations concerning their practical application and the availability of language resources. The proposed method estimates uncertainty using Monte Carlo Dropout with K iterations and subsequently performing adaptation J times. These additional computations result in increased inference time in real-world applications. Empirical evidence suggests that larger values of J lead to a linear increase in time costs in practical scenarios. Although the number of J on the WMT21 benchmark was limited in our experiments, the exact cost associated with achieving successful adaptation for new models or datasets remains uncertain. In terms of language resources, the majority of MT metric benchmarks still focus on the News domain, leaving a dearth of multidomain MQM benchmarks for conducting more meta-evaluation experiments during the preparation of this paper. To address these limitations, it is imperative to explore the performance of the proposed methodology on a wider range of out-ofdistribution benchmarks in the future. Furthermore, as highlighted by the reviewer, it is also important to note that the proposed methodology does not consistently exhibit performance improvements on certain specific test sets. One possible explanation for this observation could be attributed to our investigation of the optimal learning rate using the WMT20 dataset. The divergence in scoring perspectives between the conventional WMT score and the MQM score might lead to discrepancies in improvement trends. ## Ethics Statement An ethical concern associated with neural metrics is the presence of unpredictable bias in the evaluation process. Unlike traditional text-based metrics, neural metrics pose challenges in mitigating evaluation bias due to their black-box nature, which also introduces potential issues like gender bias inherent in pre-trained language models. While our current study does not investigate the bias problem, reducing uncertainty in the evaluation process may help contribute to mitigating the potential risks associated with generating biased results. ## Acknowledgment This work was supported in part by the Science and Technology Development Fund, Macau SAR (Grant Nos. FDCT/060/2022/AFJ, FDCT/0070/2022/AMJ), the National Natural Science Foundation of China (Grant No. 62206076), the Research Program of Guangdong Province (Grant No. 2220004002576), Shenzhen College Stability Support Plan (Grant Nos. GXWD20220811173340003, GXWD20220817123150002), Shenzhen Science and Technology Program (Grant No. RCBS20221008093121053) and the Multi-year Research Grant from the University of Macau (Grant No. MYRG2020-00054-FST). This work was performed in part at SICC which is supported by SKL-IOTSC, and HPCC supported by ICTO of the University of Macau. We would like to thank the anonymous reviewers and meta-reviewer for their insightful suggestions. ## References Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. *ArXiv preprint*, abs/1607.06450. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. 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Test-time training with self-supervision for generalization under distribution shifts. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 9229–9248. PMLR. Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek Wong, and Lidia Chao. 2022. UniTE: Unified translation evaluation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8117–8127, Dublin, Ireland. Association for Computational Linguistics. Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, and Trevor Darrell. 2021. Tent: Fully test-time adaptation by entropy minimization. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. Runzhe Zhan, Xuebo Liu, Derek F. Wong, and Lidia S. Chao. 2021a. Difficulty-aware machine translation evaluation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 26–32, Online. Association for Computational Linguistics. Runzhe Zhan, Xuebo Liu, Derek F. Wong, and Lidia S. Chao. 2021b. Variance-aware machine translation test sets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with BERT. In *8th International* Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Discussed in the "Limitation" section after the conclusion but before the references. A2. 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chang-etal-2023-multi

Multi-{CLS} {BERT}: An Efficient Alternative to Traditional Ensembling

https://aclanthology.org/2023.acl-long.48

Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERT{\_}Base model can even outperform the corresponding BERT{\_}Large model. We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4-times less computation and memory.

# Multi-Cls Bert**: An Efficient Alternative To Traditional Ensembling** Haw-Shiuan Chang∗† **Ruei-Yao Sun**∗† Amazon USA [email protected] [email protected] ## Abstract Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERTBase model can even outperform the corresponding BERTLarge model. We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4-times less computation and memory. ## 1 Introduction BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) is one of the most widely-used language model (LM) architectures for natural language understanding (NLU) tasks. We often fine-tune the pretrained BERT or its variants such as RoBERTa (Liu et al., 2019) so that the LMs learn to aggregate all the contextualized word embeddings into a single CLS embedding for a downstream text classification task. ∗indicates equal contribution †The work is done while the authors were at UMass Kathryn Ricci∗ **Andrew McCallum** CICS, UMass, Amherst 140 Governors Dr., Amherst, MA, USA [email protected] [email protected] ![0_image_0.png](0_image_0.png) ![0_image_1.png](0_image_1.png) Classic 5 BERT Ensemble ![0_image_2.png](0_image_2.png) During fine-tuning, different initializations and different training data orders significantly affect BERT's generalization performance, especially with a small training dataset (Dodge et al., 2020; Zhang et al., 2021a; Mosbach et al., 2021). One simple and popular solution to the issue is to finetune BERT model multiple times using different random seeds and ensemble their predictions to improve its accuracy and confidence estimation. Although very effective, the memory and computational cost of ensembling a large LM is often prohibitive (Xu et al., 2020; Liang et al., 2022). Naturally, we would like to ask, "Is it possible to ensemble BERT models at no extra cost?" To answer the question, we propose Multi-CLS BERT, which enjoys the benefits of ensembling without sacrificing efficiency. Specifically, we input the multiple CLS tokens to BERT and encourage the different CLS embeddings to aggregate the information from different aspects of the input text. As shown in Figure 1, the proposed Multi-CLS BERT shares all the hidden states of the input text 821 and only ensembles different ways of aggregating the hidden states. Since the input text is usually much longer than the number of inputted CLS embeddings, Multi-CLS BERT is almost as efficient as the original BERT. Allen-Zhu and Li (2020) discovered that the key of an effective ensembling model is the diversity of individual models and the models trained using different random seeds have more diverse predictions compared to simply using dropout (Srivastava et al., 2014; Gal and Ghahramani, 2016) or averaging the weights of the models during training (Fort et al., 2019). To ensure the diversity of CLS embeddings without fine-tuning Multi-CLS BERT using multiple seeds, we propose several novel diversification techniques. For example, we insert different linear layers into the transformer encoder for different CLS tokens. Furthermore, we propose a novel reparametrization trick to prevent the linear layers from learning the same weights during fine-tuning. We test the effectiveness of these techniques by modifying the multi-task pretraining method proposed by Aroca-Ouellette and Rudzicz (2020), which combines four self-supervised losses. In our experiments, we demonstrate that the resulting Multi-CLS BERT can significantly improve the accuracy on GLUE (Wang et al., 2019b) and SuperGLUE (Wang et al., 2019a), especially when the training sizes are small. Similar to the BERT ensemble model, we further show that multiple CLS embeddings significantly reduce the expected calibration error, which measures the quality of prediction confidence, on the GLUE benchmark. ## 1.1 Main Contributions - We propose an efficient ensemble BERT model that does not incur any extra computational cost other than inserting a few CLS tokens and linear layers into the BERT encoder. Furthermore, we develop several diversification techniques for pretraining and fine-tuning the proposed MultiCLS BERT model.1 - We improve the GLUE performance reported in Aroca-Ouellette and Rudzicz (2020) using a better and more stable fine-tuning protocol and verify the effectiveness of its multi-task pretraining methods in GLUE and SuperGLUE with different training sizes. - Building on the above state-of-the-art pretraining and fine-tuning for BERT, our experiments and analyses show that Multi-CLS BERT significantly outperforms the BERT due to its similarity to a BERT ensemble model. The comprehensive ablation studies confirm the effectiveness of our diversification techniques. ## 2 Method In sections 2.1 and 2.2, we first review its state-ofthe-art pretraining method from Aroca-Ouellette and Rudzicz (2020). In Section 2.3, we modify one of its losses, quick thoughts (QT), to pretrain our multiple embedding representation. In Section 2.4, we encourage the CLS embeddings to capture the fine-grained semantic meaning of the input sequence by adding hard negatives during the pretraining. To diversify the CLS embeddings, we modify the transformer encoder in Section 2.5 and propose a new reparametrization method during the fine-tuning in Section 2.6. ## 2.1 Multi-Task Pretraining After testing many self-supervised losses, ArocaOuellette and Rudzicz (2020) find that combining the masked language modeling (MLM) loss, TFIDF loss, sentence ordering (SO) loss (Sun et al., 2020), and quick thoughts (QT) loss (Logeswaran and Lee, 2018) could lead to the best performance. The MLM loss is to predict the masked words and the TFIDF loss is to predict the importance of the words in the document. Each input text sequence consists of multiple sentences. They swap the sentence orders in some input sentences and use the CLS embedding to predict whether the order is swapped in the SO loss. Finally, QT loss is used to encourage the CLS embeddings of the consecutive sequences to be similar. To improve the state-of-the-art pretraining method, we modify the multi-task pretraining method by using multiple CLS embeddings to represent the input sequence and using non-immediate consecutive sentences as the hard negative. Our training method is illustrated in Figure 2. ## 2.2 Quick Thoughts Loss Two similar sentences tend to have the same label in a downstream application, so pretraining should pull the CLS embeddings of these similar sentences closer. The QT loss achieves this goal by assuming ![2_image_0.png](2_image_0.png) consecutive text sequences are similar and encouraging their CLS embeddings to be similar. Aroca-Ouellette and Rudzicz (2020) propose an efficient way of computing QT loss in a batch by evenly splitting each batch with size B into two parts. The first part contains B/2 text sequences randomly sampled from the pretrained corpus, and the second part contains each of the B/2 sentences that are immediately subsequent to those in the first part. Then, for each sequence in the first part, they use the consecutive sequence in the second part as the positive example and the other B/2 − 1 sequences as the negative examples. We can write the QT loss for the sequences containing sentences 1, 2, 3, and 4 as $$L_{QT}(s^{1-2},s^{3-4})=-\log(\frac{\exp(\text{Logit}_{s^{1}-2,s^{3-4}}^{QT}}{\sum_{s}\exp(\text{Logit}_{s^{1}-2,s}^{QT})}),\tag{1}$$ where s is the sentences in the second part of the batch, LogitQT s 1−2,s3−4 = ( c 1−2 ||c 1−2||) T c 3−4 ||c 3−4|| is the score for classifying sequence s 3−4as the positive example, c 1−2 ||c 1−2|| is the L2-normalized CLS embedding for sentences 1 and 2. The normalization is intended to stabilize the pretraining by limiting the gradients' magnitudes. ## 2.3 Multiple Cls Embeddings A text sequence could have multiple facets; two sequences could be similar in some facets but dissimilar in others, especially when the text sequences are long. The QT loss squeezes all facets of a sequence into a single embedding and encourages all facets of two consecutive sequences to be similar, potentially causing information loss. Some facets might better align with the goal of a downstream application. For example, the facets that contain more sentiment information would be more useful for sentiment analysis. To preserve the diverse facet information during pretraining, we propose multi-CLS quick thoughts loss (MCQT). The loss integrates two ways of computing the similarity of two sequences. The first way computes the cosine similarity between the most similar facets, and the second computes the cosine similarity between the summations of all facets. We linearly combine the two methods as the logit of the two input sequences: $$\text{Logit}_{s^{1}-2}^{MC},_{s^{3}-4}=\lambda\max_{i,j}(\frac{\mathbf{c}_{i}^{1-2}}{||\mathbf{c}_{i}^{1-2}||})^{T}\frac{\mathbf{c}_{j}^{3-4}}{||\mathbf{c}_{j}^{3-4}||}+$$ $$(1-\lambda)(\frac{\sum_{i}\mathbf{c}_{i}^{1-2}}{||\sum_{i}\mathbf{c}_{i}^{1-2}||})^{T}\frac{\sum_{j}\mathbf{c}_{j}^{3-4}}{||\sum_{j}\mathbf{c}_{j}^{3-4}||}.\tag{2}$$ $\mathbf{a}=\mathbf{a}$. where λ is a constant hyperparameters; c 1−2 kand c 3−4 kare the CLS embeddings of sentences 1-2 and sentences 3-4, respectively. The first term only considers the most similar facets to allow some facets to be dissimilar. Furthermore, the term implicitly diversifies CLS embeddings by considering each CLS embedding independently. In contrast, the second term encourages the CLS embeddings to work collaboratively, as in a typical ensemble model, and also let every CLS embedding receive gradients more evenly. Notice that we sum the CLS embeddings before the normalization so that the encoder could predict the magnitude of each CLS embedding as its weight in the summation. To show that the proposed method can improve the state-of-the-art pretraining methods, we keep the MLM loss and TFIDF loss unchanged. For the sentence ordering (SO) loss, we project the K hidden states h c k into the embedding h SO with the hidden state size D for predicting the sentence order: h SO = L SO(⊕kh c k ), where ⊕kh c k is the concatenation of K hidden states with size K × D. ## 2.4 Hard Negative For a large transformer-based LM, distinguishing the next sequence from random sequences could be easy. The LM can achieve low QT loss by outputting nearly identical CLS embeddings for the sentences with the same topic while ignoring the fine-grained semantic information (Papyan et al., 2020). In this case, multiple CLS embeddings might become underutilized. The hard negative is a common method of adjusting the difficulties of the contrastive learning (Baldini Soares et al., 2019; Cohan et al., 2020). Our way of collecting hard examples is illustrated in the bottom-left block of Figure 2. To efficiently add the hard negatives in the pretraining, we split the batch into three parts. For each sequence in the first part, we would use its immediate next sequence in the second part as the positive example, use the sequence after the next one in the third part as the hard negative, and use all the other sequences in the second or the third part as the easy negatives. We select such sequence after the next one as our hard negatives because the sequence usually share the same topic with the input sequence but is more likely to have different fine-grained semantic facets compared to the immediate next sequence. After adding the hard negative, the modified QT loss of the three consecutive sequences becomes ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) ## 2.5 Architecture-Based Diversification Initially, we simply input multiple special CLS tokens ([C1], ..., [CK]) after the original CLS token, [CLS0], and take the corresponding hidden states as the CLS embeddings, but we found that the CLS embeddings quickly become almost identical during the pretraining. Subsequently, instead of using the same final transformation head HQT for all CLS hidden states, we use a different linear layer LO,k in the final head HMC kto transform the hidden state h c k for the kth CLS. We set the bias term in LO,k to be the constant 0 because we want the differences between the CLS to be dynamic and context-dependent. Nevertheless, even though we differentiate the resulting CLS embeddings ck = HMC k(h c k ), the hidden states h c k before the transformation usually still collapse into almost identical embeddings. To solve the collapsing problem, we insert multiple linear layers Ll,k into the transformer encoder. In Figure 3, we illustrate our encoder architecture built on the BERTBase model. After the 4th transformer layer, we insert the layers L4,k to transform the hidden states before inputting them to the 5th layer. Similarly, we insert L8,k between the 8th transformer layer and 9th transformer layer. For BERTLarge, we insert Ll,k(.) after layer 8 and layer 16. Notice that although the architecture looks similar to the adapter (Houlsby et al., 2019) or prefix-tuning (Li and Liang, 2021), our purpose is to diversify the CLS embeddings rather than freezing parameters to save computational time. ## 2.6 Fine-Tuning As shown in Figure 3, we input multiple CLS tokens into the BERT encoder during fine-tuning and pool the corresponding CLS hidden states into the single CLS embedding for each downstream task fine-tuning in order to avoid overfitting and increasing computational overhead. As a result, we can use the same classifier architecture on top of MultiCLS BERT and BERT, which also simplifies their comparison. We discover that simply summing all the CLS hidden states still usually makes the hidden states and the inserted linear layers (e.g., LO,k) almost identical after fine-tuning. To avoid collapsing, we aggregate the CLS hidden states by proposing a novel re-parameterization trick: $${\mathbf{c}}^{M C F T}=\sum_{k}\left(L_{O,k}^{F T}({\mathbf{h}}_{k}^{c})\right),\qquad\qquad(4)$$ where L F T O,k(h c k ) = (WO,k − $=\frac{1}{M}\sum_{k\ell}\mathbf{W}_{\ell}$. ## K Pk′ Wo,K′)H C K , And Wo,K Is The Linear Weights Of Lo,K. Then, If All The L F T O,K Become Identical (I.E., ∀K,Wo,K = 1 K Pk′ Wo,K′), L F T O,K(H C K ) = 0 = C*Mcf T* . However, Gradient Descent Would Not Allow The Model To Constantly Output The Zero Vector, So L F T O,K Remains Different During The Fine-Tuning. 3 Experiments The parameters of neural networks are more restricted as more training samples are available (MacKay, 1995) and the improvement of deep ensemble models comes from the diversity of individual models (Fort et al., 2019), so the benefits of ensembling are usually more obvious when the training set size is smaller. Therefore, in addition to using the full training dataset, we also test the settings where the models are trained by 1k samples (Zhang et al., 2021a) or 100 samples from each task in GLUE (Wang et al., 2019b) or SuperGLUE (Wang et al., 2019a). Another benefit of the 1k- and 100-sampling settings is that the average scores would be significantly influenced by most datasets rather than by only a subset of relatively small datasets (Card et al., 2020). ## 3.1 Experiment Setup To accelerate the pretraining experiments, we initialize the weights using the pretrained BERT models (Devlin et al., 2019) and continue the pretraining using different loss functions on Wikipedia 2021 and BookCorpus (Zhu et al., 2015). All of the methods are based on uncased BERT as in Aroca-Ouellette and Rudzicz (2020). We compare the following methods: - **Pretrained**: The pretrained BERT model released from Devlin et al. (2019). - MTL: Pretraining using the four losses selected in Aroca-Ouellette and Rudzicz (2020): MLM, QT, SO, and TFIDF. We remove the continue learning procedure used in ERNIE (Sun et al., 2020) because we find that simply summing all the losses leads to better performance (see our ablation study in Section 3.3). - **Ours (K=5,** λ): The proposed Multi-CLS BERT method using 5 CLS tokens. We show the results of setting λ = {0, 0.1, 0.5, 1} in Equation 2. We reduce the maximal sentence length by 5 to accommodate the extra 5 CLS tokens. - **Ours (K=1)**: We set K = 1 in our method to verify the effectiveness of using multiple embeddings. During fine-tuning, the CLS embedding is a linear transformation of the single facet CLS = LO,1(h f 1 ). The GLUE and SuperGLUE scores are significantly influenced by the pretraining random seeds (Sellam et al., 2021) and fine-tuning random seeds (Dodge et al., 2020; Zhang et al., 2021a; Mosbach et al., 2021). To stably evaluate the performance of different pretraining methods, we pretrain models using four random seeds and fine-tune each pretrained model using four random seeds, and report the average performance on the development set across all 16 random seeds. To further stabilize the fine-tuning process and reach better performance, we follow the fine-tuning suggestions from Zhang et al. (2021a) and Mosbach et al. (2021), including training longer, limiting the gradient norm, and using Adam (Kingma and Ba, 2015) with bias term and warmup. | GLUE | SuperGLUE | | | | | | | | |---------------------|--------------|--------------|--------|--------|--------|-------|-------|-------| | Configuration ↓ | Model Name ↓ | Model Size ↓ | 100 | 1k | Full | 100* | 1k* | Full | | Pretrained | 109.5M | 55.71 | 71.67 | 82.05 | 57.18 | 61.55 | 65.04 | | | ± 0.62 | ± 0.15 | ± 0.08 | ± 0.43 | ± 0.37 | ± 0.36 | | | | | MTL | 109.5M | 59.29 | 73.26 | 83.30† | 57.50 | 62.94 | 66.33 | | | ± 0.27 | ± 0.13 | ± 0.07 | ± 0.41 | ± 0.36 | ± 0.33 | | | | | Ours (K=1) | 111.3M | 57.84 | 73.28 | 83.40 | 57.31 | 63.35 | 66.29 | | | ± 0.32 | ± 0.13 | ± 0.07 | ± 0.35 | ± 0.18 | ± 0.18 | | | | | Ours (K=5, λ = 0) | 118.4M | 61.54 | 74.14 | 83.41 | 58.29 | 63.71 | 66.80 | | | ± 0.32 | ± 0.12 | ± 0.07 | ± 0.33 | ± 0.26 | ± 0.25 | | | | | Ours (K=5, λ = 0.1) | 118.4M | 61.80 | 74.10 | 83.47 | 58.20 | 63.61 | 66.74 | | | ± 0.35 | ± 0.13 | ± 0.05 | ± 0.31 | ± 0.27 | ± 0.26 | | | | | Ours (K=5, λ = 0.5) | 118.4M | 60.49 | 74.02 | 83.47 | 58.41 | 63.78 | 66.80 | | | ± 0.35 | ± 0.12 | ± 0.08 | ± 0.38 | ± 0.25 | ± 0.24 | | | | | Ours (K=5, λ = 1) | 118.4M | 59.86 | 73.75 | 83.43 | 57.84 | 63.56 | 66.39 | | | ± 0.34 | ± 0.14 | ± 0.07 | ± 0.40 | ± 0.22 | ± 0.22 | | | | | BERT Base | MTL | 335.2M | 61.39 | 75.30 | 84.13 | 59.03 | 65.21 | 69.16 | | ± 0.37 | ± 0.27 | ± 0.11 | ± 0.54 | ± 0.38 | ± 0.37 | | | | | Ours (K=1) | 338.3M | 59.19 | 75.35 | 84.59 | 57.35 | 64.67 | 69.24 | | | ± 0.43 | ± 0.21 | ± 0.07 | ± 0.42 | ± 0.43 | ± 0.41 | | | | | Ours (K=5, λ = 0) | 350.9M | 63.19 | 75.73 | 84.51 | 59.46 | 65.43 | 69.56 | | | ± 0.49 | ± 0.26 | ± 0.05 | ± 0.44 | ± 0.38 | ± 0.31 | | | | | Ours (K=5, λ = 0.1) | 350.9M | 64.24 | 76.27 | 84.61 | 59.88 | 65.58 | 70.03 | | | ± 0.40 | ± 0.12 | ± 0.08 | ± 0.43 | ± 0.26 | ± 0.25 | | | | | Ours (K=5, λ = 0.5) | 350.9M | 63.02 | 75.95 | 84.49 | 59.42 | 65.84 | 69.79 | | | ± 0.42 | ± 0.10 | ± 0.08 | ± 0.34 | ± 0.25 | ± 0.25 | | | | | Ours (K=5, λ = 1) | 350.9M | 62.07 | 75.85 | 84.61 | 58.74 | 65.00 | 69.04 | | | ± 0.45 | ± 0.17 | ± 0.07 | ± 0.50 | ± 0.29 | ± 0.27 | | | | | BERT Large | | | | | | | | | ## 3.2 Main Results Our results are presented in Table 1. We can see that **Ours (K=5)** is consistently better than other baselines and that the improvement is larger in datasets with fewer training samples. For example, in GLUE 100, it achieves 61.80 on average using BERTBase with 118.4M parameters, which outperforms MTL using BERTLarge with 335.2M parameters (61.39). Please see Appendix E for the scores of individual tasks. MTL significantly improves the scores of original BERT model (**Pretrained**), confirming the effectness of the QT, SO, and TFIDF losses. Compared to MTL, **Ours (K=1)** is slightly better in GLUE 1k and GLUE Full, but worse in GLUE 100. We observe that λ = 0.1 usually performs well, which justifies the inclusion of both the highest logit and average logit in Equation 2. The λ = 0 model has significantly worse performance only in BERTLarge model. This suggests that the benefits of Multi-CLS BERT depend on our pretraining method and maximizing the highest logit stabilizes the pretraining of a larger model. ## 3.3 Ablation Study In our ablation studies, we would like to test the effectiveness of the design choices in our baseline MTL and our best model, **Ours (K=5,** λ = 0.1). The model variants we test include: - **MLM only**: Removing the QT, SO, and TFIDF losses in MTL. That is, we simply continue training **Pretrained** using only the MLM loss. - **CMTL+**: The best pretrained method reported in Aroca-Ouellette and Rudzicz (2020). It uses the continual learning method (Sun et al., 2020) to weight each loss in MTL. ## - Mlm+So+Tfidf: Mtl Without The Qt Loss. - **No Inserted Layers**: Removing the Ll,k(.) in the transformer encoder from our method. - **No Hard Negative**: Removing the hard negatives described in Section 2.4 from our method. - **Sum Aggregation**: Simply summing the facets (i.e., using LO,k to replace L F T O,k in Equation 4). This baseline removes the proposed reparametrization trick to test its effectiveness. - Default: **Ours (K=k,** λ = 0.1), where k = {1, 3, 5, 10}. - SWA: Stochastic weight averaging (Ruppert, 1988; Izmailov et al., 2018) averages the weights along the optimization trajectory. GLUE SuperGLUE* Model ↓ Model Description ↓ K ↓ 100 1k 100 1k ![6_image_2.png](6_image_2.png) Pretrained 1 56.85 71.68 57.90 62.14 ![6_image_0.png](6_image_0.png) MLM only 1 55.38 70.74 57.39 61.77 CMTL+ 1 58.65 72.57 56.88 62.63 MLM + SO + TFIDF 1 60.35 72.65 57.88 62.60 MTL 1 59.53 73.12 57.51 62.95 1 57.76 73.30 57.53 63.22 3 61.09 73.95 57.85 63.31 5 **62.62 74.49** 58.82 **63.86** 10 60.99 73.59 58.25 62.82 SWA 1 57.31 72.91 - - Ensemble on Dropouts 1 58.45 72.86 - - Ensemble on FT Seeds 1 60.07 75.20 - - 5 63.34 75.35 - - No Inserted 1 58.06 73.18 57.97 63.34 Layers 5 60.12 73.35 56.46 62.00 No Hard 1 58.44 73.30 57.19 63.33 Negative 5 61.77 74.18 **58.89 63.86** Sum Aggregation 5 58.87 73.94 57.41 63.82 No Hard 1 60.36 75.69 58.47 65.04 Negative 5 63.23 75.77 **60.33 65.75** Default 1 60.01 76.03 57.38 65.10 5 **64.33 76.38** 59.99 65.51 - **Ensemble on Dropouts**: Running the forward pass of **Ours (K=1)** with dropout using 5 different seeds and averaging their prediction probabilities for each class in each task. - **Ensemble on FT Seeds**: Fine-tuning **Ours (K=1)** or **Ours (K=5,** λ = 0.1) using 5 different seeds and averaging their prediction probabilities. Our results are presented in Table 2. We can see that continuing training using **MLM only** loss degrades the performance, which indicates that our improvement does not come from training BERT longer. Removing QT loss results in mixed results. The better performance of MTL compared to CMTL+ suggests that the continual training technique used in Aroca-Ouellette and Rudzicz (2020) is harmful with our evaluation settings. Removing the inserted layers (**No Inserted Layers**) or removing the re-parametrization trick (Sum Aggregation) makes the performance of **Ours** (K=5, λ = 0.1) close to the **Ours (K=1)** baseline. This result highlight the importance of diversity of CLS embeddings. The performance of **Ours** (K=3) and **Ours (K=10)** is usually better than **Ours** (K=1), but are worse than **Ours (K=5)**. In both BERTBase and BERTLarge models, removing hard negatives degrades the GLUE scores but slightly increases the SuperGLUE scores. | Inference | GLUE* (ECE) | | | |------------------------|---------------|--------|-------| | Time (s) | 100 | 1k | | | Ours (K=1) | 0.2918 | 25.22 | 19.32 | | Ours (K=5, λ = 0.1) | 0.3119 | 15.46 | 17.01 | | ± 0.0002 | ± 1.99 | ± 1.64 | | | ± 0.0004 | ± 1.79 | ± 1.64 | | | Ensemble of Ours (K=1) | 1.4590 | 13.85 | 10.80 | | ± 0.0012 | ± 0.97 | ± 0.88 | | ![6_image_1.png](6_image_1.png) | GLUE* 100 | GLUE* 1k | | |------------------|------------|-------| | Multi-CLS vs ENS | 32.57 | 41.35 | | Dropout vs ENS | 37.17 | 45.53 | | Least vs ENS | 39.57 | 48.85 | | ENS vs ENS | 38.67 | 50.14 | In GLUE 100 and 1k, we do not get good results by using other efficient ensembling methods such as SWA and **Ensemble on Dropouts**. This suggests that the gradient descent trajectory and different dropout maps might not produce prediction diversity sufficient for an effective BERT ensemble model (Fort et al., 2019). On the other hand, ensembling the models that are fine-tuned using different random seeds indeed boosts the performance at the expense of high computational costs. The ensembled Multi-CLS BERT (**Ensemble on FT Seeds K=5**) still outperforms the ensembled K=1 baseline, but ensembling makes their performance differences smaller. These results imply that the improvements of Multi-CLS BERT overlap with the improvements of a BERT ensemble model. ## 3.4 Ensembling Analysis We compare the inference time and expected calibration error (ECE) (Naeini et al., 2015) of using multiple CLS embeddings, using a single CLS embedding, and ensembling BERT models with different fine-tuning seeds in Table 3. A lower ECE means a better class probability estimation. For example, if a model outputs class 1 with 0.9 probability for 100 samples, ECE = 0 means that 90 samples among them are indeed class 1. Table 3 shows that **Ours (K=5)** is much faster than the BERT ensemble and almost as efficient as **Ours (K=1)**, because a BERT ensemble needs to run for multiple forward passes and we reduce the maximal sentence length by 5 in **Ours (K=5)**. Additionally, the ECE of **Ours (K=5)** is lower than Ours (K=1) but not as low as the ECE from ensembling BERT models with different fine-tuning seeds. That is, without significantly increasing inference time, ensembling multiple CLS embeddings improves the output confidence, even though not as much as ensembling BERT models. Next, we analyze the correlation of uncertainty estimation from different methods in Table 4. When ensembling BERT models with different dropout maps (**Dropout**) or different fine-tuning seeds (ENS), we can estimate the prediction uncertainty by the variance of the prediction probability from each individual BERT model. We can also use one minus prediction probability as the uncertainty (**Least**). In **Multi-CLS**, we measure the disagreement among the CLS embeddings as the uncertainty3and would like to see how many top20% most uncertain samples from the disagreement of CLS embeddings are also the top-20% most uncertain samples for a BERT ensemble model. Table 4 reports the ratio of the number of the overlapping uncertain samples from two estimation methods to the number of 20% samples in the development set. We can see that the ratio from Multi-CLS BERT and the BERT ensemble model (**Multi-CLS vs ENS**) is close to the ratios from other uncertainty estimations and the BERT ensemble model (Dropout vs ENS, **Least vs ENS**, and ENS vs ENS). This shows that different CLS embeddings can classify the uncertain samples differently, as is the case for the different BERT models in a BERT ensemble model. In Appendix C, we visualize the CLS embeddings of some uncertain samples to show how different CLS embeddings solve a task in different ways. ## 4 Related Work Due to its effectiveness, ensembling BERT in a better or more efficient way has recently attracted researchers' attention. Nevertheless, the existing approaches often need to rely on distillation (Xu et al., 2020; Matsubara et al., 2022; Zuo et al., 2022) or still require significant extra computational cost during training and testing (Kobayashi et al., 2022; Liang et al., 2022). Some recent vision models can also achieve ensembling almost without extra computational cost by sharing the weights (Wen et al., 2020), partitioning the model into subnetworks (Havasi et al., 2021; Zhang et al., 2021b), or partitioning the embeddings (Lavoie et al., 2022). However, it is unknown if the approaches are applicable to the pretraining and fine-tuning of language models. Similar to Multi-CLS BERT, mixture of softmax (MoS) (Yang et al., 2018) also uses multiple embeddings to improve the pretraining loss. Recently, Narang et al. (2021); Tay et al. (2022) have found that MoS is one of the few modifications that can improve on the original BERT architecture on the NLU benchmarks. Nevertheless, Narang et al. (2021) also point out that MoS requires significant extra training cost to compute the multiplication between each hidden state and all the word embeddings in the vocabulary. Chang et al. (2021) propose represent the sentence using multiple embeddings and demonstrate its improvement over the single embedding baseline on unsupervised sentence similarity tasks. Similar to our Equation 2, their non-negative sparse coding loss also encourages multiple sentence embeddings to collaborate during pretraining. Nevertheless, our loss is more computationally efficient and is designed to improve downstream supervised tasks rather than similarity tasks. Some approaches also represent a text sequence using multiple embeddings, such as contextualized word embeddings (Khattab and Zaharia, 2020; Luan et al., 2021) for information retrieval applications, sentence embeddings (Liu and Lapata, 2019; Iter et al., 2020; Mysore et al., 2022; Sul and Choi, 2023), or entity pair embeddings (Xue et al., 2022). However, the goal of this approach is to improve the representation of a relatively long text sequence and it is unknown if its benefits could be extended to the GLUE tasks that require fine-tuning and often involve only one or two sentences. ## 5 Conclusion In this work, we propose representing the input text using K CLS embeddings rather than using the single CLS embedding in BERT. Compared to BERT, Multi-CLS BERT significantly increases the GLUE and SuperGLUE scores and reduces the expected calibration error in GLUE, while its only added cost is to reduce the maximal text length by K and add a little extra time for computing the inserted linear transformations. Therefore, we recommend the wide use of multiple CLS embeddings for the almost free performance gain. To solve the collapsing problem of CLS embeddings, we modify the pretraining loss, BERT architecture, and fine-tuning loss. The ablation study shows that all of these modifications contribute to the performance improvement of Multi-CLS BERT. In our analyses for investigating the source of the improvement, we find that a) ensembling the original BERT leads to greater improvement than ensembling the Multi-CLS BERT and b) the disagreement of different CLS embeddings highly correlates with the disagreement of the BERT models from different fine-tuning seeds. Both findings support our perspective that Multi-CLS BERT is an efficient ensembling method. ## 6 Acknowledgement We thank Jay Yoon Lee and the anonymous reviewers for their constructive feedback. This work was supported in part by the Center for Data Science and the Center for Intelligent Information Retrieval, in part by the Chan Zuckerberg Initiative under the project Scientific Knowledge Base Construction, in part by the IBM Research AI through the AI Horizons Network, in part using high performance computing equipment obtained under a grant from the Collaborative R&D Fund managed by the Massachusetts Technology Collaborative, and in part by the National Science Foundation (NSF) grant numbers IIS-1922090 and IIS-1763618. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor. ## 7 Limitations Our methods are evaluated using BERT as many previous recent work such as Aroca-Ouellette and Rudzicz (2020); Dodge et al. (2020); Sellam et al. (2021); Gu et al. (2021); Qin et al. (2021); Wang et al. (2021); Xu et al. (2022); Zhou and Srikumar (2022); Hou et al. (2022); Wang et al. (2022a); Liu et al. (2022); Zhao et al. (2022); Zhou et al. (2022); Zheng et al. (2022); Fu et al. (2022). Our limited computational resources do not allow us to conduct similar experiments on RoBERTa (Liu et al., 2019) because pretraining RoBERTa requires much powerful GPUs and a much larger CPU memory to store the corpora. For the similar reason, we are unable to test our methods on larger language models. We are also not able to conduct a more comprehensive search for the pretraining and finetuning hyperparameters. We haven't tested if the multiple embedding representation could also improve other language model architectures such as XLNet (Yang et al., 2019), or other fine-tuning methods such as prompt (Radford et al., 2019; Li and Liang, 2021), or adapter (Houlsby et al., 2019; Wang et al., 2022b). Our conclusion mainly draws from the overall scores of GLUE or SuperGLUE benchmarks, which only include English datasets and might contain some dataset selection bias (Dehghani et al., 2021). Although much more efficient, Multi-CLS BERT is still worse than the classic BERT ensemble model in terms of expected calibration error and accuracy when more training data are available (e.g., in GLUE 1k). We also do not know if MultiCLS BERT could provide efficient and high-quality uncertainty estimation for other applications such as active learning (Pop and Fulop, 2018). ## 8 Ethical And Broader Impact Multi-CLS BERT can provide better confidence estimation compared to BERT while better efficiency compared to the classic BERT ensemble. This work might inspire prospective efficient ensembling approaches that produce more robust predictions (Clark et al., 2019b) with lower the energy consumption. On the other hand, the readers of the paper might not notice the limitations of the study (e.g., the confidence estimation of Multi-CLS BERT is still sometimes far behind the classic BERT ensemble model) and mistakenly believe that Multi-CLS BERT has all the benefits of the classic BERT ensemble model. ## References Zeyuan Allen-Zhu and Yuanzhi Li. 2020. 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Moebert: from bert to mixture-of-experts via importance-guided adaptation. *ArXiv preprint*, abs/2204.07675. 8 ## A Appendix Overview In the appendix, we first describe the details of our methods and evaluation protocol in Appendix B. Then, we visualize the disagreement of CLS embeddings of some samples in Appendix C and provide a diversity metric during pretraining in Appendix D. Finally, we compare the performance of individual tasks in Appendix E. ## B Experiment Details We first describe the architecture details and pretraining details of our methods and baselines in Appendix B.1. Then, we list the hyperparameter setup in the fine-tuning in Appendix B.2. Finally, we explain the details of the ensemble baselines and their related analyses in Appendix B.3. ## B.1 Our Models And Baselines The models built on BERTBase are pretrained using two billion tokens and each batch contains 30 sequences. The models built on BERTLarge are pretrained using one billion tokens and each batch contains 48 sequences. The learning rate is 2·10−5 and the warmup ratio is 0.001 for the pretraining stage. We implement Multi-CLS BERT by modifying the code of Aroca-Ouellette and Rudzicz (2020) 4. We use [unused0] - [unused(K-1)] tokens in the original BERT tokenizer as our input CLS tokens [C1] - [CK]. We still keep the original CLS tokens to increase the comparability with the MTL baseline. We use NVIDIA GeForce RTX 2080, 1080, and TITAN X, M40 GPUs for the BERTBase experiments and use GeForce RTX 8000 and Tesla M40 for the BERTLarge experiments. In Table 1, the model size excludes the top classifier parameters used in each task. We test **CMTL+** using the default hyperparameters of Aroca-Ouellette and Rudzicz (2020) and we do not try different hyperparameters or different schedules of pretraining losses. **No Inserted** Layers only removes the Ll,k(.) while still using different HMC kon top during pretraining. SWA averages the weights of every model checkpoint that is evaluated using the validation dataset. ## B.2 Fine-Tuning We start from the default evaluation hyperparameters used in Aroca-Ouellette and Rudzicz (2020) 4https://github.com/StephAO/olfmlm and modify the settings based on the suggestions from Zhang et al. (2021a) and Mosbach et al. (2021). We find that the best hyperparameters depend on the training size. For example, batch size 16 works well in GLUE Full but is much worse than batch size 4 in GLUE 100. Furthermore, the performance of the default hyperparameters on some tasks is suboptimal or unstable even after averaging the performance from 16 trials. Therefore, we coarsely tune the hyperparameters to maximize and stabilize the performance of the **Ours (K=1)** baseline under the memory and computational time constraints in our GPUs. The preliminary results suggest that the hyperparameters also maximize the performance of MTL. Next, we list fine-tuning hyperparameters for all the tasks5. Our fine-tuning stops after 20 epochs, 60k batches, or consecutive 10k batches without a validation improvement (whichever comes first). We use the first 5k validation samples to select the best fine-tuned model checkpoints for the evaluation. The maximal gradient norm is 1. The maximal length for sentences and CLS tokens is 128 for GLUE and 256 for SuperGLUE. For each task, we select the best learning rate from c · 10−5and c = 1, 2, 3, 4, 5, 7. When running large datasets in GLUE Full and SuperGLUE Full (MNLI, QQP, QNLI, SST-2, BoolQ, MultiRC, and WiC) using BERTLarge, we use learning rates c = 2, 4, 6, 8, 10, 14 to accelerate the training. The batch sizes for GLUE 100, 1k, Full are 4, 8, 16, respectively. The batch size for SuperGLUE is 4 except that the BERTLarge models use 8 in SuperGLUE 1k and Full. For BERTBase, the warmup ratio is 0.1. For BERTLarge, the warmup ratio is 0.2 and the weight decay is 10−6. For each fine-tuning random seed, we randomly select a different subset in the settings where only 100 or 1k training samples are available. For the datasets with less than 500 training samples in SuperGLUE and SuperGLUE 1k (i.e., CB and COPA), we repeat the experiments 32 times to further stabilize the scores. For the pre-trained BERT baseline, we use 16 fine-tuning random seeds. To reduce the computational cost, we use two pretraining random seeds and four fine-tuning random seeds in our ablation study in Table 2. Compared to other tasks, ReCoRD needs to be trained much longer than other tasks in Super- GLUE, so we only use one fine-tuning seed for each of the four pretrained models with different seeds. Our fine-tuning stops after 600k batches (BERTBase) / 300k batches (BERTLarge) or consecutive 160k batches without a validation improvement (whichever comes first). To stabilize the performance of each model on ReCoRD, we use the first 40k validation samples to select the best fine-tuned model checkpoints. We set batch size as 8 and learning rate as 1 · 10−5for BERTBase. For BERTLarge, we set batch size as 32 and learning rate as 2 · 10−5. ## B.3 Ensemble Models Ensemble on FT Seeds (K=1) in Table 2 is the same as **Ensemble of Ours (K=1)** in Table 3. **Ensemble on FT Seeds (K=5)** in Table 2 is the same as ENS in Table 4. **Ensemble on Dropouts** in Table 2 is the same as **Dropout** in Table 4. All results are the average of four models that use four different pretrained models and the best learning rate among c · 10−5(c = 1, 2, 3, 4, 5, 7) in the finetuning stage. In Table 3, we compute the expected calibration error (ECE) (Naeini et al., 2015) by $$\sum_{j=1}^{10}{\frac{|B_{j}|}{N}}|\mathrm{acc}(j)-\mathrm{conf}(j)|,\qquad\qquad(5)$$ where acc(j) is the model accuracy in the jth bin Bj , N is the number of validation samples, and conf(j) = 1 |Bj | Px∈Bj maxyP(y|x) is the average of the highest prediction probability P(y|x) in the jth bin. We put the samples into 10 equal-size bins according to their highest prediction probability maxyP(y|x). In Table 3, we use Tesla M40 to measure the inference time of the models built on BERTBase. We set batch size 16 and run 1000 batches to get the average inference time of one batch in every GLUE task. We repeat the experiments five times and report their average and standard error. For the ensemble model, we assume the time of averaging multiple prediction probabilities is negligible and directly multiply the inference time of **Ours (K=1)** by 5. In Table 4, we would like to see if CLS embeddings disagree with each other as other ensemble baselines did. In **Multi-CLS**, we compute the uncertainty of each sample x as the average variance of prediction probability of each CLS embedding meanl (varkP(y = l|*x, k*)) and estimate the prediction probability of the kth CLS embedding by $$P(y=l|x,k)=\frac{\exp\left(\mathbf{q}_{l,k}^{T}L_{O,k}^{FT}(\mathbf{h}_{k}^{c}(x,y_{gt}))\right)}{\sum_{i}\exp\left(\mathbf{q}_{i,k}^{T}L_{O,k}^{FT}(\mathbf{h}_{k}^{c}(x,y_{gt}))\right)},\tag{6}$$ where $L^{FT}(\mathbf{h}_{k}^{c}(x,y_{gt}))$ is the $\mathbf{C}\mathbf{I}$-subadditive where L F T O,k(h c k (*x, y*gt)) is the CLS embedding of the input x after fine-tuning, and qi,k = 1 Ni Pygt=i L F T O,k(h c k (*x, y*gt)) is the ith class embedding for the kth CLS embedding, which is computed by averaging the kth CLS embeddings of the input x with the ith class label. In Table 4, the two ensemble models for **ENS vs** ENS use the same set of 5 fine-tuning seeds and the two **Ours (K=5,** λ = 0.1) pretrained with different random seeds. Both uncertainty estimation models for Multi-CLS vs ENS, **Dropout vs ENS**, and Least vs ENS are based on the same pretrained Ours (K=5, λ = 0.1) model. ## C Visualization Of Cls Embeddings Table 5–16 compare the CLS embeddings of **Ours** (K=1) and **Ours (K=5,** λ = 0.1) after fine-tuning to illustrate how different CLS embeddings capture distinct aspects of an input sentence in solving a task. For each task, we select one sample (a sentence or a sentence pair) from the validation set whose CLS embeddings disagree with each other. For each selected sample, we visualize its nearest-neighboring sentences in the validation set with respect to each CLS embedding. The nearest neighbors for the kth CLS embedding are determined by the cosine similarity between the respective kth CLS embedding of the input sentence and other sentences. Beside each sentence or sentence pair, we show their ground truth label and the model's prediction. In **Ours (K=5,** λ = 0.1), two representative sentences are selected from the top-three nearest neighbors for each CLS, and each CLS is manually annotated with terms that summarize those aspects that are shared by the neighbors and relate to the query sentence. For comparison, accompanying tables show the top-ten nearest neighbors for **Ours** (K=1). In almost all the classification tasks, we observe that CLS 3 and CLS 5 vote for the same class (i.e., their embeddings are close to the neighbors with the same class prediction). On the other hand, CLS 1 and CLS 4 vote for another class in these examples where the CLS embeddings disagree. The observation suggests that the similarity of CLS embeddings after the pretraining stage correlates with their similarity after the fine-tuning. ## D Diversity Measurement Between Two Cls Embeddings We find that cosine similarities between the CLS embeddings are not a good measurement of their diversity. For different CLS k, if their hidden states h c k are identical but their output linear layers LO,k have different biases, the cosine similarity between CLS embeddings could be small but their diversity is also small. Motivated by the visualization in Appendix C, we found that the diversity between two CLS embeddings (k1 and k2) could be estimated by their similarity differences to their neighbors. If two CLS embeddings collapse, their dot products to their neighbors should perfectly correlated with each other and their resulting nearest neighbors would be the same. Thus, we estimate the diversity between CLS embeddings during pretraining by $$\mbox{Corr}\left([(\mathbf{c}_{k_{1},i}^{1-2})^{T}\mathbf{c}_{k_{1},j}^{3-4}]_{i,j},[(\mathbf{c}_{k_{2},i}^{1-2})^{T}\mathbf{c}_{k_{2},j}^{3-4}]_{i,j}\right),\tag{7}$$ where $\mathbf{c}_{k_{1},i}^{1-2}$ is the $k_{1}$th CLS embedding of the $i$th sample for sentence 1 and 2 in the batch, c 3−4 k1,j is the k1th CLS embedding of the jth sample for sentence 3 and 4 in the batch, [(c 1−2 k1,i) T c 3−4 k1,j ]i,j is a sequence containing all the pairwise dot products of k1th CLS embeddings in the batch, and Corr is the pearson correlation coefficient. Lower correlation means more diverse. We use the metric to test our diversification methods and detect the collapsing during pretraining. Without using the diversity tricks we developed (e.g., inserting linear layers into the transformer encoder), this metric would be greater than 0.99 and the improvement would be greatly reduce in downstream applications (see our ablation study in Table 2). In contrast, our final best model reaches around 0.9–0.95 in this metric. We found that if we use the fine-tuning re-parameterization trick during pretraining, we can have a lower correlation value (i.e., more diverse CLS embeddings), but the performance on GLUE is much worse. This indicates that there is an ideal diversity level for the consecutive sentence detection task during pretraining. ## E Performance Of Individual Tasks The GLUE tasks include CoLA (Warstadt et al., 2019), SST-2 (Socher et al., 2013), MRPC (Dolan and Brockett, 2005) , QQP6, STS-B (Cer et al., 2017), MNLI (Williams et al., 2018), QNLI (Rajpurkar et al., 2016), RTE (Bentivogli et al., 2009), and WNLI (Levesque et al., 2012). The SuperGLUE tasks include BoolQ (Clark et al., 2019a), CB (De Marneffe et al., 2019), COPA (Roemmele et al., 2011), MultiRC (Khashabi et al., 2018), ReCoRD (Zhang et al., 2018), RTE, WiC (Pilehvar and Camacho-Collados, 2019), and WSC (Levesque et al., 2012). We report the individual task results of GLUE 100 and 1k in Table 17, the results of GLUE Full in Table 18, the results of SuperGLUE 100 and 1k in Table 19, the results of SuperGLUE Full in Table 20, the results of the top 20% uncertain sample overlapping ratio in Table 22, and the results of ECE in Table 21. In Table 18, we also compare the GLUE score of our MTL baseline with the scores reported in Aroca-Ouellette and Rudzicz (2020). In GLUE 100 and SuperGLUE 100, multiple embeddings are almost always better. In GLUE 1k and Full, the improvement is smaller, so the baselines perform better in some individual tasks. We also observe that different downstream tasks might prefer different lambda. In Table 21, we compute the p value using Chernoff bound: $$P(X>(1+\delta)\mu)<\left(\frac{e^{\delta}}{(1+\delta)^{(1+\delta)}}\right)^{\mu},\quad(8)$$ $$=(0.2)^{2}4N,\,N$$ where µ = (0.2)24N, N is the number of samples in the validation set, δ = P4 i=1 Si µ − 1, and Siis the observed size of overlapping at the ith trial. | Task: CoLA Prediction | Label | Sentence | Summary | |----------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|-----------------------------------|-----------| | Query: unacceptable | un | | | | acceptable | I lent the book partway to Tony. | | | | CLS-space Neighbors (K=5): CLS 1 acceptable acceptable I gave it to Pete to take to the fair. | Gave to | | | | acceptable | un | | | | acceptable | Sue gave to Bill a book. | | | | CLS 2 unacceptable | un | | | | acceptable | We wanted to invite someone, but we couldn't decide who to. | Incorrect or | | | un- | extra word | | | | un | | | | | acceptable | Jessica crammed boxes at the truck. | | | | acceptable CLS 3 unacceptable | un | | | | acceptable | We wanted to invite someone, but we couldn't decide who to. | Incorrect or extra word | | | un | | | | | acceptable | un | | | | acceptable | I hit that you knew the answer. | First person | | | CLS 4 acceptable | acceptable | The paper was written up by John. | Writing | | acceptable | acceptable | John owns the book. | | | CLS 5 unacceptable | un | | | | acceptable | Chris was handed Sandy a note by Pat. | Extra word Writing | | | un- | Giving | | | | un | | | | | acceptable | What Mary did Bill was give a book. | | | | acceptable | | | | | Table 5: Visualization of Ours (K=5, λ = 0.1) using a sample in CoLA. The neighbors from CLS 2, 3, and 5 | | | | Table 5: Visualization of **Ours (K=5,** λ = 0.1) using a sample in CoLA. The neighbors from CLS 2, 3, and 5 are unacceptable sentences that often contain extra words, as in the query. The neighbors from CLS 1 and 2 are semantically related to the query. | Task: CoLA Prediction | Label | Sentence | |-------------------------|----------------------------------|------------| | Query: unacceptable | un | | | acceptable | I lent the book partway to Tony. | | | CLS-space Neighbors (K=1): unacceptable unacceptable I presented John with it dead. unacceptable acceptable Nora sent the book. unacceptable unacceptable There seemed to be intelligent. unacceptable unacceptable The book what inspired them was very long. unacceptable unacceptable The book was by John written. unacceptable acceptable I met the man who grows peaches. unacceptable acceptable We persuaded Mary to leave and Sue to stay. unacceptable unacceptable I hit that you knew the answer. unacceptable unacceptable We think that Leslie likes ourselves. unacceptable acceptable This flyer and that flyer differ. Table 6: Visualization of Ours (K=1) using the sample in CoLA. | | | | Task: SST-2 Prediction | Label | Sentence | Summary | |---------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------| | Query: negative | negative | An occasionally funny, but overall limp, fish-out-of-water story. | | | CLS-space Neighbors (K=5): CLS 1 negative positive Based on a devilishly witty script by Heather McGowan and Niels Mueller, the | Fun or funny | | | | film gets great laughs, but never at the expense of its characters | Commas Script | | | | negative | positive | McConaughey's fun to watch, the dragons are okay, not much fire in the script. | | | CLS 2 | Stunning or thrilling but negative | | | | negative | negative | If looking for a thrilling sci-fi cinematic ride, don't settle for this imposter. | Sci-fi | | CLS 3 positive | positive | Funny but perilously slight. | Positive overall qualified | | positive | positive | A movie that successfully crushes a best selling novel into a timeframe that mandates | but | | that you avoid the Godzilla sized soda. | | | | | CLS 4 | Visually rather stunning, but ultimately a handsome-looking bore, the true | | | | negative | negative | creativity would have been to hide Treasure Planet entirely and completely reimagine it. | | | negative | negative | Passable entertainment, but it's the kind of motion picture that won't make much of | Positive | | a splash when it's released, and will not be remembered long afterwards. | statement, but negative | | | | negative | negative | It showcases Carvey's talent for voices, but not nearly enough and not without taxing every drop of one's patience to get to the good stuff. | Liquid | | CLS 5 positive | positive | The terrific and bewilderingly underrated Campbell Scott gives a star performance that is nothing short of mesmerizing. | Mesmerizing or intense | | positive | positive | ... an otherwise intense, twist-and-turn thriller that certainly shouldn't hurt talented | and | | young Gaghan's resume. | positive | | | | Table 7: Visualization of Ours (K=5, λ = 0.1) using a sample in SST-2. The neighbors from CLS 2 and 4 share the | | | | Table 7: Visualization of **Ours (K=5,** λ = 0.1) using a sample in SST-2. The neighbors from CLS 2 and 4 share the same "postive, but negative" template as in the query. Like the query, CLS 1, 3, and 5 capture the positive aspects. Some CLSs also capture the semantic aspects of the query such as script, *sci-fi*, or *liquid*. | Task: SST-2 Prediction | Label | Sentence | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|-------------------------------------------------------------------| | Query: positive | negative | An occasionally funny, but overall limp, fish-out-of-water story. | | CLS-space Neighbors (K=1): positive positive In a way, the film feels like a breath of fresh air, but only to those that allow it in. positive positive A painfully funny ode to bad behavior. positive positive Two hours fly by - opera's a pleasure when you don't have to endure intermissions - and even a novice to the form comes away exhilarated. positive positive Huston nails both the glad-handing and the choking sense of hollow despair. positive positive The movie's relatively simple plot and uncomplicated morality play well with the affable cast. positive positive So much facile technique, such cute ideas, so little movie. positive positive A psychological thriller with a genuinely spooky premise and an above-average cast, actor Bill Paxton's directing debut is a creepy slice of gothic rural Americana. positive positive The primitive force of this film seems to bubble up from the vast collective memory of the combatants. The continued good chemistry between Carmen and Juni is what keeps this slightly disappointing positive positive sequel going, with enough amusing banter - blessedly curse-free - to keep both kids and parents entertained. positive positive This flick is about as cool and crowd-pleasing as a documentary can get. Table 8: Visualization of Ours (K=1) using the sample in SST-2. | | | | Task: MRPC Prediction | Label | Sentence Pair | Summary | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------| | Query: | S1: A man arrested for allegedly threatening to shoot and kill a city councilman from Queens was ordered held on $100,000 bail during an early morning court appearance Saturday. S2: The Queens man arrested for allegedly threatening to shoot City Councilman Hiram Monserrate was held on $100,000 bail Saturday, a spokesman for the Queens district attorney said. | | | | CLS-space Neighbors (K=5): CLS 1 equivalent equivalent S1: Myanmar's pro-democracy leader Aung San Suu Kyi will return home late Friday but will remain in detention after recovering from surgery at a Yangon hospital, her personal physician said. S2: Myanmar's pro-democracy leader Aung San Suu Kyi will be kept under house arrest following her release from a hospital where she underwent surgery, her personal physician said Friday. equivalent equivalent | Comments Politics | | | | S1: Bob Richter, a spokesman for House Speaker Tom Craddick, had no comment about the ruling. S2: Bob Richter, spokesman for Craddick, R-Midland, said the speaker had not seen the ruling and could not comment. | | | | | CLS 2 equivalent | equivalent | S1: They were being held Sunday in the Camden County Jail on $100,000 bail. S2: They remained in Camden County Jail on Sunday on $100,000 bail. | Thousands Justice | | not | not | | | | equivalent | equivalent | S1: "More than 70,000 men and women from bases in Southern California were deployed in Iraq. | Crime or threat | | equivalent | equivalent | S2: In all, more than 70,000 troops based in Southern California were deployed to Iraq. | | | CLS 3 | S1: Robert Walsh, 40, remained in critical but stable condition Friday at Staten Island University Hospital's north campus. | | | | equivalent | equivalent | S2: Walsh, also 40, was in critical but stable condition at Staten Island University Hospital last night. | Time | | S1: Blair's Foreign Secretary Jack Straw was to take his place on Monday to give a statement to parliament on the European Union. S2: Blair's office said his Foreign Secretary Jack Straw would take his place on Monday to give a statement to parliament on the EU meeting the prime minister attended last week. | | | | | CLS 4 equivalent | equivalent | S1: Franklin County Judge-Executive Teresa Barton said a firefighter was struck by lightning and was taken to the Frankfort Regional Medical Center. S2: A county firefighter, was struck by lightning and was in stable condition at Frankfort Regional Medical Center. | Comments | | not | not | | | | equivalent | equivalent | S1: Myanmar's pro-democracy leader Aung San Suu Kyi will return home late Friday but will remain in detention after recovering from surgery at a Yangon hospital, her personal physician said. S2: Myanmar's pro-democracy leader Aung San Suu Kyi will be kept under house arrest following her release from a hospital where she underwent surgery, her personal physician said Friday. | | | CLS 5 | Medical or justice | | | | equivalent | equivalent | S1: Unable to find a home for him, a judge told mental health authorities they needed to find supervised housing and treatment for DeVries somewhere in California. | | | equivalent | equivalent | S2: The judge had told the state Department of Mental Health to find supervised housing and treatment for DeVries somewhere in California. | Court's ruling | | S1: A former employee of a local power company pleaded guilty Wednesday to setting off a bomb that knocked out a power substation during the Winter Olympics last year. S2: A former Utah Power meter reader pleaded guilty Wednesday to bombing a power substation during the 2002 Winter Olympics. | | | | | equivalent | equivalent | | | | Table 9: Visualization of Ours (K=5, λ = 0.1) using a sample in MRPC. The neighbors from CLS 2, 3, and 5 focus | | | | Table 9: Visualization of **Ours (K=5,** λ = 0.1) using a sample in MRPC. The neighbors from CLS 2, 3, and 5 focus on different aspects of the query. The neighbors from CLS 1 and 4 are someone's comments as in the query and might not be equivalent. Several CLSs are also related to justice. | Task: MRPC Prediction | Label | Sentence Pair | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------| | Query: | S1: A man arrested for allegedly threatening to shoot and kill a city councilman from Queens was ordered held on $100,000 bail during an early morning court appearance Saturday. | | | equivalent | equivalent | S2: The Queens man arrested for allegedly threatening to shoot City Councilman Hiram Monserrate was held on $100,000 bail Saturday, a spokesman for the Queens district attorney said. | | CLS-space Neighbors (K=1): S1: The Justice Department Aug. 19 gave pre-clearance for the Oct. 7 date for the election to recall Gov. Gray Davis, saying it would not affect minority voting rights. equivalent equivalent S2: The Justice Department on Aug. 19 sanctioned the Oct. 7 date for recall election, saying it would not affect voting rights. S1: The worm attacks Windows computers via a hole in the operating system, an issue Microsoft on July 16 had warned about. equivalent equivalent S2: The worm attacks Windows computers via a hole in the operating system, which Microsoft warned of 16 July. equivalent equivalent S1: O'Brien was charged with leaving the scene of a fatal accident, a felony. S2: Bishop Thomas O'Brien, 67, was booked on a charge of leaving the scene of a fatal accident. S1: "There is no conscious policy of the United States, I can assure you of this, to move the equivalent equivalent dollar at all," he said. S2: He also said there is no conscious policy by the United States to move the value of the dollar. S1: The AFL-CIO is waiting until October to decide if it will endorse a candidate. equivalent equivalent S2: The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries. S1: Speaking for the first time yesterday, Brigitte's maternal aunt said his family was unaware he had was in prison or that he had remarried. equivalent equivalent S2: Brigitte's maternal aunt said his family was unaware he had been sent to prison, or that he had remarried in Sydney. S1: Rosenthal is hereby sentenced to custody of the Federal Bureau of prisons for one day with credit for time served," Breyer said to tumultuous cheers in the courtroom. S2: "Rosenthal is hereby sentenced to custody of the Federal Bureau of Prisons for one day with credit for time served." equivalent not equivalent S1: Police say CIBA was involved in the importation of qat, a narcotic substance legal in Britain but banned in the United States. equivalent equivalent S2: Mr McKinlay said that CIBA was involved in the importation of qat, a narcotic substance legal in Britain but banned in the US. S1: Judge Craig Doran said it wasn't his role to determine if Hovan was "an evil man" but maintained that "he has committed an evil act." equivalent equivalent S2: Judge Craig Doran said he couldn't determine if Hovan was "an evil man" but said he "has committed an evil act." S1: But MTA officials appropriated the money to the 2003 and 2004 budgets without notifying riders or even the MTA board members considering the 50-cent hike, Hevesi found. equivalent equivalent S2: MTA officials appropriated the surplus money to later years' budgets without notifying riders or the MTA board members when the 50-cent hike was being considered, he said. Table 10: Visualization of Ours (K=1) using the sample in MRPC. | | | | Task: MNLI Prediction | Label | Sentence Pair | Summary | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------| | Query: | S1: There is very little left of old Ocho the scant remains of Ocho Rios Fort are | | | | contradiction | contradiction | probably the oldest and now lie in an industrial area, almost forgotten as the tide of progress has swept over the town. S2: There is nothing left of the Ocho Rios Fort. | | | CLS-space Neighbors (K=5): CLS 1 S1: After the purge of foreigners, only a few stayed on, strictly confined to Dejima neutral contradiction Island in Nagasaki Bay. S2: A few foreigners were left free after the purge on foreigners. | Size or | | | | S1: 'Publicity.' Lincoln removed his great hat, making a small show of dusting it | quantity | | | | neutral | neutral | off. S2: Lincoln took his hat off. | | | CLS 2 | S1: There is no tradition of clothes criticism that includes serious analysis, or even of costume criticism among theater, ballet, and opera critics, who do have an august | | | | neutral | neutral | writerly heritage. S2: Clothes criticism is not serious. | Historical places or heritage | | S1: All of the islands are now officially and proudly part of France, not colonies as | Negation | | | | neutral | neutral | they were for some three centuries. S2: The islands are part of France now instead of just colonies. | | | CLS 3 | S1: And yet, we still lack a set of global accounting and reporting standards that | | | | contradiction | neutral | reflects the globalization of economies, enterprises, and markets. | Industry | | S2: The globalization of economies is not reflected in global accounting standards. S1: The technology used to capture and evaluate information in response to the RFP | | | | | contradiction | contradiction | permits LSC to compile and assess key information about the delivery system at the program, state, regional, and national level. | Negation | | S2: There is no way for the LSC to compile information about delivery systems. | | | | | CLS 4 | Region | | | | neutral | neutral | S1: Scotland became little more than an English county. | Historical | | S2: Scotland was hardly better than an English county. | places | | | | Minimization or | | | | | neutral | neutral | S1: Just as in ancient times, without the River Nile, Egypt could not exist. S2: Without the Nile River, Egypt could not exist. | negation | | CLS 5 | S1: Beside the fortress lies an 18th-century caravanserai, or inn, which has been | | | | contradiction | neutral | converted into a hotel, and now hosts regular folklore evenings of Turkish dance | Buildings | | and music. | or | | | | S2: The 18th century caravanserai is now a hotel. | properties | | | | S1: Diamonds are graded from D to X, with only D, E, and F considered good, D | | | | | contradiction | contradiction | being colorless or river white, J slightly tinted, Q light yellow, and S to X yellow. | Contrast | | S2: There is no difference between diamonds, all having the same properties. | | | | | Table 11: Visualization of Ours (K=5, λ = 0.1) using a sample in MNLI. Only one sentence in the neighbors of | | | | Table 11: Visualization of **Ours (K=5,** λ = 0.1) using a sample in MNLI. Only one sentence in the neighbors of CLS 3 contains negation. Only the premise in the neighbors from CLS 5 makes a comparison. Both CLSs vote for the contradiction class. Several CLSs are related to buildings or historical places. | Task: MNLI Prediction | Label | Sentence Pair | |-------------------------|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------| | Query: | S1: There is very little left of old Ocho the scant remains of Ocho Rios Fort are probably the | | | contradiction | contradiction | oldest and now lie in an industrial area, almost forgotten as the tide of progress has swept over the town. S2: There is nothing left of the Ocho Rios Fort. | | CLS-space Neighbors (K=1): contradiction contradiction S1: It was utterly mad. S2: It was perfectly normal. contradiction neutral S1: Fixing current levels of damage would be impossible. S2: Fixing the damage could never be done. contradiction contradiction S1: It was still night. S2: The sun was blazing in the sky, darkness nowhere to be seen. contradiction contradiction S1: That's their signal S2: That isn't their signal. contradiction contradiction S1: It is extremely dangerous to Every trip to the store becomes a temptation. S2: Even with every trip to the store, it never becomes a temptation. S1: The Revolutionaries couldn't be dissuaded from destroying most of the cathedral's statues, contradiction contradiction although 67 were saved (many of the originals are now housed in the Musée de l'Oeuvre NotreDame next door). S2: All of the cathedrals statues were saved by the Revolutionaries. contradiction contradiction S1: It was deserved. S2: It was not deserved at all contradiction entailment S1: And far, far away- lying still on the tracks- was the back of the train. S2: The train wasn't moving but then it started up. S1: Even if you're the kind of traveler who likes to improvise and be adventurous, don't turn contradiction contradiction your nose up at the tourist offices. S2: There's nothing worth seeing in the tourist offices. contradiction contradiction S1: Cybernetics had always been Derry's passion. S2: Derry knew nothing of cybernetics. Table 12: Visualization of Ours (K=1) using the sample in MNLI. | | | | Task: QNLI Prediction | Label | Sentence Pair | Summary | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------| | Query: entailment | entailment | S1: What factors negatively impacted Jacksonville following the war? S2: Warfare and the long occupation left the city disrupted after the war. | | | CLS-space Neighbors (K=5): CLS 1 entailment entailment S1: When was the Russian policy "indigenization" defunded? S2: Never formally revoked, it stopped being implemented after 1932. S1: How did Luther describe his learning at the university? | Time | | | | entailment | entailment | S2: He was made to wake at four every morning for what has been described as "a day of rote learning and often wearying spiritual exercises." | | | CLS 2 | S1: How did the 2001 IPCC report compare to reality for 2001-2006? | | | | entailment | not | S2: The study compared IPCC 2001 projections on temperature and sea level change | | | entailment | with observations. | Change | | | S1: Who led the most rapid expansion of the Mongol empire? | | | | | entailment | entailment | S2: Under Genghis's successor Ogedei Khan the speed of expansion reached its peak. | | | CLS 3 not | not | S1: During which period did Jacksonville become a popular destination for the rich? S2: This highlighted the visibility of the state as a worthy place for tourism. Jacksonville | | | entailment | entailment | S1: What brought the downfall of Jacksonville filmmaking? | | | not | not | Duration of | | | S2: Over the course of the decade, more than 30 silent film studios were established, | | | | | entailment | entailment | time | | | earning Jacksonville the title of "winter film capital of the world". | | | | | CLS 4 | S1: How did the new king react to the Huguenots? | | | | entailment | entailment | S2: Louis XIV gained the throne in 1643 and acted increasingly aggressively to force the Huguenots to convert. | Change | | entailment | not | S1: What did Luther begin to experience in 1536? | | | entailment | S2: In December 1544, he began to feel the effects of angina. | | | | CLS 5 | S1: What brought the downfall of Jacksonville filmmaking? | | | | not | not | S2: Over the course of the decade, more than 30 silent film studios were established, | | | entailment | entailment | earning Jacksonville the title of "winter film capital of the world". | Negative event | | not | S1: What cycle AC current system did Tesla propose? | | | | not | S2: He found the time there frustrating because of conflicts between him and the | | | | entailment | entailment | other Westinghouse engineers over how best to implement AC power. | | | Table 13: Visualization of Ours (K=5, λ = 0.1) using a sample in QNLI. The neighbors from CLS 1, 2, and 4 are | | | | Table 13: Visualization of **Ours (K=5,** λ = 0.1) using a sample in QNLI. The neighbors from CLS 1, 2, and 4 are about time or changes. The neighbors from CLS 3 and 5 are about *Jacksonville* or negative events. | Task: QNLI Prediction | Label | Sentence Pair | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|-------------------------------------------------------------------------------------------------------------------------------------------------| | Query: entailment | entailment | S1: What factors negatively impacted Jacksonville following the war? S2: Warfare and the long occupation left the city disrupted after the war. | | CLS-space Neighbors (K=1): S1: How many Africans were brought into the United States during the slave trade? entailment entailment S2: Participation in the African slave trade and the subsequent treatment of its 12 to 15 million Africans is viewed by some to be a more modern extension of America's "internal colonialism". S1: Which country used to rule California? S2: Though there is no official definition for the northern boundary of southern California, such a division has existed from the time when Mexico ruled California, and political disputes raged between the Californios of Monterey in the upper part and Los Angeles in the lower part of Alta California. entailment entailment S1: In what area of this British colony were Huguenot land grants? entailment entailment S2: In 1700 several hundred French Huguenots migrated from England to the colony of Virginia, where the English Crown had promised them land grants in Lower Norfolk County. S1: Who was responsible for the new building projects in Jacksonville? entailment entailment S2: Mayor W. Haydon Burns' Jacksonville Story resulted in the construction of a new city hall, civic auditorium, public library and other projects that created a dynamic sense of civic pride. S1: What did Tesla first receive after starting his company? S2: The company installed electrical arc light based illumination systems designed by Tesla and entailment entailment also had designs for dynamo electric machine commutators, the first patents issued to Tesla in the US. S1: In what year did the university first see a drop in applications? entailment entailment S2: In the early 1950s, student applications declined as a result of increasing crime and poverty in the Hyde Park neighborhood. entailment entailment S1: What was Fresno's population in 2010? S2: The 2010 United States Census reported that Fresno had a population of 494,665. S1: What was the percentage of Black or African-Americans living in the city? S2: The racial makeup of the city was 50.2% White, 8.4% Black or African American, 1.6% entailment entailment Native American, 11.2% Asian (about a third of which is Hmong), 0.1% Pacific Islander, 23.4% from other races, and 5.2% from two or more races. S1: Where did Marin build first fort? entailment entailment S2: He first constructed Fort Presque Isle (near present-day Erie, Pennsylvania) on Lake Erie's south shore. entailment not S1: How old was Tesla when he became a US citizen? entailment S2: In the same year, he patented the Tesla coil. Table 14: Visualization of Ours (K=1) using the sample in QNLI. | | | | Task: STS-B Prediction | Label | Sentence Pair | Summary | |------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------|--------------| | Query: 2.009 | 2.000 | S1: Volkswagen skids into red in wake of pollution scandal S2: Volkswagen's "gesture of goodwill" to diesel owners | | | CLS-space Neighbors (K=5): CLS 1 2.633 3.800 S1: Rosberg emulates father with Monaco win | Motor | | | | S2: FORMULA 1: Rosberg stays modest despite Monaco win | vehicles Racing | | | | 2.754 | 3.000 | S1: A motorcross driver going by during a race S2: A race car driver performs in the race of his life. | | | CLS 2 1.710 | 1.400 | S1: A golden dog is running through the snow. S2: A pack of sled dogs pulling a sled through a town. | Colors | | 1.917 | 1.400 | Action | | | S1: The black and white dog is running on the grass. S2: A black and white dog swims in blue water. | | | | | CLS 3 2.952 | 2.600 | S1: Obama endorses same-sex marriage S2: Obama's delicate dance on same-sex marriage | Politics and | | 2.071 | 1.000 | economics | | | S1: Spanish jobless rate soars past 25 per cent S2: US jobless rate seen rising, offering Obama no relief | | | | | CLS 4 | S1: Presumably the decision of drivers to slow down in response to work zone signage is influenced by many factors. | Motor | | | 0.337 | 0.000 | S2: This short talk deals with issues of "cheating slightly" :Dan Ariely: Our buggy | vehicles | | moral code . | Morality | | | | 0.946 | 0.600 | S1: Saudi gas truck blast kills at least 22 S2: Nigeria church blast kills at least 12 | | | CLS 5 | S1: Stocks dipped lower Tuesday as investors opted to cash in profits from Monday's big rally despite a trio of reports suggesting modest improvement in the economy. S2: Wall Street moved tentatively higher Tuesday as investors weighed a trio of reports showing modest economic improvement against an urge to cash in profits from Monday's big rally. | | | | 3.820 | 2.800 | Politics and economics | | | 2.952 | 2.600 | S1: Obama endorses same-sex marriage S2: Obama's delicate dance on same-sex marriage | | | Table 15: Visualization of Ours (K=5, λ = 0.1) using a sample in STS-B. The neighbors from CLS 1 and 4 are | | | | Table 15: Visualization of **Ours (K=5,** λ = 0.1) using a sample in STS-B. The neighbors from CLS 1 and 4 are about motor vehicles. The neighbors from CLS 3 and 5 are about politics and economics. | Task: STS-B Prediction | Label | Sentence Pair | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|--------------------------------------------------------------------------------------------------------------------| | Query: 2.009 | 2.000 | S1: Volkswagen skids into red in wake of pollution scandal S2: Volkswagen's "gesture of goodwill" to diesel owners | | CLS-space Neighbors (K=1): 2.133 2.600 S1: Large silver locomotive engine in a shed. S2: The silver train is parked in a station. 2.537 3.200 S1: An AeroMexico jet taxing along a runway. S2: a silver AreoMexico Jet Liner sitting on the tarmac. 2.189 2.800 S1: Two women holding checkered flags near an orange car. S2: Two ladies in skimpy clothes pose next to an old fashioned car. 1.963 2.400 S1: Two dogs in the snow S2: Two dogs play in the grass. 2.041 1.600 S1: Three dogs are playing in the white snow. S2: Two dogs are playing in the grass. 2.402 3.200 S1: Once you open it up to toxins, the answer is clearly no, boiling is not enough. S2: Boiling eliminates only a certain class of contaminants that can make you ill. 1.990 1.400 S1: A golden dog is running through the snow. S2: A pack of sled dogs pulling a sled through a town. 2.230 2.200 S1: Man sitting on a bench drink from a mug surrounded by rugs. S2: A man is sitting on one of two red benches and staring into a kiosk. S1: If you can get over the "ick factor," you have an easily-applied source of organic nitrogen fertilizer close at hand. 2.426 2.000 S2: The NPK numbers on the fertilizer represents the percent, by weight, of Nitrogen, P2O5 and K2O, respectively. S1: Try switching to rats; weanling rats if you need something smaller. 2.248 3.400 S2: As mentioned in previous answers, rats and gerbils can be offered instead of mice or in a rotation with mice. Table 16: Visualization of Ours (K=1) using the sample in STS-B. | | | GLUE 100 (BERT Base) CoLA SST MRPC STS-B QQP MNLI QNLI RTE Avg. MCC Acc F1 Spearman F1 Acc Acc Acc - Pretrained **18.62** 75.41 80.44 62.16 59.09 38.51 59.99 54.56 55.71 ± 1.96 ± 1.95 ± 0.57 ± 3.68 ± 0.94 ± 0.69 ± 1.33 ± 0.79 ± 0.62 MTL 9.90 70.67 81.64 78.88 59.74 43.50 73.54 57.49 59.29 ± 1.18 ± 0.63 ± 0.19 ± 0.57 ± 0.73 ± 0.87 ± 0.56 ± 1.15 ± 0.27 Ours (K=1) 11.24 70.24 80.97 78.10 58.94 40.99 68.69 55.18 57.84 ± 1.09 ± 1.30 ± 0.33 ± 0.61 ± 0.73 ± 0.56 ± 0.99 ± 1.19 ± 0.32 Ours (K=5, λ = 0) 17.44 74.31 **81.98 79.53 61.98** 44.47 **75.94** 58.44 61.54 ± 1.36 ± 1.19 ± 0.22 ± 0.70 ± 0.54 ± 0.67 ± 0.48 ± 1.38 ± 0.32 Ours (K=5, λ = 0.1) 17.61 **75.49** 81.68 79.25 61.70 **46.09** 75.12 59.17 **61.80** ± 1.75 ± 0.96 ± 0.14 ± 0.66 ± 0.63 ± 0.84 ± 0.56 ± 1.48 ± 0.35 Ours (K=5, λ = 0.5) 13.52 74.24 81.60 79.49 61.78 44.46 74.14 57.25 60.49 ± 1.94 ± 0.95 ± 0.15 ± 0.51 ± 0.49 ± 0.62 ± 0.66 ± 1.34 ± 0.35 Ours (K=5, λ = 1) 10.59 74.56 81.28 77.93 60.80 43.42 74.83 57.17 59.86 ± 1.68 ± 0.89 ± 0.16 ± 0.67 ± 0.82 ± 0.95 ± 0.70 ± 1.20 ± 0.34 GLUE 100 (BERT Large) CoLA SST MRPC STS-B QQP MNLI QNLI RTE Avg. MTL 15.64 79.61 81.48 74.92 61.41 43.61 77.28 58.66 61.39 ± 1.60 ± 1.63 ± 0.19 ± 0.90 ± 0.47 ± 0.86 ± 0.53 ± 1.24 ± 0.37 Ours (K=1) 16.87 73.21 81.35 77.48 57.58 41.51 70.79 56.20 59.19 ± 1.99 ± 1.57 ± 0.18 ± 0.63 ± 0.75 ± 0.87 ± 1.90 ± 0.57 ± 0.43 Ours (K=5, λ = 0) **22.41** 80.54 82.01 76.06 61.60 46.35 77.73 60.27 63.19 ± 1.97 ± 2.11 ± 0.23 ± 1.97 ± 0.96 ± 0.86 ± 0.64 ± 1.23 ± 0.49 Ours (K=5, λ = 0.1) 22.02 **82.67** 81.81 **78.44 63.49** 46.94 77.58 63.51 **64.24** ± 2.79 ± 0.72 ± 0.22 ± 0.63 ± 0.66 ± 0.74 ± 0.65 ± 0.70 ± 0.40 Ours (K=5, λ = 0.5) 18.59 80.47 **82.02** 77.16 61.18 **47.04** 77.43 61.91 63.02 ± 2.54 ± 1.33 ± 0.12 ± 0.40 ± 0.69 ± 0.77 ± 0.56 ± 1.27 ± 0.42 Ours (K=5, λ = 1) 15.76 79.98 81.83 76.73 62.27 45.27 **77.99** 59.24 62.07 ± 2.65 ± 1.22 ± 0.18 ± 1.19 ± 0.74 ± 1.09 ± 0.49 ± 1.11 ± 0.45 GLUE 1k (BERT Base) CoLA SST MRPC STS-B QQP MNLI QNLI RTE Avg. Pretrained 42.71 87.08 86.98 85.93 70.01 58.05 77.97 64.34 71.67 ± 0.54 ± 0.18 ± 0.20 ± 0.16 ± 0.22 ± 0.62 ± 0.33 ± 0.65 ± 0.15 MTL 41.57 86.82 87.44 87.18 71.92 62.01 82.39 66.54 73.26 ± 0.68 ± 0.16 ± 0.24 ± 0.15 ± 0.20 ± 0.26 ± 0.26 ± 0.53 ± 0.13 Ours (K=1) 39.69 86.76 87.49 87.53 71.56 62.07 83.69 66.79 73.28 ± 0.63 ± 0.21 ± 0.31 ± 0.10 ± 0.28 ± 0.19 ± 0.23 ± 0.58 ± 0.13 Ours (K=5, λ = 0) 42.24 86.98 87.69 87.91 **73.09 63.08 83.94** 68.00 **74.14** ± 0.59 ± 0.20 ± 0.34 ± 0.13 ± 0.15 ± 0.25 ± 0.18 ± 0.51 ± 0.12 Ours (K=5, λ = 0.1) 42.60 86.90 **87.76 88.05** 72.81 62.63 83.68 **68.10** 74.10 ± 0.52 ± 0.24 ± 0.33 ± 0.14 ± 0.21 ± 0.58 ± 0.13 ± 0.49 ± 0.13 Ours (K=5, λ = 0.5) **42.75** 86.78 87.55 87.88 72.56 62.71 83.66 68.00 74.02 ± 0.49 ± 0.19 ± 0.31 ± 0.11 ± 0.21 ± 0.39 ± 0.17 ± 0.51 ± 0.12 Ours (K=5, λ = 1) 40.08 **87.36** 87.54 87.74 72.83 62.79 83.53 67.98 73.75 ± 0.90 ± 0.13 ± 0.23 ± 0.10 ± 0.16 ± 0.43 ± 0.14 ± 0.30 ± 0.14 GLUE 1k (BERT Large) CoLA SST MRPC STS-B QQP MNLI QNLI RTE Avg. MTL 49.10 89.84 87.53 87.85 73.04 62.70 84.74 67.52 75.30 ± 0.76 ± 0.18 ± 0.24 ± 0.10 ± 0.18 ± 1.85 ± 0.17 ± 0.75 ± 0.27 Ours (K=1) 46.89 89.54 **88.41** 87.61 72.58 64.51 **85.20** 67.61 75.35 ± 0.90 ± 0.21 ± 0.25 ± 0.16 ± 0.22 ± 0.35 ± 0.16 ± 1.24 ± 0.21 Ours (K=5, λ = 0) 49.76 89.93 87.38 87.91 72.65 63.50 85.00 69.66 75.73 ± 0.63 ± 0.14 ± 0.38 ± 0.11 ± 0.26 ± 1.83 ± 0.23 ± 0.41 ± 0.26 Ours (K=5, λ = 0.1) **49.80 89.94** 87.27 **88.31 73.84 65.34** 85.17 70.83 **76.27** ± 0.69 ± 0.14 ± 0.27 ± 0.08 ± 0.19 ± 0.32 ± 0.11 ± 0.38 ± 0.12 Ours (K=5, λ = 0.5) 48.66 89.71 87.21 88.20 73.62 65.14 85.18 70.02 75.95 ± 0.43 ± 0.11 ± 0.36 ± 0.09 ± 0.16 ± 0.28 ± 0.15 ± 0.33 ± 0.10 Ours (K=5, λ = 1) 48.43 89.90 87.02 87.86 73.22 64.64 85.07 70.64 75.85 ± 0.83 ± 0.17 ± 0.39 ± 0.08 ± 0.18 ± 0.83 ± 0.15 ± 0.40 ± 0.17 | GLUE Full (BERT Base) | | | | | | | | | | |-------------------------|--------|--------|----------|--------|--------|--------|--------|--------|-------| | CoLA | SST | MRPC | STS-B | QQP | MNLI | QNLI | RTE | Avg. | | | 8.5k | 67k | 3.5k | 5.7k | 363k | 392k | 108k | 2.5k | - | | | MCC | Acc | F1 | Spearman | F1 | Acc | Acc | Acc | - | | | MTL† | 49.4 | 91.2 | 89.1 | 88.3 | 89.0 | 82.0 | 90.5 | 70.8 | 81.4 | | Pretrained | 59.09 | 92.71 | 89.82 | 88.13 | 87.29 | 84.33 | 91.11 | 64.42 | 82.05 | | ± 0.37 | ± 0.07 | ± 0.18 | ± 0.06 | ± 0.09 | ± 0.07 | ± 0.09 | ± 0.42 | ± 0.08 | | | MTL | 59.36 | 92.44 | 90.18 | 89.86 | 88.01 | 84.44 | 91.61 | 70.81 | 83.30 | | ± 0.28 | ± 0.06 | ± 0.14 | ± 0.04 | ± 0.04 | ± 0.05 | ± 0.04 | ± 0.46 | ± 0.07 | | | Ours (K=1) | 58.64 | 92.83 | 90.83 | 89.99 | 87.96 | 84.66 | 91.60 | 70.81 | 83.40 | | ± 0.40 | ± 0.06 | ± 0.12 | ± 0.05 | ± 0.06 | ± 0.07 | ± 0.05 | ± 0.32 | ± 0.07 | | | Ours (K=5, λ = 0) | 58.38 | 92.53 | 90.84 | 89.94 | 87.91 | 84.48 | 91.59 | 71.74 | 83.41 | | ± 0.34 | ± 0.09 | ± 0.14 | ± 0.04 | ± 0.04 | ± 0.06 | ± 0.06 | ± 0.38 | ± 0.07 | | | Ours (K=5, λ = 0.1) | 58.67 | 92.78 | 90.67 | 90.01 | 87.95 | 84.56 | 91.59 | 71.76 | 83.47 | | ± 0.27 | ± 0.08 | ± 0.19 | ± 0.03 | ± 0.09 | ± 0.06 | ± 0.05 | ± 0.20 | ± 0.05 | | | Ours (K=5, λ = 0.5) | 59.01 | 92.70 | 90.96 | 89.99 | 87.86 | 84.62 | 91.66 | 71.14 | 83.47 | | ± 0.22 | ± 0.08 | ± 0.17 | ± 0.04 | ± 0.07 | ± 0.08 | ± 0.07 | ± 0.51 | ± 0.08 | | | Ours (K=5, λ = 1) | 58.66 | 92.69 | 90.64 | 89.96 | 87.88 | 84.55 | 91.58 | 71.76 | 83.43 | | ± 0.28 | ± 0.08 | ± 0.20 | ± 0.02 | ± 0.10 | ± 0.07 | ± 0.06 | ± 0.35 | ± 0.07 | | | GLUE Fulll (BERT Large) | | | | | | | | | | | CoLA | SST | MRPC | STS-B | QQP | MNLI | QNLI | RTE | Avg. | | | MTL | 62.42 | 93.94 | 90.93 | 90.10 | 86.26 | 84.53 | 92.45 | 72.49 | 84.13 | | ± 0.26 | ± 0.12 | ± 0.22 | ± 0.06 | ± 0.11 | ± 0.19 | ± 0.06 | ± 0.75 | ± 0.11 | | | Ours (K=1) | 62.62 | 93.82 | 91.26 | 89.89 | 86.36 | 85.09 | 92.56 | 75.17 | 84.59 | | ± 0.32 | ± 0.11 | ± 0.10 | ± 0.06 | ± 0.07 | ± 0.03 | ± 0.06 | ± 0.44 | ± 0.07 | | | Ours (K=5, λ = 0) | 62.81 | 93.93 | 90.69 | 90.04 | 86.36 | 84.84 | 92.53 | 74.96 | 84.51 | | ± 0.19 | ± 0.10 | ± 0.15 | ± 0.06 | ± 0.08 | ± 0.11 | ± 0.05 | ± 0.29 | ± 0.05 | | | Ours (K=5, λ = 0.1) | 62.63 | 93.86 | 91.03 | 90.25 | 86.42 | 84.96 | 92.59 | 75.16 | 84.61 | | ± 0.36 | ± 0.08 | ± 0.15 | ± 0.05 | ± 0.06 | ± 0.09 | ± 0.05 | ± 0.45 | ± 0.08 | | | Ours (K=5, λ = 0.5) | 62.26 | 94.03 | 90.92 | 90.11 | 86.39 | 84.84 | 92.56 | 74.87 | 84.49 | | ± 0.34 | ± 0.05 | ± 0.11 | ± 0.05 | ± 0.07 | ± 0.12 | ± 0.07 | ± 0.49 | ± 0.08 | | | Ours (K=5, λ = 1) | 63.30 | 93.97 | 90.83 | 90.11 | 86.33 | 84.99 | 92.43 | 74.98 | 84.61 | | ± 0.23 | ± 0.09 | ± 0.18 | ± 0.05 | ± 0.13 | ± 0.10 | ± 0.06 | ± 0.46 | ± 0.07 | | SuperGLUE 100 (BERT Base) BoolQ CB COPA MultiRC RTE WiC WSC Avg. Acc Acc F1 Acc F1 EM Acc Acc Acc - Pretrained 61.21 77.68 **74.53** 59.63 53.81 1.27 54.41 55.78 60.16 57.18 ± 0.30 ± 1.02 ± 2.42 ± 1.05 ± 1.23 ± 0.21 ± 0.58 ± 0.54 ± 1.06 ± 0.43 MTL 61.97 77.23 72.73 59.69 52.74 1.41 56.13 56.03 61.67 57.50 ± 0.11 ± 1.27 ± 2.24 ± 1.09 ± 1.16 ± 0.24 ± 1.10 ± 0.39 ± 0.62 ± 0.41 Ours (K=1) 61.53 76.34 70.84 58.81 54.53 1.56 57.27 56.03 61.66 57.31 ± 0.20 ± 1.27 ± 1.97 ± 0.68 ± 0.83 ± 0.18 ± 0.94 ± 0.45 ± 0.53 ± 0.35 Ours (K=5, λ = 0) 62.01 **79.14** 72.13 60.44 55.34 3.09 **58.17** 56.99 61.91 58.29 ± 0.15 ± 0.73 ± 1.82 ± 0.57 ± 0.47 ± 0.41 ± 1.09 ± 0.42 ± 0.55 ± 0.33 Ours (K=5, λ = 0.1) 62.04 78.79 72.67 60.63 54.31 3.24 58.15 56.74 61.24 58.20 ± 0.16 ± 1.06 ± 1.34 ± 0.65 ± 0.74 ± 0.39 ± 1.35 ± 0.38 ± 0.57 ± 0.31 Ours (K=5, λ = 0.5) **62.09** 78.03 71.98 **61.19** 55.72 3.33 57.60 **57.54** 61.78 **58.41** ± 0.14 ± 0.95 ± 1.83 ± 1.13 ± 0.76 ± 0.40 ± 1.14 ± 0.60 ± 0.61 ± 0.38 Ours (K=5, λ = 1) 61.94 77.80 69.21 59.94 **55.96 3.97** 57.76 56.29 **62.57** 57.84 ± 0.25 ± 0.77 ± 2.39 ± 0.59 ± 0.45 ± 0.32 ± 1.17 ± 0.42 ± 0.32 ± 0.40 SuperGLUE 100 (BERT Large) BoolQ CB COPA MultiRC RTE WiC WSC Avg. MTL 62.03 78.14 74.21 64.31 **55.76** 1.32 58.24 56.42 **62.28** 59.03 ± 0.13 ± 1.80 ± 3.23 ± 1.04 ± 1.40 ± 0.29 ± 1.34 ± 0.35 ± 0.63 ± 0.54 Ours (K=1) 60.49 77.36 71.39 61.63 52.96 1.18 57.04 55.79 61.43 57.35 ± 0.38 ± 0.92 ± 2.38 ± 1.13 ± 1.04 ± 0.18 ± 0.74 ± 0.42 ± 0.74 ± 0.42 Ours (K=5, λ = 0) 62.08 78.90 75.26 **64.63** 51.08 3.46 61.07 **57.38** 61.37 59.46 ± 0.07 ± 1.45 ± 2.40 ± 1.13 ± 1.23 ± 0.37 ± 0.58 ± 0.69 ± 1.07 ± 0.44 Ours (K=5, λ = 0.1) 62.18 80.36 77.08 64.19 51.48 **3.61 62.43** 57.37 61.13 **59.88** ± 0.01 ± 1.46 ± 2.46 ± 0.93 ± 1.47 ± 0.35 ± 0.47 ± 0.77 ± 0.71 ± 0.43 Ours (K=5, λ = 0.5) **62.19 80.69 77.28** 63.25 52.87 2.99 60.07 56.80 61.24 59.42 ± 0.01 ± 0.95 ± 1.79 ± 0.89 ± 1.00 ± 0.35 ± 0.77 ± 0.57 ± 0.76 ± 0.34 Ours (K=5, λ = 1) 62.06 79.14 73.22 62.69 50.01 3.14 60.81 57.24 61.44 58.74 ± 0.08 ± 1.47 ± 3.05 ± 0.88 ± 1.54 ± 0.41 ± 1.00 ± 0.49 ± 0.62 ± 0.50 SuperGLUE 1k (BERT Base) BoolQ CB COPA MultiRC RTE WiC WSC Avg. Pretrained 62.89 **87.00 85.63** 60.94 55.37 5.19 59.39 60.40 64.54 61.55 ± 0.27 ± 0.80 ± 1.51 ± 0.53 ± 0.81 ± 0.72 ± 0.56 ± 0.44 ± 0.34 ± 0.37 MTL 63.38 85.49 82.85 60.91 56.52 7.44 65.96 63.61 **65.61** 62.94 ± 0.39 ± 0.79 ± 1.37 ± 0.52 ± 0.69 ± 0.64 ± 1.06 ± 0.21 ± 0.32 ± 0.36 Ours (K=1) **63.87** 86.83 84.28 60.63 58.68 7.86 66.34 65.00 64.09 63.35 ± 0.45 ± 0.47 ± 0.65 ± 0.39 ± 0.19 ± 0.14 ± 0.38 ± 0.29 ± 0.35 ± 0.18 Ours (K=5, λ = 0) 63.27 86.49 82.18 **62.88** 59.03 7.76 **67.60** 65.18 65.03 63.71 ± 0.41 ± 0.46 ± 0.90 ± 0.64 ± 0.72 ± 0.45 ± 0.48 ± 0.34 ± 0.37 ± 0.26 Ours (K=5, λ = 0.1) 63.20 86.38 82.63 62.53 59.16 **8.36** 67.11 65.11 64.45 63.61 ± 0.39 ± 0.57 ± 0.98 ± 0.61 ± 0.34 ± 0.23 ± 0.58 ± 0.27 ± 0.38 ± 0.27 Ours (K=5, λ = 0.5) 63.25 86.83 84.14 61.97 **59.57** 8.10 66.71 **65.38** 64.78 **63.78** ± 0.38 ± 0.57 ± 0.91 ± 0.51 ± 0.20 ± 0.41 ± 0.49 ± 0.42 ± 0.42 ± 0.25 Ours (K=5, λ = 1) 63.12 86.88 83.97 61.66 58.57 7.70 66.83 65.15 64.56 63.56 ± 0.42 ± 0.51 ± 0.78 ± 0.52 ± 0.46 ± 0.22 ± 0.41 ± 0.39 ± 0.36 ± 0.22 SuperGLUE 1k (BERT Large) BoolQ CB COPA MultiRC RTE WiC WSC Avg. MTL 63.86 **88.67 87.83** 67.22 56.56 7.52 68.68 66.16 64.67 65.21 ± 0.42 ± 0.87 ± 1.46 ± 0.73 ± 0.64 ± 0.53 ± 0.83 ± 0.29 ± 0.29 ± 0.38 Ours (K=1) 63.22 87.11 85.15 66.09 **59.81** 6.86 67.89 65.45 **65.28** 64.67 ± 0.35 ± 0.85 ± 1.74 ± 0.63 ± 0.35 ± 0.87 ± 0.77 ± 0.23 ± 0.49 ± 0.43 Ours (K=5, λ = 0) 63.73 87.61 86.19 **70.12** 54.34 7.39 69.09 66.84 64.85 65.43 ± 0.47 ± 0.75 ± 1.11 ± 0.66 ± 3.53 ± 0.79 ± 0.61 ± 0.33 ± 0.33 ± 0.38 Ours (K=5, λ = 0.1) **64.73** 87.51 87.14 68.09 58.56 **8.96** 68.85 66.57 64.31 65.59 ± 0.52 ± 0.60 ± 0.82 ± 0.65 ± 0.34 ± 0.20 ± 0.53 ± 0.43 ± 0.46 ± 0.25 Ours (K=5, λ = 0.5) 63.55 87.83 87.45 68.88 58.66 8.86 **69.78** 66.64 64.44 **65.84** ± 0.46 ± 0.52 ± 0.74 ± 0.78 ± 0.37 ± 0.21 ± 0.45 ± 0.36 ± 0.36 ± 0.25 Ours (K=5, λ = 1) 63.83 86.72 84.79 67.75 56.87 8.14 68.33 **66.93** 64.84 65.00 ± 0.38 ± 0.68 ± 1.06 ± 0.59 ± 0.76 ± 0.61 ± 0.44 ± 0.30 ± 0.38 ± 0.29 Table 19: The scores on the development set of the tasks in SuperGLUE except for ReCoRD. We compare different methods using BERTBase and BERTLarge in SuperGLUE 100 and 1k. | SuperGLUE Full (BERT Base) | | | | | | | | | | | | | |------------------------------|--------|--------|---------|--------|--------|--------|--------|--------|--------|--------|--------|-------| | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC | ReCoRD | Avg. | | | | | | 9.4k | 250 | 400 | 5.1k | 2.5k | 6k | 554 | 101k | - | | | | | | Acc | Acc | F1 | Acc | F1 | EM | Acc | Acc | Acc | F1 | EM | - | | | Pretrained | 74.01 | 87.00 | 85.63 | 60.94 | 65.93 | 16.72 | 65.76 | 66.85 | 64.33 | 58.78 | 58.10 | 65.04 | | ± 0.34 | ± 0.80 | ± 1.51 | ± 0.53 | ± 0.13 | ± 0.17 | ± 0.44 | ± 0.29 | ± 0.40 | ± 0.62 | ± 0.62 | ± 0.36 | | | MTL | 77.46 | 85.49 | 82.85 | 60.91 | 65.45 | 16.03 | 72.09 | 69.77 | 65.56 | 59.10 | 58.38 | 66.33 | | ± 0.24 | ± 0.79 | ± 1.37 | ± 0.52 | ± 0.13 | ± 0.15 | ± 0.59 | ± 0.25 | ± 0.32 | ± 0.39 | ± 0.40 | ± 0.33 | | | Ours (K=1) | 77.46 | 86.83 | 84.28 | 60.63 | 65.34 | 15.89 | 72.19 | 70.76 | 64.55 | 57.68 | 56.98 | 66.29 | | ± 0.13 | ± 0.47 | ± 0.65 | ± 0.39 | ± 0.19 | ± 0.18 | ± 0.55 | ± 0.16 | ± 0.32 | ± 0.97 | ± 0.96 | ± 0.18 | | | Ours (K=5, λ = 0) | 77.57 | 86.49 | 82.18 | 62.87 | 65.79 | 16.03 | 72.77 | 70.68 | 65.14 | 60.20 | 59.48 | 66.80 | | ± 0.31 | ± 0.46 | ± 0.90 | ± 0.64 | ± 0.11 | ± 0.20 | ± 0.44 | ± 0.21 | ± 0.26 | ± 0.57 | ± 0.56 | ± 0.25 | | | Ours (K=5, λ = 0.1) | 77.29 | 86.38 | 82.63 | 62.53 | 65.66 | 16.17 | 72.24 | 70.58 | 65.31 | 60.27 | 59.55 | 66.74 | | ± 0.16 | ± 0.57 | ± 0.98 | ± 0.61 | ± 0.13 | ± 0.24 | ± 0.59 | ± 0.18 | ± 0.28 | ± 0.48 | ± 0.48 | ± 0.26 | | | Ours (K=5, λ = 0.5) | 76.84 | 86.83 | 84.14 | 61.97 | 65.58 | 15.84 | 72.11 | 70.88 | 65.81 | 59.85 | 59.10 | 66.80 | | ± 0.27 | ± 0.57 | ± 0.91 | ± 0.51 | ± 0.13 | ± 0.22 | ± 0.39 | ± 0.20 | ± 0.40 | ± 0.48 | ± 0.48 | ± 0.24 | | | Ours (K=5, λ = 1) | 76.69 | 86.88 | 83.97 | 61.66 | 65.33 | 16.23 | 71.54 | 70.43 | 65.14 | 58.62 | 57.88 | 66.39 | | ± 0.27 | ± 0.51 | ± 0.78 | ± 0.52 | ± 0.20 | ± 0.18 | ± 0.68 | ± 0.32 | ± 0.30 | ± 0.97 | ± 0.95 | ± 0.22 | | | SuperGLUE Full (BERT Large) | | | | | | | | | | | | | | MTL | 77.78 | 88.67 | 87.83 | 67.22 | 65.93 | 16.68 | 71.97 | 71.08 | 64.37 | 69.60 | 68.85 | 69.16 | | ± 0.35 | ± 0.87 | ± 1.46 | ± 0.73 | ± 0.19 | ± 0.29 | ± 1.08 | ± 0.22 | ± 0.24 | ± 0.60 | ± 0.61 | ± 0.37 | | | Ours (K=1) | 78.04 | 87.11 | 85.15 | 66.09 | 65.96 | 16.49 | 75.54 | 70.62 | 65.02 | 70.12 | 69.43 | 69.24 | | ± 0.40 | ± 0.85 | ± 1.74 | ± 0.63 | ± 0.15 | ± 0.28 | ± 0.43 | ± 0.14 | ± 0.36 | ± 0.10 | ± 0.10 | ± 0.41 | | | Ours (K=5, λ = 0) | 78.21 | 87.61 | 86.19 | 70.12 | 64.91 | 15.62 | 73.34 | 71.73 | 65.26 | 69.28 | 68.58 | 69.56 | | ± 0.25 | ± 0.75 | ± 1.11 | ± 0.66 | ± 0.71 | ± 1.18 | ± 0.58 | ± 0.20 | ± 0.38 | ± 0.45 | ± 0.47 | ± 0.31 | | | Ours (K=5, λ = 0.1) | 78.70 | 87.51 | 87.14 | 68.09 | 65.83 | 17.67 | 75.36 | 71.41 | 65.44 | 69.88 | 69.25 | 69.98 | | ± 0.20 | ± 0.60 | ± 0.82 | ± 0.65 | ± 0.13 | ± 0.25 | ± 0.32 | ± 0.24 | ± 0.52 | ± 0.35 | ± 0.40 | ± 0.24 | | | Ours (K=5, λ = 0.5) | 78.54 | 87.83 | 87.45 | 68.88 | 66.06 | 16.66 | 74.83 | 71.49 | 64.83 | 68.87 | 68.15 | 69.79 | | ± 0.39 | ± 0.52 | ± 0.74 | ± 0.78 | ± 0.16 | ± 0.33 | ± 0.62 | ± 0.20 | ± 0.36 | ± 0.52 | ± 0.51 | ± 0.25 | | | Ours (K=5, λ = 1) | 77.49 | 86.72 | 84.79 | 67.75 | 65.77 | 17.09 | 74.46 | 70.97 | 64.79 | 68.45 | 67.73 | 69.04 | | ± 0.29 | ± 0.68 | ± 1.06 | ± 0.59 | ± 0.27 | ± 0.41 | ± 0.47 | ± 0.21 | ± 0.40 | ± 0.37 | ± 0.39 | ± 0.27 | | GLUE 100 ECE (BERT Base) CoLA SST MRPC QQP MNLI QNLI RTE Avg. Ours (K=1) 27.15 19.96 10.90 32.18 32.70 23.37 30.26 25.22 Ours (K=5, λ = 0.1) 24.21 14.40 20.06 16.01 17.02 6.53 9.96 15.46 GLUE 1k ECE (BERT Base) CoLA SST MRPC QQP MNLI QNLI RTE Avg. Ours (K=1) 21.72 10.19 15.21 13.93 35.21 15.58 23.43 19.32 Ours (K=5, λ = 0.1) 20.50 7.52 16.11 14.36 32.67 15.00 12.88 17.01 GLUE Full ECE (BERT Base) CoLA SST MRPC QQP MNLI QNLI RTE Avg. Ours (K=1) 14.90 3.07 10.45 5.28 4.67 2.38 22.20 8.99 Ours (K=5, λ = 0.1) 15.06 4.23 5.97 4.85 4.43 3.52 23.35 8.77 Table 21: The comparison of expected calibration error (ECE) in the classification tasks of GLUE. | GLUE 100 (BERT Base) | | | | | | | | | |------------------------|-------|-------|-------|-------|-------|--------|--------|-------| | CLS vs ENS | 32.93 | 47.13 | 56.79 | 25.70 | 21.70 | 21.47† | 22.27† | 32.57 | | Dropout vs ENS | 33.65 | 47.56 | 54.63 | 30.88 | 24.76 | 38.28 | 30.45† | 37.17 | | Least vs ENS | 38.70 | 48.28 | 61.11 | 26.84 | 24.66 | 42.40 | 35.00 | 39.57 | | ENS vs ENS | 35.34 | 42.53 | 59.88 | 31.87 | 26.58 | 43.13 | 31.36† | 38.67 | | GLUE 1k (BERT Base) | | | | | | | | | | CLS vs ENS | 53.25 | 46.98 | 46.91 | 34.76 | 32.63 | 49.45 | 25.45† | 41.35 | | Dropout vs ENS | 45.55 | 54.31 | 46.30 | 49.91 | 37.76 | 53.50 | 31.36† | 45.53 | | Least vs ENS | 59.01 | 60.92 | 51.54 | 48.24 | 37.68 | 54.53 | 30.00† | 48.85 | | ENS vs ENS | 57.09 | 59.48 | 50.31 | 50.62 | 41.00 | 56.14 | 36.36 | 50.14 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? section 7 ✓ A2. Did you discuss any potential risks of your work? section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? section 1 ✓ A4. Have you used AI writing assistants when working on this paper? Grammarly ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 ✓ B1. Did you cite the creators of artifacts you used? Section D B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Not applicable. Left blank. ## C ✓ **Did You Run Computational Experiments?** Section 3 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section B.2 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 3 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section B.1 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ai-fang-2023-fly

On-the-fly Cross-lingual Masking for Multilingual Pre-training

https://aclanthology.org/2023.acl-long.49

In multilingual pre-training with the objective of MLM (masked language modeling) on multiple monolingual corpora, multilingual models only learn cross-linguality implicitly from isomorphic spaces formed by overlapping different language spaces due to the lack of explicit cross-lingual forward pass. In this work, we present CLPM (Cross-lingual Prototype Masking), a dynamic and token-wise masking scheme, for multilingual pre-training, using a special token $[\mathcal{C}]_{x}$ to replace a random token $x$ in the input sentence. $[\mathcal{C}]_{x}$ is a cross-lingual prototype for $x$ and then forms an explicit cross-lingual forward pass. We instantiate CLPM for the multilingual pre-training phase of UNMT (unsupervised neural machine translation), and experiments show that CLPM can consistently improve the performance of UNMT models on $\{De, Ro, Ne \} \leftrightarrow En$. Beyond UNMT or bilingual tasks, we show that CLPM can consistently improve the performance of multilingual models on cross-lingual classification.

## On-The-Fly Cross-Lingual Masking For Multilingual Pre-Training Xi Ai College of Computer Science Chongqing University [email protected] ## Abstract In multilingual pre-training with the objective of MLM (masked language modeling) on multiple monolingual corpora, multilingual models only learn cross-linguality implicitly from isomorphic spaces formed by overlapping different language spaces due to the lack of explicit cross-lingual forward pass. In this work, we present CLPM (Cross-lingual Prototype Masking), a dynamic and token-wise masking scheme, for multilingual pre-training, using a special token [C]x to replace a random token x in the input sentence. [C]x is a cross-lingual prototype for x and then forms an explicit crosslingual forward pass. We instantiate CLPM for the multilingual pre-training phase of UNMT (unsupervised neural machine translation), and experiments show that CLPM can consistently improve the performance of UNMT models on {De, Ro, Ne} ↔ En. Beyond UNMT or bilingual tasks, we show that CLPM can consistently improve the performance of multilingual models on cross-lingual classification. ## 1 Introduction With tied weights across the languages and the help of language identifications (Johnson et al., 2017), multilingual models only have access to monolingual corpora in different languages. Stemming from BERT/MLM (Devlin et al., 2019) and GPT (Radford et al., 2018; Alec Radford, 2020), for cross-lingual knowledge, multilingual pre-training with the objective of MLM on multiple monolingual corpora is introduced by XLM (Lample and Conneau, 2019), explored by MASS (Song et al., 2019) and mBART (Liu et al., 2020; Lewis et al., 2020), and scaled by XLM-R (Conneau et al., 2020) and mT5 (Xue et al., 2021). Essentially, in multilingual MLM pre-training, models are encouraged to learn implicit crosslinguality from both linguistic similarities and shared tokens (Karthikeyan et al., 2020; Wu and Dredze, 2019; Pires et al., 2019; Dufter and Bin Fang College of Computer Science Chongqing University [email protected] Schütze, 2020) for translation and cross-lingual transfer. However, it does not learn any explicit and principled cross-lingual forward pass from inputs to outputs, only relying on the isomorphic space that emerged from multilingual MLM pretraining by overlapping language spaces agnostically. Given the nature of translation and crosslingual transfer, models should understand explicit cross-lingual forward passes initiating crosslingual knowledge directly. Considering this aspect, beyond the *implicit* and *agnostic* cross-linguality, we are interested in the question: can models learn explicit and *principled* cross-linguality in multilingual pre-training without any supervision? Following this idea, for multilingual pre-training, we present a dynamic and token-wise masking scheme, CLPM (Cross-lingual Prototype Masking), to compute a special token [C]x representing a cross-lingual prototype for a selected token x and then replace x with [C]x instead of the standard token [M] in multilingual MLM pre-training. We present an example in Table 1. Significantly, when predicting the selected and replaced x, we model an explicit cross-lingual forward pass from the cross-lingual prototype [C]x to x. Source The investment fund that owned the building had to make a choice . [M] The [M] fund [M] owned [M] building [M] to make a choice . [C]x The [C]x1 fund [C]x3 owned [C]x5 building [C]x7 to make a choice . Table 1: Examples of [C]x and [M]. {x1, x3, x5, x7} at position {1, 3, 5, 7} are randomly selected for replacing. Then, we compute the [C]x set {[C]x1 , [C]x3 , [C]x5 , [C]x7} for replacing and pre-train MLM without any other change, treating [C]x as [M]. In multilingual pre-training, computing [C]x is a challenge on multiple monolingual corpora without any supervision from parallel corpora, translation tables (Dufter and Schütze, 2020; Ren et al., 2019b; Chaudhary et al., 2020), or data augmentation processes (Krishnan et al., 2021; Chaudhary 855 et al., 2020; Tarunesh et al., 2021). Fortunately, we find that suitable candidates can be dynamically obtained in the multilingual embedding space, considering the relevance between the selected token and the tokens in the other language. Meanwhile, naive token-to-token relevance is reported to misrepresent morphological variations (Artetxe et al., 2020; Czarnowska et al., 2020; Kementchedjhieva et al., 2020), which limits the improvements for translation and cross-lingual transfer tasks. Thus, we approximate multiple candidates in the other language for [C]x, expecting to cover morphological variations. Unfortunately, the input dependency is perturbed by [C]x because [C]x is not agnostic and not static as [M] but dynamically obtained from the other language. Eventually, it potentially results in a lack of learning internal structures of languages. To alleviate this pain but still use [C]x, we alternate between [M] and [C]x, where [M] is agnostic and does not perturb input language domain. We attempt UNMT and (zero-shot) cross-lingual transfer tasks. For UNMT, we consider X ↔ En on a rich-resource language De, a low-resource language Ro, and a dissimilar language Ne. Intuitively, CLPM yields improvements because of the dynamical approximations of token-level crosslingual information. We then justify this on crosslingual word similarity tasks from MUSE (Lample et al., 2018b). Beyond UNMT, we experiment with the cross-lingual classification task on XNLI (Conneau et al., 2018) to test general cross-lingual transfer CLPM improves within a pivoting-based framework.par We have three contributions. 1) We present CLPM, a dynamic and token-wise masking scheme using special tokens [C]x, to form crosslingual forward passes in multilingual pre-training. [C]x is a generalized representation from multiple cross-lingual candidates. 2) CLPM substantially improves the performance of X ↔ En baseline UNMT models by 3% ∼ 8% on rich-resource and low-resource languages and can facilitate training on dissimilar languages. 3) Beyond UNMT tasks or bilingual tasks, CLPM can be used for crosslingual classification tasks. ## 2 Cross-Lingual Prototype Masking Notation Lx is the language ID of language Langx. Pn stands for positions. ER is the embedding for R. d is the model/embedding dimension. ## 2.1 Forward Pass In Attention Given an input sentence X = {x0, x1*, ..., x*n} in the language *Lang*x, the self-attention layer (Vaswani et al., 2017) performs on the sum of X*input* = {Ex0 + ELx + EP0 , ..., Exn + ELx + EPn }, which is considered in previous works of multilingual pre-training (Liu et al., 2020; Song et al., 2019; Lample and Conneau, 2019). For predicting xi, the attention score (Bahdanau et al., 2015; Luong et al., 2015) ei,j = (Exi + ELx + EPi ) TWT q Wk(Exj + ELx + EPj ) between query vector qi and key vector kj within the same sentence can be decomposed: $$\begin{array}{c}{{e_{i,j}=\underbrace{E_{x_{i}}^{T}W_{q}^{T}W_{k}E_{x_{j}}}_{a}+}}\\ {{\underbrace{E_{L_{x}}(\cdot)}_{b}+\underbrace{E_{P_{i}}(\cdot)}_{c}+\underbrace{E_{P_{j}}(\cdot)}_{d}}}\end{array}\tag{1}$$ where Wq and Wk are linear transformation for the query vector qi and key vector kj respectively, and i and j stands for position indexes. Terms (b), (c), and (d) introduce the inductive bias towards language *Lang*x, position Pi, and position Pj respectively. When predicting xi, we have the forward pass: {xi, xj\i} → xi, where xj\i denotes all the tokens around position i, and the prediction of xiis conditioned by {xi, xj\i}. The forward pass is *monolingual* because both two sides are in the same language. In optimization, we can compute gradients from the backward pass: ∂εxi ∂Exi and ∂εxi ∂Exj , where εxi is the predicting error. 2.2 MLM with [M] **and CBOW** Suppose xiis randomly selected to be replaced by [M]. Term (a) is changed to ET [M]WT q WkExj . Since [M] does not provide prior information of xi, Term (a) forms a built-in CBOW 1 model (Continuous Bag-of-Words (Mikolov et al., 2013)) learning CBOW or bidirectional information. The forward pass {[M], xj\i} → xiis still *monolingual* in multilingual pre-training because [M] is shared and agnostic for all the languages. However, the model is significantly encouraged to predict xi by understanding neighboring tokens xj\iin the sentence, i.e., the surrounding context or bidirectional information. Moreover, since [M] is overlapping and 1For instance, given X = {x0, [M], x2, x3}, we have the forward pass: {xi = [M], xj\i = (x0, x1, x3)} → x2 if predicting x2, where {xi = [M], xj\i = (x0, x1, x3)} models (non-standard) CBOW (4-gram). shared, and xj\i are potentially overlapping tokens in different languages, it refines the morphology of different languages to overlap each other for forming the isomorphic spaces (Karthikeyan et al., 2020; Wu and Dredze, 2019; Pires et al., 2019; Dufter and Schütze, 2020) and leverages domain adaptation (Ganin et al., 2016) or language adaptation (Ai and Fang, 2022b). ## 2.3 Mlm With [C]X Although the forward pass {[M], xj\i} → xi significantly enables the model to learn both crosslingual and monolingual knowledge from the shared token [M] (Dufter and Schütze, 2020) and structural information of the neighboring tokens xj\i(Karthikeyan et al., 2020; Pires et al., 2019) in multilingual MLM pre-training, learning crosslinguality is *implicit and limited*. Our idea is, we can replace [M] with xi's cross-lingual prototype [C]xi that we explicitly have a principled *crosslingual* forward pass: {[C]xi , xj\i} → xi. In this way, we inject weak but explicit cross-lingual supervision into the model in multilingual pre-training. Therefore, we replace the selected xi with its [C]xi instead of [M] as presented in the example (Table 1), and Term (a) is modified to ET [C]xi WT q WkExj accordingly. 2.4 On-the-fly [C]x To obtain [C]xi without any cross-lingual supervision in multilingual pre-training, the starting point is the output distribution over the vocabulary V shared by all the languages. Given the multilingual model Net, we set Net to *the inference mode*, not the MLM pre-training mode, and the probability of xiis obtained from the *sof tmax* layer Qxi = exp(h T xi&Lx Oxi ) PV k=1 exp(h T xi&Lx Oxk ) , where hxi&Lx ∈ Net(Ex + ELx) is the contextualized representation of xi, Ex = {Ex0 , Ex1 , . . . , Exn } is the embedding of the input sentence, and Ox is factorized from the output matrix2 O. Recall that, in Eq. 1, the language embedding ELx of the language *Lang*x associated with the token x introduces inductive bias towards *Lang*x, so that hxi&Lx is biased by ELx towards *Lang*x and generalized from Exi . In this way, the output distribution over the vocabulary is biased by ELx towards *Lang*x, and the dot-products distinguish relevant tokens from irrelevant tokens for xi. Intuitively, we can fool the 2Note that, in most of the cases, the output matrix shares all the parameters with the embedding matrix. model by inputting Ex + ELy 3. The result is that hxi&Ly ∈ Net(Ex + ELy) is biased by ELy towards *Lang*y but still generalized from Exi . We expect hxi&Ly to be an agnostic representation that is relevant to xi and *Lang*y. Then, we can factorize Oy from the output matrix and rank the dot product h T xi&Ly Oy to search relevant tokens for xiin Langy from the output space. We will discuss the inspiration later, and in our experiment, we show a case study that some useful candidates in the other language are obtained. We approximate a relevant candidate set P Y xi in the other language *Lang*y and compute a weighted average of candidates' embeddings, where P Y xi contributes to low variance and rich information. Formally, we define E[C]xi =Py∈P Y xi EyW y xi , where P Y xi ⊂ V ocY , *V oc*Y is the entries of the other language in the multilingual vocabulary, 0 ≤ W y xi ≤ 1 is the weight of the candidate y ∈ P Y xi P and y∈P Y xi W y xi = 1. Given the model Net, we have 4 steps to compute [C]xi dynamically: - **Step 1:** We set Net to *the inference mode* Net ˜ , input Ex + ELy to Net ˜ , and obtain the representation hxi&Ly ∈ Net ˜ (Ex + ELy) for the selected token xi. - **Step 2:** We factorize Oy from the output matrix O and calculate a full-sized set Q = (h T xi&Ly Oy0 , ..., hT xi&Ly Oyv), where v equals the size of *V oc*Y . - **Step 3:** We select a candidate set P Y xi = (Ey j *, ..., E*y k ) from the embedding space, according to the Top-K dot products in Q. - **Step 4:** We compute the weight set W y xi = sof tmax(ET y jEx, ..., ET y kEx) and the final output E[C]xi =Py∈P Y xi EyW y xi . Note that, multilingual models like XLM-R (Conneau et al., 2020) do not require language embeddings, i.e., eliminating ELx . In this scenario, we can simply eliminate ELy in **Step 1** without other modifications, and we still obtain cross-lingual candidates over *V oc*Y in **Step 2** to compute the crosslingual prototype for the selected token xi. To select tokens for *V oc*Y , the minimum frequency is 1e − 5 in the monolingual corpora of Langy. Meanwhile, some tokens are shared among 3Empirical studies and alternatives of Ex + ELxand Ex's nearest neighbors are presented in Appendix C.1. different languages. We set the minimum frequency of shared tokens to 1e − 3 in the monolingual corpora. These settings are used to limit the searching bound for more meaningful candidates. Inspiration Our recipe takes inspiration from early experiments. We pre-train a small multilingual model (12 layers and 256 d) and use our recipe to search for candidates. As presented in Table 2, a multilingual model can infer some crosslingual candidates with our recipe because of the cross-lingual transfer phenomenon, and we can generalize these candidates for cross-lingual prototypes. Meanwhile, we are aware that the multilingual model has to be pre-trained or properly initialized in order to infer cross-lingual candidates by itself. We will discuss initialization later. ## 2.5 Alternation Between [M] And [C]X In our experiment (see row 12 ∼ 15 of Table 7 in Appendix), we find that we can get benefits from alternating between [M] and [C]x. Intuitively, only using [C]x might perturb bidirectional knowledge and result in the lack of language knowledge, whereas the model can learn bidirectional information from using [M] in multilingual MLM pre-training. We also note similar observations in previous works (Chaudhary et al., 2020; Ren et al., 2019a), which use translation tables for pre-training. Another side effect we observe is that the model might pay more attention to "prototype-word" translation knowledge instead of understanding bidirectional knowledge. Thus, to encourage the model to learn both strong bidirectional knowledge from [M] and cross-lingual knowledge from [C]x, in t% of the time of the MLM pre-training time, we use [C]x for masking. For the remaining (100 − t)% of the time, we still use [M]. Hence, we have dual objectives in multilingual MLM pre-training: LMLM = L[C]x +L[M]. With these dual objectives in mind, we can simply extend the MLM's masking strategy to: ([SAME], [RAN], [M], [C]x) with (10%, 10%,(80 − t)%, t%). ## 2.6 Discussion We discuss some important components of our method. For these discussions, we provide empirical studies and show the observation of these components in §Robustness and Model Variation. [M] vs. [C]x 1) [M] is static in the embedding space with an explicit entry, used by running a lookup operation. Meanwhile, it is used to replace all randomly selected tokens, which is *unified*. 2) In contrast, [C]xi or E[C]xi is dynamically approximated during training, which is *token-wise*. Choice of K The memory usage is proportional to the size of K. Meanwhile, large K potential increases noise for unambiguous [C]x. 2) On the other hand, a small K may reduce the searching bound that computing proper [C]x is hard. For instance, K = 1 only yields median improvements in our experiment. Our empirical study shows that it is robust to a range of K from 2 to 5, considering a trade-off between GPU memory problems and expected performance improvements. Initialization The random initialization may raise problems. 1) x may find some geometric close but irrelevant tokens with large dot products in *V oc*Y , which results in a trivial candidate set. 2) The *inference mode* with random initialization is trivial. To this end, we only pre-train the multilingual model by MLM with [M] at the first several iterations for warm-up to form the multilingual embedding space and activate the *inference mode*, as discussed in §Inspiration. After the warm-up, the multilingual embedding space and the inference mode are initialized in a few-shot style somewhat to avoid trivial candidates. Then, we run the alternation. In our experiments, we find that this warm-up can help the model obtain new samples with cross-lingual prototypes from the other language. Efficiency On-the-fly [C]x will increase the training time. However, only a subset of tokens (typically, 15% (Devlin et al., 2019)) of the input text stream is selected for masking, and we only need to compute [C]x for a sub-set of all the selected tokens. In our experiment, we find our method spends additional ≈ 15% time on training. Tokenization Tokenizations generating "middle" tokens, sub-tokens, or non-standard word tokens might impact [C]x, e.g., BPE. However, the impact is relatively small given that: 1): the vocabularies and monolingual corpora are dominant by the standard words rather than non-standard word token, e.g., over 50% BPE vocabulary for translation task De ↔ En) are standard words and they make up for over the 80% of the total token frequency in the monolingual corpora; 2): all the representations are contextualized that sub-tokens and non-standard ![4_image_0.png](4_image_0.png) ![4_image_1.png](4_image_1.png) Masked It [C]x1 Masked [C]x0 word tokens still represent semantics and syntactic meanings related to their original standard words (refer to the case study in Appendix C.2). ## 3 Empirical Study And Experiment All the links of datasets, libraries, scripts, and tools marked with ⋄ are listed in Appendix F. A preview version of the code is submitted, and we will open the source code on GitHub. Pre-training Setting We use Adam optimizer (Kingma and Ba, 2015) with β1 = 0.9,β2 = 0.999, ϵ = 1e − 8, *warm*_up step (Vaswani et al., 2017) and lr = 1e − 4. Dropout regularization is set to rate = 0.1. Readers can refer to Appendix D.1 for details. Model Configuration Our Transformer model (Vaswani et al., 2017) is identical to XLM (Lample and Conneau, 2019), which consists of a 6-layer encoder and 6-layer decoder with 1024 word embedding size and hidden size and 4096 feed-forward filter size. We add a learnable language embedding and a learnable position embedding to each token of the input sentence for the encoder and decoder (P and L in Eq.1 ). We have some default configurations for our method based on the study of model robustness (see §Robustness and Model Variation): 1) t% = 40% that we make a balance between the two objectives: [M] and [C]x; 2) K = 3 that we consider top-3 candidates for the cross-lingual prototypes; 3) the warm-up step is 50k that [M] is only used at the first 50k iterations; 4) we consider BPE for tokenization in all our experiments. Multilingual Task We consider three multilingual tasks: 1) UNMT for evaluation on translation tasks, 2) cross-lingual word similarity for evaluation on cross-lingual embedding tasks, and 3) zeroshot cross-lingual classification for evaluation on cross-lingual transfer tasks. ## 3.1 Mlm Instance We adapt our method to three MLM instances to pre-train the multilingual model:1) XLM (Lample and Conneau, 2019), 2) MASS (Song et al., 2019), and 3) mBART (Liu et al., 2020), which can be used to pre-train a multilingual model. Readers can refer to the original report or Appendix D.2 for more instructions on these MLM instances. Significantly, to minimize changes for evaluation and comparison, we only have two changes. The first change we make is extending the masking strategy: ([SAME], [RAN], [M]) with (10%, 10%, 80%) to ([*SAME*], [RAN], [M], [C]x) with (10%, 10%,(80 − t)%, t%). Secondly, as presented in Table 1, we only apply CLPM to the input of the source side or the encoder and do not change the shifted input of the decoder in these MLM instances. Any other component is identical to the reported MLM instances. We reimplement all the baseline models on our machine with our configurations, using official XLM⋄, Tensor2Tensor⋄, and HuggingFace⋄ as references. We compare the results of our reimplementation with the reported results on the same test set to ensure that the difference is less than 2% in overall performance (see Appendix E for result comparison). Then, we can confirm our reimplementation. ## 3.2 Unmt Setup We consider similar language pairs {De, Ro} ↔ En, using the same dataset and test set as previous works (Lample and Conneau, 2019). Meanwhile, we share the FLoRes⋄ (Guzmán et al., 2019) task to evaluate a dissimilar language pair Ne ↔ *English* (Nepali). We learn shared BPE (Sennrich et al., 2016b), selecting the most frequent 60K codes from paired languages with the same criteria in Lample and Conneau (2019). The model is pre-trained around 400K iterations on only monolingual corpora in different languages. And, after around 400K training iterations for translation with the standard pipeline⋄ (Artetxe et al., 2018b; Song et al., 2019), according to baseline models' BLEU scripts, we report BLEU computed by multi-BLEU.perl⋄ or *sacreBleu*⋄ (Post, 2018) with default rules. See more details in Appendix D.3. Result Table 3 shows the results on the {De, Ro, Ne} ↔ En test sets. Applying [C]x consistently improves the performance of baseline models on all the similar language pairs by 3% ∼ 8% and on the dissimilar pair by 2.5 ∼ 7 BLEU. The performance on the dissimilar pair is very close to SOTA: mBART25 (Liu et al., 2020), but they use 25 languages from CC25 (Wenzek et al., 2020) for pre-training. Our method slightly outperforms two dictionary-based works (Dufter and Schütze, 2020; Chaudhary et al., 2020) which require static translation tables from pre-trained word models, golden dictionaries, or bilingual lexicon induction (e.g., UBWE). Intuitively, as reported in (Artetxe et al., 2020; Kementchedjhieva et al., 2019; Czarnowska et al., 2019; Vania and Lopez, 2017), such word translation tables are reported to misrepresent morphological variations and are not contextualized properly, which limit the improvements for sentence translation. For further analyses, we conduct a case study to observe the attention weights on [C]x after pretraining, which is visualized in Appendix C.2. We observe that the model outputs prominent attention weights on [C]x for predicting replaced tokens, so that it relies on [C]x. In other words, the model understands [C]x in the context. We can confirm the effectiveness. Concretely, CLPM shows significant effectiveness on nouns, entities, terminol- ![5_image_0.png](5_image_0.png) ogy words, etc., where the attention weights on the corresponding [C]x are dominant. Meanwhile, the model can understand phrases, sub-tokens, and syntax structures to predict a replaced token of the phrase because the model pays equal/similar attention to each token of the phrase. We attribute this phenomenon to both the alternation between [C]x and [M] and involving neighboring tokens in {[C]xi , xj\i} → xithat the model captures token dependencies from the cross-lingual prototype or a synonym in the other language. Finally, the employment of multiple candidates is important because the model could learn morphological or relevant variations from [C]x in the other language (refer to Appendix C.1), e.g., understanding relevant variations <welches, welcher, **welche**> from [C]x, which is essential for further translation learning in unsupervised manners. ## Dose Clmp Introduce New Samples With Crosslingual Prototypes From The Other Language? In addition to §Case Study, we are still interested in the representation of E[C]x or whether CLMP introduces new examples with cross-lingual prototypes from the other language. Intuitively, if the weights obtained in Step 4 are {c1 = 0.9, c2 = 0.05, c3 = 0.05}, the representation is similar to the candidate c1, and then c1 is a soft translation of x. If the weights are {c1 = 0.4, c2 = 0.3, c3 = 0.3}, the representation could be different from any one of {c1, c2, c3}. Thus, the representation depends on the contributions of the candidates. To further understand E[C]x , we jointly train a discriminator to distinguish between two languages in the pre-training phase. The discriminator is trained | Language pair | De ↔ En | Ro ↔ En | Ne ↔ En | | | | |-----------------------------------------------------------------------------------------------|-----------|-----------|-----------|------|------|-----| | multi-BLEU.perl⋄ with default rules | | | | | | | | XLM(Lample et al., 2018c) | 34.3 | 26.4 | 31.8 | 33.3 | 0.5 | 0.1 | | + word translation tables (Chaudhary et al., 2020) ⋆ | 35.1 | 27.4 | 33.6 | 34.4 | 4.1 | 2.2 | | + [C]x | 35.9 | 28.1 | 34.4 | 35.3 | 6.6 | 2.8 | | MASS(Song et al., 2019) | 35.2 | 28.3 | 33.1 | 35.2 | | | | + nearest neighbor from UBWE (Dufter and Schütze, 2020) ⋆ | 36.1 | 28.8 | 34.1 | 36.4 | 5.1 | 2.8 | | + [C]x | 36.7 | 29.2 | 34.7 | 36.9 | 7.1 | 3.4 | | sacreBleu⋄ with standard settings: nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 | | | | | | | | mBART(Liu et al., 2020) + CC25 (Wenzek et al., 2020) | 34.0 | 29.8 | 30.5 | 35.0 | 10.0 | 4.4 | | + [C]x (w/o CC25) | 35.4 | 30.1 | 32.5 | 36.7 | 7.0 | 3.2 | to recognize which language an embedding or a representation belongs to. We use all the embedding instances to train the discriminator. Then, we make zero-shot classification for E[C]x to observe which language E[C]x belongs to. We report the result in Figure 1. This figure suggests that CLMP introduces unseen cross-lingual prototypes for the model. We suspect that ECx potentially yields a generalized representation from multiple relevant candidates in other languages. This is different from the method family based on translation tables. Significantly, translation tables are instances/embeddings in the embedding space, whereas cross-lingual prototypes do not exist in the embedding spaces and are new generalized samples for the model. ## 3.3 Robustness And Model Variation We have some default configurations, as presented in row 2 of Table 4. This combination is obtained in our experiments. We report the results to observe the impact of K (the number of cross-lingual candidates), the warm-up initialization, the tokenization method, and the alternation t% in Appendix B. Meanwhile, in this experiment, we discuss a mean average style for cross-lingual candidates instead of the weighted average used in the default configuration, reporting results in Appendix B. Additionally, we study alternatives for initialization and training efficiency. The result is presented in Table 7. For consistency, the row number is consistent with the full results in Appendix B. Row 11 As aforementioned, CLPM requires additional time to compute [C]x. To be fair, we reduce the training steps, so that the training time is almost similar to the baseline model (row 1). CLPM outperforms the baseline model but requires fewer training steps, which indicates that the explicit and principled cross-lingual forward pass is more ef- Row 17 We use UBWE (unsupervised bilingual word embedding) to initialize the bilingual embedding space. In the first 50k pre-training steps (equal to default warm-up steps), since the model parameters are still randomly initialized, we do not follow Step 1, 2, and 3 in on-the-fly [C]x and directly find relevant candidates based on the dot products ET y iEx, i.e., only need Step 4. Intuitively, ET y iEx is reliable to rank the candidates and compute the weights for [C]x because UBWE provides cross-lingual entries. After 50k pre-training steps, we normally run on-the-fly [C]x. We observe that adapting UBWE consistently improves the performance by 2% on the similar language and 0.5 ∼ 1 BLEU on the dissimilar language because UBWE provides additional cross-lingual supervision. See all the results in Table 8. Row 18 Vulic et al. ´ (2020) suggest seed dictionaries for unsupervised tasks in practice. Following this idea, we download a 1k seed dictionary from Panlex⋄. In the first 50k pre-training steps, we simply replace the selected token with its translation in the seed dictionary. For the out-of-thedictionary but selected token, we replace it with normal [M]. After 50k pre-training steps, if the selected token is in the dictionary, the translation is added to [C]x as a candidate in Step 4 when running on-the-fly [C]x. We find that compared to the UBWE scenario, this adaptation achieves similar results on the rich-resource language De ↔ En (+ 1.5%) but stronger results on the dissimilar language Ne ↔ En (+ 8%). All the results are presented in Table 8. | Row | Model | t | Tokenization | Warm-up | Steps | K | [C]x type | De ↔ En | | |-------|------------------------------|-----|----------------|--------------------|------------------------------|-----|-------------|-----------|------| | 1 | [M] (baseline) | - | BPE | - | 400K | - | - | 34.3 | 26.4 | | 2 | [C]x (our baseline, default) | 40% | BPE | 50K | 400K | 3 | weighted | 35.9 | 28.1 | | 11 | [C]x | + | + | + | 350K (similar training time) | + | + | 35.1 | 27.2 | | 17 | [C]x | + | + | UBWE | + | + | + | 36.5 | 28.8 | | 18 | [C]x | + | + | 1k seed dictionary | + | + | + | 36.9 | 29.1 | ![7_image_0.png](7_image_0.png) | MUSE | score | |-------------------------------|---------| | XLM(Lample and Conneau, 2019) | 0.55 | | +[C]x | 0.61 | | MASS(Song et al., 2019)⋆ | 0.60 | | +[C]x | 0.64 | | mBART(Liu et al., 2020)⋆ | 0.59 | | +[C]x | 0.64 | ## 3.4 Cross-Lingual Word Similarity Setup Given the idea of our method, we consider cross-lingual mappings of tokens. Therefore, we are interested in the isomorphism of languages' embedding spaces. To further investigate, the pretrained UNMT model is evaluated on MUSE⋄ (Lample et al., 2018b) with the provided test sets and tools, which is used to test cross-lingual word similarities on En ↔ De. This test can generally evaluate the degree of the isomorphism of languages' embedding spaces. We reuse the pretrained models in our UNMT experiment. After restoration, we extract words required by the test set via shared lookup tables. For words split into 2+ sub-tokens, we average all the sub-tokens. Result We evaluate the performance by similarities, reporting the result in Table 5. Applying [C]x can increase the similarities of parallel words from {*En, De*}, consistently improving the performance of the models on this task. It indicates that [C]x helps the models learn token-level crosslinguality in pre-training. ## 3.5 Cross-Lingual Classification Setup Beyond UNMT tasks or translation tasks, CLPM can consistently improve cross-lingual transfer. Then, we attempt the cross-lingual classification task on XNLI (Conneau et al., 2018) to test general cross-linguality [C]x improves. For this | Model | Avg (Acc.) | |---------------------------------------------------|--------------| | mBERT baseline (Wu and Dredze, 2019) | 66.3 | | XLM (Lample and Conneau, 2019) | 71.5 | | + word translation tables(Chaudhary et al., 2020) | 72.7 | | + [C]x | 74.0 | | + MT (Lample and Conneau, 2019) | 75.1 | ![7_image_1.png](7_image_1.png) test, we follow the standard and basic experiment (Lample and Conneau, 2019) to train a 12-layer Transformer encoder with 80k BPE on Wikipedia dumps⋄ of 15 XNLI languages. To pre-train the encoder on En corpora, considering the zero-shot classification based on finetuning En NLI dataset, we randomly compute [C]x from other languages with equal probability to avoid the cross-lingual bias. For pre-training on corpora of other languages, we only compute [C]x in the En entries. Note that, although we have different strategies of [C]x for the languages, we still concatenated all the corpora of the languages for joint pre-training. After pre-training, we deploy a randomly initialized linear classifier and finetune the encoder and the linear classifier on the En NLI dataset with minibatch size 16. We make zero-shot classifications for other languages. See more details in Appendix D.3. Result We report the result in Table 6. CLPM shows effectiveness on this task, outperforming baseline models. It indicates that [C]x can improve cross-lingual transfer. Meanwhile, [C]x underperforms XLM + MT that uses parallel corpora to improve cross-linguality. As discussed earlier, [C]x can provide token-level cross-lingual knowledge at the very least but is less effective than golden sentence-level knowledge. Although XLM + MT uses additional datasets, it somewhat sets an upper bound. On the other hand, our method outperforms dictionary-based methods (+ word translation tables). Similar to the observation in UNMT, we attribute to the effectiveness of using multiple candidates to capture morphological variations. However, to avoid cross-lingual bias, we use En as a pivot or anchor point. This could be a potential problem for further adaptation to other multilingual tasks. See limitations in Appendix A. ## 4 Related Work And Comparison (Ren et al., 2019a; Chaudhary et al., 2020; Lample et al., 2018c) leverage translation tables as entries for the other languages, which are automatically generated from statistical models, e.g., n-gram models. The model forms an explicit crosslingual forward pass: {[M], xj\i} → ti, where ti is the entry of the other language for xi. In contrast, our method has two significant differences: 1) we focus on the left side, adapting our [C]x to the inputs of MLM; 2) our method does not rely on token/phrase-level translation tables. Dufter and Schütze (2020) present a cross-lingual forward pass: {nn, xj\i} → xi, where nn is xi's nearest neighbor of the other language in the space of UBWE. However, UBWE is static and fixed without any interaction with the multilingual model. It might limit what it can be ultimately used for translation (Sun et al., 2019; Artetxe et al., 2018b; Lample et al., 2018a). We present a dynamic approach to obtain candidates of the other language from the model itself, which is inspired by (Ai and Fang, 2021b; Sennrich et al., 2016a). The benefit is that embeddings and representations are contextualized when pre-training MLM on monolingual corpora in different languages (Lample and Conneau, 2019). Although it is not reliable at the very early pre-training, we provide a compromised initialization for this problem. We also consider multiple candidates for cross-lingual prototypes instead of nn, which is softer and can cover morphological or relevant variations in the other language. On the other hand, considering cross-lingual prototypes is not a novel idea for cross-linguality, (Wang et al., 2019; Huang et al., 2019; Ai and Fang, 2021a) present methods to leverage crosslingual prototypes to guide encoding and decoding, forming a cross-lingual forward pass by modifying inner representations of encoding and decoding: {[M], xj\i} → {[M], hxj , hyi} → xi, where hyi is an approximation of xi's inner representation in encoding and decoding from the other language. It results in a different direction. We also employ the alternation strategy that can be viewed as linguistic code-switching (Scotton and Ury, 1977) somewhat, where the model is pre-trained in more linguistic varieties. In learning models, linguistic code-switching performs as data augmentation processes (Krishnan et al., 2021; Chaudhary et al., 2020; Tarunesh et al., 2021) with the help of static translation tables or lexicon induction in supervised manners. However, lexicon induction datasets or translation tables have been reported to misrepresent morphological variations and overly focus on named entities and frequent words (Artetxe et al., 2020; Czarnowska et al., 2020; Kementchedjhieva et al., 2020). In contrast, CLPM is dynamic and unsupervised, leveraging contextualized representations and multiple morphological variations in the model's embedding space. Meanwhile, translation tables are instances/embeddings in the embedding space, whereas cross-lingual prototypes do not exist in the embedding spaces and are new generalized samples for the model. This distinction is observed from the discriminator in Figure 1. ## 5 Conclusion In this work, we present CLPM, an alternative masking scheme, to compute special tokens [C]x for masking in multilingual MLM pre-training. [C]x is the cross-lingual prototype for the selected word x, computed from multiple candidates dynamically and token-wise. Compared to the standard masking scheme [M], [C]x automatically forms an explicit cross-lingual forward pass in attention mechanism, consistently improving cross-linguality in multilingual MLM pre-training. 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Our method provides a general framework to leverage cross-lingual prototypes for multilingual MLM pre-training, but the scope of the study is limited. We believe there are some other solutions. For instance, we can leverage linguistic varieties for masking, but the question is how to obtain linguistic varieties without using parallel corpora. Perhaps, we can consider word frequencies because Zipf's law indicates that words appear with different frequencies, and one may suggest similar meaning words appear with relatively similar frequencies in a pair of languages. Most importantly, solutions should further consider morphological variations, since in this paper we prove morphological variations are significantly beneficial. We have some default configurations for our method, as presented in row 2 of Table 7. In this experiment, we observe the impact of K (the number of cross-lingual candidates), the warm-up initialization, the tokenization method, and the alternation t%. We consider the weighted average of crosslingual candidates for [C]x, and additionally we consider the mean average style in this experiment. For initialization, we further study alternatives. The result is presented in Table 7. Row 3 ∼ 6 Models with a common choice of K (1 ∼ 5) outperform the baseline model. However, K = 1 (a single candidate) yields median improvements. Meanwhile, when K = 1, our method is similar to (Dufter and Schütze, 2020; Chaudhary et al., 2020) who employ static and word translation tables (e.g., UBWE and dictionary) for obtaining a single candidate, and they have similar results. Intuitively, the model cannot capture morphological variations and synonyms in the other language when only using one candidate, as discussed in the experiment of UNMT, but they are important in translation. It proves the significance of using multiple candidates. In this work, we present a general masking scheme for multilingual MLM pre-training on multiple monolingual corpora. Experiments show that our method can work for similar languages (including low-resource and high-resource ones) and dissimilar languages. However, we only experiment with dissimilar language Ne. More experiments are required for dissimilar and distant languages. When computing [C]x for more than 3 languages, to avoid cross-lingual bias, we adapt our method to a pivoting-based framework, using En as a pivot or anchor point. Although we show this framework can work for cross-lingual classification tasks, this could be a potential problem for further adaptation to other multilingual tasks, which requires further Row 7 ∼ 9 Warm-up is necessary to facilitate [C]x. Although a small amount of warm-up steps is enough, it is a disadvantage of [C]x somewhat. We believe there is a significant potential for development of other new alternatives. We present two options in row 17 and row 18 (see the following Row Model t Tokenization Warm-up Steps K [C]x type De ↔ En ![13_image_1.png](13_image_1.png) ![13_image_2.png](13_image_2.png) ![13_image_3.png](13_image_3.png) ![13_image_4.png](13_image_4.png) 1 [M] (baseline) - BPE - 400K - - 34.3 26.4 2 [C]x (our baseline, default) 40% BPE 50K 400K 3 weighted 35.9 28.1 3 [C]x + + + + 1 + 34.9 27.3 4 [C]x + + + + 2 + 35.8 27.9 5 [C]x + + + + 4 + 36.0 28.0 6 [C]x + + + + 5 + 35.9 28.1 7 [C]x + + 20k + + + 35.1 27.1 8 [C]x + + 100K + + + 35.8 28.0 9 [C]x + + 200K + + + 35.3 27.5 10 [C]x + Word-level + + + + 35.8 28.0 11 [C]x + + + 350K (similar training time) + + 35.1 27.2 12 [C]x 10% + + + + + 35.6 28.0 13 [C]x 70% + + + + + 34.8 27.2 14 [C]x from 0 to 70% + + + + + 35.4 27.7 15 [C]x only [C]x (no [M]) + + + + + 30.1 21.5 16 [C]x + + + + + mean 35.3 27.8 17 [C]x + + UBWE + + + 36.5 28.8 18 [C]x + + 1k seed dictionary + + + 36.9 29.1 ![13_image_6.png](13_image_6.png) ## Text). Row 10 Also, we can see there is no significant difference between the word-level tokenization and the BPE tokenization. Although the BPE tokenization gains slightly better performance, the improvement we believe is from the effectiveness of the BPE tokenization itself, not the discrepancy of [C]x. Row 11 As aforementioned, CLPM requires additional time to compute [C]x. To be fair, we reduce the training steps, so that the training time is almost similar to the baseline model (row 1). In a similar training time, CLPM outperforms the baseline model but requires fewer training steps, which indicates that the explicit and principled cross-lingual forward pass is more efficient (per step) than implicit isomorphic space formation for cross-linguality. Row 12 ∼ 14 We alternate between [C]x and [M] because we consider learning the morphology and internal structure of languages from [M] like BERT. Note that the baseline model (row 1) is equivalent to t = 0 (only use [M]). We observe that t = {10%, 40%, 70%} significantly outperform t = {0}. This confirms our intuition that the UNMT model greedily obtains the explicit cross-linguality from [C]x and the bidirectional/language knowledge from [M]. We also consider the scenario that we increase t from 0 to 70% linearly, achieving competitive performance ## With T = {10%, 40%, 70%}. ![13_Image_0.Png](13_Image_0.Png) ![13_Image_5.Png](13_Image_5.Png) ![13_Image_7.Png](13_Image_7.Png) ![13_Image_8.Png](13_Image_8.Png) ![13_Image_9.Png](13_Image_9.Png) Row 15 We have a question: does [Cx] hurt learning language knowledge? Although [M] itself cannot provide any supervision, the model can learn strong language knowledge by understanding bidirectional information. Therefore, using [Cx] instead of [M] potentially fails in learning language knowledge, even though the cross-lingual forward pass:{[C]xi , xj\i} → xiinvolves neighboring tokens. To investigate, we experiment with only using [C]x. Compared to only using [M], only using [Cx] does degrade the performance of UNMT. We suspect that 1) the translation is not fluent due to the lack of learning bidirectional knowledge with the help of [M] and 2) the model pays more attention to prototype-word mappings instead of the context. However, applying the alternation strategy can mitigate the pain, and row 12 ∼ 15 show the alternation strategy can consistently improve performance on translation. Our intuition is that cross-linguality and language knowledge are essential for translation, similar to the observation in (Zhang et al., 2021; Ai and Fang, 2022a). Row 16 As we consider the weighted average of the candidate set, we are aware that the mean average style is also an alternative. The test shows that the weighted average style outperforms the mean average style. We conjecture that the weighted average style can compute more reliable cross-lingual prototypes because, for some unambiguous tokens, the mean average style may pay more attention to low-weight candidates. For instance, if the weights in Step 4 are {0.9, 0.15, 0.05}, computing [C]x is forced to pay more attention to "0.05" by the mean average style, which is unnecessary. On the other hand, the margin is not large. We suspect that the candidate set covers morphological variations and synonyms. Therefore, they have similar weights after the *sof tmax* normalization, which results in a similar output from the weighted average and the mean average. Row 17 Inspired by UBWE (unsupervised bilingual word embedding) (Lample et al., 2018a; Artetxe et al., 2018a, 2016, 2017), we are aware that we can pre-train cross-lingual embeddings for the multilingual model before multilingual MLM pre-training instead of the random initialization with the warm-up. To this end, we use the MUSE⋄ (Lample et al., 2018a)'s UBWE method to initialize the bilingual embedding space. In the first 50k pre-training steps (equal to default warm-up steps), since the model parameters are still randomly initialized, we do not follow Step 1, 2, and 3 in on-thefly [C]x and directly find relevant candidates based on the dot products ET y iEx, i.e., only need Step 4. Intuitively, ET y iEx is reliable to rank the candidates and compute the weights for [C]x, especially at the early iterations, because UBWE provides cross-lingual entries. After 50k pre-training steps, we normally run on-the-fly [C]x. We observe that adapting UBWE consistently improves the performance by 2% on the similar language and 0.5 ∼ 1 BLEU on the dissimilar language because UBWE provides additional cross-lingual supervision. All the results are presented in Table 8. Row 18 (Vulic et al. ´ , 2020) suggest seed dictionaries for unsupervised tasks in practice. Following this idea, we download a 1k seed dictionary from Panlex⋄. In the first 50k pre-training steps, we simply replace the selected token with its translation in the seed dictionary. For the out-of-thedictionary but selected token, we replace it with normal [M]. After 50k pre-training steps, if the selected token is in the dictionary, the translation is added to [C]x as a candidate in Step 4 when running on-the-fly [C]x. We find that compared to the UBWE scenario, this adaptation achieves similar results on the rich-resource language De ↔ En (+ 1.5%) but stronger results on the dissimilar language Ne ↔ En (+ 8%). All the results are presented in Table 8. ## C Additional Experiment C.1 Alternatives Given an input word and the current model Net, we compute [C]x by 1) computing the contextualized representation by setting the model to the inference mode with the target language embedding Net ˜ (Ex + ELy), 2) computing *sof tmax* over the contextualized representations in the output (embedding) layer, 3) selecting the Top-k embeddings with the highest *sof tmax* score, and (4) computing a weighted average over the selected embeddings. Essentially, we use the target language embedding for biasing the representations towards the target language. The question remains as to how well it works. Meanwhile, two alternatives are interesting: 1) Net ˜ (Ex + ELx), which uses the source language embedding to compute representations; 2) Top-k Nearest Embedding, which computes candidates by using Top-k Nearest Embeddings in the embedding space without using the inference mode. In Table 9, we provide an empirical study for Net ˜ (Ex + ELy), Net ˜ (Ex + ELx), and Top-k Nearest Embedding. Our observations are: - Top-k Nearest Embedding seems to find overshared tokens. For instance, in \#3, it finds [C]x8 = <to, for, by> for <to>, where <to, for, by> are shared by all the languages. With cross-lingual transfer in mind, we believe that a candidate set only covering over-shared tokens is not a good one, e.g., <to, for, by> is not a good candidate set crossing En to De. Meanwhile, Top-k Nearest Embedding is not good at finding strong candidates. - Net ˜ (Ex + ELx) is better than Top-k Nearest Embedding because Net ˜ (Ex + ELx) do not obtain too much over-shared tokens. - Compared to Net ˜ (Ex + ELx), Net ˜ (Ex + ELy) (our suggestion) will change the score of the full-sized set Q = (h T xi&Ly Oy0 , ..., hT xi&Ly Oyv) (Step 2). These scores are very dense, so that small changes cause significant differences. Then, Net ˜ (Ex + ELy) is better to rank candidates than Net ˜ (Ex + ELx). | Language pair | De ↔ En | Ro ↔ En | Ne ↔ En | | | | |---------------------------------------------------------------------------------------|-----------|-----------|-----------|------|-----|-----| | XLM(Lample et al., 2018c) | 34.3 | 26.4 | 31.8 | 33.3 | 0.5 | 0.1 | | + UBWE ⋆ | 34.0 | 27.0 | 33.3 | 34.1 | 4.9 | 1.3 | | + [C]x | 35.9 | 28.1 | 34.4 | 35.3 | 6.6 | 2.8 | | + [C]x + UBWE (for wam-up with Step 1,2 and 3) | 36.5 | 28.8 | 35.1 | 36.0 | 8.3 | 3.2 | | + [C]x + 1K seed dictionary (Vulic et al. ´ , 2020) (for warm-up with Step 1,2 and 3) | 36.5 | 28.9 | 35.7 | 36.5 | 9.1 | 4.0 | In conclusion, Net ˜ (Ex + ELy) shows the advance in: 1) it does not consider too many over-shared tokens; 2) Net ˜ (Ex + ELy) with the target language embedding is better to rank candidates than Net ˜ (Ex + ELx); 3) Net ˜ (Ex + ELy) can cover multiple morphological or relevant candidates (e.g., [C]x5 = <metres, metre, **yards**> in \#4 ) for generalizing information by weighted average. In this way, Net ˜ (Ex +ELy) finds better cross-lingual prototypes, which results in better generalized information by weighted average. ## C.2 Case Study To further probe the results, we use pre-trained weights from UNMT and compute [C]x for the selected tokens of sentences, obtaining 3 candidates for each token. *We observe attention weights on* [C]x. Our case study of Table 2 shows that for predicting replaced tokens, the model outputs prominent attention weights on corresponding [C]x, so that it relies on [C]x to predict the replaced tokens. Since [C]x is the cross-lingual prototype, the model can learn cross-linguality from the [C]x. We can confirm the effectiveness of [C]x. For example, to predict <Meter> (Figure 2c), our method finds possible translation for [C]x5 = <metres, metre, **yards**>, and the attention weight on its [C]x5 dominates others. We conjecture that our method shows significant effectiveness on nouns, entities, terminology words, etc. because parallel, analogical, or relevant words of these words in other languages might be easily inferred. Meanwhile, it shows the importance of using multiple candidates because the model might understand linguistic varieties. Besides, in this way, the model can yield generalized representations from [C]x in the other language (Step 4), which might be useful for translation and cross-lingual transfer. Furthermore, as discussed in §2.6, the model can handle sub-word tokens because for predicting <in@@> (Figure 2a), the model pays similar attention to its [C]x17 and its neighboring token <accuracy>, where <in@@> and <accuracy> are ![15_image_0.png](15_image_0.png) split from <inaccuracy>. It indicates that the model can consider the sub-token's cross-lingual prototype in the context. We attribute this phenomenon to both the alternation between [C]x and [M] and involving neighboring tokens in {[C]xi , xj\i} → xi that the model captures token dependencies from the cross-lingual prototype in the other language with the same semantic. Surprisedly, to predict <which> (Figure 2a) with its [C]x14 = <**welches**, welcher, **welche** >, the model seems to understand some syntax structures because the model pays more attention to <,> than <introduced>, where [C]x14 and <,> might jointly represent the syntax structure <, which>. Recall the discriminator 1, which confirms that cross-lingual prototypes belong to one language but do not exist in the embedding space, i.e., not used in discriminator training. The model cannot only rely on cross-lingual prototypes to recover masked tokens because cross-lingual prototypes are not translations. The model has to consider both cross-lingual prototypes and the context, understanding the generalized information of crosslingual prototypes in the context. The case study confirms this as attention weights observed from neighboring tokens around [C]x. ## D Experiment Setting D.1 Pre-Training Our code is implemented on Tensorflow 2.2 (Abadi et al., 2016). We use Adam optimizer (Kingma and Ba, 2015) with β1 = 0.9,β2 = 0.999, ϵ = 1e − 8, and lr = 1e − 4. Dropout regularization is set to rate = 0.1. The mini-batch size is set to 8192 tokens for all experiments. We sample sentences from different languages with the balance strategy (Lample and Conneau, 2019). ## D.2 Mlm Instance We adapt our method to three MLM instances: XLM (Lample and Conneau, 2019), MASS (Song | Net ˜ (Ex + ELy ) ([C]x) | Net ˜ (Ex + ELx ) | Top-k Nearest Embedding | | |----------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------|--------------------------------------| | #1 | The investment fund that owned the building had to make a choice . [EOS] | | | | Reference | Der Investmentfonds, dem das Gebäude gehörte , musste sich entscheiden . [EOS] | | | | Masked | The [C]1 [C]2 that [C]4 [C]5 [C]6 [C]7 to [C]9 a choice . [EOS] | | | | investment = [C]2 | Aufsichts@@, Förder@@, Einnahmen | Aufsichts@@, Förder@@, Einnahmen | Milliarden, Denkmalschutz, Kritiken | | fund = [C]x2 | wurf, funde, Förderung | funde, Förderung, wurf | Nachlass, funde, firma | | owned = [C]x4 | gehörte, kaufte, Eigentum | Eigentum, gehörte, kaufte | entstammte, geprägten, erbaute | | building = [C]x6 | Gebäude, gebäude, Anlage | Gebäude, gebäude, Gebäudes | gebäude, gebäudes, Gebäude | | had = [C]x7 | kam, hatte, war | kam, hatte, gab | entstammte, Seinen, Zur | | make = [C]x9 | Stand@@, machten, macht | machten, Stand@@, macht | Ist, bestritt, bestes | | #2 | He learned his craft from Hans Drei@@ er , with whom he worked on several films . [EOS] | | | | Reference | Sein Handwerk lernte er bei Hans Dreier , mit dem er an mehreren Filmen arbeitete . [EOS] | | | | Masked | He [C]x1 his craft [C]x4 Hans [C]x6 [C]x7 , [C]x9 whom he [C]x12 on several films . | | | | learned = [C]x1 | stammte, stammten, stammt | stammte, stammten, stammt | entstammte, erlernte, studierte | | from = [C]x4 | von, Von, vom | von, Von, vom | Von, Vom, ; | | Drei@@ = [C]x6 | Drei@@, Zwei@@, Vier@@ | Drei@@, Zwei@@, Mehr@@ | Drei@@, drei@@, Fünf@@ | | er = [C]x7 | er, es, der | er, es, der | er, sie, es | | with = [C]x9 | mit, in, Mit | mit, in, Mit | Mit, Beim, wobei | | worked=[C]x12 | arbeitete, wirkte, arbeiteten | wirkte, arbeitete, gearbeitet | promovierte, kandidierte, studierte | | #3 | It was hampered by the need for ranges to be estimated by eye , which introduced significant in@@ accuracy . [EOS] | | | | Reference | Erschwert wurde dies durch die Notwendigkeit , Entfernungen mit dem Auge abzuschätzen, was zu erheblichen Ungenauigkeiten führte . [EOS] | | | | Masked | It [C]x1 hampered by [C]x4 need [C]x6 ranges [C]x8 be estimated by [C]x12 , [C]x14 introduced significant [C]x17 accuracy . [EOS] | | | | was = [C]x1 | war, wurde, als | war, ,, wurde | (, welches, Was | | hampered = [C]x2 | hauptsächlich, Gesundheit@@, durchgeführt | hauptsächlich, Gesundheit@@, durchgeführt | angesichts, hinsichtlich, entstammte | | the = [C]x4 | den, die, [EOS] | die, den, [EOS] | die, :, den | | for = [C]x6 | für, dafür, in | für, dafür, in | für, Für, in | | to = [C]x8 | to, dem, sich | to, dem, erweitert | to, for, by(×) | | which = [C]x14 | welches, welcher, welche | welches, welcher, welche | welches, welchen, welcher | | in@@ = [C]x17 | inen, höher, . | inen, unge@@, höher | inen, unter@@, auf@@ | | #4 | Die Gleis@@ anlage war so ausgestattet , dass dort elektrisch betriebene Wagen eingesetzt werden konnten . [EOS] | | | | Reference | The track system was equipped in such a way that electrically operated cars could be used there . [EOS] | | | | Masked | [C]x0 Gleis@@ [C]x2 [C]x3 so [C]x5 [C]x6 [C]x7 dort elektrisch [C]x10 [C]x11 eingesetzt werden konnten . [EOS] | | | | Die = [C]x0 | The, In, [EOS] | The, In, Decline | His, Her, The | | anlage = [C]x2 | facility, facilities, Complex | facility, facilities, Complex | anime, HMS, { | | war = [C]x3 | was, crew. remained | was, crew. remained | was, :, ; | | ausgestattet = [C]x5 | equipped, fitted, yan | equipped, fitted, engines | whose, equipped, dae | | , = [C]x6 | ,, [EOS], ; | ,, ;, [EOS] | ,, ;, [EOS] | | dass = [C]x7 | why, how, whether | why, whether, resources | whether, why, unlike | | betriebene = [C]x10 | operated, like, isha | like, operated, isha | Romanized, whose, starring | | Wagen = [C]x11 | drove, cars, GP | drove, cars, GP | Stakes, fled, dancer | | #5 | In den nächsten Tagen soll eine endgültige Entscheidung durch das wissenschaftliche Programm@@ komitee fallen . [EOS] | | | | Reference | A final decision is to be made by the scientific program committee in the next few days . [EOS] | | | | Masked | In den [C]x2 Tagen soll [C]x5 endgültige [C]x7 durch das [C]x10 Programm@@ [C]x12 fallen . [EOS] | | | | nächsten = [C]x2 | next, past, host | next, past, Next | next, nearest, longest | | eine = [C]x5 | a, someone, formed | a, someone, formed | someone, a, Her | | Entscheidung = [C]x7 | vision, left, Note | vision, left, Note | Shortly, p.m., { | | wissenschaftliche = [C]x11 | scientific, research, journal | scientific, research, journal | peer, doctoral, remembered | | komitee = [C]x12 | committee, Congress, body | committee, Congress, body | {, Laboratory, certified | | #6 | Sie befindet sich auf 425 Meter Höhe nahe dem Schlos@@ sberg . [EOS] | | | | Reference | It is located at an altitude of 425 meters near the Schlossberg. [EOS] | | | | Masked | [C]x0 | sich auf 425 [C]x5 | dem Schlos@@ [C]x10 . [EOS] | | [C]x1 | [C]x6 [C]x7 | | | | auf = [C]x3 | on, in, below | in, on, an | an, in, On | | Meter = [C]x5 | metres, metre, yards | metres, metre, yards | metres, meters, metre | | Höhe = [C]x6 | elevation, depth, sales | elevation, depth, sales | altitude, elevation, excess | | nahe = [C]x7 | near, inside, security | near, inside, security | near, Near, nicknamed | | sberg = [C]x10 | say, sort, sing | say, sort, sing | p.m., re, Bros. | | Table 9: Examples of [C]x and alternatives. Although we compute generalized information from the candidate set | | | | ![17_image_0.png](17_image_0.png) et al., 2019), and mBART (Liu et al., 2020), which can be used to pre-train the multilingual model. We follow the instructions of these three MLM instances that each selected token is replaced with the probabilities ([SAME], [RAN], [M]) = (10%, 10%, 80%). XLM XLM is similar to BERT (Devlin et al., 2019) but uses text streams of an arbitrary number of sentences. Following the instruction, we randomly select 15% of the tokens from the input sentence for replacing. MASS MASS is different from XLM and BERT but similar to SpanBERT (Joshi et al., 2020), using spans to replace consecutive tokens. Given an input sentence with length N, we randomly select consecutive tokens with length N/2 for replacing. mBART mBART applies spans to replace consecutive tokens for a text instance of two concatenated random sentences and perturbs the order of the two concatenated sentences for prediction. We randomly select 35% of the tokens in each instance for replacing by sampling a span length according to a Poisson distribution λ = 3.5 and swap the two sentences within each instance. Significantly, to minimize changes for evaluation, we only have two changes. * We extend the masking strategy: $([SAME],[RAN],[M])$ with $(10\%,10\%,80\%)$ to $([SAME],[RAN],[M],[C]_{x})$ with $(10\%,10\%,(80-t)\%,t\%)$. - Secondly, as presented in Table 1, we only apply CLPM to the input of the source side or the encoder. Other components of the framework are identical to the reported MLM instances, and we do not change the shifted input of the decoder in seq2seq learning (Sutskever et al., 2014). ## D.3 Setup UNMT Setup We consider the same dataset used in previous works. Specifically, we first retrieve monolingual corpora {*De, En*} from WMT 2018⋄ (Bojar et al., 2018) including all available NewsCrawl datasets from 2007 through 2017 and monolingual corpora Ro from WMT 2016⋄ (Bojar et al., 2016) including *NewsCrawl* 2016. We report {De, Ro} ↔ En on *newstest2016*. Meanwhile, we share the FLoRes⋄ (Guzmán et al., 2019) task to evaluate a dissimilar language pair Ne ↔ *English* (Nepali). We download the 872 dataset and test set with provided script. Ne is tokenized by Indic-NLP Library⋄. For others, we use the Moses tokenizer⋄ developed by (Koehn et al., 2007). We use fastBPE⋄ to learn shared BPE (Sennrich et al., 2016b), selecting the most frequent 60K tokens from concatenated corpora of paired languages with the same criteria in (Lample and Conneau, 2019). The model is pre-trained around 400K iterations on only monolingual corpora of paired languages. Then, we still train MLM but eventually train the translation task on synthetic parallel sentences by running on-the-fly backtranslation (Sennrich et al., 2016a), which is the standard pipeline⋄ of UNMT (Artetxe et al., 2018b; Song et al., 2019). After around 400K iterations, according to baseline models' BLEU scripts, we report BLEU computed by *multi-BLEU.perl*⋄ or sacreBleu⋄ (Post, 2018) with default rules. In the training phase, we use Adam optimizer (Kingma and Ba, 2015) with parameters β1 = 0.9,β2 = 0.997 and ϵ = 10−9, and a dynamic learning rate with *warm*_up = 8000 (Vaswani et al., 2017) (learning_*rate* ∈ (0, 7e−4]) is employed. We set dropout regularization with a drop rate *rate* = 0.1 and label smoothing with *gamma* = 0.1 (Mezzini, 2018). Cross-ling Classification Setup Beyond UNMT tasks or bilingual tasks, our method can be applied to multilingual tasks. Then, we attempt the cross-lingual classification task on XNLI (Conneau et al., 2018) to test general cross-linguality [C]x improves. For this test, we follow the standard and basic experiment (Lample and Conneau, 2019) to train a 12-layer Transformer encoder with 80k BPE on Wikipedia dumps⋄ of 15 XNLI languages. To tokenize {*Zh, T h*}, we use Stanford Word Segmenter⋄ and PyThaiNLP⋄ respectively. For the others, we use the Moses tokenizer⋄ with default rules. Similarly, we use fastBPE⋄ and the balanced strategy (Lample and Conneau, 2019) to learn BPE. While there are two settings in this task, we only report the results of the zero-shot classification. To pre-train the encoder on En corpora, considering the zero-shot classification based on finetuning En NLI dataset, we randomly compute [C]x from other languages with equal probability to avoid the cross-lingual bias. For pre-training on corpora of other languages, we only compute [C]x in the *English* entries. Note that, although we have different strategies of [C]x for different languages, we still concatenated all the corpora of the languages for joint pre-training. After pre-training on the corpora, we deploy a randomly initialized linear classifier and finetune the encoder and the linear classifier on the En NLI dataset with minibatch size 16. We use Adam optimizer (Kingma and Ba, 2015) with lr = 5e − 4 and linear decay of lr. After finetuning, we make zero-shot classifications for other languages. ## E Result E.1 Unmt We compare our reimplementation with reported results in Table 10. ## E.2 Cross-Lingual Classification We show the results of XNLI for each language in Table 11. ## F Source We list all the links of dataset, tools, and other sources in Table 12. | Language pair | De ↔ En | Ro ↔ En | Ne ↔ En | | | | |-----------------------------------------------------------------------------------------------|-----------|-----------|-----------|------|------|-----| | multi-BLEU.perl⋄ with default rules | | | | | | | | XLM(Lample et al., 2018c) reported | 34.3 | 26.4 | 31.8 | 33.3 | 0.5 | 0.1 | | XLM(Lample et al., 2018c) ⋆ | 33.9 | 26.3 | 0.6 | 0.2 | | | | + [C]x | 35.9 | 28.1 | 34.4 | 35.3 | 6.6 | 2.8 | | MASS(Song et al., 2019) reported | 35.2 | 28.3 | 33.1 | 35.2 | | | | MASS(Song et al., 2019)⋆ | 35.0 | 28.0 | 0.9 | 0.3 | | | | + [C]x | 36.7 | 29.2 | 34.7 | 36.9 | 7.1 | 3.4 | | sacreBleu⋄ with standard settings: nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 | | | | | | | | mBART(Liu et al., 2020) reported +CC25 | 34.0 | 29.8 | 30.5 | 35.0 | 10.0 | 4.4 | | mBART(Liu et al., 2020)⋆ | 33.7 | 29.4 | 2.0 | 1.1 | | | | + [C]x | 35.4 | 30.1 | 32.5 | 36.7 | 7.0 | 3.2 | | Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | Avg | |---------------------------------------------------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|-------| | baseline(Conneau et al., 2018) | 73.7 | 67.7 | 68.7 | 67.7 | 68.9 | 67.9 | 65.4 | 64.2 | 64.8 | 66.4 | 64.1 | 65.8 | 64.1 | 55.7 | 58.4 | 65.6 | | mBERT (Wu and Dredze, 2019) | 82.1 | 73.8 | 74.3 | 71.1 | 66.4 | 68.9 | 69 | 61.6 | 64.9 | 69.5 | 55.8 | 69.3 | 60.0 | 50.4 | 58.0 | 66.3 | | XLM (Lample and Conneau, 2019) | 83.2 | 76.5 | 76.3 | 74.2 | 73.1 | 74.0 | 73.1 | 67.8 | 68.5 | 71.2 | 69.2 | 71.9 | 65.7 | 64.6 | 63.4 | 71.5 | | + word translation tables(Chaudhary et al., 2020) | 72.7 | | | | | | | | | | | | | | | | | + [C]x | 84.8 | 78.1 | 78.0 | 76.7 | 75.8 | 76.6 | 74.7 | 71.6 | 71.9 | 74.2 | 71.8 | 74.9 | 67.4 | 67.2 | 66.5 | 74.0 | | + MT (Lample and Conneau, 2019) | 85.0 | 78.7 | 78.9 | 77.8 | 76.6 | 77.4 | 75.3 | 72.5 | 73.1 | 76.1 | 73.2 | 76.5 | 69.6 | 68.4 | 67.3 | 75.1 | Table 11: Performance of cross-lingual classification on XNLI. MT stands for additional parallel corpora. | Item | Links | |----------------------------------------------|----------------------------------------------------------------------------------------| | WMT 2016 | http://www.statmt.org/wmt16/translation-task.html | | WMT 2018 | http://www.statmt.org/wmt18/translation-task.html | | FLoRes | https://github.com/facebookresearch/flores | | Indic-NLP Library | https://github.com/anoopkunchukuttan/indic_nlp_library | | XLM | https://github.com/facebookresearch/XLM | | multi-BLEU.perl | https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-BLEU.perl | | Moses tokenizer | https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl | | Kytea | http://www.phontron.com/kytea/ | | XTREME | https://github.com/google-research/xtreme | | fastBPE | https://github.com/glample/fastBPE | | MUSE | https://github.com/facebookresearch/MUSE | | Cambridge Dictionary | https://dictionary.cambridge.org/ | | WikiExtractor | https://github.com/attardi/wikiextractor | | PyThaiNLP | https://github.com/PyThaiNLP/pythainlp | | Stanford Word Segmenter (Chang et al., 2008) | https://nlp.stanford.edu/software/segmenter.html | | Tensor2Tensor | https://github.com/tensorflow | | HuggingFace | https://huggingface.co Table 12: Links of source. | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. B4. 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Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. 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abulimiti-etal-2023-kind

How About Kind of Generating Hedges using End-to-End Neural Models?

https://aclanthology.org/2023.acl-long.50

Hedging is a strategy for softening the impact of a statement in conversation. In reducing the strength of an expression, it may help to avoid embarrassment (more technically, {``}face threat{''}) to one{'}s listener. For this reason, it is often found in contexts of instruction, such as tutoring. In this work, we develop a model of hedge generation based on i) fine-tuning state-of-the-art language models trained on human-human tutoring data, followed by ii) reranking to select the candidate that best matches the expected hedging strategy within a candidate pool using a hedge classifier. We apply this method to a natural peer-tutoring corpus containing a significant number of disfluencies, repetitions, and repairs. The results show that generation in this noisy environment is feasible with reranking. By conducting an error analysis for both approaches, we reveal the challenges faced by systems attempting to accomplish both social and task-oriented goals in conversation.

# How About Kind Of Generating Hedges Using End-To-End Neural Models? ## Alafate Abulimiti1,2, Chloé Clavel3**, Justine Cassell**1,4 1INRIA, Paris 2 ENS/PSL <[email protected]> 3 LTCI, Insitut Polytechnique de Paris, Telecom Paris <[email protected]> 4 Carnegie Mellon University <[email protected]> ## Abstract Hedging is a strategy for softening the impact of a statement in conversation. In reducing the strength of an expression, it may help to avoid embarrassment (more technically, "face threat") to one's listener. For this reason, it is often found in contexts of instruction, such as tutoring. In this work, we develop a model of hedge generation based on i) fine-tuning stateof-the-art language models trained on humanhuman tutoring data, followed by ii) reranking to select the candidate that best matches the expected hedging strategy within a candidate pool using a hedge classifier. We apply this method to a natural peer-tutoring corpus containing a significant number of disfluencies, repetitions, and repairs. The results show that generation in this noisy environment is feasible with reranking. By conducting an error analysis for both approaches, we reveal the challenges faced by systems attempting to accomplish both social and task-oriented goals in conversation. ## 1 Introduction When people interact, they attend not just to the task at hand, but also to their relationship with their interlocutors (Tracy and Coupland, 1990). One key aspect of the relationship that people attend to, while engaging in contexts as diverse as sales (Gremler and Gwinner, 2008; Planken, 2005), education (Glazier, 2016; Murphy and Rodríguez-Manzanares, 2012) and healthcare (DiMatteo, 1979; Leach, 2005), is what is referred to as *rapport*, a sense of harmony and mutual understanding between participants in a conversation (Spencer-Oatey, 2005; Tickle-Degnen and Rosenthal, 1990). Indeed, higher levels of rapport are correlated with better performance in each of these domains. Zhao et al. (2014) describes rapport as built upon a base of mutual attentiveness, face management, and coordination. This base is built primarily by conversational strategies, or ways of speaking (including nonverbal and paraverbal behaviors) that ![0_image_0.png](0_image_0.png) ![0_image_1.png](0_image_1.png) manage rapport throughout a conversation. Key conversational strategies include self-disclosure, reference to shared experience, praise, and *hedging* - giving instructions or conveying information in an indirect manner when it might otherwise sound rude or overly demanding. End-to-end large language models (LLM), of the kind that are increasingly popular and powerful, do a good job at carrying out the propositional or information-carrying aspects of conversation, and a relatively good job of maintaining the coherence of a conversation, but they are not as good at changing how they say something as a function of a relationship with the human user, while humans are, for the most part, quite good at this. However, since saying things in a specific manner - for example, through a hedge - helps task performance, it is an important topic for dialogue systems. Linguists define hedges as a way of diminishing face threat (meaning the "positive social value a person effectively claims for himself" (Goffman, 1967) by attenuating the extent or impact of an expression (Brown and Levinson, 1987; Fraser, 2010). Figure 1 shows a typical example of hedging in a peer tutoring setting, where the tutor uses two hedges ("I think" and "could" rather than "should") to deliver a hint for the next step of solving an algebra equation. Tutoring is one context in which hedges are found in abundance and where recognizing them might be important for intelligent tutoring systems, as attested by the number of computational ap877 proaches that attempt to do so (see section 2). Interestingly, even unskilled tutors use them. In fact, research on peer tutoring has shown that when rapport between a peer tutor and tutee is low, but the tutor is confident in his/her skills, that tutor tends to use more hedges, and this results in more problems attempted by the student and more problems successfully solved (Madaio et al., 2017). In this paper, then, we work towards the development of a generation module for a virtual peer tutor that, like real peer tutors, is able to choose the manner of delivering information in such a way. Specifically, we address two research questions: RQ1: How good are end-to-end large language models used alone for generating hedges when finetuned on a peer-tutoring dialogue dataset? Are the models able to implicitly learn when and how to generate hedges? The first question may be answered by comparing the performance of various fine-tuned models. If the end-to-end models cannot learn to hedge implicitly, we might attempt to drive the models to generate the utterances by providing the correct labels. We assume that the correct labels can be provided by another module of the system, so we compare the reranking method with the fine-tuning method, as the former is simple, powerful, and widely used for text generation. Consequently, the second question is: RQ2: Can we improve these models by using a reranking approach? If so, what are the remaining errors and why do they occur? ## 2 Related Work Considerably more computational methods exist to determine *what* a dialogue system should say than how to say it. However, more recently, with the increased power of end-to-end models to find information and convey it accurately, we can now turn to ensuring that the end-to-end model simultaneously also meets social goals, to increase the impact and acceptability of what is conveyed. ## 2.1 Theoretical Approaches To Hedges As described above, a hedge can soften the impact of an utterance that might otherwise seem rude, such as a demand ("could you pass the salt") or an instruction ("you might want to pour the coffee over the sink"). Madaio et al. (2017) has attested to the frequent use of hedges in the peer-tutoring setting, and their positive impact on performance, perhaps because hedges in this context might reduce a tutee's embarrassment at not knowing the correct answer (Rowland, 2007). In linguistic terms, hedging is a rhetorical strategy that attenuates the full force of an expression (Fraser, 2010) and for this reason, it has been covered in linguistic pragmatics and the study of politeness. Two main categories of hedges are identified in the literature: **Propositional Hedges** and **Relational Hedges** (Prince et al., 1982). Propositional Hedges (called **Approximators** by (Prince et al., 1982)) refer to uncertain (Vincze, 2014), fuzzy (Lakoff, 1975) and vague (Williamson, 2002) language use, such as "kind of". Relational Hedges (called **Shields** in (Prince et al., 1982)) indicate that the expression is subjective or an opinion, as in "*I think* that is incorrect". **Attribution Shields** are a subtype of relational hedges that attribute the opinion to others, such as "everyone says you should stop smoking". **Apologizers** (Raphalen et al., 2022) are apologies that mitigate the strength of an utterance, as in "I'm sorry but you have to do your homework". While the different types of hedges operate in different ways, they all serve the same mitigation functions in conversation. For this reason, in what follows - a first attempt at generating hedges — we collapse the different sub-classes and refer only to hedges and non-hedges. ## 2.2 Computational Approaches Some prior work has looked at the detection of conversational strategies and in particular work by Zhao and colleagues (Zhao et al., 2014, 2016b,a). Madaio et al. (2017) built a classifier to detect hedging and achieved an accuracy of 81%. Recent work by Raphalen et al. (2022) improved the detection of different types of hedges and achieved a weighted F1 score of 0.97. Hedging is a particular kind of indirectness, and therefore as we look at prior work in the area, we include approaches to the generation of indirect speech. The plan-based generation of indirect speech acts has existed almost as long as dialogue systems themselves (Clark, 1979; Brown, 1980; Perrault, 1980). More recently, other relevant aspects of politeness have also been addressed. For example, Porayska-Pomsta and Mellish (2004) operationalized the important notion of face in politeness theory to generate polite sentences with a template pool. Although contemporary dialogue systems tend to integrate indirect speech (Miehle et al., 2022; Briggs et al., 2017), generating hedges with powerful language models, and particularly as a function of the social context, has not been explored. Our desire to look at the social context leads us to train on spontaneous dialogue that is substantially noisier, owing to natural conversational phenomena such as disfluency. This differs from the majority of prior work, trained on written or acted corpora (Li et al., 2017; Rashkin et al., 2019). ## 2.3 Generation Techniques Different techniques have been used in the past to generate responses of a particular kind for dialogue systems. Madaan et al. (2020) used n-gram TFIDF to identify source style words and generate target politeness style utterances by replacing these words. Niu and Bansal (2018) generated politeness formulations by using reinforcement learning with a trained politeness classifier. Similar to our approach, the explicit knowledge of politeness is only given to the classifier. Liu et al. (2021) constructed an emotional support dataset with eight different dialogue strategies and fine-tuned the pre-trained language models by connecting the label tokens to the beginning of each utterance in order to create a dialogue generator that can produce the target responses without focusing on the social context. The reranking method is also widely used in text generation tasks. Hossain et al. (2020) used a simple and effective pipeline where they retrieved the original texts from the database, then edited with a Transformer (Vaswani et al., 2017) model, and then reranked the text by generation scores. Soni et al. (2021) first applied reranking to conversational strategy generation by controlling the level of self-disclosure in the outputs of DialoGPT (Zhang et al., 2020b). The authors of LaMDA (Thoppilan et al., 2022) used various classifiers to rerank and filter out inappropriate responses. Recently, ChatGPT (OpenAI, 2022) used reinforcement learning with human feedback, and has shown impressive performance. In the articles above, most algorithms were trained on written dialogue datasets, which facilitated the task. However, our spontaneous dialogue dataset may lead the way for cutting-edge models trained on a real-world, face-to-face interactional dataset. ## 3 Methodology 3.1 Task Description Let D = {d1, d2, d3*, ...d*n} be a set of dialogues, where each dialogue d = {u1, u2, u3*...u*m} is composed of m turns, where uiis a turn. Each tutor turn (and each tutee turn, although we will not examine the tutee turns further here) is labeled as hedge or non-hedge; we call lithe label of ui. A fixed window size ω of the dialogue history is assigned to each utterance: hi = {umax(1,i−ω), ui−ω+1*, ...u*i−1}. The goal of this work is to train a generator (G) that can produce a tutor's utterance u′i that matches a given hedge strategy (i.e., hedge or non-hedge) li, according to the dialogue history hi. ## 3.2 Corpus The dataset we used in the current work is the same as that used in our prior work (Raphalen et al., 2022; Goel et al., 2019; Zhao et al., 2014). 24 American teenagers aged 12 to 15, half boys and half girls, were assigned to same-gender pairs. They took turns tutoring each other in linear algebra once a week for five weeks, for a total of 60 hours of face-to-face interaction. Each interaction was composed of two tutoring periods, where the teens took turns being the tutor, with a social period at the beginning and between the two tutoring periods. For the purposes of the earlier work the corpus was annotated for hedges, as well as the subcategories of hedges, at the clause level. For our purposes, since generation happens at the level of the turn, we merge the clauses and their labels into speaker turns and turn-level hedge labels (see Appendix A for the merge strategy). Our goal is to create a hedge generation module that can produce an appropriate hedge strategy for a tutor giving an instruction, according to what has been said before as indicated by the dialogue history. For this reason we kept all turns in the dialogue history, even though our model is trained to generate only the tutor's turns (and not those of the tutee). There are 6562 turns in these interactions, of which 5626 contain non-hedges and 936 hedges. Being authentic interaction, there are disfluencies ("so just yeah just um"), repetitions ("that would be then that would be"), repairs ("oh wait, actually the x would go here"), and other spoken phenomena such as one-word clauses. These phenomena make generating hedges challenging since the language models we use are primarily trained on written dialogues, which do not contain most of these features. However, our work allows us to see how far we can go with authentic spoken data. ## 3.3 Methods We combine two techniques for generating the tutor's turn: *Fine-tuning* an existing generation model and *Re-ranking* the generated outputs to match the desired hedge strategy. ## 3.3.1 Fine Tuning Method First, we want to evaluate how well the model performs when hedge information is implicitly taught through fine-tuning. We fine-tuned the generation model with the training set of the peer-tutoring corpus. Each utterance ui = (w1*, ..., w*n) is composed of n tokens, the dialogue history hi as input to the generation model. We apply cross-entropy loss between ui and u′i , where u′ ∈ R|V |, V is the vocabulary. $$J(u_{i},u_{i}^{\prime})=-\frac{1}{n}\sum_{j=1}^{j=|V|}u_{i,j}\log(u_{i,j}^{\prime})\qquad(1)$$ ## 3.3.2 Reranking Method Since a hedge classifier was developed for prior work in our lab (Goel et al., 2019; Raphalen et al., 2022), we can use it to determine whether a generated text is a hedge or not and then inform the generator of the decision in order to regulate the output. This is known as reranking, and is what we use here as our second generation strategy. 1) We first pretrain our generator as in fine tuning. We then apply this generator to the test set to generate 501candidate utterances for each dialogue history (Figure 2). 2) These candidates are first ranked by their sentence scores (i.e., the final outputted token's log probability for each sentence). 3) We then use the hedge classifier described above to filter out the utterances that do not match the selected strategy (i.e., hedge or non-hedge). 4) We keep utterances that match the selected hedge strategy. If more than one candidate matches the strategy, we pick the first one that matches, which means the one with the highest sentence score. 5) If none of the candidates matches the selected hedge strategy, we output the one that has the highest sentence score. 1See Appendix C for the details ## 4 Experimental Setting 4.1 Data Processing We randomly split the final dataset based on a 60:20:20 ratio. Of these, 60% is the training set, 20% is the validation set, and 20% is the test set. Since our dataset is highly unbalanced, if we used it as is the results would be too biased towards non-hedges. In that approach the gap between the results of different models would not be clear because non-hedges are so much more frequent. For this reason, we manually balance by randomly selecting 235 non-hedge turns to balance the 235 hedges in the test set, and combine the data to form a new balanced test set. On the other hand, in order to have a large enough training set, we retain all tutor turns from the complete dataset, which therefore consists of 701 hedge turns and 4455 non-hedge turns, resulting in a dataset that is very skewed, but has more turns. While the complete dataset contains a relatively small number of hedge turns, we believe that preserving the natural data distribution is crucial for addressing our first research question. Underscoring the wisdom of this approach, the results we obtained on perplexity and the BARTscore (that are indicative of fluency in the generated responses, as described below) demonstrate that the models were able to generate responses with reasonable fluency and quality despite the small number of hedge turns. ## 4.2 Sota Pretrained Language Models We compare the performance of different state-ofthe-art (SOTA) free open-source pretrained models as our generators: BART, DialoGPT, and BlenderBot. BART (Lewis et al., 2020) uses an encoder-decoder architecture, trained on books and Wikipedia data, and performs well on tasks as varied as Q&A (SQuAD (Rajpurkar et al., 2016)), text generation, text classification (MNLI (Williams et al., 2018) ), and text summarization tasks (ELI5 (Fan et al., 2019)). It is pretrained by distorting the format of the input text in various ways, and this training helps us to visualize its possible application to noisy spontaneous spoken dialogues. DialoGPT (Zhang et al., 2020b) is a dialogue version of GPT-2 (Radford et al., 2019), an autoregressive language model with a multi-layer Transformer (Vaswani et al., 2017) decoder as its model architecture. It is trained on 140 million conversational exchanges extracted from Reddit comment ![4_image_0.png](4_image_0.png) threads. BlenderBot (Roller et al., 2021) uses the standard Seq2Seq Transformer architecture, but incorporates a number of dialogue training sets: Empathetic Dialogue (Rashkin et al., 2019), PersonaChat (Zhang et al., 2018), ConvAI2 (Dinan et al., 2020), and other datasets that, while largely handcrafted, focus on personality and emotions, enabling it to potentially develop some version of social skills. ## 4.3 Evaluation Metrics To evaluate performance, we used the most widely used set of reference-based metrics for natural language generation tasks (Liu et al., 2021; Ziems et al., 2022). Since these metrics have not been used for conversational strategies, we add an unsupervised reference-free metric, the BART score (Yuan et al., 2021). The BART score formulates the evaluation process as a text generation task using a pre-trained model. The score represents the probability of generating a hypothesis given a source text. The higher BART score represents better text from different perspectives (e.g., informativeness, factuality). In this paper, we denote the dialogue history as the source text and the generated utterance as the hypothesis. For comparison, we calculate the BART score between the dialogue history and the real response in the test dataset, giving a result of −6.44. We also evaluated the relevance of the generated hedge strategy using an F1 score. The results using these metrics are presented in Table 2. The detailed description of the metrics used is in Appendix B. ## 4.4 Human Evaluation While the metrics described above are important for comparison with the performance of other work in the field, they do not obviate the need for human annotation. We therefore asked two annotators to ignore sub-categories and annotate only hedge or non-hedge on each tutor turn of the model's output, with access to 4 prior turns of the dialogue history. During a training phase the annotators reached an inter-rater reliability of over .7 Kripendoff's alpha (Krippendorff, 2004) which indicates substantial agreement. One of the annotators then finished the remainder of the annotation. We computed the F1 scores for the label of the generated utterances with respect to the real tutor turn's label. A higher F1 score indicates that the approach is better suited to generate the correct hedge strategy (see Table 2). We also asked the annotators to pay attention to whether the output was unnatural and to note it if so. The annotators reported no concerns with the naturalness of the generated utterances. The concept of fluency has recently gained popularity in the dialogue community (Li et al., 2019; See et al., 2019), but the current definition of fluency varies. More fundamentally, evaluations of this kind are more applicable to written text or scripted dialogues (Pang et al., 2020; D'Haro et al., 2019). as they cannot handle disfluencies (e.g., hesitations, repetitions, false starts) of the kind that are common in spontaneous spoken dialogues, and that may serve to give the speaker time to plan the next utterance (Biber et al., 1999; Thornbury and Slade, 2006). We therefore did not assess fluency in this work. fi ## 5 Results 5.1 Rq1: How Well Do End-To-End Models Perform Alone For Generating Hedges? Table 2 compares the performance of the generation models. BlenderBot outperforms the other 2 models on most metrics,although with similar perfi formance to DialoGPT, on BLEU and ROUGE-L. The discrepancy between BlenderBot and BART in each score is relatively wide. This discrepancy is most apparent on measures that compute scores based on n-gram-level overlaps (BLEU, ROUGE). To find the reason for this discrepancy, we calculate the average length of the outputs of the 3 models and observe 5.2 words for BART, 11.8 words for BlenderBot, and 14.5 words for DialoGPT, while the average length of the tutor's utterances in test data is 15.2 words. The average length of the output of DialoGPT is therefore close to that of the test set. This further explains DialoGPT's strong performance on the BLEU and ROUGE scores. On the other hand, BART tends to generate shorter turns, consequently demonstrating lower scores on metrics that require the calculation of repetition grams to yield scores. Note that in similar tasks, the best model was Blenderbot with a BLEU 2 score of 6.21, in the case of emotional support conversational strategy generation (Liu et al., 2021), while DialoGPT reached 5.52. The best score in the positive text reframing task, meanwhile, was 11.0 for BLEU 1 (Ziems et al., 2022), while BART reached 10.1 and GPT-2 reached 4.2. Table 1 shows that BART has the lowest perplexity score, indicating that BART is more adaptive to our dataset compared to the other two models. This may be due to its pre-training approaches (see Section 4.2) that corrupt input texts with an arbitrary noising function. These approaches enable more accurate predictions in our noisy real-world dataset. BART BlenderBot DialoGPT $$\frac{\mathrm{BAH}}{34.9}$$ 34.9 69.3 72.4 ![5_image_0.png](5_image_0.png) Table 1: Language Model (LM) Perplexity (the lower is the better In response to our first research question, then, the performance of all three models was comparable but very limited. This suggests that the finetuning approach does not allow language models to learn hedge knowledge implicitly. We therefore next turn to an approach that may improve performance by screening utterances with a given label. ## 5.2 Rq2: Does Reranking Improve Hedge Generation? Table 2 shows the performance of each model for the reranking method. BlenderBot once again per- | Models | BlenderBot DialoGPT BART | R_BlenderBot R_DialoGPT R_BART | | | | | |-----------|----------------------------|----------------------------------|-------|--------|-------|-------| | BLEU_1 | 11.2 | 11.4 | 2.7 | 12.3 | 10.9∗ | 6.0∗ | | BLEU_2 | 5.8 | 4.7 | 1.5 | 6.2 | 3.9∗ | 3.1∗ | | ROUGEL | 8.6 | 9.1 | 8.1 | 11.0 | 8.4 | 9.7 | | CHRF | 17.6 | 17.0 | 9.3 | 17.6∗ | 17.5∗ | 12.2∗ | | BARTScore | -3.92 | -5.62 | -4.33 | -3.98∗ | -4.79 | -4.24 | | BERTScore | 39.9 | 38.3 | 38.5 | 40.5 | 37.5 | 39.4 | | 0.54 | 0.41 | 0.44 | 0.84 | 0.64 | 0.85 | | forms well on all metrics and has a virtually identical F1 score to BART. Additionally, we find some interesting similarities among models: 1) BlenderBot and DialoGPT outperform BART in both the fine-tuning and the reranking methods (Table 2) with respect to reference-based metrics such as BLEU, ROUGE-L, etc., and 2) DialoGPT still underperforms the other two models in terms of F1 score, and in the reranking condition the gap widens. This result could suggest that 1) the pretraining of the models (i.e., DialoGPT, BlenderBot) on dialogue datasets may help to generate longer utterances, and therefore to improve the referencebased metrics performance, and 2) the autoregressive model (e.g., DialoGPT) may not be suitable for the generation of social dialogue such as hedges. ## 5.3 Comparing Fine-Tuning And Reranking To summarize results on the fine-tuning versus reranking approaches we observe that: 1) With the help of a hedge classifier, the reranking approach can do a good job at generating hedges, 2) BlenderBot is better suited to the task of generating long utterances, as described in Section 5.1. This could be because BlenderBot is pretrained with various social dialogue datasets, giving it a certain ability to generate the social aspects of dialogue. Table 2 shows that models deployed with the reranking method have relatively higher or comparable Bart scores, but greatly improved performance on the F1 score (from .54 to .85). This result, too, underscores the advantages of the reranking method. ## 5.4 Error Analysis While BlenderBot showed strong performance when using reranking, a certain number of generated utterances still did not match the real tutor labels. When a matching utterance type cannot be found in a limited pool of candidates, we could have chosen to increase the candidate pool to promote the probability of selecting a match. However, in this early effort to generate hedges, we want to ensure sufficient quality in the generated output but also explore the limitations of current language models for generating socially relevant phenomena on the basis of a spontaneous spoken interaction dataset. We can learn about the limitations of these models by examining places where the system did not generate the desired strategy (that is, generated a hedge when the real tutor did not or vice versa). We first divide these strategy mismatches into *overgeneration errors*, where the generator generates a hedge where it should not and *under-generation* errors when it does not generate a hedge but should. Among the 1395 annotated turns outputted by the 3 generators, there are 13.3% of *over-generation* errors and 86.7% *under-generation errors*. These errors are particularly interesting in the context of reranking, as it relied strongly on the hedge classifier. The hedge classifier selected the most suitable utterances, and yet the model still produced the wrong strategy - or at the very least mismatches with the strategy of the real tutor. Therefore, we analyze the generated utterances corresponding to these two types of errors and identify two potential causes. First, there are still some places where the model generates a hedge where it should generate a nonhedge. As we mentioned in Section 4.4, we invited humans to annotate the models' outputs in terms of hedge labels. We compare the human-annotations of the model output (where they labeled the output as hedge or non-hedge) with the output of the BERT-based classifier on the same generated utterances to calculate the F1 score. We find that there is a difference of about 9 points between the F1 score for human annotation (85%) shown in Table 2, and the F1 score for the same BERT-based hedge classifier (94%) reported in Raphalen et al. (2022). We assume that the classifier we used may have misclassified some generated utterances and we therefore label them as **Classification Errors**. This category accounts for 92.5% of *over-generation errors*, and 15.3% of *under-generation errors*. Second, the basic functionality of an end-toend language model of this kind is to produce the most coherent next utterance based on the dialogue history. This may result in the language model privileging coherence of content over style of delivery. That is, the model may not be able to find an appropriate strategy match among the coherent candidates, even when the candidate pool size is 50. We label this a **Goal Mismatch** as the propositional or content coherence goals of the system may be trumping the social goals, We found 84.7% in *under-generation errors* and 7.5% in *overgeneration errors*. 18% of the cases where the pool did not include the right strategy. An example of each type of error is given in Figure 3. The first example belongs to the **Classification Error** type, where the classifier misclassified the system response (i.e. "We just found that the answer is two x equals three") as a hedge. In the second example, the tutor is trying to help the tutee to approach the answer step by step, but the tutee cannot come up with a worked idea. Here it is clear that the tutee is flailing and it is therefore probably not advisable to increase the student's stress with a volley of questions that the tutee can clearly not answer. The tutor thus uses a hedge as a response. Conversely, the generator produces a question. The generated utterance is "What do you think we should do, what's the next step". This example corresponds to our **Goal Mismatch Error**. It shows that the generator may not understand the social context, but is looking for a coherent response. The **Goal Mismatch Error** is perhaps the most interesting of the errors, and thus to verify our hypothesis - that the coherence goals of the models may impede the social goals - we looked into the nature of the relationship between rapport (between tutor and tutee) and the generation of hedges. As described above, Madaio et al. (2017) found that hedges are generated when rapport is low. Since our corpus contained rapport annotations for every 30 seconds of the interaction, we looked at the rapport level in play when the model over-generated and under-generated hedges. Since rapport is annotated from 1 to 7 in the dataset, for convenience, we divided it into 3 levels: high (5-7), medium (3-5), and low rapport (1-3), as shown in Table 3. ![6_image_0.png](6_image_0.png) Table 3: Goal Mismatch Errors Distribution ![7_image_0.png](7_image_0.png) As only 3 errors appear in the category of *overgeneration error*, we cannot obtain a meaningful conclusion due to size. However, the generators generate fewer hedges when rapport is low, an under-generation error, in contradiction to studies showing that speakers are more careful about threatening the face of (or embarrassing) their interlocutors when the social bond between them is weak (Madaio et al., 2017). We believe that this is because more hedges are found in low rapport interaction. Therefore, we count the hedge distribution of the low and high rapport interaction in the test dataset. 264 hedges are found in low rapport interaction, and 42 in high rapport interaction. This distribution corresponds to the fact that a hedge is most likely to happen in low rapport interactions. The under-generation errors are the cases where there should be hedges but non-hedges were generated. In the test dataset, more hedges occur in low rapport, and the generator under-generates more in low rapport, because there are more hedges that should be generated in low rapport. So, the generators make more errors in low rapport interaction due to an imbalance in hedge distribution between low and high rapport interaction. Goal Mismatch error directly addresses our primary question 1: How effectively do end-to-end models perform when generating hedges on their own? Due to this fundamental discrepancy between competing goals, end-to-end language models are unable to inherently learn and discern when to apply hedges appropriately. ## 5.4.1 Lexical Diversity Of The Generated Output As we have seen, LLMs can generate a hedge or non-hedge with the help of the reranking method. However, do language models spontaneously use different types of hedges in a human-like way? To investigate this question, we applied the rule-based hedge classifier from (Raphalen et al., 2022) to automatically annotate the utterances generated by models in subcategories of hedges (as defined in Section 2.1), and we compare the models' and humans' distributions of different hedge strategies. The rule-based classifier used linguistic patterns to identify each hedge subcategory. We have preferred here to use the rule-based classifier rather than the machine learning classifiers to avoid the dependence on and bias of probabilistic learningbased classifiers. Indeed, learning-based classifiers may be biased towards predicting the categories that are the most frequent in the dataset. Furthermore, the rule-based classifier reaches a 94.7 F1 score (Raphalen et al., 2022), which is comparable to the best performance (96.7 F1 score) using the Light Gradient-Boosting Machine (LGBM) (Ke et al., 2017) classifier. The above results show that the model can spontaneously learn to use different types of hedges. Indeed, the models are capable of carrying out linguistic diversity on hedges based on learning from real human dialogues. ## 6 Conclusion And Future Work In this paper, we have shown that the reranking method helps LLMs to generate hedges - an important social conversational strategy that can avoid face threats towards an interlocutor by attenuating an impact of an expression. We find that an implicit fine-tuning approach (i.e., without any supervision by a hedge label) is not sufficient for generating hedges, but a reranking method significantly improves performance in generating hedges, with a final F1 score of .85 for the BART model and .84 for the BlenderBot model. We also performed an error analysis on the generated results and found that two types of errors occur in the reranking method: Classification, and **Goal Mismatch**. The vast majority of errors fall into the category of Goal Mismatch, indicating an important conflict between contemporary language models' primary goal of ensuring coherence and the social goal of managing face, which is indispensable for human conversation. While we were able to generate hedges, we were not able to necessarily generate them where they were needed most. That is, conversational strategies are adaptive in the sense that they respond to conversational strategies uttered by the previous speaker (Zhao et al., 2014). We conclude that, going forward, we will need a way of adding an underlying representation of the social state of the dialogue to improve dialogue generation. In this paper we addressed the question of how to generate hedges, but when to generate hedges remains an important and unexplored question. In future work, we may first explore the temporal relationships between the hedge and other conversational information (e.g., other conversational strategies, level of rapport) by sequential rule mining techniques, then apply RL-based methods to investigate in a more detailed manner the optimal way to predict where hedges should occur. In this context, we note that ChatGPT can generate a hedge when requested explicitly to do so, but does not generate hedges of its own volition (so to speak), for example, when face-threatening acts such as instruction are engaged in. We began this paper by describing the need for hedges in instructional dialogues such as those engaged in by intelligent tutoring systems. The current dataset consists of authentic real-world tutoring sessions, but as carried out by untrained teenagers. We note that peer tutoring is a powerful method of teaching, used in classrooms around the world, and previous work shows that when untrained peer tutors use hedges, their tutees attempt more problems and solve more problems correctly (Madaio et al., 2017). However, they are inexperienced and so in future work it will be important to investigate the interaction between trained tutors and tutee as well, for instance, by using the TeacherStudent Chatroom Corpus (Caines et al., 2020). We believe that the methods and results from the current work will facilitate the investigation of expert tutors in future research. ## Broader Impact Since the 1990s, research has shown the the importance of intelligent tutoring systems as effective learning environment,s and supports for classroom learning (Anderson et al., 1995). Peer tutoring plays a powerful role as well, as peer tutors can motivate learners to try harder, as well as helping them to succeed, and it is particularly effective for low-achieving learners (Cassell, 2022). But virtual peer tutors have not yet achieved their potential, in part because of the difficulty of generating the social infrastructure of peer learning as well as the content of the matter being tutored. This paper, whose data comes from a corpus of peer tutoring dialogues, should therefore be seen as a step in the right direction. ## Acknowledgments We thank the anonymous reviewers for their helpful feedback. We express sincere gratitude to the members of the ArticuLab at Inria Paris for their invaluable assistance in the successful completion of this research, and to the members of the ArticuLab at Carnegie Mellon Pittsburgh for answering our questions about their prior work. This study received support from the French government, administered by the Agence Nationale de la Recherche, as part of the "Investissements d'avenir" program, with reference to ANR-19-P3IA-0001 (PRAIRIE 3IA Institute). ## Limitations Several limitations apply to the current study. While research shows that multimodal signals play an important role in conversational strategies (Zhao et al., 2016b), we did not take them into account. It is an open question as to how to render large language models capable of generating multimodal behaviors. A second limitation concerns the recent arrival on the scence of ChatGPT, that has shown impressive performance. However the models are not free, and therefore were not included. As noted above, another important limitation is the untrained status of the tutors in our corpus, who are teenagers, and not trained tutors. Their use of hedges, therefore, comes from their knowledge of everyday social interaction, and not from expertise in teaching. In looking at the data, we find a few places where, as instructors ourselves, we believe that a hedge is important, even though the real (teenage) tutor did not use one. The last limitation is that, while we focused only on generating hedge or non-hedge, there are actually 3 different kinds of hedges, that function differently. We hope to extend this work and take advantage of a text style transfer technique to generate more kinds of hedges in future work. ## Ethical Statement The corpus used here comes from earlier work by the last author and her colleagues, and was used in accordance with the original experimenters' Institutional Review Board (IRB). 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In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3682–3700, Dublin, Ireland. Association for Computational Linguistics. ## A Clauses To Turns C Implementation Details B Metrics compute overlap measures in longer utterances. To avoid generated utterances that are too long for the BLEU score, we use Rouge-L as a complementary metric. CHRF (Popovic´, 2015) is comparable to BLEU; however, while BLEU is word-level, CHRF is character-level, based on character n-gram computation. Our transcribed dataset also shows some disfluencies and repetitions represented by individual characters. Therefore, we expect this metric to result in character-level overlap scores. BERTScore (Zhang et al., 2020a) embeds the generated utterances and the reference with word vectors using the BERT model and computes pairwise cosine similarity for each generated word vector and each word in the reference, then the recall of the generated sequences is calculated. BERTScore is distinct from the previous two metrics in that it computes similarity across semantic space and has been shown to have a strong correlation with human judgment at the segment level. BARTScore (Yuan et al., 2021) formulates the text generation evaluation as a text generation task from pretrained language models in an unsupervised fashion. When the generated text is better, the training model will get a higher score by converting the generated text to reference or source text. BART score can be applied to different evaluations (e.g., informativeness, coherence, and factuality). Perplexity (Chen et al., 1998) calculates language model perplexity. Perplexity quantifies the level of uncertainty when an LM generates a new token. In our task formulation, a dialogue is composed of tutor-tutee turns. However, in the corpus considered for this study, the available annotations are at the clause2level. The choice of annotation unit was made because the annotation in hedges was part of a larger annotation campaign dedicated to the annotation of various conversational strategies (e.g., praise) at the clause level. This corpus contains 23 156 clauses, of which 21 192 contain non-hedges and 1 964 hedges. In order to obtain annotations at a turn level, we apply the simplest way to merge the hedge labels. If one or multiple clauses of one turn are annotated as hedges, this turn is labeled as a hedge. The implementation of all models was based on the Transformer library3, in addition, the PytorchLightning4library was used for training control. We apply AdamW (Loshchilov and Hutter, 2018) as our optimizer with a learning rate 10e−5. All the models are trained with 10 epochs but with an Early-stopping mechanism on validation loss, which means when the validation loss remains for 2 epochs, the training will stop to prevent overfitting. We use the base version of the BART model, the small version of BlenderBot, and also the small version of DialoGPT. For the reranking method, we use beam search as our decoding strategy. To prevent repetition, we allow the 2 grams to oc-3github.com/huggingface/transformers 4github.com/Lightning-AI/lightning BLEU (Papineni et al., 2002) calculates the word overlaps between reference and candidate utterances in n-grams (n=1, 2, 3). We do not assume that higher BLEU scores are equivalent to better task completion. Instead, BLEU is used to indicate that the generated utterances retain certain desired keywords. ROUGE-L (Lin, 2004) supplements BLEU by computing the longest common subsequence of generated utterances and references, allowing it to 2A clause consists of a subject and a verb and expresses a complete thought (Berry and Brizee, 2010). ![13_image_0.png](13_image_0.png) cur only once, and the repetition penalty = 1.2 is also applied. All models were fine-tuned on an Nvidia Quadro RTX 8000 GPU. A complete configuration of the hyperparameters used for each model is reported in the GitHub repository with the code of the paper: github.com/neuromaancer/ hedge_generation. Moreover, we apply beam search for the decoding strategy, as it reduces the risk of missing hidden high-probability word sequences by retaining the n most likely words in each generation output and ultimately selecting the utterances with the highest overall probability. To avoid repeating the same subsequences, we apply a penalty to the repeated 2-gram unit. In terms of the size of the candidate pool, logically, the more candidates generated, the more chances that one of them is the right hedge strategy (i.e., hedge or non-hedge), so we fix our candidate pool size to 50, as a compromise between the likelihood of obtaining a hedge and the speed of generation. ## D Figures Figure 4: Hedge subcategories distribution in models' outputs compared with human. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7: Limitation after the Section 6: Conclusion and future work ✓ A2. Did you discuss any potential risks of your work? Section 8 Ethical Statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? In the abstract section ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** Section 4: Experimental Setting ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4: Experimental Setting The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4: Experimental Setting ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5: Results ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4: Experimental Setting D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 3.2 Corpus and Section 4.4 Human Evaluation ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? The instructions are described in other paper, the author used the dataset under a NDA. For the anonymity, we didn't cite these papers in the blind review session, but we will cite them in the final version of the paper. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? 4.4 Human Evaluation ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? 4.4 Human Evaluation ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section 3.2 Corpus ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 3.2 Corpus

wang-etal-2023-diffusiondb

{D}iffusion{DB}: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

https://aclanthology.org/2023.acl-long.51

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: \url{https://poloclub.github.io/diffusiondb}.

# Diffusion**Db: A Large-Scale Prompt Gallery Dataset For Text-To-Image** Generative Models Zijie J. Wang Evan Montoya David Munechika Haoyang Yang Benjamin Hoover Duen Horng Chau College of Computing, Georgia Tech {jayw|emontoya30|david.munechika|alexanderyang|bhoov|polo}@gatech.edu ## Abstract ![0_Image_0.Png](0_Image_0.Png) With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DIF-FUSIONDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DIFFUSIONDB is publicly available at: https: //poloclub.github.io/diffusiondb. ## 1 Introduction Recent diffusion models have gained immense popularity by enabling high-quality and controllable image generation based on text prompts written in natural language (Rombach et al., 2022; Ramesh et al., 2022; Saharia et al., 2022). Since the release of these models, people from different domains have quickly applied them to create awardwinning artworks (Roose, 2022), synthetic radiology images (Chambon et al., 2022), and even hyper-realistic videos (Ho et al., 2022). However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, Fig. 1: DIFFUSIONDB is the first large-scale dataset featuring 6.5TB data including 1.8 million unique Stable Diffusion prompts and 14 million generated images with accompanying hyperparameters. It provides exciting research opportunities in prompt engineering, deepfake detection, and understanding large generative models. and can often feel random and unprincipled (Liu and Chilton, 2022). Willison et al. (2022) analogize writing prompts to wizards learning "magical spells": users do not understand why some prompts work, but they will add these prompts to their "spell book." For example, to generate highly-detailed images, it has become a common practice to add special keywords such as "trending on artstation" and "unreal engine" in the prompt. Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different downstream tasks (Branwen, 2020; Reynolds and McDonell, 2021). As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images (Liu and Chilton, 2022). Our work helps researchers tackle these critical challenges, through three major **contributions**: - DIFFUSIONDB **(Fig. 1), the first large-scale** prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion (Rombach et al., 2022) using 1.8 million 893 unique prompts and hyperparameters specified by real users. We construct this dataset by collecting images shared on the Stable Diffusion public Discord server (§ 2). We release DiffusionDB with a CC0 1.0 license, allowing users to flexibly share and adapt the dataset for their use. In addition, we open-source our code 1 that collects, processes, and analyzes the images and prompts. - Revealing prompt patterns and model errors. The unprecedented scale of D IFFUSION DB paves the path for researchers to systematically investigate diverse prompts and associated images that were previously not possible. By characterizing prompts and images, we discover common prompt patterns and find different distributions of the semantic representations of prompts and images. Our error analysis highlights partic- ular hyperparameters and prompt styles can lead to model errors. Finally, we provide evidence of image generative models being used for potentially harmful purposes such as generating misinformation and nonconsensual pornography (§ 3). - Highlighting new research directions. As the first-of-its-kind text-to-image prompt dataset, DIFFUSIONDB opens up unique opportunities for researchers from natural language processing (NLP), computer vision, and human-computer interaction (HCI) communities. The scale and diversity of this human-actuated dataset will provide new research opportunities in better tooling for prompt engineering, explaining large generative models, and detecting deepfakes (§ 4). We believe D IFFUSION DB will serve as an important resource for researchers to study the roles | CFG Scale Prompt NSFW | | | |-------------------------|-----------------------|------------| | 100 | 1.11.0 | 0.15525 | | Sampler | Image NSFW | | | (512) | 0.04811 | | | k_lms | | | | Step | CFG Scale Prompt NSFW | | | 50 | 7.0 | 0.01437 | | Sampler | Image Size | Image NSFW | | k_Ims | (512, 512) 0.02996 | | of prompts in text-to-image generation and design nxt-generation human-AI interaction tools. 1 Code: https://github.com/poloclub/diffusiondb ## 2 Constructing Diffusiondb We construct D IFFUSION DB (Fig. 2) by scraping user-generated images from the official Stable Diffusion Discord server. We choose Stable Diffusion as it is currently the only open-source large text-toimage generative model, and all generated images have a CC0 1.0 license that allows uses for any purpose (StabilityAI, 2022b). We choose the official public Discord server as it has strict rules against generating illegal, hateful, or NSFW (not suitable for work, such as sexual and violent content) images, and it prohibits sharing prompts with personal information (StabilityAI, 2022a). Our construction process includes collecting images (§ 2.1), linking them to prompts and hyperparameters (§ 2.2), applying NSFW detectors (§ 2.3), creating a flexible file structure (§ 2.4), and distributing the dataset (§ 2.5). We discuss D IFFU - SIONDB's limitations and broader impacts in § 7, § 8, and a Data Sheet (Gebru et al., 2020) ( ‡ A). | User Hash | | | | |------------------------|-------------------|----------------|------------| | Prompt | ilenam | | | | 9dba5021-cd9b- | 856498039 | | | | a keeshond puppy, | 481089cb827f2 | | | | watercolor painting | 43a3-ac0a- | 63b26445dc0f1 | Timestamp | | by jean - baptiste | b0f8ed4afeeb.webp | 81e08dcfd4ad2e | 2022-08-14 | | monge, muted colors | a212abcf29f3fdf | 21:51:00+0000 | | | 7ec3c11cf | | | | | Prompt | User Hash | | | | poignant portrait | fa5c8b9f-3789- | 9e1ee59715df53 | 1596176968 | | black and white photo | 46a4-8d8a- | 70f703859a2b0 | Timestamp | | of an old couple | 8619783e31f55 | | | | 6cbe5f104acf.webp | 2022-08-20 | | | | smiling at each other, | c0582398ccf71 | 08:12:00+0000 | | | nostalgia, love | 9d9f7c68d58 | | | ![1_image_0.png](1_image_0.png) ## 2.1 Collecting User Generated Images We download chat messages from the Stable Diffusion Discord channels with DiscordChatExporter (Holub, 2017 ), saving them as HTML files. We focus on channels where users can command a bot to run Stable Diffusion Version 1 to generate images by typing a prompt, hyperparameters, and the number of images. The bot then replies with the generated images and used random seeds. ## 2.2 Extracting Image Metadata We use Beautiful Soup (Richardson, 2007) to parse HTML files, mapping generated images with their prompts, hyperparameters, seeds, timestamps, and the requester's Discord usernames. Some images are collages, where the bot combines n generated images as a grid (e.g., a 3×3 grid of n = 9 images); these images have the same prompt and hyperparameters but different seeds. We use Pillow (Clark, 2015) to split a collage into n individual images and assign them with the correct metadata and unique filenames. Finally, we compress all images in DIF-FUSIONDB using lossless WebP (Google, 2010). ## 2.3 Identifying Nsfw Content The Stable Diffusion Discord server prohibits generating NSFW images (StabilityAI, 2022a). Also, Stable Diffusion has a built-in NSFW filter that automatically blurs generated images if it detects NSFW content. However, we find DIFFUSIONDB still includes NSFW images that were not detected by the built-in filter or removed by server moderators. To help researchers filter these images, we apply state-of-the-art NSFW classifiers to compute NSFW scores for each prompt and image. Researchers can determine a suitable threshold to filter out potentially unsafe data for their tasks. NSFW Prompts. We use a pre-trained multilingual toxicity prediction model to detect unsafe prompts (Hanu and Unitary team, 2020). This model outputs the probabilities of a sentence being toxic, obscene, threat, insult, identity attack, and sexually explicit. We compute the text NSFW score by taking the maximum of the probabilities of being toxic and sexually explicit (Fig. 3 Top). NSFW Images. We use a pre-trained EfficientNet classifier to detect images with sexual content (Schuhmann et al., 2022). This model predicts the probabilities of five image types: drawing, hentai, neutral, sexual, or porn. We compute the image NSFW score by summing the probabilities of hentai, sexual, and porn. We use a Laplacian convolution kernel with a threshold of 10 to detect images that have already been blurred by Stable Diffusion and assign them a score of 2.0 (Fig. 3 Bottom). As Stable Diffusion's blur effect is strong, our blurred image detector has high precision and recall (both 100% on 50k randomly sampled images). NSFW Detector Accuracy. To access the accuracy of these two pre-trained state-of-the-art NSFW detectors, we randomly sample 5k images and 2k prompt texts and manually annotate them with two binary NSFW labels (one for image and one for prompt) and analyze the results. As the percentage of samples predicted as NSFW (score > 0.5) is small, we up-sample positive samples for annota- ![2_image_0.png](2_image_0.png) tion, where we have an equal number of positive and negative examples in our annotation sample. After annotation, we compute the precisions and recalls. Because we have up-sampled positive predictions, we adjust the recalls by multiplying false negatives by a scalar to adjust the sampling bias. The up-sampling does not affect precisions. Finally, the precisions, recalls and adjusted recalls are 0.3604, 0.9565, and 0.6661 for the prompt NSFW detector, and 0.315, 0.9722, and 0.3037 for the image NSFW detector. Our results suggest two detectors are progressive classifiers. The lower adjusted recall of the prompt NSFW detector can be attributed to several potential factors, including the use of a fixed binary threshold and the potential discrepancy in the definition of NSFW prompts between the detector and our annotation process. ## 2.4 Organizing D**Iffusion**Db We organize DIFFUSIONDB using a flexible file structure. We first give each image a unique filename using Universally Unique Identifier (UUID, Version 4) (Leach et al., 2005). Then, we organize images into 14,000 sub-folders—each includes 1,000 images. Each sub-folder also includes a JSON file that contains 1,000 key-value pairs mapping an image name to its metadata. An example of this image-prompt pair can be seen in Fig. 2. This modular file structure enables researchers to flexibly use a subset of DIFFUSIONDB. We create a metadata table in Apache Parquet format (Apache, 2013) with 13 columns: unique image name, image path, prompt, seed, CFG scale, sampler, width, height, username hash, timestamp, image NSFW score, and prompt NSFW ![3_image_0.png](3_image_0.png) score. We store the table in a column-based format for efficient querying of individual columns. ## 2.5 Distributing D**Iffusion**Db We distribute DIFFUSIONDB by bundling each image sub-folder as a Zip file. We collect Discord usernames of image creators (§ 2.2), but only include their SHA256 hashes in the distribution—as some prompts may include sensitive information, and explicitly linking them to their creators can cause harm. We host our dataset on a publicly accessible repository2 under a CC0 1.0 license. We provide scripts that allow users to download and load DIFFUSIONDB by writing two lines of code. We discuss the broader impacts of our distribution in § 7, § 8, and the Data Sheet (‡ A). To mitigate the potential harms, we provide a form for people to report harmful content for removal. Image creators can also use this form to remove their images. ## 3 Data Analysis To gain a comprehensive understanding of the dataset, we analyze it from different perspectives. We examine prompt length (§ 3.1), language (§ 3.2), characteristics of both prompts (§ 3.3) and images (§ 3.4). We conduct an error analysis on misaligned prompt-image pairs (§ 3.5) and provide empirical evidence of potentially harmful uses of image generative models (§ 3.6). ## 3.1 Prompt Length We collect prompts from Discord, where users can submit one prompt to generate multiple images and experiment with different hyperparameters. Our dataset contains 1, 819, 808 unique prompts. We tokenize prompts using the same tokenizer as used in Stable Diffusion (Platen et al., 2022). This tokenizer truncates tokenized prompts at 75 tokens, excluding special tokens <|startoftext|> 2Public dataset repository: **https://huggingface.co/** datasets/poloclub/diffusiondb and <|endoftext|>. We measure the length of prompts by their tokenized length. The prompt length distribution (Fig. 4) indicates that shorter prompts (e.g., around 6 to 12 tokens) are the most popular. The spike at 75 suggests many users submitted prompts longer than the model's limit, highlighting the need for user interfaces guiding users to write prompts within the token limit. ## 3.2 Prompt Language We use a pre-trained language detector (Joulin et al., 2017) to identify the languages used in prompts. 98.3% of the unique prompts in our dataset are written in English. However, we also find a large number of non-English languages, with the top four being German (5.2k unique prompts), French (4.6k), Italian (3.2k), and Spanish (3k). The language detector identifies 34 languages with at least 100 unique prompts in total. Stable Diffusion is trained on LAION-2B(en) (Schuhmann et al., 2022) that primarily includes images with English descriptions, thus our findings suggest that expanding the training data's language coverage to improve the user experience for non-English communities. ## 3.3 Characterizing Prompts In this section, we explore the characteristics of prompts in DIFFUSIONDB. We examine the syntactic (§ 3.3.1) and semantic (§ 3.3.2) features of prompt text via interactive data visualizations. Lastly, We discuss the implications of our findings and suggest future research directions. ## 3.3.1 Prompt Syntactic Features To characterize the composition of prompts, we parse phrases from all 1.8M unique prompts. We split each prompt by commas and then extract named entities (NE) and noun phrases (NP) from each separated component using use Spacy (Honnibal et al., 2020). If there is no noun phrase in a comma-separated component, we extract the whole component (C) as a phrase. We keep track of each NP's root to create a hierarchy of noun phrases. For example, for the prompt "draw baby yoda in a loading screen for grand theft auto 5, highly detailed, digital art, concept art," we extract six phrases: "baby yoda" (NE), "a loading screen" (NP with root "screen"), "grand theft auto 5" (NE), "highly detailed" (C), "digital art' (NP with root "art"), and "concept art" (NP with root "art"). We group ![4_image_0.png](4_image_0.png) "digital art" and "concept art" into the same hierarchy as they share the same NP root "art." Visualizing Prompt Phrases. We create an interactive circle packing visualization3to gain an understanding of the distribution and relationships between different phrases (Fig. 5). Circle packing (Wang et al., 2006) is a technique to visualize hierarchical data, and each phrase is represented as a circle whose size encodes the phrase's frequency in the dataset. We position sibling noun phrases (e.g., phrases sharing the same NP root) inside their parent phrase's circle through a front-chain packing algorithm (Wang et al., 2006). Viewers can hover over a circle to see the corresponding phrase and its frequency. Viewers can also click a circle (Fig. 5A) to zoom into that sub-tree to see more details about a phrase (Fig. 5-B1) or a sub-phrase (Fig. 5-B2). Insights and implications. Our interactive visualization reveals that key phrases such as "highly detailed," "intricate," and "greg rutkowski" 3Phrase visualization: **https://poloclub.github.io/** diffusiondb/explorer\#phrase are commonly used in prompts (Fig. 5A). The hierarchical visualization also surfaces popular image styles specified by users, such"digital painting," "oil painting," and "portrait painting" for painting styles (Fig. 5-B1) and "studio lighting," "volumetric lighting", and "atmospheric lighting" for lighting. These phrases can be unfamiliar to Stable Diffusion users, especially beginners, which highlights the importance of helping users develop prompting vocabularies. Researchers can leverage DIFFUSIONDB and our visualization to design tutorials and user interfaces that integrate exemplar prompts to guide users in describing their desired images. ## 3.3.2 Prompt Semantic Features In addition to analyzing the syntactic characteristics of prompts, we also analyze their semantic features. We use a pre-trained CLIP model (Radford et al., 2021) to extract semantic features (Ramesh et al., 2022). We use a frozen CLIP ViT-L/14 text encoder (the same model used in Stable Diffusion) to convert prompts into 768-dimension vectors. ![5_image_0.png](5_image_0.png) Visualizing Prompt Embeddings. To study the distribution of prompts in high-dimensional space, we use UMAP (McInnes et al., 2020) to project 768-dimensional vectors into 2-D vectors for easy visualization. UMAP is a popular dimensionality reduction technique that is better at preserving the global structure of data and more scalable to large datasets compared to t-SNE (van der Maaten and Hinton, 2008) and PCA (Hotelling, 1936). We use grid search to fine-tune hyperparameters n_neighbors (60) and min_dist (0.1) so that prompts are more spread out in a 2-D space. We develop an interactive visualization tool4to explore prompts' semantic embeddings (Fig. 6). We use Kernel Density Estimation (KDE) (Rosenblatt, 1956) with a standard multivariate Gaussian kernel and Silverman bandwidth (Silverman, 2018) to estimate the distribution of prompts' UMAP representations. Then, we visualize the estimated distribution as a contour plot. To summarize prompts that are in the same region, we create four grids with varying granularity and pre-compute keywords for each grid tile, by treating all prompts in the tile as a document and selecting the top 4 keywords with the highest TF-IDF scores. Interactions. Our visualization shows keywords of tiles that are close to high-density regions and prompt clusters by default. Viewers can hover over a tile to see its keywords, pan and zoom in to see more details of specific regions, and click a button to display each prompt as a small dot that viewers can hover over to read its prompt text. Insights and implications. Our semantic embedding visualization (Fig. 6) highlights two popular prompt categories: art-related prompts (left in the plot) and photography-related prompts (dark blue regions on the right). These two groups appear distant from each other in the UMAP space, suggesting that the prompts for art and photography typically have distinct semantic representations. Interestingly, photography prompts appear to contain two clusters: one for non-human objects (top right) and another for celebrities (bottom right). Small prompt clusters outside the central area often feature artist names. Our findings suggest that future researchers can leverage the prompt usage distribution to fine-tune generative models to tailor to specific popular prompt categories. ## 3.4 Characterizing Images We visualize5the CLIP embedding distribution of 2 million unique image instances randomly sampled from DIFFUSIONDB (Fig. 7) by defining the unique key as the combination of the image's prompt and hyperparameters CFG scale, step, size, and seed. We use the UMAP model that was previously trained on the prompt embeddings to project the image embeddings into the same 2-D space. Finally, we apply the same method we used for our prompt embedding visualization (§ 3.3.2) to generate a contour plot and grid label overlays. Insights and implications. Our image embedding visualization reveals that generated images have a different distribution from their prompts in the CLIP embedding space. For example, the 5Image embedding visualization: **https://poloclub.** github.io/diffusiondb/explorer/\#image-embedding "movie" cluster in the prompt embedding has been replaced by the "portrait" cluster in the image embedding. This suggests the semantic representations of prompts and their generated images may not be perfectly aligned. One hypothesis is that large image generative models face limitations when generating photorealistic human faces (Borji, 2022), and therefore some images generated with movie-related prompts appear to be closer to art and portrait regions in the embedding space. ## 3.5 Stable Diffusion Error Analysis We leverage DIFFUSIONDB to discover Stable Diffusion generation failure cases and examine potential causes. To surface poor image generations, we compute CLIP embeddings for all prompts and images in DIFFUSIONDB. We then select promptimage pairs with a large cosine distance (d) between their embeddings. The cosine distances have a normal distribution (N (0.7123, 0.04132) ). In this analysis, we focus on 13,411 "bad" promptimage pairs (1) with a distance that is larger than 4 standard deviations from the mean and (2) the image was not blurred by Stable Diffusion (§ 2.3). Impacts of hyperparameters. We conduct a logistic regression test to analyze the relationship between Stable Diffusion hyperparameter values (e.g., CFG scale, step, width, and height) and the likelihood of generating an image that is semantically different from its prompt. The results reveal that all four hyperparameters are negatively correlated with the likelihood of generating a bad image. The correlation is statistically significant with a p-value of less than 0.0001 for all four variables. Furthermore, we find the distribution of selected sampler options when generating bad images is significantly different from the overall distribution (X2 = 40873.11, p < 0.0001). CFG scale controls how much the generated image looks like the prompt. We find some users specify negative CFG scales that make images look different from their prompts (large cosine distance d). In the example shown on the right, a user generates an image using a prompt about "superman" with all default hyperparameters values, except for setting CFG scale to -1. This results in an image featuring a bowl of soup instead of "superman". A small step could also generate underdeveloped images that look different from the specified prompts. As demonstrated in the example on the right, a user generates an image about "plague doctor" with all default hyperparameter values, except for setting step to 2, which leads to a blurry image. Stable Diffusion struggles with generating images with a small size or large aspect ratios. The dissimilar image shown on the right is generated with default hyperparameters except for a size of (64,512). Impacts of prompts. Despite controlling all hyperparameters to be close to default values, we still find 1.1k unique bad image-prompt pairs. Most of these instances have non-English prompts, very short prompts, or prompts consisting primarily emojis (see an example on the right). The token lengths of these instances are significantly lower than the overall token length (one-tailed t = −23.7203, p < 0.0001). The English prompt frequency among these instances is also significantly lower than the overall frequency (X2 = 1024.56, p < 0.0001). Interestingly, we also find that Stable Diffusion sometimes generates unexpected images even when prompts are meaningful English sentences. Future researchers can use our error analysis and failure cases to check potentially mislabeled training data. Implications. Our study reveals Stable Diffusion ![6_image_0.png](6_image_0.png) can make mistakes when generating images with certain hyperparameter values or prompt styles. Negative CFG scales, small steps, or small sizes contributes to generating images dissimilar to prompts. Short and non-English prompts can also lead to errors. To improve the quality of future generative models, researchers can expand the training data to cover these edge cases. There are opportunities for researchers to design user interfaces that can help users understand the impact of different hyperparameters and guide them in choosing values that fit their specific use cases. ## 3.6 Potentially Harmful Uses To identify potentially malicious uses of Stable Diffusion, we use named entity recognition to analyze prompts. We find that many prompts include names of influential politicians, such as over 65k images generated with a prompt including "Donald Trump" and over 48k images with "Joe Biden." Some prompts portray these politicians in negative lights, ranging from depicting them "as Gollum with hair" to "arrested in handcuffs." Additionally, we find female celebrities are frequently used in prompts, with a high frequency after artists and influential politicians. Some of these prompts are presented in a sexual context that could be considered nonconsensual pornography. Through keyword search, we discover prompts generating misinformation that could cause harm. For example, the prompt "scientists putting microchips into a vaccine" may harm public trust in medical institutions by potentially validating conspiracy theories. Similarly, the prompt "Russian soldiers in gas masks found the last surviving ukrainian after a nuclear war to liberate ukraine" depicts false images of the Russo-Ukrainian War and could lead to new forms of propaganda. Our findings highlight the crucial need for further research on the broader impacts of large generative models and ways to regulate and mitigate their harms. ## 4 Enabling New Research Directions The unprecedented scale and diversity of DIFFU-SIONDB bring new exciting research opportunities to help users generate images more effectively and efficiently, and enable researchers to improve, explain, and safeguard generative models. Prompt Autocomplete. With DIFFUSIONDB, researchers can develop an autocomplete system to help users construct prompts. For example, one can use the prompt corpus to train an n-gram model to predict likely words following a prompt part. Alternatively, researchers can use *semantic autocomplete* (Hyvönen and Mäkelä, 2006) by categorizing prompt keywords into ontological categories such as subject, style, quality, repetition, and magic terms (Oppenlaender, 2022). This allows the system to suggest related keywords from unspecified categories, for example suggesting style keyword "depth of field" and a magic keyword "award-winning" to improve the quality of generated images. Additionally, researchers can also use DIFFUSIONDB to study prompt *auto-replace* by distilling effective prompt patterns and creating a "translation" model that replaces weaker prompt keywords with more effective ones. Generation through Search. As DIFFUSIONDB contains 14 million images, this dataset might have already included images with a user's desired effects. Thus, a user can quickly search images in DIFFUSIONDB instead of running Stable Diffusion, which can be slow and costly. Lexica (Shameem, 2022), an AI start-up, provides such a search engine, where users can search Stable Diffusion images by natural language or images. Researchers can also construct a structured index of images and prompts, such as building a semantivisual image hierarchy of images (Li et al., 2010) or a *hierarchical topic model* of prompts (Griffiths et al., 2003), to help users easily discover and explore images and prompts with similar styles. Improving Generative Models. With DIFFU-SIONDB, a large and diverse collection of Stable Diffusion usage logs, researchers not only can identify weak points and failure modes of Stable Diffusion but also gain insights into user preferences. For example, we demonstrate that researchers can use joint text-image embeddings between prompts and images to detect generation misalignments (§ 3.5). Additionally, DIFFUSIONDB provides important metadata such as username hash and timestamp for each generated image. By analyzing these metadata fields, researchers can trace the evolution chain of prompts, parameters, and images, which offers valuable insights into how users develop mental models of large generative models and their preferences of generated images. This understanding can inform future researchers to enhance generative models and design interfaces that facilitate better image-generation experiences. Explainable Generation. As generative models have been gaining immense popularity, there is a call for explainable creativity (Llano et al., 2022). Many explanation techniques use input permutation that computes feature attribution scores by running a model on slightly-modified input values (Lundberg and Lee, 2017). DIFFUSIONDB contains 14 million prompt-image pairs including similar prompts with minor differences, such as "a happy dog" and "a sad dog", allowing researchers to investigate how individual keywords affect the generation process. Deepfake Detection. Breakthroughs in generative models raise concerns about deepfakesfake images of real individuals for unethical purposes (Wiggers, 2022). DIFFUSIONDB is valuable for detecting deepfakes, as it contains a largescale collection of model-generated images and their metadata. Researchers can use this collection to train ML models to identify synthetic artifacts and train classifiers that classify synthetic images from real images (Mirsky and Lee, 2022). ## 5 Related Work Text-to-text Prompting. Researchers have been studying prompt engineering for text-to-text generation (e.g., Liu et al., 2022; Lu et al., 2022; Rubin et al., 2022). To facilitate this line of research, researchers develop PromptSource (Bach et al., 2022), a dataset of 2k text prompts along with a framework to create and share prompts. In contrast, our work focuses on text-to-image prompting, and DIFFUSIONDB has an unprecedented scale of 14 million real prompt-image pairs. Text-to-image Prompting. There is a growing interest in text-to-image prompt engineering research from NLP, Computer Vision, and HCI communities (e.g., Qiao et al., 2022; Pavlichenko and Ustalov, 2022). For example, Oppenlaender (2022) identifies six types of prompt modifiers through an ethnographic study, and Liu and Chilton (2022) proposes design guidelines for textto-image prompt engineering by experimenting with 1,296 prompts. Closest in spirit to DIFFU-SIONDB is Lexica (Shameem, 2022) which allows users to search over 5 million Stable Diffusion images with their prompts, but it does not release its internal database. In comparison, DIFFUSIONDB is open-source and publicly available to everyone. ## 6 Conclusion We present DIFFUSIONDB, the first large-scale text-to-image prompt dataset, containing 14 million images with their prompts and hyperparameters collected from the Stable Diffusion discord server. We release the dataset with a CC0 1.0 license and open source all collection and analysis code, broadening the public's access to cutting-edge AI technologies. We discuss findings on prompt and image patterns. We hope our work will serve as a cornerstone for the future development of large generative modes and tools that help users use these modes. ## 7 Limitations We discuss four limitations of our work: the inclusion of unsafe content, potential biases in data sources, a limited measure of image quality and generalizability to different generative models. - **Inclusion of unsafe images and prompts.** We collect images and their prompts from the Stable Diffusion Discord server (§ 2). The Discord server has rules against users generating or sharing harmful or NSFW (not suitable for work, such as sexual and violent content) images. The Stable Diffusion model used in the server also has an NSFW filter that blurs the generated images if it detects NSFW content. However, we observe that DIFFUSIONDB includes some NSFW images that were not detected by the NSFW filter or removed by the server moderators. To mitigate the potential harm, we compute and share the likelihood of an image or a prompt containing unsafe content using the state-of-theart NSFW detectors (§ 2.3). In addition, we provide a Google Form on the DIFFUSIONDB website where users can report harmful or inappropriate images and prompts. We will closely monitor this form and remove reported images and prompts from DIFFUSIONDB. - **Potential biases of the data source.** The 14 million images in DIFFUSIONDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DIFFUSIONDB might not represent novice users. Similarly, the prompts in DIFFUSIONDB might not generalize to domains that require specific knowledge, such as medical images (Chambon et al., 2022). - **Limited measure of image quality.** We use joint text-image CLIP embeddings between prompts and images to detect generation misalignments (§ 3.5). While the CLIP embedding distance can indicate the degree of alignment between the prompts and generated images, it does not provide a measure of the overall image quality. When constructing our dataset, we have considered including image properties such as entropy, variance, and the most common colors to help users gauge image qualities. However, these metrics do not provide a good measure of the overall image quality as well. To better mea- sure image quality, future researchers can recruit annotators to rate images in DIFFUSIONDB. - **Generalizability.** Previous research has shown a prompt that works well on one generative model might not give the optimal result when used in other models (Borji, 2022). Therefore, different models can need users to write different prompts. For example, many Stable Diffusion prompts use commas to separate keywords, while this pattern is less seen in prompts for DALL-E 2 (Ramesh et al., 2022) or Midjourney (Holz, 2022). Thus, we caution researchers that some research findings from DIFFUSIONDB might not be generalizable to other text-to-image generative models. ## 8 Ethics Statement In this section, we discuss two main ethical considerations of DIFFUSIONDB. - **Copyright.** By using the Stable Diffusion Discord server, all users agree to the entirety of CC0 1.0 Universal Public Domain Dedication. This includes waiving any intellectual property rights related to any content shared on the server (StabilityAI, 2022b). All prompts and images in the Discord server are considered to be public domain and can be used by anyone for any purpose. Also, we release DIFFUSIONDB under the CC0 1.0 license (§ 2.5). - **Privacy.** While it is possible that some prompts may contain sensitive information, this is not common because the Stable Diffusion Discord has strict rules against writing personal information in the prompts and has moderators in place to remove violative messages. To further protect user privacy, we have anonymized the usernames of all users in our dataset (§ 2.4). Users also have the option to remove their prompts and images from our dataset through an online form (§ 2.5). We provide a thorough discussion on the limitations and broader impacts of DIFFUSIONDB in its Data Sheet (Gebru et al., 2020) (‡ A). ## Acknowledgements We thank Stability AI for releasing Stable Diffusion and hosting the Stable Diffusion Discord server. We especially appreciate the Stable Diffusion Discord moderators and users for creating an open and friendly online community that makes our work possible. We also extend our appreciation to Hugging Face for hosting our dataset. Lastly, we would like to acknowledge the anonymous reviewers for their valuable feedback and insightful comments that helped improve our paper. This work was supported in part by a J.P. Morgan PhD Fellowship, NSF grants IIS-1563816, DARPA GARD, gifts from Cisco, Bosch, and NVIDIA. 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Stable Diffusion Dream Studio beta Terms of Service. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. *Journal of Machine* Learning Research, 9. Weixin Wang, Hui Wang, Guozhong Dai, and Hongan Wang. 2006. Visualization of large hierarchical data by circle packing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Kyle Wiggers. 2022. Deepfakes for all: Uncensored AI art model prompts ethics questions. Simon Willison, Adam Stacoviak, and Jerod Stacoviak. 2022. Stable Diffusion Breaks the Internet. ## A Data Sheet For D**Iffusion**Db Motivation For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. The DIFFUSIONDB project was inspired by important needs in research focused on diffusion models and prompt engineering. As large text-to-image models are relatively new, there is a pressing need to understand how these models work, how to write effective prompts, and how to design tools to help users generate images. To tackle these critical challenges, we present DIFFUSIONDB, the first large-scale prompt dataset with 14 million real prompt-image pairs. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The dataset was created by Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau at the Georgia Institute of Technology. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. Funded in part by J.P. Morgan PhD Fellowship, NSF grants IIS-1563816, DARPA GARD, and gifts from Cisco, Bosch, and NVIDIA. Any other comments? None. | Composition | |---------------| What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. Each instance consists of an image generated by the Stable Diffusion model and the prompt as well as parameters that were input into the model to generate the image. The input parameters include seed, CFG scale, sampler, width, height, username hash, timestamp, image NSFW score How many instances are there in total (of each type, if appropriate)? There are 14 million instances in total. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). The dataset is a sample of instances. It represents a sample of images from the Stable Diffusion discord server. No tests were run to determine representativeness. What data does each instance consist of? "Raw" data (e.g., unprocessed text or images)or features? In either case, please provide a description. Each instance consists of the image generated by the Stable Diffusion model (with a unique id), along with the prompt used to generate the image and the model parameters as a JSON file. Is there a label or target associated with each instance? If so, please provide a description. The labels associated with each image are the prompt and other input parameters. Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. Everything is included. No data is missing. Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit. Not applicable. Are there recommended data splits (e.g., training, development/validation, testing)? If 905 so, please provide a description of these splits, explaining the rationale behind them. No. This dataset is not for ML model benchmarking. Researchers can use any subsets of it. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. No. All images and prompts are extracted as is from the Discord chat log. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? The dataset is entirely self-contained. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals' nonpublic communications)? If so, please provide a description. Unknown to the authors of the datasheet. It is possible that some prompts contain sensitive information. However, it would be rare, as the Stable Diffusion Discord has rules against writing personal information in the prompts, and there are moderators removing messages that violate the Discord rules. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. We collect images and their prompts from the Stable Diffusion discord server. Even though the discord server has rules against users sharing any NSFW (not suitable for work, such as sexual and violent content) and illegal images, DIFFUSIONDB still contains some NSFW images and prompts that were not removed by the server moderators. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. No. Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how. No. Any other comments? None. Collection How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, modelbased guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. The data was directly observed from the Stable Diffusion Discord Channel. It was gathered from channels where users can generate images by interacting with a bot, which consisted of messages of user generated images and the prompts used to generate those images. What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated? The data was gathered using a DiscordChatExporter (Holub, 2017), which collected images and chat messages from each channel specified. We then extracted and linked prompts to images using Beautiful Soup (Richardson, 2007). Random images and prompts were selected and manually verified to validate the prompt-image mapping. If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? DIFFUSIONDB does not sample from a larger set. However, DIFFUSIONDB-2M is a sample from a larger set. For certain messages, there would exist a collage of n images (e.g., n = 2, 4, 9) with identical prompts consolidated into a single image. These images were split and a single image would be randomly selected to include in DIFFUSIONDB-2M from n images with equal probability of any image being selected. This saved space and prioritized unique prompts. Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? Students conducted the data collection process and were compensated with stipend or course credits. Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created. All messages were generated in August 2022 and messages were collected between October 18th and 24th 2022. DIFFUSIONDB includes the generation timestamps of all images. Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation. There were no ethical review processes conducted. Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)? The data was directly obtained from individual messages in the Discord server. Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself. Users of the channel were not notified about this specific gathering of data but agree to forfeit any intellectual property rights claims by using Stable Diffusion. In addition, users are instructed that the images are public domain and can be used by anyone for any purpose. The exact language is as follows (StabilityAI, 2022b): Note, that while users have forfeited copyright (and any/all intellectual property right claims) on these images, they are still public domain and can be used by anyone for any purpose, including by the user. Feel free to use images from DreamStudio Beta and the Stable Diffusion beta Discord service for anything, including commercial purposes. Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented. By using the server and tools, users consented to the regulations posed by Stability AI LTD, the company that both made Stable Diffusion and runs the Discord server. This implies consent by using the tool. The exact wording is as follows: By your use of DreamStudio Beta and the Stable Diffusion, you hereby agree to forfeit all intellectual property rights claims, worldwide, and regardless of legal jurisdiction or intellectual property law applicable therein, including forfeiture of any/all copyright claim(s), to the Content you provide or receive through your use of DreamStudio Beta and the Stable Diffusion beta Discord service. This message is contained in the rules and terms of service section of the Stable Diffusion Discord (StabilityAI, 2022a,b). In conjunction with the previous statement about images being public domain (CC0 1.0 license), it is established that the images made by using Stable Diffusion can be used for other purposes. If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate). Users will have the option to report harmful content or withdraw images they created through a Google Form listed on the DIFFUSIONDB website: https://github.com/poloclub/diffusiondb. Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting 907 documentation. No analysis has been conducted. Any other comments? None. | Preprocessing | |-----------------| Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remaining questions in this section. The Discord chat logs include collage images, where each collage contains a grid of images that share the same prompt but have different seeds. We use Pillow (Clark, 2015) to split a collage into individual images. For DIFFUSIONDB, we include all split images. However, for DIFFUSIONDB-2M, we only include one randomly selected split image to save space and prioritize unique prompts. Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the "raw" data. Raw data was not saved. Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point. All our data collection and preprocessing code is available at: **https://github.com/poloclub/** diffusiondb. Any other comments? None. | Uses | |--------| Has the dataset been used for any tasks already? If so, please provide a description. No. Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. No. What (other) tasks could the dataset be used for? This dataset can be used for (1) prompt autocomplete, (2) generating images through search, (3) detecting deepfake, (4) debugging image generation, (5) explaining image generation, and more. Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms? There is minimal risk for harm: the data were already public. Personally identifiable data (e.g., discord usernames) were removed during the collection/preprocessing phases. Are there tasks for which the dataset should not be used? If so, please provide a description. All tasks that utilize this dataset should follow the licensing policies and the regulations (StabilityAI, 2022b) posed by Stability AI, the company that both made Stable Diffusion and runs the official Discord server. Any other comments? None. Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. Yes, the dataset is publicly available on the internet. | Distribution | |----------------| How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? The dataset is distributed on the project website: https://poloclub.github.io/diffusiondb. The dataset shares the same DOI as this paper. When will the dataset be distributed? The dataset is released on October 25th, 2022. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. All images generated by stable diffusion discord services are under the CC0 1.0 License, and therefore so are images in this dataset. In addition, the distribution of the dataset is under the Terms of Use (StabilityAI, 2022b) posed by Stability AI, the company that both made Stable Diffusion and runs the official Discord server. Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. All images in this dataset have a CC0 1.0 License and follows the Stability AI's Terms of Use (StabilityAI, 2022b). Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. No. Any other comments? None. | Maintenance | |---------------| Who will be supporting/hosting/maintaining the dataset? The authors of this paper will be supporting and maintaining the dataset. How can the owner/curator/manager of the dataset be contacted (e.g., email address)? The contact information of the curators of the dataset is listed on the project website: https://poloclub.github.io/diffusiondb. Is there an erratum? If so, please provide a link or other access point. There is no erratum for our initial release. Errata will be documented in future releases on the dataset website. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)? Yes, we will monitor the Google Form where users can report harmful images and creators can remove their images. We will update the dataset bimonthly. Updates will be posted on the project website https://poloclub.github.io/diffusiondb. If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. People can use a Google Form linked on the project website to remove specific instances from DIFFUSIONDB. Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers. We will continue to support older versions of the dataset. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description. Anyone can extend/augment/build on/contribute to DIFFUSIONDB. Potential collaborators can contact the dataset authors. ## Any Other Comments? None. Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 and Appendix A ✓ A2. Did you discuss any potential risks of your work? Section 8 and Appendix A ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** We Created A New Dataset, Described In Section 2. ✓ B1. Did you cite the creators of artifacts you used? Section 2 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 2.5. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 2. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 2.5. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Abstract, section 2 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 2 and 3 ## C ✗ **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Not applicable. Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Not applicable. Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

cattan-etal-2023-key

From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization

https://aclanthology.org/2023.acl-long.52

Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed Key Points, and quantifies their prevalence. While key points are more expressive than word clouds and key phrases, making sense of a long, flat list of key points, which often express related ideas in varying levels of granularity, may still be challenging. To address this limitation of KPA, we introduce the task of organizing a given set of key points into a hierarchy, according to their specificity. Such hierarchies may be viewed as a novel type of Textual Entailment Graph. We develop ThinkP, a high quality benchmark dataset of key point hierarchies for business and product reviews, obtained by consolidating multiple annotations. We compare different methods for predicting pairwise relations between key points, and for inferring a hierarchy from these pairwise predictions. In particular, for the task of computing pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision.

# From Key Points To Key Point Hierarchy: Structured And Expressive Opinion Summarization Arie Cattan1∗ Lilach Eden2∗ Yoav Kantor2 **Roy Bar-Haim**2 1Computer Science Department, Bar Ilan University 2IBM Research [email protected] {lilache, yoavka, roybar}@il.ibm.com ## Abstract Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed *key points*, and quantifies their prevalence. While key points are more expressive than word clouds and key phrases, making sense of a long, flat list of key points, which often express related ideas in varying levels of granularity, may still be challenging. To address this limitation of KPA, we introduce the task of organizing a given set of key points into a hierarchy, according to their specificity. Such hierarchies may be viewed as a novel type of *Textual Entailment Graph*. We develop THINKP, a high quality benchmark dataset of key point hierarchies for business and product reviews, obtained by consolidating multiple annotations. We compare different methods for predicting pairwise relations between key points, and for inferring a hierarchy from these pairwise predictions. In particular, for the task of computing pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision. https://github.com/IBM/kpa-hierarchy ## 1 Introduction Many organizations face the challenge of extracting insights from large collections of textual comments, such as user reviews, survey responses, and feedback from customers or employees. Current text analytics tools summarize such datasets via word clouds (Heimerl et al., 2014) or key phrases (Hasan and Ng, 2014; Merrouni et al., 2019), which are often too crude to capture fine-grained insights. ∗ Equal contribution. Work done while the first author was an intern at IBM Research. Multi-document summarization methods, on the other hand (Chu and Liu, 2019; Bražinskas et al., 2020a,b; Angelidis et al., 2021; Louis and Maynez, 2022), do not quantify the prevalence of each point in the summary, and are not well-suited for representing conflicting views (Bar-Haim et al., 2021). Key Point Analysis (KPA) is a recent opinion summarization framework that aims to address the above limitations (Bar-Haim et al., 2020b). KPA extracts concise sentences and phrases termed Key Points (KPs), which represent the most salient points in the data, and quantifies the prevalence of each KP as the number of its matching input sentences. One remaining shortcoming of KPA, however, is that it generates a flat list, which does not capture the relations between the key points. For example, consider the sample set of key points in Figure 1 (left), which was automatically extracted from reviews of one of the hotels in the Yelp Open Dataset1. The results do not provide a high level view of the main themes expressed in the reviews. It is hard to tell which key points convey similar ideas, and which key points support and elaborate on a more general key point. As the number of key points in the summary increases, such output becomes even harder to consume. In this work we introduce *Key Point Hierarchies* (KPH) as a novel structured representation of opinion summaries. Organizing the key points in a hierarchy, as shown in Figure 1 (right), allows the user to quickly grasp the high-level themes in the summary (the hotel is beautiful, the shows are great, comfortable rooms, *great service*), and drill down on each theme to get more fine-grained insights, e.g., from "The personnel were great" to *"check-in* was quick and easy". Furthermore, key points that (nearly) convey the same meaning (e.g., *"Housekeeping was fantastic"*, and *"The cleaning crew is* great") are clustered together and represented as a 1https://www.yelp.com/dataset 912 ![1_image_0.png](1_image_0.png) single node in the hierarchy. This structured output makes KPA results more consumable, informative, and easier to navigate. KPH can be viewed as a new type of textual entailment graph (§2). We develop THINKP (Tree HI*erarchy of* Naturally-occuring Key P*oints*), the first benchmark dataset for Key Point Hierarchies, created from KPA summaries of user reviews in multiple domains (§4). Due to the complexity of KPH annotation, THINKP was created by consolidating multiple annotations, to ensure its high quality. We explore different methods for automatic KPH construction from a given set of key points (§5). Following previous work on entailment graphs (§2), this is formulated as a two-step approach. We first compute local scores predicting the directional relation between each pair of key points. We then construct a hierarchy guided by these local pairwise predictions. We present novel methods and algorithmic improvements for each of the above subtasks. In particular, for the task of predicting pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision. We release the THINKP dataset to encourage further research on this challenging task. Overall, our work contributes to several lines of research, including key point analysis, opinion summarization, entailment graphs, and distributional methods for natural language inference. Furthermore, as we demonstrate in §4.3, our novel THINKP dataset captures diverse types of inferences between pairs of naturally-occurring texts, making it an interesting resource for NLI research in general. ## 2 Background Key Point Analysis. Bar-Haim et al. (2020a,b) proposed *Key Point Analysis (KPA)* as a summarization framework that provides both textual and quantitative summary of the main points in a collection of comments. KPA extracts a set of concise, high-quality sentences or phrases, termed Key Points, and maps each of the input sentences to its corresponding key points. The prevalence of each key point is quantified as the number of its matching sentences. KPA summaries are more expressive than the commonly-used word clouds and key phrases, while adding an important quantitative dimension that is missing from plain text summaries. The KPA algorithm aims to extract a set of key points that provide high coverage of the data, while removing redundancies. It employs two supervised models: one for assessing the quality of key point candidates, and another one for computing a match score between a sentence and a candidate key point. Bar-Haim et al. (2021) adapted KPA to business reviews, by introducing several extensions to the original algorithm. In particular, they integrated sentiment analysis into KPA, creating separate summaries for positive and negative sentences. They also developed a specialized key point quality model for the business reviews domain. Entailment Graphs. Most of the prior work on entailment graphs has focused on learning entailment relations between predicates, while satisfying some global constraints such as transitivity (Berant et al., 2010), soft transitivity (Chen et al., 2022), and other types of soft constraints (Hosseini et al., 2018). Levy et al. (2014) extended the notion of entailment graphs to instantiated predicates. Most similar to our Key Point Hierarchies are entailment graphs over text fragments, introduced by Kotlerman et al. (2015). Their motivating scenario was summarizing customer feedback, for which they developed a benchmark dataset. However, the text fragments in this dataset were extracted manually. The approach proposed in the current work, which first finds the most salient points in the data using KPA, and then constructs a hierarchy from the extracted key points, allows fully-automatic generation of structured summaries for large collections of opinions, views or arguments. Constructing hierarchies over automatically-extracted key points, which are often noisy and imperfect, represents a more realistic scenario, and makes both manual annotation of KPHs and their automatic construction more challenging. ## 3 Key Point Hierarchies Figure 1 illustrates the transformation of a flat key point list into a Key Point Hierarchy (KPH). Formally, given a list of key points K = {k1, k2*, ..., k*n}, we define a KPH H = (V, E) as a directed forest, that is, H is a Directed Acyclic Graph (DAG) where each node has no more than one parent. The vertices V are clusters of key points {C1*, ..., C*m} that convey similar ideas, and the directed edges ϵij ∈ E represent hierarchical relations between clusters Ci and Cj . Similar to Kotlerman et al. (2015), a directed edge Ci −→ Cj indicates that the key points in Ci provide elaboration and support for the key points in Cj . By transitivity, this relation extends to any two clusters Ci and Ck such that there is a directed path in H from Cito Ck, which we denote as Ci ❀ Ck. Accordingly, we define R(H) as the set of directional relations between pairs of key points (*x, y*) that can be derived from H as: $${\mathcal{R}}(H)=\{(x,y))\mid C_{x}=C_{y}\lor C_{x}\sim C_{y}\}$$ where Cx, Cy ∈ V are the clusters of x and y respectively. Considering the example in Figure 1, R(H) includes the relations *"Housekeeping was* fantastic" −→ "The personnel were great", "Housekeeping was fantastic" −→ "Friendly service all around", "Housekeeping was fantastic" −→ *"The* cleaning crew is great", and so on. We chose a hierarchical representation over a more general graph structure since it results in a simpler output that is easier to consume. In addition, this greatly simplified the annotation process. We found that hierarchical representation works well in practice, as the vast majority of the nodes in our dataset did not have more than one potential parent. This is in line with previous work, which suggested that entailment graphs tend to have a tree-like structure (Berant et al., 2012). ## 4 Think**P: A Dataset For Key Point** Hierarchies In this section we present THINKP, a benchmark dataset of key point hierarchies. To build THINKP, we first apply Key Point Analysis to reviews of businesses and products from multiple domains (§4.1). A KPH is then constructed manually from the set of key points extracted for each business or product (§4.2). We provide statistics on the resulting dataset, as well as qualitative analysis of the types of inferences it includes (§4.3). ## 4.1 Key Point Set Generation The first step in creating the dataset was to run KPA on the reviews of selected businesses and products. Our implementation follows (Bar-Haim et al., 2021), who suggested several extensions of KPA for analyzing business reviews.2 For each business, two separate summaries of positive and negative key points are created. To obtain a diverse dataset, we considered three different domains, from two data sources: Yelp. This dataset includes 7M written business reviews, where each business may be classified into multiple categories, in varying levels of granularity. We apply KPA to a sample of businesses that include at least one of the following categories: RESTAURANTS, HOTELS, and ART & ENTER-TAINMENT, and had at least 1,000 reviews. For the KPH annotation, we selected four restaurants (which we refer to as the RESTAURANTS domain), and four businesses categorized as ART & EN-TERTAINMENT, out of which three were hotels (hereafter, the *Hotels & Entertainment* domain, or HOTELS for brevity). Each domain includes two positive and two negative KPA summaries. Here, we focused on laptops and tablets from the PC domain, for which we could expect a rich and diverse set of key points discussing various aspects such as size, ease of use, design etc. Eventually, we annotated a KPH for three positive and one negative KPA summaries. ## 4.2 Kph Annotation Annotating complex structures such as KPHs is a challenging task, since it involves global, interdependent decisions. Furthermore, the annotator needs to consider different types of hierarchical relations that may hold between the key points, as we further discuss in Section 4.3. Finally, user reviews make extensive use of informal and figurative language. For example, *"The food is outrageous!"* should be interpreted as great food; "Elevators should go up and down, not diagonal" means that the elevators were scary and "Internet was a joke to get to work" indicates a poor WiFi signal. To overcome these challenges and obtain a highquality dataset, three annotators individually constructed a KPH for each KPA summary (§4.2.1); The annotators then met to resolve their disagreements and reach a consolidated KPH (§4.2.2). ## 4.2.1 Creating An Initial Kph To construct an initial KPH, annotators were shown the key points one by one in a descending order according to the number of their matched sentences. For each key point, they first decided whether it conveys the same idea as any previously seen key point, in which case it was added to an existing cluster. If not, a new node was added to the KPH, and the annotator dragged it to its right position in the hierarchy. Since key points with many matches tend to be more general, the key point ordering facilitated top-down construction of the KPH. At any point in the annotation process, annotators had a complete view of the KPH constructed so far, and could adjust it by modifying previous decisions, including both clustering and hierarchical relations. Each KPH was annotated separately by three of the authors and took about one hour to complete per annotator. Our annotation guidelines are detailed in Appendix A.1. Since the key points were extracted automatically, some of them did not satisfy the desired properties of a key point - a concise and self contained sentence or phrase that discusses a single point with a certain polarity (Bar-Haim et al., 2021). To avoid noise in THINKP, annotators could mark such bad key points as candidates for removal from the final KPH. As our annotation tool, we used CoRefi (Bornstein et al., 2020), an interface for cross-document coreference annotation with Cattan et al. (2021)'s extension for annotating a forest of clusters, which we adapted to handle key points (see Appendix A.2). ## 4.2.2 Kph Consolidation To obtain the final KPHs, the three annotators met to discuss and resolve the differences in their individual KPHs annotations. This is a complex process because both clusters and the relations between them can differ. We therefore separated the consolidation process into two subsequent stages: clustering and hierarchy. In the first phase, following the reviewer mode in CoRefi (Bornstein et al., 2020), annotators were shown one key point at a time with their original clustering decisions. In case of disagreement, the annotators discussed and reached a joint decision, which automatically modified their original KPH accordingly. At the end of this stage, the initial KPH of each of the annotators was modified to include the exact same nodes. In the second phase, since each key point has a single parent, we could easily identify the remaining disagreements by comparing the parent of each node across the different annotators. To support this consolidation phase, we enhanced CoRefi with the ability to identify and highlight both clustering and hierarchy disagreements between any number of annotators (see Appendix A.3 for more details). Consolidating multiple annotations was also efficient due to the hierarchical structure of the KPH and took about an hour per KPH. ## 4.2.3 Dataset Quality Assessment To verify the quality of the resulting dataset, we asked two additional annotators to annotate and consolidate a portion of THINKP (3 RESTAU-RANTS, 2 HOTEL and 2 PC).4 We then evaluated their individual and consolidated KPHs against our consolidated annotation, as follows. In each domain, we compared the two sets of annotated KPHs by taking the union of the KP relations induced by the KPHs in each set (Eq. 1), and computing the F1 score over the two resulting sets of relations. The 4See Appendix A.4 for more details about annotators training. | REST | HOTEL | PC | Total | | |---------------|---------|------|---------|-------| | #KPHs | 4 | 4 | 4 | 12 | | #Key points | 181 | 208 | 128 | 517 | | #Filtered KPs | 21 | 17 | 48 | 86 | | # R(H) | 850 | 302 | 266 | 1,418 | Table 1: Statistics of THINKP. R(H) is the set of key point relations that can be derived from a KPH H (§3). final F1 was obtained by macro-averaging over the three domains. The annotators' performance after consolidation reached an F1 of 0.756, indicating substantial agreement.5 Furthermore, consolidation was shown to increase individual performance by 5-6 points. ## 4.3 Dataset Properties Table 1 shows some statistics for the THINKP dataset. Overall, THINKP includes 12 KPHs, 517 key points, and 1,418 key points relations (R(H)) out of the total 24,430 key point pairs. Due to its size, we did not split THINKP into development and test sets, but rather used the entire dataset for evaluation. As described in Section 4.2.1, during the annotation, we filter a relatively small number of key points (14%), mostly from the PC domain. This is mainly because the key point quality model that we used was not trained on this domain. From a qualitative perspective, THINKP has several appealing properties that make it a valuable benchmark for NLI. First, recall that the KPA algorithm aims to remove similar key points to avoid redundancy in the summary (Bar-Haim et al., 2020b). Hence, remaining equivalent key points in THINKP are mostly non-trivial paraphrases that are challenging to detect (e.g., *"Took forever to get our room"* ↔ *"Lines to check in are ridiculous"*). In addition, hierarchical relations between key points represent diverse types of inferences. Table 2 shows a few examples of common relations we observed by analyzing a sample from the dataset. Finally, THINKP comprises naturally-occurring texts and relations, coming from real-world data. ## 5 Automatic Kph Construction We use a two-step approach to automatically build a KPH from a set of key points. In the first step, we predict directional scores between all pairs of 5We do not report Kappa because decisions are mutually dependent. key points (§5.1). In the second step, we construct a hierarchy based on the local scores (§5.2). ## 5.1 Scoring Pairwise Key Point Relations Given a pair of key points (*i, j*), we aim to predict whether a directional relation i −→ j holds between i and j, by computing a likelihood score s(*i, j*) ∈ [0, 1]. We experimented with both existing baselines and new methods we developed for this task. Due to the size of THINKP, it was not used to fine-tune the scoring models (§4.3). Baselines. Identifying directional relations between two key points is closely related to two existing tasks: Textual Entailment, also known as Natural Language Inference (NLI) (Dagan et al., 2007) and matching arguments to key points (Bar-Haim et al., 2020a). Accordingly, we implemented two baselines: (1) NLI, a RoBERTa model (Liu et al., 2019) fine-tuned on the MNLI dataset (Williams et al., 2018) to predict whether i *entails* j 6and (2) *KPA-Match*, a RoBERTa model trained on the ArgKP dataset (Bar-Haim et al., 2020a) to predict whether i *matches* j, following (Bar-Haim et al., 2021)'s implementation. Directional Distributional Similarity. Geffet and Dagan (2005) introduced the distributional inclusion hypothesis for lexical entailment (Geffet and Dagan, 2004), which suggests that the context surrounding an entailing word w1 is naturally expected to occur also with the entailed word w2. Specifically, for each word w, they built a sparse feature vector where the value of the i-th entry is the PMI of the i-th word in the dictionary with w. Many distributional similarity metrics have been proposed to predict directional relations such as hyponymy between a pair of words, based on their distributional feature vectors. Among these methods are WeedsPrec (Weeds and Weir, 2003), BInc (Szpektor and Dagan, 2008), ClarkeDE (Clarke, 2009) and APinc (Kotlerman et al., 2009). In this work, we argue that this distributional inclusion hypothesis may be extended to identify directional relations between two key points. Indeed, if i −→ j, it is likely that an input sentence that matches the key point i will also match j. For example, the sentence "The beds were really comfortable, I literally knocked out as soon as my head touched the pillow." matches both *"The beds* 6https://huggingface.co/roberta-large-mnli | Relation Type | Examples Housekeeping needs worked on ←− The beds weren't even made right The room was poorly maintained ←− The air conditioning was not functioning right. The device itself is so difficult to use ←− Transferring data was a nightmare! Customer service is a joke ←− No help moving rooms | |-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Support / Elaboration Part-of | The hardware is fantastic ←− Sound is surprisingly good The theatre is great ←− The entrance is absolutely beautiful. | | IS-A | The toiletries they offer are the worst ←− not even good shampoo in room Food varieties was very limited ←− Desert selection was below average as well | Table 2: Examples of relations between key points in THINKP. were awesome" and *"The rooms are comfortable"*. Therefore, we construct a feature vector for each key point k, whose length is equal to the number of input sentences. The value at the i-th position in this vector is the likelihood that the i-th sentence matches k, as predicted by the KPA matching model (§4.1). Then, we apply the aforementioned distributional similarity metrics to predict a directional score s(*i, j*). We only report the performance of *APinc* as it slightly outperformed other metrics. Additionally, we implemented a simple variant of WeedsPrec, in which the entries in the feature vectors are binary (match/no match). This metric, termed Binary Inclusion (*BinInc*), computes the ratio between the number of sentences matched by KPA to both i and j and the number sentences matched to i. Intuitively, when most of the sentences that were mapped to i were also mapped to j, it is a strong indication that i −→ j. ## Combining Nli With Distributional Methods. As further discussed in Section 6, we empirically found that the NLI model and the distributional methods have complementary strengths. The NLI model performs better on RESTAURANTS, whereas the distributional methods perform better on the HOTEL and PC domains. Furthermore, even within each domain, those two methods produce very different rankings, as indicated by a low Spearman correlation between their output scores (see Appendix C for more details). To take advantage of the strengths of both approaches, we explored two alternatives for combining BinInc, the best-performing distributional method (as shown in Section 6), with NLI: 1. Averaging the output scores of NLI and BinInc (denoted *NLI+BinInc-Avg*). 2. Fine-tuning the NLI model on weak labels created by the BinInc model (denoted NLI+BinInc-WL). Specifically, we first apply the *BinInc* method to a large number of unlabeled KPA summaries and obtain local scores between all pairs of key points. We then convert these pairwise scores to the NLI format, where we consider all pairs above some threshold as entailment and the others as neutral. Finally, we fine-tune the NLI model on this automatically-generated training data and use the resulting model to predict the local scores s(*i, j*) on THINKP. Implementation details and statistics on the silver data are detailed in Appendix B. ## 5.2 Hierarchy Construction We proceed to construct a KPH by determining its semantic clusters and the hierarchical relations between them. Intuitively, we would like to generate a KPH such that the set of pairwise key point relations induced by its structure are consistent with the local directional scores: high-scoring relations should be included, and low-scoring relations should be excluded. We explored several alternatives for constructing a KPH, described below. Each of these methods employs a decision threshold τ over the local scores, which needs to be tuned over some development data. Reduced Forest. Berant et al. (2012) described a simple transformation of a directed graph G into a forest of clusters. In our case, we start with a graph that includes the key points as nodes, and the directional edges e(*i, j*) for pairs with local score s(i, j) > τ . The reduced forest is constructed as follows: (a) the condensation of G is computed by contracting each strongly connected component into a single vertex that represents a cluster of nodes in G. The resulting DAG is transformed into a forest by (b) taking its transitive reduction, and (c) heuristically selecting a single parent for each node with multiple parents. We select the larger cluster as a parent, and as a tie breaker, we use the mean over all the pairwise scores s(*i, j*) such that i is in the child cluster and j is in the parent cluster. As defined by Berant et al., G is a *Forest Reducible Graph (FRG)* if after applying step b above, none of the nodes has multiple parents. ## Tree Node And Component Fix (Tncf). Given a directed graph with local edge weights that are either positive (predicting pairwise entailment between connected nodes) or negative (predicting non-entailment), the optimal entailment graph may be defined as the transitive subgraph in which the sum of the edge weights is maximized (Berant et al., 2012). Berant et al. showed that this problem is NPHard, even when further constraining the resulting graph to be forest-reducible. To address the computational complexity of finding an exact solution, Berant et al. presented an efficient approximation algorithm, termed *Tree-nodefix (TNF)* that generates forest-reducible entailment graphs, and showed empirically that the quality of the resulting graphs is close to the exact solution found via Integer Linear Programming (ILP). Starting from some initial FRG, their algorithm iteratively improves the graph objective function by removing and reattaching one node at a time, while keeping the graph forest-reducible. Berant et al. (2015) proposed an extension for this algorithm, termed *Tree-Node-and-ComponentFix (TNCF)*, where in each iteration a whole cluster may be re-attached, in addition to individual nodes. We found this extension beneficial. Since a KPH is also a forest of clusters, the TNF and TNCF algorithms are directly applicable to our setting. Following Berant et al. (2012) we defined the edge weights as wi,j = s(*i, j*) − τ so that local scores below the threshold τ are considered negative. One difference between the original TNF implementation and ours is the initialization: while they used (Berant et al., 2011)'s exact solution, computed via ILP for a sparse configuration, we take a simpler approach and start with the reduced forest described above, constructed with the same threshold τ . Greedy. As an alternative to the TNF/TNCF algorithms, we also adapted the greedy algorithm proposed by Cattan et al. (2021) for the task of hierarchical cross-document coreference resolution, which also generates a forest of clusters. First, key point clusters are obtained by agglomerative clustering with average linkage and distance threshold of 1−τ , where the distance metric between two key points i and j is defined as 1−min(s(*i, j*), s(*j, i*)). Second, we define the score of the directional edge between two clusters (C1, C2) as the average of the s(*i, j*) scores between the key points in the two clusters: $$S({\mathcal{C}}_{1},{\mathcal{C}}_{2})={\frac{1}{|{\mathcal{C}}_{1}|\cdot|{\mathcal{C}}_{2}|}}\sum_{i\in{\mathcal{C}}_{1}}\sum_{j\in{\mathcal{C}}_{2}}s(i,j)\quad(2)$$ The KPH is constructed by repeatedly adding the highest-scoring edge (if the score is above the τ threshold), skipping edges that would violate the definition of the KPH as a directed forest. The process is terminated when no more edges can be added. Note that unlike the TNF/TNCF algorithms, the Greedy algorithm does not modify existing clusters and edges in each iteration, but only adds new edges. Greedy with Global Score (Greedy GS). One limitation of the Greedy algorithm is that the edge scoring function is *local* and hence ignores indirect relations between clusters that would result from adding the edge. For example, consider a KPH with three clusters {*A, B, C*} such that B −→ A. The criterion to add the edge C −→ B will consider only S(*C, B*) but not S(*C, A*), which corresponds to the indirect relation C ❀ A. To address this issue, we modified the algorithm to consider the relations between each cluster and all its ancestors in the resulting KPH, as follows: $$\begin{array}{r l}{E_{k+1}=E_{k}\cup{\mathrm{argmax}}\,O({\mathcal{V}},E_{k}\cup\epsilon)}&{{}}\\ {\epsilon{\in}E^{*}\backslash E_{k}}&{{}}\\ {O({\mathcal{V}},{\mathcal{E}})=\sum_{{\mathcal{C}}_{i}\in{\mathcal{V}}}\sum_{{\mathcal{C}}_{j}\in A_{{\mathcal{V}},{\mathcal{E}}}({\mathcal{C}}_{i})}S({\mathcal{C}}_{i},{\mathcal{C}}_{j})}&{{}}\end{array}$$ where Ek is the set of edges in the resulting KPH after k iterations, E∗is the set of all edges scoring above τ and AV,E (C) denotes the set of ancestors of C in H(V, E). ## 6 Evaluation Predicting Local Pairwise Relations. Figure 2 compares the performance of the different local scoring methods (§5.1). For each domain, we consider all the key point pairs in the dataset, and show ![7_image_0.png](7_image_0.png) the Precision/Recall curve and the Area Under the Curve (AUC) for each method. AUC results are also summarized in Table 3. We first observe that applying the *KPA-match* model indirectly via the distributional methods (*APinc* and *BinInc*) outperforms its direct application in two out of the three domains, and increases the average AUC from 0.237 to 0.277/0.288, respectively. The NLI model has a clear advantage over the distributional methods in the RESTAURANTS domain, but is much worse for HOTEL and PC. Both *NLI+BinInc-Avg* and *NLI+BinInc-WL* models are able to combine the complementary strengths of NLI and *BinInc* and outperform all the stand-alone models. Model combination via weak labeling (*NLI+BinInc-WL*) achieves the best performance in all three domains by a large margin (+0.11 average AUC improvement over the best stand-alone method). To further assess the contribution of model combination in the weak labeling setting, we also tested a configuration in which the silver data is labeled by the NLI model (denoted *NLI-WL*). The results are shown on the last row of Table 3. While the performance is better than NLI alone (demonstrating the value of weak labeling), it is still far below *NLI+BinInc-WL*. Overalll, the results affirm the importance of both model combination and the weakly-labeled data for local scoring performance. Hierarchy Construction. Next, we compare different methods for constructing a KPH from the set of local pairwise scores (§5.2). We use the scores from the best performing local method, NLI+BinInc-WL, as found in the previous experiment. We use the F1 measure as defined in Section 4.2.3 as our evaluation metric, similar to Kotlerman et al. (2015). Since THINKP has no ![7_image_1.png](7_image_1.png) development set (§4.3), we employ a leave-one-out scheme to tune the threshold τ . Specifically, for each KPA summary S, we find the threshold that maximizes the F1 score of the three other KPHs in the same domain and predict a KPH for S using this threshold. We then compute the F1 score for the predicted KPHs in each domain. The results are summarized in Table 4. *TNCF* achieves the best overall performance on THINKP with an average F1 of 0.526, substantially improving the *Reduced Forest* baseline. The *Greedy GS* algorithm is the top performer in the Restaurants domain (F1=0.641). Adding a global scoring function to the greedy algorithm improves the performance by 0.059 (from 0.45 to 0.509). We also evaluated the quality of the predicted relations using only the local scores, with a threshold determined via leave-one-out, as before (last row in Table 4). While the resulting set of relations may not represent a valid hierarchy, it still provides an interesting reference point for comparison with the various KPH construction algorithms. We can see that both *Greedy GS* and *TNCF* improve the local results by a substantial margin (+0.028 and +0.045, resp.). These two global methods not only satisfy the constraints of generating a valid KPH, but also improve the pairwise relation prediction of the local scorer. ## 7 Conclusion We introduced Key Point Hierarchies as a novel representation for structured, expressive opinion summaries. We explored several approaches for automatic hierarchy construction from a given set of key points, which were evaluated on a new benchmark dataset we developed for this task. We also NLI 0.428 0.172 0.232 0.277 KPA-Match 0.331 0.173 0.207 0.237 APinc 0.279 0.256 0.297 0.277 BinInc 0.304 0.286 0.274 0.288 NLI+BinInc-Avg 0.472 0.320 0.316 0.369 NLI+BinInc-WL **0.486 0.364 0.345 0.398** NLI-WL 0.466 0.243 0.233 0.314 REST HOTEL PC Avg. Table 3: Evaluation of local scoring methods (AUC for Recall ≥ 0.1) Table 4: Evaluation of hierarchy construction algorithms (F1 scores). All methods use the *NLI+BinInc-WL* local scores. proposed a novel distributional representation for key points, which we leveraged via weak supervision to achieve substantial improvement on the subtask of predicting pairwise key point relations. While our initial results are promising, there is still much room for improvement, and we hope that releasing our dataset would encourage the community to further promote this line of research. ## Limitations Key Point Hierarchies may be valuable for summarizing opinions and views in multiple domains, including reviews, survey responses, customer feedback, political debates etc. However, in this work, we only demonstrated their value for business and product reviews, leaving other types of data to future work. Also, we only attempted to create KPHs for English reviews, for which an abundance of resources is available, including a huge number of written reviews and high-quality trained models, e.g. for NLI and key point matching. Applying these methods to low-resource languages is expected to be far more challenging. Finally, the quality of the resulting KPHs depends on the quality of the extracted key points provided as input, which may vary across different domains. To alleviate this problem in THINKP, we manually filtered out problematic key points from the dataset (§4.2). | REST | HOTEL | PC | Avg. | | |-----------------|---------|-------|--------|-------| | Reduced Forest | 0.597 | 0.335 | 0.396 | 0.443 | | TNCF | 0.614 | 0.460 | 0.505 | 0.526 | | Greedy | 0.512 | 0.424 | 0.416 | 0.450 | | Greedy GS | 0.641 | 0.433 | 0.451 | 0.509 | | Local (no tree) | 0.568 | 0.437 | 0.439 | 0.481 | ## Acknowledgments The first author is partially supported by the PBC fellowship for outstanding PhD candidates in data science. ## References Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, and Mirella Lapata. 2021. Extractive opinion summarization in quantized transformer spaces. 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In *Proceedings of the 2020 Conference on Empirical* Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. ## A Data Collection A.1 Annotation Guidelines We began the annotation process of THINKP by drafting guidelines in which we describe the KPH structure (§3) and define the annotation task as follows. *"Given two key points A and B, (1) if A* and B roughly convey the same idea or opinion, they should be clustered together in the same node (e.g. Friendly service all around vs. Staff was nice and helpful) and (2) if B elaborates on A and supports it, then B should be placed under A in the hierarchy (e.g., the rooms are comfortable ←− The bed was very comfy)". Importantly, as key points are automatically extracted from human reviews written by different people in their own vocabulary, we advise to ignore subtle differences because they do not reflect different opinions. For example, *"Not* much choice of fruits and desserts" and "Dessert selection was below average as well" should be considered equivalent because *"Dessert"* usually includes fruits. ## A.2 Annotation Figure 3 shows the COREFI interface that we use to annotate THINKP. For each key point, annotators decide whether to add it to an existing cluster or to create a new node in the hierarchy. ## A.3 Consolidation As described in the paper (§4.2.2), we split the consolidation stage into two subsequent steps: clustering and hierarchy, illustrated in Figures 4 and 5. For the clustering step (Figure 4), we extend the reviewer algorithm in COREFI (Bornstein et al., 2020) with the ability to review multiple annotations for the same input. In case of disagreement, we display a red thumb-down at the bottom left of the annotation interface and the annotators discuss to reach a joint decision. Each clustering decision automatically modifies their original KPHs. Considering the example in Figure 4 with a clustering disagreement for the key point *"The directions also leave a lot to be* desired (KP1)": annotator A1 grouped it together with *"The device itself is so difficult to use (KP2)"* whereas annotator A2 left it as a standalone node in the KPH (indicated by the + button in purple). Now, if A1 and A2 decide to follow A1's decision, A2's original KPH will be automatically modified to include a grouped node {*The device itself is so* difficult to use, The directions also leave a lot to be desired} (instead of two separated nodes) whose children will be the concatenation of the initial children of KP1 and KP2. On the other hand, if A1 and A2 decide to follow A2's decision, a new node "The directions also leave a lot to be desired" will be added in A1's KPH. In this case, the children of the initial grouped node will stay under *"The* device itself is so difficult to use". This automatic process ensures that the original KPHs will include the exact same nodes. In the second step, as shown in Figure 5, as the nodes in the two KPHs are identical, a disagreement will occur when a cluster C ∈ V has a different direct parent in each KPH. To identify the next disagreement, annotators can click on the "Go To Next Disagreement" button to highlight the key point in blue and its direct parent in violet on both KPHs. Once all hierarchical disagreements have been resolved, the structure of both KPHs will be ![11_image_0.png](11_image_0.png) ## A.4 Annotators Training To assess the quality of THINKP (§4.2.3), we provided a team of in-house annotators with the same annotation guidelines (§A.1), while explicitly mentioning the purpose of the data collection. Following (Bornstein et al., 2020), we also provided them an automated walk-through tutorial to get familiar with the tool functionalities (§A.2). As part of the training, we asked the annotators to construct a KPH for 2 different businesses and gave them detailed feedback. Finally, we gave them a test and proceeded with the annotators who passed the test. ## B Implementation Details As described in Section 5.1, our best local scorer is obtained by fine-tuning an NLI model on weaklylabeled data, automatically collected as follows. We first applied KPA to reviews from 152 YELP businesses. The resulting KPA summaries included 38 key points on average. We then ran the *BinInc* method on all possible key point pairs in each KPA summary. After fixing the decision threshold to 0.5, we obtained 5,379 positive pairs and 295K negative pairs. In the final dataset that was used to train the model, we downsampled the negative examples so that the ratio between positive and negative examples was 1:5.7 We train our model using PyTorch (Paszke et al., 2019), PytorchLightning (Falcon et al., 2019) and the Transformers library (Wolf et al., 2020) for 5 epochs with a batch size of 64 and a learning rate of 1e-7. ## C Analysis Figure 6 shows the Spearman correlation coefficients between the output scores of the different local methods that we define in Section 5.1. NLI has a low correlation with the distributional methods (*APinc* and *BinInc*) in each of the three domains. This indicates that NLI and the distributional methods rank the key point pairs quite differently. 7We experimented with multiple ratios (1:1, 1:2, 1:3, 1:5, 1:10) as well as considering all the pairs and found that the 1:5 ratio achieves the best performance. ![12_image_0.png](12_image_0.png) D ## Datasets - The Yelp and Amazon datasets used in this work have been released for academic use, and accordingly, we have only used them for academic research. - The authors have reviewed the THINKP dataset and verified that it does not contain any personal information or offensive content. ![13_image_0.png](13_image_0.png) ![14_image_0.png](14_image_0.png) ![14_image_1.png](14_image_1.png) Figure 6: Spearman correlations between the scores of the local methods ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The last section (unnumbered), immediately following the conclusion ✗ A2. Did you discuss any potential risks of your work? We carefully reviewed the guidelines and could not think of potential risks worth mentioning in the paper. ✓ A3. Do the abstract and introduction summarize the paper's main claims? See abstract and the first section (Introduction). ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4.1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? See Appendix D. The exact terms of use and licensing information for the dataset we release will be provided upon its release. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? See Appendix D. The exact terms of use and licensing information for the dataset we intend to release will be provided upon its release. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Section 6 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 5 (we specified the model we used, RoBERTa-large) ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 6 and Appendix B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 6 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix B ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 4.2 And Appendix A ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix A D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Appendix A, in particular A.4 ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not relevant for this annotation task ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not relevant, only two annotators

pei-etal-2023-use

When to Use What: An In-Depth Comparative Empirical Analysis of {O}pen{IE} Systems for Downstream Applications

https://aclanthology.org/2023.acl-long.53

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one{'}s applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.

# When To Use What: An In-Depth Comparative Empirical Analysis Of Openie Systems For Downstream Applications Kevin Peia, Ishan Jindalb, Kevin Chen-Chuan Changa, Chengxiang Zhaia**, Yunyao Li**c aUniversity of Illinois at Urbana-Champaign, bIBM Research, cApple {kspei2,kcchang,czhai}@illinois.edu, [email protected], [email protected] ## Abstract Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use for which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application. ## 1 Introduction Open Information Extraction (OpenIE) is the task of extracting relation tuples from plain text (Angeli et al., 2015). In its simplest form, OpenIE extracts information in the form of tuples consisting of *subject*(S), *predicate*(P), *object*(O), and any additional arguments(A). OpenIE is an open domain, intended to be easy to deploy in different domains without fine-tuning, with all relations extracted regardless of type. The increasing availability of semi-automatically generated training datasets (Cui et al., 2018) as well as significant advances in deep learning techniques have led to the development of state-of-the-art neural models (Cui et al., 2018; Garg and Kalai, 2018). Since its introduction in Etzioni et al. (2008), OpenIE has attracted a large amount of attention by the research community as a tool for a wide range of downstream NLP tasks (Mausam, 2016). However, there is no real consensus on which OpenIE model is best for each application. One example of this lack of consensus in summarization, where different papers use OLLIE (Christensen et al., 2014), | Sentence Bill Gates, former CEO of Microsoft, is a Harvard dropout. OpenIE Extractions (Bill Gates, was, former CEO of Microsoft) (Bill Gates, is, a Harvard dropout) Applications QA Who was former CEO of Microsoft? Where did Bill Gates dropout of? Slot Filling (?, was, former CEO of Microsoft) (?, is, a Harvard dropout) | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Table 1: Sample relation tuples and examples of how different applications use OpenIE extractions. MinIE (Ponza et al., 2018), and Stanford CoreNLP (Cao et al., 2018; Zhang et al., 2021) for extraction. Different applications may also have different requirements.As an example, choosing a model that assumes all relations only have a subject and object may not be suitable for event schema induction since that excludes any event schemas with more than two entities. The papers that introduce new OpenIE models and datasets do not specify how downstream applications would be impacted by the different assumptions those models make about extracted relations. We find that prior OpenIE surveys are also insufficient to find the best OpenIE model for a given application. The only previous application-focused OpenIE survey we found was Mausam (2016). However, this survey does not identify the desired properties of OpenIE for those applications or provide an empirical comparison of OpenIE systems. Glauber and Claro (2018), Claro et al. (2019), and Zhou et al. (2022) also do not provide an empirical application-focused survey. Another obstacle is the lack of apples-to-apples comparisons between OpenIE models. Comparisons should keep the training set, benchmark, and evaluation metric constant to eliminate confounders. Unfortunately, the papers that intro- | Answering | Slot Filling | Event Schema | | | | |----------------------------------|----------------|----------------|---------------------------|----|----| | Question | Induction | Summarization | Knowledge Base Population | | | | HR: Higher Recall | ✓ | ✓ | ✓ | ✓ | ✓ | | HP: Higher Precision | ✓ | ✓ | | | | | N-ary: N-ary Relation Extraction | ✓ | ✓ | ✓ | | | | IN: Inferred Relation Extraction | ✓ | ✓ | ✓ | ✓ | | | FE: Fast Extraction | ✓ | | | | | duce new OpenIE models often do not provide this apples-to-apples comparison. For example, CopyAttention (Cui et al., 2018), SpanOIE (Zhan and Zhao, 2020), IMoJIE (Kolluru et al., 2020b), and OpenIE6 (Kolluru et al., 2020a) all compare their model to models trained on different training sets. OpenIE6 reports performance on the WiRe57 benchmark which Multi2OIE (Ro et al., 2020) does not, but Multi2OIE reports performance on the ReOIE2016 benchmark which OpenIE6 does not. Because the training set can greatly affect the performance of a neural model, we focus on selecting both the appropriate OpenIE model and training set, which we refer to as an *OpenIE System*. To resolve our lack of understanding, we focus on one particular question: How do I choose a particular OpenIE system for a given application? Different implicit assumptions about OpenIE may have a significant impact on the performance of downstream applications such as the assumptions that all relations are verb-based (Zhan and Zhao, 2020) or that all relations have only a subject and object (Kolluru et al., 2020b). To answer this question an apples-to-apples comparison must be conducted for different application settings. Because it is impractical to find the best model for every application given the many possible applications of OpenIE, we instead characterize applications based on what properties they desire from OpenIE such as the desire for N-ary relation extraction by event schema induction. We provide an extensive apples-to-apples comparison of neural OpenIE models such that a practitioner can utilize our practical observations to effectively select a neural OpenIE model and training set for their downstream application. Finally, we apply our recommendations to a downstream Complex QA task. In summary, our contributions are as follows: - We propose a taxonomy that covers OpenIE training sets, benchmarks, and neural models. - We present an extensive empirical comparison of different models on different datasets with recommendations based on the results. - We perform a case study on Complex QA to show the efficacy of our recommendations. To the best of our knowledge, our survey is the only application-focused empirical survey on OpenIE datasets, metrics, and neural OpenIE models. ## 2 Motivating Applications In this section, we identify the properties of OpenIE desired by 5 downstream applications: *Slot Filling*, Question Answering (QA), Summarization, *Event* Schema Induction, and *Knowledge Base Population*. We survey how OpenIE is used and the properties explicitly desired by papers corresponding to the application, either as motivation for choosing a given OpenIE model or within a case study as a property that would improve performance. The desired properties we observe are **Higher** Recall, Higher Precision, N-ary Relation Extraction, **Inferred Relation Extraction**, and **Fast Extraction**. We define an "Inferred Relation" (IN) to be a relation that contains words that are not in the original sentence. For example, given the sentence "Bill Gates, former CEO of Microsoft, is a Harvard dropout", the relation (Bill Gates, was, former CEO of Microsoft) can be inferred even though "was" is not in the original sentence. We define an "N-ary Relation" (*N-ary*) to be a relation with more arguments than just (subject, predicate, object). For example, the relation *(Alice, went, to* the store, today) has an additional argument *today*. Table 2 provides a summary the explicitly desired properties of downstream applications. | Training Sets Test Sets | |---------------------------| | Dataset | Creation Method | Source | #Extractions | #IN | #N-ary | |-----------|-------------------------|-------------------------------|----------------|-------|----------| | SpanOIE | Weak Labeling | Wikipedia | 2,175K | 2K | 231K | | OIE4 | Weak Labeling | Wikipedia | 181K | 3K | 34K | | IMoJIE | Weak Labeling | Wikipedia | 215K | 3K | 0 | | LSOIE | Weak Labeling | QA-SRL 2.0 Wikipedia, Science | 101K | 0 | 32K | | OIE2016 | Weak Labeling | QA-SRL | 1,730 | 359 | 708 | | WiRe57 | Manual Annotation | Wikipedia and Newswire | 343 | 173 | 79 | | ReOIE2016 | Manual Annotation | OIE2016 | 1,508 | 155 | 611 | | CaRB | Crowdsourced Annotation | OIE2016 | 5,263 | 736 | 683 | | LSOIE | Weak Labeling | QA-SRL 2.0 Wikipedia, Science | 22,376 | 0 | 4,920 | Slot Filling Slot Filling is a task where an incomplete tuple must be completed using information from a given corpus (Chen et al., 2019). For example, the incomplete tuple *(Obama, born in, ?)* must be completed as *(Obama, was born in, Honolulu)* using information from the corpus. OpenIE can be used to extract complete tuples which fill slots in an incomplete tuple using entity linking. Soderland et al. (2013), Angeli et al. (2015), Soderland et al. (2015b), and Soderland et al. (2015a) take advantage of how correct relations often appear multiple times to match empty slots to the highest precision OpenIE tuple. They state in their case studies they would benefit from IN extraction and Soderland et al. (2015b) and Soderland et al. (2015a) state they would benefit from *N-ary* extraction. These two properties allow more relation surface forms to be extracted, which increases the chance an incomplete tuple can be linked to a complete tuple. Question Answering We focus on two subtasks of Question Answering (QA) that utilize OpenIE: Open-domain QA (OpenQA) and Complex QA. OpenQA involves answering questions given a large database (Fader et al., 2014a). Complex QA involves using information from multiple sentences to find answers and requires inferring relationships between multiple entities (Chali et al., 2009). Fader et al. (2013, 2014b), Yin et al. (2015), and Clark et al. (2018) are OpenQA methods that use retrieval-based methods to match OpenIE extractions to questions. By rewriting queries into incomplete tuples, such as rewriting "Where was Obama born?" into *(Obama, born in, ?)*, it is possible to use extracted relations to answer queries by filling in the missing slots in the query. For ComplexQA, Khot et al. (2017) and Lu et al. (2019) generate graphs from extracted relation tuples, then reason over these graphs to answer questions. In all QA applications surveyed, high recall (HR) is desired, with Lu et al. (2019) using a custom OpenIE method specifically for higher recall. Yin et al. (2015)'s case studies state that *N-ary* would be beneficial while Lu et al. (2019) uses a custom OpenIE method that supports IN. Summarization OpenIE addresses the problems of redundancy and fact fabrication in summarization. Redundancy is when a fact is repeated multiple times in the summary. To combat redundancy, OpenIE is used to ensure that the generated summary does not have repeated relations (Christensen et al., 2014; Zhang et al., 2021). Fact fabrication is when a fact that is not supported by the text being summarized is in the summary. To combat fact fabrication, OpenIE is used to ensure that the generated summary only contains relations from the original text (Cao et al., 2018; Zhang et al., 2021). In summarization tasks, HR is useful to ensure summaries contain all information, with Ponza et al. (2018) citing greater diversity of extractions as a way to improve performance. high precision (HP) is also desired by Zhang et al. (2021) in order to reduce redundant extractions. Event Schema Induction Event Schema Induction is the automatic discovery of patterns that indicate events, agents, and the agents' roles within that event. Extracted relations can be used to find surface forms of events, with redundant tuples being used to induce event schemas. The open nature of OpenIE allows for events to be found regardless of the domain or surface form. HR is useful for Event Schema Induction for the same reason it is useful for Slot Filling: finding more surface forms allows for more event schemas to be induced (Balasubramanian et al., 2013; Romadhony et al., 2019; Sahnoun et al., 2020). Sahnoun et al. (2020) also specifically desire IN so that more event schemas can be learned, while Balasubramanian et al. (2013) state that *N-ary* would improve performance. Knowledge Base Population The relations extracted by OpenIE can be used to automatically populate knowledge bases (KBs), creating new nodes and edges. Muhammad et al. (2020) and Kroll et al. (2021) use learning-based OpenIE models because of their ability to generalize to unseen relations and achieve HR. Kroll et al. (2021) also explicitly chooses Stanford CoreNLP and OpenIE6 for their fast extraction times (FE). ## 3 Openie Datasets In this section, we discuss the differences between different OpenIE training sets and benchmarks and their shortcomings. We provide statistics about different datasets in Table 3. ## 3.1 Training Datasets Given how data-hungry deep learning models are and how costly it is to manually label OpenIE datasets, most OpenIE training sets are weakly labeled using high confidence extractions from prior OpenIE models. CopyAttention (Cui et al., 2018), **SpanOIE** (Zhan and Zhao, 2020), and **OIE4** (Kolluru et al., 2020b) are training sets consisting of high confidence OpenIE4 extractions from Wikipedia. SpanOIE includes extractions of all confidences unlike CopyAttention and OIE4 which only contain extractions above a certain confidence threshold. The **IMoJIE** dataset (Kolluru et al., 2020b) attempts to get higher quality labels by combining Wikipedia extractions from OpenIE4, ClausIE, and RNNOIE, using a common scoring metric to combine extractions and filter out repeated extractions. The **LSOIE** training set (Solawetz and Larson, 2021) is composed of automatically converted Semantic Role Labeling (SRL) extractions with high inter-annotator agreement from the Wikipedia and Science domain of the crowdsourced QA-SRL Bank 2.0 dataset. Because this dataset is derived from SRL, all relations are assumed to be verbbased and none are inferred. ## Issues With Existing Training Sets Current OpenIE training sets are limited to Wikipedia and Science domains, which may not generalize to certain other domains. Additionally, all OpenIE training sets are weakly labeled, leading to noisy labels which may limit the capabilities of neural OpenIE models. For example, there are instances in LSOIE where the gold relation does not contain a negation it should, resulting in a completely different semantic meaning. It is an open question of how much noise exists within these training sets. ## 3.2 Benchmarks OIE2016 (Stanovsky and Dagan, 2016) is a benchmark for OpenIE automatically derived from the crowdsourced QA-SRL dataset annotated on PropBank and Wikipedia sentences. WiRe57 (Léchelle et al., 2018) consists of expert annotations for 57 sentences. CaRB (Bhardwaj et al., 2019) uses crowdsourcing to re-annotate the sentences in the OIE2016 benchmark. ReOIE2016 (Zhan and Zhao, 2020) uses manual annotation to re-annotate OIE2016 to attempt to resolve problems arising from incorrect extraction. LSOIE (Solawetz and Larson, 2021) has benchmarks derived using the same sources and rules as the training sets. BenchIE (Gashteovski et al., 2021) is derived from CaRB and is based on the idea that extracted relations need to exactly match at least one relation out of a "fact set" of semantically equivalent manually annotated gold standard relations. ## Are Existing Benchmarks Sufficient? Given how the OIE2016 benchmark has been reannotated three times, there is no real consensus on how to annotate OpenIE. For example, CaRB labels prepositions as part of the object and not the predicate, but OIE2016 and ReOIE2016 do not. As a result, it is very difficult for a single model to do well on all benchmarks because each one makes different assumptions. Although there are common principles that guide OpenIE labeling, namely Assertedness, *Minimal Propositions/Atomicity*, and Completeness and Open Lexicon (Stanovsky and Dagan, 2016; Léchelle et al., 2018; Bhardwaj et al., 2019), these principles are vague enough to be interpreted in different ways. ## 4 Evaluation Metrics In this section, we describe the different evaluation metrics used to evaluate OpenIE models and discuss their shortcomings. OIE2016 introduces *lexical matching*, which treats evaluation as a binary classification task. A predicted relation is matched to a gold standard relation if the heads of the predicate and all arguments | Model | Problem Formulation | N-ary | IN | |-----------|-----------------------|---------|------| | SpanOIE | Labeling | ✓ | | | IMoJIE | Generation | | | | Multi2OIE | Labeling | ✓ | | | IGL-OIE | Labeling | ✓ | | | CIGL-OIE | Labeling | ✓ | | | OpenIE6 | Labeling | ✓ | | | DetIE | Labeling | ✓ | | ## Are The Same. WiRe57 and **CaRB** use *word-level matching*, which calculate recall and precision based on the proportion of matching tokens in the predicted and gold standard relations. WiRe57 gives a greater penalty to recall than CaRB if there are fewer predicted relations than gold standard relations. BenchIE uses *sentence-level matching*, which requires an exact match of the predicate and arguments to a relation in the fact set. Because of BenchIE's reliance on fact sets which other benchmarks lack, the BenchIE metric is only compatible with BenchIE and no other metrics can be used with the BenchIE dataset. As a result, an applesto-apples comparison of the BenchIE dataset and metric with other datasets and metrics is not possible, so we do not report performance on BenchIE. ## Is Auc A Useful Metric? When comparing OpenIE systems, we place a greater emphasis on F1 score than AUC. The original implementations of CaRB, OIE2016, and WiRe57 use the trapezoidal rule to calculate AUC which leads to inflated AUC scores for certain systems without low recall points. As a result, we consider the highest F1 score on the PR curve to be a better metric than AUC. ## Are Existing Metrics Sufficient? All existing OpenIE metrics are lexical metrics, and lexical metrics are merely a proxy for comparing the semantic meanings of the predicted relations with the gold standard relations. For instance, existing OpenIE metrics only give small penalties for omitting negations from predicted relations, even though this changes the semantic meaning. This issue can be also observed in lexical metrics used for summarization (Saadany and Orasan, 2021). ## 5 Neural Openie Models In this section, we describe neural OpenIE models and the properties and assumptions they make that set them apart. Neural OpenIE models can be categorized based on how they formulate the OpenIE problem: as a text generation or labeling problem. We provide overviews of the models in Table 4. ## 5.1 Generative Problem Formulation Generative OpenIE models cast OpenIE as a sequence-to-sequence problem, taking the sentence as input and attempting to generate all relations in the sentence as output. The generative models we survey rely on a copy mechanism to copy vocabulary from the original sentence, meaning they can not extract IN relations. CopyAttention (Cui et al., 2018) generates extractions using GloVe embeddings and a 3-layer stacked Long Short-Term Memory (LSTM) as the encoder and decoder. IMoJIE (Kolluru et al., 2020b) builds upon CopyAttention by using BERT embeddings and introducing *iterative extraction* to combat repeated extractions. *Iterative extraction* is repeated extraction from the same sentence with previously extracted relations appended to the end so the model can identify what relations have previously been extracted. ## 5.2 Labeling Problem Formulation Labeling OpenIE models cast OpenIE as a sequence labeling problem, usually using a BIO tagging scheme to label tokens in the sentence. They can be subdivided into Piecewise and Holistic Labeling models. ## 5.2.1 Piecewise Labeling Piecewise labeling models first label predicates and then label arguments for each extracted predicate to extract relation tuples. RnnOIE (Stanovsky et al., 2018) is a bi-directional LSTM (BiLSTM) transducer inspired by SRL that uses BIO tags. SpanOIE (Zhan and Zhao, 2020) is also based on SRL, using a BiLSTM to perform span classification instead of BIO tagging. In span classification, spans of tokens of varying length are classified as parts of the relation instead of individual tokens. Span classification allows for the use of span features, which can be richer than word-level features. Multi2OIE's (Ro et al., 2020) novelty is multihead attention and BERT embeddings. After labeling the predicates, multi-head attention is used between the predicate and the rest of the sentence to label the arguments. MILIE (Kotnis et al., 2021) introduces *iterative* prediction, the process of extracting one argument of the relation tuple at a time, for multilingual OpenIE. Extraction can be performed predicate, subject, or object first, in case other languages benefit from different extraction orders. Uniquely, piecewise labeling models label all predicates in a sentence simultaneously and assume that for each predicate, there is only one set of arguments. This means that they can not extract multiple relations that share the same predicate, unlike generative and holistic labeling models. ## 5.2.2 Holistic Labeling Holistic labeling models label predicates and arguments simultaneously. OpenIE6 (Kolluru et al., 2020a) introduces grid labeling, constraint rules, and conjunction rules. Grid labeling is the simultaneous extraction of multiple relations from a sentence. Constraint rules penalize certain things like repeated extractions or not extracting a relation for a head verb. Conjunction rules split relations containing conjunctions into two separate relations. IGL-OIE is the first stage, using only grid labeling; CIGL-OIE is the second stage, adding in constraint rules; OpenIE6 is the final stage, adding conjunction rules. DetIE (Vasilkovsky et al., 2022) uses ideas from single-shot object detection to make predictions more quickly than previous methods. Labeling models generally can not label tokens that are not in the original sentence, meaning they can not extract IN relations. However, the more recent models IGL-OIE, CIGL-OIE, OpenIE6, and DetIE explicitly add "be", "of", and "from" to the end of sentences to allow for the extraction of inferred relations with those predicates. ## 5.3 Model Hyperparameters The sensitivity to hyperparameters of the models we survey is unclear. Of the works we survey, Multi2OIE and OpenIE6 describe how they perform hyperparameter tuning and provide the hyperparameters they tested. SpanOIE, IMoJIE, and DetIE do not provide details of how they obtained the hyperparameters they use. None of these works provide an in-depth analysis of how the performance was affected by different hyperparameter values. As a result, we perform our own sensitivity analysis using Multi2OIE. The results of this analysis can be found in Appendix B. In our own experiments, we observed only minor increases in performance from changing the hyperparameters in a few cases. On average, the performance changes were negligible. When making recommendations, we consider the performance over many different combinations of model, training, and test set. Minor differences in a handful of cases do not impact our overall conclusions. As a result, we use the default hyperparameters used by Ro et al. (2020) for Multi2OIE. Because other models did not report any particular sensitivity to hyperparameters, we generalize this result to all models we use and use the final set of hyperparameters those authors use. ## 5.4 Existing Model Limitations Models are often developed with specific datasets in mind. Some papers introducing new models also introduce new training sets such as CopyAttention (Cui et al., 2018), SpanOIE (Zhan and Zhao, 2020), and IMoJIE (Kolluru et al., 2020b) which may influence model assumptions. SpanOIE also introduces its own manually annotated benchmark, which may have informed the assumptions SpanOIE makes. The lack of consensus on how to label OpenIE makes it difficult to perform apples-to-apples comparisons because certain models can not extract some relations due to the assumptions they make. OpenIE has also largely been limited to English. MILIE makes assumptions that allow for different extraction methods depending on the language, but other OpenIE models that support multilingual extraction largely treat extraction from other languages the same as extraction from English. Multilingual OpenIE remains an open field of study. ## 6 Experiments In this section, we describe how we compare OpenIE models and datasets for the sake of recommendation. To find the best system for different applications, we test whether the properties of OpenIE models and training sets have a statistically significant effect on accuracy in test sets with corresponding properties.We are also interested in how the choice of model affects efficiency in order to satisfy the fast extraction property (FE). We answer the following questions: R1: How does whether a model supports N-ary relation (*N-ary*) extraction and whether the training set contains *N-ary* affect the F1 score of a model on test sets with or without *N-ary*? R2: How does whether a model supports inferred relation (IN) extraction and whether the training set contains IN affect the F1 score of a model on test sets with or without IN? R3: How does the model type affect efficiency as measured by the number of sentences processed per second (Sen./Sec)? ## 6.1 Experimental Setup Models: We compare SpanOIE, *IMoJIE*, Multi2OIE, the 3 stages of OpenIE6: *IGL-OIE*, CIGL-OIE, and *OpenIE6*, and *DetIE*. For each model, we train them with their paper's original dev set and their original hyperparameters. We run all experiments on a Quadro RTX 5000 GPU. Training Datasets: We train models on the SpanOIE, OIE4, *IMoJIE*, and *LSOIE* training sets. We combine the Science and Wikipedia domain for both the training and benchmark of LSOIE, ensuring there are no duplicate sentences from overlapping sentences in the domains. Due to the input structure of SpanOIE and Multi2OIE, they can not be trained on training datasets with inferred relations. Subsequently, we remove any inferred relations from the training sets of those models. Similarly, as IMoJIE , OpenIE6, and DetIE can not extract N-ary relations, we convert all N-ary relations in the training set into binary relations by moving additional arguments into the object. For instance, the relation (Alice, went, to the store, today) is converted into (Alice, went, to the store today). Inferred and N-ary relations were not removed from the gold standards of the test sets. Benchmarks: We evaluate all the models on the publicly available English benchmarks *OIE2016*, WiRe57, ReOIE2016, *CaRB*, and *LSOIE*. Evaluation Metrics: We use *OIE2016*'s, WiRe57's, and *CaRB*'s metrics for evaluation. We perform student's t-test between OpenIE system, test set, and evaluation metric configurations to answer R1, R2, and R3. For R1 and R2 the t-scores are computed using the per-sentence F1 scores of each method. For R3 the t-scores are computed using the mean sentences per second for each training set and test set combination for a given model. ## 7 Results In this section, we perform an apples-to-apples comparison among different OpenIE systems to determine the SoTA OpenIE model and the best general-purpose OpenIE training dataset. Best OpenIE Model We compare the different models on different evaluation metrics averaged across different training and test sets in Table 5. We observe that across all evaluation metrics Multi2OIE and CIGL-OIE have the highest or second highest F1 score. We also observe that IGLOIE and CIGL-OIE are the most efficient models. Best OpenIE Training Set Because performance on a test set is also greatly dependent on the training set depending on the domain and generation methods, we determine the best training set for each test set. In Table 6, we compare different training and test set combinations with different evaluation metrics averaged across models. We observe that the models trained on LSOIE perform best on the OIE2016 and LSOIE test sets. This is because the LSOIE training set and the OIE2016 and LSOIE test sets are derived from different versions of QA-SRL and generated using the same rules. On the WiRe57, ReOIE2016, and CaRB test sets, we observe that the models trained on the OIE4 and SpanOIE training sets generally perform the best. It is likely because the OIE4 and SpanOIE training sets contain both *N-ary* and IN relations like the WiRe57, ReOIE2016, and CaRB test sets while LSOIE and IMoJIE don't. Of the two models with the highest average CaRB F1 scores, Multi2OIE and CIGL-OIE, Multi2OIE has higher average precision while CIGL-OIE has higher average recall. CIGL-OIE tends to extract longer objects than Multi2OIE as seen in Table 7, which may explain this difference. Overall, OpenIE models have the poorest performance when extracting the object, which may be due to the variance in object length from additional arguments compared to the subject and predicate. ## 7.1 Research Questions To answer our research questions, we perform student's t-test using the CaRB F1 scores of the highest scoring model, training set, and test set combinations for each setting. We perform comparisons of OpenIE systems, where one aspect (model or training set) is changed and the other aspects are kept constant. Then, we choose the test set and evaluation metric for the two settings that results in the highest t-score between methods. For R1, we conclude (1) regardless of training set, the best *N-ary* models perform better than the best non-*N-ary* models; (2) regardless of the model, training on the best *N-ary* training sets results in higher performance than training on the best non- | Model | Sen./Sec. | CaRB | WiRe 57 | | | | | |-----------|-------------|--------|-----------|-------|-------|-------|-------| | P | R | F1 | P | R | F1 | | | | SpanOIE | 13.40 | 0.474 | 0.464 | 0.433 | 0.474 | 0.374 | 0.375 | | IMoJIE | 2.07 | 0.598 | 0.431 | 0.488 | 0.598 | 0.355 | 0.428 | | Multi2OIE | 29.22 | 0.626 | 0.501 | 0.552 | 0.624 | 0.419 | 0.488 | | IGL-OIE | 84.07 | 0.574 | 0.442 | 0.497 | 0.574 | 0.365 | 0.434 | | CIGL-OIE | 68.80 | 0.490 | 0.531 | 0.503 | 0.489 | 0.429 | 0.442 | | OpenIE6 | 28.36 | 0.394 | 0.518 | 0.438 | 0.394 | 0.463 | 0.413 | | DetIE | 29.06 | 0.603 | 0.436 | 0.502 | 0.603 | 0.353 | 0.435 | | Training Set | Test Set | CaRB | WiRe 57 | | | | | |----------------|-----------------------|-------------------|-------------------|-------|-------------------|-------|-------| | P | R | F1 | P | R | F1 | | | | SpanOIE | OIE2016 | 0.495 | 0.491 | 0.478 | 0.493 | 0.410 | 0.433 | | OIE4 | OIE2016 | 0.541 | 0.487 | 0.510 | 0.540 | 0.404 | 0.458 | | LSOIE | OIE2016 | 0.629 | 0.537 | 0.569 | 0.629 | 0.443 | 0.509 | | IMoJIE | OIE2016 | 0.469 | 0.433 | 0.424 | 0.468 | 0.363 | 0.381 | | SpanOIE | WiRe57 | 0.420 | 0.372 | 0.386 | 0.423 | 0.199 | 0.263 | | OIE4 | WiRe57 | 0.473 | 0.378 | 0.420 | 0.472 0.211 0.290 | | | | LSOIE | WiRe57 | 0.355 | 0.210 | 0.261 | 0.355 | 0.127 | 0.184 | | IMoJIE | WiRe57 | 0.436 | 0.364 | 0.378 | 0.434 0.215 0.264 | | | | SpanOIE | ReOIE2016 | 0.650 0.625 | 0.618 0.650 0.612 | 0.612 | | | | | OIE4 | ReOIE2016 0.725 0.568 | 0.606 0.725 0.555 | 0.599 | | | | | | LSOIE | ReOIE2016 | 0.632 | 0.525 | 0.562 | 0.632 | 0.513 | 0.555 | | IMoJIE | ReOIE2016 | 0.620 | 0.570 | 0.560 | 0.619 | 0.551 | 0.548 | | SpanOIE | CaRB | 0.539 | 0.440 | 0.472 | 0.535 | 0.306 | 0.377 | | OIE4 | CaRB | 0.606 | 0.446 | 0.512 | 0.606 | 0.311 | 0.408 | | LSOIE | CaRB | 0.539 | 0.344 | 0.415 | 0.539 | 0.252 | 0.337 | | IMoJIE | CaRB | 0.539 | 0.414 | 0.446 | 0.536 | 0.300 | 0.354 | | SpanOIE | LSOIE | 0.470 | 0.561 | 0.501 | 0.470 | 0.516 | 0.479 | | OIE4 | LSOIE | 0.505 | 0.558 | 0.529 | 0.505 | 0.512 | 0.505 | | LSOIE | LSOIE | 0.658 | 0.676 | 0.659 | 0.658 | 0.622 | 0.629 | | IMoJIE | LSOIE | 0.441 | 0.492 | 0.444 | 0.441 | 0.460 | 0.431 | N-ary training sets. Therefore **if an application** benefits from *N-ary*, then the best OpenIE system should include either a *N-ary* model, *N-ary* training set, or both, with both being preferred. For R2, we conclude that (1) IN models are better than non-IN models when there is either a IN training and IN test set, or a non-IN training and non-IN test set; (2) IN training sets are better than non-IN training sets when there is an IN model and IN test set. Therefore **if an application benefits** from IN**, then the chosen training set and model** should either both be IN **or both be non-**IN. For R3, we compare the efficiency of the sole generative model, IMoJIE, to the efficiency of every other model. We observe that every other model is faster than IMoJIE and the difference is statistically significant. This matches expectations, since it has been previously shown that IMoJIE is slower than other OpenIE models (Kolluru et al., 2020a).Therefore **if an application is concerned** | Sentence | According to the 2010 census, the population of the town is 2,310. | |------------|-----------------------------------------------------------------------| | Multi2OIE | (the population of the town; is; 2,310) | | CIGL-OIE | (the population of the town; is; According to the 2010 census, 2,310) | ## About Efficiency, Then The Chosen Openie Model Should Not Be A Generative Model. 8 A Case Study: Complex Qa To verify our recommendations, we perform a case study using QUEST (Lu et al., 2019), a Complex QA method that uses OpenIE to extract entities and predicates from the question and from documents to generate knowledge graphs. The nodes are entities derived from subjects and objects, while the edges are predicates. The knowledge graph is matched to the entities in the question and traversed to find potential answers. Because more extractions result in a larger knowledge graph, QUEST benefits from HR which the authors use their own rule-based OpenIE method to achieve. ## 8.1 Experimental Setup To test our recommendations, we replace the OpenIE method used by the authors with Multi2OIE trained on SpanOIE, CIGL-OIE trained on OIE4, and OpenIE6 trained on OIE4. We chose these models and training sets because they have the highest overall CaRB recall and F1 scores. One caveat is that in order for QUEST to connect entities from multiple sentences, they must have the same surface form. Because OpenIE methods often extract long subjects and objects that include adjectives and modifiers, if the subject or object of an extraction contains entities extracted by QUEST, | OpenIE | Questions | Documents | MRR | P@1 | Hit@5 | |-----------|-------------|-------------|-------|-------|---------| | QUEST | CQ-W | Top 10 | 0.132 | 0.080 | 0.167 | | CIGL-OIE | CQ-W | Top 10 | 0.111 | 0.060 | 0.167 | | OpenIE6 | CQ-W | Top 10 | 0.104 | 0.060 | 0.147 | | Multi2OIE | CQ-W | Top 10 | 0.094 | 0.053 | 0.140 | we add additional relations using those entities. For example, in the sentence "Hector Elizondo was nominated for a Golden Globe for his role in Pretty Woman," QUEST may extract the entities "Hector Elizondo," "Golden Globe," and "Pretty Woman." If an OpenIE method were to extract the triple ("Hector Elizondo", "was nominated", "for a Golden Globe for his role in Pretty Woman"), we would add the additional extractions *("Hector Elizondo", "was nominated", "Golden Globe")* and *("Hector Elizondo", "was nominated", "Pretty* Woman"). QUEST also replaces pronouns with the entities they refer to because nodes in the knowledge graph can not be made using pronouns.We replace pronouns using the same method QUEST does before running any OpenIE method. We run QUEST using the CQ-W question set and search for answers in the Top-10 Google document set used in their paper. Because CIGL-OIE has the highest CaRB recall and OpenIE6 has the highest WiRe57 recall, we expect that using either of them will result in higher downstream performance than using Multi2OIE. ## 8.2 Evaluation We compare the Mean Reciprocal Rank (MRR), Precision@1 (P@1), and Hit@5 for each OpenIE model. The results of our case study are summarized in Table 8. We observe higher performance of CIGL-OIE and OpenIE6 than Multi2OIE on QUEST, which matches our expectations based on the higher recall of CIGL-OIE and OpenIE6 and the desired property of HR but not HP for QA. Our case study demonstrates the applicability of our empirical study to the use of OpenIE methods in downstream applications. An important note is that oftentimes a great deal of pre- and post-processing is necessary to adapt OpenIE for different downstream applications. Removing pronouns and adding additional entitybased extractions was necessary to achieve reasonable performance in QUEST. Even after modifying Multi2OIE, CIGL-OIE, and OpenIE6 in this way, their performance is less than the original performance of QUEST. As a result, it is important to not just consider the performance and properties of OpenIE models, but also how to adapt models to their specific needs. ## 9 Challenges And Future Directions Even with the introduction of neural models, OpenIE systems still have significant room for improvement. In Table 2 we state that canonicalizing extractions is desired by QA while extracting from imperative sentences is desired by both QA and summarization, but no existing model or dataset addresses these properties. In sections 3.1 and 3.2 we note the lack of consensus on how to label OpenIE and the issues with weak labeling. Existing metrics also have issues with semantic meaning as discussed in section 4, which is exacerbated by errors caused by weak labeling. The lack of consensus in how to label OpenIE relations results in a diverse set of models as we discuss in section 5.4. The different assumptions these models make are also largely constrained to English syntax, leaving future work in multilingual OpenIE open. ## 10 Conclusion In this paper, we presented an application-focused empirical comparison of recent neural OpenIE models, training sets, and benchmarks. Our experiments showed that the different properties of OpenIE models and datasets affect the performance, meaning it is important to choose the appropriate system for a given application and not just choose whatever model is state-of-the-art. We hope that this survey helps users identify the best OpenIE system for their downstream applications and inspires new OpenIE research into addressing the properties desired by downstream applications. ## Limitations Although this work aims to be as comprehensive as possible, there are several limitations to this paper. Our comparisons only consider neural OpenIE models despite rule-based methods being very popular among downstream applications. This is because of the lack of recent surveys on neural OpenIE methods and the difficulties we personally encountered when trying to determine which OpenIE method was state-of-the-art. We acknowledge that there are many cases where rule-based methods may be preferable to neural models due to being faster or more tailor-made for a specific application. However, we feel that focusing on neural OpenIE methods is not a detriment because we are interested in which methods work best "out of the box". Based on the results reported in these neural OpenIE papers, we believe they are currently the best out-of-the-box OpenIE models using the metrics we report in this paper on the test sets covered in this paper. The corpora we chose are all limited to English. As a result, our results are not generalizable to any downstream task that relies on different languages. In our experiments, we do not report results for the BenchIE test set or using the BenchIE metric. This is because the BenchIE test set uniquely can only be evaluated using the BenchIE metric, and the BenchIE metric can only be applied to the BenchIE test set. We do not feel that its exclusion hurts our final conclusions about the relative performance of OpenIE methods. We perform a case study using Complex QA only, which we generalize to other applications. For our case study, we were unable to replicate the results reported in the original QUEST paper (Lu et al., 2019). We have been in correspondence with the authors to address this issue, but we still feel that our results are valid given that we use the publicly available code and data and adapted it to use our OpenIE methods to the best of our ability. Similarly, we report different results to the efficiency and performance of DetIE reported in the original paper (Vasilkovsky et al., 2022). We have been in contact with the original authors and differences in efficiency can be attributed to differing hardware while differences in performance can be attributed to different preprocessing of training and test sets. For instance, the authors of DetIE do not remove duplicate sentences when combining the Science and Wiki domains of LSOIE. We do not make specific observations based on the different evaluation metrics, mainly focusing on CaRB and WiRe57 F1 score for our evaluation. We give our experimental results within appendix A so that future researchers can make observations and draw conclusions based on OIE2016. ## Ethics Statement We did not create any of the models, datasets, or applications covered in this paper. Any ethical issues with the preexisting OpenIE datasets we use ## References Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D Manning. 2015. Leveraging linguistic structure for open domain information extraction. 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A survey on neural open information extraction: Current status and future directions. *arXiv preprint arXiv:2205.11725*. ## A Empirical Results Model Performance In this section, we report the empirical results of training each model on a variety of training sets and evaluating them on a variety of test sets with different evaluation metrics. Sen./Sec. refers to the number of sentences that could be processed per second, which we use to compare the efficiency of different models. We report Precision (P), Recall (R), F1 Score (F1), and Area Under the Curve (AUC) for the OIE2016, WiRe57, and CaRB metrics. We make observations using these results in Section 7. Table 9 shows the performance of different OpenIE models trained on different training sets on the OIE2016 benchmark. Table 10 shows performance on WiRe57. Table 11 shows performance on ReOIE2016. Table 12 shows performance on CaRB. Table 13 shows performance on LSOIE. Research Questions We also report the empirical results of our student's t-tests comparing different OpenIE systems, which we use to answer the research questions we raise in section 6. For each research question, we report the number of statistical significance tests that had a t-score above or below 0 and had a p-value above or below 0.05. We use these results to answer those research questions in section 7.1. Table 14 shows the results of the statistical significance tests used to answer R1 from section 6. Table 15 shows results for R2. Table 16 shows results for R3. Model Training set Test set Sen./Sec OIE2016 WiRe57 CaRB P R F1 AUC P R F1 AUC P R F1 AUC SpanOIE SpanOIE OIE2016 16.65 0.704 0.792 0.745 0.675 0.576 0.376 0.455 0.296 0.576 0.459 0.511 0.362 IMoJIE SpanOIE OIE2016 2.61 0.755 0.851 0.8 0.614 0.575 0.389 0.464 0.212 0.575 0.466 0.515 0.253 Multi2OIE SpanOIE OIE2016 28.21 0.724 0.915 0.809 0.719 0.558 0.439 0.491 0.29 0.566 0.521 0.542 0.348 IGL-OIE SpanOIE OIE2016 67.55 0.733 0.768 0.75 0.585 0.551 0.347 0.426 0.211 0.551 0.419 0.476 0.253 CIGL-OIE SpanOIE OIE2016 50.61 0.711 0.981 0.824 0.737 0.375 0.474 0.419 0.212 0.375 0.592 0.459 0.263 OpenIE6 SpanOIE OIE2016 38.38 0.519 0.975 0.678 0.532 0.269 0.492 0.348 0.177 0.269 0.556 0.362 0.2 DetIE SpanOIE OIE2016 26.42 0.775 0.787 0.781 0.699 0.55 0.351 0.429 0.272 0.55 0.423 0.478 0.328 SpanOIE OIE4 OIE2016 16.19 0.703 0.813 0.754 0.692 0.584 0.37 0.453 0.293 0.584 0.454 0.511 0.36 IMoJIE OIE4 OIE2016 3.44 0.695 0.824 0.754 0.495 0.553 0.399 0.464 0.196 0.553 0.474 0.51 0.231 Multi2OIE OIE4 OIE2016 31.14 0.747 0.864 0.801 0.72 0.595 0.4 0.478 0.261 0.597 0.491 0.539 0.32 IGL-OIE OIE4 OIE2016 70.02 0.718 0.84 0.774 0.661 0.544 0.39 0.455 0.257 0.544 0.48 0.51 0.313 CIGL-OIE OIE4 OIE2016 49.26 0.718 0.92 0.806 0.726 0.529 0.436 0.478 0.289 0.529 0.537 0.533 0.356 OpenIE6 OIE4 OIE2016 24.20 0.557 0.922 0.694 0.615 0.413 0.467 0.438 0.278 0.415 0.523 0.463 0.314 DetIE OIE4 OIE2016 26.29 0.787 0.855 0.82 0.764 0.563 0.366 0.443 0.286 0.563 0.453 0.502 0.354 SpanOIE LSOIE OIE2016 15.36 0.657 0.804 0.723 0.666 0.657 0.432 0.521 0.358 0.657 0.521 0.581 0.432 IMoJIE LSOIE OIE2016 1.00 0.852 0.766 0.807 0.577 0.719 0.339 0.461 0.216 0.719 0.411 0.523 0.261 Multi2OIE LSOIE OIE2016 31.00 0.758 0.894 0.821 0.767 0.728 0.484 0.582 0.401 0.728 0.585 0.649 0.483 IGL-OIE LSOIE OIE2016 68.27 0.762 0.823 0.791 0.634 0.636 0.394 0.487 0.27 0.636 0.485 0.551 0.331 CIGL-OIE LSOIE OIE2016 52.40 0.74 0.947 0.831 0.738 0.568 0.494 0.528 0.314 0.568 0.618 0.592 0.391 OpenIE6 LSOIE OIE2016 24.56 0.542 0.924 0.683 0.563 0.41 0.541 0.466 0.279 0.41 0.609 0.49 0.315 DetIE LSOIE OIE2016 26.16 0.857 0.879 0.868 0.816 0.687 0.419 0.521 0.354 0.687 0.528 0.597 0.445 SpanOIE IMoJIE OIE2016 7.16 0.188 0.975 0.316 0.579 0.084 0.394 0.138 0.213 0.084 0.428 0.14 0.232 IMoJIE IMoJIE OIE2016 1.68 0.779 0.905 0.837 0.607 0.551 0.381 0.451 0.191 0.551 0.451 0.496 0.225 Multi2OIE IMoJIE OIE2016 31.58 0.764 0.842 0.801 0.739 0.596 0.378 0.463 0.252 0.599 0.453 0.516 0.302 IGL-OIE IMoJIE OIE2016 63.00 0.775 0.797 0.786 0.592 0.545 0.323 0.406 0.194 0.545 0.396 0.459 0.238 CIGL-OIE IMoJIE OIE2016 49.62 0.775 0.928 0.845 0.69 0.509 0.375 0.432 0.21 0.509 0.482 0.495 0.269 OpenIE6 IMoJIE OIE2016 36.42 0.582 0.91 0.71 0.511 0.386 0.416 0.4 0.184 0.386 0.484 0.43 0.215 DetIE IMoJIE OIE2016 26.75 0.856 0.709 0.775 0.658 0.606 0.275 0.379 0.221 0.606 0.337 0.433 0.271 Table 9: A table that lists performance of different OpenIE systems on the OIE2016 benchmark. Model Training set Test set Sen./Sec OIE2016 WiRe57 CaRB P R F1 AUC P R F1 AUC P R F1 AUC SpanOIE SpanOIE WiRe57 9.10 0.87 0.72 0.788 0.673 0.464 0.194 0.274 0.142 0.464 0.372 0.413 0.272 IMoJIE SpanOIE WiRe57 0.91 0.863 0.644 0.738 0.465 0.461 0.154 0.231 0.061 0.461 0.313 0.373 0.123 Multi2OIE SpanOIE WiRe57 23.17 0.9 0.758 0.823 0.698 0.498 0.203 0.288 0.097 0.498 0.391 0.438 0.186 IGL-OIE SpanOIE WiRe57 9.34 0.916 0.638 0.753 0.604 0.482 0.167 0.248 0.097 0.482 0.333 0.394 0.189 CIGL-OIE SpanOIE WiRe57 7.75 0.889 0.84 0.864 0.77 0.281 0.195 0.231 0.069 0.283 0.406 0.333 0.145 OpenIE6 SpanOIE WiRe57 4.93 0.74 0.831 0.783 0.641 0.304 0.28 0.291 0.127 0.28 0.408 0.332 0.167 DetIE SpanOIE WiRe57 27.16 0.948 0.743 0.833 0.724 0.47 0.197 0.278 0.145 0.47 0.381 0.421 0.28 SpanOIE OIE4 WiRe57 9.07 0.895 0.743 0.812 0.704 0.526 0.217 0.307 0.166 0.526 0.397 0.453 0.303 IMoJIE OIE4 WiRe57 1.19 0.823 0.665 0.735 0.433 0.414 0.189 0.26 0.059 0.414 0.35 0.379 0.109 Multi2OIE OIE4 WiRe57 19.65 0.921 0.717 0.807 0.67 0.537 0.197 0.289 0.104 0.537 0.37 0.439 0.194 IGL-OIE OIE4 WiRe57 8.19 0.931 0.673 0.782 0.653 0.452 0.174 0.251 0.111 0.457 0.337 0.388 0.22 CIGL-OIE OIE4 WiRe57 6.82 0.9 0.787 0.84 0.742 0.436 0.196 0.27 0.123 0.436 0.391 0.413 0.247 OpenIE6 OIE4 WiRe57 3.47 0.799 0.755 0.777 0.662 0.451 0.295 0.357 0.192 0.451 0.397 0.423 0.261 DetIE OIE4 WiRe57 27.02 0.929 0.843 0.884 0.813 0.491 0.209 0.293 0.156 0.491 0.405 0.444 0.302 SpanOIE LSOIE WiRe57 8.52 0.759 0.534 0.627 0.469 0.357 0.135 0.196 0.092 0.357 0.209 0.263 0.142 IMoJIE LSOIE WiRe57 0.46 0.961 0.574 0.719 0.534 0.351 0.094 0.148 0.026 0.351 0.182 0.24 0.052 Multi2OIE LSOIE WiRe57 18.31 0.851 0.534 0.656 0.485 0.44 0.128 0.198 0.067 0.44 0.202 0.276 0.106 IGL-OIE LSOIE WiRe57 9.54 0.92 0.571 0.705 0.549 0.32 0.099 0.151 0.034 0.32 0.183 0.233 0.063 CIGL-OIE LSOIE WiRe57 7.65 0.933 0.694 0.796 0.671 0.301 0.114 0.165 0.044 0.301 0.223 0.256 0.082 OpenIE6 LSOIE WiRe57 3.81 0.766 0.688 0.725 0.554 0.311 0.194 0.239 0.086 0.311 0.247 0.275 0.114 DetIE LSOIE WiRe57 27.02 0.916 0.571 0.704 0.547 0.403 0.124 0.19 0.087 0.403 0.223 0.287 0.157 SpanOIE IMoJIE WiRe57 7.33 0.303 0.898 0.454 0.585 0.087 0.274 0.133 0.149 0.087 0.364 0.141 0.198 IMoJIE IMoJIE WiRe57 1.17 0.911 0.778 0.84 0.622 0.517 0.224 0.313 0.116 0.517 0.404 0.454 0.207 Multi2OIE IMoJIE WiRe57 24.83 0.9 0.706 0.791 0.692 0.539 0.195 0.287 0.12 0.539 0.373 0.44 0.228 IGL-OIE IMoJIE WiRe57 10.36 0.934 0.7 0.8 0.65 0.48 0.157 0.236 0.08 0.485 0.291 0.364 0.144 CIGL-OIE IMoJIE WiRe57 7.83 0.926 0.799 0.858 0.744 0.44 0.196 0.271 0.099 0.44 0.395 0.417 0.197 OpenIE6 IMoJIE WiRe57 5.76 0.802 0.781 0.792 0.648 0.452 0.292 0.355 0.144 0.459 0.393 0.424 0.2 DetIE IMoJIE WiRe57 27.71 0.965 0.65 0.777 0.639 0.526 0.165 0.251 0.126 0.526 0.328 0.404 0.25 Table 10: A table that lists performance of different OpenIE systems on the WiRe57 benchmark. | Model | Training set | Test set | Sen./Sec | OIE2016 | WiRe57 | CaRB | | | | | | | | | | |-----------|----------------|------------|------------|-----------|----------|--------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | P | R | F1 | AUC | P | R | F1 | AUC | P | R | F1 | AUC | | | | | | SpanOIE | SpanOIE | ReOIE2016 | 16.87 | 0.741 | 0.842 | 0.788 | 0.733 | 0.772 | 0.595 | 0.672 | 0.527 | 0.772 | 0.61 | 0.681 | 0.54 | | IMoJIE | SpanOIE | ReOIE2016 | 2.71 | 0.773 | 0.84 | 0.805 | 0.627 | 0.785 | 0.601 | 0.681 | 0.456 | 0.785 | 0.607 | 0.684 | 0.46 | | Multi2OIE | SpanOIE | ReOIE2016 | 27.70 | 0.737 | 0.932 | 0.823 | 0.753 | 0.749 | 0.688 | 0.717 | 0.586 | 0.749 | 0.698 | 0.723 | 0.596 | | IGL-OIE | SpanOIE | ReOIE2016 | 67.16 | 0.762 | 0.784 | 0.773 | 0.653 | 0.756 | 0.557 | 0.641 | 0.455 | 0.756 | 0.569 | 0.649 | 0.465 | | CIGL-OIE | SpanOIE | ReOIE2016 | 49.33 | 0.688 | 0.991 | 0.812 | 0.733 | 0.437 | 0.663 | 0.527 | 0.35 | 0.437 | 0.69 | 0.535 | 0.365 | | OpenIE6 | SpanOIE | ReOIE2016 | 37.80 | 0.498 | 0.988 | 0.662 | 0.532 | 0.314 | 0.628 | 0.419 | 0.268 | 0.314 | 0.636 | 0.42 | 0.272 | | DetIE | SpanOIE | ReOIE2016 | 26.63 | 0.802 | 0.801 | 0.802 | 0.722 | 0.734 | 0.55 | 0.629 | 0.477 | 0.734 | 0.562 | 0.636 | 0.487 | | SpanOIE | OIE4 | ReOIE2016 | 16.72 | 0.729 | 0.839 | 0.78 | 0.726 | 0.815 | 0.604 | 0.694 | 0.548 | 0.815 | 0.617 | 0.702 | 0.56 | | IMoJIE | OIE4 | ReOIE2016 | 3.00 | 0.75 | 0.155 | 0.257 | 0.095 | 0.756 | 0.119 | 0.205 | 0.075 | 0.756 | 0.119 | 0.206 | 0.075 | | Multi2OIE | OIE4 | ReOIE2016 | 27.74 | 0.773 | 0.869 | 0.818 | 0.746 | 0.813 | 0.635 | 0.713 | 0.55 | 0.813 | 0.647 | 0.72 | 0.561 | | IGL-OIE | OIE4 | ReOIE2016 | 64.23 | 0.751 | 0.877 | 0.809 | 0.72 | 0.732 | 0.615 | 0.668 | 0.52 | 0.732 | 0.629 | 0.677 | 0.531 | | CIGL-OIE | OIE4 | ReOIE2016 | 51.78 | 0.74 | 0.948 | 0.831 | 0.776 | 0.698 | 0.675 | 0.686 | 0.564 | 0.698 | 0.697 | 0.698 | 0.582 | | OpenIE6 | OIE4 | ReOIE2016 | 23.30 | 0.559 | 0.938 | 0.701 | 0.642 | 0.506 | 0.671 | 0.577 | 0.467 | 0.506 | 0.679 | 0.58 | 0.472 | | DetIE | OIE4 | ReOIE2016 | 26.36 | 0.798 | 0.858 | 0.827 | 0.771 | 0.757 | 0.569 | 0.65 | 0.5 | 0.757 | 0.587 | 0.662 | 0.516 | | SpanOIE | LSOIE | ReOIE2016 | 16.33 | 0.65 | 0.814 | 0.723 | 0.672 | 0.69 | 0.53 | 0.6 | 0.448 | 0.69 | 0.536 | 0.603 | 0.453 | | IMoJIE | LSOIE | ReOIE2016 | 1.03 | 0.836 | 0.726 | 0.778 | 0.525 | 0.747 | 0.409 | 0.529 | 0.279 | 0.747 | 0.414 | 0.533 | 0.283 | | Multi2OIE | LSOIE | ReOIE2016 | 31.24 | 0.759 | 0.845 | 0.8 | 0.736 | 0.746 | 0.582 | 0.654 | 0.49 | 0.746 | 0.586 | 0.657 | 0.495 | | IGL-OIE | LSOIE | ReOIE2016 | 69.48 | 0.742 | 0.786 | 0.763 | 0.602 | 0.626 | 0.453 | 0.525 | 0.312 | 0.626 | 0.472 | 0.538 | 0.325 | | CIGL-OIE | LSOIE | ReOIE2016 | 53.49 | 0.715 | 0.93 | 0.808 | 0.716 | 0.548 | 0.559 | 0.553 | 0.351 | 0.548 | 0.582 | 0.564 | 0.365 | | OpenIE6 | LSOIE | ReOIE2016 | 24.94 | 0.518 | 0.924 | 0.664 | 0.53 | 0.374 | 0.562 | 0.45 | 0.275 | 0.374 | 0.574 | 0.453 | 0.281 | | DetIE | LSOIE | ReOIE2016 | 27.39 | 0.847 | 0.85 | 0.848 | 0.785 | 0.692 | 0.493 | 0.575 | 0.417 | 0.692 | 0.513 | 0.589 | 0.434 | | SpanOIE | IMoJIE | ReOIE2016 | 7.36 | 0.175 | 0.993 | 0.298 | 0.584 | 0.099 | 0.527 | 0.166 | 0.289 | 0.099 | 0.535 | 0.167 | 0.294 | | IMoJIE | IMoJIE | ReOIE2016 | 1.84 | 0.802 | 0.947 | 0.868 | 0.65 | 0.713 | 0.592 | 0.647 | 0.388 | 0.713 | 0.603 | 0.653 | 0.395 | | Multi2OIE | IMoJIE | ReOIE2016 | 30.72 | 0.794 | 0.863 | 0.827 | 0.793 | 0.812 | 0.606 | 0.694 | 0.534 | 0.817 | 0.614 | 0.701 | 0.542 | | IGL-OIE | IMoJIE | ReOIE2016 | 68.80 | 0.799 | 0.817 | 0.808 | 0.644 | 0.728 | 0.508 | 0.599 | 0.403 | 0.728 | 0.53 | 0.614 | 0.42 | | CIGL-OIE | IMoJIE | ReOIE2016 | 49.48 | 0.796 | 0.919 | 0.853 | 0.723 | 0.671 | 0.579 | 0.621 | 0.431 | 0.674 | 0.622 | 0.647 | 0.464 | | OpenIE6 | IMoJIE | ReOIE2016 | 40.95 | 0.584 | 0.925 | 0.716 | 0.514 | 0.483 | 0.601 | 0.535 | 0.33 | 0.483 | 0.623 | 0.544 | 0.342 | | DetIE | IMoJIE | ReOIE2016 | 26.83 | 0.905 | 0.717 | 0.8 | 0.683 | 0.829 | 0.442 | 0.577 | 0.404 | 0.829 | 0.46 | 0.592 | 0.421 | Table 11: A table that lists performance of different OpenIE systems on the ReOIE2016 benchmark. Model Training set Test set Sen./Sec OIE2016 WiRe57 CaRB P R F1 AUC P R F1 AUC P R F1 AUC SpanOIE SpanOIE CaRB 17.14 0.81 0.778 0.794 0.704 0.609 0.273 0.377 0.219 0.609 0.403 0.485 0.324 IMoJIE SpanOIE CaRB 3.12 0.836 0.794 0.814 0.639 0.629 0.283 0.39 0.17 0.629 0.416 0.5 0.25 Multi2OIE SpanOIE CaRB 22.39 0.826 0.878 0.851 0.793 0.59 0.315 0.411 0.22 0.609 0.458 0.523 0.326 IGL-OIE SpanOIE CaRB 69.67 0.831 0.771 0.8 0.672 0.611 0.267 0.371 0.184 0.611 0.399 0.483 0.275 CIGL-OIE SpanOIE CaRB 52.62 0.789 0.986 0.876 0.818 0.379 0.331 0.354 0.148 0.379 0.508 0.434 0.228 OpenIE6 SpanOIE CaRB 41.02 0.643 0.981 0.777 0.671 0.335 0.406 0.367 0.181 0.338 0.489 0.399 0.223 DetIE SpanOIE CaRB 25.79 0.866 0.788 0.825 0.735 0.595 0.266 0.368 0.212 0.595 0.406 0.483 0.324 SpanOIE OIE4 CaRB 16.92 0.804 0.777 0.79 0.701 0.646 0.28 0.39 0.23 0.646 0.413 0.503 0.339 IMoJIE OIE4 CaRB 3.83 0.804 0.816 0.81 0.572 0.624 0.304 0.408 0.17 0.624 0.442 0.517 0.247 Multi2OIE OIE4 CaRB 33.37 0.838 0.831 0.835 0.761 0.647 0.298 0.408 0.213 0.647 0.442 0.525 0.317 IGL-OIE OIE4 CaRB 72.82 0.82 0.834 0.827 0.734 0.607 0.298 0.399 0.219 0.607 0.438 0.509 0.323 CIGL-OIE OIE4 CaRB 58.49 0.814 0.908 0.858 0.796 0.584 0.326 0.418 0.237 0.584 0.479 0.526 0.35 OpenIE6 OIE4 CaRB 24.93 0.685 0.903 0.779 0.716 0.518 0.395 0.448 0.281 0.518 0.482 0.499 0.346 DetIE OIE4 CaRB 26.28 0.862 0.843 0.852 0.785 0.614 0.277 0.382 0.223 0.614 0.425 0.502 0.343 SpanOIE LSOIE CaRB 16.59 0.741 0.731 0.736 0.636 0.561 0.244 0.34 0.191 0.561 0.334 0.418 0.26 IMoJIE LSOIE CaRB 1.05 0.896 0.702 0.788 0.569 0.615 0.195 0.296 0.109 0.615 0.281 0.386 0.157 Multi2OIE LSOIE CaRB 33.89 0.818 0.81 0.814 0.738 0.611 0.267 0.372 0.189 0.611 0.369 0.461 0.262 IGL-OIE LSOIE CaRB 67.65 0.825 0.743 0.782 0.616 0.529 0.215 0.305 0.127 0.529 0.304 0.386 0.178 CIGL-OIE LSOIE CaRB 49.70 0.814 0.897 0.853 0.753 0.475 0.273 0.346 0.149 0.475 0.386 0.426 0.21 OpenIE6 LSOIE CaRB 28.14 0.667 0.898 0.766 0.627 0.403 0.333 0.365 0.168 0.403 0.389 0.396 0.198 DetIE LSOIE CaRB 26.27 0.904 0.8 0.849 0.762 0.578 0.234 0.334 0.185 0.578 0.343 0.43 0.27 SpanOIE IMoJIE CaRB 7.41 0.265 0.979 0.417 0.619 0.131 0.4 0.198 0.226 0.131 0.438 0.202 0.248 IMoJIE IMoJIE CaRB 1.77 0.863 0.914 0.888 0.696 0.633 0.306 0.413 0.179 0.633 0.457 0.531 0.266 Multi2OIE IMoJIE CaRB 31.22 0.848 0.813 0.83 0.771 0.645 0.28 0.39 0.201 0.648 0.418 0.508 0.301 IGL-OIE IMoJIE CaRB 73.88 0.865 0.803 0.833 0.681 0.615 0.252 0.357 0.165 0.615 0.384 0.473 0.252 CIGL-OIE IMoJIE CaRB 55.01 0.855 0.909 0.881 0.768 0.563 0.286 0.379 0.178 0.574 0.437 0.496 0.274 OpenIE6 IMoJIE CaRB 37.82 0.715 0.898 0.796 0.633 0.498 0.365 0.421 0.204 0.503 0.44 0.47 0.252 DetIE IMoJIE CaRB 27.16 0.932 0.69 0.793 0.667 0.67 0.21 0.32 0.175 0.67 0.327 0.439 0.273 Table 12: A table that lists performance of different OpenIE systems on the CaRB benchmark. | Model | Training set | Test set | Sen./Sec | OIE2016 | WiRe57 | CaRB | | | | | | | | | | |-----------|----------------|------------|------------|-----------|----------|--------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | P | R | F1 | AUC | P | R | F1 | AUC | P | R | F1 | AUC | | | | | | SpanOIE | SpanOIE | LSOIE | 18.56 | 0.745 | 0.851 | 0.794 | 0.742 | 0.537 | 0.388 | 0.451 | 0.298 | 0.537 | 0.551 | 0.544 | 0.423 | | IMoJIE | SpanOIE | LSOIE | 2.92 | 0.631 | 0.866 | 0.73 | 0.499 | 0.53 | 0.516 | 0.523 | 0.244 | 0.53 | 0.537 | 0.534 | 0.253 | | Multi2OIE | SpanOIE | LSOIE | 27.55 | 0.618 | 0.909 | 0.736 | 0.646 | 0.525 | 0.596 | 0.558 | 0.364 | 0.525 | 0.628 | 0.571 | 0.383 | | IGL-OIE | SpanOIE | LSOIE | 205.07 | 0.636 | 0.815 | 0.714 | 0.582 | 0.529 | 0.484 | 0.505 | 0.295 | 0.529 | 0.506 | 0.517 | 0.308 | | CIGL-OIE | SpanOIE | LSOIE | 159.43 | 0.634 | 0.975 | 0.769 | 0.653 | 0.379 | 0.601 | 0.464 | 0.284 | 0.379 | 0.633 | 0.474 | 0.3 | | OpenIE6 | SpanOIE | LSOIE | 123.55 | 0.458 | 0.965 | 0.622 | 0.468 | 0.268 | 0.562 | 0.363 | 0.215 | 0.268 | 0.58 | 0.366 | 0.222 | | DetIE | SpanOIE | LSOIE | 29.52 | 0.664 | 0.806 | 0.728 | 0.671 | 0.519 | 0.466 | 0.491 | 0.354 | 0.519 | 0.489 | 0.503 | 0.371 | | SpanOIE | OIE4 | LSOIE | 19.48 | 0.737 | 0.848 | 0.788 | 0.736 | 0.541 | 0.382 | 0.447 | 0.294 | 0.541 | 0.541 | 0.541 | 0.416 | | IMoJIE | OIE4 | LSOIE | 3.62 | 0.61 | 0.89 | 0.724 | 0.442 | 0.52 | 0.541 | 0.53 | 0.239 | 0.52 | 0.564 | 0.541 | 0.248 | | Multi2OIE | OIE4 | LSOIE | 39.12 | 0.642 | 0.877 | 0.742 | 0.637 | 0.547 | 0.517 | 0.532 | 0.309 | 0.547 | 0.547 | 0.547 | 0.327 | | IGL-OIE | OIE4 | LSOIE | 196.72 | 0.628 | 0.896 | 0.738 | 0.659 | 0.521 | 0.54 | 0.53 | 0.361 | 0.521 | 0.566 | 0.543 | 0.378 | | CIGL-OIE | OIE4 | LSOIE | 191.90 | 0.617 | 0.945 | 0.747 | 0.692 | 0.505 | 0.587 | 0.543 | 0.392 | 0.505 | 0.621 | 0.557 | 0.414 | | OpenIE6 | OIE4 | LSOIE | 64.24 | 0.47 | 0.924 | 0.623 | 0.587 | 0.394 | 0.537 | 0.455 | 0.342 | 0.394 | 0.557 | 0.462 | 0.354 | | DetIE | OIE4 | LSOIE | 30.26 | 0.667 | 0.854 | 0.749 | 0.712 | 0.51 | 0.482 | 0.496 | 0.364 | 0.51 | 0.509 | 0.51 | 0.385 | | SpanOIE | LSOIE | LSOIE | 18.09 | 0.715 | 0.888 | 0.792 | 0.762 | 0.666 | 0.474 | 0.554 | 0.394 | 0.666 | 0.65 | 0.658 | 0.541 | | IMoJIE | LSOIE | LSOIE | 1.09 | 0.741 | 0.891 | 0.809 | 0.563 | 0.748 | 0.571 | 0.648 | 0.379 | 0.748 | 0.597 | 0.664 | 0.395 | | Multi2OIE | LSOIE | LSOIE | 37.98 | 0.662 | 0.935 | 0.775 | 0.707 | 0.745 | 0.676 | 0.709 | 0.557 | 0.745 | 0.703 | 0.723 | 0.579 | | IGL-OIE | LSOIE | LSOIE | 201.64 | 0.679 | 0.891 | 0.771 | 0.651 | 0.697 | 0.611 | 0.652 | 0.485 | 0.697 | 0.65 | 0.673 | 0.515 | | CIGL-OIE | LSOIE | LSOIE | 183.46 | 0.643 | 0.978 | 0.776 | 0.705 | 0.621 | 0.717 | 0.666 | 0.529 | 0.621 | 0.767 | 0.686 | 0.566 | | OpenIE6 | LSOIE | LSOIE | 65.63 | 0.473 | 0.954 | 0.633 | 0.529 | 0.438 | 0.723 | 0.546 | 0.428 | 0.438 | 0.75 | 0.553 | 0.447 | | DetIE | LSOIE | LSOIE | 28.19 | 0.739 | 0.893 | 0.809 | 0.776 | 0.694 | 0.579 | 0.631 | 0.49 | 0.694 | 0.618 | 0.654 | 0.523 | | SpanOIE | IMoJIE | LSOIE | 7.19 | 0.226 | 0.996 | 0.368 | 0.61 | 0.085 | 0.389 | 0.139 | 0.211 | 0.085 | 0.439 | 0.142 | 0.238 | | IMoJIE | IMoJIE | LSOIE | 2.98 | 0.681 | 0.945 | 0.792 | 0.532 | 0.517 | 0.497 | 0.507 | 0.225 | 0.517 | 0.523 | 0.52 | 0.236 | | Multi2OIE | IMoJIE | LSOIE | 33.67 | 0.651 | 0.882 | 0.749 | 0.703 | 0.554 | 0.502 | 0.527 | 0.333 | 0.554 | 0.527 | 0.54 | 0.348 | | IGL-OIE | IMoJIE | LSOIE | 218.05 | 0.691 | 0.863 | 0.767 | 0.567 | 0.517 | 0.443 | 0.477 | 0.241 | 0.517 | 0.472 | 0.493 | 0.256 | | CIGL-OIE | IMoJIE | LSOIE | 189.39 | 0.678 | 0.934 | 0.785 | 0.6 | 0.489 | 0.503 | 0.496 | 0.262 | 0.489 | 0.551 | 0.518 | 0.286 | | OpenIE6 | IMoJIE | LSOIE | 124.62 | 0.502 | 0.924 | 0.651 | 0.452 | 0.353 | 0.506 | 0.416 | 0.207 | 0.353 | 0.534 | 0.425 | 0.219 | | DetIE | IMoJIE | LSOIE | 30.12 | 0.742 | 0.755 | 0.748 | 0.657 | 0.569 | 0.377 | 0.454 | 0.296 | 0.569 | 0.4 | 0.47 | 0.314 | Table 13: A table that lists performance of different OpenIE systems on the LSOIE benchmark. Table 14: Statistical significance tests to answer R1. Each number represents the number of test set and evaluation metric combinations with the corresponding t-score and p-value. When t-score is greater than 0, non-N-ary outperforms N-ary, and when t-score is less than 0, N-ary outperforms non-N-ary. | Independent Var. | Constants | p-value ≤ 0.05 | p-value > 0.05 | | | |---------------------|-----------------------------|------------------|------------------|----|----| | t-score > 0 | t-score < 0 | t-score > 0 | t-score < 0 | | | | non-N-ary model vs. | non-N-ary train, N-ary test | 2 | 5 | 3 | 5 | | N-ary model | N-ary train, N-ary test | 3 | 5 | 1 | 6 | | non-N-ary train vs. | non-N-ary model, N-ary test | 0 | 11 | 0 | 4 | | N-ary train | N-ary model, N-ary test | 4 | 9 | 0 | 2 | Table 15: Statistical significance tests to answer R2. Each number represents the number of test set and evaluation metric combinations with the corresponding t-score and p-value. When t-score is greater than 0, non-IN outperforms IN, and when t-score is less than 0, IN outperforms non-IN. | Independent Var. | Constants | p-value ≤ 0.05 | p-value > 0.05 | | | |---------------------------|-------------------|------------------|------------------|----|----| | t-score > 0 | t-score < 0 | t-score > 0 | t-score < 0 | | | | non-IN train, IN test | 9 | 0 | 2 | 1 | | | non-IN model vs. IN model | IN train, IN test | 0 | 4 | 7 | 1 | | non-IN train, non-IN test | 0 | 1 | 2 | 0 | | | IN train, non-IN test | 3 | 0 | 0 | 0 | | | non-IN model, IN test | 6 | 6 | 0 | 0 | | | non-IN train vs. IN train | IN model, IN test | 2 | 7 | 0 | 3 | | non-IN model, non-IN test | 2 | 1 | 0 | 0 | | | IN model, non-IN test | 2 | 0 | 1 | 0 | | | Configuration 1 | Configuration 2 | t-Score | p-value | | | |-------------------|-------------------|-----------|-----------|---------|----------| | Model | Sen./Sec | Model | Sen./Sec | | | | IMoJIE | 2.070 | Multi2OIE | 29.225 | -21.621 | 1.50E-15 | | IMoJIE | 2.070 | IGL-OIE | 84.072 | -5.501 | 2.63E-05 | | IMoJIE | 2.070 | CIGL-OIE | 68.800 | -4.929 | 9.31E-05 | | IMoJIE | 2.070 | OpenIE6 | 28.357 | -5.813 | 1.31E-05 | ## B Hyperparameter Sensitivity Study In this section, we report the empirical results of training Multi2OIE on a variety of hyperparameters. For each combination of training and test set, we start with the original hyperparameters used by Ro et al. (2020), then modify one. The different hyperparameter values we test are values the authors test in their hyperparameter search. The hyperparameters the authors change are the number of epochs used for training, the dropout rate for the multi-head attention blocks, the dropout rate for the argument classifier, the batch size, the learning rate, the number of multi-head attention heads, the number of multi-head attention blocks, and the number of dimensions for the position embeddings. The original hyperparameter values Ro et al. (2020) use are in table 17. Table 18 shows the CaRB score of Multi2OIE trained with different hyperparameters, averaged over all training and test sets. Table 19 shows the CaRB score averaged over all training sets on the OIE2016 test set. Table 20 shows the CaRB score averaged over all training sets on the WiRe57 test set. Table 21 shows the CaRB score averaged over all training sets on the ReOIE2016 test set. Table 22 shows the CaRB score averaged over all training sets on the CaRB test set. Table 23 shows the CaRB score averaged over all training sets on the LSOIE test set. The largest difference in CaRB F1 score from the original model hyperparameters was for Multi2OIE tested on WiRe57. However, it should be noted that WiRe57 only consists of 57 sentences with 343 relations. An incorrect prediction on a single sentence may lead to a significant F1 difference overall. Therefore, we feel that this difference is not due to sensitivity to hyperparameters, but rather due to the sensitivity of WiRe57. For other test sets, we observe much smaller effects of different | Hyperparameter | Value | |-------------------------------|---------| | Epochs | 1 | | Multi-head Attention Dropout | 0.2 | | Argument Classifier Dropout | 0.2 | | Batch Size | 128 | | Learning Rate | 3e-5 | | Multi-head Attention Heads | 8 | | Multi-head Attention Blocks | 4 | | Position Embedding Dimensions | 64 | hyperparameters on the CaRB score. | Average Difference from Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | |----------------------------------------------------|---------------|---------------|---------|---------|---------|---------| | Increase | Decrease | | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | Hyperparameter Changed | | | | | | | | Epochs | 2 | 0.0027 | -0.0028 | -0.0007 | 0.0200 | -0.0130 | | 3 | 0.0028 | -0.0025 | -0.0003 | 0.0160 | -0.0090 | | | Multi-head | 0.0 | 0.0028 | -0.0039 | -0.0023 | 0.0020 | -0.0150 | | Attention Dropout | 0.1 | 0.0006 | -0.0027 | -0.0015 | 0.0030 | -0.0120 | | Argument | 0.0 | 0.0003 | -0.0013 | -0.0011 | 0.0040 | -0.0110 | | Classifier Dropout | 0.1 | -0.0005 | 0.0002 | -0.0003 | 0.0050 | -0.0110 | | Batch Size | 64 | 0.0005 | -0.0001 | -0.0004 | 0.0040 | -0.0050 | | Learning Rate | 2e-5 | -0.0010 | 0.0029 | 0.0012 | 0.0070 | -0.0050 | | 5e-5 | 0.0031 | -0.0061 | -0.0033 | 0.0090 | -0.0160 | | | Multi-head Attention Heads | 4 | -0.0008 | 0.0013 | 0.0008 | 0.0150 | -0.0150 | | Multi-head Attention Blocks | 2 | 0.0011 | -0.0009 | -0.0006 | 0.0040 | -0.0100 | | Position Embedding | 128 | -0.0007 | -0.0044 | -0.0033 | 0.0030 | -0.0130 | | Dimensions | 256 | -0.0019 | 0.0023 | 0.0010 | 0.0140 | -0.0110 | Table 18: CaRB scores averaged over all training and test set combinations when using Multi2OIE. Each row represents a change of a single hyperparameter from the final hyperparameters used by Ro et al. (2020). The different hyperparameter values tested are the same ones tested by Ro et al. (2020). | Average Difference from | | | | | | | | |-----------------------------------------------------------------------------------------------------|-----------------------------|--------------------------|-------------|-------------|---------|---------|---------| | Test Set | Hyperparameter | Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | Changed | Increase | Decrease | | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | | OIE2016 | Epochs | 2 | 0.0017 | -0.0067 | -0.0040 | -0.0010 | -0.0100 | | 3 | 0.0027 | -0.0020 | -0.0003 | 0.0040 | -0.0050 | | | | OIE2016 | Multi-head | 0.0 | 0.0013 | -0.0020 | -0.0010 | 0.0020 | -0.0030 | | Attention Dropout | 0.1 | 0.0020 | -0.0020 | -0.0007 | 0.0020 | -0.0050 | | | OIE2016 | Argument | 0.0 | 0.0020 | -0.0020 | -0.0003 | 0.0000 | -0.0010 | | Classifier Dropout | 0.1 | 0.0040 | 0.0017 | 0.0023 | 0.0050 | -0.0020 | | | OIE2016 | Batch Size | 64 | 0.0007 | 0.0017 | 0.0010 | 0.0040 | -0.0020 | | OIE2016 | Learning Rate | 2e-5 | 0.0003 | 0.0007 | 0.0010 | 0.0070 | -0.0050 | | 5e-5 | 0.0043 | -0.0073 | -0.0033 | 0.0050 | -0.0110 | | | | OIE2016 | Multi-head Attention Heads | 4 | 0.0030 | 0.0017 | 0.0020 | 0.0070 | -0.0010 | | OIE2016 | Multi-head Attention Blocks | 2 | 0.0003 | -0.0013 | -0.0010 | 0.0040 | -0.0040 | | OIE2016 | Position Embedding | 128 | 0.0007 | -0.0080 | -0.0050 | -0.0010 | -0.0110 | | Dimensions | 256 | -0.0017 | -0.0027 | -0.0023 | 0.0030 | -0.0110 | | | Table 19: CaRB scores averaged over all training sets on the OIE2016 test set when using Multi2OIE. | | | | | | | | | Average Difference from | | | | | | | | |---------------------------|------------------------------|--------------------------|-------------|-------------|---------|---------|---------| | Test Set | Hyperparameter | Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | Changed | Increase | Decrease | | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | | WiRe57 | Epochs | 2 | 0.0047 | 0.0013 | 0.0037 | 0.0200 | -0.0130 | | 3 | 0.0087 | 0.0030 | 0.0063 | 0.0160 | -0.0030 | | | | 0.0 | 0.0077 | -0.0097 | -0.0070 | -0.0020 | -0.0150 | | | | WiRe57 | Multi-head Attention Dropout | 0.1 | 0.0050 | -0.0057 | -0.0023 | 0.0030 | -0.0120 | | WiRe57 | Argument | 0.0 | 0.0017 | -0.0060 | -0.0047 | 0.0040 | -0.0110 | | Classifier Dropout | 0.1 | -0.0007 | -0.0047 | -0.0033 | 0.0010 | -0.0110 | | | WiRe57 | Batch Size | 64 | 0.0067 | -0.0033 | -0.0017 | 0.0020 | -0.0050 | | WiRe57 | Learning Rate | 2e-5 | 0.0043 | 0.0000 | 0.0010 | 0.0070 | -0.0030 | | 5e-5 | 0.0063 | -0.0080 | -0.0053 | 0.0090 | -0.0160 | | | | WiRe57 | Multi-head Attention Heads | 4 | -0.0020 | 0.0020 | 0.0020 | 0.0150 | -0.0150 | | WiRe57 | Multi-head Attention Blocks | 2 | 0.0013 | -0.0020 | -0.0013 | 0.0030 | -0.0100 | | WiRe57 | Position Embedding | 128 | 0.0000 | -0.0080 | -0.0060 | 0.0030 | -0.0130 | | Dimensions | 256 | -0.0007 | 0.0033 | 0.0037 | 0.0140 | -0.0060 | | Table 20: CaRB scores averaged over all training sets on the WiRe57 test set when using Multi2OIE. | Average Difference from Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | | |-------------------------------------------------------------------------------------------------------|------------------------------|---------------|----------|---------|---------|---------|---------| | Test Set | Hyperparameter Changed | Increase | Decrease | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | | ReOIE2016 | Epochs | 2 | -0.0023 | -0.0043 | -0.0037 | -0.0010 | -0.0090 | | 3 | -0.0030 | -0.0070 | -0.0060 | -0.0040 | -0.0090 | | | | 0.0 | 0.0010 | -0.0040 | -0.0017 | 0.0000 | -0.0040 | | | | ReOIE2016 | Multi-head Attention Dropout | 0.1 | -0.0017 | -0.0040 | -0.0030 | -0.0020 | -0.0040 | | ReOIE2016 | Argument | 0.0 | -0.0020 | 0.0007 | -0.0003 | 0.0020 | -0.0020 | | Classifier Dropout | 0.1 | -0.0060 | 0.0020 | -0.0013 | 0.0000 | -0.0020 | | | ReOIE2016 | Batch Size | 64 | -0.0050 | 0.0017 | -0.0010 | 0.0000 | -0.0020 | | ReOIE2016 | Learning Rate | 2e-5 | -0.0037 | 0.0047 | 0.0017 | 0.0060 | -0.0010 | | 5e-5 | -0.0037 | -0.0060 | -0.0050 | -0.0030 | -0.0080 | | | | ReOIE2016 | Multi-head Attention Heads | 4 | -0.0037 | 0.0023 | 0.0003 | 0.0040 | -0.0050 | | ReOIE2016 | Multi-head Attention Blocks | 2 | 0.0013 | -0.0007 | -0.0003 | 0.0000 | -0.0010 | | ReOIE2016 | Position Embedding | 128 | -0.0043 | -0.0027 | -0.0033 | 0.0000 | -0.0060 | | Dimensions | 256 | -0.0043 | 0.0043 | 0.0010 | 0.0060 | -0.0050 | | | Table 21: CaRB scores averaged over all training sets on the ReOIE2016 test set when using Multi2OIE. | | | | | | | | | Average Difference from | | | | | | | | |--------------------------------------------------------------------------------------------------|-----------------------------|--------------------------|-------------|-------------|---------|---------|---------| | Test Set | Hyperparameter | Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | Changed | Increase | Decrease | | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | | CaRB | Epochs | 2 | 0.0070 | -0.0030 | -0.0003 | 0.0020 | -0.0030 | | 3 | 0.0027 | -0.0033 | -0.0017 | 0.0010 | -0.0040 | | | | CaRB | Multi-head | 0.0 | 0.0040 | -0.0040 | -0.0020 | 0.0000 | -0.0030 | | Attention Dropout | 0.1 | -0.0023 | -0.0020 | -0.0020 | 0.0000 | -0.0030 | | | CaRB | Argument | 0.0 | 0.0003 | -0.0003 | -0.0003 | 0.0010 | -0.0030 | | Classifier Dropout | 0.1 | 0.0010 | -0.0003 | -0.0003 | 0.0000 | -0.0010 | | | CaRB | Batch Size | 64 | 0.0007 | -0.0003 | -0.0003 | 0.0010 | -0.0010 | | CaRB | Learning Rate | 2e-5 | -0.0017 | 0.0020 | 0.0007 | 0.0010 | 0.0000 | | 5e-5 | 0.0053 | -0.0047 | -0.0020 | 0.0010 | -0.0060 | | | | CaRB | Multi-head Attention Heads | 4 | -0.0010 | -0.0007 | -0.0010 | 0.0030 | -0.0030 | | CaRB | Multi-head Attention Blocks | 2 | 0.0043 | -0.0023 | -0.0003 | 0.0010 | -0.0010 | | CaRB | Position Embedding | 128 | 0.0017 | -0.0027 | -0.0017 | 0.0000 | -0.0040 | | Dimensions | 256 | -0.0007 | 0.0000 | 0.0000 | 0.0020 | -0.0030 | | | Table 22: CaRB scores averaged over all training sets on the CaRB test set when using Multi2OIE. | | | | | | | | | Average Difference from | | | | | | | | |---------------------------------------------------------------------------------------------------|-----------------------------|--------------------------|-------------|-------------|---------|---------|---------| | Test Set | Hyperparameter | Original Hyperparameters | Max CaRB F1 | Max CaRB F1 | | | | | Changed | Increase | Decrease | | | | | | | CaRB P | CaRB R | CaRB F1 | | | | | | | LSOIE | Epochs | 2 | 0.0027 | -0.0013 | 0.0007 | 0.0080 | -0.0040 | | 3 | 0.0030 | -0.0030 | 0.0003 | 0.0080 | -0.0040 | | | | LSOIE | Multi-head | 0.0 | 0.0000 | 0.0003 | 0.0003 | 0.0010 | 0.0000 | | Attention Dropout | 0.1 | 0.0000 | 0.0003 | 0.0003 | 0.0010 | -0.0010 | | | LSOIE | Argument | 0.0 | -0.0007 | 0.0010 | 0.0003 | 0.0010 | 0.0000 | | Classifier Dropout | 0.1 | -0.0007 | 0.0023 | 0.0010 | 0.0020 | 0.0000 | | | LSOIE | Batch Size | 64 | -0.0003 | -0.0003 | 0.0000 | 0.0020 | -0.0020 | | LSOIE | Learning Rate | 2e-5 | -0.0043 | 0.0073 | 0.0017 | 0.0050 | -0.0030 | | 5e-5 | 0.0030 | -0.0047 | -0.0007 | 0.0040 | -0.0040 | | | | LSOIE | Multi-head Attention Heads | 4 | -0.0003 | 0.0013 | 0.0007 | 0.0020 | -0.0010 | | LSOIE | Multi-head Attention Blocks | 2 | -0.0017 | 0.0017 | 0.0000 | 0.0010 | -0.0010 | | LSOIE | Position Embedding | 128 | -0.0013 | -0.0007 | -0.0007 | 0.0000 | -0.0010 | | Dimensions | 256 | -0.0020 | 0.0067 | 0.0027 | 0.0050 | 0.0000 | | | Table 23: CaRB scores averaged over all training sets on the LSOIE test set when using Multi2OIE. | | | | | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 10 ✗ A2. Did you discuss any potential risks of your work? We do not believe our observations can be used for adversarial attacks or have malicious effects. We train models that are already publicly available on data that is also already publicly available. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Sections 3-7 ✓ B1. Did you cite the creators of artifacts you used? Sections 3-7, links to the code and datasets used are in the code and data files attached to the submission ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We did not plan to use the artifacts for any commercial applications because we were writing a survey paper. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We did not plan to use the artifacts for any commercial applications because we were writing a survey paper. We were using them purely for research purposes. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The data we use are relations in sentences. We do not believe these data may lead to a violation of privacy. The source for the sentences were scientific articles, news articles, and Wikipedia, which we believe do not contain offensive content. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ## C ✓ **Did You Run Computational Experiments?** Sections 5, 7 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We did not believe the models we used were large enough to warrant this discussion, and we ran all models on a single GPU. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 5.1, experimental setup ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 6, Appendix B, we did not include error bars but we describe how we obtained our results and how we averaged them to reach our conclusions. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 5.1, experimental setup ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

weerasooriya-etal-2023-subjective

Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful {C}rowd{O}pinion with Population-level Learning

https://aclanthology.org/2023.acl-long.54

Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content. Conventionally, annotator disagreements are resolved before any learning takes place. However, researchers are increasingly identifying annotator disagreement as pervasive and meaningful. They also question the performance of a system when annotators disagree. Particularly when minority views are disregarded, especially among groups that may already be underrepresented in the annotator population. In this paper, we introduce CrowdOpinion, an unsupervised learning based approach that uses language features and label distributions to pool similar items into larger samples of label distributions. We experiment with four generative and one density-based clustering method, applied to five linear combinations of label distributions and features. We use five publicly available benchmark datasets (with varying levels of annotator disagreements) from social media (Twitter, Gab, and Reddit). We also experiment in the wild using a dataset from Facebook, where annotations come from the platform itself by users reacting to posts. We evaluate CrowdOpinion as a label distribution prediction task using KL-divergence and a single-label problem using accuracy measures.

# Subjective Crowd Disagreements For Subjective Data: Uncovering Meaningful Crowdopinion **With Population-Level Learning** Tharindu Cyril Weerasooriya 1*, Sarah Luger2**, Saloni Poddar**1, Ashiqur R. KhudaBukhsh1**, Christopher M. Homan**1 1Rochester Institute of Technology, USA 2Orange Silicon Valley *[email protected] ## Abstract This paper contains content that can be offensive or disturbing. Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content. Conventionally, annotator disagreements are resolved before any learning takes place. However, researchers are increasingly identifying annotator disagreement as pervasive and meaningful. They also question the performance of a system when annotators disagree. Particularly when minority views are disregarded, especially among groups that may already be underrepresented in the annotator population. In this paper, we introduce *CrowdOpinion*, an unsupervised learning based approach that uses language features and label distributions to pool similar items into larger samples of label distributions. We experiment with four generative and one density-based clustering method, applied to five linear combinations of label distributions and features. We use five publicly available benchmark datasets (with varying levels of annotator disagreements) from social media (Twitter, Gab, and Reddit). We also experiment in the wild using a dataset from Facebook, where annotations come from the platform itself by users reacting to posts. We evaluate CrowdOpinion as a label distribution prediction task using KL-divergence and a single-label problem using accuracy measures. ## 1 Introduction Long term exposure to offensive, threatening, and hate speech posts through any public-facing social media platform can lead to depression or even physical injuries, specially at a younger age (Pedalino and Camerini, 2022). This is a persistent problem in social and web content where the impact could be not limited to just the targeted parties but expand to anyone in the community consuming the content ![0_image_0.png](0_image_0.png) Figure 1: Examples from DSI (Sap et al., 2019), from human annotation for Twitter posts on whether they are intended to be offensive. These examples show how offense cannot generalize, and in cases when a majority of the annotators are not offended the input for a classifier is the majority voice. (Benson, 1996; Fauman, 2008; Chandrasekharan et al., 2017; Müller and Schwarz, 2020). Language used by content creators in social media (see Figure 1) with a subtle tone and syntax can hide the offensive content from the purview (Basile et al., 2019; Zubiaga et al., 2019) or machine learning classifiers (Kumar et al., 2021). This challenge has ethical and legal implications in many countries as these governments have imposed restrictions for platforms to identify and remove such harming content (Kralj Novak et al., 2022; Saha et al., 2019) citing the right for safety. The ML classifiers generally rely on human feedback (Eriksson and Simpson, 2010; Dong et al., 2019). Because humans, as content creators or annotators (content moderators), are subjective in their opinions (Alm, 2011). Their feedback is essential to understanding subjective web or social media content. The standard practice is to ask multiple annotators about each post and then use the majority opinion or ML-based methods to determine the ground truth label (see Figure 2). Typically, minority views are completely removed from the dataset before it is published. Yet these views are often meaningful and important 950 ![1_image_0.png](1_image_0.png) (Aroyo and Welty, 2014; Kairam and Heer, 2016; Plank et al., 2014; Chung et al., 2019; Obermeyer et al., 2019; Founta et al., 2018). Figure 1 shows three tweets with offensive language that have been labeled by multiple annotators about the tweeter's intent (Sap et al., 2019). In each case, the majority of annotators considers the offensiveness to be not intended. Yet a minority considers it to be *intended*. A classifier trained on such language data after these minority opinions are removed would not know about them. This is dangerous because abusers often obscure offensive language to sound unintended in case they are confronted (Sang and Stanton, 2022). And so, removing minority opinions could have dramatic impacts on the model's performance if, say, it was trying to detect users creating hateful or offensive content on a social platform. Consequently, a growing body of research advocates that published datasets include ALL annotations obtained for each item (Geng, 2016; Liu et al., 2019; Klenner et al., 2020; Basile, 2020; Prabhakaran et al., 2021). And a substantial body of research is studying annotator disagreement (Aroyo and Welty, 2014; Kairam and Heer, 2016; Plank et al., 2014; Chung et al., 2019; Obermeyer et al., 2019; Founta et al., 2018; Binns et al., 2017). Unfortunately, most existing datasets are based on 3–10 annotators per label, far too few, statistically speaking, to represent a population. Thus, learning over such a sparse space is challenging. Liu et al. (2019) show that clustering in the space of label distributions can ameliorate the sparseness problem, indicating that data items with similar label distributions likely have similar interpretations. Thus, a model can pool labels into a single collection that is large enough to represent the underlying annotator population. Recent work by Davani et al. (2022), studying annotator disagreement with majority vote and multi-label learning methods, has called out the need for cluster-based modeling to understand annotator disagreements. The lack of annotator-level labels also hinders studying the annotator behaviors using methods that utilize those granular-level labels (Dawid and Skene, 1979; Rodrigues and Pereira, 2018; Gordon et al., 2022; Collins et al., 2022; Liu et al., 2023). We see this as a benefit to *CrowdOpinion* (CO) we propose, a technique applicable at a broader level for understanding and predicting annotator disagreements which mitigate granular-level annotations. The **motivation** behind *CrowdOpinion* is to reduce inequity and bias in human-supervised machine learning by preserving the full distribution of crowd responses (and their opinions) through the entire learning pipeline. We focus our methods on web and social media content due to its subjectivity. Our contributions to this core problem in AI and NLP is a learning framework1that uses unsupervised learning in Stage 1 on both the labels AND data features to better estimate soft label distributions. And in Stage 2, we use these labels from Stage 1 to train and evaluate with a supervised learning model. We consider the following three questions. Q1: *Does mixing language features and labels* lead to better ground truth estimates than those that use labels only? This focuses on the first stage as a standalone problem and is difficult to answer directly, as "ground truth" from our perspective is the *distribution of labels from a hidden population* of would-be annotators, of which we often only have a small sample (3-10 annotators) per data item. We study four generative and one distance-based clustering methods, trained jointly on features and label distributions, where we vary the amount of weight given to features versus labels. Q2: *Does mixing features and labels in the first* stage lead to better label distribution learning in the second? We use the label distributions obtained from the first-stage models from Q1 as feedback for supervised learning. We compare our results with baselines from pooling based on labels only (Liu et al., 2019), predictions trained on the majority label for each item without clustering, and predictions trained on the label distribution for each item ![2_image_0.png](2_image_0.png) ![2_image_1.png](2_image_1.png) but without any other first-stage modeling. Our results show improvement over unaggregated baselines. Q3: Do our methods lead to better single-label learning (SL)? Since most applications consider only single-label prediction, we measure the model performance on single-label prediction via accuracy. ## 1.1 Beyond Experiments Humans have annotated our benchmark datasets for specific tasks. However, this is not always the case in practice. Social networks have introduced *reactions* that allow users to react to platform content. We study this use case by predicting these reactions for Facebook posts (Wolf, 2016) as a special case. Among the top 100 posts from Facebook (entropy > 1.2), 26 were about Donald Trump, with most of the label distribution mass divided between "like", "haha", and "angry". Another 26 posts were about politics (but not Trump), with the label distribution mass generally divided between "angry" and "sad". There were only two non-English posts and no sports-related posts. And interestingly, except for two non-English posts, all of the other top posts had a substantial portion of their mass on "angry". The bottom 100 set (entropy < 0.04) contains 46 posts about sports and 13 non-English posts. There was only one political post (and it was not about Trump). The label distribution pattern in this set was more dominated by "like" (> 98%), followed by reactions of either "love" or "haha". "Like" was also dominant in the high entropy posts, but not to such a degree; based on this observation and (Tian et al., 2017), we eliminate it from our experiments. Figure 3 illustrates some nuances in meaning that different label distributions reveal. All three are negative posts about Barack Obama, and all have most of their mass on "like". DFBE1 and DFBE2 have similar distributions, in contrast to DFBE3 where, besides "like", the distribution mass falls mainly on "haha" and "angry". Perhaps this is because, in contrast to the first two posts which are from anonymous sources, the criticism on DFBE3 comes from a political rival, and maybe this provides a concrete target for ridicule? ## 1.2 Facebook'S Special Case "Like" was the original Facebook reaction and platform users may find it a quick, default, and intuitive interaction. The over-representation of "like" on Facebook exemplifies how this dataset is an unusual human annotation case. It is unique not only in the human labeling behavior, but also in the resulting label distribution. ## 2 Methods - **Crowdopinion** In conventional, nondistributional supervised learning, clustering might happen over the feature space only as a form of data regularization (Nikulin and McLachlan, 2009); the labels, being strictly categorical and nondistributional, would be scalar and thus too simple to benefit from extensive modeling. In our setting, each data item xi ∈ X is associated with a vector yi ∈ Y, representing the empirical distribution of ALL annotator responses, which we view as *sample* of a larger, hidden population. Our approach, *CrowdOpinion* (CO) is two-staged and summarized in Algorithm 1. In Stage 1, we cluster together related data items and share among them a label distribution yˆi based on all labels from all items in each cluster. This stage resembles, in function, a deep vein of label estimation research begun by Dawid and Skene (Dawid and Skene, 1979; Carpenter, 2008; Ipeirotis et al., 2010; Pasternack and Roth, 2010; Weld et al., 2011; Raykar and Yu, 2012; Kairam and Heer, 2016; Gordon et al., 2021), except that (a) our output is an estimate of the distribution of label responses by the underlying population of annotators, not a single label, and (b) yiin their models is a vector with one dimension for each annotator. To better handle the label sparseness common in most datasets, our yi has one dimension for each label choice, representing the proportion of annotators who made that choice. Stage 2 performs supervised learning on these new item, label distribution pairs (xi, yˆi). Note that nearly any pair of clustering C and supervised learning H algorithms can be used 952 Algorithm 1: CO-C-H-w 1 *Parameters:* 2 Clustering (or pooling) algorithm C 3 Hypothesis space H 4 Mixing parameter w ∈ [0, 1] 5 *Inputs:* 6 Data features with empirical label distributions (xi, yi)1≤i≤n // BOTH xi and yi are vectors! 7 *Procedure:* 8 Stage 1: 9 Perform clustering with C on BOTH item features and labels, weighted and concatenated together: (w · xi,(1 − w) · yi)1≤i≤n 10 Let (ˆxi, yˆi) be the centroid of the cluster πj associated with each (xi, yi) 11 Stage 2: Perform supervised learning on (xi, yˆi) over hypothesis space H for stages one and two, respectively. Liu et al. (2019) performed the same kind of label regularization only using the label space Y, it is a baseline for our methods (w = 0). Our main technical innovation is to perform label regularization based on the *weighted joint feature and label* space w · X × (1 − w)· Y, where w ∈ [0, 1] is the mixing parameter that determines the relative importance of X versus Y during clustering. We consider four clustering models C used by Liu et al. (2019): a (finite) multinomial mixture model (FMM) with a Dirichlet prior over π ∼ Dir(*p, γ* = 75), where p is the number of clusters and each cluster distribution πj is a multinomial distribution with Dirichlet priors Dir(*d, γ* = 0.1), where d is the size of the label space, using the bnpy library (Hughes and Sudderth, 2013), a Gaussian mixture model (GMM) and a K-means model (KM) from scikit-learn, and the Gensim implementation of Latent Dirichlet Allocation (LDA) (Reh˚u ˇ ˇrek and Sojka, 2010). Each of these models takes as a hyperparameter the number of clusters p. We perform parameter search (4 ≤ p ≤ 40) on the number of clusters, choosing arg minp Pi KL((xi, yi)w,(ˆxi, yˆi)w), i.e., the p that minimizes the total KL divergence between the raw and clustered label distribution, where, e.g., (xi, yi)w denotes (w · xi,(1 − w) · yi), i.e., the weighted concatenation of xi and yi. We also consider a soft, distance-based clustering method, called *neighborhood-based pooling* (NBP) in the context of PLL (Weerasooriya et al., 2020). For each data item i it averages over all data items j within a fixed Kullback-Liebler (KL) ball of radius r: ## YˆI = {Yj | Kl((Xi, Yi)W∥(Xj , Yj )W) < R}. (1) Here, the hyperparameter is the diameter r of the balls, rather than the number of clusters, and there is one ball for each data item. We perform hyperparameter search (0 ≤ r ≤ 15) via methods used in (Weerasooriya et al., 2020). Table 2 summarizes model selection results using these methods. The supervised model (CNN) for H is a 1D convolutional neural network (Kim, 2014), with three convolution/max pool layers (of dimension 128) followed by a dropout (0.5) and softmax layer implemented with TensorFlow. The input to the model is a 384-dimension-vector text embedding, described below. Table 3 summarizes the supervised-learning based classification results. We compare our methods against four baselines. PD is our CNN model but with no clustering; it is trained directly on the raw empirical label distributions (yi). SL the same model, but trained on one-hot encodings of most frequent label in each yi. **DS+CNN** uses the Dawid and Skene (1979) model for C and H = CNN. CO-C**-CNN-**0 is from Liu et al. (2019), which clusters on labels only. We represent language features for both our unsupervised learning and classification experiments using a state-of-the-art pre-trained paraphrase-MiniLM-L6-v2 transformer model using SBERT (sentence-transformers) library (Reimers and Gurevych, 2019). We identified this pre-trained model based on STS benchmark scores at the time of writing. The feature vector size for each post is 384. ## 3 Experiments 3.1 Dataset Descriptions As our approach focuses on human disagreement, we identified datasets that contain multiple annotators and multiple label choices per data item. We conducted our experiments on publicly available human-annotated English language datasets generated from social media sites (Facebook, Twitter, and Reddit). Each dataset consists of 2,000 posts and employs a 50/25/25 percent for train/dev/test split. Larger datasets are downsampled with random selection to 2,000 for a fairer comparison be- | Dataset | No. of ants. | Total data | No. of label | Avg. | |----------------|----------------|--------------|----------------|--------| | (per item) | items | choices | Entropy | | | DFB (Facebook) | Avg. 862.3 | 8000 | 5 | 0.784 | | DGE (Reddit) | Avg. 4 | 54263 | 28 | 0.866 | | DJQ1 (Twitter) | 10 | 2000 | 5 | 0.746 | | DJQ2 (Twitter) | 10 | 2000 | 5 | 0.586 | | DJQ3 (Twitter) | 10 | 2000 | 12 | 0.993 | | DSI (Reddit) | Avg. 3 | 45318 | 4 | 0.343 | | $\mathcal{D}_{\texttt{J02}}$ | $\mathcal{D}_{\texttt{J03}}$ | $\mathcal{D}_{\texttt{SI}}$ | |:-------------------|:-------------------:|:-------------------:|:-------------------:| | NBP | NBP | K-Means | | | | 0.133 | 0.023 | 0.050 | | | 2.8 | 10.2 | 35 | | | 0.75 | 0 | 1.0 | | | Table 1: Experimental datasets summary: We calculated entropy per data item and averaged it over the dataset to measure uncertainty. DFB (Wolf, 2016), DGE (Demszky et al., 2020), DJQ1-3 (Liu et al., 2016), and DSI (Sap et al., 2019). Dataset DFB DGE DJQ1 DJQ2 DJQ3 DSI Model NBP NBP NBP NBP NBP K-Means KL (↓) 0.070 0.020 0.123 0.133 0.023 0.050 r/p 3 0.8 5.6 2.8 10.2 35 w 0.5 0 0.25 0.75 0 1.0 Table 2: Optimal label aggregation model summary with the parameters and KL-divergence. Here r/p is the number of clusters for the generative models and r is the neighborhood size for distance-based clustering. K-Means is the optimum model for DSI , while NBP (distance-based clustering) is the optimal model for the remaining five datasets. tween them. The datasets vary in content, number of annotators per item, number of annotator choices, and source of content. More detailed descriptions of the datasets are included in the Appendix. ## 3.2 Results To address Q1, i.e., whether mixtures of data features and labels in Stage 1 lead to better ground truth population estimates, Table 2 shows the model name, hyperparameter values, and mean KL divergence between the cluster centroid yˆi and each item's empirical distribution yi of the best cluster model for each dataset. The best choice for w varies considerably across the datasets. The two datasets, D*GE,JQ*3 with the largest number of choices (28 and 12, respectively) both selected models with w = 0, i.e., the label distributions alone provided the best results. This was somewhat surprising, especially considering that in both cases the number of annotators per item is less than the number of label choices. We suspected that such sparse distributions would be too noisy to learn from. But apparently the size of these label spaces alone leads to a rich, meaningful signal. On the other extreme, the dataset with the fewest annotators (DSI ) per item selected a model with w = 1, i.e., it used only item features, and not the label distributions, to determine the clusters. This is what we would expect whenever there is relatively low confidence in the label distributions, which should be the case with so few labels per item. Interestingly, it was the only dataset that did ## Not Select Nbp (K-Means). In general, the mean KL-divergence for all selected models was quite low, suggesting that the items clustered together tended to have very similar label distributions. One might expect for there to be more divergence the higher w is, because clustering with higher w relies less directly on the label distributions. But, reading across the the results, there does not appear to be any relationship between w and KL-divergence. The datasets themselves are very different from one another, and so perhaps it is unlikely that something as simple as the mixing parameter w would change the final label assignment. For Q2, i.e., whether mixtures of data features and labels in Stage 1 improve the label distribution prediction in Stage 2, we measure the mean KL(yi∥H(xi)), where H is one of the supervised learning models trained on each of the clustering models. For all datasets, the best cluster-based models in Table 3 outperform the baselines from Table 3. Among the clustering models, as with Q1 there is a lot of variation among which values for w give the best performance. But while the differences appear significant, they are not substantial, suggesting that subtle differences in the data or the inductive biases of particular clustering models are driving the variance. It is interesting to note that **DS+CNN** is always close to the worst model and often the worst by far. This may be because (a) that model treats disagreement as a sign of poor annotation and seeks to eliminate it, whereas our model is designed to preserve disagreement (b) DS models individual annotator-item pairs and the datasets we study here (which are representative of most datasets currently available) have very sparse label sets, and so overfitting is a concern. For Q3, Table 3 (bottom) shows the classification prediction results, where evaluation is measured by accuracy, i.e., the proportion of test cases where the arg max label of the (ground truth) training input label distribution is equal to that of the arg max predicted label distribution. Here the results are mixed between the non-clustering (Table 4) and clustering (Table 4) models, and the variation in terms of significance and substance is in line with Q1. Once again, **DS+CNN** is the overall worst performer, even though here the goal is single-label inference, i.e., exactly what DS is designed for. | KL-Divergence (↓) | | | | | | | |---------------------|-------------------------|-------------|-------------|-------------|-------------|-------------| | Dataset | DFB | DGE | DJQ1 | DJQ2 | DJQ3 | DSI | | Baselines PD | 0.857±0.006 2.011±0.001 | 1.092±0.004 | 1.088±0.003 | 1.462±0.00 | 0.889 ±0.00 | | | DS+CNN | - | 3.247±0.012 | 1.042±0.005 | 1.035±0.003 | 3.197±0.034 | 1.514±0.067 | | Model (C) | GMM | LDA | GMM | K-Means | LDA | FMM | | KL, w = 0 | 0.684±0.001 1.987±0.001 | 0.427±0.01 | 0.510±0.001 | 0.823±0.001 | 0.860±0.026 | | | w = | 0.75 | 0.50 | 1.0 | 0.25 | 1.0 | 1.0 | | KL | 0.680±0.001 1.995±0.001 | 0.450±0.001 | 0.499±0.001 | 0.884±0.001 | 0.991±0.003 | | ![5_image_0.png](5_image_0.png) Table 3: KL-divergence(↓) results for the CO-C-CNN-w models from Algorithm 1, using various choices for clustering C and feature-label mixing w. Here w = 0 is the baseline from Liu et al. (2019); Weerasooriya et al. (2020) that uses label distributions in the clustering stage, and w = 1 means that only data feature are used. The *best* score is included in the table. Full set of results included in Appendix Table 6.The *best* score for each dataset bolded. Accuracy (↑) Dataset DFB DGE DJQ1 DJQ2 DJQ3 DSI | Accuracy (↑) | | | | | | | |----------------|-------------|-------------|-------------|--------------|-------------------------|-------------| | Dataset | DFB | DGE | DJQ1 | DJQ2 | DJQ3 | DSI | | Others | - | 0.652 | 0.82 | 0.76 | 0.81 | - | | DS+CNN | - | 0.168±0.003 | 0.684±0.004 | 0.658±0.003 | 0.061±0.031 0.508±0.067 | | | Baselines PD | 0.780±0.001 | 0.987±0.001 | 0.601±0.001 | 0.800±0.001 | 0.880±0.020 | 0.734±0.001 | | SL | 0.790±0.005 | 0.942±0.003 | 0.701±0.002 | 0.810 ±0.001 | 0.888±0.030 0.759±0.002 | | | Model (C) | GMM | LDA | GMM | NBP | LDA | LDA | | Acc. (↑),w = 0 | 0.785±0.001 | 0.949±0.001 | 0.891±0.01 | 0.873±0.001 | 0.880±0.001 0.932±0.001 | | | w = | 1.0 | 1.0 | 0.75 | 0.25 | 0.75 | 0.5 | | Acc. (↑) | 0.798±0.001 | 0.950±0.001 | 0.901±0.01 | 0.897±0.001 | 0.883±0.001 | 0.920±0.045 | Post Model KL hired fired quitting other way raise hours complains support going home none other DJQ3E1 Thank you Alice for all Annotations 0 0 0 0 0 0 5 1 0 0 4 0 the attention u caused CO-FMM-CNN-0 0.706 0.044 0.003 0.009 0.009 0.009 0.015 0.208 0.017 0.060 0.042 0.318 0.265 today at work CO-FMM-CNN-1 1.11 0.07 0.063 0.136 0.084 0.091 0.002 0.293 0.019 0.019 0.043 0.071 0.098 CO-NBP-CNN-0.75 0.63 0.05 0.082 0.062 0.023 0.048 0.005 0.382 0.056 0.011 0.021 0.134 0.123 DJQ3E2 Going to work 4PM to 12AM is NOT what I Annotations 0 0 1 0 1 1 4 1 5 0 1 0 want to do.. I have my CO-FMM-CNN-0 0.597 0.028 0.000 0.019 0.009 0.019 0.038 0.323 0.028 0.118 0.192 0.157 0.064 black sweatpants CO-FMM-CNN-1 1.860 0.028 0.047 0.148 0.000 0.000 0.000 0.220 0.000 0.380 0.050 0.127 0.000 spread out, though CO-NBP-CNN-0.75 0.522 0.002 0.047 0.138 0.000 0.039 0.021 0.220 0.001 0.244 0.080 0.207 0.001 Table 5: Two examples from DJQ3. In the first example the author's sarcasm is missed by 4 out of 10 annotators who label the comment as *none of the above but job related* and in the second, a similar sentiment is labeled as *going to work* when *hours* or complaining about work are chosen by others. The act of "laying out [work] clothes" was not noted by many annotators. ## 4 Discussions And Ethical Considerations Our results for **Qs 2–3** show that cluster-based aggregation universally improves the performance of distributional learning. This seems to confirm that clustering is a powerful tool for combating label sparseness to predict population-level annotator responses. However, results were mixed for singlelabel learning. Also, among the clustering methods in both distributional and single-label learning, there was relatively little variance in performance as w varies. The latter is certainly a negative result with respect to the technical AI question of whether or not to use both data features and label distributions in cases when we do cluster. But it is positive in that, combined with the overall superior performance of clustering for population-level learning, it shows that *either* label features or label distributions are adequate for realizing the benefits of clustering as a means of label distribution regularization. It also suggests that annotator disagreements are, in fact, meaningful and essential. To gain a better sense of how these methods can be used to address annotator inequality, we extract examples from DJQ3 (Table 5), DF B (Figure 5), and DSI (Figure 4). We select examples from among the data items with the lowest KLdivergence scores between their empirical label distributions and their predictions according to the CO-FMM-CNN-0 model. We report their predicted distributions according to this model and two other models at a data item level. Here, we see that the predicted distributions seem to differ from the empirical distributions and each other in meaningful ways. This is because ![6_image_0.png](6_image_0.png) ![6_image_2.png](6_image_2.png) r/Incels ![6_image_1.png](6_image_1.png) r/darkjokes ![6_image_3.png](6_image_3.png) ![6_image_4.png](6_image_4.png) our models rely on other items with similar label distributions or language to normalize reactions. For instance, in example DFBE4, we see that the heavy annotator response to sad (795 responses) is retained when w = 0 (0.910), when only labels determine the clusters, but it decreases dramatically (to 0.165 and 0.126) as w increases. These examples show that when we introduce text into the clustering phase, the overall performance may not change, but qualitative differences may be quite significant at the item level. The examples in Figure 4 were surfaced by randomly sampling Reddit DSI for posts whose predictions, using our models, differed from the human annotation. These examples all elicit ways of interpreting social media posts that contrast model predictions, human annotator choices, and our observations about offensiveness and toxicity. Example DSIE4, (Figure 4a) is an offensive joke that mocks women and people with a mental health disorder called borderline personality disorder ("BPD"). In contrast, the human annotation was split between *not intended to be offensive* and probably intended to be offensive. No human chose intended to be offensive, yet our algorithm predicted it might be, reflecting the deniability that comes from phrasing offensive speech as a "joke." Example DSIE5, (Figure 4c) is a joke about rape and older women. It is offensive because it associates rape with sex as opposed to rape with violence and sex with procreation. This is a challenging case for a typical ML classifier—there is no majority, and the label polarities are also opposite. In this case, our prediction correctly identifies the majority label. This may be due to our models grouping similar data items of similar content, supporting items such as this when there is contrasting confidence in human annotators. Example DSIE6 (Figure 4b) is offensive because it makes light of the hate group KKK wearing hoods by identifying them with an NWA song and film about African American teenagers ("boyz n the hood"). The PLL prediction also indicates that this post may have been *intended to be offensive*. But the human annotator thought it was *probably* not intended to be offensive. This is another case where our prediction aligns with our judgment. Example DSIE7, (Figure 4d) is offensive because it alludes to a woman being dead and thus not having agency; it seems threatening. Two human annotators chose this to be *probably intended* to be offensive, and one annotator considered it not intended to be offensive. The prediction finds this intended to be offensive. A commonality among these examples is that they all contain an element of deniability—the poster can always claim they were only joking. One challenge with content moderation is where to draw the line. When does the potential harm of letting an offensive post through outweigh the winnowing of free discourse? The answer often depends on context. The population-level learning approach we advocate here can help provide a more nuanced view into annotator response. It may also provide context on opinions to inform decisions about what should and should not be censored. Our work also supports the findings from (Sap et al., 2021), where they studied the underlying reasons why annotators disagree on subjective content, such as offensive language annotation. The examples show how the proposed models can identify offensive content even with unreliable training data (human annotations). ## 5 Conclusion Human annotation is often an expensive-to-acquire, challenging, and subjective resource for supervised machine learning. The obstacles to using human decisions in ML classification tasks are even more apparent when the problem domain is social media content. The nuance, disagreement, and diversity of opinions by humans augment and enrich the complex decisions machine learning attempts to surface. To gain as much utility as possible from this valuable resource, we propose and subsequently *CrowdOpinion* to retain these human judgments in the data prediction pipeline for as long as possible. First, this work introduces a novel method for mixing language features and label features into label distribution estimators to improve populationlevel learning. Then, we evaluated our approach against different baselines and experimented with datasets containing varying amounts of annotator disagreements. Our results suggest that (i) clustering is an effective measure for countering the problem of label sparseness when learning a populationlevel distribution of annotator responses, (ii) data features or label distributions are equally helpful as spaces in which to perform such clustering, and thus (iii) label distributions are meaningful signals that reflect the content of their associated items. ## Limitations Evaluation: We evaluate work as a single-label learning problem (accuracy) and a probability distribution (KL). These metrics do not fully capture the nuances of the crowd (Inel et al., 2014). We hope to build on this work by moving beyond general population-level predictions to predictions on subpopulations of interest, such as vulnerable communities. We hope to develop better methods for evaluating and assessing the performance of population-level learning. The range of mixing (w =) of the language features and labels in our experiments could be further delved into. Our experiments cover weights ranging from 0 to 100 in quartiles, but this parameter, as a hyperparameter, could benefit from additional experiments in finer ranges. Datasets: Our experimental datasets have been primarily in English. In addressing the ability to generalize, we hope to explore other offensive or hate speech-related datasets from other languages. The challenge of evaluating our models with other languages is acquiring a dataset with annotatorlevel labels, a rare resource for English datasets and challenging for other languages. Finally, we hope our methods open the discussion to building nuanced systems that capture human disagreement while studying subjective content on social media. Computation: As our experiments follow a twostage setup, the first phase (data mixing) of it can be further optimized to run on GPUs similar to the second phase (classification), which is running on GPU through the TensorFlow/Keras implementation. The first phase utilizes libraries through Sckitlearn, BNPY, and scripts through Python (NBP), which can be a bottleneck for implementing the work and expanding. ## Ethical Considerations Our analysis constitutes a secondary study of publicly available datasets and thus is considered exempt from a federal human subjects research perspective. However, as with any study that involves data collected from humans, there is a risk that it can be used to identify people (Hovy and Spruit, 2016; Kralj Novak et al., 2022). We understand these risks and train and test our models on anonymized data to minimize them. In addition, it is essential to note that any methods identifying marginalized voices can also aid in selective censorship. Our models in Stage 1 and Stage 2, generate rich soft label distributions, this can be helpful for ML models to learn from a representative label. 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Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2019. Detection and Resolution of Rumours in Social Media: A Survey. ACM Computing Surveys, 51(2):1–36. ## A Dataset Sources 1. DGE by Demszky et al. (2020) - Available at https://github.com/ google-research/google-research/ tree/master/goemotions 2. DJQ1−3 by Liu et al. (2016) - Available at https://github.com/Homan-Lab/pldl_ data 3. DSI by Sap et al. (2019) - Available at https://homes.cs.washington.edu/ ~msap/social-bias-frames/index.html 4. DF B available at Wolf (2016) ## A.1 Goemotions (Dge) This is one of the largest, hate-speech related datasets of around 58,000 Reddit comments collected by Demszky et al. (2020). The comments are annotated by a total of 82 MTurkers with 27 emotions or "neutral," yielding 28 annotation labels total: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, and *neutral*. The number of annotations per item varies from 1 to 16. ## A.2 Jobs (Djq1-3) Liu et al. (2016) asked five annotators each from MTurk and F8 platforms to label work related tweets according to three questions: point of view of the tweet (DJQ1: 1st person, 2nd person, 3rd person, *unclear*, or *not job related*), subject's employment status (DJQ2: employed, *not in labor* force, not employed, *unclear*, and *not job-related*), and employment transition event (DJQ3: *getting* hired/job seeking, getting fired, quitting a job, losing job some other way, *getting promoted/raised*, getting cut in hours, complaining about work, offering support, going to work, coming home from work, *none of the above but job related*, and not job-related). ## A.3 Sbic Intent (Dsi) The Social Bias Inference Corpus (DSI) dataset is made up of ∼45,000 posts from Reddit, Twitter, and hate sites collected by Sap et al. (2019). It was annotated with respect to seven questions: offensiveness, intent to offend, lewdness, group implications, targeted group, implied statement, in-group language. Out of these predicates, we consider only the intent to offend question (as it had the richest label distribution patterns) with the label options: Intended, Probably Intended, *Probably Not Intended*, and *Not Intended*. The number of annotations per data item varies between 1 and 20 annotations. ## A.4 Facebook (Dfb) The original multi-lingual dataset is Facebook posts written on the 144 most-liked pages during 4 months in 2016. The posts all come from pages hosted by news entities or public figures with a large fanbase interacting through comments and reactions. Each item consists of the post text (we remove all non-text data) and we take as the label set the (normalized) distribution of the post's reactions: like, love, haha, wow, sad, and *angry*. However, as *like* tends to dominate, following Tian et al. (2017) we eliminate that reaction before we normalize. We perform language detection 2and subsample 2,000 English-only posts. The annotations per item varies widely from 50 to 71,399. In contrast to other datasets, DFB is a special case since annotations for it come from users of the social network. The users are "reacting" to a post in contrast to a human annotator annotating a post for a specified task. The randomness of users reacting to a post and posts being from different domains make it a special case. ## B Experimental Setup Our experimental setup consists of the following configurations; Setup \#1 - Ubuntu 18.04, Intel i67600k (4 cores) at 4.20GHz, 32GB RAM, and nVidia GeForce RTX 2070 Super 8GB VRAM. Setup \#2 - Debian 9.8, Intel Xeon (6 cores) at 2.2GHz, 32GB RAM, and nVidia Tesla P100 12GB VRAM. For a single pass through on a dataset, the estimated time of completion is 8 hours per language representation model on Setup \#2, which is the slowest out of the two. In our experimental setup, we compare our language based models to other PLDL models based on annotations and baselines from prior research. For comparison sake, we built our own experimental setup similar to the models used by Liu et al. (2019); Weerasooriya et al. (2020). 2Google Translate Language Detection https://bit.ly/ 33g7Ct3 Experiments tracked with "Weights and Biases" by Biewald (2020). ## C Complete Set Of Results For Co See Table 6 for KL-Divergence and Table 7 and for accuracy results. ## D Entropy Distributions See Figure 6 for the Histograms. ## E Model Selection Parameters | Dataset | w = 0 | w = 0.25 | w = 0.50 | w = 0.75 | w = 1 | | |----------------------------------|---------|------------|------------|------------|---------|------| | Neighborhood Based Pooling Model | | | | | | | | DFB | r | 0.8 | 1.4 | 3.0 | 3.6 | 4.6 | | KL | 0.085 | 0.093 | 0.070 | 0.080 | 0.098 | | | DGE | r | 0.8 | 1.1 | 0.6 | 0.9 | 10.6 | | KL | 0.020 | 0.032 | 0.252 | 0.363 | 0.232 | | | DJQ1 | r | 3.5 | 5.6 | 3.4 | 5.6 | 2.8 | | KL | 0.133 | 0.123 | 0.120 | 0.131 | 0.456 | | | DJQ2 | r | 3.2 | 3.5 | 2.4 | 2.8 | 5.5 | | KL | 0.134 | 0.135 | 0.137 | 0.133 | 0.512 | | | DJQ3 | r | 10.2 | 5 | 6.1 | 8.7 | 3 | | KL | 0.023 | 0.024 | 0.027 | 0.028 | 0.884 | | | DSI | r | 2.4 | 9.3 | 4.8 | 9.8 | 11.4 | | KL | 0.160 | 0.176 | 0.180 | 0.190 | 0.350 | | | Data- | Baseline | CO-C-CNN-w | | | | |------------------------|--------------|--------------|-------------|-------------|--------------| | set | w = 0 | w = 0.25 | w = 0.50 | w = 0.75 | w = 1 | | C =FMM Clustering | | | | | | | DFB | 0.707±0.003 | 0.686±0.004 | 0.687±0.004 | 0.689±0.003 | 0.686±0.003 | | DGE | 2.011± 0.002 | 2.010±0.001 | 2.008±0.002 | 2.005±0.001 | 2.004±0.002 | | DJQ1 | 0.458±0.001 | 0.464±0.007 | 0.468±0.011 | 0.46±0.004 | 0.461±0.006 | | DJQ2 | 0.515±0.001 | 0.522±0.009 | 0.517±0.005 | 0.515±0.003 | 0.518±0.007 | | DJQ3 | 0.887±0.001 | 0.892±0.004 | 0.889±0.005 | 0.889±0.003 | 0.890±0.003 | | DSI | 0.991±0.003 | 0.992±0.005 | 0.993±0.003 | 0.927±0.027 | 0.86±0.026 | | C =GMM Clustering | | | | | | | DFB | 0.684±0.001 | 0.683±0.003 | 0.682±0.001 | 0.680±0.001 | 0.685±0.002 | | DGE | 1.999± 0.001 | 1.998±0.001 | 2.002±0.006 | 2.000±0.003 | 1.998± 0.003 | | DJQ1 | 0.450±0.001 | 0.467±0.001 | 0.447±0.004 | 0.437±0.001 | 0.427±0.01 | | DJQ2 | 0.513±0.002 | 0.512±0.001 | 0.510±0.003 | 0.514±0.001 | 0.516±0.004 | | DJQ3 | 0.880±0.001 | 0.881±0.001 | 0.870±0.001 | 0.885±0.001 | 0.889±0.005 | | DSI | 0.882±0.008 | 0.877±0.024 | 0.904±0.021 | 0.9±0.031 | 0.894±0.026 | | C = K-Means clustering | | | | | | | DFB | 0.680±0.0 | 0.687±0.001 | 0.680±0.001 | 0.688±0.001 | 0.684±0.0 | | DGE | 1.998±0.001 | 1.999±0.002 | 2.002±0.006 | 2.001±0.004 | 2.000±0.004 | | DJQ1 | 0.457±0.001 | 0.456±0.0 | 0.457±0.001 | 0.447±0.001 | 0.434±0.001 | | DJQ2 | 0.499±0.001 | 0.510±0.001 | 0.510±0.002 | 0.512±0.002 | 0.513±0.001 | | DJQ3 | 0.874±0.001 | 0.883±0.001 | 0.853±0.001 | 0.888±0.001 | 0.889±0.001 | | DSI | 0.857±0.008 | 0.886±0.024 | 0.889±0.028 | 0.895±0.028 | 0.894±0.027 | | C = LDA Clustering | | | | | | | DFB | 0.684±0.0 | 0.683±0.0 | 0.684±0.0 | 0.684±0.0 | 0.684±0.0 | | DGE | 1.987±0.0 | 1.997±0.0 | 1.995±0.0 | 1.999±0.002 | 1.999±0.001 | | DJQ1 | 0.458±0.001 | 0.457±0.001 | 0.456±0.001 | 0.459±0.001 | 0.458±0.001 | | DJQ2 | 0.512±0.0 | 0.514±0.001 | 0.515±0.0 | 0.513±0.001 | 0.512±0.001 | | DJQ3 | 0.884±0.0 | 0.885±0.0 | 0.880±0.001 | 0.834±0.0 | 0.823±0.0 | | DSI | 0.932±0.0 | 0.980±0.0 | 0.92±0.045 | 0.867±0.018 | 0.905±0.023 | | C =NBP Pooling | | | | | | | DFB | 0.688±0.003 | 0.686±0.001 | 0.687±0.002 | 0.688±0.004 | 0.69±0.007 | | DGE | 2.002±0.005 | 2.0±0.002 | 2.001±0.005 | 2.001±0.001 | 2.010±0.003 | | DJQ1 | 0.469±0.009 | 0.485±0.026 | 0.479±0.021 | 0.475±0.012 | 0.457±0.0 | | DJQ2 | 0.520±0.007 | 0.519±0.01 | 0.519±0.007 | 0.522±0.01 | 0.513±0.001 | | DJQ3 | 0.897±0.012 | 0.889±0.005 | 0.894±0.006 | 0.889±0.007 | 0.883±0.0 | | DSI | 0.900±0.024 | 0.895±0.025 | 0.894±0.028 | 0.890±0.019 | 0.889±0.027 | | Data- | Baseline | CO-C-CNN-w | | | | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------|--------------|----------|----------|-------| | set | w = 0 | w = 0.25 | w = 0.50 | w = 0.75 | w = 1 | | C = FMM Clustering | | | | | | | DFB | 0.780±0.001 0.777±0.010 0.789±0.001 0.787±0.001 | 0.790±0.001 | | | | | DGE 0.949±2e −16 0.949±2e −16 0.923±2e −16 0.910±2e −16 0.948±2e −16 DJQ1 0.892±0.0 0.890±0.0 0.878±0.0 0.880±0.0 0.892±0.0 DJQ2 0.890±0.0 0.812±0.0 0.890±0.0 0.870±0.0 0.830±0.0 DJQ3 0.878±0.002 0.880±0.002 0.870±0.003 0.881±0.002 0.880±0.002 DSI 0.949±0.0 0.950±0.0 0.940±0.0 0.941±0.0 0.942±0.0 C = GMM Clustering DFB 0.785±0.001 0.789±0.001 0.787±0.001 0.798±0.001 0.783±0.001 DGE 0.940±0.001 0.949± 0.001 0.942±0.006 0.949±0.003 0.950±0.003 DJQ1 0.891±1e −16 0.888±1e −16 0.880±1e −16 0.901±1e −16 0.890±0.0 −16 0.875±1e −16 0.865±1e −16 0.800±1e −16 0.801±0.0 DJQ2 0.870±1e DJQ3 0.880±0.002 0.881±1e −16 0.875±0.001 0.870±0.002 0.871±0.002 DSI 0.949±0.0 0.947±0.0 0.945±0.0 0.944±0.0 0.943±0.0 C = K-Means Clustering DFB 0.780±0.001 0.783±0.001 0.786±0.001 0.773±0.001 0.765±0.001 DGE 0.940±0.000 0.930±0.000 0.930±0.000 0.902±0.000 0.938±0.000 DJQ1 0.890±0.0 0.891±0.0 0.893±0.0 0.890±0.0 0.870±0.0 DJQ2 0.873±0.0 0.870±0.0 0.875±0.0 0.872±0.0 0.870±0.0 DJQ3 0.881±0.0 0.878±0.0 0.875±0.0 0.870±0.0 0.830±0.001 DSI 0.775 ±0.008 0.777±0.007 0.76±0.028 0.773±0.009 0.759±0.023 C = LDA Clustering DFB 0.784±0.0 0.782±0.0 0.787±0.0 0.788±0.0 0.789±0.0 DGE 0.949±0.0 0.930±0.0 0.935±0.0 0.932±0.0 0.950±0.0 DJQ1 0.891±0.0 0.893±0.0 0.890±0.0 0.891±0.0 0.891±0.0 DJQ2 0.873±0.0 0.875±0.0 0.870±0.0 0.878±0.0 0.879±0.0 DJQ3 0.880±0.0 0.881±0.0 0.882±0.0 0.883±0.0 0.879±0.001 DSI 0.932±0.0 0.980±0.0 0.92±0.045 0.867±0.018 0.905±0.023 C = NBP Clustering DFB 0.785±0.0 0.781±0.0 0.780±0.0 0.787± 0.0 0.785±0.0 DGE 0.850±0.0 0.820±0.0 0.810±0.0 0.800±0.0 0.805±0.0 DJQ1 0.890±0.0 0.879±0.0 0.890±0.0 0.789±0.005 0.892±0.0 DJQ2 0.873±0.0 0.897±0.0 0.880±0.0 0.820±0.0 0.865±0.0 DJQ3 0.880±0.002 0.879±0.002 0.865±0.002 0.879±0.002 0.881±0.0 DSI 0.755±0.036 0.767±0.019 0.758±0.034 0.761±0.016 0.762±0.025 | | | | | | ![14_image_0.png](14_image_0.png) | Dataset | w = 0 | w = 0.25 | w = 0.50 | w = 0.75 | w = 1 | w = 0 | w = 0.25 | w = 0.50 | w = 0.75 | w = 1 | | |---------------|-----------|------------|------------|------------|---------|---------|------------|------------|------------|---------|----| | FMM Model | GMM Model | | | | | | | | | | | | DFB | p | 4 | 30 | 36 | 4 | 32 | 26 | 17 | 37 | 26 | 11 | | KL | 0.704 | 1.551 | 1.587 | 1.273 | 1.598 | 0.702 | 0.696 | 0.706 | 0.702 | 1.432 | | | DGE | p | 24 | 36 | 6 | 16 | 20 | 25 | 34 | 24 | 26 | 26 | | KL | 2.053 | 2.121 | 3.312 | 3.941 | 4.804 | 2.191 | 2.361 | 3.460 | 3.442 | 5.198 | | | DJQ1 | p | 15 | 6 | 7 | 9 | 6 | 31 | 11 | 36 | 27 | 4 | | KL | 0.465 | 0.458 | 0.468 | 0.461 | 0.903 | 0.497 | 0.714 | 0.770 | 0.785 | 0.751 | | | DJQ2 | p | 9 | 8 | 5 | 5 | 5 | 34 | 14 | 30 | 23 | 6 | | KL | 0.516 | 0.511 | 0.514 | 0.514 | 1.194 | 0.537 | 0.826 | 0.876 | 0.869 | 0.878 | | | DJQ3 | p | 9 | 20 | 8 | 21 | 10 | 17 | 24 | 37 | 23 | 11 | | KL | 0.965 | 1.406 | 1.371 | 1.586 | 1.457 | 0.903 | 0.902 | 0.918 | 0.905 | 1.491 | | | DSI | p | 21 | 30 | 37 | 4 | 5 | 12 | 13 | 10 | 35 | 33 | | KL | 0.942 | 0.940 | 0.932 | 0.566 | 0.355 | 0.849 | 0.711 | 1.935 | 1.989 | 1.932 | | | K-Means Model | LDA Model | | | | | | | | | | | | DFB | p | 21 | 35 | 34 | 30 | 32 | 9 | 19 | 16 | 5 | 8 | | KL | 0.702 | 0.710 | 0.733 | 0.705 | 0.715 | 0.680 | 0.584 | 0.687 | 0.689 | 0.690 | | | DGE | p | 27 | 34 | 19 | 31 | 28 | 14 | 17 | 14 | 4 | 17 | | KL | 2.322 | 2.593 | 3.541 | 4.430 | 4.293 | 1.907 | 1.997 | 1.985 | 2.494 | 2.938 | | | DJQ1 | p | 35 | 21 | 35 | 35 | 22 | 37 | 35 | 14 | 22 | 10 | | KL | 0.471 | 0.463 | 0.467 | 0.477 | 0.463 | 0.450 | 0.449 | 0.435 | 0.480 | 0.470 | | | DJQ2 | p | 11 | 16 | 34 | 30 | 33 | 19 | 7 | 5 | 19 | 9 | | KL | 0.515 | 0.512 | 0.540 | 0.519 | 0.538 | 0.500 | 0.510 | 0.512 | 0.509 | 0.514 | | | DJQ3 | p | 35 | 19 | 29 | 14 | 32 | 5 | 5 | 4 | 5 | 18 | | KL | 0.969 | 0.938 | 0.948 | 0.912 | 0.953 | 0.889 | 0.887 | 0.886 | 0.880 | 0.890 | | | DSI | p | 38 | 19 | 17 | 31 | 35 | 6 | 15 | 4 | 18 | 31 | | KL | 0.856 | 0.564 | 0.108 | 0.100 | 0.050 | 0.935 | 0.935 | 0.496 | 0.397 | 0.296 | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 4.2 ✓ A2. Did you discuss any potential risks of your work? Section 4.1 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Appendix A ✓ B1. Did you cite the creators of artifacts you used? Appendix A ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We have cited the original owner (research papers) ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We have cited the original owner (research papers) ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 4.1 ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appendix A ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3.1 ## C ✓ **Did You Run Computational Experiments?** Section 3 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix B C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? No response. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

varshney-baral-2023-post

Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in {QA}

https://aclanthology.org/2023.acl-long.55

Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. {`}Selective prediction{'} partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question {`}what to do after abstention{'}. To this end, we present an explorative study on {`}Post-Abstention{'}, a task that allows re-attempting the abstained instances with the aim of increasing **coverage** of the system without significantly sacrificing its **accuracy**. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements {--}performance metric of the Post-Abstention task{--} both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.

## Post-Abstention: Towards Reliably Re-Attempting The Abstained Instances In Qa Neeraj Varshney And Chitta Baral Arizona State University Abstract Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in realworld applications. *Selective prediction* partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question '*what to do after abstention*'. To this end, we present an explorative study on 'Post-Abstention', a task that allows re-attempting the abstained instances with the aim of increasing *coverage* of the system without significantly sacrificing its *accuracy*. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements –performance metric of the Post-Abstention task– both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems. ## 1 Introduction Despite remarkable progress made in Natural Language Processing (NLP), even the state-of-the-art systems often make incorrect predictions. This problem becomes worse when the inputs tend to diverge from the training data distribution (Elsahar and Gallé, 2019; Miller et al., 2020; Koh et al., 2021). Incorrect predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. Selective prediction partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. By avoiding potentially incorrect predictions, it allows maintaining high task accuracy and thus improves the system's reliability. Selective prediction has recently received considerable attention from the NLP community leading to development of several methods (Kamath et al., 2020; Garg and Moschitti, 2021; Xin et al., 2021; Varshney et al., 2022d). While these contributions are important, selective prediction leaves us with a pertinent question: *what to do after abstention?* In this work, we address the above question and present an explorative study on '**Post-Abstention**', a task that allows re-attempting the abstained instances with the aim of increasing *coverage* of the given selective prediction system without significantly sacrificing its *accuracy*. Figure 1 illustrates the benefit of employing a post-abstention method; a model that achieves an accuracy of 70% is first enabled with the selective prediction ability that increases the accuracy to 85% but answers only 71% instances. Then, a post-abstention method is employed (for the 29% abstained instances) that assists the system in answering 9% more instances raising the coverage to 80% without considerably dropping the overall accuracy. We note that this task allows re-attempting all the abstained instances but does not require the system to necessarily output predictions for all of them i.e. the system can abstain even after utilizing a post-abstention method (when it is not sufficiently confident even in its new prediction). This facet not only allows the system to maintain its performance but also provides opportunities of sequentially applying stronger post-abstention methods to reliably and optimally increase the coverage in stages. We provide mathematical formulation of the post-abstention task and explore several baseline methods to solve it (Section 2). To evaluate the efficacy of these methods, we conduct comprehensive experiments with 11 Question-Answering datasets from MRQA shared task (Fisch et al., 2019) in 967 ![1_image_0.png](1_image_0.png) both in-domain and out-of-domain settings (Section 3). Our post-abstention methods lead to overall risk improvements (performance metric of the proposed task) of up to 21.81 in the in-domain setting and 24.23 in the out-of-domain setting. To further analyze these results, we study several research questions, such as 'what is the extent of overlap between the instances answered by different postabstention methods', 'what is the distribution of model's original confidence on instances that get answered in the post-abstention stage', and 'how often do the system's predictions change after applying post-abstention methods'. In Section 4, we show that these investigations lead to numerous important and interesting findings. In summary, our contributions are as follows: 1. We present an **explorative study on 'PostAbstention'**, a task that aims at increasing the coverage of a given selective prediction system without significantly sacrificing its *accuracy*. 2. We **explore several baseline post-abstention** methods and evaluate them in an extensive experimental setup spanning 11 QA datasets in both in-domain and out-of-domain settings. 3. We show that the proposed post-abstention methods **result in overall risk value improvements** of up to 21.81 and 24.23 in the in-domain and out-of-domain settings respectively. 4. Our **thorough analysis** leads to several interesting findings, such as (a) instances answered by different post-abstention methods are not mutually exclusive i.e. there exist some overlapping instances, (b) instances that get answered in the post-abstention stage are not necessarily the ones on which the given system was initially most confident, etc. We believe our work will encourage further research in Post-Abstention, an important step towards improving the reliability of NLP systems. ## 2 Post-Abstention In this section, we first provide background for post-abstention (2.1) and then describe the task (2.2) and its approaches (2.3). ## 2.1 Background Post-abstention, as the name suggests, is applicable for a system that abstains from answering i.e. a selective prediction system. A system can typically abstain when its prediction is likely to be incorrect. This improves the reliability of the system. Such a system typically consists of two functions: a predictor (f) that gives the model's prediction on an input (x) and a selector (g) that determines if the system should output the prediction made by f: $$(f,g)(x)={\begin{cases}f(x),&{\mathrm{if~g(x)=1~}}\\ A b s t a i n,&{\mathrm{if~g(x)=0~}}\end{cases}}$$ Typically, g comprises of a prediction confidence estimator g˜ and a threshold th that controls the level of abstention for the system: ## G(X) = 1[˜G(X)) > Th] A selective prediction system makes trade-offs between *coverage* and *risk*. Coverage at a threshold th is defined as the fraction of total instances answered by the system (where *g > th* ˜ ) and risk is the error on the answered instances. With decrease in threshold, coverage will increase, but the risk will usually also increase. The overall selective prediction performance is measured by the *area under Risk-Coverage curve* (ElYaniv et al., 2010) which plots risk against coverage for all confidence thresholds. Lower AUC is better as it represents lower average risk across all confidence thresholds. In NLP, approaches such as Monte-Carlo Dropout (Gal and Ghahramani, 2016), Calibration (Kamath et al., 2020; Varshney et al., 2022c,d; Zhang et al., 2021), Error Regularization (Xin et al., 2021) and Label Smoothing (Szegedy et al., 2016) have been studied for selective prediction. In this work, we consider MaxProb (Hendrycks and Gimpel, 2017), a technique that uses the maximum softmax probability across all answer candidates as the confidence estimator. We use this simple technique because the focus of this work is on postabstention i.e. the next step of selective prediction. However, we note that the task formulation and the proposed methods are general and applicable to all selective prediction approaches. ## 2.2 Task Formulation We define the post-abstention task as follows: Given a selective prediction system with an abstention threshold, the post-abstention task allows re-attempting the abstained instances with the aim of improving the coverage without considerably degrading the accuracy (or increasing the risk) of the given system. Next, we mathematically describe the task and its performance evaluation methodology. Let the coverage and risk of the given selective prediction system at abstention threshold th be covth and *risk*th respectively. A post-abstention method re-attempts the originally abstained instances (where *g < th* ˜ ) and outputs the new prediction for the ones where it is now sufficiently confident. This typically leads to an increase in the coverage of the system with some change in the risk value; let the new coverage and risk be cov′th and *risk*′th respectively. From the risk-coverage curve of the given system, we calculate its risk at coverage cov′th and compare it with *risk*′th to measure the efficacy of the post-abstention method (refer to Figure 2). For a method to have a positive impact, its risk (*risk*′th) should be lower than the risk of the given system at coverage cov′th. We summarize this performance evaluation methodology in Figure 2. To get an overall performance estimate of a post- ![2_image_0.png](2_image_0.png) abstention method, we compile these differences in risk values for all confidence thresholds and calculate an aggregated value. The higher the overall improvement value, the more effective the method is. We note that this evaluation methodology is fair and accurate as it conducts pair-wise comparisons at **equal coverage** points. An alternative performance metric could be AUC but it computes the overall area ignoring the pair-wise comparisons which are crucial for our task because the coverage points of the original system would be different from those achieved by the post-abstention method. ## 2.3 Approaches 2.3.1 **Ensembling Using Question Paraphrases** It is well known that even state-of-the-art NLP models are often brittle i.e. when small semanticpreserving changes are made to the input, their predictions tend to fluctuate greatly (Jia and Liang, 2017; Belinkov and Bisk, 2018; Iyyer et al., 2018; Ribeiro et al., 2018; Wallace et al., 2019). Ensembling the predictions of the model on multiple semantically equivalent variants of the input is a promising approach to address this issue (Anantha et al., 2021; Vakulenko et al., 2021) as it can reduce the spread or dispersion of the predictions. ![3_image_0.png](3_image_0.png) We leverage the above technique in reattempting the abstained questions i.e. we first generate multiple paraphrases of the input instance and then aggregate the model's predictions on them. We use BART-large (Lewis et al., 2019) model fine-tuned on Quora Question Corpus (Iyer et al., 2017), PAWS (Zhang et al., 2019), and Microsoft Research Paraphrase Corpus (Dolan and Brockett, 2005) for paraphrasing and explore the following strategies for aggregating the model predictions: - **Mean**: In this strategy, we calculate the average confidence assigned to each answer candidate across all predictions. Then, we select the candidate with the highest average confidence as the system's prediction. Note that the system will output this prediction only if its confidence surpasses the abstention threshold. - Max: Here, like the *mean* strategy, we select the answer candidate with the highest average confidence but we use the maximum confidence assigned to that candidate as its prediction confidence. This is done to push the most confident prediction above the abstention threshold. ## 2.3.2 Re-Examining Top N Predictions (Retop) State-of-the-art models have achieved impressive performance on numerous NLP tasks. Even in cases where they fail to make a correct prediction, they are often able to rank the correct answer as one of their top N predictions. This provides opportunities for re-examining the top N predictions to identify the correct answer in case of abstention. To this end, a model that can estimate the correctness of a prediction can be leveraged. Following this intuition, we develop an **auxiliary model** that takes the context, question, and a prediction as input and assigns a score indicating the likelihood of that prediction to be correct. This model can be used for each of the top N predictions given by the QA model to select the one that is most likely to be the correct answer. Training Auxiliary Model: We first create data instances by annotating (context, question, prediction) triplets conditioned on the correctness of the QA system's predictions and then train a classification model using this data. This model is specific to the given QA system and essentially learns to distinguish its correct and incorrect predictions. - **Annotate (context, question, prediction)** triplets: We utilize the trained QA model to get its top N predictions for each training instance. Then, we annotate each (context, question, prediction) triplet based on the prediction's correctness i.e. a correct prediction is annotated as '1' and an incorrect prediction is annotated as '0'. Figure 3 illustrates this annotation step. - **Train a classification model**: Then, a binary classification model is trained using the annotated dataset collected in the previous step. This model specifically learns to distinguish the correct predictions of the QA model from the incorrect ones. Softmax probability assigned to the label '1' corresponds to the likelihood of correctness for each prediction. Note that we use the QA model's top N predictions to collect the '0' annotations instead of randomly selecting candidates because this procedure results in highly informative negative instances (that are probable predictions and yet incorrect) and not easy/obvious negatives. This can help the auxiliary model in learning fine-grained representations distinguishing correct and incorrect predictions. Leveraging Auxiliary Model: For an abstained instance, we compute the likelihood value for each of the top N predictions given by the QA model using our trained auxiliary model. Then, we calculate the overall confidence (c) of each prediction (p) as a weighted average of the QA model's probability (sq) and the auxiliary model's likelihood score (sa) i.e. cp is calculated as: $$c_{p}=\alpha*s_{q}^{p}+(1-\alpha)*s_{a}^{p}$$ where α is a weight parameter. We incorporate QA model's probability as it provides more flexibility to compute the overall confidence. Finally, prediction with the highest overall confidence is selected as the new prediction. We differentiate this method from existing methods such as calibration in Appendix C. ## 2.3.3 Human Intervention (Hi) In intolerant application domains such as biomedicals where incorrect predictions can have serious consequences, human intervention is the most reliable technique to answer the abstained instances. Human intervention can be in various forms such as providing relevant knowledge to the model, asking clarifying questions (Rao and Daumé III, 2018) or simplifying the input question. In this work, we explore a simple human intervention approach in which the system provides multiple predictions instead of only one prediction for the abstained instances. The human can then select the most suitable prediction from the provided predictions. Performance of this method can be approximated based on the presence of the correct answer in the predictions provided to the human. Note that the above approach would answer all the abstained instances and hence the coverage would always be 100%. This implies that with the increase in abstention threshold, the risk would monotonically decrease as multiple predictions would be returned for a larger number of instances. In addition to the above approach, we also explore a **REToP-centric** HI approach in which the system returns multiple predictions only when REToP surpasses the confidence threshold in the postabstention stage. Similar to REToP, it abstains on the remaining instances. Finally, we note that comparing the performance of HI approaches with other post-abstention approaches would be unfair as other approaches return only a single prediction. Therefore, we present HI results separately. ## 3 Experiments And Results 3.1 Experimental Setup Datasets: We experiment with SQuAD 1.1 (Rajpurkar et al., 2016) as the source dataset and the following 10 datasets as out-of-domain datasets: NewsQA (Trischler et al., 2017), TriviaQA (Joshi et al., 2017), SearchQA (Dunn et al., 2017), HotpotQA (Yang et al., 2018), and Natural Questions (Kwiatkowski et al., 2019), DROP (Dua et al., 2019), DuoRC (Saha et al., 2018), RACE (Lai et al., 2017), RelationExtraction (Levy et al., 2017), and TextbookQA (Kim et al., 2019). We use the preprocessed data from the MRQA shared task (Fisch et al., 2019) for our experiments. Implementation Details: We run all our experiments using the huggingface (Wolf et al., 2020) implementation of transformers on Nvidia V100 16GB GPUs with a batch size of 32 and learning rate ranging in {1−5}e−5. We generate 10 paraphrases of the question in Ensembling method, reexamine top 10 predictions, vary α in the range 0.3 − 0.7 for REToP method, and vary the number of predictions in the range 2 to 5 for HI methods. Since the focus of this work is on post-abstention, it's crucial to experiment with models that leave sufficient room for effectively evaluating the ability of post-abstention methods. For that reason, we experiment with a small size model (BERT-mini having just 11.3M parameters) from Turc et al. (2019) for our experiments. However, we note that our methods are general and applicable for all models. ## 3.2 Results 3.2.1 Retop Table 1 shows the post-abstention performance of REToP for selected abstention thresholds. The last column ('*Total Risk Improvement*') in this table corresponds to the overall improvement aggregated over all confidence thresholds. It can be observed that REToP achieves considerable risk improvements both in the in-domain setting (21.81 on SQuAD) and the out-of-domain settings (24.23 on TextbookQA, 21.54 on HotpotQA, 20.42 on RE, etc). Next, we analyze these results in detail. ## Higher Improvement On Moderate Confidences: In Figure 4, we plot risk improvements achieved by REToP on SQuAD (in-domain) and HotpotQA (out-of-domain) datasets for all confidence thresholds. These plots reveal that the improvement is Dataset Model **0.2 0.32 0.36 0.48 0.54 0.60 0.68 Total Risk** Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ **Improvement**↑ Given (G) 96.65 32.45 87.24 28.10 83.34 26.69 69.94 21.91 62.57 19.91 56.23 17.98 47.92 15.43 SQuAD REToP 99.73 **33.75** 97.27 **31.93** 95.08 **30.85** 80.88 **24.84** 72.44 **21.82** 63.73 **19.19** 52.65 **16.43** (in-domain) G@REToPcov - 34.00 - 32.77 - 31.67 - 25.82 - 22.59 - 20.24 - 16.83 **21.81** HotpotQA Given (G) 97.54 67.65 89.56 65.88 85.39 65.13 71.75 62.71 64.77 61.56 58.19 60.34 49.25 58.29 REToP 99.93 **68.17** 98.63 **67.39** 96.9 **66.61** 82.88 **63.61** 73.55 **61.89** 64.36 **60.53** 52.96 **58.34** G@REToPcov - 68.30 - 67.92 - 67.47 - 64.52 - 63.04 - 61.55 - 59.01 **21.54** RE Given (G) 97.59 44.49 89.01 40.51 85.41 39.04 74.08 34.16 66.86 30.54 60.58 27.94 54.10 24.20 REToP 99.93 **45.38** 98.95 **44.39** 97.52 **43.79** 85.89 **38.67** 77.61 **34.57** 69.54 **31.12** 59.33 **25.39** G@REToPcov - 45.47 - 45.01 - 44.43 - 39.22 - 35.51 - 32.10 - 27.33 **20.42** RACE Given (G) 89.02 80.5 71.07 77.04 66.17 75.56 51.34 72.54 43.47 69.62 36.2 68.85 29.97 63.86 REToP 99.41 82.24 92.28 **80.71** 86.94 **79.35** 62.91 **73.82** 51.48 **71.76** 42.28 **69.47** 33.09 **65.92** G@REToPcov - 81.94 - 81.00 - 80.00 - 75.00 - 72.54 - 69.72 - 66.37 **15.10** NewsQA Given (G) 93.90 69.76 80.91 66.40 75.5 64.91 60.30 60.79 53.30 58.8 47.17 56.62 39.32 54.11 REToP 99.48 **71.03** 96.13 **70.24** 93.21 69.64 70.85 **63.71** 60.73 **60.67** 52.04 **58.07** 42.09 **54.94** G@REToPcov - 71.31 - 70.36 - 69.61 - 63.81 - 61.01 - 58.33 - 55.02 **5.10** SearchQA Given (G) 96.15 86.68 81.77 85.67 75.77 85.34 58.64 84.08 50.22 83.58 42.67 83.33 34.46 82.55 REToP 99.92 87.06 97.58 86.81 93.92 **86.48** 71.49 **84.76** 59.46 **84.04** 48.6 **83.48** 37.08 **82.75** G@REToPcov - 87.04 - 86.79 - 86.52 - 85.07 - 84.15 - 83.56 - 82.77 **1.78** TriviaQA Given (G) 96.67 67.31 86.89 65.05 82.54 63.82 68.81 60.39 61.44 58.39 55.11 56.48 47.12 54.03 REToP 99.86 **68.07** 97.07 **67.33** 93.72 **66.23** 76.72 62.40 67.93 60.25 59.55 **57.77** 49.29 54.89 G@REToPcov - 68.09 - 67.42 - 66.60 - 62.32 - 60.12 - 57.95 - 54.83 **0.70** NQ Given (G) 92.37 63.78 79.04 59.99 74.87 58.77 60.60 53.51 54.03 51.00 47.94 48.31 41.70 45.27 REToP 98.71 **65.34** 93.04 **63.39** 89.30 **62.62** 70.65 **56.90** 61.68 **53.54** 53.24 **50.10** 43.75 **46.44** G@REToPcov - 65.67 - 63.93 - 63.02 - 57.43 - 53.80 - 50.68 - 46.45 **10.70** DROP Given (G) 95.74 88.46 81.17 87.38 76.11 87.33 62.34 86.23 53.69 85.38 48.77 84.45 43.05 85.01 REToP 99.53 88.64 92.95 **87.83** 88.42 88.04 69.00 **86.31** 58.55 **85.57** 51.90 **84.49** 44.18 85.09 G@REToPcov - 88.63 - 88.19 - 87.88 - 86.69 - 85.91 - 84.87 - 84.94 **3.63** DuoRC Given (G) 97.20 68.68 87.87 66.41 84.21 65.82 71.09 62.42 64.16 61.47 57.16 59.91 50.03 58.46 REToP 99.87 **69.45** 98.33 **69.17** 96.14 68.68 80.75 **64.69** 71.95 **62.59** 62.56 **60.70** 52.90 **58.69** Original@cov - 69.51 - 69.02 - 68.4 - 64.77 - 62.74 - 60.92 - 59.32 **4.32** TBQA Given (G) 94.34 67.14 80.9 63.32 75.65 61.92 57.49 56.02 49.63 52.14 41.45 51.04 34.07 50.00 REToP 99.53 **68.38** 95.01 **67.23** 91.68 **66.18** 68.20 **58.34** 58.55 **54.77** 47.37 **51.26** 37.26 **49.64** G@REToPcov - 68.56 - 67.30 - 66.23 - 59.41 - 56.02 - 52.60 - 50.71 **24.23** more on moderate thresholds as compared to low thresholds. We attribute this to the high difficulty of instances that remain to be re-attempted at low thresholds i.e. only the instances on which the given system was highly underconfident are left for the post-abstention method. It has been shown that model's confidence is negatively correlated with difficulty (Swayamdipta et al., 2020; Rodriguez et al., 2021; Varshney et al., 2022b) implying that the remaining instances are tough to be answered correctly. This justifies the lesser improvement in performance observed at low thresholds. In-Domain vs Out-of-Domain Improvement: REToP achieves higher performance improvement on the in-domain dataset than the out-of-domain datasets (on average). This is expected as the auxil- ![5_image_0.png](5_image_0.png) iary model in REToP is trained using the in-domain training data. However, it still has good performance on out-of-domain datasets as the auxiliary model learns fine-grained representations to distinguish between correct and incorrect predictions. Furthermore, the improvement on out-of-domain Dataset Ens. REToP REToP ***HI on** (α = 0.6) (α = 0.65) (REToP) SQuAD 0.29 21.81 20.02 47.85 HotpotQA 0.93 21.54 19.00 37.88 RE 21.72 20.42 17.61 46.65 RACE 16.72 15.10 14.17 36.26 NewsQA 11.92 5.10 5.10 26.41 SearchQA 17.05 1.78 2.23 20.08 TriviaQA 9.50 0.70 1.47 17.21 NQ 13.40 10.70 10.89 31.95 DROP 1.57 3.63 2.99 8.08 DuoRC -1.69 4.32 5.90 20.26 TBQA -6.93 24.23 23.73 45.18 Total 84.48 **129.33** 123.11 337.81 data varies greatly across datasets (from 0.7 on TriviaQA to 24.23 on TextbookQA). ## 3.2.2 **Comparing Post-Abstention Approaches** We provide the performance tables for other postabstention approaches in Appendix. However, we compare their total risk improvement values in Table 2. In the in-domain setting, REToP achieves higher improvement than Ensembling method. This is because the auxiliary model in REToP has specifically learned to distinguish the correct and incorrect predictions from the training data of this domain. However, in some out-of-domain cases, Ensembling outperforms REToP (SearchQA, TriviaQA, NewsQA). Overall, REToP leads to a consistent and higher risk improvement on average. Ensembling also leads to a minor degradation in a few out-of-domain datasets (DuoRC and TextbookQA). Next, we analyze the performance of human intervention (HI) methods. ## 3.2.3 Human Intervention (Hi) We study two variants of HI method. In the first variant, multiple predictions (n=2) are returned for all the abstained instances. This makes the coverage to be 100% for all the confidences; therefore, we present only the risk values in Table 3. As expected, with increase in abstention threshold, the risk decreases because multiple predictions get outputted for a larger number of instances. Selection of operating threshold for an application depends on the trade-off between risk that can be tolerated and human effort required to select the most suitable prediction from a set of predictions returned by the system. For example, a low threshold can Dataset **0.0 0.2 0.4 0.6 0.8** SQuAD 34.15 33.72 30.9 28.05 26.3 HotpotQA 68.33 68.19 66.56 63.65 61.57 RE 45.52 45.35 43.39 41.28 39.31 RACE 82.05 81.6 80.12 78.19 77.15 NewsQA 71.46 71.2 69.42 67.21 65.29 SearchQA 87.06 86.92 85.64 83.98 82.94 TriviaQA 68.13 67.9 66.62 64.21 62.47 NQ 66.09 65.67 63.63 61.06 59.31 DROP 88.69 88.69 87.56 86.36 85.7 DuoRC 69.55 69.42 68.15 66.42 65.22 TBQA 68.73 68.46 67.07 64.74 64.01 be selected for tolerant applications like movie recommendations and a high threshold for tolerant applications like house robots. In the second variant of HI method, we study a **REToP-centric** approach in which the system returns multiple predictions only when REToP surpasses the confidence threshold in the postabstention stage. The last column in Table 2 shows the risk improvements achieved by this approach (n=2). Note that REToP re-examines the top N predictions and selects one while this method outputs multiple predictions and requires a human to select the most suitable one. These results indicate that though REToP achieves good performance, there is still some room for improvement. ## 3.2.4 Ensembling Using Paraphrases Comparing the performance of Mean and Max Ensembling strategies reveals that Max increases the coverage more than the Mean strategy but it also increases the risk considerably. Thus, pushing the instance's confidence to surpass the abstention threshold fails to provide risk improvements. However, such a technique could be employed in scenarios where risk degradation can be tolerated. ## 4 Analysis What is the distribution of model's original confidence on the instances that get answered after applying post-abstention method? In Figure 5, we show the distribution of model's original confidence on SQuAD instances that get answered by REToP at abstention threshold 0.5. Green-colored bars represent the number of instances answered from each confidence bucket. *We found that REToP* answers a large number of instances from the high ![7_image_1.png](7_image_1.png) confidence buckets; however, instances from even low confidence buckets get answered. This can further be controlled using the weight parameter (α) in the overall confidence computation. ## How Often Do The System'S Predictions Change After Applying Retop And What Is Its Impact? REToP can either boost the confidence of the top most prediction of the given model or can select a different answer by re-examining its top N predictions. In Figure 6, we specifically analyze the latter scenario i.e. the instances on which REToP's prediction differs from the original model's prediction. At a threshold of 0.5, the original system abstains on 3411 SQuAD instances and after applying REToP, it answers 1110 of those instances. Out of these 1110 instances, the REToP changes the prediction on 186 instances. The original prediction is incorrect in more cases (99 vs 87) and after applying REToP, the system gives 116 correct predictions and only 70 incorrect. This implies that by overriding the original system's prediction, REToP improves the system's accuracy. However, in some cases, it also changed a correct prediction to incorrect but such cases are lesser than the former. ## To What Extent Do The Instances Answered By Different Post-Abstention Methods Overlap? In Figure 7, we demonstrate the Venn diagram of SQuAD instances answered by REToP and Ensembling (Mean) approaches at abstention threshold 0.5. REToP answers 1110 instances while Ensembling answers 277 and there 127 common instances between the two approaches. This indicates that the two sets are not mutually exclusive i.e. there are some instances that get targeted by both the ap- ![7_image_0.png](7_image_0.png) ![7_image_2.png](7_image_2.png) proaches; however, there are a significant number of instances that are not in the intersection. This result motivates studying composite or sequential application of different post-abstention methods to further improve the post-abstention performance. ## 5 Conclusion And Discussion In this work, we formulated 'Post-Abstention', a task that allows re-attempting the abstained instances of the given selective prediction system with the aim of increasing its *coverage* without significantly sacrificing the *accuracy*. We also explored several baseline methods for this task. Through comprehensive experiments on 11 QA datasets, we showed that these methods lead to considerable performance improvements in both in-domain and out-of-domain settings. We further performed a thorough analysis that resulted in several interesting findings. Looking forward, we believe that our work opens up several avenues for new research, such as exploring test-time adaptation, *knowledge hunting*, and other human intervention techniques like asking clarification questions as post-abstention methods (discussed in Appendix D). Studying the impact of composite or sequential application of multiple post-abstention methods in another promising direction. Furthermore, prior selective prediction methods can also be repurposed and explored for this task. We plan to pursue these crucial research directions in our future work. Finally, we hope our work will encourage further research in this important area and facilitate the development of more reliable NLP systems. ## Limitations The proposed post-abstention methods require additional computation and storage. Despite this additional requirement, we note that this is not a serious concern as current devices have high storage capacity and computation hardware. Furthermore, additional computation for training auxiliary model in REToP is required only once and just an inference is required at evaluation time which has a much lower computation cost. Moreover, the risk mitigation that comes with the post-abstention methods weighs much more than the computational or storage overhead in terms of importance. Secondly, human-intervention techniques require a human to be a participant and contribute in the answering process. However, these approaches do not expect the participating human to be an expert in the task. 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In *Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics:* System Demonstrations, pages 270–278, Online. Association for Computational Linguistics. Yuan Zhang, Jason Baldridge, and Luheng He. 2019. PAWS: Paraphrase adversaries from word scrambling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1298–1308, Minneapolis, Minnesota. Association for Computational Linguistics. ## Appendix A Ensembling (Mean) Performance Table 5 shows the performance of using Ensembling (Mean) as a post-abstention method for a few selected abstention threshold values. For each dataset, we provide three rows: the first row ('*Given*') shows the coverage and risk values of the given selective prediction system at specified abstention thresholds, the second row ('Ens') shows the coverage and risk after applying the postabstention method on the abstained instances of the given selective prediction system, and the final row ('G@Enscov') shows the risk of the given selective system at the coverage achieved by Ens method. For the post-abstention method to be effective the risk in the second row should be less than that in the third row and the magnitude of difference corresponds to the improvement. The last column 'Total Risk Improvement' shows the overall improvement aggregated over all confidence thresholds ranging between 0 and 1 at an interval of 0.02. ## B Dataset Statistics Table 4 shows the statistics of all evaluation datasets used in this work. SQuAD corresponds to the in-domain dataset while the remaining 10 datasets are out-of-domain. We use the preprocessed data from the MRQA shared task (Fisch et al., 2019). ## C Differentiating Retop From Calibration REToP is different from calibration based techniques presented in (Kamath et al., 2020; Varshney et al., 2022c) in the following aspects: (a) Firstly, REToP does not require a held-out dataset unlike calibration based methods that infer the model on the held-out dataset to gather instances on which the model in incorrect. (b) Secondly, the auxiliary model trained in REToP predicts the likelihood of correctness of (context, question, prediction) triplet i.e. it is used for each of the top N prediction individually. This is in contrast to calibrators that predicts a single score for an instance and ignores the top N predictions. (c) Finally, we use the entire context, question, and the prediction to predict its correctness likelihood score unlike feature-based calibrator models in which a random-forest model is trained using just syntax-level features such as length of question, | Dataset | Size | Dataset | Size | |-----------|--------|-----------|--------| | SQuAD | 10507 | HotpotQA | 5901 | | RE | 2948 | RACE | 674 | | NewsQA | 4212 | SearchQA | 16980 | | TriviaQA | 7785 | NQ | 12836 | | DROP | 1503 | DuoRC | 1501 | | TBQA | 1503 | | | semantic similarity of prediction with the question, etc. ## D Other Post-Abstention Techniques Asking clarifying questions to the user in order to get information about the question has started to received considerable research attention in conversational, web search, and information retrieval settings (Aliannejadi et al., 2021, 2020; Zamani et al., 2020a; Zhang et al., 2020; Zamani et al., 2020b). These techniques can be leveraged/adapted for the post-abstention task. Test-time adaptation is another promising research area in which the model is adapted at testtime depending on the instance. This is being studied in both computer vision (Chen et al., 2022) and language processing (Wang et al., 2021; Banerjee et al., 2021). Cascading systems in which stronger and stronger models are conditionally used for inference is also an interesting avenue to explore with respect to Post-Abstention (Varshney and Baral, 2022; Li et al., 2021; Varshney et al., 2022a). ## E Coverage 100% For Human Intervention Methods We believe that the ability to identify situations when there is no good answer in the top N returned candidates is a very difficult task (for the humans also) and it requires even more cognitive skills than just selecting the best answer from the provided answer candidates. Because of this reason, the coverage is 100%. ## F Comparison With Other Selective Prediction Methods In this work, we presented a new QA setting and studied the performance of several baseline methods for this task. The focus of this work is on studying the risk improvement that can be achieved in this problem setup. We consciously do not pitch the approaches for this task as competitors of the existing selective prediction approaches. In fact, these approaches are **complimentary** to the selective prediction approaches. A post-abstention method can be used with any selective prediction method as the first step. Dataset Model **0.2 0.32 0.36 0.48 0.54 0.60 0.68 Total Risk** Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ Cov↑ Risk↓ **Improvement**↑ Given (G) 96.65 32.45 87.24 28.10 83.34 26.69 69.94 21.91 62.57 19.91 56.23 17.98 47.92 15.43 SQuAD Ens 97.64 32.88 89.51 28.93 87.64 28.24 72.46 22.71 65.12 20.58 58.37 18.7 49.59 15.89 (in-domain) G@Enscov - 32.96 - 29.09 - 28.26 - 22.58 - 20.65 - 18.66 - 15.91 0.29 HotpotQA Given (G) 97.54 67.65 89.56 65.88 85.39 65.13 71.75 62.71 64.77 61.56 58.19 60.34 49.25 58.29 Ens 98.59 67.84 91.93 66.23 90.41 65.92 75.65 63.17 68.45 62.22 61.31 60.72 52.26 58.88 G@Enscov - 67.9 - 66.37 - 66.04 - 63.4 - 62.14 - 60.91 - 58.94 0.93 RE Given (G) 97.59 44.49 89.01 40.51 85.41 39.04 74.08 34.16 66.86 30.54 60.58 27.94 54.10 24.20 Ens 98.27 44.56 92.2 41.35 90.57 40.71 77.44 34.87 70.86 31.45 64.86 29.08 56.07 24.74 G@Enscov - 44.82 - 42.27 - 41.42 - 35.58 - 32.47 - 30.02 - 25.54 21.72 RACE Given (G) 89.02 80.5 71.07 77.04 66.17 75.56 51.34 72.54 43.47 69.62 36.2 68.85 29.97 63.86 Ens 91.69 80.42 73.89 77.71 71.51 77.18 53.71 72.65 46.88 70.25 40.21 69.0 31.6 64.79 G@Enscov - 80.88 - 77.31 - 77.13 - 72.93 - 71.43 - 70.11 - 65.09 16.72 NewsQA Given (G) 93.90 69.76 80.91 66.40 75.5 64.91 60.30 60.79 53.30 58.8 47.17 56.62 39.32 54.11 Ens 95.56 70.24 83.52 67.14 81.13 66.49 63.01 61.53 55.75 59.45 49.53 57.19 41.17 54.21 G@Enscov - 70.18 - 67.02 - 66.46 - 61.63 - 59.67 - 57.33 - 54.67 11.92 SearchQA Given (G) 96.15 86.68 81.77 85.67 75.77 85.34 58.64 84.08 50.22 83.58 42.67 83.33 34.46 82.55 Ens 98.0 86.82 87.31 85.79 84.7 85.61 65.65 84.1 56.86 83.65 48.46 83.16 38.73 82.36 G@Enscov - 86.83 - 86.05 - 85.87 - 84.52 - 84.03 - 83.59 - 82.94 17.05 TriviaQA Given (G) 96.67 67.31 86.89 65.05 82.54 63.82 68.81 60.39 61.44 58.39 55.11 56.48 47.12 54.03 Ens 98.01 67.58 89.88 65.71 87.99 65.15 72.31 60.95 65.0 59.13 58.47 56.9 49.67 54.38 G@Enscov - 67.64 - 65.76 - 65.3 - 61.38 - 59.25 - 57.55 - 54.94 9.5 NQ Given (G) 92.37 63.78 79.04 59.99 74.87 58.77 60.60 53.51 54.03 51.00 47.94 48.31 41.70 45.27 Ens 94.59 64.35 83.46 60.82 81.32 60.16 64.83 54.7 58.05 52.17 51.8 49.8 44.33 46.31 G@Enscov - 64.43 - 61.31 - 60.79 - 55.03 - 52.61 - 50.01 - 46.82 13.4 DROP Given (G) 95.74 88.46 81.17 87.38 76.11 87.33 62.34 86.23 53.69 85.38 48.77 84.45 43.05 85.01 Ens 97.6 88.48 85.63 87.72 83.17 87.28 65.34 86.15 56.55 85.65 50.37 84.54 44.78 84.99 G@Enscov - 88.47 - 87.72 - 87.52 - 86.05 - 85.63 - 84.54 - 84.84 1.57 DuoRC Given (G) 97.20 68.68 87.87 66.41 84.21 65.82 71.09 62.42 64.16 61.47 57.16 59.91 50.03 58.46 Ens 98.0 68.86 90.34 67.11 88.61 66.84 73.82 63.36 66.96 62.19 59.96 60.78 51.57 58.4 Original@cov - 68.91 - 67.18 - 66.69 - 63.18 - 61.79 - 60.07 - 58.91 -1.69 TBQA Given (G) 94.34 67.14 80.9 63.32 75.65 61.92 57.49 56.02 49.63 52.14 41.45 51.04 34.07 50.00 Ens 95.94 67.55 84.3 64.17 81.1 63.33 62.28 56.94 53.96 54.25 45.78 52.33 37.72 51.15 G@Enscov - 67.45 - 64.33 - 63.38 - 57.05 - 54.38 - 52.03 - 50.53 -6.93 ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? We have Limitations Section at the end of the paper after Conclusion A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** References ✓ B1. Did you cite the creators of artifacts you used? We use the publicly available standard NLP datasets in this work with appropriate citations and references. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We do not create any artifcats in this reserach. We use the publicly available standard NLP datasets in this work with proper citations and references. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Sections 3 And 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Sections 3 and 4 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Sections 3 ✓ C3. 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qian-etal-2023-unilg

{U}ni{LG}: A Unified Structure-aware Framework for Lyrics Generation

https://aclanthology.org/2023.acl-long.56

As a special task of natural language generation, conditional lyrics generation needs to consider the structure of generated lyrics and the relationship between lyrics and music. Due to various forms of conditions, a lyrics generation system is expected to generate lyrics conditioned on different signals, such as music scores, music audio, or partially-finished lyrics, etc. However, most of the previous works have ignored the musical attributes hidden behind the lyrics and the structure of the lyrics. Additionally, most works only handle limited lyrics generation conditions, such as lyrics generation based on music score or partial lyrics, they can not be easily extended to other generation conditions with the same framework. In this paper, we propose a unified structure-aware lyrics generation framework named UniLG. Specifically, we design compound templates that incorporate textual and musical information to improve structure modeling and unify the different lyrics generation conditions. Extensive experiments demonstrate the effectiveness of our framework. Both objective and subjective evaluations show significant improvements in generating structural lyrics.

# Unilg: A Unified Structure-Aware Framework For Lyrics Generation Tao Qian1,2, Fan Lou2, Jiatong Shi3, Yuning Wu1, Shuai Guo1, Xiang Yin2**, Qin Jin**1∗ 1Renmin University of China, P.R.China 2ByteDance AI Lab 3Carnegie Mellon University, U.S.A {qiantao, yuningwu, shuaiguo, qjin}@ruc.edu.cn, tianzhong.t, yinxiang.stephen}@bytedance.com, [email protected] ## Abstract As a special task of natural language generation, conditional lyrics generation needs to consider the structure of generated lyrics1and the relationship between lyrics and music. Due to various forms of conditions, a lyrics generation system is expected to generate lyrics conditioned on different signals, such as music scores, music audio, or partially-finished lyrics, etc. However, most of the previous works have ignored the musical attributes hidden behind the lyrics and the structure of the lyrics. Additionally, most works only handle limited lyrics generation conditions, such as lyrics generation based on music score or partial lyrics, they can not be easily extended to other generation conditions with the same framework. In this paper, we propose a unified structure-aware lyrics generation framework named UniLG. Specifically, we design compound templates that incorporate textual and musical information to improve structure modeling and unify the different lyrics generation conditions. Extensive experiments demonstrate the effectiveness of our framework. Both objective and subjective evaluations show significant improvements in generating structural lyrics. ## 1 Introduction Great progress has been made in natural language generation (NLG) with pre-trained language models in recent years (Lewis et al., 2020; Radford et al., 2019; Brown et al., 2020). Lyrics generation is also a special task of NLG (Chen and Lerch, 2020; Gill et al., 2020). Different from general natural language, lyrics eventually need to be presented with music after the composition. This requires the lyrics to follow song-writing rules (i.e., the structure of lyrics), such as clear paragraphs with chorus and verse concepts. However, most Figure 1: Example chorus parts of a song. We use different ![0_image_0.png](0_image_0.png) colors for different beats2 within the bar3and rhythm patterns are shown in 4/4 time signatures4. The same melody and rhythm pattern may repeat several times when meeting the chorus parts of the song. The melody and rhythm patterns can hint the correspondences between lyric sentences, e.g. the same or similar sentences. previous works ignore the musical concepts behind the lyrics and do not consider the structure of lyrics (Sheng et al., 2021; Qian et al., 2022). To explicitly model the structure of lyrics, some works introduce additional structural labels (e.g., sentence-level chorus and verse label), which inevitably require much effort for additional human annotation (Potash et al., 2015; Lu et al., 2019). To avoid the huge annotation cost, other works either adopt predefined formats (e.g., the number of syllables in each sentence) or linguistic tags (e.g., PoS, Part-of-Speech) to inject structural information (Li et al., 2020; Castro and Attarian, 2018). Nevertheless, given that those methods cannot directly indicate the structure of lyrics, the generated lyrics are still difficult to realize the musical concepts (e.g., chorus and verse). Moreover, most works only focus on certain lyrics generation conditions, such as generating lyrics given music scores or partially-finished lyrics etc., which hinders the application of a lyrics generation model in various scenarios. To mitigate the issues in previous works, we propose a unified structure-aware lyrics genera-2https://en.wikipedia.org/wiki/Beat_(music) 3https://en.wikipedia.org/wiki/Bar_(music) 44/4 denotes that each beat is a 1/4 note and each bar has 4 beats. To simplify the description, we state our method with a 4/4 time signature, for it's widely used in songwriting. The English version is provided in Appendix I due to space issues. 983 ![1_image_0.png](1_image_0.png) tion framework named UniLG. As illustrated in Figure 1, the chorus parts of the songs share the same melody, so that the corresponding lyrics follow a similar pattern. Such a phenomenon inspires us that shared musical signals indicate the structure of lyrics and can be used to infer the relation across lyrics. Therefore, we design a compound template (i.e., a sequence of tuples) that incorporates both textual and musical information to model the structure of lyrics. The template is designed with rhythmic concepts in mind, and it can be extracted from different sources (e.g., audio, music score, etc.). As shown in Figure 2, the general interface in the template enables UniLG to generate lyrics based on various conditional signals without re-training the model. Additionally, we propose a cycle-consistency loss to enforce the reconstruction of the musical information from the generated lyrics to further improves the performance. To verify our proposed framework, we collect a test dataset named Song8k with chorus and verse labels for each sentence. Both objective and subjective evaluations on the test dataset demonstrate the effectiveness of our framework. In summary, the main contributions of this work are as follows: - we propose a unified structure-aware lyrics generation framework named UniLG; - we design a compound template that incorporates textual and musical information to achieve structure modeling and enable lyrics generation in various conditions; - we introduce a cycle-consistency loss to validate the impact of musical information and further boost the performance; - extensive experiments demonstrate the effectiveness of our method that achieves better structural modeling in lyrics generation. ## 2 Related Work The existing lyrics generation approaches can be categorized into two types: 1) free lyrics generation, which generates lyrics either from scratch or based on some prefix prompts (Radford et al., 2019; Brown et al., 2020); and 2) conditional lyrics generation, which generates lyrics conditioned on control signals (e.g., music score, audio, etc.) (Saeed et al., 2019; Fan et al., 2019). In this work, we focus on conditional lyrics generation. Recent works have shown the effectiveness of pre-trained language models in NLG (Lewis et al., 2020; Brown et al., 2020; Radford et al., 2019). As a special task of NLG, lyrics generation also follows the trend of using pre-trained language models. However, the pre-trained language models are trained with general text corpus and fail to consider the structure of lyrics (e.g., chorus and verse parts of the song), which is a salient feature for lyrics. Several works adopt pre-trained Transformer variants, such as GPT-2, as the backbone to improve the performance of lyrics generation but ignore the structure of lyrics as well (Zhang et al., 2020; Lee et al., 2019; Bao et al., 2019; Sheng et al., 2021; Qian et al., 2022). To achieve structural modeling, some works attempt to annotate the structural information of lyrics, however, this requires additional expensive human annotation (Potash et al., 2015; Lu et al., 2019). To avoid human labeling, SongNet chooses corpus with pre-defined formats (e.g., Ci5and Sonnet), while some works regard the linguistic tags of lyrics as the structure information of lyrics (Li et al., 2020; Castro and Attarian, 2018). However, SongNet can not provide diverse representations for sentences, and the construction of linguistic tags is inconvenient and not humanfriendly. In addition, these methods cannot represent the structure of lyrics explicitly. Moreover, most previous lyrics generation works ignore the musical properties hidden behind the lyrics, that is, the lyrics will eventually be presented together with the music. To overcome all the above limitations, we propose a compound template in our framework that can be conveniently constructed. It provides discriminative representations and incorporates both textual and musical information to 5https://en.wikipedia.org/wiki/CI ![2_image_0.png](2_image_0.png) ## 3 Method Our proposed unified framework for lyrics generation (UniLG) contains two highlights: 1) it considers the structure of lyrics in the generation; 2) it can handle different lyrics generation conditions with different control signals, such as the music score, or music audio, or partial lyrics, etc. For the structure of lyrics, as illustrated in Figure 1, the melody implies the structure of lyrics, which can be leveraged for lyrics structure modeling. However, large-scale (melody, lyrics) parallel data is generally difficult to obtain. We, therefore, propose using rhythm patterns6that preserves the inter-correlation of lyrics as musical information to explicitly represent the structure of lyrics. As explored in previous works (Ju et al., 2021; McAuliffe et al., 2017), the defined rhythm patterns can be efficiently extracted from lyrics and different rhythmic sources (e.g., music score, music audio, etc.) without extra human annotation. For handling various control signals of different lyrics generation conditions, the model should have the capacity to process different types of inputs, such as music score, music audio, rhythm patterns, partially-finished lyrics, etc. Therefore, we design a compound template (i.e., a sequence of tuples) that can incorporate both textual and musical information. So any type of input can be converted into a compound template, and then lyrics can be generated based on the compound template. Figure 2 illustrates the overview of our proposed lyrics generation framework UniLG. We propose an intermediate compound template as a bridge between the rhythmic sources (e.g., audio, music score, etc.) and lyrics in UniLG. Specifically, the lyrics generation is decomposed into a two-stage pipeline consisting of an Input-to-Template stage and a Template-to-Lyric stage. In this section, we first describe the compound template in detail. We then present the two stages during training respectively. Finally, we discuss the inference procedure of UniLG to illustrate how to handle various control signals in different lyrics generation conditions with our unified framework. ## 3.1 Compound Template To model the structure of the lyrics, the compound templates are designed to incorporate both musical and textual information. As shown in Figure 3, a compound template consists of five components, Masked Lyric M (or Lyric L), Bar A, Beat B, Segment S, and Intro-position P. These components can be categorized into three aspects: semantic information, musical information, and textual information. The details of these aspects with corresponding components are as follows: Semantic Information Aspect We introduce Lyric Symbols and *Masked Lyric Symbols* to leverage the pre-trained language model and achieve semantic control. Lyric Symbols: We denote the Lyric, a sequence of Chinese character tokens, as L = (l1, l2*, ..., l*n) = (li) n i=1, where li stands for the i th element of L, li *∈ C ∪ E*, n is the length of L. C refers to the set of Chinese characters and E = { ⟨/s⟩, ⟨bos⟩, ⟨eos⟩ } is a set of special tokens, including the separation token between sentences ⟨/s⟩, the start of sequence token ⟨bos⟩, and end of sequence token ⟨eos⟩. Masked Lyric Symbols: We denote the Masked Lyric as M = (mi) n i=1, where mi stands for the i th element of M, and mi *∈ C ∪ E ∪ {⟨*m⟩}, where ⟨m⟩ stands for mask token, which is widely used in masked language modeling (MLM) (Kenton and Toutanova, 2019; Lewis et al., 2020). Musical Information Aspect As illustrated in Figure 3, the inter-correlation of lyrics can be preserved in the musical information, and two kinds of musical symbols, *Beat Symbols* and *Bar Symbols*, are designed to represent the musical information at a different level. Beat Symbols: The Beat B = (bi) n i=1 denotes the local musical information, where bi (the i th element of B, bi *∈ B∪E*), denotes the local musical information of mi and li. B = {**Beat**i} 3 i=0, and Beat0, Beat1, **Beat**2, and **Beat**3 stand for 1 st, 2 nd, 3 rd, and 4 th beat in a bar. Bar Symbols: The Bar A = (ai) n i=1 denotes the global musical information, where ai (the i th element of A, ai *∈ A∪ E*) denotes the bar information of the bi. A = {Bari} 511 i=0, and token Barj stands for the j th bar7. And ai also indicates that the word mi and li are supposed to be sung at bar ai. Textual Information Aspect Similar to SongNet, the Intro-position and segment symbols are adopted to model the textual information at word and sen-7the number of bars is no more than 512 from our data. tence level (Li et al., 2020). In the following sections, we name the sub-sequence of any component between special symbols in E as a sentence. Segment Symbols: The segment symbols provide global textual information to the compound template. We denote Segment as S = (si) n i=1, where si (the i th element of S, si *∈ S ∪ E*) denotes the sentence position of the mi and li. S = {Segi} 255 i=0 and Segj stands for the j th sentence. For example, the lyrics shown in Figure 3 is the 10th and 11th sentences (Seg10 and Seg11) . Intro-Position Symbols: The Intro-Position P = (pi) n i=1 denotes the local textual information, where pi (the i th element of P, pi *∈ P ∪ E*) denotes the local position within the sentence of the mi and li. P = {Posi} 31 i=0 and the token Posi stands for the i th reversed local position within the sentence or the distance to the end of token of the sentence. For example, in Figure 3, the Pos8 means this position is 8 tokens away from the last token of the corresponding segment (Pos0 in Seg10). The compound template is a tuple sequence consisting of five components, including Masked Lyric M (or Lyric L), Beat B, Bar A, Segment S, and Intro-Position P. As shown in the blue and green dotted box in the bottom of Figure 3, we can construct the template based on these components. ## 3.2 Input-To-Template Module In this subsection, we discuss the construction procedure of the compound template given the lyrics L = (li) n i=1 in length n during training. To be specific, we first extract symbols (defined in Section 3.1) from lyrics L. Then, we combine them to construct the compound template: (1) Masked Lyric M = (mi) n i=1. Similar to MLM, the M is constructed by randomly masking 85% of elements that are not in E of lyrics L. (2) Bar A = (ai) n i=1 and Beat B = (bi) n i=1. According to the time signatures, the bar information A can be obtained given beat information B. And the Beat B is extracted from lyrics L through a Lyric-to-Beat model (details in Appendix A), which predicts rhythm patterns in B for given lyrics L. (3) Segment S = (si) n i=1, and Intro-poistion P = (pi) n i=1. As shown in Figure 3, the special tokens of E appear in the position for all components, in other words, they have the same format information, and the S and P can be extracted from either M, B, A, or L. For a sequence Q ∈ {*M, B, A, L*}, 986 S can be construct by counting the number of ⟨/s⟩ (if the number is c) before each position i and replace Segc with the corresponding i th token not in E. Similarly, for a sequence Q ∈ {*M, B, A, L*}, P can be constructed by counting the distance away from the nearest ⟨/s⟩ in the right (if the distance is c) for each position i and replace Posc with the corresponding i th token not in E. As shown in the blue dotted box in the bottom of Figure 3, the compound template T, a tuple sequence including Masked Lyric M, Beat B, Bar A, Segment S, and Intro-Position P, can be formulated as T = (ti) n i=1 = (<mi, bi, ai, si, pi>) n i=1. ## 3.3 Template-To-Lyric Module Through the Input-to-Template module, we construct the template T and obtain paired lyrictemplate data. With such data, we adopt a pretrained encoder-decoder Transformer language model MT5 as backbone (Xue et al., 2021b). Figure 3 illustrates the procedure of the Template-toLyric module and the details are as follows: Encoder Inputs and Decoder Inputs We define H0E and H0D as the inputs of the Encoder and the Decoder respectively and their formulations are: $$\begin{array}{l}{{H_{E}^{0}=E_{\mathrm{T}}=\mathrm{LN}(E_{M}+E_{B}+E_{A}+E_{S}+E_{P})}}\\ {{H_{D}^{0}=E_{\mathrm{L}}=\mathrm{LN}(E_{L}+E_{B}+E_{A}+E_{S}+E_{P}),}}\end{array}$$ $$(1)$$ where LN(∗) denotes the layer normalization and E∗ stands for token embedding sequences of ∗. Similar to the definition of the T in Section 3.2, the L denote the compound template that is a sequence of tuples: L = (li) n i=1 = (<li, bi, ai, si, pi>) n i=1, where the M is replaced by L in the T to obtain the L as shown in Figure 3. Encoder and Decoder The Encoder and Decoder each consist of N Transformer layers. HtE and HtD denote the output of the t th encoder layer and decoder layer respectively. As shown in Figure 3, the output of Encoder and Decoder HN E and HN D are: $$\begin{array}{r l}{H_{E}^{N}=}&{{}{\mathrm{Encoder}}(H_{E}^{0})}\\ {H_{D}^{N}=}&{{}{\mathrm{Decoder}}(H_{E}^{N},H_{D}^{0}*{\mathrm{Mask}}_{D}),}\end{array}$$ $$\left(2\right)$$ where MaskD denotes a causal decoder mask. And there is a projection layer for HN D to get the final distribution of the predicted lyrics. Training with Cycle-consistency Loss The main training loss is to minimize the negative loglikelihood over the lyrics L = (li) n i=1 given the ![4_image_0.png](4_image_0.png) template T = (ti) n i=1 as shown in the gray dotted line in Figure 4: $$\begin{array}{r l}{{\mathcal{L}}_{\mathrm{TL}}=-\log P(L|\mathbb{L},\mathbb{T}\,)}\\ {=-\sum_{i=1}^{n}\log P(l_{i}|1_{<i};\,\texttt{t}_{1},...,\,\texttt{t}_{n}),}\end{array}$$ $$(3)$$ where L = (li) n i=1 denote the compound template. The l<i stands for sequence (l1,l2,...,li−1). As illustrated by the orange dotted line in Figure 4, we introduce the cycle-consistency loss (CCL) to enhance the impact of musical information. The Lyric-to-Beat model reconstructs the beat sequence from the predicted lyrics of the language model. The formulation of CCL is as follows: $$\begin{array}{c}{{{\mathcal{L}}^{\prime}{}_{\mathrm{LB}}=-\log P(B|L^{\prime})}}\\ {{=-\Sigma_{i=1}^{n}\log P(b_{i}|b_{<i};l_{1}^{\prime},...,l_{n}^{\prime}),}}\end{array}$$ $$\mathbf{\Phi}(4)$$ ′1*, ..., l*′n),(4) where L′ = (l′i ) n i=1 denotes the predicted lyrics by the language model, and B = (bi) n i=1 denotes the Beat of input template T as in Figure 4. Finally, the training objective of Template-toLyric model is to minimize the loss Ltot: $${\mathcal{L}}_{\mathrm{tot}}={\mathcal{L}}_{\mathrm{T2L}}+\alpha*{\mathcal{L}}_{\mathrm{L2B}}^{\prime},$$ $$({\mathfrak{H}})$$ $$\mathrm{CCL}.$$ where α is a hyper-parameter to weigh CCL. ## 3.4 Inference Procedure In this subsection, we describe the inference procedure of UniLG for various lyrics generation conditions. The major steps are shown in Algorithm 1, including "*Beat Construction*", "*Masked Lyric Construction*", and "*Components Construction*". Given the template T by Algorithm 1, the Template-toLyric module generates the Lyric L and the L autoregressively. "*Beat Construction*" is a method to construct the Beat B from raw input X (e.g., beat, lyric, music score, audio, etc.)8. "*Beat Construction*" consists of "*Sentence Length Generation*" and "*Beat* Generation". "*Sentence Length Generation*" generates a sequence of numbers with each number denoting the length of one sentence9. "*Beat Generation*" generates the Beat based on the outputs from "*Sentence Length Generation*". For example, if "*Sentence Length Generation*" generate a sequence S = [3, 2], "*Beat Generation*" may return B = [⟨bos⟩, Beat1, Beat3, Beat0, ⟨/s⟩, **Beat**0, Beat1, ⟨/s⟩, ⟨eos⟩]. To achieve content controllable generation, we use keywords K to construct the Masked Lyrics M. Based on the keyword 10, the model generates the lyrics in the MLM manner. The keywords can be either user-specified or sampled from the training corpus, which should appear in the generated lyrics. "*Masked Lyric Construction*" is a method to construct the masked lyrics M condition on Beat B and keywords K. Similar to the construction of P and S in Section 3.2, given Beat B, M can be constructed by replacing the token that is not in E with the mask token or keywords in K randomly. "*Components Construction*" is a method to obtain the other components given M and B as described in Section 3.2, and organize all components for the template. ## 4 Experimental Settings 4.1 Dataset We collect lyrics of 249,007 Chinese pop songs from Internet as the base of our experiments. Lyric-Template Dataset. We use the pre-trained Lyric-to-Beat model to extract the lyric-template dataset from 249,007 lyrics. We randomly select 8000 songs for the validation and test set respectively, and the remaining songs are used for training. The data statistics are shown in Appendix B. 8The details of the Lyric-to-Beat, **MIDI-to-Beat**, and Audio-to-Beat modules are discussed in Appendix D. 9The sentence means the sub-sequence between special symbols. 10If keywords are empty, we will randomly select some popular words as keywords. Algorithm 1 Template Construction In Inference Input: X: the raw input;K: keywords. Output: T, generated compound template. Def *Beat Construction*(X): Case of X: a beat sequence : B = X a lyric sequence: B = **Lyric-to-Beat**(X) a MIDI file: B = **MIDI-to-Beat**(X) a audio file: B = **Audio-to-Beat**(X) None: S = *Sentence Length Generation*() B = *Beat Generation*(S) ## End Case Return B B = *Beat Construction*(X) M = *Masked Lyric Construction*(B, K) return T = *Components Construction*(B, M) Additional Dataset: Song8k. We also annotate 8,000 songs with structure labels (sentence-level chorus and verse label) for evaluation and we name this dataset Song8k. For dataset settings, we use all 8,000 songs for further evaluation in the Templateto-Lyric module. ## 4.2 Baselines In Model Comparison We compare with two baselines in the experiments: 1) MT5, a pre-trained Transformer language model (Xue et al., 2021b); 2) SongNet, a format-controlled text generation model (Li et al., 2020). MT5 and SongNet construct their inputs with the same corpus as the lyric-template dataset. MT5, SongNet, and UniLG have similar parameters and all models use the same pre-trained model as initialization for a fair comparison. The details of the model configuration, training, and decoding settings are reported in Appendix E and G. ## 4.3 Objective Evaluation Metrics We use three kinds of objective evaluation metrics: general level, low level, and high level (more details can be found in Appendix F). General Level: Besides perplexity (PPL), we use Integrity metric to evaluate the sentence integrity (Li et al., 2020), which calculates the average probability of the separation token given previous tokens. Low Level: We use Format F1 and Beat F1 to evaluate the degree of consistency between the generated lyrics and the given textual format (Segment and Intro-Position) and rhythm patterns (Beat) in the template. High Level: We use Song8k and a pre-trained model (details are in Appendix C) to evaluate the quality of the structure of generated lyrics. Specifically, the model predicts a chorus or verse label for each sentence in generated lyrics and compares it with the human annotations to obtain Structure F1. ## 4.4 Subjective Evaluation Metrics As illustrated in Section ??, the Beat is important for the compound template and may have a big impact on our framework. We conduct subjective experiments for "*Beat Construction*", including "*Sentence Length Generation*" and "*Beat Generation*". Besides, we also conduct subjective experiments for model comparison. For each subjective experiment, we invite 43 annotators to evaluate the generated lyrics. Each annotator is required to score lyrics concerning four aspects. Each aspect is rated with an opinion score from 1 to 5 (from bad to excellent). The four aspects are as follows: 1) **Coherence**: the overall consistency of the topic of the entire song; 2) **Fluency**: the fluency of the semantic correlation within a sentence and between the sentence; 3) **Correlation**: the structural or semantic similarity among sentences, such as the distribution of words and corresponding relationships of sentences; 4) **Fascination**: the degree of fascinating sentences in annotators' opinion. ## 5 Experiments Results In this section, we report and analyze both objective and subjective experimental results. We also show some cases in Appendix H to verify the ability of UniLG to handle different generation conditions. ## 5.1 Objective Results Model Comparison We compare MT5, SongNet, and UniLG on the Song8k and the test set of the lyric-template dataset. The results of the model comparison (in Table 1) show that MT5 achieves the best results in PPL and Integrity. Our UniLG outperforms baselines in Format F1, Beat F1, and Structure F1.The Structure F1 shows that our framework does generate the lyrics with better structure, which indicates that the musical information improves the structural modeling. Ablation Study We further ablate our UniLG to verify the impact of musical and textual information as well as the CCL. From the results shown in Table 2, we see that the textual information (Seg&Pos), musical information (Bar&Beat), and CCL play crucial roles in the overall performance. These modules of our framework show significant improvement, especially on the metrics of Beat F1 and Structure F1. The CCL may enhance the musical information to boost performance in Format F1, Beat F1, and Structure F1, but at the same time may introduce noise and cause degradation in general metrics (PPL and Integrity). The effectiveness of the CCL further proves that the musical information behind the lyrics does benefit the structure-aware lyric generation. We notice that the musical information (Bar&Beat) degrades the performance of the framework more than the textual information and CCL. This may be due to there being extra position embeddings for input data in MT5 model. When it comes to music information, missing Bar&Beat leads to a complete loss of information, while missing the Seg&Pos only partially loses position information. ## 5.2 Subjective Results Template Construction As the template directly affects the Template-to-Lyric module, we perform the subjective evaluation on different settings of "*Sentence Length Generation*" and "*Beat Generation*" in Algorithm 1 to investigate the impact of the compound template. For "*Sentence Length Generation*", we have 2 candidate settings: 1) Random, the length of the sentence is randomly chosen from 6 to 12; 2) 2gram, the next sentence length is generated conditioned on the length of the previous sentence. We generate 40 songs in 6 to 16 sentences for each setting. Given two number sequences generated by "*Sentence Length Generation*" of two settings, the two Beat can be generated by the same method of "*Beat Generation*", whose setting is chosen randomly. The results in Table 3 show that Random and 2-gram strategies achieve comparable performance and different sentence length generation strategies have little influence on models. For "*Beat Generation*", we have 3 candidate settings: 1) Random, the beat information for each character is randomly chosen from B; 2) Rule, the beat is non-decreasing than the previous one; 3) Sample, we compute the statistics of the beat sequence of each length in the lyric-template dataset and sample the beat sequence conditioned on the sequence length. We generate 40 songs in 6 to 16 sentences for each setting ("*Sentence Lengths Generation*" uses 2-gram). The result in Table 4 shows | Dataset | PPL(↓) | Intergrity(↓) | Format F1(%, ↑) | Beat F1(%, ↑) | Structure F1(%, ↑) | | |-----------|----------|-----------------|-------------------|-----------------|----------------------|-------| | MT5 | T-L | 1.96 | 1.92 | 77.08 | 14.63 | - | | SongNet | T-L | 2.62 | 2.39 | 86.36 | 31.19 | - | | UniLG | T-L | 2.41 | 2.11 | 87.39 | 32.88 | - | | MT5 | S8 | 1.99 | 2.10 | 76.11 | 14.37 | 50.02 | | SongNet | S8 | 2.68 | 2.66 | 85.79 | 31.56 | 50.68 | | UniLG | S8 | 2.19 | 2.14 | 88.91 | 34.25 | 53.71 | Table 2: Ablation experiments on the test set of lyric-template dataset and Song8k. T-L and S8 stand for lyrictemplate dataset and Song8k respectively. CCL denotes the cycle-consistency loss in Section ??. | Dataset | PPL(↓) | Integrity(↓) | Format F1(%, ↑) | Beat F1(%, ↑) | Structure F1(%, ↑) | | |------------|----------|----------------|-------------------|-----------------|----------------------|-------| | UniLG | T-L | 2.41 | 2.23 | 87.39 | 31.82 | - | | - Bar&Beat | 2.62 | 2.43 | 83.52 | 21.35 | - | | | - Seg&Pos | 2.44 | 2.22 | 85.67 | 31.62 | - | | | - CCL | 2.45 | 2.21 | 85.84 | 30.42 | - | | | UniLG | S8 | 2.19 | 2.14 | 88.91 | 34.25 | 53.71 | | - Bar&Beat | 2.58 | 2.61 | 86.72 | 31.65 | 51.08 | | | - Seg&Pos | 2.23 | 2.22 | 86.68 | 31.52 | 50.98 | | | - CCL | 2.19 | 2.12 | 88.04 | 32.42 | 52.34 | | that musical information does influence the lyric generation, and the Sample method, which leads to more natural rhythm patterns, achieves the best performance on all metrics. | Random | 2-gram | | |-------------|-------------|-------------| | Coherence | 3.31 ± 0.07 | 3.31 ± 0.08 | | Fluency | 3.26 ± 0.07 | 3.27 ± 0.07 | | Correlation | 3.11 ± 0.08 | 3.11 ± 0.08 | | Fascination | 2.98 ± 0.08 | 3.06 ± 0.07 | Table 3: The MOS score of different settings in "*Sentence Length Generation*". | Random | Rule | Sample | | |-------------|-------------|-------------|-------------| | Coherence | 3.19 ± 0.08 | 3.25 ± 0.08 | 3.32 ± 0.08 | | Fluency | 3.03 ± 0.07 | 3.24 ± 0.07 | 3.30 ± 0.07 | | Correlation | 2.94 ± 0.09 | 3.06 ± 0.09 | 3.11 ± 0.08 | | Fascination | 2.96 ± 0.08 | 2.99 ± 0.09 | 3.09 ± 0.09 | Table 4: The MOS score of different settings in "Beat Generation". Model Comparison We also conduct the subjective comparison of UniLG with two baselines: MT5 and SongNet. We adapt 2-gram for "*Sentence* Length Generation" and Sample for *Beat Generation* in model comparison. We generate 120 songs in 6 to 16 sentences by each model with the same Masked Lyrics. The results in Table 5 show that our UniLG outperforms the baselines, which further Table 5: The MOS score of model comparison. validates the effectiveness of our framework. Table 1 and 5 prove that our compound template enables a stronger structure modeling ability than SongNet. This may be because that the compound template provides discriminative representations for lyrics under the guidance of musical information. The MT5 achieves better PPL and Integrity in Table 1 but gets lower MOS results in Fluency in Table 5. This indicates that MT5 may pay too much attention to the fluency of the text but lacking the logical correlation between sentences. ## 5.3 Case Studies We also show some cases in Appendix H to verify the ability of UniLG to handle different generation conditions. Although lyrics generated conditioned on the templates constructed by automatic method *Template Construction* are less satisfying (cases in Figure 5), the handcrafted template or template extracted by other resources achieves satisfying results as shown in Figure 6 and 7. These cases demonstrate that the template is human- | MT5 | SongNet | UniLG | | |-------------|-------------|-------------|-------------| | Coherence | 3.25 ± 0.05 | 3.33 ± 0.04 | 3.40 ± 0.04 | | Fluency | 3.08 ± 0.05 | 3.16 ± 0.05 | 3.25 ± 0.04 | | Correlation | 3.03 ± 0.05 | 3.11 ± 0.04 | 3.19 ± 0.04 | | Fascination | 2.99 ± 0.06 | 3.07 ± 0.05 | 3.15 ± 0.06 | understandable and can be manipulated by users directly as in Section 3.1. The results in Figure 8 and 9 indicate that the template acts as a bridge between lyrics and the rhythmic sources (e.g., audio, music score, etc.), which enables our UniLG to generate lyrics conditioned on different signals. ## 6 Conclusion In this paper, we propose UniLG, a unified structure-aware lyric generation framework. With our designed compound template to indicate the structure of lyrics with textual and musical information, which acts as a bridge between the rhythmic sources and lyrics, UniLG can handle different lyrics generation conditions. We also introduce a cycle-consistency loss to enhance the impact of musical information to improve performance. Extensive experiments demonstrate the effectiveness of our framework, achieving significant improvement in both objective and subjective evaluations. We will explore topic-driven lyrics generation in our future work. ## Limitations The limitations of our work include: 1) In our work, the structure of lyrics is the chorus and verse parts of songs, and it is learned in a data-driven manner, which highly relies on data quality. 2) The settings of the Lyric-to-Beat model will limit the effect of our model. For this work, we make an assumption that all songs are with 4/4 time signatures for the Lyric-to-Beat model. If the time signature is not 4/4, we need to re-train the Lyricto-Beat model.3) Better "*Beat Construction*" can be investigated, such as using a language model to generate the beat sequence. We only explore the simple method and achieve satisfying results. 4) The model trained from scratch may not achieve satisfying results. And a GPU with at least 20G memory may be needed to use the pre-trained language model (MT5) to reproduce our work. ## Ethics Statement Under the review of the company's legal team, the data collected for research is under legally correct copyright. The artifacts we used (e.g., MT5, other codes, etc) are consistent with their intended use and meet corresponding licenses. The mother tongue of all annotators is Chinese and the annotators are recruited by the human resources departments and the payment is adequate enough (The annotators receive an hourly wage of 80 RMB, about 12 USD) according to the laws and regulations of our country. Before the experiments, we report key information about the requirements for human annotators, including the evaluation criteria and the usage of their annotations. We have used the data under the consensus of the industry and research and the final information used for research does not include any protected category. ## Acknowledgements This work was partially supported by the National Natural Science Foundation of China (No. 62072462) and the National Key R&D Program of China under Grant No.2020AAA0108600. ## References Hangbo Bao, Shaohan Huang, Furu Wei, Lei Cui, Yu Wu, Chuanqi Tan, Songhao Piao, and Ming Zhou. 2019. Neural melody composition from lyrics. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 499–511. Springer. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Pablo Samuel Castro and Maria Attarian. 2018. 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Previous works usually generate a fixed rhythm pattern with a rule-based method and lots of handcraft design is needed which constrains the diversity of rhythm patterns (Fukayama et al., 2010; Monteith et al., 2012). Following the method in previous work and the released project11, we obtain the lyric-beat dataset for training (Ju et al., 2021; Ren et al., 2020; Xue et al., 2021a). With such lyric-beat data, we adopt the sequence-to-sequence (Seq2Seq) framework to train this model: $$\begin{array}{l}{\cal L}_{\rm L2B}=-\log P(B|L)\\ =-\Sigma_{i=1}^{n}\log P(b_{i}|b_{<i};l_{1},...,l_{n}),\end{array}\tag{6}$$ where $L=(l_{i})_{i=1}^{n}$ and $B=(l_{i})_{i=1}^{n}$ denote the lyric and beat sequence and n indicate the length of the sequence (Vaswani et al., 2017). The b<i stands for sequence (b1,b2,...,bi−1). We conduct objective experiments with different settings on the lyric-beat dataset. The results are shown in Table 6 and we chose the MT5-based model for our framework. The Lyric-to-Beat model achieves average perplexity of 1.13 and accuracy of 92.18%. Due to high accuracy, the Lyric-toBeat model provides a more efficient method than previous work to obtain the paired lyrics and beats data (Ju et al., 2021). Table 6: The results of the Lyric-to-Beat model on lyricbeat testset, where L means the numbers of encoder and decoder layers, H means the attention heads of each layer, and D means the dimension of the hidden state. | PPL | Beat Acc(%) | | |--------------------|---------------|-------| | L=4,H=4,D=256 | 1.37 | 90.01 | | L=8,H=6,D=512 | 1.42 | 91.34 | | L=8,H=6,D=512(MT5) | 1.13 | 92.18 | ## B Statics Of Lyric-Template Dataset Under the review of our legal team, the data for research is under legally correct copyright. And the statistics of this Lyric-Template dataset are as shown in Table 7. 11https://github.com/microsoft/muzic/tree/main/ telemelody Table 7: The statistics of Lyric-Template dataset. ## C Details Of Lyric-To-Structure Model Inspired by (Zhang et al., 2019), we train a Lyricto-Structure model on Song8k to verify the performance of our framework. With Song8k, we split 50 songs each for validation and test sets and others for training the Lyric-to-Structure model. Similar to the Lyric-to-Beat model in Appendix A, the Lyricto-Structure model predicts structure information for given lyrics. With Song8k dataset (mentioned in Section 4.1), we construct the lyric-structure dataset to train the Lyric-to-Structure model. We adopt the Seq2Seq framework to train this model: | data samples | 249,007 | |---------------------------|-----------| | average sents. per sample | 37.01 | | average words per sample | 293.36 | $$\begin{array}{l}{\cal L}_{\rm L2S}=-\log P(S|L)\\ =-\Sigma_{i=1}^{m}\log P(s_{i}|s_{<i};l_{1},...,l_{n}),\end{array}\tag{7}$$ where $L=(l_{i})_{i=1}^{n}$ and $S=(s_{i})_{i=1}^{m}$ stands for $i=1,...,n$. lyrics sequence, li, si (si ∈ {Chorus, Verse}) stands for i th token in L and S, and n indicate the length of lyrics and m indicate the sentence numbers of lyrics (it's also the length of S). The Lyric-to-Structure model achieves average perplexity of 1.78 and accuracy of 80.66%. ## D **Module Details In Inference Procedure** Algorithm In this section, we will provide more details about the Lyric-to-Beat, **MIDI-to-Beat**, and **Audio-toBeat** modules. Note that the relationship between generated lyrics and inputs is only rhythm patterns and the semantic information should be introduced through masked lyrics (as Algorithm 1 in Section 3.4) in our framework. Notice that the UniLG only produces lyrics and these outputs can be produced by using the alignment of input signals and templates. ## D.1 Lyric-To-Beat Module This module contains a Lyric-to-Beat model as described in Appendix A. The Lyric-to-Beat model extracts the beat sequence from the lyrics. ## D.2 Midi-To-Beat Module Similar to TeleMelody (Ju et al., 2021), we extract the melody track from MIDI files and calculate the beats information of notes in melody track12. ## D.3 Audio-To-Beat Module We use Audio-to-MIDI tools13 to transcript the audio to MIDI file and use **MIDI-to-Beat** to extract the beats information from audio file. ## D.4 Video-To-Beat Module* Similar to recent video background music generation work, we can extract the visual beats14 from videos and map visual beats to our beats information (Di et al., 2021). But we haven't done this part yet. ## E Model Configuration And Training Settings Lyric-to-Beat Model In recent years, pre-trained auto-regressive language models have improved various downstream tasks' performance significantly. We adopt MT5-based15 Lyric-to-Beat model in Seq2Seq framework (Xue et al., 2021b). The Lyric-to-Beat model consists of 8 encoder layers and 8 decoder layers and 6 attention heads for each layer. The hidden size of each layer is 512. The model is trained on a GeForce RTX 3090, and the batch size is 32 with 4096 tokens for each sample in the batch. Dropout with the rate of 0.1 is used for training and the activate function is gatedgelu (Shazeer, 2020). The model is finetuning with Adam optimizer with a learning rate of 0.0005 for 40,000 steps on the lyric-beat dataset (Kingma and Ba, 2015). Lyric-to-Structure Model Inspired by BERTscore, we train a standard Seq2Seq Transformer to evaluate the performance of the structural modeling (Zhang et al., 2019). The Lyric-to-Structure model consists of 4 encoder layers and 4 decoder layers, and 4 attention heads for each layer. The hidden size of each layer is 256. The model is trained on a GeForce RTX 3090, and the batch size is 32 with 4096 tokens for each sample in the batch. Dropout with the rate of 0.2 is used for training. The model is trained with Adam optimizer with a learning rate of 0.0005 for 40,000 steps on the Song8k dataset (Kingma and Ba, 2015). Model Comparison Similar to the Lyric-to-Beat module, we use the MT5-small from hugging face as initialization for following models (Wolf et al., 2020). MT5 We fine-tune the MT5-small version with masked lyrics and lyrics data with Adam optimizer with a learning rate of 0.0001 and 8,000 warmup steps for 5 epochs on the lyric-template dataset. We use Masked Lyric and Lyric of the compound template as the input for the encoder and decoder of MT5, respectively, under the standard Seq2Seq framework. The MT5 doesn't incorporate any musical and texture information. SongNet We rewrite SongNet in the MT5 framework. Based on MT5, we tune the model with Adam optimizer with a learning rate of 0.0001 and 8,000 warmup steps for 5 epochs on the lyrictemplate dataset. SongNet constructs its input with masked lyrics, intro-position, and segments of the compound template. We use Segment, Introposition, Masked Lyric and Lyric of the compound template as the input for the encoder and decoder of SongNet, respectively, under the standard Seq2Seq framework. The SongNet doesn't incorporate any musical information. UniLG The parameters are the same as MT5. The Template-to-Lyric model is trained with The hyper-parameter of the CCL α (in Section 3.3) is determined by performance on the validation set and is 0.03. And the Lyric-to-Beat model for CCL is an MT5-based model in Table 6. The UniLG train with Adam optimizer with a learning rate of 0.0001 and 8,000 warmup steps for 5 epochs on the lyric-template dataset. ## F Definition Of Objective Metrics Integrity The Integrity is the metric to evaluate the sentence integrity (Li et al., 2020). Integrity calculates the average probability of the separation token given previous tokens. The formulation of Integrity is: $$\text{Integrity}=\frac{1}{|Y|}\Sigma_{y\in Y}2^{-\log P(y_{|y|}|y_{1},...,y_{|y|-1})},\tag{8}$$ where Y is one piece of song, and y is one sentence of Y . |y| denotes the length of sentence y and |Y | denotes the number of sentences in Y . F1 scores For given two sequences A = (ai) n i=1 and A′ = (a′i ) m i=1, we define the formulation of Format F1 score is that: $$\mathbf{F1}(A,A^{\prime})={\frac{2*\Sigma_{i=1}^{\min(n,m)}[a_{i}==a_{i}^{\prime}]}{n+m}},\quad(9)$$ where [*] denote 1 for * is true or 0 otherwise. There are several F1 scores in our experiments, including Format F1, Beat F1, and Structure F1 scores. Format F1 score We use P = (pi) n i=1 stands for n positions of separation tokens in template of input data. And P′ = (p′i ) m i=1 stands for m positions of separation tokens in corresponding generated results. The formulation of Format F1 score is: Format F1= F1(*P, P*′). Beat F1 score Similarly, we use B = (bi) n i=1 stands for beat sequence with n tokens of input data. With the help of the Lyric-to-Beat model (details in Appendix A), we predicted beat sequence from generated lyrics with m tokens and annotate predicted beat sequence as B′ = {b′1 , b′2 , ..., b′m}. The formulation of Beat F1 score is: Beat F1= F1(*B, B*′). Structure F1 score Similar to Format F1 score, we use S = {s1, s2*, ..., s*n} stands for structure information sequence with n tokens of annotated 8,000 songs. We use the Lyric-to-Structure model (details in Appendix C) to predict structure information from generated lyrics. We use S′ = {s′1 , s′2 , ..., s′m} to stand for predicted structure information. The formulation of the Structure F1 score (Struc. F1) is Struc. F1= F1(*S, S*′). ## G Models' Decoding Settings In Experiments For the Lyric-to-Beat and Lyric-to-Structure modules, we use top-k sampling and the k in top-k is 2, the beams are 1, and the temperature is 0.5. For the Template-to-Lyric module, we use a sample-based beam search strategy 16 in subjective experiments with temperature 2.0, k of top-k 48, beams is 12, repetition penalty is 1.5, and score time decay is 0.98 and adopts top-k sampling decoding strategy in objective experiments for efficiency with temperature 1.5, k of top-k 32, beams is 1, and repetition penalty is 1.1. 16https://github.com/huggingface/transformers/ blob/c4d4e8bdbd25d9463d41de6398940329c89b7fb6/ src/transformers/generation_utils.py ## H Case Studies For Different Conditional Signals We show some cases for different inputs with rhythm resources. In the following figures (Figure 5-9), we use dotted boxes to address the corresponding or chorus parts. It's obvious that the automatic template construction is less satisfying because the template may not contain chorus and verse parts. The template which is handcrafted or extracted from other resources achieves satisfying results. It's clear that our framework can capture the correspondence between sentences with musical and textual structural information. And our framework can generate lyrics with structure and handle multiple inputs with rhythm resources. It is worth noticing that UniLG cannot rely on multiple modalities at the same time. The essence is still to generate lyrics given beats, ignoring the additional information of multimodal inputs. And our new experiments such that the different input modalities of the same song are given as input to the framework show consistency between beats and lyrics as same as Figures 5, 6, 7, 8, and 9. The only differences of results in experiments indicate that the Audio-to-Beat suffers the performance of Audio-to-MIDI tools. To avoid redundant content, we don't include these results. ## I Examples And Instructions For Other Languages In Figure 1 and 10, the rhythm patterns shown are the **start** beat of the corresponding word and it's flexible for any time signature and any-to-any correspondence between notes and words. To simplify the description, we only state our method with a 4/4 time signature, for it's widely used in songwriting. In Figure 1 and 10, the same rhythm pattern may repeat several times when meeting the chorus parts of the song through the melody are not exactly the same. These figures also illustrate the concept of chorus parts of lyrics, similar sentences with correspondence. It is obvious that the basic elements of Chinese words (Chinese characters) and English words (words) have different attributes in phonemes. The correspondence between notes and Chinese characters is one-to-many. However, the correspondence between notes and English words is many-to-many. Even though, the UniLG needs little adjustments except for generation lyrics on the music score and audio. For many-to-many correspondence lan- Sentence Lengths: 7,4,7,9,7,8,7,6,7,10 ![13_image_2.png](13_image_2.png) Sentence Lengths: ![13_image_1.png](13_image_1.png) ![13_image_0.png](13_image_0.png) guages, the procedure of extraction beats from MIDI should add a post-procession, that randomly skips 0 - 2 notes for each note and memorizes this information as the alignments for final outputs. | Sentence Lengths: 10, 7, 10, 7, 1, 8 Rhythm Pattern: 3 0 0 1 1 2 2 3 3 0 3 0 1 1 2 2 3 3 0 0 1 1 2 2 3 3 0 3 0 1 1 2 2 3 1 3 0 1 1 2 2 3 3 0 1 1 2 2 3 0 0 1 1 2 2 3 3 0 1 1 2 1 3 0 1 1 2 2 3 3 0 1 1 2 2 3 0 0 1 1 2 2 3 3 0 1 1 2 1 3 0 1 1 2 2 3 3 0 1 1 2 2 3 0 0 1 1 2 2 3 3 0 1 1 2 0 1 1 2 0 1 1 2 | Sentence Lengths: 10, 7, 10, 7, 1, 8 Generated Lyrics: !"#$%&'()* +,-./01 !"#$%&'()* 2345678 9 !:";'<= >+?'@AB 5CDD+'EF GHIJ 9 KLMNOPQ 5%R'(ST UVWXYZ[\ GHIJ 9 ]2^_(`a bc!d(ec 5CDDf'gc hiIj bcIj kcIj | Sentence Lengths: 10, 7, 10, 7, 1, 8 Generated Lyrics: lmnopqrs(t "u"vwxy %&zec('({| -}~YÄÅ 9 lmno(ÇÉ !:ÑÖhÜá ]m$à"2câ äHãå ç lmno(ÇÉ 5C!éèêë ;líì(îïñ .Cóò 9 lmno(ÇÉ !:ÑôhÜá ]m$à"2câ éèêë éèöõ éèÇÉ | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---| | Sentence Lengths: 4, 7, 7, 3, 3, 13, 7, 3, 3, 7, 6, 10, 7, 3, 3, 7, 6, 7, 3, 3, 7, 6, 6 Raw Input Lyrics: !"#$ %&'()*+ %&'()*+ ,-. /01 23(4+56"78)9: ;;<=>?@ ABC DEE :FG01HI JKL01F )MNO01PQRS TUVW:XY Z[\ X': ]^_`Ka] 3b/:01 ;;<=>?@ ABC DEE :FG01HI cde:01 fgO:01 | Sentence Lengths: 4, 7, 7, 3, 3, 13, 7, 3, 3, 7, 6, 10, 7, 3, 3, 7, 6, 7, 3, 3, 7, 6, 6 Predicted Rhythm Pattern: 3 0 0 1 3 0 0 1 1 2 2 3 0 0 1 1 2 2 0 1 2 0 1 2 0 1 2 2 3 3 9 9 1 1 2 2 3 3 0 0 1 1 2 2 0 1 2 0 1 2 3 0 1 1 2 2 3 0 1 2 3 3 0 2 3 3 0 0 1 1 2 2 3 3 0 0 1 1 2 2 0 1 2 0 1 2 3 0 0 1 1 2 2 0 1 2 3 3 0 3 0 0 1 1 2 2 0 1 2 0 1 2 3 0 0 1 1 2 2 0 1 2 3 3 0 0 1 2 3 3 0 | Sentence Lengths: 4, 7, 7, 3, 3, 13, 7, 3, 3, 7, 6, 10, 7, 3, 3, 7, 6, 7, 3, 3, 7, 6, 6 Generated Lyrics without keywords: Khij k$lmnoQ 3pqrstu mbQ RvQ cwxyzcwxm{Q|} #~ÄOÅÇ mbQ RvQ kÉÑÖQÜá kÉÑàâä àâä2ãå~Qçé Qèkêëíì îïë îïë Qèkêëíì kñórcQ kOòôöcQ 3õd 3úò ùK$QOûü mhsO†ê °Q¢£†ê Sentence Lengths: 4, 7, 7, 3, 3, 13, 7, 3, 3, 7, 6, 10, 7, 3, 3, 7, 6, 7, 3, 3, 7, 6, 6 Generated Lyrics with keywords: !I§I •N¶c)ß® •©¶c:*+ ™™´ ™™´ A¨¨~+YÆØ°õ:^ ±N¶c3ú² ™™´ AEE ³&k¥µ¶I ÖµN·õF K¸¹2NzØêRI ºª,I,zº ½[\ æ[\ •^ªøí¿L öc•A£ª K¸ñë¬?I A£ª Êk°3Æ^3ã KóÖ«»… Q©~kç | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---| ![15_image_0.png](15_image_0.png) ![15_image_1.png](15_image_1.png) ![15_image_2.png](15_image_2.png) | e the formal production of the production of the production of the second the second the second the | | | | |-------------------------------------------------------------------------------------------------------|------------------------------------|-------------------------|--------------------------------------| | & ... | 10.11.2020303303000101020203303030 | | | | Rhythm Pattern | 1 2 2 2 2 3 3 3 | 1 2 2 2 2 3 3 3 | | | Chorus lyrics l Wher -ev -er you go | What -ev -er you do l | In will be resigning to | here wait -ing for you | | $ | a pr | | | | Rhythm Pattern | 11 22 22 33 33 33 | 10 11 21 21 31 31 | 0 1 1 1 2 2 3 3 | | Chorus lyrics 2 What several its takes | Or how my heart breaks | 1 will will be a right | here wait -ing for you | $ ... Figure 10: Example chorus parts of a song in English. Similar to Figure 1, we use different colors for different beats within the bar and rhythm patterns are shown in 4/4 time signatures. These lines show similar melody with similar lyrics. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section: Limitation ✓ A2. Did you discuss any potential risks of your work? Section: Limitation ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section: Abstract, Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section: Method, Experimental Settings, Experiments Results ✓ B1. Did you cite the creators of artifacts you used? Section: Related Work, Method, Experimental Settings, Experiments Results ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section: Experimental Settings, Ethics Statement, Appendix B Statics of Lyric-Template Dataset ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section: Experimental Settings, Experiments Results, Appendix A Details of Lyric-to-Beat Model, E Model Configuration and Training Settings, G Models' Decoding Settings in Experiments ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section: Appendix B Statics of Lyric-Template Dataset ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section: Experimental Settings, Appendix E Model Configuration and Training Settings ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section: Experimental Settings, Appendix A Details of Lyric-to-Beat Model, B Statics of LyricTemplate Dataset, C Details of Lyric-to-Structure Model ## C ✓ **Did You Run Computational Experiments?** Section: Experiments Results, Appendix A Details of Lyric-to-Beat Model, C Details of Lyric-toStructure Model The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section: Appendix A Details of Lyric-to-Beat Model, C Details of Lyric-to-Structure Model, E Model Configuration and Training Settings ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section: Appendix E Model Configuration and Training Settings, G Models' Decoding Settings in Experiments ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section: Experiments Results, Appendix A Details of Lyric-to-Beat Model, B Statics of Lyric-Template Dataset, C Details of Lyric-to-Structure Model, ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section: Appendix A Details of Lyric-to-Beat Model, C Details of Lyric-to-Structure Model ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section: Experiments Results D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. We report key information about the requirements for human annotators, and we report this in Ethics Statement. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section: Ethics Statement ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section: Ethics Statement ✓ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section: Ethics Statement ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section: Ethics Statement

zhang-etal-2023-fc

{FC}-{KBQA}: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering

https://aclanthology.org/2023.acl-long.57

The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained modeling of the logical expression, or inexecutability issues due to the fine-grained modeling of disconnected classes and relations in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to both ensure the generalization ability and executability of the logical expression. The main idea of FC-KBQA is to extract relevant fine-grained knowledge components from KB and reformulate them into middle-grained knowledge pairs for generating the final logical expressions. FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than the baseline. Our code is now available at GitHub \url{https://github.com/RUCKBReasoning/FC-KBQA}.

# Fc-Kbqa: A Fine-To-Coarse Composition Framework For Knowledge Base Question Answering Lingxi Zhang1**, Jing Zhang**1∗ , Yanling Wang1, Shulin Cao2**, Xinmei Huang**1, Cuiping Li1, Hong Chen1**, Juanzi Li**2 1School of Information, Renmin University of China, Beijing, China 2Department of Computer Science and Technology, Tsinghua University, Beijing, China {zhanglingxi, zhang-jing, wangyanling,huangxinmei, licuiping, chong}@ruc.edu.cn {caosl19}@mails.tsinghua.edu.cn, {lijuanzi}@tsinghua.edu.cn ## Abstract The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained modeling of the logical expression, or inexecutability issues due to the fine-grained modeling of disconnected classes and relations in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA (FC-KBQA) to both ensure the generalization ability and executability of the logical expression. The main idea of FC-KBQA is to extract relevant finegrained knowledge components from KB and reformulate them into middle-grained knowledge pairs for generating the final logical expressions. FC-KBQA derives new state-of-theart performance on GrailQA and WebQSP, and runs 4 times faster than the baseline. Our code is now available at GitHub https://github. com/RUCKBReasoning/FC-KBQA. ## 1 Introduction Question answering over knowledge bases (KBQA) aims to provide a user-friendly way to access largescale knowledge bases (KBs) by natural language questions. Existing KBQA methods (Zhang et al., 2023) can be roughly categorized into retrievalbased and semantic-parsing (SP) based methods. The former (Feng et al., 2021; He et al., 2021a; Zhang et al., 2022) directly scores the relevance between the question and answer candidates, thus it is difficult to resolve the complex questions. On the contrary, some KBQA approaches, such as (Das et al., 2021; Kapanipathi et al., 2021; Qiu et al., 2020; Sun et al., 2020), are based on semantic parsing (denoted as SP-based), which can address complex questions and achieve promising results on i.i.d. datasets. SP-based methods first translate the questions into logical expressions such as SPARQL and then execute them against KB to yield answers. ∗Corresponding author. Figure 1: Illustration of generalization tasks in KBQA. ![0_image_0.png](0_image_0.png) ![0_image_1.png](0_image_1.png) Each question is paired with a logical expression that consists of different components. Components involved in the training data are colored in non-green color, while unseen components are colored in green. Figure 2: Results of the pilot study. The coarse-grained method directly matches the question with the logical expression (i.e., the composition of components), while the fine-grained method matches the question with each component candidate and then composes them to derive the logical expression. The exact match accuracy of logical expressions on compositional generalization test data and zero-shot generalization test data is shown on the right of the figure. As illustrated in Figure 1, a logical expression consists of multiple components such as classes and relations. Most existing SP-based approaches fail with logical expressions that contain unseen compositions of components (called compositional generalization) or unseen components (called zero-shot generalization). To address the above problem, GrailQARank (Gu et al., 2021) proposes a BERT-based rank model to match the given question with each logical expression candidate, which leverages the generalization abilities of the pre-trained language models. On top of that, RNG-KBQA (Ye et al., 2022) further uses a pre-trained generation model, which takes top-5 ranked logical expressions as the additional input beyond the question to generate the target logical expression. Behind these mainstream models, a logical expression is viewed as an inseparable unit during modeling. Actually, logical expressions are coarse-grained because they can be decomposed into relatively fine-grained components including relations, classes, entities, and logical skeletons (See examples in Figure 3). Such coarse-grained modeling entangles representations of fine-grained components, thereby overfitting the seen compositions during the training process, which weakens the model's compositional generalization ability. Meanwhile, even though pre-trained language models can deal with zero-shot components to some extent, compositional overfit reduces their ability to identify individual unseen components with zero-shot generalization. To demonstrate the above idea, we perform a pilot study (Cf. the detailed settings in Section 4.1) with two preliminary experiments: one calculates the similarity score between a question and each coarse-grained logical expression to obtain the most relevant one, and the other searches the most relevant fine-grained components to form the final logical expression of a question. We observe that the fine-grained modeling derives more accurate logical expressions on both the compositional task and zero-shot task (Cf. Figure 2). It could be explained that fine-grained modeling focuses exclusively on each component, avoiding overfitting of seen compositions in the training data. Although some studies attempt to leverage fine-grained components, they only consider partial fine-grained components such as relations, classes, and entities (Chen et al., 2021), or suffer from inexecutability due to disconnected fine-grained components in real KBs (Shu et al., 2022). Thus, to both ensure the generalization ability and executability of logical expressions, we propose a Fine-to-Coarse composition framework for KBQA (FC-KBQA), which contains three submodules. The overview of our model is shown in Figure 4. The first module is fine-grained component detection, which detects all kinds of finegrained component candidates from Freebase by their semantic similarities with the question. Such component detection guarantees the generalization ability in both compositional and zero-shot tasks. The second module is the middle-grained component constraint, which efficiently prunes and composes the fine-grained component candidates by ensuring the components' connectivity in the KB. The final module is the coarse-grained component composition, which employs a seq-to-seq generation model to generate the executable coarse-grained logical expression. In addition to encode the finegrained components, the middle-grained components are also encoded to enhance the model's reasoning capacity, so as to improve the executability of the generated logical expression. In contrast to previous work (Cao et al., 2022b; Chen et al., 2021; Shu et al., 2022) that only uses the knowledge constraints to guide the decoding process, we emphasize injecting them into the encoding process, because the encoder which learns bidirectional context could better suit natural language understanding (Du et al., 2022). We conduct extensive experiments on widely used GrailQA, WebQSP, and CWQ datasets. GrailQA (Gu et al., 2021) is a KBQA benchmark focusing on generalization problems. FCKBQA derives new state-of-the-art performance on GrailQA-Dev (+7.6% F1 gain and +7.0% EM gain respectively). Meanwhile, FC-KBQA also obtains good performance on WebQSP and CWQ. Moreover, FC-KBQA runs 4 times faster than the state-of-the-art baseline RNG-KBQA. The ablation studies demonstrate the effect of our middlegrained encoding strategy. Contributions. (1) We conduct a pilot study to reveal an intriguing phenomenon - a fine-grained understanding of the logical expression helps enhance the generalization ability of SP-based KBQA methods, which is rarely discussed before. (2) We propose a fine-to-coarse composition framework FC-KBQA to address the generalization problem, which takes advantage of the idea of fine-grained modeling. (3) We devise a middle-grained component constraint that is injected into both the encoder and the decoder to guide the seq-to-seq model in producing executable logical expressions. (4) FC-KBQA not only maintains efficiency but also achieves significant improvement on GrailQA. ## 2 Related Work Coarse-Grained SP-based Methods. Many efforts are paid to solve generalization problems on SP-based KBQA. Some approaches, such as (Lan and Jiang, 2020; Gu et al., 2021), use a rank-based model that takes advantage of a coarse-level match between the question and the logical expressions or query graphs. They first enumerate numerous query graph candidates based on KBs and then they rank them according to how relevant they are to the question. Another line of approaches, in addition to the rank-based ones, makes use of a generation model. KQAPro (Cao et al., 2022a) leverages BART to directly convert questions into logical expressions. Additionally, RNG-KBQA (Ye et al., 2022) further injects top-k ranked logical expressions as an additional input to the question. CBR-KBQA (Das et al., 2021) injects analogous questions and their corresponding logical expressions from the training data to increase the generalization. All of the aforementioned methods are pure coarse-level frameworks that treat each coarse-grained logical expression as a separate unit. Fine-Grained SP-based Methods. Many researchers have been motivated to address the generalization issue by the notion of utilizing decomposed components, such as class, relation, and logical skeleton. Some approaches (Wang et al., 2020; Zhao et al., 2022; Li et al., 2023) retrieve the relevant schema item such as relation and column as additional fine-grained input information, while another line of approaches (Dong and Lapata, 2018) extracts the skeleton of logical expression as the decoder guide. Such methods primarily concentrate on the grammar of logical expression and often ignore the knowledge constraint, which is essential in large-scale KB. They usually focus on KBs or DBs that contain a small number of relations where a logical expression can be easy to be executable. Program Transfer (Cao et al., 2022b), ReTrack (Chen et al., 2021), and TIARA (Shu et al., 2022) simply apply KB constraints to control the generation of the decoding process. As opposed to them, we make use of middle-grained KB constraints during both the encoding and the decoding processes to help the model better adapt to KB and ensure executability. ## 3 Problem Definition Knowledge Base (KB). A KB is comprised by ontology {(C × R × C)} and relational facts {(E × R × (E ∪ C))}, where *R, C,* and E denote relation set, class set, and entity set respectively. Notably, we consider literal as a special type of entity. Specifically, an ontology triple (cd*, r, c*r) consists of a relation r ∈ R, a domain class cd which denotes the class of the subject entities, and a range class cr which denotes the class of the object entities. Each class has multiple entities, thus an ontology triplet can be instantiated as several relational facts. For example, both (e1*, r, e*2) and (e3*, r, e*4) correspond to (cd*, r, c*r), where e1, e3 ∈ cd and e2, e4 ∈ cr. Figure 3 illustrates a KB subgraph. SP-based KBQA. Given a natural question q, KBQA models aim to find a set of entities denoted by A ⊆ E from KB as the answers to q. Instead of directly predicting A, SP-based KBQA models translate q to an executable logical expression denoted by s such as SPARQL, lambda-DCS (Liang et al., 2013), query graph (Lan and Jiang, 2020), and s-expression (Gu et al., 2021). We select s-expression as our used logical expression since it could provide a good trade-off on compactness, compositionality, and readability (Gu et al., 2021). The **logical skeleton** of an s-expression can be derived by removing all the relations, classes, and entities in the expression and only keeping function operators and parentheses. Specifically, we replace relations, classes, entities, literals with special tokens "<rel>", "<class>", "<entity>", "<literal>" respectively. Figure 3 shows an executable logical expression on the KB and its corresponding logical skeleton. We unitedly name the relations, classes, entities, and logical skeleton in an s-expression as the **fine-grained** component, while the complete s-expression is the **coarse-grained logical expression**. ## 4 Approach 4.1 Pilot Study As analyzed in Section 1, considering the logical expression as a unit will lead to entangled representations of fine-grained components and thus weakens generalization ability. Here we study the necessity of fine-grained modeling by testing how coarse-grained and fine-grained matching methods perform when selecting a question's logical expression from the corresponding candidate pool. Dataset. To simplify the experiment, we extract a toy dataset that only involves 1-hop logical expressions from GrailQA. Then, for the relation r and the class c in such logical expressions, we study the compositional generalization where the composition (*r, c*) is unseen or zero-shot generalization where the individual r or c is unseen in the training data. For each question with its ground-truth logical expression, we select 100 logical expressions ![3_image_0.png](3_image_0.png) that share the same domain as the ground truth as the coarse-grained expression candidates. For fair comparison, we separate all of the relations, classes, and logical skeletons from the coarse-grained candidates as the fine-grained component candidates. Methods. We aim to find the target logical expression of a given question by a ranking model trained with a contrastive loss (Chen et al., 2020), which is also used by RNG-KBQA (Ye et al., 2022). The coarse-grained method concatenates a question and a candidate logical expression to feed into BERT, then the output embedding of [CLS] is fed into a linear layer to compute the similarity score. The fine-grained method follows the above pipeline, but the input is the concatenation of a question and a fine-grained candidate component, then scores each logical expression candidate by summing up the normalized question-component similarity scores. For both methods, we compute accuracy by evaluating whether the ground-truth logical expression owns the highest score in the candidate pool. ## Observation - Fine-Grained Modeling Can Better Solve The Generalization Problems On Kbqa. The matching accuracy is reported in Figure 2. The fine-grained method outperforms the coarsegrained method in both composition generalization and zero-shot generalization tasks. A possible explanation is the fine-grained matching focuses solely on each component and is simple to learn, which better capture the semantic information of each component and also well adaptable to express the various compositions of components. The coarse-grained matching, on the other hand, attempts to describe all of the components as a whole composition, limiting the ability to express unseen compositions and components. Inspired by this, we propose FC-KBQA in the next section. ## 4.2 Model Overview We propose a fine-to-coarse composition framework FC-KBQA bridged by a middle-grained KB constraint. Figure 4 illustrates the overall framework, which contains three parts: Fine-grained Component Detection. Given a question, we extract relation candidates and class candidates from the whole KB based on semantic similarity. Simultaneously, we adopt an entity linker to detect mentioned entities and use a seq-toseq model to generate logical skeletons. Middle-grained Component Constraint. Based on the detected components, we devise an efficient way to check the connectivity of component pairs on the KB, including class-relation pairs, relationrelation pairs, and relation-entity pairs. We only keep the executable component pairs to guarantee the executability of final logical expression. Coarse-grained Component Composition. Finally, a seq-to-seq model takes the concatenation of the question and the reformulated components as input to generate the logical expression. In particular, the middel-grained components are injected into both the encoder and the decoder to ensure the executability of the final logical expressions. ## 4.3 Fine-Grained Component Detection Relation and Class Extraction. Taking the relation extractor as the example, given a question q, we aim to extract relations in q. First, we apply BM25 (Robertson et al., 2009) to recall the relation candidates from the KB based on the surface overlaps between relations' names and q. Then we apply BERT (Devlin et al., 2019) as the cross-encoder to measure the semantic similarity between q and each relation candidate r. We describe r using the relation domain, the relation name, and the relation range and let the BERT input be "[CLS] q [D] domain(r) [N] name(r) [R] range(r) [SEP]", where [CLS], [SEP], [D], [N], and [R] are the special tokens. To better distinguish the spurious relations, ![4_image_0.png](4_image_0.png) we sample the relations that share the same domain as the ground-truth relation as the negatives for training. The trained model is used to retrieve the set of top-k relations, denoted by Rq. The class extractor works in the same way as the relation extractor. We represent the class using its name and domain, and use other classes in the same domain as negatives. Cq represents the set of the top-k relevant classes. Entity Linking. A common paradigm of finding topic entities in KBQA methods is to first leverage a NER tool (Finkel et al., 2005) to detect mentions and then apply an entity disambiguation model to link them to entities in KB. However, some nounphrase mentions such as "rich media" are hard to be detected by the NER tool, and some ambiguous entities could not be distinguished by the pure entity names. To address both issues, we equip the NER tool1 with a trie tree-based mention detection method and propose a relation-aware pruning method to filter the mentions. Specifically, we build a trie tree (Fredkin, 1960) with the surface names of all entities in the KB. Then we can search noun phrase mentions in the question efficiently and link them to the KB by 1We follow GrailQA which utilizes an open BERT-NER tool on GitHub (https://github.com/kamalkraj/BERT-NER). BLINK (Wu et al., 2020) to obtain the corresponding entities Eq. After that, we propose a relation awared pruning strategy to prune Eq by removing the entities that could not link to any relations in Rq. Finally, following GrailQA (Gu et al., 2021), we choose the entity with the highest popularity. We define regular expressions to extract literals such as digits and years appearing in q. Logical Skeleton Parsing. Logical skeleton parsing aims to transform a given question q into a logical skeleton l. Because the logical skeleton is domain-independent, the parsing process could be generalized across domains. We adopt T5 (Raffel et al., 2020), a state-of-the-art generation model to parse logical skeletons. Since many entity names contain tokens such as "and" and "of" that may cause the logical skeleton to be incorrectly determined, we mask each mention m ∈ Mq with the special token "<entity0>", "<entity1>", ..., in order of appearance. For example, we change "Thomas was the designer of what ship?" to "<entity0> was the designer of what ship?". We notice that a common error is parsing out logical skeleton with wrong relation numbers, for example "<rel>" instead of "<rel><rel>". Instead of increasing beam numbers, we manually add grammar rules, such as add "<rel><rel>" as the second candidate when "<rel>" is T5's top-1 prediction. The set of the top-2 logical skeleton candidates is denoted as Lq. ## 4.4 Middle-Grained Component Constrain After deriving the candidate components according to Section 4.3, the KB-based constraint is required to guarantee the composed logical expression is executable. A straightforward idea is to fill the logical skeleton with candidate relations, classes, and entities, and execute them one by one to check executability. However, such enumeration is inefficient, since all combinations of candidate components should be considered. Therefore, we incorporate the middle-grained component pairs which are connected in KB. Such pairs can be produced efficiently to keep the model's efficiency. The middle-grained component pairs include class-relation pairs, relation-relation pairs, and relation-entity pairs. For each class c ∈ Cq and each relation r ∈ Rq, if r is connected with the domain class c, we add (*c, r*) into the classrelation pair set Pc−r. For example in Figure 3, the class "railway.railway" is linked with the relation "rail.railway.terminuses", so the pair (railway.railway, rail.railway.terminuses) is executable and will be added into Pc−r. If the range class of r is c, we add the pair of c and the reverse relation of r. We construct executable relationrelation pair set Pr−r by checking each relation pair (r1 ∈ Rq, r2 ∈ Rq). If r2's domain class does not match r1's range class, we directly remove this pair to maintain efficiency, otherwise, we reformulate (r1, r2) to a logical expression and execute on KB to check its connectivity. For each relation-entity pair (*r, e*), we first check whether the logical skeleton candidates contain the <entity> placeholder or not. If not, we leave Pr−e empty; otherwise we directly take the result of the relationpruning strategy for entities in Section 4.3. ## 4.5 Coarse-Grained Component Composition We apply a generation model based on T5 to compose all the above fine-grained and middle-grained component candidates and output an executable logical expression by a controlled decoder. Encoding Process. Before feeding the finegrained and middle-grained component candidates into the generator, we sort the middle-grained candidates according to their similarity scores to the question. By doing this, the order can reveal the pattern of which pair is more likely to appear in the ground-truth logical expression. In intuition, such a pattern will help to generate more accurate logical expressions. To accomplish this, we take the logits of the fine-grained component detection in section 4.3 as the similarity score between the question and each class/relation component, and then calculate the similarity score between the question and a middle-grained component pair by summing the scores of contained single components. The encoding of such middle-grained component improves the generator's reasoning capacity in terms of capturing the knowledge constraints. We use ";" to separate each element (a component or a component pair). To explicitly inform the model the type of each component, we place "[REL]", "[CL]", "[ENT]", and "[LF]" before each relation, class, entity, and logical skeleton respectively. For example, we organize the input of encoder as "query;[CL]c1[REL]r1;[REL]r1 [REL]r2;[CL]c2[REL]r3;[ENT]e1;[LF]l1;[LF]l2". Decoding Process. The middle-grained components are also used to produce a dynamic vocabulary to constrain the decoding process. The generated token ytis confined to the tokens involved in the dynamic vocabulary at each step t. We initialize the dynamic vocabulary with the union of tokens from the detected entities, tokens from the detected classes in Pc−r, i.e., usually the answer type, and the keywords such as "JOIN" in logical skeleton. Then we update the dynamic vocabulary by the relations paired with r in Pr−r if the last generated component is r or by the relations paired with c in Pc−r if it is c. ## 5 Experiment 5.1 Experimental Settings Dataset. We evaluate our method on GrailQA (Gu et al., 2021), WebQSP (Yih et al., 2016), and CWQ (Talmor and Berant, 2018), all of which are based on Freebase. GrailQA focuses on generalization problems which involved up to 4-hop logical expressions and complex operations. WebQSP is an i.i.d. benchmark that required 2-hop reasoning. Although CWQ is not designed to solve generalization problem, we can still separate out the zero-shot test set with all the unseen relations and classes, yielding 576/3519 zero-shot/all test set. Evaluation Metrics. To measure the accuracy of logical expression, we use the well-adopted exact match (EM) which measures the exact equivalence Table 1: Results of overall evaluation on GrailQA-LeaderBoard (%). Overall I.I.D. Compositional Zero-Shot EM F1 EM F1 EM F1 EM F1 GrailQA-Rank (Gu et al., 2021) 50.6 58.0 59.9 67.0 45.5 53.9 48.6 55.7 GrailQA-Trans (Gu et al., 2021) 33.3 36.8 51.8 53.9 31.0 36.0 25.7 29.3 ReTrack (Chen et al., 2021) 58.1 65.3 84.4 87.5 61.5 70.9 44.6 52.5 RNG-KBQA (Ye et al., 2022) 68.8 74.4 86.2 89.0 63.8 71.2 63.0 69.2 FC-KBQA(Ours) **73.2 78.7 88.5 91.2 70.0 76.7 67.6 74.0** between the query graph of the predicted and the gold logical expression. We also calculate the F1 score based on the predicted and gold answers. Baselines. On GrailQA, we mainly compare with the published works on the leaderboard, including GrailQA-Rank (Gu et al., 2021), GrailQATrans (Gu et al., 2021), Retrack (Chen et al., 2021), RNG-KBQA (Ye et al., 2022). They are all SPbased models that target generalization problems in KBQA. On WebQSP and CWQ, we compare our method with the retrieval-based models including GraphNet (Pu et al., 2018),PullNet (Sun et al., 2019) and NSM (He et al., 2021b), and the SP-based models including QGG (Lan and Jiang, 2020), RNG-KBQA (Ye et al., 2022), and PI Transfer (Cao et al., 2022b). We evaluate F1 for the retrieval-based models, while evaluate both F1 and EM for the SP-based methods. We compare all the baselines that have the results on the two datasets or publish the codes that can be executed. ## 5.2 Overall Evaluation Performance. In Table 1 and Table 2, we evaluate the performance of FC-KBQA on different datasets. For the baselines, we directly take their results reported in the original papers. To be noted, on the extracted zero-shot test set of CWQ, the results for some models remain empty because their full codes are not released. As shown in Table 1, our model outperforms all the baselines, especially on the compositional and zero-shot test tasks. Compared with RNG-KBQA, the state-of-the-art published model, we have an absolute gain of 4.3% and 4.4% in terms of F1 score and EM respectively. We also outperform on the extracted zero-shot CWQ test set by 11.3% in terms of F1, as for an unseen complex question, parsing out correct knowledge components and logical skeletons is much easier than directly parsing the coarse-grained logical expression correctly. Since the fine-grained module solely focuses on each component and thus leads Table 2: F1 Evaluation on WebQSP and CWQ (%). to a higher component accuracy, FC-KBQA also outperforms on the i.i.d test set of WebQSP. On the original test set of CWQ, we only under-perform PI Transfer which leverages a pre-train process on a large-scale wiki data that is out scope of CWQ. Efficiency. Both RNG-KBQA and GrailQA-Rank enumerate all the logical expressions in a 2-hop KB subgraph (enumeration), so it is time-consuming for the rank model to score thousands of logical expressions for each question (candidate selection). Conversely, our FC-KBQA just retrieves the most relevant components (candidate selection) and then enumerates the component pairs based on the filtered candidates (enumeration), which greatly reduces the inference time. Besides enumeration and candidate selection, a seq-to-seq model is used to generate the final logical expression (final composition). In the same 24GB GPU and Intel Gold 5218 CPU, the experimental results in Figure 5 show that our model runs 4 times faster than baselines. ## 5.3 Ablation Studies GrailQA does not provide ground truth for the test set, so we conduct the ablation studies on the public Grail-Dev to investigate how the fine- and middlegrained components affect the performance. As shown in Table 3, we develop four model variants. (1) **-Knowledge** removes all the finegrained and middle-grained components except for the logical skeleton. (2) **-Knowledge Pairs** replaces the middle-grained components, such as | WebQSP | CWQ | | | |-------------|---------|-----------|------| | Overall | Overall | Zero-Shot | | | GraphNet | 66.4 | 32.8 | 22.3 | | PullNet | 68.1 | 47.2 | - | | NSM | 74.3 | 48.8 | 31.6 | | QGG | 74.0 | 40.4 | 28.9 | | RNG-KBQA | 75.6 | 42.3 | 33.3 | | PI Transfer | 76.5 | 58.7 | - | | Ours | 76.9 | 56.4 | 53.1 | Overall I.I.D. Compositional Zero-Shot EM F1 EM F1 EM F1 EM F1 T5-base 22.7 23.4 61.8 64.1 28.3 29.0 0.3 0.3 RNG-KBQA 71.4 76.8 86.5 88.9 61.6 68.8 69.0 74.8 Enhanced RNG-KBQA 72.8 78.2 86.6 90.2 61.7 69.3 71.5 76.7 FC-KBQA **79.0 83.8 89.0 91.5 70.4 77.3 78.1 83.1** –Knowledge 23.1 24.0 62.1 64.2 29.5 31.0 0.3 0.3 –Knowledge Pairs 53.6 55.6 70.2 72.3 44.0 46.0 50.3 52.2 –Logical Skeleton 78.0 80.8 85.2 86.8 68.5 71.9 79.2 81.8 –Decode Constraint 77.5 83.1 88.3 91.1 67.8 76.3 76.8 82.5 ![7_image_0.png](7_image_0.png) class-relation pairs and relation-relation pairs with the corresponding fine-grained candidates, such as classes and relations. (3) **-Logical Skeleton** gets rid of the logical skeleton. (4) **-Decode Constraint** deletes the dynamic vocabulary created with the middle-grained components. The results show that removing "knowledge" reduces model performance by 60% F1 score, and replacing "knowledge pairs" with pure fine-grained components also reduces model performance by 28% F1, indicating that encoding the middlegrained components can significantly improve the model's reasoning capacity. To further demonstrate that encoding such middle-grained components can also help improve other model's performance, we create Enhanced RNG-KBQA by taking the top-10 ranked results from its ranking model and formulating them into middle-grained component pairs to be injected into its encoder. The results in Table 3 show that middle-grained reformulation improves the performance of RNG-KBQA. Middle-grained component pairs, like coarse-grained logical expressions, can guarantee connectivity, but they are more compact and much shorter. As a result, because PLMs have a maximum input length, the middle-grained formulation can inject more components and is more likely to cover the components involved in the target logical expression. Removing "logical skeleton" can result in a 3.0% F1 drop, indicating that skeleton is useful for guiding the question understanding even though it is less important than the knowledge. Removing "decode constraint" in the decoder can also have an effect on model performance, but is much weaker than removing "knowledge pairs" in the encoder, indicating that injecting the knowledge constraints in the encoding process is more useful than in the decoding process, because the encoder learns the bidirectional context, which is better suited to natural language understanding. This is also a significant difference from the existing knowledge constrained decoding methods. Both "Knowledge Pairs" and "Decode Constraint" are proposed for addressing the inexecutability issue, which guarantee all generated logical expressions are executable. Removing either reduces the accuracy, which indicates that high executability can improve the model performance. ## 5.4 Error Analysis We randomly select 50 error cases on GrailQA and summarize the error into three main categories: error entity (60%), error relation and class (35%), and error logical skeleton (40%). We also analysis the error cases while our model fails but some baseline methods can answer successfully resolve them. A typical mistake is on logical expressions that involve KB-specific component composition. For example, in Freebase, "coach" is represented by the join of "sports.sports_team.coaches" and "sports.sports_team_coach_tenure.coach". Our fine-to-coarse model only predicts the previous relation but is unable to recall "sports.sports_team_coach_tenure.coach", while some coarse-grained methods are able to memorize such composition and provide the correct answer. ## 6 Conclusion This paper proposes FC-KBQA, a Fine-to-Coarse composition framework for KBQA. The core idea behind it is to solve the entanglement issue of mainstream coarse-grained modeling by the fine-grained modeling, and further improve the executability of logical expression by reformulating the finegrained knowledge into middle-grained knowledge pairs. Benefiting from this, FC-KBQA achieves new state-of-the-art performance and efficiency on the compositional and zero-shot generalization KBQA tasks. This fine-to-coarse framework with middle-grained knowledge injection could be inspiring for generalization on other NLP tasks. ## 7 Limitations Although our model achieves good performance in solving the compositional and zero-shot generalization problems, there is still room for improvement on the i.i.d datasets. The fine-grained module in our framework cannot take advantage of explicit composition information when the component compositions in the testing set and training set significantly overlapp. For example, in Freebase, "Who is the coach of FC Barcelona?" is answered by the join of relation "sports.sports_team.coaches" and "sports.sports_team_coach_tenure.coach". Our fine-grained extractor may fail to recall "sports.sports_team_coach_tenure.coach" and instead select "base.american_football.football_coac -h.coach" as the candidate since 'football coach" is more relevant to the question than "coach tenure" in semantics. The only coarse-grained model, however, can directly memorize the pattern because such composition appears frequently in the training data. Therefore, compared to conventional models that completely memorize composition patterns, our model may only have minor advantages. Another limitation is that we cannot guarantee the generalization on other KBs such as WikiData because gaps between KBs may bring negative impact. For example, relations in Freebase are often more specific (ice_hockey.hockey_player.hockey_position, soccer.football_player.position_s), while relations in Wikidata are more general (position_played_on_team). We consider it as a direction for our future work. ## 8 Ethics Statement This work focuses on the generalization issue of knowledge base question answering, and the contribution is fully methodological. Hence, there are no direct negative social impacts of this work. For experiments, this work uses open datasets that have been widely used in previous work and are without sensitive information as we know. The authors of this work follow the ACL Code of Ethics and the application of this work have no obvious issue that may lead to the risk of ethics. ## Acknowledgments This work is supported by National Natural Science Foundation of China (62076245, 62072460, 62172424,62276270); Beijing Natural Science Foundation (4212022). ## References Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, and Hanwang Zhang. 2022a. Kqa pro: A dataset with explicit compositional programs for complex question answering over knowledge base. In *Proceedings of the* 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6101–6119. Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, and Jinghui Xiao. 2022b. 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Rng-kbqa: Generation augmented iterative ranking for knowledge base question answering. In *Proceedings of the 60th Annual* Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6032–6043. Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. 2016. The value of semantic parse labeling for knowledge base question answering. In *Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics* (Volume 2: Short Papers), pages 201–206. Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, and Hong Chen. 2022. Subgraph retrieval enhanced model for multi-hop knowledge base question answering. In *Proceedings of the 60th* Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5773– 5784. Lingxi Zhang, Jing Zhang, Xirui Ke, Haoyang Li, Xinmei Huang, Zhonghui Shao, Shulin Cao, and Xin Lv. 2023. A survey on complex factual question answering. *AI Open*, 4:1–12. ## A Implementation Detail Chen Zhao, Yu Su, Adam Pauls, and Emmanouil Antonios Platanios. 2022. Bridging the generalization gap in text-to-SQL parsing with schema expansion. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5568–5578, Dublin, Ireland. Association for Computational Linguistics. KB Environment. To execute the SPARQL, we build a virtuoso database with the latest official data dump of Freebase2. Pilot Study. To simulate the generalization problems, the training set and test set are drawn from GrailQA's training set and test set, respectively. To build the toy train set, we choose two thousand cases with only the 1-hop logical expression from the GrailQA train set. In addition, for the compositional test set, we select the 1-hop cases from the GrailQA test set, which contains seen single relations and classes but unseen class-relation pairs beyond the train set. For the zero-shot test set, we select the 1-hop cases that involve both a class and a relation that does not appear in the toy train set. To be noted, as coarse-grained modeling involves the enumeration of logical expressions to obtain candidates, and the enumeration is nearly impossible for 2-hop logical expressions due to the large amount (greater than 2,000,000). So, we simplify the pilot study to only 1-hop questions that involve the composition of a class and a relation, which can also support comparing fine-grained and coarse-grained modeling. For both the coarse-level and fine-level matching methods, we apply a BERT-based-uncased model. Both models are trained for 5 epochs with a batch size of 8 and a learning rate of 2e-5. To demonstrate the capacity of the models and make an objective comparison, we also employ the contractive loss with a random negative sample for both strategies. Extraction Model. For both the relation extractor and class extractor, we also apply the BERT-baseduncased model. The encoder accepts the concatenation of the question q and relation r or the class c as the input, and then a linear layer projects the output [CLS] embedding into a similarity score s(*q, r*) or s(*q, c*). The BERT is fine-tuned by optimizing a contrastive loss (Chen et al., 2020), Table 4: Entity linking accuracy (%). | Accuracy | | |-------------------------|------| | GrailQA | 68.0 | | RNG-KBQA | 81.6 | | Ours | 87.2 | | –Relation-aware Pruning | 83.0 | $${\mathcal{L}}\left(q,r_{p o s}\right)=-\log{\frac{e^{s\left(q,r_{p o s}\right)}}{e^{s\left(q,r_{p o s}\right)}+\sum_{r^{\prime}\in\left\{r_{n e g}\right\}}e^{s\left(q,r^{\prime}\right)}}}$$ ′) where rpos is one of the golden relations extracted from the target logical expression, and {rneg} is the set of the negative relations sampled from relation set which shares the same domain as rpos. We sample 48 negative candidates for each sample and fine-tune BERT-base-uncased for 10 epochs with a batch size of 8 and a learning rate of 2e-5. Generation Model. We initiate both of our seqto-seq models with T5-based provided by the huggingface library (Wolf et al., 2020). For logical skeleton parsing, we fine-tune for 5 epochs with a batch size of 4 and a 4-step gradient accumulation. For the final composition model, we fine-tune for 10 epochs with a batch size of 8 and a 4-step gradient accumulation. To be noted, both the designed rules for logical skeleton parsing and vocabulary constraints in decoding process will not be used in the training process, and both training object follow the regular BART. ## B Component Detection Models. Entity Linking. As shown in Figure 4, compared with the entity linking (EL) strategy in RNGKBQA, our EL strategy gains 5.6% accuracy improvement. The reasons include (1) the trie tree considers all entities' surface names, ensuring the high coverage of entity candidates, (2) the relationaware pruning strategy can effectively remove hard negatives with similar mentions but completely different semantics. Relation and Class Extraction. Figure 6 depicts the effects of varying different sizes (k) of relations and classes. With the increase of k, the relation or class coverage represented by accuracy begins to grow slowly and attends to be stable when k is around 10. Meanwhile, the complexity of composition enumeration grows exponentially with k. Thus, to balance efficiency and performance, we choose top-10 relations and top-10 classes. Logical Skeleton Parsing. Table 5 displays the effectiveness of logical skeleton parsing techniques for various beam searches. "Raw Question" refers to directly parsing the raw question into the logical skeleton, while "+Mask" refers to parsing using our entity mask strategy. For both the strategies, in addition to the top-1, top-2, and top-3 beam search results, we also report the results of Top2(R) which add "<rel><rel>" as the top-2 candidate if "<rel>" is the top-1 prediction, vice versa. We can see that our designed entity mask strategy and rule-based beam search can contribute to the logical skeleton parsing. The rules significantly improve the performance as 1-hop relation and 2-hop relations are quite mix up in KBs. For example, the semantic-grained one-hop relation "program producer" could be represented by a 1hop relation ("tv.tv_producer.programs_produced" in domain TV) or a 2-hop relations ("broadcast.content.producer" and "radio.radio_subject.programs_with_this_subject" in domain radio). ## C Running Example We here give a running example of our framework for better understanding. As shown in Figure 4, given the question "the terminuses of Antonio belongs to what railway?", we first propose fine-grained component detection. We retrieve candidate classes "railway", "railway_terminus", "railway_type", ... and candidate relations "railway.terminuses", "railway.branches_to", "transit_line.terminuses",..., and candidate entities "Antonio" which is a football player,"Antonio" which is a city ,..., and logical skeleton candidates. Then, we apply the middle-grained constrain, for example, for class-relation pairs, "railway" is connected to "railway.terminuses" in KB but not connected to "railway.branches_to"; for relationrelation pairs,"railway.terminuses" shares matched domain and range with "railway.branches_to" but not share with "transit_line.terminuses"; for entities, the football player "Antonio" does not match any candidate relations and will be pruned. Finally, we put question, all connected class-relation pairs, all connected relation-relation pairs, all entities that have not been pruned and logical skeleton candidates into the composition model and generate logical expression. ![12_image_0.png](12_image_0.png) | Top-1 Top-2 Top-3 Top-2(R) | | | | | |------------------------------|------|------|------|------| | Raw Question | 83.2 | 86.1 | 86.7 | 94.0 | | +Mask | 85.5 | 87.4 | 88.6 | 95.3 | ## D Case Study Figure 7 shows some cases that our FC-KBQA and RNG-KBQA predicted. Example(a) shows a simple one-hop case, but RNG-KBQA tends to generate a more complex logical expression because it frequently occurs in the training set. With sample cases where the surface name of the gold relation has a clear overlap with the question, Example(b) demonstrates how the composition of each component causes RNG-KBQA to fail. As seen in example(c), the entanglement of knowledge and logical skeleton causes RNG-KBQA to predict some straightforward logical operators like "COUNT" incorrectly. These restrictions can be overcome by our proposed FC-KBQA. (a) How many holy orders practicing religions are there? FC-KBQA : RNG-KBQA: (COUNT(AND religion.religion (JOIN religion.religion.practices m.Of4prp))) (COUNT (AND religion.adherents (JOIN (R religion.religion.collective_term_for_adherents) (JOIN religion.religion.practices m.Of4prp)))) X (b) Which wine has the maximum percent new oak? FC-KBQA : (ARGMAX wine.wine wine.wine.percent_new_oak) ^ (ARGMAX food.beer food.beer.original_gravity) X RNG-KBQA: (c) How many comic book writers are professional documentary filmmakers? FC-KBQA: (COUNT (AND comic_books.comic_book_writer (JOIN (R people.profession.people_with_this_profession) m.03qsd25))) (ARGMIN comic_books.comic_book_writer people.person.height_meters) X RNG-KBQA: Figure 7: Case Study on GrailQA. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 5 ✓ B1. Did you cite the creators of artifacts you used? Section 5 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The datasets we used are all publicly available. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? The datasets we used are all publicly available, and we only use them for evaluation. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The datasets we used are all publicly available. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? The datasets we used are all publicly available. ✗ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. The datasets we used are all publicly available. The readers can refer to the original paper for the statistics. ## C ✓ **Did You Run Computational Experiments?** Appendix ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

wachowiak-gromann-2023-gpt

Does {GPT}-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models

https://aclanthology.org/2023.acl-long.58

Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal. We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated. To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor{'}s source domain without any pre-set domains. We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets. When provided 12 few-shot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15{\%} in English and 34.65{\%} in Spanish. GPT{'}s most common error is a hallucinated source domain for which no indicator is present in the sentence. Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain.

# Does Gpt-3 Grasp Metaphors? Identifying Metaphor Mappings With Generative Language Models Lennart Wachowiak King's College London [email protected] Dagmar Gromann University of Vienna [email protected] ## Abstract Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal. We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated. To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor's source domain without any pre-set domains. We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets. When provided 12 fewshot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. GPT's most common error is a hallucinated source domain for which no indicator is present in the sentence. Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain. ## 1 Introduction Metaphor processing with pre-trained language models (e.g. Conneau et al., 2020; Brown et al., 2020) has been dominated by metaphor detection, that is, the classification of expressions into metaphoric or literal (e.g. Aghazadeh et al., 2022; Leong et al., 2020). In metaphor interpretation, a common approach is to paraphrase metaphoric expressions into literal ones (e.g. Stowe et al., 2021a). Few approaches target metaphor identification, e.g. predicting the source domain of a metaphor in a linguistic sequence. For instance, Rosen (2018) relies on grammatical constructs and pre-defined labels. Instead, in this paper, we test a generative language model's ability to predict the source domain given a target domain and sequence without grammatical assumptions or fixed source domain labels. Conceptual metaphor theory (CMT) (Lakoff and Johnson, 1980) starts from the assumption that metaphors represent a powerful cognitive mechanism to transfer physical knowledge structures to abstract domains. In natural language, *He was* bombarded by insults or Your words pierce my heart transfers the concrete domain of weapons to the abstract domain of words in the metaphor WORDS ARE WEAPONS. On the assumption that our cognitive organization relies on metaphors, automatically identifying metaphoric transfer holds the promise of contributing to more human-like computational models. From the overall success of pre-trained language models in metaphor detection, a certain degree of metaphoric knowledge in these models can be assumed (Aghazadeh et al., 2022). This paper aims to evaluate whether this inherent knowledge extends beyond contextual clues to predict the concrete domains in the metaphoric transfer. Detecting a metaphor entails contrasting the physical with the abstract meaning of a sequence. However, the source domain is frequently a noncontextual attribute (Aghazadeh et al., 2022), while the target domain can be found directly using contextual clues. For instance, in the above example, pierce is more implicitly related to WEAPONS than the explicit *words* is to WORDS. To determine the accuracy of the predicted source domains from fine-tuning and few-shot prompting GPT-3 (Brown et al., 2020), we manually evaluate the results. To this end, we propose a classification of error types from too generic domains to relying on words in the sentence that are not connected to the metaphor, which we call trigger words. This provides further intuition on the nature and extent of metaphoric knowledge encoded in pre-trained language models. We compare methods to elicit metaphoric knowledge without any assumptions on grammar or source domains and test if it extends across languages, i.e., Spanish in addition to English. Finally, we evaluate its generalization by testing on two distinct datasets and a set of nonmetaphoric sentences. ## 2 Preliminaries Two major pillars that build the foundation for this approach are conceptual metaphors and generative language models, which we briefly introduce here. ## 2.1 Conceptual Metaphors The idea of metaphoric projection from a physical source domain to an abstract target domain is deeply rooted in the tradition of embodied cognition, which assumes that higher-level cognition is shaped by physical experiences (Barsalou, 1999). For instance, actual physical movement recruits similar areas in the brain as communicating with action verbs (Durand et al., 2018; Gibbs, 2006). Conceptual metaphors are deeply entrenched in our knowledge organization system and utilized in everyday communication to convey thoughts more precisely. In a large-scale study, Prabhakaran et al. (2021) evaluate the persuasiveness of metaphors and show that metaphoricity in political posts increases social media engagement. Citron and Goldberg (2014) show that metaphoric emotional language elicits a higher emotional response by recipients than literal use. To provide complex analyses of metaphoricity in language and analyze the metaphoric knowledge of generative language models, we believe that identifying concrete metaphoric projections in natural language is required. ## 2.2 Generative Language Models Large generative language models are trained with the objective of predicting the next token in a sequence. During inference, this allows them to be prompted with some text by a user and then generate what they predict to be most likely to come next. Scaled to large training corpora based on web-data and multi-billion parameter architectures, this simple objective resulted in models such as GPT-3 (Brown et al., 2020) or its open-source variants BLOOM (Luccioni et al., 2022) and OPT-175 (Zhang et al., 2022). For a specific task, these models can be used either in a zero-shot, few-shot, or fine-tuning manner. For zero-shot text completion, the model is prompted with an instance of a task without being provided any example solution of other task instances. In comparison, for few-shot completions, the prompt already contains some samples of the task and the respective solutions. In both variants, the model weights are not changed anymore, only the prompt differs. In contrast, when fine-tuning the model, its weights are optimized to predict the task-specific output given some input/output task samples. ## 3 Related Work Tong et al. (2021) provide a recent overview of architectures used for metaphor detection, available datasets, and further metaphor-related tasks. An overview by Rai and Chakraverty (2020) takes many different approaches to computational metaphor processing into account, additionally reflecting on the different theoretical and linguistic views on the definition of metaphors. While there are many metaphor-related tasks, the closest to ours are presented in the sections on paraphrasing and connecting source and target domains. Detection. Metaphor detection, the simplest form of computational metaphor processing, is a binary classification task in which each word of a sentence is labeled as being used metaphorically or literally. In a 2020 shared task on metaphor detection, finetuning pre-trained language models led to the best results (Leong et al., 2020). To achieve small improvements in accuracy, different approaches enrich the model input by, for instance, providing dictionary definitions of the words being classified (Babieno et al., 2022) or concreteness measures that indicate to what extent something can be experienced via the senses (Brysbaert et al., 2014). Commonly used datasets for this task are the VU Amsterdam Metaphor (VUA) Corpus (Steen et al., 2010) and the TOEFL corpus (Klebanov et al., 2018), both human-annotated based on different protocols. Model Insights. Other research explores the embeddings generated by language models and how they relate to metaphoricity. Pedinotti et al. (2021) show that BERT's likelihood scores show a decreasing likelihood from literal sentences to conventional and novel metaphors and, lastly, to nonsense sentences; thus, BERT's scores correlate with human-annotated plausibility scores. Moreover, for different layers, they explore cosine similarities between words used metaphorically, e.g., the flowers nodded *in the wind*, and their metaphorical paraphrases and literal synonyms. Similarly, Aghazadeh et al. (2022) investigate which layers of different language models encode metaphoric knowledge across different languages and datasets via probing. Paraphrasing. One common approach to metaphor interpretation is paraphrasing the metaphorical expression using only literal words. For example, the phrase *to devour a novel* could be rephrased as *to enjoy a novel*. An example of metaphor interpretation is the work by Mao et al. (2018), who propose to query WordNet for possible candidate translations, from which the best is selected based on similarities in the embedding space. On the other hand, there is also research on generating metaphoric paraphrases given a literal sentence as input. Recent work in metaphoric paraphrasing uses text-to-text models, such as T5 or BART (Stowe et al., 2021b,a). Most recently, Liu et al. (2022) proposed a new task for which they created a dataset of novel metaphors in the form of similes, for example The meteor was as bright as (New York City | coal), which the language model has then to interpret as *very* bright or *not bright at all*. A fine-tuned RoBERTa model outperforms various GPT variants on the task and comes close to human performance. The authors also show that the reverse of the tasks, i.e., predicting the metaphoric language given the literal answer, is more difficult. Connecting Source and Target Domains. Trying to automate the process of identifying metaphor mappings is not a new endeavor. For instance, given manually collected metaphoric phrases of a specific target domain, Chung et al. (2004) propose to facilitate the identification of source domains by querying WordNet senses and the ontology SUMO. More recent research makes use of syntactical patterns metaphoric language often occurs in (Sullivan, 2013), thereby narrowing down the pool of sentences considered as metaphoric candidates. Dodge et al. (2015) use such patterns to find metaphor candidates that are further analyzed by identifying evoked frames and checking for whether the frames relate in MetaNet. Given a target domain and a corpus, they can use this system to see which source domains are frequently used to metaphorically talk about a target domain. This system, however, is limited by existing frame resources and relies on pre-defined grammatical structures. Also querying an existing database, Ge et al. (2022) use hypernym relations from WordNet to identify the source and target domains for pairs of literally used nouns and literally or metaphorically used verbs or adjectives. While the target domain identification reaches an accuracy of 87.3%, the source domain identification only reaches 67.3% based on the manual evaluation of a small subset of the data. Shutova et al. (2017) explore unsupervised methods for identifying clusters of source and target concepts as well as the connections between them. They limit their approach to verb–noun constructions, from which the verbs constitute the source domain clusters and the nouns the target domain clusters. Mohler et al. (2016) provide a dataset with sentences from government discourse annotated with scores from -1 to 3 to indicate the level of metaphoricity. More importantly, 7,941 sentences are annotated for source–target domain mappings with 108 different source domains. Rosen (2018) uses this dataset to build a model to predict the source domain of a metaphor given a contextual sentence and a target domain referent. Compared to our approach, this work presupposes that a given sentence is metaphoric while also depending on specific grammatical dependencies when constructing the model input. Most importantly, it is limited to the 77 labels sub-sampled from the overall available 108 domains as experiments are done using feed-forward neural networks and LSTMs instead of text-to-text networks. Rosen also shows that the inter-annotator agreement for the original source domain annotations is rather low with a Cohen's kappa of 0.544, which indicates the difficulty and potential ambiguity of the task. In contrast to the existing work on computational extraction of source and target domains, our approach does not rely on any assumptions about grammatical structure or word types that supposedly indicate metaphorical language. Moreover, we are not limited to a pre-defined set of source or target domains due to the text-to-text approach. ## 4 Method 4.1 Task In our experiments, we use GPT-3 to predict a metaphor's source domain given a sentence and a target domain. For example, a prompt to identify the conceptual metaphor underlying the sentence You are wasting my time could look like this: Extract the conceptual metaphor from the following sentence : $${\mathrm{a}}\,{\mathrm{t}}\,{\mathrm{i}}\,{\mathrm{o}}\,{\mathrm{n}}\,{\mathrm{s}}\,{\mathrm{h}}\,{\mathrm{i}}\,{\mathrm{p}}\quad{\mathrm{i}}\,{\mathrm{s}}\quad{\mathrm{a}}\,{\mathrm{t}}$$ Sentence : Our r el ati o n s hi p i s at crossroads Target Domain: Relationship Source Domain: Journey Sentence: You are wasting my time Target Domain: Time Source Domain: <<model completion>> ## In This Prompt, The Model Is Provided With One Example Of A Metaphor Mapping, Which Is Relationship Is A Journey. Afterwards, It Is Provided With The Sentence And Target Domain For Which We Want To Know The Source Domain. A Correct Prediction, In This Case, Would Be Time Is Money Or Time Is A Resource. 4.2 Dataset The main dataset was gathered by retrieving all natural language examples annotated with source and target domain from Lakoff's Master Metaphor List1, called Metaphor List in the following. For this task, we randomly selected 446 sentences, with a maximum of three per metaphor, i.e., per unique combination of source and target domain. To ensure that the model does not simply assume all sentences to be metaphoric, we use non-metaphoric English sentences from the VUA corpus (Steen et al., 2010) by extracting sentences for which each word is labeled as literal by the annotators. For instance, *He did not even see an English newspaper* is an example of a non-metaphoric sentence. From the extracted non-metaphoric sentences, we manually chose 50 to be added to our dataset as many of the sentences were wrongly labeled or extremely short. The resulting dataset is split into a train, validation, and test set detailed in Table 1. A unique combination of source and target domain, for example, BELIEFS (target) ARE PLANTS (source), does not appear in the validation or test set if it already appeared in the training set. This allows us to test whether the model can generalize to new, unseen metaphors. As the Metaphor List data only contains a limited number of domain combinations, the validation and test set contain the same combinations of source and target domains, however, with different unique sentences. Entirely new domain combinations in the test set are evaluated via sentences from additional datasets. To test the ability to generalize across datasets, 1http://www.lang.osaka-u.ac.jp/~sugi moto/MasterMetaphorList/metaphors/index. html copyright (c) 1994 by George Lakoff, University of California, Berkeley we use sentences from the LCC dataset (Mohler et al., 2016) (CC BY-NC-SA v4.0), where we use the provided source and target domains and the raw sentences without indication of the precise metaphor location. From the English and Spanish sentences, we use a subset of maximally 10 sentences per target domain, resulting in a set of 284 (EN) and 110 (ES) sentences. In comparison to the Metaphor List samples, the LCC dataset consists of much longer sentences using complicated, expert language from the political domain. All multilingual samples, as well as sentences from the LCC corpus, are solely used as hold-out test set and do not play a role in the model and prompt selection process. These sentences, thus, test the model's generalization ability to new source domains, a different language, i.e., Spanish, and more complex sentences. Model and prompt selection is based on the validation set created from the Metaphor List samples and the non-metaphoric VUA sentences. The number of samples from the training set that are actually used depends on the prompting type. ## 4.3 Experiments And Evaluation Using two automated evaluation metrics (Sec. 4.3.1), we compare few-shot prompts and finetuned models on the validation set (Sec. 4.3.2). The test set evaluation is done manually (Sec. 4.3.3) ## 4.3.1 Evaluation Metric While we manually evaluate the model on the test set, we use two automatically computed scores to evaluate on the validation set. The validation performance is used to select the best way to prompt or fine-tune the model. As the first score, we compute the embedding similarity of the gold standard source domain and the GPT-3-generated domain. We compute the similarity using the Gensim library (Reh˚u ˇ ˇrek and Sojka, 2010) with 300-dimensional GloVe vectors (Pennington et al., 2014). To provide more context to the automated evaluation, we also use knowledge graph embeddings. We rely on the KGvec2go Web API (Portisch et al., 2020) created from the resources WordNet, Wiktionary, DBpedia, and WebIsALOD. We average the four returned similarity scores based on the different resources, called KB score in the following. 4.3.2 Prompt Selection To see with what prompts the model returns the best source domains, we vary the number of labeled | Dataset | Train | Val. Sentences | Test Sentences | Target | Source | |--------------------|---------|---------|-----|----------|----------| | Sentences | Domains | Domains | | | | | Metaphor List | 117 | 105 | 224 | 91 | 94 | | VUA non-metaphoric | 15 | 15 | 15 | 47 | - | | LCC EN | 0 | 0 | 284 | 30 | 90 | | LCC ES | 0 | 0 | 110 | 11 | 67 | | Total | 132 | 120 | 633 | 179 | 251 | Table 1: Number of sentences and unique target and source domains in the different datasets. few-shot samples provided at the beginning of each prompt. We compute the scores described in Section 4.3.1 for generations obtained through prompts containing 2, 4, 6, 8, and 12 labeled samples. That means, in each few-shot setting, the model has at least 2 examples of correct domain mappings for orientation. For each of these five prompt variations, we choose three distinct sets of training samples. Thus, we generate three solutions to evaluate. This allows us to observe how much the model depends on specific training samples, and we can compute average scores and standard deviations. Moreover, we also fine-tune GPT-3 by using our samples to train the model for 4 epochs, during which the model's weights are adapted, instead of just providing the samples as few-shot samples in the prompt. After fine-tuning, the model does not require any few-shot samples in the prompt, but can directly classify a sample from the validation set. We fine-tune two variants: (1) a model fine-tuned with all 132 sentences from the training set; and (2) a model fine-tuned with 34 sentences from the training set, one per unique source domain. The experiments of comparing different prompts with each other and the fine-tuned variants use the validation set. With GPT-3 being proprietary licensed by the OpenAI, L.L.C Terms of Use, the text generations with its API cost 42.73$. Our code is available online2. For all generations, we set the temperature parameter to 0, which means that the text generation model samples words in a greedy fashion, i.e., it always generates the most likely next word. Increasing the temperature changes the likelihood with which words are sampled. For now, a temperature of 0 allows us to generate words in a deterministic, repeatable fashion. However, future experiments could include the temperature as a hyperparameter to be optimized. The GPT-3 architectures we 2https://github.com/lwachowiak/Metaphor-ExtractionWith-GPT-3 used are davinci-002, the most powerful available model variant at the time of the experiments3, and curie-001, the second most powerful variant. ## 4.3.3 Manual Evaluation Issues with the gold standard source domains, as well as the fact that the source domains can be phrased with different expressions and differ in their level of precision, make it difficult for the automated scores to be reliable enough to directly derive an accuracy score from them. Thus, to compute the final accuracy on the test set, we manually check the model's output. After experimenting with the different prompting styles on the validation set, we choose the model with the best combined KB score and embedding similarity for manual evaluation on the hold-out test set. Two annotators, the authors of this paper, manually evaluate the correctness of the generated answers for English. Both annotators independently evaluated the model output and then discussed disagreements. One annotator evaluates the answers for Spanish. The source domain was considered correct if it corresponded to the gold standard or was deemed correct by the annotator(s). While hard to automate, for humans it is often easy to detect a close correspondence between a gold domain, e.g. "musical harmony", and a predicted domain, e.g. "music". In difficult cases, annotators, following the Metaphor Identification Procedure (MIP) (Group, 2007), analyze words for their more basic, physical meaning and see if these are in concordance with the predicted source domain. For instance, the gold standard for You make me sick! is "nausea", whereas *sick* is also defined as physically ill and thus related to the predicted "disease" domain. To gather more insights into the type of issues that can be observed from the predicted source domains, all predictions deemed 3In November 2022, OpenAI released davinci-003, an InstructGPT variant (Ouyang et al., 2022). ![5_image_0.png](5_image_0.png) ## 5 Results This section describes the results from the experiments that determine the manually evaluated test set predictions, their accuracy, and types of errors. 5.1 Prompt Selection Results Figure 1 shows the automatically computed scores on the validation set achieved by davinci-002 and curie-001 with different numbers of few-shot samples. We can see that davinci-002 outperforms the smaller architecture by about 0.15 to 0.2 points. The highest embedding similarity and highest KB score are achieved by davinci-002 when prompted with 12 different few-shot training samples, achieving an embedding similarity of 0.505 and a KB score of 0.553. However, the standard deviation is very high for the models prompted with 12 samples, thus, showing the importance of the quality of those samples. Due to this fluctuation in performance, the average KB score over all three runs is highest for davinci-002 models prompted with 8 samples, and the average embedding score is highest for davinci-002 models prompted with 4 samples. The prompt based on 12 few-shot samples that led to the overall best results is available in the appendix. In comparison, the fine-tuned models perform better than the curie-001 models but worse than the davinci-002 models. Fine-tuning a model with 36 samples, each with a unique source domain, leads to an embedding similarity of 0.303 and a KB score of 0.386. Fine-tuning GPT-3 on all available training samples results in improved scores of 0.413 and 0.513. Examining the completions of the model fine-tuned on all samples, we can see that it sticks more to the source domains already present in the training data while also predicting fewer distinct source domains overall: the completions from the best performing few-shot variant contain 74 unique source domains, from which 7 are also present in the training data; the completions of the model fine-tuned on 36 contain 78 unique source domains, from which 13 are present in the training data; and the completions of the model fine-tuned on all data contain only 50 unique source domains, from which 18 are present in the training data. ## 5.2 Manual Evaluation Results We used the best prompt identified in the previous section to generate the source domains for the test set samples. The correctness of the generations was manually verified by two annotators. We used Cohen's Kappa, a chance-corrected coefficient of agreement, to compute the inter-annotator agreement. Across all test data points, we obtained a Cohen's Kappa of 0.51, corresponding to a moderate agreement according to Landis and Koch (1977). After disagreements were resolved through discussion, we computed the model's accuracy, which is reported by dataset in Table 2. The model achieved an accuracy of 81.33% on the Metaphor List corpus, 53.74% on the English part of the LCC corpus, and 34.65% on the Spanish part of the LCC corpus. In addition, the model was able to achieve an accuracy of 42.11% in predicting a sentence is nonmetaphoric instead of predicting a source domain. Averaged by sample, this results in an accuracy of 60.22%. The decrease in performance on the LCC test set is not surprising as the sentences are on average much longer and often use domain-specific language. Moreover, the target domains specified by the LCC gold standard are often much harder to identify in the sentence as they are less precisely matched to the sentence's words. To provide insights into the adequacy of the evaluation metrics, we evaluate their correlation with the manual annotation decisions. As we have an ordinal variable (correctly classified, wrongly classified) and a continuous variable (KB score and embedding similarity), we used Spearman's rank correlation coefficient. We achieve a correlation of 0.43 for the KB score and 0.40 for the embedding similarity. Both scores are statistically significant with p < 0.05, and can be interpreted as a moderate correlation (Dancey and Reidy, 2007). Dataset Accuracy Inter-annotator Agreement Cohen's Kappa Agreement in % Metaphor List 81.33% 0.55 (Moderate) 87.6% VUA non-metaphoric 42.11% 0.89 (Almost perfect) 94.7% LCC EN 53.74% 0.45 (Moderate) 72.5% LCC ES 34.65% - - Average (weighted by samples) 60.22% 0.51 (Moderate) 79.7% Average (unweighted) 52.96% 0.63 (Substantial) 84.9% Table 2: Manually evaluated test performance ## 5.3 Type Of Errors We manually classified all errors on the English test sets based on the typology presented in Table 3: wrong with trigger, wrong without trigger, too literal, should be non-metaphoric, should be metaphoric, too specific, too general, wrong subelement mapping. Trigger here refers to words in the input that are clearly related to the predicted source domain. For instance, any mentions of animal-related terms, e.g. bullish mindset or trough of poverty, led the model to predict "animals" as source domain. The most common error class is being wrong without any trigger in the sentence, followed by erroneous predictions of non-metaphoric and being wrong with trigger. Some instances indicate a misinterpretation of words, e.g. *dumbfounded* likely leads to the entertaining prediction of "being_stupid". Furthermore, interesting errors can be found in the category of wrong subelement mappings, where the model identifies the general source domain but fails to pick the correct element of that domain for its prediction. For instance, in the sentence *China is a fertile ground for revolt*, the gold standard refers to "plants", and the model predicts "land", which is in the same domain of cultivation but not entirely the correct domain. Similarly, when a metaphor involved movement and locations and the true source domain referred to only one of them, the model regularly picked the wrong subelement. For instance, the model wrongly predicts EXISTANCE IS MOTION for the sentence *It came into existence*, where the true source domain would have been "location". For the Spanish LCC data, one annotator classified erroneous predictions according to our error typology. A vast majority of 62.12% of errors were predictions of non-metaphoric sequences which should be metaphoric, followed by 19.70% wrong without trigger. A trend to predict "family" without any trigger in the sentence for the target domain "government" in half of its occurrences could be observed. In the 13.64% cases of wrong with trigger, the model's predictions mostly represented literal English translations of context words from the Spanish sentence. All source domain predictions were made in English, which was expected given that the source and target domains in the prompt were also in English. In total, 12 LCC sentences were disregarded since the gold standard was faulty. ## 6 Discussion We experimented with different GPT-3 variants and prompts containing varying numbers of few-shot samples to see whether GPT-3 can generate the source domain of a conceptual metaphor mapping given a context and a target domain. The best results were achieved with a long few-shot prompt containing 12 example completions. The largest model variant davinci-002 strongly outperformed the next biggest variant and a fine-tuned GPT-3. We also saw that fine-tuning the model can lead to a decrease in expressiveness, that is, fewer unique source domains being generated. In our case, this might be because the model fine-tuned on all data sees each source domain around three times per training. It might be possible to counteract the decrease in expressiveness by increasing the temperature parameter, thus, making less probable generations more likely. Manually coding the errors made by the model, we saw that the model often fabricates source domains for which no related words are present in the sentence. Other common errors included predicting a literal meaning although a metaphor was present, and generating wrong source domains based on trigger words that were not metaphorically related | Error Code | Definition | Example | % of All Errors | | | | | |----------------------------------------------------------|-------------------------------------------------------------------------|----------------------|--------------------|----------|---------------|-------|------| | Sentence | Wrong Prediction | | | | | | | | Wrong with trigger | The | model | predicts | a | | | | | wrong source domain due to words in the sentence related to that domain | The arms race | COMPETITION IS | 21.31 | | | | | | WAR | | | | | | | | | Wrong without | The | model | predicts | a | | | | | trigger | wrong | source | domain | | | | | | without | any | noticeable | | | | | | | triggers for that domain in the sentence | Sally gave the idea | IDEAS ARE CHILDREN | 27.32 | | | | | | to Sam | | | | | | | | | Too literal | The model predicts a literal relationship instead of a metaphoric mapping | I'm down to my bottom dollar | MONEY | IS | IN | | | | VESTMENT | 7.10 | | | | | | | | Should be nonmetaphoric | The | model | predicted | a | | | | | metaphoric source domain instead of non-metaphoric | They saw him advancing | MOVING IS COMING | 7.65 | | | | | | Should | be | The model wrongly predicted non-metaphoric | Under the cover of | DARKNESS | IS | 25.14 | | | metaphoric | darkness | non-metaphoric | | | | | | | Too specific | The predicted metaphor is more specific than what the sentence implies. | He | finally | caught | SCHEDULE | IS | 2.73 | | up to schedule | PEOPLE | | | | | | | | Too general | The predicted source domain is too unspecific | The | idea | slipped | MIND IS SPACE | 1.09 | | | through my fingers | | | | | | | | | Wrong | subele | | | | | | | | ment mapping | The model predicts an aspect of the correct source domain, however, it is not the exact element | Let's strip away the | IMPORTANCE IS | 7.65 | | | | | unimportant details | CLOTHING | | | | | | | | Table 3: The different types of errors made by the model | | | | | | | | to the target domain. Discerning whether to predict a source domain for a given sentence or to label it as non-metaphoric seems to be quite challenging for the model as well. Analyzing the errors of large language models as done here is essential to build appropriate trust or distrust in the model and allow for the use of error-correction methods in the future, for instance, the selection of better prompts or training samples. In the context of analyzing the model's misclassifications, we also experienced issues with the dataset, e.g. unintuitive metaphor mappings or lack of contextual clues for the provided target domain. The dataset's quality strongly affected the Spanish test results and clearly indicated that more multilingual resources for metaphor identification are needed. The difference in the nature and quality of the datasets is also the main reason for the strong variation in accuracy results. The Metaphor List dataset provides prototypical, general language examples, while the LCC dataset annotated real-world, domain-specific expert language. This affects the complexity as well as the length of sentences, both contributing to the difference in accuracy across datasets. Application. Using GPT-3 to analyze metaphors used in an unlabeled corpus comes with two problems: (1) we do not know what target domains are the right ones to provide to the model, (2) there will be an overwhelming amount of output given that most sentences contain at least subtle metaphoric language that will largely not even be relevant to the domain we are interested in. Therefore, it would be useful to first filter sentences based on seed words whose usage interests us or that belong to a specific target domain we want to analyze (Wachowiak et al., 2022). As such an approach already narrows down the candidate sentences to a pre-specified target domain, we can include that target domain in the prompt for the language model. Lastly, it might help to restrict the context window around the words of interest so that the model is not distracted by other metaphors in the sentence. However, to confirm this, further research is needed. Considering precise element mappings. As the capabilities of large neural language models continue to grow, it will be interesting to see if they can identify not only the correct source domains but also precise element-wise mappings between the concepts of the target and source domain. For example, the conceptual metaphor LOVE IS A JOURNEY involves mapping lovers to travelers, difficulties to roadblocks, and progress to distance traveled forward. Querying such an element-wise mapping could be facilitated through a set of the target domain's core elements being provided to the model. OpenAI Transparency Issues. An issue with researching the capabilities of large language models, such as GPT-3, is the accessibility and transparency. While GPT-3 variants are easily accessible via an API, the model stays a black-box, and researchers can not investigate the specific model weights. Moreover, there is no explicit mapping available of how the models advertised on the website relate to those described in OpenAI's papers (Leike, 2022). Lastly, the model variant accessible for fine-tuning differs from the one accessible for direct zero- and few-shot text generation, which might also explain the drop in performance observed in our metaphor extraction task. On the other hand, comparable models for which the weights are publicly released, such as BLOOM (Luccioni et al., 2022) or OPT175 (Zhang et al., 2022), have the issue that they are not hosted anywhere. Thus, researchers must provide the infrastructure to run them, which is only possible for very few academic institutions. ## 7 Conclusion We analyzed how well GPT-3 can identify source domains of metaphors in natural language. Across three different datasets in English and Spanish, GPT-3 predicts the source domain with an accuracy of 60.22%. The best performance was achieved given 12 few-shot examples in the prompt, although the average performance was highest with 4 to 8 few-shot examples. However, the model still suffers from specific error types, such as hallucinating domains without any indicators being present. We believe future iterations of large language models like GPT-3 will become important tools in computational metaphor analysis, where one investigates conceptual metaphors in different domains, for instance, literature or political discourse. In the future, we want to experiment with using large language models to generate complete metaphors, i.e., generate both, source domain and target domain, given a sentence. We also plan to use the developed techniques in corpus analyses. ## Limitations The approach of identifying source domains relies on having a contextual sentence but also a target domain available. The datasets available for evaluation do not always provide precise target domains. For example, the LCC dataset provides the target domain *gun ownership* for the sentence *I just don't* know what it will take for people in this country to embrace gun safety, or the target domain *climate* change for the sentence *The event is billed as the* largest meeting of influential figures within the renewable energy field. This mismatch often makes it difficult to provide precise source domains. A similar problem also exists when wanting to use our source domain prediction approach in the wild as we have to somehow provide the model with a target domain. While we can provide a target domain by selecting sentences based on seed-word lists designed for specific domains, we do not know how precisely this matches the target domain occurring in the sentence. In a multilingual setting, the issue becomes more pressing since there are very few multilingual metaphor datasets and for semiautomated approaches the seed-word lists would have to be provided for each language. Another challenge is connected to the fact that the model output requires time-consuming manual evaluation to obtain a precise accuracy score. However, deciding what counts as a correct source domain can be difficult and might change depending on how strictly the annotators apply certain rules. For instance, whether an annotator sees a predicted source domain as too general or too specific is a matter of degree. Overall, this makes it hard to benchmark different approaches across papers, which is why further investigation of automated metrics, as presented in this paper, is crucial. Lastly, there are issues regarding the accessibility of large neural language models, such as GPT-3, and the transparency of OpenAI's API as described in the discussion section. ## Ethics Statement Metaphor identification represents an analysis of people's usage of language in communication as well as its grounding in the physical world. Using metaphoric language has been shown to increase the speaker's persuasiveness and the listener's emotional response. On the one hand, people might unconsciously use metaphors and might not appreciate their language being automatically analyzed in this regard. On the other hand, a model able to identify metaphors can be trained to actively utilize metaphoric language and thus become more persuasive and elicit a higher emotional response. In the long run, this could be viewed as a means to train language models to become more manipulative in their interaction with humans, e.g. in speech assistance or chat applications. The proposed approach served the purpose of probing the extent of metaphoric knowledge in a pre-trained language model and not to train it to manipulate users. As a matter of fact, the proposed method can also be utilized to detect the extent of metaphoric language produced by a language model and, thus, counteract this development. Nevertheless, we propose that the aspect of metaphoricity in language models might be worth including in discussions on ethics in AI. The nature of the datasets utilized herein might also represent a number of biases. The Metaphor List has been introspectively curated by a white male Western person, i.e., George Lakoff, while the LCC dataset stems from online websites and political debates in American English respectively Mexican Spanish where the profile of the annotators remains unclear. Thus, the first bias is that not all genders, communities of speakers, and language varieties have been represented in this experiment. Second, the domains are limited to political and general language domains and the results might differ when applied to other domains. Third, the coverage of languages is limited to two due to the lack of datasets and annotators, i.e., for Russian in the case of the LCC dataset. Thus, it would be interesting and important to extend the scope of the experiment to investigate the utilization of metaphoric language by different speaker profiles of different languages and language varieties in the future. ## References Ehsan Aghazadeh, Mohsen Fayyaz, and Yadollah Yaghoobzadeh. 2022. 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In *Proceedings of the 3rd Workshop on Figurative Language* Processing (FLP), pages 44–53, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics. Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022. OPT: open pre-trained transformer language models. *CoRR*, abs/2205.01068. ## Appendix The 12 few-shot samples included in the best identified prompt and used for the generation of the completions on the test set: Extract the conceptual metaphor from the following sentence : Sentence : I ' ve l o st all hope of a solution . Ta rget Domain : hope Source Domain : p o s se s si o n s Extract the conceptual metaphor from the following sentence : Sentence : Even in backruptcy he managed to hang onto his car collection . Ta rget Domain : p o s se s si o n Source Domain : holding Extract the conceptual metaphor from the following sentence : Sentence : A ti g re s s in bed . Ta rget Domain : l u st Source Domain : animal Extract the conceptual metaphor from the following sentence : S e nt e n c e : He ' s r e a l l y hi g h . Ta rget Domain : eupho ria Source Domain : up Extract the conceptual metaphor from the following sentence : S e nt e n c e : We we re made f o r e a c h o t h e r . Ta rget Domain : love Source Domain : pa rt −whole Extract the conceptual metaphor from the following sentence : S e nt e n c e : Many t h e o r i e s s p r a n g up o ut o f the fe rtile soil of his discoveries . Ta rget Domain : t h e o ri e s Source Domain : beings Extract the conceptual metaphor from the following sentence : Sentence : Her blood ran cold Ta rget Domain : f e a r Source Domain : cold Extract the conceptual metaphor from the following sentence : Sentence : the contagion of democratic ideas Ta rget Domain : b eli e f Source Domain : di sea se Extract the conceptual metaphor from the following sentence : Sentence : She i s made of toughe r s t u f f . Ta rget Domain : p e r s o n alit y Source Domain : substance Extract the conceptual metaphor from the following sentence : Sentence : Things are at a stand still . Ta rget Domain : p r o g re s s Source Domain : motion Extract the conceptual metaphor from the following sentence : Sentence : She took inventory of her beliefs . Ta rget Domain : b eli e f s Source Domain : commodities Extract the conceptual metaphor from the following sentence : Sentence : But he he said , don ' t wash i t I wanna wear i t . Ta rget Domain : washing Source Domain : not metaphoric ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 6 & Limitations section ✓ A2. Did you discuss any potential risks of your work? Section 6 & Ethics section ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract & Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? We did not use any AI writing assistants and all contents of the paper were written exclusively by the authors. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 & 5 ✓ B1. Did you cite the creators of artifacts you used? Section 1 & 2 & 3 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 4.3.2 ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 4 ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 4.3.3 ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 4 & 5 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.2 ## C ✓ **Did You Run Computational Experiments?** Section 4 & 5 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4 & 5 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 4.3.3 & 5.2 & 5.3 ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? The authors of this paper performed the manual evaluation themselves. ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? The authors of this paper performed the manual evaluation themselves. ✗ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? The authors of this papers were the evaluators so no consent form was needed. ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? There were no ethical concerns with the evaluation method. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? We re-used two already published datasets and only manually evaluated the model's predictions.

thorn-jakobsen-etal-2023-right

Being Right for Whose Right Reasons?

https://aclanthology.org/2023.acl-long.59

Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are {`}right for the right reasons{'}. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups (balanced across age and ethnicity). Such data enables us to ask both what demographics our predictions align with and whose reasoning patterns our models{'} rationales align with. We find systematic inter-group annotator disagreement and show how 16 Transformer-based models align better with rationales provided by certain demographic groups: We find that models are biased towards aligning best with older and/or white annotators. We zoom in on the effects of model size and model distillation, finding {--}contrary to our expectations{--} negative correlations between model size and rationale agreement as well as no evidence that either model size or model distillation improves fairness.

# Being Right For Whose **Right Reasons?** Terne Sasha Thorn Jakobsen*123, Laura Cabello*3**, Anders Søgaard**3 1Copenhagen Center for Social Data Science 2Copenhagen Research Center for Mental Health 3University of Copenhagen [email protected], [email protected], [email protected] ## Abstract Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are 'right for the right reasons'. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups (balanced across age and ethnicity). Such data enables us to ask both what demographics our predictions align with and whose reasoning patterns our models' rationales align with. We find systematic inter-group annotator disagreement and show how 16 Transformer-based models align better with rationales provided by certain demographic groups: We find that models are biased towards aligning best with older and/or white annotators. We zoom in on the effects of model size and model distillation, finding - contrary to our expectations - negative correlations between model size and rationale agreement as well as no evidence that either model size or model distillation improves fairness. ## 1 Introduction Transparency of NLP models is essential for enhancing protection of user rights and improving model performance. A common avenue for providing such insight into the workings of otherwise opaque models come from explainability methods (Páez, 2019; Zednik and Boelsen, 2022; Baum et al., 2022; Beisbart and Räz, 2022; Hacker and Passoth, 2022). Explanations for model decisions, also called *rationales*, are extracted to detect when models rely on spurious correlations, i.e., are right for the wrong reasons (McCoy et al., 2019), or to analyze if they exhibit human-like inferential *These authors contributed equally to this work. ![0_image_0.png](0_image_0.png) semantics (Piantadosi and Hill, 2022; Ray Choudhury et al., 2022). Furthermore, model rationales are used to evaluate how well models' behaviors align with humans, by comparing them to humanannotated rationales, constructed by having annotators mark *evidence* in support of an instance's label (DeYoung et al., 2019). Human rationales are, in turn, used in training to improve models by guiding them towards what features they should (or should not) rely on (Mathew et al., 2021; Rajani et al., 2019). While genuine disagreement in labels is by now a well-studied phenomenon (Beigman Klebanov and Beigman, 2009; Plank et al., 2014; Plank, 2022), little attention has been paid to disagreement in rationales. Since there is evidence that human rationales in ordinary decision-making differ across demographics (Stanovich and West, 2000), we cannot, it seems, blindly assume that what counts as a rationale for one group of people, e.g,. young men, also counts as a rationale for another group of people, e.g., elderly women. This dimension has not been explored in fairness research either. Could it be that some models that exhibit perfor1033 mance parity, condition on factors that align with the rationales of some groups, but not others? Contributions We present a collection of three existing datasets with demographics-augmented annotations to enable profiling of models, i.e., quantifying their alignment1 with rationales provided by different socio-demographic groups. Such profiling enables us to ask *whose* right reasons models are being right for. Our annotations span two NLP tasks, namely *sentiment classification* and common-sense reasoning, across three datasets and six demographic groups, defined by age {Young, Old} and ethnicity {Black/African American, White/Caucasian, Latino/Hispanic}. We investigate label and rationale agreement across groups and evaluate to what extent groups' rationales align with 16 Transformer-based models' rationales, which are computed through attention- and gradient-based methods. We observe that models generally align best with older and/or white annotators. While larger models have slightly better prediction performance, model size does not correlate positively with neither rationale alignment nor fairness. Our work constitutes multi-dimensional research in off-the-beaten-track regions of the NLP research manifold (Ruder et al., 2022). We make the annotations publicly available.2 ## 2 Fairness And Rationales Fairness generally concerns the distribution of resources, often across society as a whole. In NLP, the main resource is system performance. Others include computational resources, processing speed and user friendliness, but *performance is king*. AI fairness is an attempt to regulate the distribution of performance across subgroups, where these are defined by the product of legally protected attributes (Williamson and Menon, 2019). NLP researchers have uniformly adopted American philosopher John Rawls' definition of fairness (Larson, 2017; Vig et al., 2020; Ethayarajh and Jurafsky, 2020; Li et al., 2021; Chalkidis et al., 2022), defining fairness as performance parity, except where it worsens the conditions of the least advantaged. Several dozen metrics have been proposed, based on Rawls' definition (Castelnovo et al., 2022), some of which are argued to be in-1We use the terms 'agreement' and 'alignment' interchangeably. 2https://github.com/terne/Being_Right_ for_Whose_Right_Reasons. consistent or based on mutually exclusive normative values (Friedler et al., 2021; Castelnovo et al., 2022). Verma and Rubin (2018) grouped these metrics into metrics based only on predicted outcome, e.g., statistical parity, and metrics based on both predicted and actual outcome, e.g., performance parity and accuracy equality. Corbett-Davies and Goel (2018) argue that metrics such as predictive parity and accuracy equality do not track fairness in case of infra-marginality, i.e., when the error distributions of two subgroups are different. For a better understanding of the consequences of inframarginality we refer to Biswas et al. (2019) and Sharma et al. (2020). Generally, there is some consensus that fairness in NLP is often best evaluated in terms of performance parity using standard performance metrics (Williamson and Menon, 2019; Koh et al., 2020; Chalkidis et al., 2022; Ruder et al., 2022). We do the same and evaluate fairness in group-model rationale agreement quantifying performance differences (understanding performance as degree of rationale agreement) across end user demographics. In doing so, we are embodying group fairness values: that individuals should be treated equally regardless of their protected attributes, i.e., group belonging. Fairness and explainability are often intertwined in the literature due to the assumption that transparency, through explainability methods, makes it possible to identify which models are right for the right reasons or, on the contrary, right by relying on spurious, potentially harmful, patterns (Langer et al., 2021; Balkir et al., 2022). This study tightens the connection between fairness and explainability, investigating whether model rationales align better with those of some groups rather than others. If so, this would indicate that models can be more robust for some groups rather than others, even in the face of performance parity on dedicated evaluation data. That is: We ask whether models are equally right for the right reasons (with the promise of generalization) across demographic groups. ## 3 Data We augment a subset of data from three publicly available datasets spanning two tasks: DynaSent (Potts et al., 2020) and SST (Socher et al., 2013) 3, for sentiment classification and CoS-E (Talmor et al., 2019; Rajani et al., 2019) for common-sense ![2_image_0.png](2_image_0.png) reasoning.4 For each dataset, we crowd-source annotations for a subset of the data. We instruct annotators to select a label and provide their rationale for their choice by highlighting supporting words in the given sentence or question. Table 1 shows statistics of the annotations collected. Annotation guidelines are explained in § 3.1 (and included in full in Appendix A) and recruitment procedures are explained in § 3.2. | Annotators | Annotations | | | |---------------|---------------|-------|-------| | ×Group | Total | Total | | | DYNASENT | 48 | 288 | 2,880 | | SST-2 | 26 | 156 | 1,578 | | COS-E | 50 | 300 | 3,000 | | TOTAL | 124 | 744 | 7,458 | | BEFORE EXCL.* | - | 929 | 9,310 | ## 3.1 Annotation Process We summarize the process of collecting annotations in Figure 2, where we depict a three-step process: recruitment, annotation and exclusion. In this section, we start by describing the second step - annotation - and explain *what* is annotated and how it is annotated. We describe our recruitment and exclusion criteria in the following section, 3.2. Annotators are directed to a Qualtrics5survey 4We use the simplified version of CoS-E released by DeYoung et al. (2019). 5https://www.qualtrics.com and presented with i) a consent form, ii) a short survey on demographics, *iii)* instructions for their annotation task and lastly, iv) a randomly selected set of n ≈ 10 instances to annotate, out of a subset of size N. As a result of this procedure, each group, for each dataset, is represented by approximately N/n annotators. Data points are annotated for both classification labels and extractive rationales, i.e., input words that motivate the classification. Existing rationale datasets are typically constructed by giving annotators 'gold standard' labels, and having them provide rationales for these labels. Instead, we let annotators provide rationales for labels they choose themselves. This lets them engage in the decision process, but it also acknowledges that annotators with different backgrounds may disagree on classification decisions. Explaining other people's choices is error-prone (Barasz and Kim, 2022), and we do not want to bias the rationale annotations by providing labels that align better with the intuitions of some demographics than with those of others. For the sentiment analysis datasets, we discard neutral instances because rationale annotation for neutral instances is ill-defined. Yet, we still allow annotators to evaluate a sentence as neutral, since we do not want to force our annotators to provide rationales for positive and negative sentiment that they do not see. DynaSent We re-annotate N = 480 instances six times (for six demographic groups), comprising 240 instances labeled as positive, and 240 instances labeled as negative in the DynaSent Round 2 test set (see Potts et al. (2020)). This amounts to 2,880 annotations, in total. Our sentiment *label* annotation follows the instructions of Potts et al. (2020). To annotate *rationales*, we formulate the task as marking "supporting evidence" for the label, following how the task is defined by DeYoung et al. (2019). Specifically, we ask annotators to mark all the words, in the sentence, they think shows evidence for their chosen label. SST-2 We re-annotate N = 263 instances six times (for six demographic groups), which are all the positive and negative instances from the Zuco dataset of Hollenstein et al. (2018) 6, comprising a mixture of train, validation and test set instances from SST-2, which we remove from the original data before training the models. Instructions for sentiment annotations build on the instructions by Potts et al., combined with a few examples from Zaidan et al. (2007). The instructions for annotating rationales are the same as for DynaSent. CoS-E We re-annotate N = 500 instances from the test set six times (for six demographic groups) and ask annotators to firstly select the answer to the question that they find most correct and sensible, and then mark words that justifies that answer. Following Chiang and Lee (2022), we specify the rationale task with a wording that should guide annotators to make short, precise rationale annotations: 'For each word in the question, if you think that removing it will decrease your confidence toward your chosen label, please mark it.' ## 3.2 Annotator Population We recruited annotators via Prolific based on two main criteria, age and ethnicity, previously identified as related to unfair performance differences of NLP systems (Hovy and Søgaard, 2015; Jørgensen et al., 2016; Sap et al., 2019; Zhang et al., 2021). Recruitment In our study, there is a trade-off between collecting annotations for a diverse set of data instances (number of tasks and sentences) and for a diverse set of annotators (balanced by demographic attributes), while keeping the study affordable and payment fair. Hence, when we want to study differences between individuals with different ethnic backgrounds, we can only study a subset of possible ethnic identities (of which there are many categories and diverging definitions). We balanced the number of annotators across *three* ethnic groups - Black/African American (B), Latino/Hispanic (L) and White/Caucasian (W) - and two age groups —below 36 (young, Y) and above 37 (old, O), excluding both - whose cross-product results in six sub-groups: {BO, BY, LO, LY, WO, WY}. We leave a two-year gap between the age groups in order to not compare individuals with very similar ages. Furthermore, the age thresholds are inspired by related studies of age differences in NLP-tasks and common practices in distinguishing groups with an age gap (Johannsen et al., 2015; Hovy and Søgaard, 2015) and around the middle ages (Zhang et al., 2021). Our threshold also serves to guarantee sufficient proportions of available crowdworkers in each group. Our ethnicity definition follows that of Prolific, which features in a question workers have previously responded to and hence are recruited by, defining ethnicity as: '[a] feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, colour, language or religion' While we do not require all annotators to be fluent in English, we instead ask about their Englishspeaking abilities in the demographics survey and find that 75% of the participants speak English "very well" and only 1% "not well", and the remaining "well". Exclusions Annotators who participated in annotating one task were excluded from participating in others. *After* annotation, we manually check whether a participant's answers to our short demographics survey correspond to their recruitment criteria. We found many discrepancies between recruitment ethnicity and reported ethnicity, especially for Latino/Hispanic individuals, who often report to identify as White/Caucasian. This highlights the difficulty of studying ethnicities as distinct, separate groups, as it is common to identify with more than one ethnicity7. Hence, the mismatches are not necessarily errors. For our experiments, we decided to exclude participants with such mismatches and recruit new participants to replace their responses (see Appendix B for further details). A smaller amount of participants were excluded due to mismatch in reported age or due 7General Social Survey as well as US Census allow respondents to report multiple ethnicities for this reason. See, e.g., a GSS 2001 report commenting on multi-ethnicity: shorturl.at/BCP49. to failing a simple attention check. We release annotations both with and without the instances excluded from our analyses. The final data after preprocessing consist of one annotation per instance for each of the six groups, i.e., six annotations per instance in total. Annotators annotated (approximately) 10 instances each. All participants were paid equally. ## 4 Experiments We first conduct an analysis of *group-group* label agreement (i.e., comparing human annotator groups with each other, measuring human agreement on the sentiment and answer labels) and rationale agreement (measuring human agreement on rationale annotations) to characterize inter-group differences. We then move to *group-model* agreement (comparing the labels and rationales of our annotator groups to model predictions and model rationales) and ask: Do models' explanations align better with certain demographic groups compared to others? In our analysis, we further focus on how rationale agreement and fairness behave depending on model size and model distillation. We probe 16 Transformer-based models8. To ease readability, we will use abbreviations following their original naming when depicting models' performance9. We fine-tune the models individually on each dataset (see Figure 3). SST-2 and CoS-E simplified10 are modeled as binary classification tasks; DynaSent is modeled as a ternary (positive/negative/neutral) sentiment analysis task. We exclude all annotated instances from the training splits; for CoS-E, we downsample the negative examples to balance both classes in the training split. After fine-tuning for 3 epochs, we select the checkpoint with the highest validation accuracy to run on our test (annotated) splits and apply two explainability methods to obtain input-based explanations, i.e., rationales, for the predictions made. 8All pretrained models can be downloaded at huggingface.co/models. 9{abv2: albert-base-v2, alv2: albert-large-v2, mlm-l6: MiniLM-L6-H384-uncased, mlm-l12: MiniLM-L12-H384uncased, axlv2: albert-xlarge-v2, dbu: distilbert-baseuncased, dr: distilroberta-base, bbu: bert-base-uncased, rb: roberta-base, mrb: muppet-roberta-base, dv3b: debertav3-base, axxlv2: albert-xxlarge-v2, blu: bert-largeuncased, rl: roberta-large, mrl: muppet-roberta-large, dv3l: microsoft/deberta-v3-large} 10CoS-E simplified represents each of the original questions into five question-answer pairs, one per potential answer, and label them as True (the right question-answer pair) or False. We measure label agreement with appropriate variants of F1 (SST-2 binary-F1; DynaSent macroF1; CoS-E mean of binary-F1 towards the negative and the positive class). CoS-E simplified represents a slightly different task (see footnote 10) from what the annotators were presented to solve (a multiclass question-answering task). To correctly measure label agreement, we evaluate whether a model predicts 'True' for the question-answer pair with the answer selected by the annotator. Therefore, to avoid misleading F1 scores if, for example, a model predominantly predict True, we report the mean of the F1 towards each class. We explain below how we measure rationale agreement. Explainability methods We analyze models' predictions through two families of post-hoc, attribution-based11 explainability methods: Attention Rollout (AR) (Abnar and Zuidema, 2020) and Layer-wise Relevance Propagation (LRP) (Bach et al., 2015), a gradient-based method. Ali et al. (2022) compare these methods, showing how their predicted rationales are frequently uncorrelated. Both AR and LRP thus provide token level rationales for a given input, but while AR approximates the relative importance of input tokens by accumulating attention, LRP does so by backpropagating 'relevance' from the output layer to the input, leading to sparser attribution scores. We rely on the rules proposed in Ali et al. (2022), an extension of the original LRP method (Bach et al., 2015; Arras et al., 2017) for Transformers, aiming to uphold the conservation property of LRP in Transformers as well. This extension relies on an "implementation trick", whereby the magnitude of any output remains intact during backpropagation of the gradients of the model. Comparing rationales Attention-based and gradient-based methods do not provide categorical relevance of the input tokens, but a vector Si with continuous values for each input sentence i. We translate Siinto a binary vector S b i following the procedure from Wang et al. (2022) for each group. We define the top-k gd tokens as rationales, where k gd is the product of the current sentence length (tokens) and the average rationale length ratio (RLR) of a group g within a dataset d. On average, RLR for SST-2 are shorter (29.6%) com-11The methods are applied at inference time and provide explanations *locally*, i.e., for each individual instance, indicating the relative importance of each input token through a score distribution. pared to DynaSent (31.9%) and CoS-E (33.0%) (see Appendix B for specific values). Models' outputs are also preprocessed to normalize different tokenizations and to match the input format given to annotators. After aligning explanations from models and annotators in the same space, we can compare them. We employ two metrics specifically designed to evaluate discrete rationales: token-level F1 (token-F1) (Equation 1) (DeYoung et al., 2019; Wang et al., 2022), and Intersection-Over-Union F1 (IOU-F1) (Equation 3) as presented in (DeYoung et al., 2019). These metrics are flexible enough to overcome the strictness of exact matching.12 ## 5 Results And Discussion Figure 3 shows group-model label agreement over our annotated data.13 Error bars show the variability between best and worst performing groups. CoS-E exhibits the lowest variability, indicating less variability in label agreement between groups. When annotators disagree on the label of an instance, it is to be expected that their rationales will subsequently be different. Therefore, to compare group-group (§ 5.1) and group-model (§ 5.2) rationales more fairly, we focus on the subset of instances where all groups are in agreement about 12Formally, $${\mathrm{token-}}F_{1}={\frac{1}{N}}\sum_{i=1}^{N}2\times{\frac{P_{i}\times R_{i}}{P_{i}+R_{i}}}$$ ``` where Pi and Ri are the precision and recall for the i th instance, computed by considering the overlapped tokens between models' and annotators' rationales. To measure Intersection-Over-Union, we define the categorical vector given by the annotators for each sample as Ai. Thereby, ``` $$\mathrm{IOU}_{i}={\frac{|S_{i}^{b}\cap A_{i}|}{|S_{i}^{b}\cup A_{i}|}}$$ i ∪ Ai|(2) $$\mathrm{and}$$ $$(2)$$ $$\text{IOU-}F_{1}=\frac{1}{N}\sum_{i=1}^{N}\left\{\begin{array}{ll}1&\text{if IOU}_{i}\geq0.5\\ 0&\text{otherwise.}\end{array}\right.\tag{3}$$ These metrics account for _plausibility_ (DeYoung et al., ![5_image_0.png](5_image_0.png) the label, i.e., instances with full label agreement. This amounts to 209, 152 and 161 instances for DynaSent, SST-2 and CoS-E, respectively. ## 5.1 Analysis Of Group-Group Agreement $$(1)$$ We first want to quantify how different the rationales of one group are to those of others, and more generally to a random population. We compare each groups' set of rationales to a random paired set of rationales, where the rationale of each instance is randomly picked from one of the five other groups. Figure 4 shows the overall agreement score, average token-F1 across datasets, and its standard deviation from 20 random seeds, i.e., 20 random combinations of paired rationales. We observe that rationales of White annotators (WO, WY) are on average more similar to others while the average difference with the rationales of minority groups like, for example, Black Young (BY), is greater. We then compute the level of rationale agreement (token-F1) between all groups (heatmaps on Figure 4) and observe that, in general, differences in group-group rationale agreement are consistent across datasets (tasks): Black Youngs (BY) have lower alignment with others, especially in sentiment analysis tasks. While the definition of rationales for DynaSent seems to be easier (higher values of agreement), it seems to be harder (lower values of agreement) for CoS-E, even when the label is agreed upon. We hypothesize this is due to the complexity of the CoS-E task itself, which also leads to more lengthy rationales, as reflected by the average RLR reported on § 4, probably in ![6_image_1.png](6_image_1.png) ![6_image_0.png](6_image_0.png) the absence of a clear motivation for the selected answer. The definition of what is *common-sense* varies across cultures and it is related to a person's background (Hershcovich et al., 2022), which makes CoS-E a highly subjective task14. Take for example the question 'Where would you find people standing in a line outside?' with these potential answers: 'bus depot', 'end of line', 'opera', 'neighbor's house' and 'meeting'. Even if there is agreement on the *correct* choice as 'bus depot', the rationale behind it could easily differ amongst people, i.e., it could be due to 'people standing', or the fact that they are standing in 'a line outside', or all together. ## 5.2 Analysis Of Group-Model Agreement Now that we have analyzed group-group agreement, we measure the alignment between groups' rationales and models' rationales. We analyze predictions from 16 Transformer-based models and employ AR and LRP to extract model rationales. Methods for comparing rationales and measuring group-model agreement are explained in Section 4. Socio-demographic fairness Figure 5 shows a systematic pattern of model rationales aligning better with the rationales of older annotators in each ethnic group (BO, LO, WO) on the sentiment datasets. The only exception is White Young (WY) annotators in SST-2, whose median token-F1 is higher than their older counterpart. We argue this is due, in part, to the data source of the tasks themselves. While DynaSent constitutes an ensemble of diverse customer reviews, SST is based on movie review excerpts from Rotten Tomatoes with a more informal language, popular amongst younger users. Findings from Johannsen et al. (2015) and Hovy 14This is specially notorious on the query type *people*. ![6_image_2.png](6_image_2.png) and Søgaard (2015) indicate that there exist grammatical differences between age groups. Johannsen et al. (2015) further showed several age and genderspecific syntactic patterns that hold even across languages. This would explain not only the noticeable group-group differences when marking supporting evidence (lexical structures) for their answers, but also the agreement disparity reflected by models fine-tuned on potentially age-biased data. Results are consistent with previous findings of ![7_image_0.png](7_image_0.png) Zhang et al. (2021), who show a variety of language models aligning better with older, white annotators, and worse with minority groups, in word prediction tasks. We observe that group-model rationale agreement does not correlate with group-model class agreement, i.e., when a model performs well for a particular group, it does not necessarily entail that its rationales, or learned patterns, align. Group-model rationale agreement evaluated with Attention Rollout and CoS-E are shown in Figure 13 in Appendix C, along with results using the complementary metric (IOU-F1). The patterns derived from them are in line with those in Figure 5: AR shows similar behaviours as LRP, but leads to larger variation between models. However CoS-E, which, as explained, is a very different task, does not seem to exhibit big group differences. This is also noticeable in Figure 6, where error bars show the distance between groups with the highest and lowest level of agreement in every model. The role of model size In general, larger language models seem to perform better on NLP tasks. In our setting, Figure 3 shows a positive trend with model size: larger models achieve, in general, higher performance. Could it be the case that larger language models also show higher rationale agreement? And, are they consequently more fair? We evaluate fairness in terms of performance parity: min-max difference between the group with the lowest and highest token-F1 (per model). Relying on min-max difference captures the widely shared intuition that fairness is always in the service of the worst off group (Rawls, 1971). Contrary to our expectations, Figure 6 shows how token-F1 scores actually *decrease* with model size - with CoS-E model rationales from LRP being the only exception to the trend. We report Spearman correlation values for each dataset and explainability method: The negative correlation between token-F1 and model size is significant in all three datasets with AR, but only in DynaSent with LRP. The positive correlation in CoS-E with LRP rationales is also significant. When we zoom in on the min-max Token-F1 gaps (error bars on Figure 6) 15, we find that performance gaps are uncorrelated with model size. Therefore, there is no evidence that larger models are more fair, i.e., rationale alignment does not become more equal for demographic groups. In the context of toxicity classification, work from (Baldini et al., 2021) also hints that size is not well correlated with fairness of models. Do distilled models align better? Knowledge distillation has been proven to be effective in model compression while maintaining model performance (Gou et al., 2021). But can it also be effective in improving NLP fairness? Xu and Hu (2022) find a consistent pattern of toxicity and bias reduction after model distillation. Chai et al. (2022) show promising results when approaching fairness without demographics through knowledge distillation. Tan et al. (2018) discuss the benefits of applying knowledge distillation to leverage model interpretability. Motivated by these findings, we take results from LRP to look closer into groupmodel rationale agreement for distilled models, which we show in Table 2. We find overall higher rationale agreement for distilled models. However, there is no evidence that distilled models are also more fair: Only minilm-l6-h384-uncased 15See Figure 14 in Appendix C.2 for a plot of the gaps themselves. | token-F1 (↑) | IOU-F1 (↑) | min-max | min-max | | |-------------------------|--------------|-----------|-----------|------| | token-F1 (↓) IOU-F1 (↓) | | | | | | minilm-l6-h384-unc. | .31 | .28 | .045 | .068 | | minilm-l12-h384-unc. | .27 | .21 | .045 | .083 | | distilbert-base-unc. | .29 | .24 | .064 | .100 | | distilroberta-base | .36 | .36 | .065 | .069 | | Avg. (16 models) | .29 | .24 | .054 | .081 | has a smaller performance gap between the best and worst-off group for both metrics compared to the average. ## 6 Conclusion In this paper, we present a new collection of three existing datasets with demographics-augmented annotations, balanced across age and ethnicity. By having annotators choose the right label and marking supporting evidence for their choice, we find that what counts as a rationale differs depending on peoples' socio-demographic backgrounds. Through a series of experiments with 16 popular model architectures and two families of explainability methods, we show that model rationales align better with older individuals, especially on sentiment classification. We look closer at model size and the influence of distilled pretraining: despite the fact that larger models perform better in general NLP tasks, we find negative correlations between model size and rationale agreement. Furthermore, from the point of view of performance parity, we find no evidence that increasing model size improves fairness. Likewise, distilled models do not seem to be more fair in terms of rationale agreement, however they do present overall higher scores. This work indicates the presence of undesired biases that *do not necessarily surface in task performance*. We believe this provides an important addendum to the fairness literature: Even if models are fair in terms of predictive performance, they may still exhibit biases that can only be revealed by considering model rationales. If models are equally right, but only right for the right reasons in the eyes of some groups rather than others, they will likely be less robust for the latter groups. ## Limitations Our analysis is limited to non-autoregressive Transformer-based models, fine-tuned with the same set of hyperparameters. Hyperparameter optimization would undoubtedly lead to better performance for some models, but we fine-tuned each model with standard hyperparameter values for solving sentiment analysis tasks (DeYoung et al., 2019) to reduce resource consumption. This should not affect the conclusions drawn from our experiments. Comparing human rationales and rationales extracted with interpretability methods such as Attention Rollout and LRP is not straightforward. Overall agreement scores depend on how model rationales are converted into categorical values (top-k gd). See Jørgensen et al. (2022) for discussion. ## Acknowledgments Many thanks to Stephanie Brandl, David Dreyer Lassen, Frederik Hjort, Emily Pitler and David Jurgens for their insightful comments. This work was supported by the Novo Nordisk Foundation. ## Ethics Statement Broader impact Although explainability and fairness are broadly viewed as intertwined subjects, very little work has studied the two concepts together (Feng and Boyd-Graber, 2019; González et al., 2021; Ruder et al., 2022). This study is a first of its kind to examine fairness issues of explainability methods and to publish human rationales with diverse socio-demographic information. We hope this work will impact the NLP research community towards more data-aware and multi-dimensional investigations of models and methods, and towards further studies of biases in NLP. Personal and sensitive data This study deals with personal and sensitive information. The responses are anonymous and cannot be used to identify any individual. Informed consent The participants were informed of the study's overall aim, the procedure and confidentiality of their responses. With this information, the participants consented to the use and sharing of their responses. Potential risks We do not anticipate any risks of participation in the study, yet we do note a recent awareness of poor working conditions among crowdworkers for AI data labeling in some countries (Williams et al., 2022). 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In *Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*, pages 4581–4588, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. ## A Annotation Guidelines And Task Examples On the next pages, we firstly show the annotation instructions given to annotators within the Qualtrics surveys. Full exports of the surveys are available in our GitHub repository.16 We created instructions specific for each dataset (DynaSent, SST-2, and CoS-E), leaning on prior work of annotating labels and rationales for these and similar datasets (Potts et al., 2020; Zaidan et al., 2007; DeYoung et al., 2019), as described in the paper, section 3.1. Figure 7, 8, and 9 shows the instructions for DynaSent, SST-2 and CoS-E, respectively, and Figure 10 shows an example of how an instance for the sentiment task and the common-sense reasoning task is annotated, i.e. how it looked from the perspective of the crowdworkers. Annotating rationales for the common-sense reasoning task is somewhat more complex than annotating rationales for sentiment: while we can ask annotators to mark 'evidence' for a sentiment label - often resulting in marking words that are positively or negatively loaded - we cannot as simply ask for 'evidence' for a common-sense reasoning answer without risking some confusion. Take, for instance, the question "Where do you find the most amount of leafs?" with the answer being 'Forest', as shown in Figure 9. Here, the term 'evidence' might be misunderstood as actual evidence for why there would be more leafs in the forest compared to a field - evidence which cannot be found within the question itself. We therefore re-phrase the rationale annotation instructions for CoS-E, following an example from Chiang and Lee (2022), and ask, "For each word in the question, if you think that removing it will decrease your confidence toward your chosen label, please mark it." Furthermore, the subset of the CoS-E dataset, that we re-annotate, consists of the more 'difficult' split of the CommonsenseQA dataset (Talmor et al., 2019; DeYoung et al., 2019). To make the task as clear as possible to the annotators, we explain, in the instructions, that the question and answer-options have been created by other crowdworkers who were instructed to create questions that could be "easily answered by humans without context, by the use of commonsense knowledge", as is described by Talmor et al. (2019). 16https://github.com/terne/Being_Right_ for_Whose_Right_Reasons. | COMPLETE LABEL AGREEMENT | | | | | | |----------------------------|-----|-----|-----|---------|-------| | DATASET | N | POS | NEG | NEUTRAL | TOTAL | | DynaSent | 480 | 105 | 102 | 2 | 209 | | SST | 263 | 79 | 73 | 0 | 152 | | CoS-E | 500 | - | - | - | 161 | Table 3: Number of instances, in our (re-)annotated data, where all annotator groups agreed upon the instance's label. ## B Annotations Overview Table 4 gives further information on the distribution of annotators, across groups and datasets, as well as ratios of rationale lengths to input lengths. Table 3 shows the number of instances in the data subsets, we work with, and the number of instances where all our annotator groups agreed on the label and that are therefore used for rationale-agreement analyses. ## C Supplementary Figures For completeness, we provide supplementary figures for all the metrics and datasets analyzed in the paper. ## C.1 Label Agreement Heatmaps in Figure 11 show the level of groupgroup label agreement across datasets. Similar to what is shown in Figure 4, BY consistently exhibit lower level of agreement. Box-plots in Figure 12 represent group-model label agreement. Each dot represents the F1-score of each model. While for Cos-E the models generally exhibit lower variability across groups, the level of agreement is also lower (as shown in Figure 3). ## C.2 Rationale Alignment Figure 13 is the extended version of Figure 5, showing the group-model rationale agreement for each dataset, each explainability method and with two metrics for measuring agreement, token-F1 and IOU-F1. The bar charts in Figure 14 shows, per model and dataset, the distance between the group with the lowest and highest agreement with the model (by token-F1), which we refer to as the "min-max token-F1 gaps" in section 5.2. We include this plot because it serves to better illustrate the gaps themselves, and how they are uncorrelated with model size, compared to what Figure 6 in the paper can convey. Instructions Please read these instructions carefully. You will be shown 10 sentences from reviews of products and services. For each, your task is to choose from one of our three labels: Positive: The sentence conveys information about the author's positive evaluative sentiment. Negative: The sentence conveys information about the author's negative evaluative sentiment. No sentiment: The sentence does not convey anything about the author's positive or negative sentiment. Here are some examples of the labels: Sentence: This is an under-appreciated little gem of a movie. (This is Positive because it expresses a positive overall opinion.) Sentence: I asked for my steak medium-rare, and they delivered it perfectly! (This is Positive because it puts a positive spin on an aspect of the author's experience.) Sentence: The screen on this device is a little too bright. (This is Negative because it negatively evaluates an aspect of the product.) Sentence: The book is 972 pages long. (This is No sentiment because it describes a factual matter with not evaluative component.) Sentence: The entrees are delicious, but the service is so bad that it's not worth going. (This is Negative because the negative statement outweighs the positive one.) Sentence: The acting is great! The soundtrack is run-of-the mill, but the action more than makes up for it. (This is Positive because the positive statements outweighs the negative.) We further ask you to specify what snippets of text, in the sentence, you think acts as supporting evidence for your chosen ![14_image_0.png](14_image_0.png) label. The sentence will be shown to you as illustrated below, and your task is to mark (by clicking on them) all the words you think shows evidence for the sentiment label you chose. Be aware that some sentences might be too long to fit on your screen. You therefore have to remember to scroll in order to see all the words that can be marked as evidence. Click the forward button below when you are ready to start the task. Figure 7: DynaSent annotation instructions. ## Instructions Please read these instructions carefully. You will be shown approximately 10 sentences from reviews of movies. For each, your task is to choose from one of our three labels: Positive: The sentence conveys information about the author's positive evaluative sentiment. Negative: The sentence conveys information about the author's negative evaluative sentiment. No sentiment: The sentence does not convey anything about the author's positive or negative sentiment. Here are some examples of the labels: Sentence: This is an under-appreciated little gem of a movie. (This is Positive because it expresses a positive overall opinion.) Sentence: he is one of the most exciting martial artists on the big screen, continuing to perform his own stunts and dazzling audiences with his flashy kicks and punches. (This is Positive because it positively evaluates an aspect of the movie.) Sentence: The acting is great! The soundtrack is run-of-the-mill, but the action more than makes up for it. (This is Positive because the positive statements outweigh the negative.) Sentence: The story is interesting but the movie is so badly put together that even the most casual viewer may notice the miserable pacing and stray plot threads. (This is Negative because the negative statement outweighs the positive one.) Sentence: A woman in peril. A confrontation. An explosion. The end. Yawn. Yawn. Yawn. (This is Negative because it puts a negative spin on the author's experience.) Sentence: don't go see this movie. (This is Negative because it recommends against seeing the movie, reflecting a negative evaluation.) Sentence: it is directed by Steven Spielberg. (This is No sentiment because it describes a factual matter with no evaluative component.) Sentence: I saw it in the local theater with my best friend. (This is No sentiment because it does not say anything about the movie.) We further ask you to specify what snippets of text, in the sentence, you think acts as supporting evidence for your chosen ![15_image_0.png](15_image_0.png) label. The sentence will be shown to you as illustrated below, and your task is to mark (by clicking on them) all the words you think shows evidence for the sentiment label you chose. Be aware that some sentences might be too long to fit on your screen. In that case you have to scroll in order to see all the words that can be marked as evidence. Click the forward button below when you are ready to start the task. Figure 8: SST-2 annotation instructions. ## Instructions (Please read these instructions carefully.) You will be shown 10 multiple-choice questions. All questions and their answer-options have been created by other crowdworkers, who where instructed to create questions that can be fairly easily answered by humans without context, by the use of common-sense knowledge. Your task is to firstly select the answer you think is most correct and sensible. We call this the label of the question. Secondly, we ask you to mark relevant words in the question that justifies your choice. Specifically, for each word in the question, if you think that removing it will decrease your confidence toward your chosen label, you should mark it. In the image below, you see an example of how the task will be presented to you. To the question "Where do you find the most amount of leafs?", the option "Forest" is selected as the correct answer and four words have been marked as justification. Where do you find the most amount of leafs? ![16_image_0.png](16_image_0.png) When marking words, be aware that some questions might be longer and not fit perfectly on your screen. In that case you have to scroll in order to see all the words that can be marked. Also, the texts may have misspellings, typos and wrongly put spaces before punctuation - pay no attention to this. Click the forward button below when you are ready to start the task. Figure 9: CoS-E annotation instructions. Sentence: The art exhibit has a lot to offer. ![17_image_0.png](17_image_0.png) ![17_image_1.png](17_image_1.png) t ![17_image_2.png](17_image_2.png) i ![17_image_3.png](17_image_3.png) Figure 10: Screenshots of the annotation tasks as they are viewed in Qualtrics surveys. | DATASET | BO | BY | LO | LY | WO | WY | TOTAL/AVG. | | |-----------|---------|-----------|-----------|-----------|-----------|-----------|--------------|-----| | Annot. | 51 | 56 | 61 | 73 | 54 | 51 | 346 | | | DynaSent | Annot.∗ | 48 (58%F) | 48 (67%F) | 48 (44%F) | 48 (40%F) | 48 (56%F) | 48 (48%F) | 288 | | RLR | 33.7 | 32.5 | 31.5 | 29.8 | 34.7 | 29.1 | 31.9 | | | Annot. | 28 | 27 | 53 | 43 | 27 | 29 | 207 | | | SST | Annot.∗ | 26 (69%F) | 26 (58%F) | 26 (38%F) | 26 (31%F) | 26 (38%F) | 26 (69%F) | 156 | | RLR | 32.1 | 25.1 | 30.7 | 27.8 | 29.1 | 32.7 | 29.6 | | | Annot. | 52 | 56 | 74 | 85 | 54 | 55 | 376 | | | CoS-E | Annot.∗ | 50 (60%F) | 50 (60%F) | 50 (40%F) | 50 (48%F) | 50 (48%F) | 50 (40%F) | 300 | | RLR | 31.9 | 32.9 | 34.1 | 32.2 | 33.3 | 33.6 | 33.0 | | ![18_image_0.png](18_image_0.png) 0.0 0.5 1.0 0.0 0.5 1.0 ![18_image_1.png](18_image_1.png) ![18_image_2.png](18_image_2.png) ![19_image_0.png](19_image_0.png) ![19_image_1.png](19_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? In section titled "Limitations", section 7. ✓ A2. Did you discuss any potential risks of your work? Section 8, "Ethics Statement" ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 on paragraph "Contributions". ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** It is described in Section 3. Used in Section 4 and 5 ✓ B1. Did you cite the creators of artifacts you used? Section 3.1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 8, "Ethics Statement" ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? In the ethics statement we specify that the intended use of our annotations is research purposes only. The datasets we use are at least intended for research purposes as well. A larger discussion does not seem relevant in this case. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Section 8, "Ethics Statement" ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3 and 4 ## C ✓ **Did You Run Computational Experiments?** Section 4 ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Our research does not focus on model development from scratch. We use known pretrained models and refer to the original library (footnote 6) in which this information is clearly stated. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Experimental setup is discussed in SEction 4. In the section 7 "Limitations, we provide further explanations. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 3 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Section 3 and Appendix C, and a printout of the full surveys/annotation task will be shared upon acceptance (an author's name and contact details appears in them).Section 3 and Ethics Statement. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 3 and Ethics Statement. ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Ethics Statement. ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Anonymous data is exempt from IRB approval at the authors' institution. ✓ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? SEction 3

yu-etal-2023-alert

{ALERT}: Adapt Language Models to Reasoning Tasks

https://aclanthology.org/2023.acl-long.60

Recent advancements in large language models have enabled them to perform well on complex tasks that require step-by-step reasoning with few-shot learning. However, it is unclear whether these models are applying reasoning skills they have learnt during pre-training , or if they are simply memorizing their training corpus at finer granularity and have learnt to better understand their context. To address this question, we introduce {pasted macro {`}OUR{'}}model, a benchmark and suite of analyses for evaluating reasoning skills of language models. {pasted macro {`}OUR{'}}model enables comparing pre-trained and finetuned models on complex tasks that require reasoning skills to solve. Our benchmark provides a test bed to asses any language model on fine-grained reasoning skills, which spans over 20 datasets and covers 10 different reasoning skills. By using {pasted macro {`}OUR{'}}model we further investigate \textit{the role of finetuning}. Our extensive empirical analysis shows that language models learn more reasoning skills such as textual entailment, abductive reasoning, and analogical reasoning during the finetuning stage compared to pretraining stage. However, we also find that when language models are finetuned they tend to overfit to the prompt template, which hurts the robustness of models causing generalization problems.

# Alert**: Adapting Language Models To Reasoning Tasks** Ping Yu♠ Tianlu Wang♠ Olga Golovneva♠ **Badr AlKhamissi**△ Siddharth Verma△ Zhijing Jin△ Gargi Ghosh♠ Mona Diab♠ **Asli Celikyilmaz**♠ ♠Meta AI △Work done at Meta AI {pingyu,aslic}@meta.com ## Abstract Recent advancements in large language models have enabled them to perform well on complex tasks that require step-by-step reasoning with few-shot learning. However, it is unclear whether these models are applying reasoning skills they have learned during pre-training, or if they are simply memorizing their training corpus at finer granularity and have learned to better understand their context. To address this question, we introduce ALERT, a benchmark and suite of analyses for evaluating reasoning skills of language models. ALERT enables comparing pre-trained and finetuned models on complex tasks that require reasoning skills to solve them. Our benchmark provides a test bed to assess any language model on fine-grained reasoning skills, which spans over 20 datasets and covers 10 different reasoning skills. To prove the efficacy of ALERT we investigate the role of finetuning. Our extensive empirical analysis shows that language models acquire reasoning skills such as textual entailment, abductive reasoning, and analogical reasoning during the finetuning stage compared to pretraining stage. Another finding is when language models are finetuned they tend to overfit to the prompt template, which hurts the robustness of models resulting in generalization problems. ## 1 Introduction Large language models (LLMs) (e.g., GPT3 (Brown et al., 2020a), PALM (Chowdhery et al., 2022), OPT (Zhang et al., 2022)) have shown increasing in-context learning capabilities with scaling up the model and data sizes. Despite this progress, even the largest of these models still struggle with tasks such as commonsense reasoning (West et al., 2022), and math word problems (Hendrycks et al., 2021b) which require arithmetic reasoning or symbolic manipulation (Rytting and Wingate, 2021). Table 1 presents some examples that require certain reasoning skills. Even The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have? The answer is 29 apples . Select the best translation into predicate logic. David teaches Chris. (c: Chris; d: David; Txy: x teaches y) (A) Tdc; (B) Tcd; (C) Tcc; (D) dTc. **The answer is** (B) Tcd . Isabella entered the hall. Olivia entered the hall. The apple is in the blue_treasure_chest. Olivia exited the hall. Isabella moved the apple to the green_basket. Question: Where does Isabella think that Olivia searches for the apple? The answer is Isabella thinks that Olivia searches for the apple in the green_basket . Table 1: Examples from tasks that require reasoning skills and generated outputs from GPT-3 series *text-davinci-003* engine. The failed outputs are highlighted in red . Predictions by ChatGPT are shown in Table 9 in Appendix. the powerful LLMs (such as *text-davinci-003*1and ChatGPT2) fail to make correct predictions. To improve large LLMs' performance on tasks that require multiple steps of reasoning, recent work used different prompting methods which included a rationale with the final answer in the form of: scratchpad for arithmetic and logical reasoning (Nye et al., 2021), chain-of-thought (CoT) (Wei et al., 2022) for practically any tasks, or adding *let's* think step-by-step (Kojima et al., 2022) to prompt models to generate explanations. Other works such as Chung et al. (2022) integrated step-by-step explanations into the finetuning stage (CoT-finetuning). While these techniques may improve the accuracy and interpretability, it is not well understood which reasoning skills they rely on or to what degree they require higher-order reasoning. It is also uncertain how frequently the stated reasoning steps actually contribute to the final task predictions. For instance, to correctly answer the questions in Table 1 a combination of logical, commonsense, math and spatial reasoning skills are required. In this work, to gain a deeper understanding of LLMs reasoning abilities in in-context learning 1https://beta.openai.com/docs/models/gpt-3. 2https://chat.openai.com/chat. 1055 settings, we introduce ALERT, a new pipeline to benchmark different LLMs on various reasoning skills and provide analysis to assess reasoning abilities. Unlike existing commonly used benchmarks (e.g., Mishra et al. (2022); Wang et al. (2022c); Srivastava et al. (2022)), ALERT can evaluate LLMs' fine-grained reasoning skills. It spans over 20 datasets and covers 10 different reasoning skills including logical, causal, commonsense, abductive, spatial, analogical, argument and deductive reasoning as well as textual entailment, and mathematics (see Figure 6). ALERT enables easy benchmarking of any LM (e.g., pre-trained, finetuned, CoTfinetuned) on a rich set of new inference methods including zero-shot, few-shot and CoT. Using ALERT, we further investigate whether finetuning can improve LMs' performance on downstream reasoning tasks. Specifically, we are interested in diagnosing what actually improved when we observe a performance increase on reasoning tasks. Is it because models have seen similar data in the finetuning stage? Or is it because models have seen prompts in a specific template and memorize the template during finetuning such as definitions provided in the NIV2 benchmark (Wang et al., 2022c)? Or does the LLM actually acquired the required reasoning skill? We investigate these three possibilities. To study the above questions, we compare three different model types (as shown in Figure 2): a pretrained model and two types of finetuned models. Specifically: - OPT (Zhang et al., 2022): A baseline LLM a pre-trained model with no finetuning (figure (A) in Figure 2); - **OPT-FT**: Meta-finetuned OPT on reference answers *without* explanations, illustrated in (figure (B) in Figure 2); - **OPT-CoT**: Meta-finetuned OPT on data with rationales (explanations) (Chung et al., 2022; AlKhamissi et al., 2023) (figure (C) in Figure 2). Using these three types of models, we investigate the role of finetuning on three dimensions: (1) Data memorization: We investigate whether the performance improvements obtained after finetuning can be attributed to using similar or sometimes the exact same data as in the evaluation datasets. To this end, we use vocabulary overlap to | Reasoning Skills | Datasets | |--------------------|---------------------------------------------------------------------------------------| | Logical | bigbench repeat copy logic, mmmlu answer generation | | Causal | plausable result generation, anli r2 entailment, anli r3 entailment, cb entailment | | Commonsense | piqa answer generation, commongen sentence generation, sciq answer generation, openbookqa question answering | | Entailment | nli r2 entailment, anli r3 entailment, cb entailment, lue entailment classification | | Mathematics | semeval closed vocabulary math, semeval geometric math, mmmlu formal logic | | Abductive | tellmewhy | | Spatial | babi t1 single supporting fact, piqa answer generation, toqa find location easy clean | | Analogical | commongen sentence generation, bard analogical reasoning causation | | Argument | argument stance classification, argument consequence classification | | Deductive | rocstories correct answer generation | measure the extent to which the evaluation data is different from the finetuning data, i.e. We investigate whether the improvement is more significant when evaluation data and finetuning data are more similar. (2) Reasoning skills transfer: We investigate if certain reasoning skills can be more successfully permeated in LLMs than other reasoning skills. To verify this, we carefully divide the evaluation datasets into groups which require different reasoning skills. We compile held-out datasets as shown in Figure 6 which require skills held-out from any of the training datasets. This way, we expect to see larger improvements on in-domain skills compared to held-out skills if reasoning skills can be transferred during finetuning stages. (3) Prompt template memorization: Our third hypothesis is that LLMs can overfit to data format used in the finetuning datasets such as training data format used in Figure 2. In other words, the consistency in data format helps LLMs better understand the instruction which then yields better performance after finetuning. To test this, we evaluate finetuned LLMs on datasets with 5 different prompt templates. Summary of findings: (i) Different from Gururangan et al. (2020), our experiments indicate that there is no strong correlation between high vocabulary overlap (between finetuning and evaluation datasets) and performance gain on reasoning evaluation datasets. This means that LLMs are not ![2_image_0.png](2_image_0.png) simply memorizing the training data during the finetuning stage; (ii) Finetuning helps improve certain reasoning capabilities of LLMs (e.g. analogical and abductive) but not all of them (e.g. commonsense reasoning); (iii) Finetuning can cause overfitting towards data format, which makes it harder for LLMs to generalize to other prompt templates, while CoT-finetuning helps to mitigate this issue as it incorporates a variety of explanations. Though many of the aspects that we study have been discussed in prior analyses of LLMs (Chung et al., 2022; Wei et al., 2021a, 2022; Kojima et al., 2022; Cobbe et al., 2021; Sanh et al., 2021), prior work has not evaluated LLMs on different reasoning skills and how these skills can be improved. Overall, by evaluating reasoning skills with ALERT, we gain new insights on how models have or have not succeeded in generalizing beyond their *training* experience. To summarize our contributions, this paper presents a meticulously designed benchmark for assessing reasoning abilities. Furthermore, a thorough investigation of *the role of finetuning* in the context of reasoning abilities, data memorization, and data format is conducted. ## 2 Motivation And Our Benchmark Motivation. The analyses in ALERT are inspired by a scientific question: To what extent do LLMs learn generalizable reasoning abilities? This question motivates our focus on measuring LLMs' performance on tasks that require contextual understanding and perform multi-step operations, which are crucial to perform well on downstream tasks. Datasets Construction. To construct the datasets of ALERT, we select datasets from NIV2 benchmark (Wang et al., 2022c) and perform the following operations: (1) Omit extremely hard tasks. We design ALERT so that it can be used to benchmark a variety of LLMs, from pre-trained, finetuned to instruction-tuned models. To select such tasks, we apply several heuristics: firstly, we manually omit tasks that heavily rely on instructions. Some tasks are hard to solve when only in-context examples (demonstrations) are provided (e.g., the example in Figure 1). Secondly, we selected only those tasks that achieved a reasonable level of performance (empirically use ROUGE-L > 5.0) when evaluated with a pre-trained model (we use the OPT-13B model). Thirdly, we omit tasks on which humans fail to get decent performance given the ground truth labels from NIV2. For example, *task963_librispeech_asr_next_word_ prediction* (Weir et al., 2020) provides a prompt "Joey's favourite food is ___", with the ground truth answer "sandwiches". Without any context or background information, the answer can be any food thus it is extremely hard for humans to accurately predict "sandwiches". (2) Remove tasks with long input context. The input sentence length of some tasks can be very long, and currently most LLMs are not designed for solving long text problems. We omit tasks with demonstration length longer than 2048 tokens. (3) Fix ground truth labels. For each reasoning task, NIV2 provides the reasoning skills required to solve the task, e.g. task102_commongen_data_to_text requires relational, analogical and commonsense reasoning. However, we found that some tasks have been labeled with incorrect reasoning skills. For example, *task393_plausible_result_generation* provides a sentence and asks LLMs to complete the sentence. The labels given by NIV2 are causal reasoning and textual entailment, but in fact this task can hardly examine an entailment skill. Accordingly, we manually fix reasoning skill labels. In addition, we only keep the predominant skill. For example, many tasks need more or less commonsense knowledge, therefore we select the related tasks that only heavily rely on commonsense knowledge to assess commonsense reasoning. Benchmark. After the above steps, we select tasks that represent a variety of reasoning skills and construct ALERT reasoning benchmark, where Table 2 shows details about our benchmark. ![3_image_0.png](3_image_0.png) ## 3 Experiment Setup 3.1 Models To perform a controlled comparison across training and prompting methods, we focus on three different models: pre-trained, meta-finetuned, and rationalebased meta-finetuned (CoT-finetuned) models. For pre-trained models, we use OPT (Zhang et al., 2022), a suite of decoder-only pre-trained transformers which are reported to yield comparable performance to GPT-3 (Brown et al., 2020b). We benchmark with OPT models of two scales: 1.3B and 13B. For finetuned models (OPT-FT), we finetune OPT models on datasets without explanations. For CoT-finetuned models (OPT-CoT), we finetune OPT models on data with rationales (explanations). We train all models in Pytorch (Paszke et al., 2017) using OPT-IML (Iyer et al., 2022) codebase3. We initialize model hyper-parameters for each model scale following OPT (Zhang et al., 2022). We pack our training examples into sequences of length 2048, left-truncating examples that overflow. We use AdamW (Loshchilov and Hutter, 2017) with 32-bit state with (β1, β2) = (0.9, 0.95), linearly warming up the learning rate for 6% steps to the maximum, followed by linearly decaying it to 0. For all 1.3B models, we use batch size of 128, and for 13B models, we use batch size of 256. ## 3.2 Finetuning Data Our finetuning corpus is comprised of 10 datasets: ProofWriter (Tafjord et al., 2020), StrategyQA (Geva et al., 2021), ECQA (Aggarwal et al., 2021), CoQA (Reddy et al., 2019), GSM8K (Cobbe et al., 2021), AQUA-RAT (Ling et al., 2017), ESNLI (Camburu et al., 2018), MATH (Hendrycks et al., 2021c), CoS-E (Rajani et al., 2019), WinoWhy (Zhang et al., 2020). These 10 finetuning datasets collectively contain 6 different reasoning skills: logical reasoning, causal reasoning, commensense reasoning, textual entailment, mathematics, abductive reasoning. In addition, these 10 datasets all come with instructions, demonstration examples and explanations. This enables fair comparison of OPT-FT and OPT-CoT models. More details about finetuning corpus can be found in Table 5 in Section A.2. More details about development data selection can be found in the Appendix. A.3. ## 3.3 Evaluation Templates Following (Wei et al., 2021b), to control for the effect of variable prompt templates, we adopt different templates (T) during inference stage in our experiments: T1: instruction + demonstration examples with explanations + "let's think step by step"; T2: instruction + "Please give a short explanation after the answer" + demonstration examples with explanations + "let's think step by step" T3: instruction + "Please give a short explanation after the answer" + demonstration examples with explanations T4: "Please give a short explanation after the answer" + demonstration examples with explanations + "Let's think step by step" T5: instructions + demonstrations For each dataset, we report the average and max score among these five templates. The final aggregated results (including aggregated average score and aggregated max score) are reported by further averaging across all datasets. Unless specified otherwise, the default score refers to the aggregated max score among five templates. Evaluation metrics. Since our benchmark contains both classification and generation tasks, we cannot use classification accuracy to evaluate all the tasks. Following FLAN (Wei et al., 2021b), we append classification choices at the end of prompts and ask models to generate answers. Thus, classification tasks can be treated as a special case of generation tasks. Accordingly, we use ROUGE-L (Lin, 2004) to measure the performance of both classification and generation tasks and report the aggregated score. Similar to Chung et al. (2022), we also use *exact-match* score which is more suitable for tasks with short answers. Additionally, we compute *relaxed-match* score which is a relaxed version of exact-match. Specifically, we normalize ground truth answers and predictions to have all text in lower case and remove punctuation and extra white spaces. ## 4 Analysis 4.1 Does Finetuning Help? Figure 3 demonstrates the performance averaged across all evaluation tasks in our benchmark. Rationale-based finetuning (OPT-CoT) has been shown to improve the performance of the 1.3B model by 3.89% in terms of the aggregated max ROUGE-L score and 3.83% in terms of the aggregated max exact-match score. As for 13B model, OPT-CoT gains the improvement by 15.22% in regard of aggregated max ROUGE-L score, 12.64% in regard of aggregated max exact-match score. However, finetuning (OPT-FT) sometimes yields worse results than the vanilla pre-trained model. ## 4.2 **What Does Llms Learn During Finetuning?** We find that CoT-finetuning improves performance on reasoning tasks in general. However, what exactly does the LLMs learn during the finetuning stage is still under explored. Thus, we study the role of finetuning from three perspectives: data memorization, reasoning skill transfer, and prompt template memorization. ## 4.2.1 Data Memorization Gururangan et al. (2020) finds that the performance gain is larger when the finetuning dataset is more dissimilar to the pre-training dataset. However, their conclusion is made by a single-task finetuning. They evaluate their model on the same dataset that was used for finetuning. A more thorough evaluation dictates that finetuned models (Wei et al., 2021b; Chung et al., 2022) be evaluated on heldout datasets. As such, in Figure 2 in blocks (B) and (C) we show two potential ways of finetuning and inference as illustrated here in our paper. To confirm that the improvement in finetuning performance is due to the increased amount of data seen during the finetuning stage, we measure the dissimilarity between the training data used in finetuning and evaluation, respectively. If higher similarity leads to better performance, it may indicate that the improvements of finetuned LLMs are due to seeing more similar data during the finetuning stage. Following (Gururangan et al., 2020), we use unigram vocabulary overlap to measure the data similarity. More specifically, we divide our tasks into three categories: The first category has 10 datasets which consists of up to 10% overlap between the finetuning data and evaluation data. The second category comprises 3 datasets with an overlap between 10% and 30%. The third category has 7 datasets with an overlap over 30%. Details can be found in Table 7 in appendix A.5. We measure the performance improvements of OPT-FT and OPT-CoT compared against the pretrained OPT model. We present both ROUGEL score (top) and relaxed-match score (down) in Figure 5. The results indicate that there is no strong correlation between the vocabulary overlap between fineuning and evaluation datasets and the performance of the model (neither a higher nor a lower vocabulary overlap always translate to a performance improvement). OPT-CoT achieves the best ROUGE-L and relaxed-match scores both in settings when there is a medium (10%-30%) level of vocabulary overlap. We don't observe a consistent pattern on OPT-FT models either. Overall, for these challenging tasks, seeing similar data during finetuning stage does not guarantee performance improvement. ## 4.2.2 Reasoning Skill Transfer Table 6 illustrates the reasoning skills present in each stage. 7 skills can be learned from pretraining data. Appendix. A.4 shows more details about pretraining data. 6 skills can be learned from finetuning data (Table 5). Using ALERT we measure a total of 10 reasoning skills in model evaluation. The average ROUGE-L scores are calculated for each reasoning skill on 6 models (1.3B OPT, 1.3B OPT-FT, 1.3B OPT-CoT, 13B OPT, 13B OPTFT, 13B OPT-CoT). Figure 7 shows the difference between OPT-FT and OPT, and the difference between OPT-CoT and OPT models' performance. For example, OPT-FT 1.3B model yields on average 3.5 less ROUGE-L points than OPT 1.3B model on the tasks of logical reasoning. Figure 7 contains 4 sub-figures, showing reasoning skills transfer results: (i) The upper left subfigure shows 7 skills that are acquired during the ![5_image_1.png](5_image_1.png) ![5_image_0.png](5_image_0.png) ![5_image_2.png](5_image_2.png) pretraining stage (OPT pretraining data), and how much improvement can be obtained through metafinetuning (OPT-FT and OPT-CoT); (ii) The bottom left sub-figure illustrates that these 3 skills are harder to acquire during the pre-training stage, and the amount of improvement that can be obtained through meta-finetuning; (iii) The upper right subfigure illustrates that such 7 skills are acquired during the meta-finetuning stage through finetuning datasets (Table 5). Do these skills show improvement measured by evaluation benchmark? (iv) The bottom right sub-figure studies the reasoning skills that were not learned in the finetuning stage, can these skills be improved through meta-finetuning? We study the answers to these questions below. From figure (ii) We observe that all four of the LLMs demonstrate enhanced reasoning capabilities on textual entailment, abductive reasoning, and analogical reasoning tasks. These abilities are not readily acquired during the pretraining stage, as the pretraining data consists only of plain text. On the other hand, skills such as commonsense reasoning or spatial reasoning can be gained during the pretraining stage, while the benefits of further finetuning are not as pronounced. Additionally, Gururangan et al. (2020) concluded that the more dissimilar the domain between pretraining and finetuning are, the higher the potential for finetuning to yield gains. We see the same trend but the domain in Gururangan et al. (2020) is defined by the vocabulary overlaps, while we define the domains by reasoning skills. From figure (iii) we can see that the reasoning skills gained during the metafinetuning stage may not necessarily transfer to the improvement of the same skills on the evaluation datasets. We also observe that finetuning with OPT-CoT enables the model to acquire a wider range of reasoning skills, resulting in stronger performance on logical and causal reasoning tasks, in addition to skills that consistently improve across all finetuned models. ## 4.2.3 Data Format Memorization We investigate whether finetuning can simply memorize the template representation of the training data, and the effect of data format on the robustness of the models. Evaluation with relaxed-match score. We compare two metrics: exact-match and relaxed-match. From Figure 3, we observe that OPT-FT is worse than OPT when exact-match is used as the metric. However, when relaxed-match is used, OPT-FT outperforms OPT as shown in Figure 8. Relaxedmatch score ignores punctuation, articles and extra whitespace. This suggests that if we decouple performance from format adherence, OPT-FT performs better than OPT. In other words, finetuning is helpful but it can make the output more noisy. This explains the reason for the performance drop when exact-match is used as the metric. ![6_image_0.png](6_image_0.png) Meta- Held-out Skills from Pretraining Data ** Finetuning data in Sec. 3.2* ![6_image_1.png](6_image_1.png) Skills in Meta-finetuning Data Skills in Meta-finetuning Data Held-out Skills from Pretraining Data *** Finetuning data in Sec. 3.2** ![6_image_2.png](6_image_2.png) Template following percentage. We check whether the model can follow the template of the demonstrations. For example, if a demonstration uses "the answer is xxx because yyy", then we check what percentage of instances can follow the exact same template as the demonstration. Figure 4 (left) shows the average template following percentage for each model. Both OPT and OPT-CoT consistently show that they can follow demonstrations' even though OPT is not pre-trained on rationales. Compared to 1.3B models, larger models demonstrate a greater overall ability to follow the template of the demonstrations. Compared to OPT and OPTCoT, OPT-FT lacks the ability to follow diverse templates. This is because the OPT-FT training process does not contain any rationale data. Finetuning causes the model to become more biased towards a particular template representation, while its ability to adapt to other templates becomes impaired. It is worth noting that despite being trained on rationales, the OPT-CoT model performs well when evaluated using non-CoT templates. Robustness To assess the robustness of each model to various templates, we compute the standard deviation of ROUGE-L scores for each model across five different templates. As we can see from Figure 4 (right), OPT is robust to different templates, while OPT-FT has difficulties adapting to changing templates. In general, finetuning (both OPT-FT and OPT-CoT) adversely affects the robustness of the model and makes the model biased towards a specific data format, however, OPT-CoT is better than general finetuning (OPT-FT). Reasoning chain quality. Following (Golovneva et al., 2022) we evaluate reasoning abilities of the models using ROSCOE scoring suite (Table 3). Looking at each score in detail (Appendix C), we found that overall across templates OPT-FT models produce shorter, less informative chains, while OPT baseline models produce long chains with high amount of self-repetitions. 13B OPT-CoT chains showed best quality despite some self-consistency and grammar issues. When comparing prompt templates, models prompted with Template 5 produce short chains, often without reasoning at all, even if they were fine-tuned on reasoning chains (OPTCoT), suggesting overfitting to the prompt template. In summary, models learn the data format representation and templates during finetuning stage. However, finetuned models contain bias towards the data formats and template it has seen, which potentially reduces the robustness of the model to more generalized settings. When comparing robustness, OPT-CoT is better than OPT-FT, but it is still not as robust as the pre-trained model. 1.3B 13B Metrics OPT OPT-FT OPT-CoT OPT OPT-FT OPT-CoT ![7_image_0.png](7_image_0.png) ## 5 Related Work LLMs that Reason. To improve LLMs' reasoning abilities, Kojima et al. (2022) shows that LLMs can be decent zero-shot reasoners by simply appending "Let's think step by step" to the prompt. Wei et al. (2022) adds a series of intermediate reasoning steps to improve LLMs' reasoning abilities. Wang et al. (2022a) further proposes to expand prompts to include rationales in each few-shot example. Fu et al. (2022) discovers that prompting with higher reasoning complexity achieves substantial gains on math word tasks. To tackle problems harder than demonstration examples, Zhou et al. (2022) first reduces a complex problem into a list of subproblems and solve subproblems sequentially. Another line of research is to improve the naive decoding strategy, Wang et al. (2022b) introduces a self-consistency strategy which selects the most consistent answer among a set of reasoning paths. Existing Reasoning Benchmarks. Many benchmarks are used for evaluating language models' performance, such as BIG-Bench (Srivastava et al., 2022), Natural Instruction V2 (NIV2) (Wang et al., 2022c), MMLU (Hendrycks et al., 2020). Although they contain some reasoning tasks, none of them are specifically designed to test models' reasoning skills. For example, NIV2 contains 172 datasets and a total of 1554 tasks, including some reasoning tasks. It has several issues which make it inappropriate to be directly used as a reasoning benchmark: (1) it is designed for instruction-tuned models and some tasks might be unsuitable for evaluating pretrained models or non-instruction finetuned models, as shown in Figure 1; (2) reasoning skills have been divided into 27 categories while some of them have large overlaps, e.g. numerical reasoning, quantitative reasoning, reasoning on numbers; (3) some reasoning labels are wrongly labeled, e.g. task393_plausible_result_generation gives textual entailment label but this task can hardly examine the entailment skill. The Curriculum benchmark (Chen and Gao, 2022) is designed for probing LLMs' reasoning abilities and covers 8 different reasoning skills. However, this work only focuses on classification tasks and it converts all examples into the Natural Language Inference (NLI) format to fit into a unified framework. We argue that the forced conversion of all datasets into the NLI format does not align with human natural conversational style. We observed that even davinci-003 fails at some simple tasks due to their forced conversion, e.g. examples in Table 1. More discussion and results are shown in the Appendix B. Finetuning LLMs. LLMs meta-finetuned on a range of NLP tasks have shown improved performance on held-out downstream tasks such as FLAN (Wei et al., 2021b), T0 (Sanh et al., 2021), Tk-Instruct (Wang et al., 2022c) and Instruct-GPT (Ouyang et al., 2022). Following this approach, we finetune OPT models and name this type of models as OPT-FT ((B) in Figure 2). Chung et al. (2022) further adds chain-of-thought data at finetuning stage and shows significant improvements. We also study this type of models and name them as OPT-CoT ((C) in Figure 2). However, from previous research it still remains unclear whether the improvement comes from simply adding more training data or finetuning on rationales actually helps. We conduct rigorous evaluations to address this question. ## 6 Conclusion We introduce ALERT, a carefully curated benchmark for evaluating reasoning abilities of LLMs. It comprises over 20 datasets and covers 10 different reasoning skills. Using this benchmark, we further investigate the impact of finetuning on these complex tasks. Our experiments reveal that LLMs do not simply memorize training data, but are capable of learning various reasoning skills, such as textual entailment, abductive reasoning and analogical reasoning. While we found that finetuning generally leads to improved performance, we also discovered some negative effects. LLMs tend to memorize the data template representation and templates seen during finetuning, thus reducing the robustness of the model to generalized settings. CoT-finetuning (OPT-CoT) can alleviate this issue to some extent, but it is still less robust compared to the vanilla pre-trained model. ## Limitations ALERT aims to encompass a wide range of reasoning skills, but some reasoning skills are missing, specifically in regards to symbolic reasoning (last letter concatenation task and coin flip (Wei et al., 2022)) and compositionality reasoning (SCAN (Lake and Baroni, 2018), COGS (Kim and Linzen, 2020) and CFQ (Keysers et al., 2019)). These reasoning skills should be included in future work. In terms of computing power, we have experimented with models that were accessible to us. We acknowledge that there are larger models that we were not able to train due to the limitations of our computational budget. During our analysis, we discovered that some datasets contain noise, where even human experts are unable to provide accurate answers for certain instances. While it is important to address this issue, it is a time-consuming process to carefully review and clean each instance in the dataset. We plan to address this in future work. ## Ethics Statement Large language models (LLMs), due to potential bias in the training data, can be prone to generate toxic and unwanted content (Weidinger et al., 2021). However, in this paper, we are focused on reasoning tasks where the model is prompted to explain its decisions, because of which our model falls under contained generation. 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From §**3.2)** We used 10 datasets for finetuning, which contain 6 different reasoning skills. ## A.3 Development Data Details Our finetuning models are tuned on pretrained LLMs on the finetuning corpus with the goal of improving the performance of unseen tasks. For example, blocks (B) and (C) in Figure 2 are showing models that are finetuned on tasks B,C,D and the goal is to achieve good results on task A. Checkpoint selection can determine the final performance of the LLMs to a very large extent. There are several ways to select checkpoints: (i) select checkpoint of the last iteration; (ii) select checkpoint based on perplexity or loss from validation datasets of finetuning corpus (validation datasets of task B, C, D); (iii) select checkpoint based on perplexity or loss from validation datasets of evaluation corpus (validation datasets of task A); In order to achieve a better performance on evaluation corpus, a common approach is to use methods like (iii) to select a checkpoint. However, we would like to prevent LLMs overfiting to the distribution of our final evaluation corpus. We initially used the method (ii) but found that it did't work well. However, this resulted in a distribution mismatch issue. We speculate this to the fact that some tasks in our finetuning corpus do not have a validation set. We thus select 3 tasks from NIV2 benchmark and compile a development set that does not have any overlaps with our finetuning data or evaluation data. There are 3 datasets used as our development set for checkpoint selection: task 247 dream answer generation (Sun et al., 2019), task 118 semeval and task 10 open vocabulary mathematical answer generation (Hopkins et al., 2019) and anli r1 entailment (Williams et al., 2022) ## A.4 Pretraining Data Analysis The pre-training corpus of OPT model (Zhang et al., 2022) contains a concatenation of datasets used in RoBERTa (Liu et al., 2019), the Pile (Gao et al., 2020), and PushShift.io Reddit (Baumgartner et al., 2020; Roller et al., 2020). RoBERTa Three datasets in RoBERTa (Liu et al., 2019) are used as pretraining corpus: BookCorpus (Zhu et al., 2015), Stories (Trinh and Le, 2018), and CCNews (Liu et al., 2019). Deductive reasoning skill and spatial reasoning skill can be learned from stories dataset. Logical reasoning skill can be learned from these three datasets. Pile A subset of the Pile (Gao et al., 2020) are used as pre-training corpus, including CommonCrawl, DM Mathematics, Project Gutenberg, HackerNews, OpenSubtitles, OpenWebText2, USPTO, and Wikipedia. Mathematics reasoning skill can be learned from DM Mathematics dataset. Causal Reasoning can be learned widely from OpenWebText2. Commensense reasoning skill can be learned from Wikipedia. PushShift.io Reddit The longest chain of comments in each thread are extracted from PushShift.io Reddit (Baumgartner et al., 2020). Argument reasoning skill can be learned from this dataset. ## A.5 Vocabulary Overlaps (Cont. From § **4.2.1)** We measure unigram vocabulary overlaps between our finetuning corpus and the evaluation corpus (reasoning benchmark). ## B Curriculum Benchmark Results (Cont. from §5) We randomly selected one dataset from each reasoning skill and reported the results of GPT-3 (Brown et al., 2020b) (text-davinci engine). Since all of the data has been converted to NLI format, we measure classification accuracy of GPT-3 model. From Table 8, we can see that even GPT-3 achieves a pretty random results on these datasets. Through our analysis, we found that it is not because those tasks are too difficult for GPT-3, it is because curriculum benchmark forcing all the data to be NLI format, resulting in unnatural data expression, which made GPT-3 fail on it. We conclude that the curriculum benchmark may be suitable for classification finetuned models, but it is not suitable for language models for in-context learning. ## C Evaluating Reasoning Chains (Cont. from §5) Following (Golovneva et al., 2022) we evaluate reasoning abilities of the models using ROSCOE scoring suite (Table 10). Chains are evaluated | Reasoning Skills | Task ID | Datasets | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | bigbench repeat copy logic (Srivastava et al., 2022) | | | | Logical | 62 | | | Reasoning | 697 | mmmlu answer generation formal logic (Hendrycks et al., 2021a) | | 393 1386 1387 1388 | plausible result generation (Weir et al., 2020) anli r2 entailment (Williams et al., 2022) anli r3 entailment (Williams et al., 2022) cb entailment (Wang et al., 2019) | | | Causal | | | | Reasoning | 80 102 591 1286 | piqa answer generation (Bisk et al., 2020) | | commongen sentence generation (Lin et al., 2020) sciq answer generation (Welbl et al., 2017) | | | | openbookqa question answering (Mihaylov et al., 2018) | | | | Commonsense Reasoning | anli r2 entailment (Williams et al., 2022) anli r3 entailment (Williams et al., 2022) cb entailment (Wang et al., 2019) | | | glue entailment classification (Wang et al., 2018) | | | | 1386 1387 1388 1344 | | | | Texual | | | | Entailment | 104 | | | Mathematics | 119 697 | semeval closed vocabulary math answer generation (Hopkins et al., 2019) semeval geometric math answer generation (Hopkins et al., 2019) mmmlu answer generation formal logic (Hendrycks et al., 2021a) | | Abductive Reasoning | 332 | tellmewhy answer generation (Lal et al., 2021) | | babi t1 single supporting fact answer generation (Weston et al., 2015) piqa answer generation (Bisk et al., 2020) tomqa find location easy clean (Nematzadeh et al., 2018) | | | | Analogical | 102 | commongen sentence generation (Lin et al., 2020) | | Reasoning | 1152 | bard analogical reasoning causation (Fulda et al., 2017) argument stance classification (Kobbe et al., 2020) | | Argument | 513 | | | Reasoning | 514 | argument consequence classification (Kobbe et al., 2020) | | Deductive Reasoning | 216 | rocstories correct answer generation (Mostafazadeh et al., 2016) | | 83 | | | | Spatial | 80 | | | Reasoning | 151 | Table 4: Details about ALERT benchmark. | | Datasets | Train Size | Val Size | Test Size | Reasoning Skills | |-------------|--------------|------------|-------------|--------------------------------------------------------------| | ProofWriter | 69,810 | 10,190 | 20,030 | Logical Reasoning, Causal Reasoning | | StrategyQA | 2,290 | - | 490 | Commonsense Reasoning | | ECQA | 7,598 | 1,090 | 2,194 | Commonsense Reasoning | | CoQA | 10,8647 | 7,983 | - | Textual Entailment | | GSM8K | 7,473 | - | 1,319 | Mathematics | | AQUA-RAT | 97,467 | 254 | 254 | Mathematics | | ESNLI | 549,367 | 9,842 | 9,824 | Commonsense Reasoning, Logical Reasoning, Textual Entailment | | MATH | 7,500 | - | 5,000 | Mathematics | | CoS-E | 9,741 | 1,221 | - | Commonsense Reasoning | | WinoWhy | 273 | - | - | Abductive Reasoning, Commonsense Reasoning | Table 5: Training corpus for meta-finetuning OPT-FT and OPT-CoT. (Cont. from § 3.2) | Task ID | Datasets | Reasoning Skills | |------------------------------------------|--------------------------------------------|----------------------------------------| | 247 | dream answer generation (Sun et al., 2019) | Logical Reasoning | | Commonsense Reasoning | | | | 118 | semeval open vocabulary mathematical | Commonsense Reasoning | | answer generation (Hopkins et al., 2019) | Mathematics | | | Textual Entailment | | | | 1385 | anli r1 entailment (Williams et al., 2022) | Commonsense Reasoning Causal Reasoning | Table 6: Dev set for checkpoint selection | Category | Datasets | Vocabulary Overlaps | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------|-----------------------| | bigbench repeat copy logic (Srivastava et al., 2022) | | | | babi t1 single supporting fact answer generation (Weston et al., 2015) | | | | semeval closed vocabulary math answer generation (Hopkins et al., 2019) semeval geometric math answer generation (Hopkins et al., 2019) tomqa find location easy clean (Nematzadeh et al., 2018) plausible result generation (Weir et al., 2020) argument stance classification (Kobbe et al., 2020) argument consequence classification (Kobbe et al., 2020) mmmlu answer generation formal logic (Hendrycks et al., 2021a) bard analogical reasoning causation (Fulda et al., 2017) | 1.59% 0.38% 7.90% 5.84% 0.94% 3.72% 6.04% 6.11% 5.35% 0.45% | | | 0% to 10% | commongen sentence generation (Lin et al., 2020) | | | 10% to 30% | tellmewhy answer generation (Lal et al., 2021) cb entailment (Wang et al., 2019) | 29.31% 28.05% 20.97% | | piqa answer generation (Bisk et al., 2020) | | | | rocstories correct answer generation (Mostafazadeh et al., 2016) sciq answer generation (Welbl et al., 2017) openbookqa question answering (Mihaylov et al., 2018) glue entailment classification (Wang et al., 2018) anli r2 entailment (Williams et al., 2022) anli r3 entailment (Williams et al., 2022) | 42.51% 57.45% 32.54% 48.2% 55.19% 43.37% 53.13% | | | over 30% | | | | Datasets | Random score | GPT-3 Davinci score | |-----------------|----------------|-----------------------| | Boolean | 34.50% | 31.80% | | Physical | 49.08% | 50.00% | | Entailment Tree | 50.88% | 54.41% | | Event Semantic | 54.08% | 55.89% | | Spatial | 49.80% | 49.30% | using *facebook/roscoe-512-roberta-base* sentence embedding model. Evaluation results are detailed in Table 10. We found that the chain quality varies between models, in particular some reasoning aspects correlate with chain length as seen in Table 11. Similar to (Chung et al., 2022), we noticed that nonfinetuned models (i.e. OPT-1.3B and OPT-13B) tend to produce long chains of reasoning, often repeating themselves, which significantly affects the quality of the chains and final scores (Figure 9). Below we explore the differences between models' outputs under four perspectives: semantic alignment, semantic similarity, logical inference and language coherence. ## C.1 Semantic Alignment Despite the fact that model 13B OPT-CoT on average outperforms other models in almost all semantic alignment scores (*Faithfulness-Step*, Faithfulness-Token, and *Info-Step*, see Table 10), The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have? The answer is 29 apples. The cafeteria started with 23 apples and bought 6 more, for a total of 23 + 6 = «23+6=29»29 apples . Select the best translation into predicate logic. David teaches Chris. (c: Chris; d: David; Txy: x teaches y) (A)Tdc;(B)Tcd;(C)Tcc;(D)dTc. The answer is (B) Tcd. In predicate logic, the predicate (in this case, "T") is written before the subject and object of the sentence. Since David (d) is the teacher and Chris (c) is the student, the correct translation is Tcd, which reads as "David teaches Chris." Isabella entered the hall. Olivia entered the hall. The apple is in the blue_treasure_chest. Olivia exited the hall. Isabella moved the apple to the green_basket. Question: Where does Isabella think that Olivia searches for the apple? The answer is It is impossible for me to answer this question as I have no information about the thoughts or actions of Isabella or Olivia. Additionally, I do not have access to browse any information beyond my training data, which ends in 2021 . ![15_image_0.png](15_image_0.png) there is no common pattern across tasks (Fig 10). The performance change between finetuned models and corresponding pretrained version are significant4 on half of the tasks (11 tasks out of 20 for *Faithfulness-** scores, and 9 out of 20 for *InfoStep*). Repetition-Token score variations exhibit different behavior. Half of the tasks have higher number of repetitions between reasoning steps for pretrained models, with OPT-FT models generally outperforming others (all performance improvements are significant). Generations produced by these models tend to be shorter in terms of the number of steps (Figure 9), so they contain less repetitions, but also less semantic overlap with the context, thus in general having lower faithfulness and informativeness. Some examples reflecting this behavior are provided in Table 12. Scores are mostly aligned across Templates (Figure 11), except Template 5, that stands out in having less aligned scores with respect to the context, but also more self-consistent across the task. This is the only template that did not have any explanation in its prompt. Manual review showed that despite CoT-finetuning, OPT-COT models tend to produce 1-step answer-only generations (see example in the Table 12, and Figure 9 for chains' length distribution), thus overfitting to the template rather than learning from finetuning. In summary, ROSCOE-SA is able to identify aligned information, but it does not guarantee highquality output. It will favor model with short explanations and high semantic overlap with the reference. We found that often OPT-FT-1.3B simply repeats one sentence from the input, instead of producing reasoning, and thus will get highest ROSCOE-SA scores on these chains, while other models that produce some sort of reasoning will be punished. ## C.2 Semantic Similarity Semantic similarity scores support previous conclusions: models, finetuned on final answers (OPT-FT) exhibit lower similarity with respect to the baseline and CoT-finetuned models, while having less repetitions (Figure 12). Again, we attribute that to the fact that these models produce short chains that lack detailed reasoning steps. | OPT 1.3B | OPT-FT 1.3B | OPT-CoT 1.3B | OPT 13B | OPT-FT 13B | OPT-CoT 13B | | |----------------------------------------------------------------------------------------------------------------------------|---------------|----------------|-----------|--------------|---------------|-------| | ROSCOE-SA Faithfulness-Step | 0.863 | 0.841 | 0.862 | 0.863 | 0.858 | 0.870 | | Faithfulness-Token | 0.936 | 0.921 | 0.938 | 0.936 | 0.923 | 0.940 | | Info-Step | 0.857 | 0.829 | 0.854 | 0.858 | 0.846 | 0.861 | | Repetition-Token | 0.618 | 0.920 | 0.683 | 0.582 | 0.857 | 0.701 | | ROSCOE-SS Info-Chain | 0.925 | 0.909 | 0.920 | 0.926 | 0.916 | 0.925 | | Repetition-Step | 0.627 | 0.923 | 0.692 | 0.591 | 0.859 | 0.708 | | ROSCOE-LI Source Consistency | 0.550 | 0.604 | 0.573 | 0.584 | 0.617 | 0.598 | | Self-Consistency | 0.848 | 0.953 | 0.875 | 0.863 | 0.944 | 0.890 | | ROSCOE-LS Perplexity-Step | 0.016 | 0.006 | 0.015 | 0.010 | 0.006 | 0.009 | | Perplexity-Chain | 0.022 | 0.006 | 0.020 | 0.016 | 0.006 | 0.013 | | Grammar | 0.725 | 0.744 | 0.666 | 0.688 | 0.705 | 0.640 | | Table 10: ROSCOE evaluation results averaged across templates. Each metric is bounded within [0, 1], where 1 indicates the | | | | | | | Table 10: ROSCOE evaluation results averaged across templates. Each metric is bounded within [0, 1], where 1 indicates the perfect score and 0 corresponds to failure. Values corresponding to the best performing model are **bolded**, second best are underscored. | Kendall's τ score | Kendall's τ p-value | | |---------------------|-----------------------|-------| | Faithfulness-Step | -0.101 | 0.000 | | Faithfulness-Token | 0.039 | 0.000 | | Info-Step | 0.054 | 0.000 | | Repetition-Token | -0.869 | 0.000 | | Info-Chain | 0.009 | 0.000 | | Repetition-Step | -0.867 | 0.000 | | Source Consistency | -0.119 | 0.000 | | Self-Consistency | -0.553 | 0.000 | | Perplexity-Step | 0.000 | 0.960 | | Perplexity-Chain | 0.369 | 0.000 | | Grammar | 0.013 | 0.000 | Table 11: Kendall correlation between evaluation perspective and number of steps in chain across all generated reasoning chains. Strong correlations (|τ | > 0.4) are **bolded**. ## C.3 Logical Inference In general, finetuned models are more self- and source-consistent than respective baselines (Figure 13, significantly outperforming nonfinetuned models on 14 out of 20 tasks. We further looked into the task 083, which is a task to find a right answer given s given single supporting fact, potentially amongst a set of other irrelevant facts. Manual review showed that although in this task finetuned models tend to produce answers that are more consistent, they often fail to select the fact that is relevant to the question asked (see "Spatial Reasoning" example in Table 12. ## C.4 Language Coherence Despite the variations in the values, *Perplexity-** score changes between models are mostly insignificant (15 out of 20 tasks, see Figure 14). Manual review showed that all models produce mostly grammatically correct content. | D | Licenses | |-------------------------------------------------------------------------------------------------------------------------------|---------------| | D.1 | Data in ALERT | | - task62: Apache 2.0 - task697: MIT - task393: MIT - task1386: CC BY-NC 4.0 - task1387: CC BY-NC 4.0 - task1388: CC BY-SA 3.0 | | - task080: AFL 3.0 - task102: MIT - task591: CC BY-NC-3.0 ## - Task1286: Apache 2.0 - task1344: CC BY 4.0 - task104: Please refer to: https://github.c om/allenai/semeval-2019-task-10\#te rms-and-conditions - task119: Please refer to: https://github.c om/allenai/semeval-2019-task-10\#te rms-and-conditions - task332: Please refer to: https://github.c om/StonyBrookNLP/tellmewhy - task083: CC BY 3.0 - task151: Please refer to: https://github.c om/kayburns/tom-qa-dataset - task1152: Apache 2.0 - task513: Please refer to: https://github.c om/dwslab/StArCon - task514: Please refer to: https://github.c om/dwslab/StArCon - task216: Please refer to: https://www.micr osoft.com/en-us/research/publicati on/a-corpus-and-cloze-evaluation-f or-deeper-understanding-of-commons ense-stories/ ## D.2 Data In Dev Set - task247: Dream dataset is intended for noncommercial research purpose only. https: //github.com/nlpdata/dream. - task118: Please refer to: https://github.c om/allenai/semeval-2019-task-10\#te rms-and-conditions - task 1385: CC BY-NC 4.0 ## D.3 Data In Training Set - ProofWriter: CC BY. Downloaded from http s://aristo-data-public.s3.amazonaws .com/proofwriter/proofwriter-datas et-V2020.12.3.zip - StrategyQA: MIT. Downloaded from https: //storage.googleapis.com/ai2i/strate gyqa/data/strategyqa_dataset.zip. - ECQA: Literature and Wikipedia passages are shared under CC BY-SA 4.0 license. Middle/High school exam passages are collected from RACE which comes with its own license. - GSM8K: MIT. Downloaded from https:// raw.githubusercontent.com/openai/gra de-school-math/master/grade_school_ math/data/train.jsonl. - AQUA-RAT: Apache License, Version 2.0. Downloaded from: https://raw.github usercontent.com/deepmind/AQuA/master /train.json - ESNLI: please refer to https://github.c om/OanaMariaCamburu/e-SNLI/commit/b ab0fa0212be9e5c6737da70c639a596f882e 931. Downloaded from: https://raw.gith ubusercontent.com/OanaMariaCamburu/e -SNLI/master/dataset/esnli_train_1.c sv - MATH: MIT. Downloaded from: https:// people.eecs.berkeley.edu/~hendrycks /MATH.tar - CoS-E: BSD-3-Clause license. Downloaded from: https://raw.githubusercontent. com/salesforce/cos-e/master/data/v1. 11/cose_train_v1.11_processed.jsonl - WinoWhy: MIT. Downloaded from: https: //raw.githubusercontent.com/HKUST-K nowComp/WinoWhy/master/winowhy.json ## E More Details About Model Training We finetune our 1.3B models on 32 V100s with batch size 8 on each GPU with totally 38 hours and 21 minutes. We finetune our 13B models on 128 V100s with batch size 4 on each GPU with totally 13 hours and 26 minutes. Following OPT-IML (Iyer et al., 2022), we use Fully Sharded Data Parallel (Artetxe et al., 2021) and the Megatron-LM Tensor Parallelism (Shoeybi et al., 2019). We inherit most model hyper-parameters for each model scale following OPT-IML. We pack our training examples into sequences of length 2048, left-truncating examples that overflow. We use Adam (Kingma and Ba, 2014) with 32-bit state with (β1, β2) = (0.9, 0.95), linearly warming up the learning rate for 60 steps to the maximum, followed by linearly decaying it to 0. ![18_image_0.png](18_image_0.png) ![19_image_0.png](19_image_0.png) ![20_image_0.png](20_image_0.png) ![20_image_1.png](20_image_1.png) ![21_image_0.png](21_image_0.png) REASONING SKILL: Logistic Reasoning, Mathematics PROMPT: Please give a short explanation after the answer. Input: Identify the conclusion of the following argument. It is hard not to verify in our peers the same weakened intelligence due to emotions that we observe in our everyday patients. The arrogance of our consciousness, which in general, belongs to the strongest defense mechanisms, blocks the unconscious complexes. Because of this, it is difficult to convince people of the unconscious, and in turn to teach them what their conscious knowledge contradicts. (Sigmund Freud, The Origin and Development of Psychoanalysis) (A)It is hard not to verify in our peers the same weakened intelligence due to emotions that we observe in our everyday patients. (B)The arrogance of our consciousness, which in general, belongs to the strongest defense mechanisms, blocks the unconscious complexes. (C)Because of this, it is difficult to convince people of the unconscious, and in turn to teach them what their conscious knowledge contradicts. (D)It is difficult to convince people of the unconscious, and in turn to teach them what their conscious knowledge contradicts. Let's think step by step. Output: The answer is D because Because of the reasons specified in the argument, it is difficult to convince people of the unconscious, and in turn to teach them what their conscious knowledge contradicts. Input: Select the best translation into predicate logic: No people drive on Mars. (Dx: x drives on Mars) (A) ∼P d (B) (∀x)(P x ∨ ∼Dx) (C) (∀x)(P x ⊃ ∼Dx) (D) ∼Dp Let's think step by step. Output: The answer is C because Answer C means for everything, if that thing is a person, it doesn't drive on Mars. So that's the correct translation. Input: Select the best translation into predicate logic. David teaches Chris. (c: Chris; d: David; Txy: x teaches y) (A)Tdc (B)Tcd (C)Tcc (D)dTc Let's think step by step. Output: The answer is A because David teaches Chris, so in the relation Txy x should be 'd' for davis and y should be 'c' for Chris. Input: Which of the given formulas of PL is the best symbolization of the following sentence? Either England's importing beef is not a necessary condition for France's subsidizing agriculture, or China promotes human rights if South Africa supplies diamonds. (A) (∼F ⊃ E) ∨ (C ≡ ∼S) (B) (∼E ⊃ F) ∨ (C ⊃ S) (C) (∼F ⊃ E) ∨ (C ≡ S) (D) ∼(F ⊃ E) ∨ (S ⊃ C) Let's think step by step. Output: The answer is D because When not defined in the question, entities, relationships or statements are shown with their first letter. In this example 'France subsidizing agriculture' is shown with F for simplicity. The correct answer is D, because it uses the 'or' operator between the two 'if' clauses. Input: Select the best translation into predicate logic. Kevin is introduced to José by Wilma. (j: José; k: Kevin; w: Wilma; Ixyz: x introduces y to z) (A)Iwjk (B)Ijkw (C)Ikjw (D)Iwkj Let's think step by step. Output: REFERENCE: D OPT 1.3B: The answer is A because all statements need to be presented with an argument. OPT-FT 1.3B: The answer is B OPT-CoT 1.3B: The answer is A because Using the 'or' operator between the statements, we have : Kevin is introduced to José, because of its 'e' factor in the equation. Wilma is introduced to Kevin because of its 'j' factor in the equation. José is introduced to Wilma because of its 'y' factor in the equation. In both cases, Wilma accepts 'x' and 'y'. OPT 13B: The answer is A because introducing a person to another person is the action of introducing. It is possible that Kevin has been introduced to Wilma but not Jose. OPT-FT 13B: The answer is A because when w is introduced, it says 'Wilma introduced Kevin to José' OPT-CoT 13B: The answer is D because the correct term is 'introduces', the relation is 'j: kevin and w: wilma to k: x', it is an 'and' relation. Using the operator 'and' allows us to choose the greatest of the 5 possibilities. REASONING SKILL: Commonsense Reasoning PROMPT: Please give a short explanation after the answer. Input: Deserts are in extremely dry environments, so liquids will naturally be (A) rainbow (B) plentiful (C) meager (D) talented Let's think step by step. Output: The answer is C because This is a good example and the output correctly answers the question. Input: Dairy has things in it that would make humans sick, so in order for dairy to be edible, this process has to take place (A) mixing (B) boiling (C) freezing (D) pasteurized Let's think step by step. Output: The answer is D because in order for dairy to be edible, this process has to take place pasteurized. So, the correct answer is "D". Input: Digestion is when stomach acid breaks down (A) a pizza (B) a house (C) a rock (D) a car Let's think step by step. Output: The answer is A because Digestion is when stomach acid breaks down a food and only option A is a food. So, this example is correct. Input: With a ruler I can measure (A) feelings (B) love (C) air (D) a phone Let's think step by step. Output: REFERENCE: D OPT 1.3B: A is a feeling, and all feelings come from feelings (A). Therefore, feelings are products of emotions (A). Therefore, emotions are created by emotions (A). Therefore, feelings and emotions are directly related (A). Therefore, emotions are created by emotions (A)... *[repeats 249 times]* OPT-FT 1.3B: A because feelings and love are measurements and air is a phone OPT-CoT 1.3B: The answer is D because With a ruler I can measure an object or range of objects and options are all to do with measuring. So, this example is correct. OPT 13B: The answer is B because With a ruler I can measure a length and only options B and D are in front of me. So, the answer is B. OPT-FT 13B: The answer is D because a phone is an object OPT-CoT 13B: The answer is A because Answer: Measure feelings With a ruler I can measure. REASONING SKILL: Casual Reasoning, Textual entailment PROMPT: Please give a short explanation after the answer. Input: Premise: If you can dream it, you can achieve it - unless you're a goose trying to play a very human game of rugby. In the video above, one bold bird took a chance when it ran onto a rugby field mid-play. Things got dicey when it got into a tussle with another player, but it shook it off and kept right on running. After the play ended, the players escorted the feisty goose off the pitch. It was a risky move, but the crowd chanting its name was well worth it. Video credit: Storyful / Nathan Swarbrick @swazza1990 <sep> Hypothesis: The crowd believed they knew the name of the goose running on the field. Let's think step by step. Output: The answer is Entailment because This is a good example, the hypothesis sentence entails the given premise. Because the crowd was chanting its name, the crowd must have believed they knew the goose's name. Input: Premise: Notley hopeful attacks on women politicians will fade in coming year After a politically nasty year that saw women politicians face a barrage of attacks on social media and angry demonstrators shouting to have them put behind bars, Premier Rachel Notley is optimistic there will be a return to civil discourse. In a year-end interview with CBC News, Notley reflected on 2016, a year that saw hateful social-media attacks against MLA Sandra Jansen, and chants of "Lock her up" directed at the premier at an anti-carbon tax rally in Edmonton. <sep> Hypothesis: Notley reflected on the chants of "lock her up" from previous to the year 2016. Let's think step by step. Output: The answer is Contradiction because The Premise states Notley reflected on the chants from 2016, not before 2016. In this example hypothesis sentence contradicts the given premise. Input: Premise: Mazda Sales (Thailand), a unit of Japanese carmaker Mazda Motor Corp, remains bullish about its sales prospects this year despite falling figures in the first six months, the Bangkok Post reported, citing its President Hidesuke Takesue. (bit.ly/1gr7HMd) —- NOTE: Reuters has not verified this story and does not vouch for its accuracy. (Bangkok Newsroom; Editing by Prateek Chatterjee) <sep> Hypothesis: Mazda Sales will be fired next week Let's think step by step. Output: The answer is Neutral because In this example the hypothesis can't be proven one way or the other based on the context. It's set in the future and no one knows the future. So, hypothesis sentence is neutral with respect to a given premise. Input: Premise: How to clear nasal congestion quickly<br>Gently blow your nose. Perhaps the easiest and quickest way to reduce nasal congestion is to simply blow your nose into a soft tissue. Unfortunately, it's not always possible to completely relieve congestion by merely blowing, but it's always a good starting point. <sep> Hypothesis: strike a pose to fix your nose Let's think step by step. Output: REFERENCE: Contradiction OPT 1.3B: The answer is Modified Contradiction because In this example the hypothesis sentence suggests the method is "Do not hit your head." In fact, that's the phrase a person who is rolling around on the ground, or who has had their head hit a hard surface while trying to get a good sleeper on the sheets should try. Why is this? A head-on collision will ... [continues to hallucinate for 23 steps] OPT-FT 1.3B: Answer: Blow your nose into a soft tissue. OPT-CoT 1.3B: The answer is Contradiction because The hypothesis states that Nastai will solve his and nasal congestion only by blowing into his nose, but that's not necessarily true, because he could have blown into his nose and caused irritation as well. OPT 13B: The answer is Yes because The answer is Yes. All the above information about clearing the nose is proven by the given premise, but if you still don't know the answer, then we suggest you go here and click on "How to clear your nose naturally" page. OPT-FT 13B: The answer: contradiction OPT-CoT 13B: The answer is contradiction because One cannot blow their nose if they have struck a pose. REASONING SKILL: Analogical Reasoning PROMPT: Please give a short explanation after the answer. Input: throw : fly. aspire : ? Let's think step by step. Output: The answer is attain because Fly is a causation of throw. Hence, the inferred analogy being causation, attain is the causation of aspire. Input: listen : hear. drop : ? Let's think step by step. Output: The answer is fall because Hear is a causation of listen. Hence, the inferred analogy being causation, the causation of drop is fall. Input: hit : shatter. nourish : ? Let's think step by step. Output: The answer is grow because Shatter is a causation of hit. Hence, the inferred analogy being causation, the causation of nourish is grow. Input: throw : fly. ignite : ? Let's think step by step. Output: REFERENCE: burn OPT 1.3B: The answer is move because fly is a causation of ignite. Hence, the inferred analogy being causation, the causation of move is fly. OPT-FT 1.3B: The answer is throw OPT-CoT 1.3B: The answer is ignite because Fire is a causation of throw. Hence, the inferred analogy being causation, the causation of ignite is fly. OPT 13B: The answer is evaporate because Fly is a causation of throw. Hence, the inferred analogy being causation, the causation of evaporate is ignite. OPT-FT 13B: The answer is fly because Ignite is a causation of throw OPT-CoT 13B: The answer is grow because Throw is a causation of grow. REASONING SKILL: Commonsense reasoning, Spatial Reasoning PROMPT: Please give a short explanation after the answer. Input: How do you remove keys from a computer keyboard for cleaning? Let's think step by step. Output: The answer is Place the tip of a knife under the corner of a key and gently twist to pop it off. because The process of removing keys from a keyboard for cleaning is descriptive and logical. Input: How can you dry wet replacement computer keyboard keys? Let's think step by step. Output: The answer is by arranging paper towels on the counter and evenly spacing out each key to dry, face side up, on top of them. because The steps involved in the process of drying wet replacement keyboard keys are logical. Input: To coat small plastic objects like replacement keyboard keys with soap Let's think step by step. Output: The answer is place them, water and liquid dishsoap in a sealed container. Shake vigorously until the container is completely full of foam. because The process of coating small plastic objects like replacement keys is descriptive and logical. Input: How do you cut gnocchi without a knife? Let's think step by step. Output: The answer is Press the edge of a spoon down into the dough to cut it. because The answer is quite detailed about the method to cut gnocchi when knife is not available. Input: How do you taste something? Let's think step by step. Output: The answer is Place it in your mouth to taste. because To taste something one has to keep it in contact with the tongue. Hence it is logical to put it in the mouth to taste. Input: How do you wear a bracelet? Let's think step by step. Output: The answer is Put it on around your wrist. because Bracelet is worn around wrists. Hence it is correct answer. Input: To find an easy way to complete levels on a video game, Let's think step by step. Output: REFERENCE: look up cheat codes to make the rules of the game different or null. OPT 1.3B: The answer is Enter key without pressing. because How to find the perfect way to complete levels on a video game is logical and descriptive. OPT-FT 1.3B: It tells you the easiest way to get all the coins and medals and the fastest way to get through the level. OPT-CoT 1.3B: The answer is play the game. because If you play a game to finish the levels then you can call it a game. OPT 13B: The answer is Complete the level as fast as possible to get a high score, by jumping through the stage as quickly as possible. because The answer is explicit and very clear that how a user can complete levels on a video game with just a hint. OPT-FT 13B: The answer is try harder or use a guide, because The process of finding an easy way to complete levels on a video game is descriptive and logical. OPT-CoT 13B: The answer is YouTube to find out how to get past a level. because YouTube is used to find out ways to complete a game easily. REASONING SKILL: Spatial Reasoning PROMPT: Instructions: You will be given a passage consisting of set of facts and a question as input. The task is to answer a question of form 'Where is <person_name>?' using one of the given facts to determine the latest location of the person. Answer should be a word/phrase describing the location from the supporting fact. Avoid answers that are incomplete or incorrect. Positive Examples: Input: Passage: Mary moved to the bathroom. John went to the hallway. Question: Where is Mary? Output: bathroom Input: Passage: Mary moved to the bathroom. John went to the hallway. Daniel went back to the hallway. Sandra moved to the garden. Question: Where is Daniel? Output: hallway Input: Passage: Mary moved to the bathroom. John went to the hallway. Daniel went back to the hallway. Sandra moved to the garden. John moved to the office. Sandra journeyed to the bathroom. Question: Where is Daniel? Output: hallway Input: Passage: John moved to the hallway. Mary travelled to the hallway. Sandra went to the bedroom. Sandra went back to the hallway. Question: Where is Mary? Output: REFERENCE: hallway OPT 1.3B: bathroom OPT-FT 1.3B: hallway OPT-CoT 1.3B: hallway OPT 13B: bathroom OPT-FT 13B: bathroom OPT-CoT 13B: hallway Table 12: Examples from tasks that require different reasoning skills and generated outputs. The failed outputs are highlighted in red. Outputs that are different from the reference, but can be valid because the question is ambiguous are highlighted in blue. All except the one are examples generated using prompt Template 4, last example used Template 5. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✗ A2. Did you discuss any potential risks of your work? We did not see any potential risks in our paper ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Our data and pretrained model may contain scientific artifacts. Section 2 (data part). Section 3 (model part) ✓ B1. Did you cite the creators of artifacts you used? Section 2 and Section 3 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section D in appendix ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 2 and Section D in appendix ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No, we use public datasets. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No, we use public datasets. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix ## C ✓ **Did You Run Computational Experiments?** Section 3 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3 and 4 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

imanigooghari-etal-2023-glot500

Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages

https://aclanthology.org/2023.acl-long.61

The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.

# Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages Ayyoob Imani∗1,2, Peiqin Lin∗1,2, Amir Hossein Kargaran1,2**, Silvia Severini**1, Masoud Jalili Sabet1, Nora Kassner1,2, Chunlan Ma1,2, Helmut Schmid1, André F. T. Martins3,4,5, François Yvon6 **and Hinrich Schütze**1,2 1CIS, LMU Munich, Germany 2Munich Center for Machine Learning (MCML), Germany 3Instituto Superior Técnico (Lisbon ELLIS Unit) 4Instituto de Telecomunicações 5Unbabel 6Sorbonne Université, CNRS, ISIR, France {ayyoob, linpq, amir, silvia}@cis.lmu.de ## Abstract The NLP community has mainly focused on scaling Large Language Models (LLMs) *vertically*, i.e., making them better for about 100 languages. We instead scale LLMs *horizontally*: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, "help" from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should not limit NLP to a small fraction of the world's languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at https://github.com/cisnlp/Glot500. ## 1 Introduction The NLP community has mainly focused on scaling Large Language Models (LLMs) *vertically*, i.e., deepening their understanding of high-resource languages by scaling up parameters and training data. While this approach has revolutionized NLP, the achievements are largely limited to high-resource languages. Examples of "vertical" LLMs are GPT3 (Brown et al., 2020), PaLM (Chowdhery et al., 2022) and Bloom (BigScience et al., 2022). In this paper, we create Glot500-m, a model that instead focuses on scaling multilingual LLMs *horizontally*, i.e., scaling to a large number of languages the great *Equal contribution. majority of which is low-resource. As LLMs are essential for progress in NLP, lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world's languages and cultures. Our goal is to address this need with the creation of Glot500-m.1 Existing multilingual LLMs support only about 100 (Conneau et al., 2020) out of the 7000 languages of the world. These supported languages are the ones for which large amounts of training data are available through projects such as Oscar (Suárez et al., 2019) and the Wikipedia dumps.2 Following Siddhant et al. (2022), we refer to the 100 languages covered by XLM-R (Conneau et al., 2020) as **head** languages and to the remaining languages as tail languages. This terminology is motivated by the skewed distribution of available data per language: for the best-resourced languages there are huge corpora available, but for the long tail of languages, only small corpora exist. This is a key problem we address: the availability of data for tail languages is limited compared to head languages. As a result, tail languages have often been ignored by language technologies (Joshi et al., 2020). Although there exists some work on machine translation for a large number of tail languages (Costa-jussà et al., 2022; Bapna et al., 2022), existing LLMs for tail languages are limited to a relatively small number of languages (Wang et al., 2019; Alabi et al., 2022; Wang et al., 2022). In this paper, we address this gap. Our work has three parts. (i) **Corpus collection.** We collect Glot2000-c, a corpus covering thousands of tail languages. (ii) Model training. Using Glot500-c, a subset of Glot2000-c, we train Glot500-m, an LLM covering 511 languages. (iii) **Validation.** We conduct an extensive evaluation of the quality of Glot500-m's representations of tail languages on a diverse suite of tasks. In more detail, **corpus collection** considers three major sources: websites that are known to publish content in specific languages, corpora with classified multilingual content and datasets published in specific tail languages. The resulting dataset Glot2000-c comprises 700GB in 2266 languages collected from ≈150 sources. After cleaning and deduplication, we create the subset Glot500-c, consisting of 511 languages and 534 *language-scripts* (where we define a language-script as a combination of ISO 639-33and script) to train Glot500-m. Our criterion for including a language-script in Glot500-c is that it includes more than 30,000 sentences. Model training. To train Glot500-m, we employ vocabulary extension and continued pretraining. XLM-R's vocabulary is extended with new tokens trained on Glot500-c. We then perform continued pretraining of XLM-R with the MLM objective (Devlin et al., 2019). Validation. We comprehensively evaluate Glot500-m on a diverse suite of natural language understanding, sequence labeling and multilingual tasks for hundreds of languages. The results demonstrate that Glot500-m performs better than XLMR-B (XLM-R-base) for tail languages by a large margin while performing comparably (or better) for head languages. Previous work on multilinguality has been hindered by the lack of LLMs supporting a large number of languages. This limitation has led to studies being conducted in settings dissimilar from realworld scenarios. For example, Dufter and Schütze (2020) use synthetic language data. And the curse of multilinguality has been primarily studied for a set of high-resource languages (Conneau et al., 2020). By creating Glot500-m, we can investigate these issues in a more realistic setting. We make code, data and trained models available to foster research by the community on how to include hundreds of languages that are currently ill-served by NLP technology. Contributions. (i) We train the multilingual model Glot500-m on a 600GB corpus, covering more than 500 diverse languages, and make it publicly available at https://github.com/cisnlp/ Glot500. (ii) We collect and clean Glot500-c, a corpus that covers these diverse languages and al3https://iso639-3.sil.org/code_tables/639 lows us to train Glot500-m, and will make as much of it publicly available as possible. (iii) We evaluate Glot500-m on pseudoperplexity and on five diverse tasks across these languages. We observe large improvements for low-resource languages compared to an XLM-R baseline. (iv) Our extensive analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, "help" from related languages and the total capacity of the model. (v) Our work addresses an important goal of NLP research: we should not limit NLP to a relatively small number of high-resource languages and instead strive to support as many languages as possible to bring the benefits of NLP to all languages and cultures. ## 2 Related Work Training multilingual LLMs using the masked language modeling (MLM) objective is effective to achieve cross-lingual representations (Devlin et al., 2019; Conneau et al., 2020). These models can be further improved by incorporating techniques such as discriminative pre-training (Chi et al., 2022) and the use of parallel data (Yang et al., 2020; Chi et al., 2021). However, this primarily benefits a limited set of languages with large corpora. Recent research has attempted to extend existing LLMs to languages with limited resources. Wang et al. (2019) propose vocabulary extension; Ebrahimi and Kann (2021) investigate adaptation methods, including MLM and Translation Language Model (TLM) objectives and adapters; Alabi et al. (2022) adapt XLM-R to 17 African languages; Wang et al. (2022) expand language models to low-resource languages using bilingual lexicons. Alternatively, parameter-efficient fine-tuning adapts pre-trained models to new languages by training a small set of weights effectively (Zhao et al., 2020; Pfeiffer et al., 2021; Ansell et al., 2022). Pfeiffer et al. (2022) address the "curse of multilinguality" by sharing a part of the model among all languages and having separate modules for each language. We show that the common perception that multilinguality increases as we add more languages, until, from some point, it starts decreasing, is naive. The amount of available data per language and the similarity between languages also play important roles (§6.8). Another approach trains LLMs from scratch for a limited number of tail languages; e.g., AfriBERTa (Ogueji et al., 2021a) and IndicNLPSuite (Kakwani et al., 2020) are LLMs for 11 African languages and 11 Indic languages. In concurrent work, Adebara et al. (2022) train a multilingual model for 517 African languages on a 42 GB corpus, but without making the model available and with an evaluation on a smaller number of languages than ours. Closely related to our work on corpus creation, Bapna et al. (2022) and Costa-jussà et al. (2022) also create NLP resources for a large number of tail languages. They train a language identifier model and extract textual data for tail languages from largescale web crawls. This approach is effective, but it requires significant computational resources and native speakers for all tail languages. This is hard to do outside of large corporations. Bapna et al. (2022) have not made their data available. Costajussà et al. (2022) have only released a portion of their data in around 200 languages. A key benefit of "horizontally" scaled multilingual LLMs is transfer from high- to low-resource languages. Our evaluation suggests that Glot500-m excels at this, but this is not the main focus of our paper. There is a large body of work on crosslingual transfer: (Artetxe and Schwenk, 2019; ImaniGooghari et al., 2022; Lauscher et al., 2020; Conneau et al., 2020; Turc et al., 2021; Fan et al., 2021; Severini et al., 2022; Choenni and Shutova, 2022; Wang et al., 2023), inter alia. ## 3 Glot2000-C 3.1 Data Collection One of the major challenges in developing NLP technologies for tail languages is the scarcity of high-quality training data. In this work, we propose a lightweight methodology that is easily replicable for academic labs. We identify tail language data previously published by researchers, publishers and translators and then crawl or download them. By crawling a few websites and compiling data from around 150 different datasets, we amass more than 700GB of text in 2266 languages. We will refer to these sources of data as *data sources*. Our data covers many domains, including religious texts, news articles and scientific papers. Some of the data sources are high-quality, verified by native speakers, translators and linguists. Others are less reliable such as web crawls and Wikipedia dumps. It is therefore necessary to clean the data. For a list of data sources, see §C. ## 3.2 Language-Scripts Some languages are written in multiple scripts; e.g., Tajik is written in both Cyrillic and Arabic scripts. Some data sources indicate the script, but others either do not or provide mixed text in multiple scripts. We detect the script for each sentence and treat each language-script as a separate entity. ## 3.3 Ngram Lms And Language Divergence We train a 3-gram character-level language model for each language-script , using KenLM (Heafield, 2011). We refer to the perplexity calculated for the corpus of language using language model as PP (, ). Similar to Gamallo et al. (2017), we define a perplexity-based divergence measure of languages and as: ## D, = Max Pp (, ), Pp (, ) We use D to filter out noisy data in §3.4 and study the effect of similar languages in LLM training in §6.7 and §6.8. For more details, see §A. ## 3.4 Data Cleaning To remove noise, we use chunk-level and corpuslevel filters. While some sources are sentence-split, others provide multiple sentences (e.g., a paragraph) as one chunk. Chunk-level filters process each chunk of text from a data source as a unit, without sentencesplitting. Some chunk-level filters are based on the notion of word: we use white space tokenization when possible and otherwise resort to sentencePiece (Kudo and Richardson, 2018) trained by Costa-jussà et al. (2022). As chunk-level filters, we employ the **sentencelevel filters** SF1–SF5 from BigScience ROOTS (Laurençon et al., 2022). SF1 Character repetition. If the ratio of repeated characters is too high, it is likely that the sentence has not enough textual content. SF2 Word repetition. A high ratio of repeated words indicates non-useful repetitive content. SF3 Special characters. Sentences with a high ratio of special characters are likely to be crawling artifacts or computer code. SF4 Insufficient number of words. Since training language models requires enough context, very small chunks of text are not useful. SF5 Deduplication. If two sentences are identical after eliminating punctuation and white space, one is removed. ![3_image_0.png](3_image_0.png) In the rest of the paper, we refer to a chunk as a **sentence'**. A sentence' can consist of a short segment, a complete sentence or a chunk (i.e., several sentences). Corpus-level filters detect if the corpus of a language-script is noisy; e.g., the corpus is in another language or consists of non-meaningful content such as tabular data. We employ filters CF1 and CF2. CF1 In case of **mismatch between language** and script, the corpus is removed; e.g., Chinese written in Arabic is unlikely to be Chinese. CF2 Perplexity mismatch. For each languagescript L1, we find its closest language-script L2: the language-script with the lowest perplexity divergence (§3.3). If L1 and L2 are not in the same typological family, we check L1/L2 manually and take appropriate action such as removing the corpus (e.g., if it is actually English) or correcting the ISO code assigned to the corpus. ## 3.5 Training Data: Glot500-C Among the 2000+ language-scripts that we collected data for, after cleaning, most have too little data for pretraining LLMs. It is difficult to quantify the minimum amount needed for pretraining. Therefore, we pick a relatively high "safe" threshold, 30,000 sentences', for inclusion of language-scripts in model training. This allows us to train the model effectively and cover many low-resource languages. Table 1 gives Glot500-c statistics. See §B for a list of language-scripts. We train Glot500-m on Glot500-c; note that while Glot500-c focuses on tail languages, it contains some data in head languages which we include in Glot500-m training to prevent catastrophic forgetting. We divide the corpus for each language into train/dev/test, reserving 1000 sentences' each for dev and test and using the rest for train. We pick 1000 parallel verses if we have a Bible translation | XLM-R-B XLM-R-L Glot500-m | | | | |-----------------------------|------|------|------| | Model Size | 278M | 560M | 395M | | Vocab Size | 250K | 250K | 401K | | Transformer Size | 86M | 303M | 86M | Table 2: Model sizes. Glot500-m and XLM-R-B have the same transformer size, but Glot500-m has a larger vocabulary, resulting in an overall larger model. and add 500 each to test and dev. These parallel verses convey identical meanings and facilitate crosslingual evaluation. We pretrain the model using only the training data. ## 4 Glot500-M 4.1 Vocabulary Extension To extend XLM-R's vocabulary, we use SentencePiece (Kudo and Richardson, 2018) with a unigram language model (Kudo, 2018) to train a tokenizer with a vocabulary size of 250K on Glot500-c. We sample data from different language-scripts according to a multinomial distribution, with =.3. The amount we sample for head languages is the same as tail languages with the lowest amount; this favors tail languages - head languages are already well learned by XLM-R. We merge the obtained tokens with XLM-R's vocabulary. About 100K new tokens were in fact old tokens, i.e., already part of XLM-R's vocabulary. We take the probabilities of the (genuinely) new tokens directly from SentencePiece. After adding the 151K new tokens to XLM-R's vocabulary (which has size 250K), the vocabulary size of Glot500-m is 401K. We could also calculate probabilities of existing and new tokens over a mixture of original XLM-R training corpus and Glot500-c (Chung et al., 2020). For head languages, the percentage of changed tokens using the new tokenizer compared to the original tokenizer ranges from 0.2% to 50%. However, we found no relationship between percentage of changed tokens and change in performance on downstream tasks. Thus, there was little effect of tokenization in our experiments. ## 4.2 Continued Pretraining We create Glot500-m by continued pretraining of XLM-R-B with the MLM objective. The optimizer used is Adam with betas (0.9, 0.999). Initial learning rate: 5e-5. Each training step contains a batch of 384 training samples randomly picked from all language-scripts. The sampling strategy across language-scripts is the same as for vocabu- | |head| |tail| measure (%) | | | | |-----------------------------|----|-----|------------| | Sentence Retrieval Tatoeba | 70 | 28 | Top10 Acc. | | Sentence Retrieval Bible | 94 | 275 | Top10 Acc. | | Text Classification | 90 | 264 | F1 | | NER | 89 | 75 | F1 | | POS | 63 | 28 | F1 | | Roundtrip Alignment | 85 | 288 | Accuracy | lary extension (§4.1). We save checkpoints every 10K steps and select the checkpoint with the best average performance on downstream tasks by early stopping. Table 2 lists the sizes of XLM-R-B, XLMR-L and Glot500-m. Except for a larger vocabulary (§4.1), Glot500-m has the same size as XLM-R-B. We train Glot500-m on a server with eight NVIDIA RTX A6000 GPUs for two weeks. Similar to XLM-R, we concatenate sentences' of a language-script and feed them as a stream to the tokenizer. The resulting output is then divided into chunks of 512 tokens and fed to the model. ## 5 Experimental Setup For most tail languages, there are no manually labeled evaluation data. We therefore adopt a mixed evaluation strategy: based partly on human labels, partly on evaluation methods that are applicable to many languages without requiring gold data. Table 3 lists all our evaluation tasks. Perplexity Following Salazar et al. (2020), we calculate pseudoperplexity (PPPL) over the heldout test set. PPPL is based on masking tokens one-by-one (not left to right). Salazar et al. (2020) give evidence that PPPL is a better measure of linguistic acceptability compared to standard leftto-right perplexity. Roundtrip Alignment For assessing the quality of multilingual representations for a broad range of tail languages without human gold data, we adopt roundtrip evaluation (Dufter et al., 2018). We first word-align sentences' in a parallel corpus based on the multilingual representations of an LLM.We then start from a word in a sentence' in language-script L1, follow the alignment links to its translations in language-script L2, then the alignment links from L2 to L3 and so on, until in the end we follow alignment links back to L1. If this "roundtrip" gets us back to , then it indicates that the LLM has similar representations for the meaning of in language-scripts L1, L2, L3, etc. In other words, the cross-lingual quality of representations is high. Vice versa, failure to get back to is a sign of poor multilingual representations. We use SimAlign (Jalili Sabet et al., 2020) and align on the sub-word level on the Bible part of test, based on the representations of the LLM computed by transformer layer 8 as suggested in the original paper. We use intersection symmetrization: each word in a sentence' is aligned to at most one word in the other sentence'. As evaluation measure we compute the percentage of roundtrips that were successes, i.e., the roundtrip starts at in L1 and returns back to . For each language-script in test, we randomly select three language-scripts as intermediate points L2, L3, L4. Since the intermediate points influence the results, we run the experiment five times with different intermediate points and report the average. All models are evaluated with the same five sets of three intermediate language-scripts. Sequence Labeling We consider two sequence labeling tasks: Named Entity Recognition (NER) and Part-Of-Speech (POS) tagging. We use the WikiANN dataset (Pan et al., 2017) for NER and version v2.11 of Universal Dependencies (UD) (de Marneffe et al., 2021) for POS. Since training data does not exist for some languages, we finetune on English (with early stopping based on dev) and evaluate zero-shot transfer on all languages covered by WikiANN/UD. We set the learning rate to 2e-5 with Adam. Sentence Retrieval Following (Hu et al., 2020), we use up to 1000 English-aligned sentences' from Tatoeba (Artetxe and Schwenk, 2019) to evaluate SentRetr (sentence retrieval). We also use 500 English-aligned sentences' from the Bible part of test. We find nearest neighbors using cosine similarity based on the average word embeddings in layer = 8 - following Jalili Sabet et al. (2020) – and compute top10 accuracy. For fair comparison and because the architectures are the same, we do not optimize the hyperparameter for Glot500-m and XLM-R-B. Text Classification We evaluate on Taxi1500 (Ma et al., 2023). It provides gold data for text classification with six classes in a large number of language-scripts of which Glot500-m supports 354. We finetune on English (with early stopping on dev) and evaluate zero-shot on test of the target language-script. Learning rate: 2e-5, batch size: ## 6 Experiments In this section, we discuss aggregate results. For detailed results, see §D and §E. ## 6.1 Results Table 4 gives results. Glot500-m outperforms XLM-R-B on all tasks for both head and tail language-scripts, except for POS on head. That Glot500-m outperforms XLM-R-B is expected for tail language-scripts (i.e., those not covered by XLM-R). For these language-scripts the improvement margin is large. Outperformance may seem counterintuitive for head language-scripts (those covered by XLM-R) since Glot500-m has the same number of (non-embedding) parameters as XLMR-B. Since the number of covered languages has greatly increased, leaving less capacity per language, we might expect underperformance. There are a few possible explanations. First, XLM-R may be undertrained, and the inclusion of more head language training data may improve their representations. Second, having more languages may improve multilinguality by allowing languages to synergize and enhance each other's representations and cross-lingual transfer. Third, there are languages similar to head languages among the tail languages, which in turn aids head languages. The gap between Glot500-m and the baselines for tail language-scripts in sequence labeling is smaller. These tasks do not require as deep an understanding of language and thus transfer from head to tail language-scripts is easier through shared tokens. Glot500-m also outperforms XLM-R-L for tail language-scripts (all tasks) and head languagescripts (3 tasks). This suggests that scaling up size is not the only way for improvements. We can also improve the quality of multilingual LLM representations by increasing the number of languages. ## 6.2 Language Coverage Table 5 compares Glot500-m vs. XLM-R-B on pseudoperplexity. For fair comparison we use word-level normalization. For 69 head languagescripts, Glot500-m underperforms XLM-R-B. This is expected as Glot500-m's training data is small for these language-scripts. Glot500-m outperforms XLM-R-B for 420 tail language-scripts. There are eight tail language-scripts for which ![5_image_0.png](5_image_0.png) Glot500-m performs worse than XLM-R-B. Five are tail languages with a similar head language where the two share a macro-language: ekk/Standard Estonian (est/Estonian), aln/Gheg Albanian (sqi/Albanian), nob/Norwegian Bokmal (nor/Norwegian), hbs/Serbo-Croatian (srp/Serbian), lvs/Standard Latvian (lav/Latvian). Since XLMR-B's pretraining corpus is large for the five head languages, its performance is good for the close tail languages. The other three languages all have a unique script: sat/Santali (Ol Chiki script), div/Dhivehi (Thaana script), iku/Inuktitut (Inuktitut syllabics). For these languages, XLM-R-B's tokenizer returns many UNK tokens since it is not trained on these scripts, resulting in an unreasonably optimistic estimate of pseudoperplexity by our implementation. Glot500-m's token-level normalized pseudoperplexity ranges from 1.95 for lhu/Lahu to 94.4 for tok/Toki Pona. The average is 13.5, the median 10.6. We analyze the five language-scripts with the highest pseudoperplexity: tok_Latn, luo_Latn, acm_Arab, ach_Latn, and teo_Latn. tok/Toki Pona is a constructed language. According to Wikipedia: "Essentially identical concepts can be described by different words as the choice relies on the speaker's perception and experience." This property can result in higher variability and higher perplexity. acm/Mesopotamian Arabic contains a large number of tweets in raw form. This may result in difficult-to-predict tokens in test. luo/Luo, ach/Acoli and teo/Teso are related Nilotic languages spoken in Kenya, Tanzania, Uganda and South Sudan. Their high perplex- | tail | head | all | | | | | | | | |-------------------------------------------------------------------------------|--------|-------|------|------|------|------|-------|-------|------| | XLM-R-B XLM-R-L Glot500-m XLM-R-B XLM-R-L Glot500-m XLM-R-B XLM-R-L Glot500-m | | | | | | | | | | | Pseudoperplexity | 304.2 | 168.6 | 12.2 | 12.5 | 8.4 | 11.8 | 247.8 | 136.4 | 11.6 | | Sentence Retrieval Tatoeba | 32.6 | 33.6 | 59.8 | 66.2 | 71.1 | 75.0 | 56.6 | 60.4 | 70.7 | | Sentence Retrieval Bible | 7.4 | 7.1 | 43.2 | 54.2 | 58.3 | 59.0 | 19.3 | 20.1 | 47.3 | | Text Classification | 13.7 | 13.9 | 46.6 | 51.3 | 60.5 | 54.7 | 23.3 | 25.8 | 48.7 | | NER | 47.5 | 51.8 | 60.7 | 61.8 | 66.0 | 63.9 | 55.3 | 59.5 | 62.4 | | POS | 41.7 | 43.5 | 62.3 | 76.4 | 78.4 | 76.0 | 65.8 | 67.7 | 71.8 | | Roundtrip Alignment | 2.6 | 3.1 | 4.5 | 3.4 | 4.1 | 5.5 | 2.8 | 3.3 | 4.7 | head tail $\begin{array}{c|c}&\text{head}\\ \hline\text{Glot500-m is better}&37\\ \text{XLM-R-B is better}&69\\ \end{array}$ ity could be related to the fact that they are tonal languages, but the tones are not orthographically indicated. Another possible explanation is that the training data is dominated by one subcorpus (Jehova's Witnesses) whereas the test data are dominated by PBC. There are orthographic differences between the two, e.g., "dong" (JW) vs. "doŋ" (PBC) for Acoli. These three languages are also spoken over a large area in countries with different standard languages, which could increase variability. Our analysis is not conclusive. We note however that the gap between the three languages and the next most difficult languages in terms of pseudoperplexity is not large. So maybe Luo, Acoli and Teso are simply (for reasons still to be determined) languages that have higher perplexity than others. ## 6.3 Training Progression To analyze the training process, we evaluate Glot500-m on sequence labeling and SentRetr at 10,000-step intervals. Figure 1 shows that performance improves rapidly at the onset of training, but then the rate of improvement slows down. This trend is particularly pronounced for tail languages in SentRetr. In comparison, sequence labeling is relatively straightforward, with the baseline (XLM-R-B, epoch 0) achieving high performance by correctly transferring prevalent classes such as *verb* and *noun* $$\frac{\mathrm{tail}}{420}$$ $$8$$ through shared vocabulary, resulting in a smaller improvement of Glot500-m vs. XLM-R-B. For SentRetr, we observe larger improvements for the Bible than for Tatoeba. This is likely due to the higher proportion of religious data in Glot500-c, compared to XLM-R's training data (i.e., CC100). The average performance on downstream tasks peaks at 480K steps. We have taken a snapshot of Glot500-m at this stage and released it. ## 6.4 Analysis Across Language-Scripts To analyze the effect of language-scripts, we select five tail language-scripts each with the largest and smallest gain when comparing Glot500-m vs. XLMR-B for SentRetr and sequence labeling. Table 6 shows that Glot500-m improves languages with scripts not covered by XLM-R (e.g., div/Dhivehi, Thaana script, see §6.2) by a large margin since XLM-R simply regards the uncovered scripts as unknown tokens and cannot compute meaningful representations for the input. The large amount of data we collected in Glot500-c also contributes to the improvement for tail languages, e.g., for tat_Cyrl (Tatar) in SentRetr Tatoeba and mlt_Latn (Maltese) in POS. See §6.7 for a detailed analysis of the effect of corpus size. On the other hand, Glot500-m achieves just comparable or even worse results for some languagescripts. We see at least three explanations. (i) As discussed in §6.2, some tail languages (e.g., nob/Norwegian Bokmal) are close to a head language (e.g., nor/Norwegian), so Glot500-m has no advantage over XLM-R-B. (ii) A language is at the low end of our corpus size range (i.e., 30,000 sentences'). Example: xav_Latn, Xavánte. (iii) Some languages are completely distinct from all other languages in Glot500-c, thus without support from any similar language. An example is mau_Latn, Huautla Mazatec. Glot500-m has a much harder time learning good representations in these cases. | lang-script | XLM-R-B | Glot500-m | gain | | |---------------|-----------|-------------|--------|------| | uig_Arab | head | 45.8 | 56.2 | 10.4 | | uig_Latn | tail | 9.8 | 62.8 | 53.0 | | hin_Deva | head | 67.0 | 76.6 | 9.6 | | hin_Latn | tail | 13.6 | 43.2 | 29.6 | | uzb_Latn | head | 54.8 | 67.6 | 12.8 | | uzb_Cyrl | tail | 6.2 | 78.8 | 72.6 | | kaa_Cyrl | tail | 17.6 | 73.8 | 56.2 | | kaa_Latn | tail | 9.2 | 43.4 | 34.2 | | kmr_Cyrl | tail | 4.0 | 42.4 | 38.4 | | kmr_Latn | tail | 35.8 | 63.0 | 27.2 | | tuk_Cyrl | tail | 13.6 | 65.0 | 51.4 | | tuk_Latn | tail | 9.6 | 66.2 | 56.6 | | high end | SentRetr Tatoeba | |------------------|--------------------| | low end high end | NER | | low end | | ## 6.5 Languages With Multiple Scripts Table 7 compares SentRetr performance XLM-R-B vs. Glot500-m for six languages with two scripts. Unsurprisingly, XLM-R performs much better for a language-script it was pretrained on ("head") than on one that it was not ("tail"). We can improve the performance of a language, even surpassing the language-script covered by XLM-R, if we collect enough data for its script not covered by XLM-R. For languages with two scripts not covered by XLM- R, the performance is better for the script for which we collect a larger corpus. For example, kaa_Cyrl (Kara-Kalpak) has about three times as much data as kaa_Latn. This explains why kaa_Cyrl outperforms kaa_Latn by 30%. Dufter and Schütze (2020) found that, after training a multilingual model with two scripts for English (natural English and "fake English"), the model performed well at zero-shot transfer if the capacity of the model was of the right size (i.e., not too small, not too large). Our experiments with real data show the complexity of the issue: even if there is a "right" size for an LLM that supports both full acquisition of languages and multilingual transfer, this size is difficult to determine and it may be different for different language pairs in a large horizontally scaled model like Glot500-m. | language-script | XLMR Glot500 | gain | language-script | XLMR Glot500 | gain | | | | |-----------------------|--------------------|--------|-------------------|------------------------|----------------------|------|------|------| | tat | C Tatar | 10.3 | 70.3 | 60.0 | uzn C Northern Uzbek | 5.4 | 87.0 | 81.6 | | nds L Low German | 28.8 | 77.1 | 48.3 | crs L Seselwa Creole | 7.4 | 80.6 | 73.2 | | | tuk L Turkmen | 16.3 | 63.5 | 47.3 | srn L Sranan Tongo | 6.8 | 79.8 | 73.0 | | | ile | L Interlingue | 34.6 | 75.6 | 41.0 | uzb C Uzbek | 6.2 | 78.8 | 72.6 | | uzb C Uzbek | 25.2 | 64.5 | 39.3 | bcl L Central Bikol | 10.2 | 79.8 | 69.6 | | | SentRetr Bible | | | | | | | | | | dtp L Kadazan Dusun | 5.6 | 21.1 | 15.5 | xav L Xavánte | 2.2 | 5.0 | 2.8 | | | kab L Kabyle | 3.7 | 16.4 | 12.7 | mauL Huautla Mazatec | 2.4 | 3.6 | 1.2 | | | pamL Pampanga | 4.8 | 11.0 | 6.2 | ahk L Akha | 3.0 | 3.2 | 0.2 | | | lvs | L Standard Latvian | 73.4 | 76.9 | 3.5 | aln L Gheg Albanian | 67.8 | 67.6 | -0.2 | | nob L Bokmål | 93.5 | 95.7 | 2.2 | nob L Bokmål | 82.8 | 79.2 | -3.6 | | | div T Dhivehi | 0.0 | 50.9 | 50.9 | mlt L Maltese | 21.3 | 80.3 | 59.0 | | | che C Chechen | 15.3 | 61.2 | 45.9 | sah C Yakut | 21.9 | 76.9 | 55.0 | | | mri L Maori | 16.0 | 58.9 | 42.9 | sme L Northern Sami | 29.6 | 73.6 | 44.1 | | | nan L Min Nan | 42.3 | 84.9 | 42.6 | yor L Yoruba | 22.8 | 64.2 | 41.4 | | | tgk C Tajik | 26.3 | 66.4 | 40.0 | quc L K'iche' | 28.5 | 64.1 | 35.6 | | | POS | | | | | | | | | | zea L Zeeuws | 68.1 | 67.3 | -0.8 | lzh HLiterary Chinese | 11.7 | 18.4 | 6.7 | | | vol L Volapük | 60.0 | 59.0 | -1.0 | nap L Neapolitan | 47.1 | 50.0 | 2.9 | | | min L Minangkabau | 42.3 | 40.4 | -1.8 | hywAWestern Armenian | 79.1 | 81.1 | 2.0 | | | wuuHWu Chinese | 28.9 | 23.9 | -5.0 | kmr L Northern Kurdish | 73.5 | 75.2 | 1.7 | | | lzh HLiterary Chinese | 15.7 | 10.3 | -5.4 | aln L Gheg Albanian | 54.7 | 51.2 | -3.5 | | ## 6.6 Analysis Across Language Families Table 8 compares SentRetr performance Glot500-m vs. XLM-R-B for seven language families that have ten or more language-scripts in Glot500-c. We assign languages to families based on Glottolog.4 Generally, XLM-R has better performance the more language-scripts from a language family are represented in its training data; e.g., performance is better for indo1319 and worse for maya1287. The results suggest that Glot500-m's improvement over 4http://glottolog.org/glottolog/family | family | |𝐿𝐺 | | |𝐿𝑋| | XLM-R-B Glot500-m gain | | | |----------|-----------|----------|--------------------------|------|------| | indo1319 | 91 | 50 | 41.5 | 61.4 | 19.9 | | atla1278 | 69 | 2 | 5.5 | 45.2 | 39.6 | | aust1307 | 53 | 6 | 13.7 | 47.0 | 33.2 | | turk1311 | 22 | 7 | 20.1 | 62.9 | 42.8 | | sino1245 | 22 | 2 | 7.6 | 38.9 | 31.3 | | maya1287 | 15 | 0 | 3.8 | 20.3 | 16.4 | | afro1255 | 12 | 5 | 13.0 | 34.3 | 21.4 | | lang-script | Glot+1 Glot500-m | | |----------------------------|--------------------|------| | rug_Latn, Roviana | 51.0 | 49.0 | | yan_Latn, Mayangna/Sumo | 46.4 | 31.8 | | wbm_Latn, Wa/Va | 49.6 | 46.4 | | ctd_Latn, Tedim Chin | 47.4 | 59.4 | | quh_Latn, Southern Quechua | 33.4 | 56.2 | | tat_Cyrl, Tatar | 58.8 | 67.2 | XLM-R is the larger, the better our training corpus Glot500-c's coverage is of a family. ## 6.7 Effect Of Amount Of Training Data We examine correlation between pretraining corpus size and Glot500-m zero-shot performance. We focus on SentRetr Bible (§5) since it supports the most head and tail languages. We find that Pearson's = .34, i.e., corpus size and performance are moderately, but clearly correlated. We suspect that the correlation is not larger because, in addition to corpus size of language itself, corpus size of languages closely related to is also an important factor (see §6.4 for a similar finding for Norwegian). We therefore also compute Pearson's between (i) performance of language on SentRetr Bible and (ii) joint corpus size of and its nearest neighbors (according to perplexity divergence, §3.3). In this case, Pearson's = .44 (for both = 3 and = 4), indicating that the corpus size of nearest neighbor languages does play a role. ## 6.8 Support Through Related Languages Building on §6.7, there is another way we can investigate the positive effect of closely related languages on performance: We can compare performance (again on SentRetr Bible) of continued pretraining on just one language (we refer to this model as Glot+1) vs. on all 511 languages represented in Glot500-c (i.e., Glot500-m). Table 9 presents results for six language-scripts selected from various language families and suggests that some languages do not receive support from related languages (top three). In that case, Glot+1 can fully concentrate on learning the isolated language and does better than Glot500-c. Other languages (bottom three) do receive support from related languages. For example, Southern Quechua (quh) seems to receive support in Glot500-m from closely related Cuzco Quechua (quz), resulting in Glot500-m outperforming Glot+1. ## 7 Conclusion And Future Work We collect and data-clean Glot500-c, a large corpus of hundreds of usually neglected tail (i.e., long-tail) languages and create Glot500-m, an LLM that is trained on Glot500-c and covers these languages. We evaluate Glot500-m on six tasks that allow us to evaluate almost all languages. We observe large improvements for both head and tail languages compared to XLM-R. Our analysis shows that no single factor fully explains the quality of the representation of a language in a multilingual model. Rather, a combination of factors is important, including corpus size, script, "help" from related languages and the total capacity of the model. This work is the first to create a language model on a dataset of several hundreds of gigabytes and to make it publicly available for such a large and diverse number of low-resource languages. In future research, we would like to train larger models to further investigate the effect of model size, distill highly multilingual models for resource-efficient deployment, explore alternatives to continued pretraining and use models for more tail language downstream tasks. ## Limitations (1) We did not perform any comprehensive hyperparameter search, which would have further consolidated our results. This decision was made due to the high cost of training multiple models. (2) Compared to current very large models, Glot500-m is comparatively small. (3) Although we have tried to minimize the amount of noise in our data, some noise is still present. ## Ethics Statement There are two issues worth mentioning in regards to this project. First, it was not feasible for us to thoroughly examine the content of the data for all languages, thus we cannot confirm the absence of discrimination based on factors such as race or sexuality. The data was solely utilized as a textual corpus, and the content should not be interpreted as an endorsement by our team. If the model is subsequently utilized for generation, it is possible that the training data may be reflected in the generated output. However, addressing potential biases within the data is an area for future research. Second, it is important to note that while the data sources utilized in this study do not explicitly prohibit the reuse of data for research purposes, some sources do have copyright statements indicating that such use is permissible while others do not. Additionally, certain sources prohibit the redistribution of data. 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Assuming a test data contains sequences of characters = ℎ1, ℎ2, · · · , ℎ, perplexity (PP) of given an n-gram character level language model is computed as follows: $$\mathcal{P}\mathcal{P}(S,M)=\sqrt{\prod_{t=1}^{T}\frac{1}{\mathbb{P}\left(c h_{t}\mid c h_{1}^{t-1}\right)}}\qquad(1)$$ where P ℎ| ℎ−1 1 is computed as by dividing the observed frequency () of ℎ−1 1ℎ by the observed frequency of ℎ−1 1in training data: $$\mathbb{P}\left(c h_{t}\mid c h_{1}^{t-1}\right)={\frac{C\left(c h_{1}^{t-1}c h_{t}\right)}{C\left(c h_{1}^{t-1}\right)}}\qquad\quad(2)$$ Given the definition of perplexity, we can determine how well a trained language model on language 1 predicts the test text of language 2 and vice-versa. The divergence between two languages is computed with the maximum of the perplexity values in both directions. Two reasons lead to the use of max: first, a symmetrical divergence is required, and second, languages differ in their complexity, so one direction of computing perplexity may result in a much lower perplexity than another. Thus, comparing perplexity results becomes difficult. As an example, the Kuanua language (ksd_Latn) has short words and a simple structure, which results in 3−gram models getting lower perplexity on its text compared to other languages. The lower the perplexity the smaller the divergence between languages. The divergence (D) between language and with trained language models of and test texts of , where is the corresponding language, computed as follows: D, = max PP ( , ), PP ( , ) (3) Runs and Data. The data used to train and test the character level n-gram models is the same data used for the training and testing of the Glot500-m. The training of the models was limited to 100, 000 sentences' per language-script. We use KenLM library (Heafield, 2011) to build n-gram models. This library uses an interpolated modified KneserNey smoothing for estimating the unseen n-grams. Our evaluation has been performed over 7 n-gram models (3 ≤ ≤ 9). Baseline and Evaluation. Language family trees were used as a baseline for evaluating the divergence measures of the proposed approach. We obtained language family tree data from Ethnologue online version (Eberhard et al., 2022). For each language, the family tree follows the general order from largest typological language family group to smallest. There is only one family tree for each language in the baseline data. Nodes in the family tree represent typological language family groups. Each node only has one parent, so if a node is common in the family tree of two languages, its parent is also common. We evaluate our perplexity method on the following binary classification task: Do the majority of a language 's nearest neighbors belong to the same typological language family group as ? Assuming languages and , with the following family trees: : 1 → 2 → 3 → 4 → 5 → 6 : 1 → 2 → 7 → 8 These 2 languages belong to the same typological family group with family tree levels of ∈ {1, 2}, but not with family tree levels of = 3 and higher. Result. When it comes to language families, the majority of studies only refer to the largest typological language family group (level = 1). Here, we also assess our methodology for other levels. The results of classification accuracy for 3−gram model, ∈ {1, 3, 7, 13, 21} and ∈ {1, 2, 3, max} are shown in Table 10. In cases where the maximum level of a tree is less than the parameter, the maximum level for that language is used. Languages without a family or no other family member in our data are excluded. We only report the 3−gram model results as it gets the best results in most configurations among other n-gram models. With increasing , the accuracy decreases, since more languages fall outside the same typological family. As increases, the accuracy decreases, because languages with faraway neighbors are being included but the number of languages in the language typological group family will remain the same. There are times when languages have a lot of loan words from other languages because of geological proximity or historical reasons (e.g, colonization), which makes them similar to the languages they borrowed words from in our method. However they are different when it comes to their typological families and our method fails in these cases. Aymara (Macrolanguage: aym_Latn) and Quechua (Macrolanguage: que_Latn), for example, had a great deal of contact and influence on each other, but they do not belong to the same typological group. As well, some of the typological families are not that large, which makes our results worse when increases. This is the case, for instance, of the Tarascan typological family which only has two members. model accuracy (%) 3-gram 1 1 84.45 3-gram 1 3 75.77 3-gram 1 7 69.08 3-gram 1 13 62.75 3-gram 1 21 55.33 3-gram 2 1 79.75 3-gram 2 3 67.63 3-gram 2 7 59.49 3-gram 2 13 51.36 3-gram 2 21 42.68 3-gram 3 1 75.05 3-gram 3 3 60.22 3-gram 3 7 49.55 3-gram 3 13 38.34 3-gram 3 21 29.84 3-gram max 1 59.31 3-gram max 3 36.89 3-gram max 7 18.81 3-gram max 13 6.87 3-gram max 21 2.89 Table 10: Detecting the typological relatedness of language with n-gram divergence: (Eq. 3); : level of typological language family group; : number of nearest language neighbors. ## B Languages The list of languages used to train Glot500-m with the amount of available data for each language is available in Tables 11, 12 and 13. On Macrolanguages The presence of language codes that are supersets of other language codes within datasets is not uncommon (Kreutzer et al., 2022). This issue becomes more prevalent in extensive collections. Within the ISO 639-3 standard, these languages are referred to as macrolanguages. When confronted with macrolanguages, if it is not feasible to ascertain the specific individual language contained within a dataset, the macrolanguage code is retained. Consequently, it is possible that in Glot2000-c and Glot500-c both the corpora for the macrolanguage and its individual languages have been included. ## C List Of Data Sources The datasets and repositories used in this project involve: AI4Bharat,5 AIFORTHAI-LotusCorpus,6 Add (El-Haj et al., 2018), AfriBERTa (Ogueji et al., 2021b), AfroMAFT (Adelani et al., 2022; Xue et al., 2021), Anuvaad,7 AraBench (Sajjad et al., 2020), AUTSHUMATO,8 Bloom (Leong et al., 2022), CC100 (Conneau et al., 2020; Wenzek et al., 2020a), CCNet (Wenzek et al., 2020b), CMU_Haitian_Creole,9 CORP.NCHLT,10 Clarin,11 DART (Alsarsour et al., 2018), Earthlings (Dunn, 2020), FFR,12 Flores200 (Costa-jussà et al., 2022), GiossaMedia (Góngora et al., 2022, 2021), Glosses (Camacho-Collados et al., 2016), Habibi (El-Haj, 2020), HinDialect (Bafna, 2022), HornMT,13 IITB (Kunchukuttan et al., 2018), IndicNLP (Nakazawa et al., 2021), Indiccorp (Kakwani et al., 2020), isiZulu,14 JParaCrawl (Morishita et al., 2020), KinyaSMT,15 LeipzigData (Goldhahn et al., 2012), Lindat,16 Lingala_Song_Lyrics,17 Lyrics,18 MC4 (Raffel et al., 2020), MTData (Gowda et al., 2021), MaCoCu (Bañón et al., 2022), Makerere MT Corpus,19 Masakhane community,20 Mburisano_Covid,21 Menyo20K (Adelani et al., 2021), Minangkabau corpora (Koto and Koto, 2020), MoT (Palen-Michel et al., 2022), NLLB_seed (Costa-jussà et al., 2022), Nart/abkhaz,22 OPUS (Tiedemann, 2012), OSCAR (Suárez et al., 2019), ParaCrawl (Bañón et al., 2020), Parallel Corpora for Ethiopian Lan-5https://ai4bharat.org/ 6https://github.com/korakot/corpus/releases/ download/v1.0/AIFORTHAI-LotusCorpus.zip 7https://github.com/project-anuvaad/ anuvaad-parallel-corpus 8https://autshumato.sourceforge.net/ 9http://www.speech.cs.cmu.edu/haitian/text/ 10https://repo.sadilar.org/handle/20.500.12185/ 7 11https://www.clarin.si/ 12https://github.com/bonaventuredossou/ffr-v1/ tree/master/FFR-Dataset 13https://github.com/asmelashteka/HornMT 14https://zenodo.org/record/5035171 15https://github.com/pniyongabo/kinyarwandaSMT 16https://lindat.cz/faq-repository 17https://github.com/espoirMur/songs_lyrics_ webscrap 18https://lyricstranslate.com/ 19https://zenodo.org/record/5089560 20https://github.com/masakhane-io/ masakhane-community 21https://repo.sadilar.org/handle/20.500.12185/ 536 22https://huggingface.co/datasets/Nart/abkhaz_ text Table 11: List of languages used to train Glot500-m (Part I). | Language-Script | |Sent| | Family | Head | Language-Script | |Sent| | Family | Head | Language-Script | |Sent| | Family | Head | |-------------------|----------|----------|----------|-------------------|----------|----------|----------|-------------------|----------|----------|--------| | hbs_Latn | 63411156 | indo1319 | vec_Latn | 514240 | indo1319 | swh_Latn | 95776 | atla1278 | yes | | | | mal_Mlym | 48098273 | drav1251 | yes | jpn_Jpan | 510722 | japo1237 | yes | alt_Cyrl | 95148 | turk1311 | | | aze_Latn | 46300705 | yes | lus_Latn | 509250 | sino1245 | rmn_Grek | 94533 | indo1319 | | | | | guj_Gujr | 45738685 | indo1319 | yes | crs_Latn | 508755 | indo1319 | miq_Latn | 94343 | misu1242 | | | | ben_Beng | 43514870 | indo1319 | yes | kqn_Latn | 507913 | atla1278 | kaa_Cyrl | 88815 | turk1311 | | | | kan_Knda | 41836495 | drav1251 | yes | ndo_Latn | 496613 | atla1278 | kos_Latn | 88603 | aust1307 | | | | tel_Telu | 41580525 | drav1251 | yes | snd_Arab | 488730 | indo1319 | yes | grn_Latn | 87568 | | | | mlt_Latn | 40654838 | afro1255 | yue_Hani | 484700 | sino1245 | lhu_Latn | 87255 | sino1245 | | | | | fra_Latn | 39197581 | indo1319 | yes | tiv_Latn | 483064 | atla1278 | lzh_Hani | 86035 | sino1245 | | | | spa_Latn | 37286756 | indo1319 | yes | kua_Latn | 473535 | atla1278 | ajp_Arab | 83297 | afro1255 | | | | eng_Latn | 36122761 | indo1319 | yes | kwy_Latn | 473274 | atla1278 | cmn_Hani | 80745 | sino1245 | yes | | | fil_Latn | 33493255 | aust1307 | yes | hin_Latn | 466175 | indo1319 | gcf_Latn | 80737 | indo1319 | | | | nob_Latn | 32869205 | indo1319 | iku_Cans | 465011 | rmn_Cyrl | 79925 | indo1319 | | | | | | rus_Cyrl | 31787973 | indo1319 | yes | kal_Latn | 462430 | eski1264 | kjh_Cyrl | 79262 | turk1311 | | | | deu_Latn | 31015993 | indo1319 | yes | tdt_Latn | 459818 | aust1307 | rng_Latn | 78177 | atla1278 | | | | tur_Latn | 29184662 | turk1311 | yes | gsw_Latn | 449240 | indo1319 | mgh_Latn | 78117 | atla1278 | | | | pan_Guru | 29052537 | indo1319 | yes | mfe_Latn | 447435 | indo1319 | xmv_Latn | 77896 | aust1307 | | | | mar_Deva | 28748897 | indo1319 | yes | swc_Latn | 446378 | atla1278 | ige_Latn | 77114 | atla1278 | | | | por_Latn | 27824391 | indo1319 | yes | mon_Latn | 437950 | mong1349 | rmy_Latn | 76991 | indo1319 | | | | nld_Latn | 25061426 | indo1319 | yes | mos_Latn | 437666 | atla1278 | srm_Latn | 76884 | indo1319 | | | | ara_Arab | 24524122 | yes | kik_Latn | 437228 | atla1278 | bak_Latn | 76809 | turk1311 | | | | | zho_Hani | 24143786 | yes | cnh_Latn | 436667 | sino1245 | gur_Latn | 76151 | atla1278 | | | | | ita_Latn | 23539857 | indo1319 | yes | gil_Latn | 434529 | aust1307 | idu_Latn | 75106 | atla1278 | | | | ind_Latn | 23018106 | aust1307 | yes | pon_Latn | 434522 | aust1307 | yom_Latn | 74818 | atla1278 | | | | ell_Grek | 22033282 | indo1319 | yes | umb_Latn | 431589 | atla1278 | tdx_Latn | 74430 | aust1307 | | | | bul_Cyrl | 21823004 | indo1319 | yes | lvs_Latn | 422952 | indo1319 | mzn_Arab | 73719 | indo1319 | | | | swe_Latn | 20725883 | indo1319 | yes | sco_Latn | 411591 | indo1319 | cfm_Latn | 70227 | sino1245 | | | | ces_Latn | 20376340 | indo1319 | yes | ori_Orya | 410827 | yes | zpa_Latn | 69237 | otom1299 | | | | isl_Latn | 19547941 | indo1319 | yes | arg_Latn | 410683 | indo1319 | kbd_Cyrl | 67914 | abkh1242 | | | | pol_Latn | 19339945 | indo1319 | yes | kur_Latn | 407169 | indo1319 | yes | lao_Laoo | 66966 | taik1256 | yes | | ron_Latn | 19190217 | indo1319 | yes | dhv_Latn | 405711 | aust1307 | nap_Latn | 65826 | indo1319 | | | | dan_Latn | 19174573 | indo1319 | yes | luo_Latn | 398974 | nilo1247 | qub_Latn | 64973 | quec1387 | | | | hun_Latn | 18800025 | ural1272 | yes | lun_Latn | 395764 | atla1278 | oke_Latn | 64508 | atla1278 | | | | tgk_Cyrl | 18659517 | indo1319 | nzi_Latn | 394247 | atla1278 | ote_Latn | 64224 | otom1299 | | | | | srp_Latn | 18371769 | indo1319 | yes | gug_Latn | 392227 | tupi1275 | bsb_Latn | 63634 | aust1307 | | | | fas_Arab | 18277593 | yes | bar_Latn | 387070 | indo1319 | ogo_Latn | 61901 | atla1278 | | | | | ceb_Latn | 18149215 | aust1307 | bci_Latn | 384059 | atla1278 | abn_Latn | 61830 | atla1278 | | | | | heb_Hebr | 18128962 | afro1255 | yes | chk_Latn | 380596 | aust1307 | ldi_Latn | 61827 | atla1278 | | | | hrv_Latn | 17882932 | indo1319 | yes | roh_Latn | 377067 | indo1319 | ayr_Latn | 61570 | ayma1253 | | | | glg_Latn | 17852274 | indo1319 | yes | aym_Latn | 373329 | ayma1253 | gom_Deva | 61140 | indo1319 | | | | fin_Latn | 16730388 | ural1272 | yes | yap_Latn | 358929 | aust1307 | bba_Latn | 61123 | atla1278 | | | | slv_Latn | 15719210 | indo1319 | yes | ssw_Latn | 356561 | atla1278 | aln_Latn | 60989 | indo1319 | | | | vie_Latn | 15697827 | aust1305 | yes | quz_Latn | 354781 | quec1387 | leh_Latn | 59944 | atla1278 | | | | mkd_Cyrl | 14717004 | indo1319 | yes | sah_Cyrl | 352697 | turk1311 | ban_Latn | 59805 | aust1307 | | | | slk_Latn | 14633631 | indo1319 | yes | tsn_Latn | 350954 | atla1278 | ace_Latn | 59333 | aust1307 | | | | nor_Latn | 14576191 | indo1319 | yes | lmo_Latn | 348135 | indo1319 | pes_Arab | 57511 | indo1319 | yes | | | est_Latn | 13600579 | yes | ido_Latn | 331239 | arti1236 | skg_Latn | 57228 | aust1307 | | | | | ltz_Latn | 12997242 | indo1319 | abk_Cyrl | 321578 | abkh1242 | ary_Arab | 56933 | afro1255 | | | | | eus_Latn | 12775959 | yes | zne_Latn | 318871 | atla1278 | hus_Latn | 56176 | maya1287 | | | | | lit_Latn | 12479626 | indo1319 | yes | quy_Latn | 311040 | quec1387 | glv_Latn | 55641 | indo1319 | | | | kaz_Cyrl | 12378727 | turk1311 | yes | kam_Latn | 310659 | atla1278 | fat_Latn | 55609 | atla1278 | | | | lav_Latn | 12143980 | indo1319 | yes | bbc_Latn | 310420 | aust1307 | frr_Latn | 55254 | indo1319 | | | | bos_Latn | 11014744 | indo1319 | yes | vol_Latn | 310399 | arti1236 | mwn_Latn | 54805 | atla1278 | | | | epo_Latn | 8737198 | arti1236 | yes | wal_Latn | 309873 | gong1255 | mai_Deva | 54687 | indo1319 | | | | cat_Latn | 8648271 | indo1319 | yes | uig_Arab | 307302 | turk1311 | yes | dua_Latn | 53392 | atla1278 | | | tha_Thai | 7735209 | taik1256 | yes | vmw_Latn | 306899 | atla1278 | dzo_Tibt | 52732 | sino1245 | | | | ukr_Cyrl | 7462046 | indo1319 | yes | kwn_Latn | 305362 | atla1278 | ctd_Latn | 52135 | sino1245 | | | | tgl_Latn | 7411064 | aust1307 | yes | pam_Latn | 303737 | aust1307 | nnb_Latn | 52041 | atla1278 | | | | sin_Sinh | 7293178 | indo1319 | yes | seh_Latn | 300243 | atla1278 | sxn_Latn | 51749 | aust1307 | | | | gle_Latn | 7225513 | indo1319 | yes | tsc_Latn | 298442 | atla1278 | mps_Latn | 50645 | tebe1251 | | | | hin_Deva | 7046700 | indo1319 | yes | nyk_Latn | 297976 | atla1278 | mny_Latn | 50581 | atla1278 | | | | kor_Hang | 6468444 | kore1284 | yes | kmb_Latn | 296269 | atla1278 | gkp_Latn | 50549 | mand1469 | | | | ory_Orya | 6266475 | indo1319 | zai_Latn | 277632 | otom1299 | kat_Latn | 50424 | kart1248 | | | | | urd_Arab | 6009594 | indo1319 | yes | gym_Latn | 274512 | chib1249 | bjn_Latn | 49068 | aust1307 | | | | swa_Latn | 5989369 | yes | bod_Tibt | 273489 | sino1245 | acr_Latn | 48886 | maya1287 | | | | | sqi_Latn | 5526836 | indo1319 | yes | nde_Latn | 269931 | atla1278 | dtp_Latn | 48468 | aust1307 | | | | bel_Cyrl | 5319675 | indo1319 | yes | fon_Latn | 268566 | atla1278 | lam_Latn | 46853 | atla1278 | | | | afr_Latn | 5157787 | indo1319 | yes | ber_Latn | 264426 | bik_Latn | 46561 | | | | | | nno_Latn | 4899103 | indo1319 | nbl_Latn | 259158 | atla1278 | poh_Latn | 46454 | maya1287 | | | | | tat_Cyrl | 4708088 | turk1311 | kmr_Latn | 256677 | indo1319 | phm_Latn | 45862 | atla1278 | | | | Language-Script |Sent| Family Head Language-Script |Sent| Family Head Language-Script |Sent| Family Head ast_Latn 4683554 indo1319 guc_Latn 249044 araw1281 hrx_Latn 45716 indo1319 mon_Cyrl 4616960 mong1349 yes mam_Latn 248348 maya1287 quh_Latn 45566 quec1387 hbs_Cyrl 4598073 indo1319 nia_Latn 247406 aust1307 hyw_Cyrl 45379 indo1319 hau_Latn 4368483 afro1255 yes nyn_Latn 241992 atla1278 rue_Cyrl 45369 indo1319 sna_Latn 4019596 atla1278 cab_Latn 240101 araw1281 eml_Latn 44630 indo1319 msa_Latn 3929084 yes top_Latn 239232 toto1251 acm_Arab 44505 afro1255 som_Latn 3916769 afro1255 yes tog_Latn 231969 atla1278 tob_Latn 44473 guai1249 srp_Cyrl 3864091 indo1319 yes mco_Latn 231209 mixe1284 ach_Latn 43974 nilo1247 mlg_Latn 3715802 yes tzh_Latn 230706 maya1287 vep_Latn 43076 ural1272 zul_Latn 3580113 atla1278 pms_Latn 227748 indo1319 npi_Deva 43072 indo1319 arz_Arab 3488224 afro1255 wuu_Hani 224088 sino1245 tok_Latn 42820 arti1236 nya_Latn 3409030 atla1278 plt_Latn 220413 aust1307 sgs_Latn 42467 indo1319 tam_Taml 3388255 drav1251 yes yid_Hebr 220214 indo1319 yes lij_Latn 42447 indo1319 hat_Latn 3226932 indo1319 ada_Latn 219427 atla1278 myv_Cyrl 42147 ural1272 uzb_Latn 3223485 turk1311 yes iba_Latn 213615 aust1307 tih_Latn 41873 aust1307 sot_Latn 3205510 atla1278 kek_Latn 209932 maya1287 tat_Latn 41640 turk1311 uzb_Cyrl 3029947 turk1311 koo_Latn 209375 atla1278 lfn_Latn 41632 arti1236 cos_Latn 3015055 indo1319 sop_Latn 206501 atla1278 cgg_Latn 41196 atla1278 als_Latn 2954874 indo1319 kac_Latn 205542 sino1245 ful_Latn 41188 atla1278 amh_Ethi 2862985 afro1255 yes qvi_Latn 205447 quec1387 gor_Latn 41174 aust1307 sun_Latn 2586011 aust1307 yes cak_Latn 204472 maya1287 ile_Latn 40984 arti1236 war_Latn 2584810 aust1307 kbp_Latn 202877 atla1278 ium_Latn 40683 hmon1336 div_Thaa 2418687 indo1319 ctu_Latn 201662 maya1287 teo_Latn 40203 nilo1247 yor_Latn 2392359 atla1278 kri_Latn 201087 indo1319 kia_Latn 40035 atla1278 fao_Latn 2365271 indo1319 mau_Latn 199134 otom1299 crh_Cyrl 39985 turk1311 uzn_Cyrl 2293672 turk1311 scn_Latn 199068 indo1319 crh_Latn 39896 turk1311 smo_Latn 2290439 aust1307 tyv_Cyrl 198649 turk1311 enm_Latn 39809 indo1319 bak_Cyrl 2264196 turk1311 ina_Latn 197315 arti1236 sat_Olck 39614 aust1305 ilo_Latn 2106531 aust1307 btx_Latn 193701 aust1307 mad_Latn 38993 aust1307 tso_Latn 2100708 atla1278 nch_Latn 193129 utoa1244 cac_Latn 38812 maya1287 mri_Latn 2046850 aust1307 ncj_Latn 192962 utoa1244 hnj_Latn 38611 hmon1336 hmn_Latn 1903898 pau_Latn 190529 aust1307 ksh_Latn 38130 indo1319 asm_Beng 1882353 indo1319 yes toj_Latn 189651 maya1287 ikk_Latn 38071 atla1278 hil_Latn 1798875 aust1307 pcm_Latn 187594 indo1319 sba_Latn 38040 cent2225 nso_Latn 1619354 atla1278 dyu_Latn 186367 mand1469 zom_Latn 37013 sino1245 ibo_Latn 1543820 atla1278 kss_Latn 185868 atla1278 bqc_Latn 36881 mand1469 kin_Latn 1521612 atla1278 afb_Arab 183694 afro1255 bim_Latn 36835 atla1278 hye_Armn 1463123 indo1319 yes urh_Latn 182214 atla1278 mdy_Ethi 36370 gong1255 oci_Latn 1449128 indo1319 quc_Latn 181559 maya1287 bts_Latn 36216 aust1307 lin_Latn 1408460 atla1278 new_Deva 181427 sino1245 gya_Latn 35902 atla1278 tpi_Latn 1401844 indo1319 yao_Latn 179965 atla1278 ajg_Latn 35631 atla1278 twi_Latn 1400979 atla1278 ngl_Latn 178498 atla1278 agw_Latn 35585 aust1307 kir_Cyrl 1397566 turk1311 yes nyu_Latn 177483 atla1278 kom_Cyrl 35249 ural1272 pap_Latn 1360138 indo1319 kab_Latn 176015 afro1255 knv_Latn 35196 nep_Deva 1317291 indo1319 yes tuk_Cyrl 175769 turk1311 giz_Latn 35040 afro1255 azj_Latn 1315834 turk1311 xmf_Geor 174994 kart1248 hui_Latn 34926 nucl1709 bcl_Latn 1284493 aust1307 ndc_Latn 174305 atla1278 kpg_Latn 34900 aust1307 xho_Latn 1262364 atla1278 yes san_Deva 165616 indo1319 yes zea_Latn 34426 indo1319 cym_Latn 1244783 indo1319 yes nba_Latn 163485 atla1278 aoj_Latn 34349 nucl1708 gaa_Latn 1222307 atla1278 bpy_Beng 162838 indo1319 csy_Latn 34126 sino1245 ton_Latn 1216118 aust1307 ncx_Latn 162558 utoa1244 azb_Arab 33758 turk1311 yes tah_Latn 1190747 aust1307 qug_Latn 162500 quec1387 csb_Latn 33743 indo1319 lat_Latn 1179913 indo1319 yes rmn_Latn 162069 indo1319 tpm_Latn 33517 atla1278 srn_Latn 1172349 indo1319 cjk_Latn 160645 atla1278 quw_Latn 33449 quec1387 ewe_Latn 1161605 atla1278 arb_Arab 159884 afro1255 yes rmy_Cyrl 33351 indo1319 bem_Latn 1111969 atla1278 kea_Latn 158047 indo1319 ixl_Latn 33289 maya1287 efi_Latn 1082621 atla1278 mck_Latn 157521 atla1278 mbb_Latn 33240 aust1307 bis_Latn 1070170 indo1319 arn_Latn 155882 arau1255 pfl_Latn 33148 indo1319 orm_Latn 1067699 yes pdt_Latn 155485 indo1319 pcd_Latn 32867 indo1319 haw_Latn 1062491 aust1307 her_Latn 154827 atla1278 tlh_Latn 32863 arti1236 hmo_Latn 1033636 pidg1258 gla_Latn 152563 indo1319 yes suz_Deva 32811 sino1245 kat_Geor 1004297 kart1248 yes kmr_Cyrl 151728 indo1319 gcr_Latn 32676 indo1319 pag_Latn 983637 aust1307 mwl_Latn 150054 indo1319 jbo_Latn 32619 arti1236 loz_Latn 964418 atla1278 nav_Latn 147702 atha1245 tbz_Latn 32264 atla1278 fry_Latn 957422 indo1319 yes ksw_Mymr 147674 sino1245 bam_Latn 32150 mand1469 mya_Mymr 945180 sino1245 yes mxv_Latn 147591 otom1299 prk_Latn 32085 aust1305 nds_Latn 944715 indo1319 hif_Latn 147261 indo1319 jam_Latn 32048 indo1319 run_Latn 943828 atla1278 wol_Latn 146992 atla1278 twx_Latn 32028 atla1278 Table 12: List of languages used to train Glot500-m (Part II). Language-Script |Sent| Family Head Language-Script |Sent| Family Head Language-Script |Sent| Family Head pnb_Arab 899895 indo1319 sme_Latn 146803 ural1272 nmf_Latn 31997 sino1245 rar_Latn 894515 aust1307 gom_Latn 143937 indo1319 caq_Latn 31903 aust1305 fij_Latn 887134 aust1307 bum_Latn 141673 atla1278 rop_Latn 31889 indo1319 wls_Latn 882167 aust1307 mgr_Latn 138953 atla1278 tca_Latn 31852 ticu1244 ckb_Arab 874441 indo1319 ahk_Latn 135068 sino1245 yan_Latn 31775 misu1242 ven_Latn 860249 atla1278 kur_Arab 134160 indo1319 xav_Latn 31765 nucl1710 zsm_Latn 859947 aust1307 yes bas_Latn 133436 atla1278 bih_Deva 31658 chv_Cyrl 859863 turk1311 bin_Latn 133256 atla1278 cuk_Latn 31612 chib1249 lua_Latn 854359 atla1278 tsz_Latn 133251 tara1323 kjb_Latn 31471 maya1287 que_Latn 838486 sid_Latn 130406 afro1255 hne_Deva 31465 indo1319 sag_Latn 771048 atla1278 diq_Latn 128908 indo1319 wbm_Latn 31394 aust1305 guw_Latn 767918 atla1278 srd_Latn 127064 zlm_Latn 31345 aust1307 bre_Latn 748954 indo1319 yes tcf_Latn 126050 otom1299 tui_Latn 31161 atla1278 toi_Latn 745385 atla1278 bzj_Latn 124958 indo1319 ifb_Latn 30980 aust1307 pus_Arab 731992 indo1319 yes udm_Cyrl 121705 ural1272 izz_Latn 30894 atla1278 che_Cyrl 728201 nakh1245 cce_Latn 120636 atla1278 rug_Latn 30857 aust1307 pis_Latn 714783 indo1319 meu_Latn 120273 aust1307 aka_Latn 30704 atla1278 kon_Latn 685194 chw_Latn 119751 atla1278 pxm_Latn 30698 book1242 oss_Cyrl 683517 indo1319 cbk_Latn 118789 indo1319 kmm_Latn 30671 sino1245 hyw_Armn 679819 indo1319 ibg_Latn 118733 aust1307 mcn_Latn 30666 afro1255 iso_Latn 658789 atla1278 bhw_Latn 117381 aust1307 ifa_Latn 30621 aust1307 nan_Latn 656389 sino1245 ngu_Latn 116851 utoa1244 dln_Latn 30620 sino1245 lub_Latn 654390 atla1278 nyy_Latn 115914 atla1278 ext_Latn 30605 indo1319 lim_Latn 652078 indo1319 szl_Latn 112496 indo1319 ksd_Latn 30550 aust1307 tuk_Latn 649411 turk1311 ish_Latn 111814 atla1278 mzh_Latn 30517 mata1289 tir_Ethi 649117 afro1255 naq_Latn 109747 khoe1240 llb_Latn 30480 atla1278 tgk_Latn 636541 indo1319 toh_Latn 107583 atla1278 hra_Latn 30472 sino1245 yua_Latn 610052 maya1287 ttj_Latn 106925 atla1278 mwm_Latn 30432 cent2225 min_Latn 609065 aust1307 nse_Latn 105189 atla1278 krc_Cyrl 30353 turk1311 lue_Latn 599429 atla1278 hsb_Latn 104802 indo1319 tuc_Latn 30349 aust1307 khm_Khmr 590429 aust1305 yes ami_Latn 104559 aust1307 mrw_Latn 30304 aust1307 tum_Latn 589857 atla1278 alz_Latn 104392 nilo1247 pls_Latn 30136 otom1299 tll_Latn 586530 atla1278 apc_Arab 102392 afro1255 rap_Latn 30102 aust1307 ekk_Latn 582595 ural1272 vls_Latn 101900 indo1319 fur_Latn 30052 indo1319 lug_Latn 566948 atla1278 mhr_Cyrl 100474 ural1272 kaa_Latn 30031 turk1311 niu_Latn 566715 aust1307 djk_Latn 99234 indo1319 prs_Arab 26823 indo1319 yes tzo_Latn 540262 maya1287 wes_Latn 98492 indo1319 san_Latn 25742 indo1319 yes mah_Latn 534614 aust1307 gkn_Latn 97041 atla1278 som_Arab 14199 afro1255 yes tvl_Latn 521556 aust1307 grc_Grek 96986 indo1319 uig_Latn 9637 turk1311 yes jav_Latn 516833 aust1307 yes hbo_Hebr 96484 afro1255 hau_Arab 9593 afro1255 yes Table 13: List of languages used to train Glot500-m (Part III). guages (Abate et al., 2018), Phontron (Neubig, 2011), QADI (Abdelali et al., 2021), Quechua-IIC (Zevallos et al., 2022), SLI_GalWeb.1.0 (Agerri et al., 2018), Shami (Abu Kwaik et al., 2018), Stanford NLP,23 StatMT,24 TICO (Anastasopoulos et al., 2020), TIL (Mirzakhalov et al., 2021), Tatoeba,25 TeDDi (Moran et al., 2022), Tilde (Rozis and Skadin,š, 2017), W2C (Majliš, 2011), WAT (Nakazawa et al., 2022), WikiMatrix (Schwenk et al., 2021), Wikipedia,26 Workshop on NER for South and South East Asian Languages (Singh, 2008), XLSum (Hasan et al., 2021). ## D Results For Each Task And Language We report the detailed results for all tasks and languages in Table 14 (Sentence Retrieval Tatoeba), 15, 16 (Sentence Retrieval Bible), 17 (NER), and 18 (POS), 19, 20 (Text Classification), 21, 22 (Round Trip Alignment). ## E Perplexity Results For All Languages Perplexity number for all languages is presented in Table 23, Table 24, and Table 25. afr_Latn 71.9 76.5 **81.1** heb_Hebr 76.3 **84.1** 76.0 pam_Latn 4.8 5.6 **11.0** amh_Ethi 35.1 37.5 **44.6** hin_Deva 73.8 **88.8** 85.6 pes_Arab 83.3 86.6 **87.6** ara_Arab 59.2 **66.8** 64.2 hrv_Latn 79.6 85.6 **89.8** pms_Latn 16.6 12.6 **54.5** arz_Arab 32.5 47.8 **63.5** hsb_Latn 21.5 23.0 **53.6** pol_Latn 82.6 **89.6** 82.4 ast_Latn 59.8 59.8 **87.4** hun_Latn 76.1 **81.8** 69.2 por_Latn 91.0 **92.1** 90.1 aze_Latn 62.6 78.3 **79.9** hye_Armn 64.6 40.0 **83.2** ron_Latn 86.0 **89.1** 82.8 bel_Cyrl 70.0 80.5 **81.4** ido_Latn 25.7 28.8 **57.6** rus_Cyrl 89.6 **91.6** 91.5 ben_Beng 54.1 68.2 **69.4** ile_Latn 34.6 41.9 **75.6** slk_Latn 73.2 **80.6** 75.9 bos_Latn 78.5 82.2 **92.4** ina_Latn 62.7 66.2 **91.4** slv_Latn 72.1 **78.0** 77.0 bre_Latn 10.3 10.9 **19.9** ind_Latn 84.3 **90.2** 88.8 spa_Latn 85.5 **89.0** 88.9 bul_Cyrl 84.4 **88.3** 86.7 isl_Latn 78.7 **84.5** 84.0 sqi_Latn 72.2 81.4 **84.7** cat_Latn 72.8 73.9 **78.7** ita_Latn 81.3 84.7 **86.4** srp_Latn 78.1 85.0 **90.0** cbk_Latn 33.2 36.0 **49.4** jpn_Jpan 74.4 **80.8** 72.6 swe_Latn 90.4 **92.4** 89.7 ceb_Latn 15.2 15.0 **41.3** kab_Latn 3.7 3.0 **16.4** swh_Latn 30.3 34.6 **44.1** ces_Latn 71.1 **81.3** 75.1 kat_Geor 61.1 **79.1** 67.7 tam_Taml 46.9 42.3 **66.4** cmn_Hani 79.5 84.8 **85.6** kaz_Cyrl 60.3 69.9 **72.3** tat_Cyrl 10.3 10.3 **70.3** csb_Latn 21.3 20.2 **40.3** khm_Khmr 41.1 45.0 **52.5** tel_Telu 58.5 50.4 **67.9** cym_Latn 45.7 45.7 **55.7** kor_Hang 73.4 **84.3** 78.0 tgl_Latn 47.6 54.2 **77.1** dan_Latn 91.9 **93.9** 91.5 kur_Latn 24.1 28.5 **54.1** tha_Thai 56.8 39.4 **78.1** deu_Latn **95.9** 94.7 95.0 lat_Latn 33.6 **48.0** 42.8 tuk_Latn 16.3 14.8 **63.5** dtp_Latn 5.6 4.7 **21.1** lfn_Latn 32.5 35.9 **59.3** tur_Latn 77.9 **85.4** 78.4 ell_Grek 76.2 **84.1** 80.2 lit_Latn 73.4 **76.8** 65.6 uig_Arab 38.8 58.3 **62.6** epo_Latn 64.9 68.5 **74.3** lvs_Latn 73.4 **78.9** 76.9 ukr_Cyrl 77.1 **88.3** 83.7 est_Latn 63.9 68.6 **69.1** mal_Mlym 80.1 **84.4** 83.8 urd_Arab 54.4 34.3 **80.9** eus_Latn 45.9 **54.4** 52.7 mar_Deva 63.5 **81.2** 77.9 uzb_Cyrl 25.2 32.2 **64.5** fao_Latn 45.0 42.7 **82.4** mhr_Cyrl 6.5 5.8 **34.9** vie_Latn 85.4 **87.9** 87.0 fin_Latn 81.9 **85.8** 72.3 mkd_Cyrl 70.5 **83.9** 81.4 war_Latn 8.0 6.5 **26.2** fra_Latn 85.7 85.8 **86.0** mon_Cyrl 60.9 **77.3** 77.0 wuu_Hani 56.1 47.4 **79.7** fry_Latn 60.1 62.4 **75.1** nds_Latn 28.8 29.0 **77.1** xho_Latn 28.9 31.7 **56.3** gla_Latn 21.0 21.2 **41.9** nld_Latn 90.3 **91.8 91.8** yid_Hebr 37.3 51.8 **74.4** gle_Latn 32.0 36.9 **50.8** nno_Latn 70.7 77.8 **87.8** yue_Hani 50.3 42.3 **76.3** glg_Latn 72.6 75.8 **77.5** nob_Latn 93.5 **96.5** 95.7 zsm_Latn 81.4 87.4 **91.8** gsw_Latn 36.8 31.6 **69.2** oci_Latn 22.9 23.2 **46.9** ace_Latn 4.4 4.6 **53.4** iba_Latn 14.4 13.6 **66.0** pan_Guru 43.2 **59.4** 48.8 ach_Latn 4.4 3.2 **40.0** ibo_Latn 5.0 3.0 **30.4** pap_Latn 12.4 9.2 **72.4** acr_Latn 2.6 3.4 **25.4** ifa_Latn 4.4 4.4 **39.2** pau_Latn 4.4 4.0 **29.8** afr_Latn 76.8 **77.2** 69.4 ifb_Latn 4.8 3.6 **36.6** pcm_Latn 13.6 10.4 **66.8** agw_Latn 5.8 3.0 **36.0** ikk_Latn 3.0 3.2 **50.6** pdt_Latn 9.2 8.6 **68.6** ahk_Latn 3.0 2.6 3.2 ilo_Latn 6.2 3.6 **55.0** pes_Arab 69.4 72.2 **80.8** aka_Latn 5.0 4.2 **57.0** ind_Latn **82.6** 80.4 72.2 pis_Latn 6.4 5.0 **57.2** aln_Latn 67.8 **72.4** 67.6 isl_Latn 62.6 **73.6** 66.0 pls_Latn 5.0 4.0 **34.4** als_Latn 51.4 48.0 **55.8** ita_Latn **75.4** 73.6 70.0 plt_Latn 26.6 28.0 **59.8** alt_Cyrl 12.6 9.0 **50.8** ium_Latn 3.2 3.0 **24.8** poh_Latn 3.4 2.4 **15.2** alz_Latn 4.6 3.8 **34.6** ixl_Latn 4.0 3.0 **18.4** pol_Latn 79.2 **79.8** 63.8 amh_Ethi 35.4 43.2 **52.8** izz_Latn 2.8 2.8 **25.6** pon_Latn 5.6 4.4 **21.6** aoj_Latn 5.0 3.0 **20.4** jam_Latn 6.6 4.4 **67.8** por_Latn **81.6** 79.8 76.6 arb_Arab 7.0 7.8 **14.6** jav_Latn 25.4 33.2 **47.4** prk_Latn 3.6 2.2 **49.8** arn_Latn 4.8 4.0 **28.4** jpn_Jpan 65.0 **71.8** 64.2 prs_Arab 79.4 78.6 **88.8** ary_Arab 2.8 4.0 **15.2** kaa_Cyrl 17.6 24.8 **73.8** pxm_Latn 3.2 3.2 **24.0** arz_Arab 5.4 4.8 **24.8** kaa_Latn 9.2 9.8 **43.4** qub_Latn 4.6 3.6 **43.4** asm_Beng 26.2 40.6 **66.6** kab_Latn 3.4 2.4 **20.6** quc_Latn 3.6 2.8 **24.8** ayr_Latn 4.8 4.8 **52.8** kac_Latn 3.6 3.2 **26.4** qug_Latn 4.8 3.6 **50.8** azb_Arab 7.4 6.8 **72.4** kal_Latn 3.4 3.6 **23.2** quh_Latn 4.6 4.4 **56.2** aze_Latn 71.0 **78.6** 73.0 kan_Knda 51.2 **67.6** 50.2 quw_Latn 6.2 4.6 **49.2** bak_Cyrl 5.4 6.4 **65.2** kat_Geor 54.2 **61.4** 51.4 quy_Latn 4.6 4.6 **61.4** bam_Latn 3.4 3.6 **60.2** kaz_Cyrl 61.4 **73.0** 56.8 quz_Latn 4.8 4.2 **68.0** ban_Latn 9.0 9.8 **33.0** kbp_Latn 2.6 2.6 **36.0** qvi_Latn 4.4 3.4 **46.8** bar_Latn 13.4 12.8 **40.8** kek_Latn 5.0 3.4 **26.4** rap_Latn 3.2 3.2 **25.6** bba_Latn 3.8 3.4 **36.8** khm_Khmr 28.4 42.6 **47.6** rar_Latn 3.2 3.0 **26.6** bbc_Latn 7.8 7.4 **57.2** kia_Latn 4.0 5.6 **33.2** rmy_Latn 6.8 5.8 **34.6** bci_Latn 4.4 3.6 **13.2** kik_Latn 3.2 2.8 **53.4** ron_Latn **72.2** 69.6 66.6 bcl_Latn 10.2 11.2 **79.8** kin_Latn 5.0 5.0 **59.4** rop_Latn 4.6 3.4 **46.0** bel_Cyrl 67.2 **72.8** 55.8 kir_Cyrl 54.8 **70.2** 66.6 rug_Latn 3.6 3.4 **49.0** bem_Latn 6.6 5.4 **58.2** kjb_Latn 4.0 3.8 **29.6** run_Latn 5.4 6.4 **54.6** ben_Beng 46.4 52.8 **53.4** kjh_Cyrl 11.0 7.8 **53.8** rus_Cyrl **75.8** 74.6 71.2 bhw_Latn 4.4 6.0 **47.8** kmm_Latn 4.8 3.8 **42.6** sag_Latn 6.0 4.4 **52.4** bim_Latn 4.2 2.8 **52.2** kmr_Cyrl 4.0 4.2 **42.4** sah_Cyrl 6.2 4.6 **45.8** bis_Latn 7.0 4.6 **48.6** kmr_Latn 35.8 40.4 **63.0** san_Deva 13.8 14.2 **27.2** bod_Tibt 2.0 1.8 **33.2** knv_Latn 2.8 2.2 9.0 san_Latn 4.6 3.8 9.8 bqc_Latn 3.4 3.0 **39.2** kor_Hang 64.0 **71.6** 61.2 sba_Latn 2.8 2.8 **37.6** bre_Latn 17.6 23.4 **32.8** kpg_Latn 5.2 3.8 **51.8** seh_Latn 6.4 4.8 **74.6** bts_Latn 6.0 5.0 **56.4** krc_Cyrl 9.2 10.2 **63.0** sin_Sinh 44.8 **56.6** 45.0 btx_Latn 11.0 9.0 **59.6** kri_Latn 2.8 2.8 **62.8** slk_Latn **75.2** 72.8 63.6 bul_Cyrl **81.2** 78.0 76.4 ksd_Latn 7.0 5.4 **42.6** slv_Latn 63.6 **64.6** 51.8 bum_Latn 4.8 3.6 **38.0** kss_Latn 2.2 2.4 6.0 sme_Latn 6.8 6.2 **47.8** bzj_Latn 7.8 4.0 **75.0** ksw_Mymr 1.6 2.0 **31.8** smo_Latn 4.4 3.4 **36.0** cab_Latn 5.8 4.6 **17.4** kua_Latn 4.8 5.4 **43.8** sna_Latn 7.0 3.6 **43.0** cac_Latn 3.6 3.0 **14.8** lam_Latn 4.6 3.6 **27.4** snd_Arab 52.2 64.6 **66.6** cak_Latn 3.4 3.4 **21.4** lao_Laoo 31.4 **52.8** 49.6 som_Latn 22.2 29.0 **33.0** caq_Latn 3.2 4.4 **30.2** lat_Latn 52.2 **57.8** 49.6 sop_Latn 5.2 4.2 **31.2** cat_Latn **86.6** 81.0 76.4 lav_Latn 74.2 **78.0** 58.8 sot_Latn 6.0 4.8 **52.2** cbk_Latn 31.8 35.6 **54.6** ldi_Latn 5.4 4.4 **25.2** spa_Latn **81.2** 78.8 80.0 cce_Latn 5.2 4.6 **51.8** leh_Latn 5.6 4.0 **58.2** sqi_Latn 58.2 58.2 **63.4** ceb_Latn 14.2 12.6 **68.0** lhu_Latn 2.0 2.0 5.0 srm_Latn 4.0 3.2 **32.4** ces_Latn 75.2 **75.8** 58.0 lin_Latn 6.6 5.4 **65.4** srn_Latn 6.8 5.2 **79.8** cfm_Latn 4.6 4.0 **46.8** lit_Latn **74.4** 71.6 62.4 srp_Cyrl 83.0 **87.0** 81.2 che_Cyrl 3.4 3.4 **14.0** loz_Latn 6.8 4.6 **49.2** srp_Latn 85.0 **87.2** 81.2 chk_Latn 5.4 4.2 **41.2** ltz_Latn 9.8 10.0 **73.8** ssw_Latn 4.8 8.4 **47.0** chv_Cyrl 4.6 4.2 **56.0** lug_Latn 4.6 4.0 **49.4** sun_Latn 22.4 25.4 **43.0** ckb_Arab 4.0 4.8 **47.2** luo_Latn 6.4 4.4 **40.8** suz_Deva 3.6 3.4 **34.2** cmn_Hani 39.2 40.8 **41.8** lus_Latn 3.8 3.8 **54.4** swe_Latn **79.8 79.8** 78.0 cnh_Latn 4.8 4.2 **55.6** lzh_Hani 25.0 31.4 **63.4** swh_Latn 47.8 48.8 **66.4** crh_Cyrl 8.8 11.2 **75.2** mad_Latn 7.6 4.4 **44.4** sxn_Latn 4.8 4.8 **25.8** crs_Latn 7.4 5.2 **80.6** mah_Latn 4.8 4.2 **35.6** tam_Taml 42.8 **56.8** 52.0 csy_Latn 3.8 5.0 **50.0** mai_Deva 6.4 9.6 **59.2** tat_Cyrl 8.2 6.2 **67.2** ctd_Latn 4.2 5.4 **59.4** mal_Mlym 49.4 **62.6** 56.8 tbz_Latn 2.6 2.6 **28.0** ctu_Latn 2.8 2.8 **21.6** mam_Latn 3.8 3.2 **12.8** tca_Latn 2.4 3.2 **15.4** cuk_Latn 5.0 3.4 **22.2** mar_Deva 66.2 69.0 **74.8** tdt_Latn 6.2 5.0 **62.2** cym_Latn 38.8 **46.0** 42.4 mau_Latn 2.4 2.4 3.6 tel_Telu 44.4 **57.2** 42.6 dan_Latn 71.6 **73.2** 63.2 mbb_Latn 3.0 3.4 **33.6** teo_Latn 5.8 3.4 **26.0** deu_Latn 78.8 **80.6** 66.6 mck_Latn 5.2 3.6 **57.4** tgk_Cyrl 4.6 4.2 **71.2** djk_Latn 4.6 4.0 **40.4** mcn_Latn 6.0 4.2 **39.2** tgl_Latn 61.0 60.6 **78.6** dln_Latn 5.2 4.8 **66.4** mco_Latn 2.6 2.6 7.0 tha_Thai 30.0 37.0 **45.4** Table 15: Top10 accuracy of XLM-R-B, XLM-R-L, and Glot500-m on Sentence Retrieval Bible (Part I). dtp_Latn 5.4 4.2 **24.2** mdy_Ethi 2.8 2.4 **31.6** tih_Latn 5.2 4.4 **51.6** dyu_Latn 4.2 2.4 **50.2** meu_Latn 5.6 4.4 **52.0** tir_Ethi 7.4 6.2 **43.4** dzo_Tibt 2.2 2.0 **36.4** mfe_Latn 9.0 6.8 **78.6** tlh_Latn 7.8 6.4 **72.4** efi_Latn 4.4 4.2 **54.0** mgh_Latn 5.2 3.4 **23.6** tob_Latn 2.2 3.0 **16.8** ell_Grek 52.6 **53.8** 48.6 mgr_Latn 4.0 4.4 **57.6** toh_Latn 4.0 4.0 **47.2** enm_Latn 39.8 39.2 **66.0** mhr_Cyrl 6.6 5.4 **48.0** toi_Latn 4.2 4.4 **47.4** epo_Latn **64.6** 59.8 56.2 min_Latn 9.4 6.2 **29.0** toj_Latn 4.2 4.0 **15.6** est_Latn 72.0 **75.6** 56.4 miq_Latn 4.4 4.4 **47.4** ton_Latn 4.2 3.8 **22.4** eus_Latn 26.2 **28.4** 23.0 mkd_Cyrl **76.6** 72.6 74.8 top_Latn 3.4 3.6 8.0 ewe_Latn 4.6 3.0 **49.0** mlg_Latn 29.0 28.4 **66.0** tpi_Latn 5.8 4.4 **58.0** fao_Latn 24.0 28.4 **73.4** mlt_Latn 5.8 5.2 **50.4** tpm_Latn 3.6 3.0 **39.6** fas_Arab 78.2 80.4 **89.2** mos_Latn 4.2 3.6 **42.8** tsn_Latn 5.4 3.6 **41.8** fij_Latn 3.8 3.0 **36.4** mps_Latn 3.2 3.2 **21.6** tso_Latn 5.6 5.0 **50.8** fil_Latn 60.4 64.4 **72.0** mri_Latn 4.2 3.8 **48.4** tsz_Latn 5.6 3.2 **27.0** fin_Latn **75.6** 75.0 53.8 mrw_Latn 6.0 4.4 **52.2** tuc_Latn 2.6 2.6 **31.4** fon_Latn 2.6 2.0 **33.4** msa_Latn 40.0 40.2 **40.6** tui_Latn 3.6 3.2 **38.0** fra_Latn **88.6** 86.8 79.2 mwm_Latn 2.6 2.6 **35.8** tuk_Cyrl 13.6 15.8 **65.0** fry_Latn 27.8 27.4 **44.0** mxv_Latn 3.0 3.4 8.8 tuk_Latn 9.6 9.6 **66.2** gaa_Latn 3.8 3.4 **47.0** mya_Mymr 20.2 27.8 **29.4** tum_Latn 5.2 4.6 **66.2** gil_Latn 5.6 3.6 **36.8** myv_Cyrl 4.6 4.0 **35.0** tur_Latn 74.4 **74.8** 63.2 giz_Latn 6.2 4.0 **41.0** mzh_Latn 4.6 3.2 **36.2** twi_Latn 3.8 3.0 **50.0** gkn_Latn 4.0 3.4 **32.2** nan_Latn 3.2 3.2 **13.6** tyv_Cyrl 6.8 7.0 **46.6** gkp_Latn 3.0 3.2 **20.4** naq_Latn 3.0 2.2 **25.0** tzh_Latn 6.0 5.2 **25.8** gla_Latn 25.2 26.6 **43.0** nav_Latn 2.4 2.8 **11.2** tzo_Latn 3.8 3.8 **16.6** gle_Latn 35.0 38.6 **40.0** nbl_Latn 9.2 11.8 **53.8** udm_Cyrl 6.0 5.0 **55.2** glv_Latn 5.8 3.6 **47.4** nch_Latn 4.4 3.0 **21.4** uig_Arab 45.8 **63.6** 56.2 gom_Latn 6.0 4.6 **42.8** ncj_Latn 4.6 3.0 **25.2** uig_Latn 9.8 11.0 **62.8** gor_Latn 3.8 3.0 **26.0** ndc_Latn 5.2 4.6 **40.0** ukr_Cyrl **66.0** 63.4 57.0 grc_Grek 17.4 23.8 **54.8** nde_Latn 13.0 15.2 **53.8** urd_Arab 47.6 47.0 **65.0** guc_Latn 3.4 2.6 **13.0** ndo_Latn 5.2 4.0 **48.2** uzb_Cyrl 6.2 7.4 **78.8** gug_Latn 4.6 3.2 **36.0** nds_Latn 9.6 8.4 **43.0** uzb_Latn 54.8 60.8 **67.6** guj_Gujr 53.8 71.2 **71.4** nep_Deva 35.6 50.6 **58.6** uzn_Cyrl 5.4 5.4 **87.0** gur_Latn 3.8 2.8 **27.0** ngu_Latn 4.6 3.4 **27.6** ven_Latn 4.8 4.2 **47.2** guw_Latn 4.0 3.4 **59.4** nia_Latn 4.6 3.2 **29.4** vie_Latn **72.8** 71.0 57.8 gya_Latn 3.6 3.0 **41.0** nld_Latn **78.0** 75.8 71.8 wal_Latn 4.2 5.4 **51.4** gym_Latn 3.6 3.8 **18.0** nmf_Latn 4.6 4.6 **36.6** war_Latn 9.8 6.6 **43.4** hat_Latn 6.0 4.2 **68.2** nnb_Latn 3.6 3.2 **42.0** wbm_Latn 3.8 2.4 **46.4** hau_Latn 28.8 36.0 **54.8** nno_Latn 58.4 67.2 **72.6** wol_Latn 4.6 4.4 **35.8** haw_Latn 4.2 3.4 **38.8** nob_Latn 82.8 **85.2** 79.2 xav_Latn 2.2 2.4 5.0 heb_Hebr 25.0 **26.0** 21.8 nor_Latn 81.2 84.2 **86.2** xho_Latn 10.4 16.2 **40.8** hif_Latn 12.2 16.4 **39.0** npi_Deva 50.6 70.8 **76.6** yan_Latn 4.2 3.4 **31.8** hil_Latn 11.0 10.8 **76.2** nse_Latn 5.2 5.0 **54.8** yao_Latn 4.4 3.8 **55.2** hin_Deva 67.0 72.8 **76.6** nso_Latn 6.0 4.2 **57.0** yap_Latn 4.0 4.0 **24.0** hin_Latn 13.6 16.0 **43.2** nya_Latn 4.0 4.6 **60.2** yom_Latn 4.8 3.6 **42.2** hmo_Latn 6.4 4.4 **48.2** nyn_Latn 4.4 4.2 **51.8** yor_Latn 3.4 3.6 **37.4** hne_Deva 13.4 14.8 **75.0** nyy_Latn 3.0 3.0 **25.6** yua_Latn 3.8 3.4 **18.2** hnj_Latn 2.8 2.8 **54.2** nzi_Latn 3.2 3.0 **47.2** yue_Hani 17.2 14.0 **24.0** hra_Latn 5.2 4.6 **52.2** ori_Orya 42.6 **62.0** 57.0 zai_Latn 6.2 4.2 **38.0** hrv_Latn 79.8 **81.8** 72.6 ory_Orya 31.4 47.0 **55.2** zho_Hani 40.4 40.2 **44.4** hui_Latn 3.8 3.0 **28.0** oss_Cyrl 4.2 3.6 **54.8** zlm_Latn 83.4 78.4 **87.0** hun_Latn 76.4 **78.2** 56.2 ote_Latn 3.6 2.4 **18.0** zom_Latn 3.6 3.4 **50.2** hus_Latn 3.6 3.2 **17.6** pag_Latn 8.0 5.0 **61.2** zsm_Latn 90.2 **91.0** 83.0 hye_Armn 30.8 33.0 **75.2** pam_Latn 8.2 7.0 **49.8** zul_Latn 11.0 16.0 **49.0** Table 16: Top10 accuracy of XLM-R-B, XLM-R-L, and Glot500-m on Sentence Retrieval Bible (Part II). ace_Latn 33.4 38.9 **44.2** heb_Hebr 51.5 **56.5** 49.0 ori_Orya **31.4** 27.6 31.0 afr_Latn 75.6 **78.3** 76.7 hin_Deva 67.0 **71.1** 69.4 oss_Cyrl 33.7 39.2 **52.1** als_Latn 60.7 61.4 **80.0** hrv_Latn 77.2 **78.9** 77.3 pan_Guru 50.0 **50.5** 48.1 amh_Ethi 42.2 40.9 **45.4** hsb_Latn 64.0 69.0 **71.2** pms_Latn 71.2 74.9 **75.9** ara_Arab 44.7 48.7 **56.1** hun_Latn 76.2 **79.8** 75.9 pnb_Arab 57.0 64.6 **65.8** arg_Latn 73.6 74.6 **77.2** hye_Armn 50.8 **61.7** 54.8 pol_Latn 77.5 **81.2** 78.1 arz_Arab 48.3 52.5 **57.4** ibo_Latn 40.8 42.8 **58.6** por_Latn 77.8 **81.2** 78.6 asm_Beng 53.2 **64.4** 64.2 ido_Latn 61.6 **78.6** 77.8 pus_Arab 37.4 39.9 **41.4** ast_Latn 78.1 82.8 **84.5** ilo_Latn 55.3 65.3 **77.1** que_Latn 59.1 55.2 **66.8** aym_Latn 40.8 38.7 **47.1** ina_Latn 54.7 **63.4** 58.0 roh_Latn 52.6 55.7 **60.3** aze_Latn 62.4 **69.2** 66.1 ind_Latn 49.0 54.1 **56.6** ron_Latn 74.8 **79.9** 74.2 bak_Cyrl 35.1 49.3 **59.4** isl_Latn 69.1 **77.2** 72.1 rus_Cyrl 63.8 **70.0** 67.6 bar_Latn 55.2 58.6 **68.4** ita_Latn 77.3 **81.2** 78.7 sah_Cyrl 47.3 49.7 **74.2** bel_Cyrl 74.2 **78.7** 74.3 jav_Latn 58.4 **61.2** 55.8 san_Deva 36.9 **37.3** 35.8 ben_Beng 65.3 **75.8** 71.6 jbo_Latn 18.0 26.3 **27.8** scn_Latn 49.9 54.8 **65.8** bih_Deva 50.7 57.1 **58.7** jpn_Jpan 19.7 **20.6** 17.2 sco_Latn 80.9 81.8 **85.6** bod_Tibt 2.5 3.0 **31.6** kan_Knda 56.9 **60.8** 58.4 sgs_Latn 42.5 47.4 **62.7** bos_Latn 74.0 **74.3** 74.2 kat_Geor 65.5 **69.5** 68.3 sin_Sinh 52.2 57.0 **57.8** bre_Latn 59.1 **63.9** 63.3 kaz_Cyrl 43.7 **52.7** 50.0 slk_Latn 75.0 **81.7** 78.5 bul_Cyrl 76.8 **81.6** 77.2 khm_Khmr 43.3 **46.2** 40.6 slv_Latn 79.4 **82.2** 80.1 cat_Latn 82.2 **85.4** 83.7 kin_Latn 60.5 58.4 **67.1** snd_Arab 41.2 **46.6** 41.8 cbk_Latn **54.6** 54.0 54.1 kir_Cyrl 44.2 **46.9** 46.7 som_Latn 55.8 55.5 **58.2** ceb_Latn 55.1 **57.8** 53.8 kor_Hang 49.1 **58.5** 50.9 spa_Latn 72.8 **73.3** 72.8 ces_Latn 77.6 **80.8** 78.3 ksh_Latn 41.3 48.3 **58.7** sqi_Latn 74.0 74.4 **76.6** che_Cyrl 15.4 24.6 **60.9** kur_Latn 58.8 65.0 **69.6** srp_Cyrl 59.7 **71.4** 66.4 chv_Cyrl 52.9 51.6 **75.9** lat_Latn 70.7 **79.2** 73.8 sun_Latn 42.0 49.7 **57.7** ckb_Arab 33.1 42.6 **75.5** lav_Latn 73.4 **77.1** 74.0 swa_Latn 65.6 69.0 **69.6** cos_Latn 54.3 **56.4** 56.0 lij_Latn 36.9 41.6 **46.6** swe_Latn 71.8 **75.9** 69.7 crh_Latn 44.3 52.4 **54.7** lim_Latn 59.9 64.7 **71.8** szl_Latn 58.2 56.7 **67.6** csb_Latn 55.1 54.2 **61.2** lin_Latn 37.4 41.3 **54.0** tam_Taml 55.0 **57.9** 55.2 cym_Latn 57.9 **60.1** 59.7 lit_Latn 73.4 **77.0** 73.5 tat_Cyrl 40.7 47.7 **68.0** dan_Latn 81.5 **84.2** 81.7 lmo_Latn 68.8 68.4 **71.3** tel_Telu 47.4 **52.5** 46.0 deu_Latn 74.3 **78.6** 75.7 ltz_Latn 47.4 55.8 **69.1** tgk_Cyrl 24.7 38.3 **68.5** diq_Latn 37.8 43.3 **53.1** lzh_Hani 15.6 **21.6** 11.8 tgl_Latn 71.0 74.7 **75.1** div_Thaa 0.0 0.0 **51.1** mal_Mlym 61.0 **63.3** 61.3 tha_Thai 4.2 1.6 3.2 ell_Grek 73.7 **78.6** 72.8 mar_Deva 60.2 **63.4** 60.7 tuk_Latn 45.6 50.7 **59.7** eml_Latn 32.9 36.1 **40.8** mhr_Cyrl 44.3 48.3 **63.1** tur_Latn 74.9 **79.3** 76.1 eng_Latn 82.7 **84.5** 83.3 min_Latn 42.9 **46.2** 41.8 uig_Arab 44.0 **50.9** 48.0 epo_Latn 63.8 **71.8** 68.0 mkd_Cyrl 74.5 **80.4** 73.3 ukr_Cyrl 75.2 **76.3** 74.2 est_Latn 72.2 **78.5** 73.5 mlg_Latn 54.9 54.3 **57.9** urd_Arab 51.2 57.8 **74.5** eus_Latn 59.0 **62.0** 58.0 mlt_Latn 43.2 48.3 **73.3** uzb_Latn 70.6 **76.2** 75.1 ext_Latn 36.9 **47.1** 46.1 mon_Cyrl 72.4 **74.3** 66.9 vec_Latn 59.0 63.3 **66.4** fao_Latn 61.1 70.8 **72.4** mri_Latn 14.2 18.3 **53.5** vep_Latn 59.8 59.3 **71.3** fas_Arab 44.6 **58.0** 51.2 msa_Latn 62.3 **70.4** 65.8 vie_Latn 68.5 **77.8** 71.3 fin_Latn 75.5 **79.1** 75.2 mwl_Latn 42.6 **47.5** 45.3 vls_Latn 68.1 73.6 **73.7** fra_Latn 77.2 **79.8** 76.0 mya_Mymr 51.3 53.4 **55.5** vol_Latn 59.2 55.6 **59.2** frr_Latn 45.4 46.8 **54.8** mzn_Arab 36.4 43.1 **44.9** war_Latn 61.9 61.4 **66.1** fry_Latn 74.3 **79.0** 77.5 nan_Latn 46.2 51.4 **82.1** wuu_Hani 29.4 **54.0** 25.1 fur_Latn 44.9 50.1 **56.4** nap_Latn 53.0 53.9 **55.7** xmf_Geor 40.2 40.0 **62.6** gla_Latn 55.5 61.4 **63.5** nds_Latn 62.4 66.7 **77.1** yid_Hebr 47.6 **52.5** 50.3 gle_Latn 70.8 **74.6** 72.2 nep_Deva 63.2 **66.4** 62.7 yor_Latn 42.2 40.1 **63.1** glg_Latn 80.2 **81.1** 79.4 nld_Latn 80.1 **83.6** 80.8 yue_Hani 24.8 **30.3** 22.6 grn_Latn 40.0 42.3 **54.7** nno_Latn 76.6 **80.4** 78.0 zea_Latn 65.2 67.4 **68.6** guj_Gujr 61.0 **61.9** 59.8 nor_Latn 76.5 **80.1** 76.7 zho_Hani 24.2 **28.8** 23.4 hbs_Latn 61.1 57.2 **61.5** oci_Latn 65.3 67.8 **70.1** afr_Latn 88.7 **89.3** 87.5 hbo_Hebr 38.9 45.7 **54.2** pol_Latn 84.7 **85.4** 82.4 ajp_Arab 62.9 67.3 **69.7** heb_Hebr 68.0 **69.2** 67.2 por_Latn 88.6 **89.8** 88.2 aln_Latn 53.5 **60.4** 52.3 hin_Deva 71.3 **75.3** 70.3 quc_Latn 28.9 29.3 **62.4** amh_Ethi 64.5 **66.2** 66.1 hrv_Latn 85.9 **86.2** 85.5 ron_Latn 83.9 **85.7** 80.6 ara_Arab 68.5 **69.7** 65.4 hsb_Latn 71.5 74.4 **83.6** rus_Cyrl 89.1 **89.7** 88.7 bam_Latn 25.4 23.5 **40.8** hun_Latn 82.6 **82.7** 81.2 sah_Cyrl 20.3 22.8 **76.8** bel_Cyrl 86.2 **86.2** 86.0 hye_Armn 85.2 **86.5** 84.0 san_Deva 18.3 **28.6** 26.1 ben_Beng 82.8 **83.8** 83.8 hyw_Armn 78.5 **82.5** 80.4 sin_Sinh 57.7 **60.1** 54.7 bre_Latn 61.6 **66.6** 60.7 ind_Latn 83.5 **84.1** 82.7 slk_Latn 85.6 **85.8** 84.4 bul_Cyrl **89.1** 88.9 88.1 isl_Latn 84.2 **85.1** 82.8 slv_Latn 78.5 **79.1** 75.9 cat_Latn 86.7 **87.9** 86.3 ita_Latn 88.3 **89.6** 87.3 sme_Latn 29.8 31.5 **73.7** ceb_Latn 49.3 49.5 **66.4** jav_Latn 73.2 **76.7** 74.1 spa_Latn 88.5 **89.0** 88.0 ces_Latn 85.0 **85.4** 84.4 jpn_Jpan 17.3 **32.2** 31.7 sqi_Latn 81.4 **82.9** 77.9 cym_Latn 65.5 **67.0** 64.4 kaz_Cyrl 77.3 **79.1** 75.9 srp_Latn 86.1 **86.6** 85.3 dan_Latn 90.7 **91.0** 90.2 kmr_Latn 73.1 **78.2** 75.5 swe_Latn 93.5 **93.7** 92.1 deu_Latn **88.4** 88.4 87.9 kor_Hang **53.7** 53.4 53.1 tam_Taml 76.1 **76.9** 75.0 ell_Grek **87.3** 87.0 85.4 lat_Latn 75.0 **80.3** 72.4 tat_Cyrl 45.0 48.8 **70.1** eng_Latn 96.3 **96.5** 96.0 lav_Latn 86.0 **86.3** 83.5 tel_Telu **85.0** 85.0 82.2 est_Latn 86.1 **86.4** 83.1 lij_Latn 48.1 48.6 **76.8** tgl_Latn 72.7 **74.8** 74.7 eus_Latn 71.3 **73.7** 61.8 lit_Latn 84.1 **84.6** 81.1 tha_Thai 46.0 54.7 **56.7** fao_Latn 77.0 80.6 **89.2** lzh_Hani 14.1 **23.1** 23.0 tur_Latn 72.9 **74.0** 70.7 fas_Arab 71.8 **74.2** 71.5 mal_Mlym **86.9** 86.7 84.4 uig_Arab 68.2 **70.2** 68.9 fin_Latn 85.2 **85.7** 80.8 mar_Deva 83.0 **85.2** 80.8 ukr_Cyrl 85.9 **86.3** 84.8 fra_Latn 86.7 **87.3** 85.4 mlt_Latn 21.0 21.9 **79.5** urd_Arab 61.0 **68.2** 62.0 gla_Latn 57.4 **61.8** 60.2 myv_Cyrl 39.7 38.6 **65.7** vie_Latn 70.9 **72.2** 67.1 gle_Latn 65.5 **68.7** 64.4 nap_Latn 52.8 17.0 **63.6** wol_Latn 25.6 25.5 **61.6** glg_Latn 83.7 **86.4** 82.6 nds_Latn 58.0 67.3 **77.2** xav_Latn 8.4 5.3 **14.0** glv_Latn 27.5 29.5 **52.7** nld_Latn 88.5 **88.8** 88.2 yor_Latn 21.7 21.4 **63.9** grc_Grek 62.0 68.1 **73.1** nor_Latn 88.1 **88.9** 88.0 yue_Hani 31.5 **42.0** 40.9 grn_Latn 8.9 7.8 **19.8** pcm_Latn 47.3 50.1 **57.1** zho_Hani 28.6 42.4 **43.1** gsw_Latn 48.7 55.9 **80.3** ace_Latn 15 25 60 iba_Latn 30 35 56 ote_Latn 6 5 36 ace_Latn 15 25 60 iba_Latn 30 35 56 ote_Latn 6 5 36 ach_Latn 9 8 34 ibo_Latn 8 6 51 pag_Latn 22 21 52 acr_Latn 10 8 46 ifa_Latn 12 12 47 pam_Latn 20 18 41 afr_Latn 54 64 57 ifb_Latn 14 11 48 pan_Guru 53 65 59 agw_Latn 11 13 54 ikk_Latn 11 7 47 pap_Latn 31 36 55 ahk_Latn 5 5 24 ilo_Latn 15 13 52 pau_Latn 12 10 41 aka_Latn 11 7 48 ind_Latn 62 66 63 pcm_Latn 25 28 46 aln_Latn 44 51 49 isl_Latn 50 60 49 pdt_Latn 17 20 53 als_Latn 45 51 50 ita_Latn 57 68 61 pes_Arab 60 70 64 alt_Cyrl 25 23 54 ium_Latn 6 7 53 pis_Latn 13 13 57 alz_Latn 13 11 34 ixl_Latn 10 7 33 pls_Latn 6 7 41 amh_Ethi 42 49 43 izz_Latn 9 6 41 plt_Latn 30 51 50 aoj_Latn 12 9 41 jam_Latn 15 14 55 poh_Latn 16 8 48 arb_Arab 27 55 45 jav_Latn 44 54 49 pol_Latn 53 63 47 arn_Latn 9 8 46 jpn_Jpan 56 66 56 pon_Latn 10 8 50 ary_Arab 16 27 40 kaa_Cyrl 35 49 59 por_Latn 61 67 57 arz_Arab 28 49 39 kab_Latn 8 7 30 prk_Latn 6 6 51 asm_Beng 44 **53 53** kac_Latn 7 8 44 prs_Arab 62 67 65 ayr_Latn 11 9 53 kal_Latn 9 7 33 pxm_Latn 9 9 43 azb_Arab 19 17 55 kan_Knda 53 63 59 qub_Latn 13 10 55 aze_Latn 56 64 61 kat_Geor 55 60 57 quc_Latn 9 7 45 bak_Cyrl 17 19 57 kaz_Cyrl 53 64 56 qug_Latn 13 8 59 bam_Latn 7 7 46 kbp_Latn 5 5 35 quh_Latn 11 10 56 ban_Latn 21 24 46 kek_Latn 6 9 45 quw_Latn 13 10 48 bar_Latn 31 42 45 khm_Khmr 51 64 59 quy_Latn 12 11 57 bba_Latn 6 6 42 kia_Latn 7 7 39 quz_Latn 11 8 56 bci_Latn 9 8 28 kik_Latn 7 6 40 qvi_Latn 9 8 59 bcl_Latn 28 27 51 kin_Latn 17 9 50 rap_Latn 8 7 50 bel_Cyrl 56 67 54 kir_Cyrl 55 63 60 rar_Latn 8 9 48 bem_Latn 13 14 43 kjb_Latn 7 9 48 rmy_Latn 16 12 47 ben_Beng 53 65 60 kjh_Cyrl 15 19 50 ron_Latn 60 70 60 bhw_Latn 11 11 47 kmm_Latn 8 6 46 rop_Latn 10 10 50 bim_Latn 7 7 47 kmr_Cyrl 8 8 44 rug_Latn 7 7 55 bis_Latn 13 12 57 knv_Latn 7 6 44 run_Latn 16 9 49 bqc_Latn 7 7 36 kor_Hang 59 70 60 rus_Cyrl 60 66 61 bre_Latn 30 49 36 kpg_Latn 9 10 57 sag_Latn 9 11 42 bts_Latn 18 17 56 krc_Cyrl 25 22 56 sah_Cyrl 10 9 52 btx_Latn 23 26 53 kri_Latn 7 9 52 sba_Latn 7 6 41 bul_Cyrl 61 70 57 ksd_Latn 10 11 53 seh_Latn 11 8 47 bum_Latn 9 9 43 kss_Latn 5 5 23 sin_Sinh 54 66 59 bzj_Latn 18 14 56 ksw_Mymr 5 5 53 slk_Latn 56 63 56 cab_Latn 9 8 41 kua_Latn 12 12 45 slv_Latn 59 66 61 cac_Latn 10 10 47 lam_Latn 5 8 28 sme_Latn 10 12 43 cak_Latn 7 8 53 lao_Laoo 56 66 64 smo_Latn 8 7 51 caq_Latn 7 7 47 lat_Latn 56 64 50 sna_Latn 13 11 42 cat_Latn 53 64 48 lav_Latn 54 66 55 snd_Arab 54 64 57 cbk_Latn 43 47 57 ldi_Latn 8 9 28 som_Latn 32 45 33 cce_Latn 13 9 47 leh_Latn 13 10 44 sop_Latn 12 8 32 ceb_Latn 28 30 49 lhu_Latn 6 6 30 sot_Latn 11 8 45 ces_Latn 50 65 53 lin_Latn 10 7 49 spa_Latn 61 69 60 cfm_Latn 8 8 55 lit_Latn 54 66 53 sqi_Latn 57 68 60 che_Cyrl 11 6 20 loz_Latn 10 10 48 srm_Latn 10 9 53 chv_Cyrl 8 7 52 ltz_Latn 22 30 52 srn_Latn 10 9 53 cmn_Hani 53 62 56 lug_Latn 16 9 45 srp_Latn 55 67 56 cnh_Latn 7 8 56 luo_Latn 12 10 39 ssw_Latn 14 17 40 crh_Cyrl 22 31 57 lus_Latn 11 7 52 sun_Latn 40 **47 47** crs_Latn 14 17 61 lzh_Hani 46 **55 55** suz_Deva 15 13 53 csy_Latn 9 7 52 mad_Latn 23 28 56 swe_Latn 60 66 56 ctd_Latn 9 8 56 mah_Latn 6 6 42 swh_Latn 47 59 56 ctu_Latn 15 14 51 mai_Deva 34 39 59 sxn_Latn 11 8 46 cuk_Latn 15 7 44 mal_Mlym 56 64 60 tam_Taml 56 61 60 cym_Latn 46 51 48 mam_Latn 10 6 31 tat_Cyrl 21 28 64 dan_Latn 51 62 50 mar_Deva 55 63 60 tbz_Latn 6 6 43 deu_Latn 56 65 53 mau_Latn 5 5 6 tca_Latn 5 5 47 djk_Latn 12 10 46 mbb_Latn 11 7 48 tdt_Latn 16 13 56 dln_Latn 10 5 52 mck_Latn 15 10 41 tel_Telu 55 65 60 dtp_Latn 9 8 39 mcn_Latn 13 9 43 teo_Latn 12 8 26 dyu_Latn 6 8 52 mco_Latn 6 7 28 tgk_Cyrl 10 7 55 dzo_Tibt 6 5 55 mdy_Ethi 6 7 47 tgl_Latn 48 60 56 Table 19: F1 of XLM-R-B, XLM-R-L, and Glot500-m on Text Classification (Part I). efi_Latn 10 9 50 meu_Latn 15 11 52 tha_Thai 56 67 61 ell_Grek 37 47 54 mfe_Latn 16 14 61 tih_Latn 11 11 56 eng_Latn 74 75 68 mgh_Latn 10 6 35 tir_Ethi 23 27 48 enm_Latn 46 56 65 mgr_Latn 14 12 46 tlh_Latn 30 26 59 epo_Latn 53 63 53 mhr_Cyrl 14 10 43 tob_Latn 6 9 52 est_Latn 62 68 53 min_Latn 27 37 50 toh_Latn 11 8 41 eus_Latn 28 33 22 miq_Latn 7 7 48 toi_Latn 14 10 40 ewe_Latn 9 9 52 mkd_Cyrl 65 69 61 toj_Latn 12 11 42 fao_Latn 33 41 55 mlg_Latn 32 51 48 ton_Latn 6 7 47 fas_Arab 62 68 62 mlt_Latn 12 11 49 top_Latn 11 10 25 fij_Latn 8 7 51 mos_Latn 7 8 41 tpi_Latn 11 13 55 fil_Latn 47 56 53 mps_Latn 11 12 54 tpm_Latn 9 8 47 fin_Latn 57 66 56 mri_Latn 9 8 47 tsn_Latn 11 8 45 fon_Latn 5 6 49 mrw_Latn 15 18 41 tsz_Latn 10 10 45 fra_Latn 57 66 57 msa_Latn 43 49 46 tuc_Latn 7 9 50 fry_Latn 31 34 37 mwm_Latn 5 6 50 tui_Latn 8 8 49 gaa_Latn 5 6 43 mxv_Latn 8 8 24 tuk_Latn 23 26 53 gil_Latn 9 8 44 mya_Mymr 45 52 54 tum_Latn 12 12 49 giz_Latn 9 10 49 myv_Cyrl 11 7 47 tur_Latn 55 66 56 gkn_Latn 8 7 40 mzh_Latn 7 9 45 twi_Latn 9 6 46 gkp_Latn 5 6 35 nan_Latn 6 6 30 tyv_Cyrl 19 18 54 gla_Latn 28 43 42 naq_Latn 8 7 42 tzh_Latn 12 13 42 gle_Latn 37 53 40 nav_Latn 7 9 25 tzo_Latn 13 11 41 glv_Latn 10 12 38 nbl_Latn 20 26 46 udm_Cyrl 10 11 51 gom_Latn 10 13 39 nch_Latn 10 8 39 ukr_Cyrl 61 67 56 gor_Latn 17 15 50 ncj_Latn 7 9 43 urd_Arab 59 65 59 guc_Latn 8 6 42 ndc_Latn 13 13 40 uzb_Latn 49 59 56 gug_Latn 11 7 44 nde_Latn 20 26 46 uzn_Cyrl 13 17 57 guj_Gujr 57 67 63 ndo_Latn 13 9 40 ven_Latn 10 8 43 gur_Latn 6 6 47 nds_Latn 16 15 42 vie_Latn 57 65 55 guw_Latn 11 9 49 nep_Deva 56 **61 61** wal_Latn 15 9 41 gya_Latn 5 5 39 ngu_Latn 8 10 50 war_Latn 19 21 41 gym_Latn 10 7 47 nia_Latn 11 9 47 wbm_Latn 7 6 52 hat_Latn 11 10 59 nld_Latn 50 59 55 wol_Latn 11 9 40 hau_Latn 34 40 47 nmf_Latn 9 7 36 xav_Latn 10 10 40 haw_Latn 8 7 41 nnb_Latn 11 8 46 xho_Latn 23 32 48 heb_Hebr 16 31 41 nno_Latn 49 56 57 yan_Latn 7 7 46 hif_Latn 22 37 42 nob_Latn 54 60 55 yao_Latn 10 8 43 hil_Latn 26 31 60 nor_Latn 53 63 55 yap_Latn 8 8 46 hin_Deva 54 70 57 npi_Deva 53 62 61 yom_Latn 13 9 35 hmo_Latn 14 13 53 nse_Latn 17 10 45 yor_Latn 11 7 51 hne_Deva 32 40 59 nso_Latn 11 7 48 yua_Latn 12 10 39 hnj_Latn 8 7 55 nya_Latn 12 10 56 yue_Hani 52 61 54 hra_Latn 10 7 49 nyn_Latn 16 7 38 zai_Latn 16 14 40 hrv_Latn 56 63 56 nyy_Latn 8 8 34 zho_Hani 55 68 55 hui_Latn 9 7 43 nzi_Latn 5 7 40 zlm_Latn 59 70 64 hun_Latn 62 69 53 ori_Orya 54 65 60 zom_Latn 11 9 50 hus_Latn 7 10 39 ory_Orya 55 64 61 zsm_Latn 61 64 63 hye_Armn 60 68 60 oss_Cyrl 6 6 47 zul_Latn 24 35 52 | Language-Script | XLM-R-B | XLM-R-L | Glot500-m Language-Script | XLM-R-B | XLM-R-L | Glot500-m Language-Script | XLM-R-B | XLM-R-L | Glot500-m | | | |-------------------|-----------|-----------|-----------------------------|-----------|-----------|-----------------------------|-----------|-----------|-------------|------|-------| | ace_Latn | 2.50 | 2.83 | 4.56 | hye_Armn | 2.32 | 3.25 | 4.91 | pam_Latn | 2.85 | 3.52 | 4.46 | | ach_Latn | 3.13 | 4.02 | 5.60 | hye_Latn | 2.34 | 2.98 | 2.44 | pan_Guru | 2.11 | 2.73 | 4.11 | | acr_Latn | 2.01 | 2.46 | 2.51 | iba_Latn | 2.77 | 3.85 | 6.01 | pap_Latn | 3.12 | 3.85 | 5.46 | | afr_Latn | 3.17 | 3.66 | 5.46 | ibo_Latn | 2.05 | 2.43 | 4.33 | pau_Latn | 2.67 | 3.09 | 4.09 | | agw_Latn | 2.51 | 2.80 | 4.09 | ifa_Latn | 1.81 | 2.40 | 3.45 | pcm_Latn | 3.81 | 4.44 | 6.47 | | ahk_Latn | 1.11 | 1.23 | 1.22 | ifb_Latn | 2.22 | 2.58 | 3.28 | pdt_Latn | 2.41 | 3.33 | 5.11 | | aka_Latn | 3.38 | 4.50 | 6.48 | ikk_Latn | 1.75 | 2.29 | 3.83 | pes_Arab | 2.66 | 3.91 | 4.81 | | aln_Latn | 4.06 | 4.92 | 7.39 | ilo_Latn | 3.06 | 3.87 | 6.24 | pis_Latn | 1.91 | 2.32 | 4.42 | | als_Latn | 3.92 | 4.85 | 6.32 | ind_Latn | 4.06 | 5.00 | 7.60 | pls_Latn | 2.14 | 2.57 | 4.02 | | alt_Cyrl | 2.91 | 3.36 | 5.32 | isl_Latn | 4.40 | 5.22 | 7.07 | plt_Latn | 3.74 | 3.99 | 6.82 | | alz_Latn | 3.78 | 4.89 | 5.94 | ita_Latn | 3.55 | 4.02 | 6.18 | poh_Latn | 0.92 | 1.10 | 1.87 | | amh_Ethi | 3.04 | 3.10 | 4.87 | ium_Latn | 2.00 | 2.27 | 3.46 | pol_Latn | 3.94 | 5.20 | 5.12 | | amh_Latn | 1.41 | 1.76 | 1.70 | ixl_Latn | 1.62 | 1.94 | 2.14 | pon_Latn | 3.53 | 4.51 | 5.18 | | aoj_Latn | 1.77 | 1.97 | 3.22 | izz_Latn | 1.65 | 2.06 | 3.12 | por_Latn | 3.61 | 4.35 | 6.12 | | arb_Arab | 1.07 | 1.47 | 2.40 | jam_Latn | 2.77 | 3.06 | 3.59 | prk_Latn | 2.10 | 2.70 | 5.40 | | arn_Latn | 2.40 | 2.79 | 4.51 | jav_Latn | 3.10 | 3.67 | 5.21 | prs_Arab | 3.54 | 4.28 | 6.92 | | ary_Arab | 0.86 | 1.10 | 2.43 | jpn_Jpan | 3.62 | 4.39 | 4.07 | pxm_Latn | 1.76 | 2.15 | 3.40 | | arz_Arab | 0.83 | 1.14 | 2.52 | kaa_Cyrl | 2.99 | 3.91 | 5.45 | qub_Latn | 2.48 | 2.97 | 4.24 | | asm_Beng | 2.82 | 2.47 | 5.21 | kaa_Latn | 2.34 | 2.96 | 3.64 | quc_Latn | 1.87 | 2.45 | 2.77 | | ayr_Latn | 2.61 | 3.09 | 3.93 | kab_Latn | 2.51 | 3.08 | 3.14 | qug_Latn | 2.44 | 2.99 | 5.34 | | azb_Arab | 2.57 | 3.16 | 4.96 | kac_Latn | 1.66 | 2.17 | 3.34 | quh_Latn | 2.91 | 3.46 | 5.43 | | aze_Cyrl | 2.76 | 3.26 | 3.62 | kal_Latn | 3.00 | 3.90 | 4.73 | quw_Latn | 2.89 | 3.50 | 5.62 | | aze_Latn | 4.24 | 5.04 | 8.00 | kan_Knda | 2.58 | 3.18 | 4.05 | quy_Latn | 2.69 | 3.15 | 5.51 | | bak_Cyrl | 2.20 | 2.38 | 4.35 | kan_Latn | 1.62 | 2.08 | 1.81 | quz_Latn | 3.33 | 3.89 | 6.07 | | bam_Latn | 3.56 | 4.29 | 5.73 | kat_Geor | 4.06 | 4.99 | 5.53 | qvi_Latn | 2.82 | 3.42 | 4.89 | | ban_Latn | 2.26 | 2.74 | 3.37 | kaz_Cyrl | 3.82 | 4.56 | 5.31 | rap_Latn | 1.31 | 1.61 | 2.31 | | bar_Latn | 3.11 | 3.81 | 3.84 | kbp_Latn | 1.47 | 1.65 | 3.32 | rar_Latn | 1.83 | 2.22 | 3.27 | | bba_Latn | 2.43 | 2.80 | 4.16 | kek_Latn | 1.91 | 2.45 | 2.70 | rmy_Latn | 2.85 | 3.68 | 4.83 | | bbc_Latn | 3.02 | 3.85 | 5.22 | khm_Khmr | 1.57 | 1.70 | 2.82 | ron_Latn | 3.33 | 4.00 | 4.99 | | bci_Latn | 2.81 | 3.18 | 3.30 | kia_Latn | 2.92 | 3.27 | 4.69 | rop_Latn | 1.60 | 2.08 | 3.46 | | bcl_Latn | 3.78 | 4.61 | 8.06 | kik_Latn | 2.28 | 2.73 | 4.38 | rug_Latn | 2.56 | 2.95 | 3.60 | | bel_Cyrl | 3.73 | 4.91 | 6.46 | kin_Latn | 2.67 | 3.26 | 4.19 | run_Latn | 3.33 | 3.98 | 6.82 | | bem_Latn | 3.06 | 3.77 | 5.69 | kir_Cyrl | 4.54 | 4.35 | 6.36 | rus_Cyrl | 4.20 | 5.05 | 7.38 | | ben_Beng | 3.29 | 3.07 | 4.99 | kjb_Latn | 2.42 | 3.03 | 3.27 | sag_Latn | 2.92 | 3.52 | 5.17 | | bhw_Latn | 2.91 | 3.47 | 5.16 | kjh_Cyrl | 3.13 | 3.81 | 5.39 | sah_Cyrl | 2.31 | 3.01 | 4.98 | | bim_Latn | 2.54 | 3.29 | 4.12 | kmm_Latn | 2.52 | 3.30 | 3.73 | san_Deva | 2.48 | 2.20 | 3.64 | | bis_Latn | 2.59 | 2.96 | 4.68 | kmr_Cyrl | 2.31 | 2.76 | 4.30 | san_Latn | 1.54 | 2.23 | 2.35 | | bod_Tibt | 0.54 | 3.39 | 2.43 | kmr_Latn | 3.75 | 4.19 | 5.70 | sba_Latn | 1.88 | 2.24 | 3.86 | | bqc_Latn | 2.44 | 3.16 | 4.61 | knv_Latn | 1.27 | 1.53 | 2.09 | seh_Latn | 3.44 | 4.20 | 4.94 | | bre_Latn | 3.32 | 3.87 | 3.79 | kor_Hang | 2.76 | 3.99 | 4.89 | sin_Sinh | 2.55 | 3.60 | 3.44 | | bts_Latn | 4.06 | 4.92 | 7.99 | kor_Latn | 0.92 | 2.40 | 0.90 | slk_Latn | 4.65 | 5.06 | 6.43 | | btx_Latn | 3.23 | 3.88 | 5.59 | kpg_Latn | 2.80 | 3.12 | 5.77 | slv_Latn | 3.11 | 4.32 | 5.23 | | bul_Cyrl | 3.56 | 4.67 | 5.88 | krc_Cyrl | 2.85 | 3.66 | 4.90 | sme_Latn | 2.70 | 3.35 | 4.40 | | bum_Latn | 3.22 | 3.73 | 4.89 | kri_Latn | 1.90 | 2.52 | 5.07 | smo_Latn | 2.26 | 2.72 | 4.34 | | bzj_Latn | 1.65 | 2.43 | 4.48 | ksd_Latn | 2.82 | 3.28 | 5.42 | sna_Latn | 2.89 | 3.39 | 5.32 | | cab_Latn | 2.16 | 2.63 | 2.98 | kss_Latn | 0.99 | 1.09 | 1.49 | snd_Arab | 3.12 | 3.92 | 5.30 | | cac_Latn | 1.51 | 1.74 | 2.86 | ksw_Mymr | 0.95 | 1.46 | 4.18 | som_Latn | 3.15 | 3.40 | 4.17 | | cak_Latn | 1.86 | 2.18 | 3.24 | kua_Latn | 4.25 | 4.92 | 7.31 | sop_Latn | 2.80 | 3.55 | 4.23 | | caq_Latn | 2.20 | 2.94 | 3.66 | lam_Latn | 2.41 | 3.09 | 4.03 | sot_Latn | 3.49 | 4.31 | 6.96 | | cat_Latn | 3.76 | 4.04 | 5.24 | lao_Laoo | 2.61 | 3.21 | 4.39 | spa_Latn | 3.71 | 4.21 | 5.86 | | cbk_Latn | 3.12 | 3.64 | 4.34 | lat_Latn | 4.65 | 5.51 | 7.44 | sqi_Latn | 4.07 | 5.07 | 6.50 | | cce_Latn | 2.96 | 3.40 | 4.86 | lav_Latn | 3.35 | 4.56 | 6.45 | srm_Latn | 1.75 | 1.96 | 3.23 | | ceb_Latn | 3.45 | 4.13 | 5.10 | ldi_Latn | 3.41 | 3.94 | 4.29 | srn_Latn | 3.40 | 3.86 | 5.98 | | ces_Latn | 4.33 | 5.27 | 7.75 | leh_Latn | 2.73 | 3.66 | 5.28 | srp_Cyrl | 6.48 | 6.50 | 10.24 | | cfm_Latn | 2.69 | 3.18 | 4.52 | lhu_Latn | 1.43 | 1.61 | 1.36 | srp_Latn | 4.16 | 5.06 | 6.31 | | che_Cyrl | 2.50 | 3.02 | 3.17 | lin_Latn | 1.78 | 2.73 | 4.61 | ssw_Latn | 3.27 | 4.02 | 5.72 | | chk_Hani | 4.88 | 6.75 | 7.08 | lit_Latn | 4.69 | 5.66 | 7.07 | sun_Latn | 2.98 | 3.69 | 4.61 | | chk_Latn | 3.20 | 3.94 | 5.36 | loz_Latn | 3.35 | 3.91 | 6.03 | suz_Deva | 1.68 | 1.66 | 2.82 | | chv_Cyrl | 2.25 | 2.77 | 4.79 | ltz_Latn | 3.73 | 3.99 | 5.16 | swe_Latn | 4.77 | 4.76 | 7.09 | | ckb_Arab | 2.38 | 3.15 | 3.86 | lug_Latn | 2.84 | 3.50 | 5.59 | swh_Latn | 4.05 | 4.99 | 7.27 | | ckb_Latn | 2.11 | 2.57 | 3.35 | luo_Latn | 3.34 | 4.09 | 4.90 | sxn_Latn | 2.08 | 2.54 | 3.06 | | cmn_Hani | 3.24 | 4.57 | 5.22 | lus_Latn | 2.43 | 2.99 | 5.20 | tam_Latn | 2.59 | 3.08 | 2.56 | | cnh_Latn | 2.17 | 2.75 | 3.62 | lzh_Hani | 3.21 | 5.56 | 5.47 | tam_Taml | 3.09 | 3.77 | 5.74 | | crh_Cyrl | 3.14 | 3.79 | 6.77 | mad_Latn | 2.65 | 3.29 | 4.45 | tat_Cyrl | 2.13 | 2.62 | 4.03 | | crs_Latn | 2.63 | 3.46 | 4.88 | mah_Latn | 2.95 | 3.59 | 4.92 | tbz_Latn | 1.62 | 2.03 | 4.22 | | csy_Latn | 2.58 | 3.02 | 4.25 | mai_Deva | 1.79 | 2.02 | 3.86 | tca_Latn | 1.29 | 1.56 | 2.77 | | ctd_Latn | 2.94 | 3.61 | 4.65 | mal_Latn | 2.67 | 3.36 | 2.71 | tdt_Latn | 3.20 | 3.48 | 5.06 | | ctu_Latn | 1.89 | 2.31 | 2.40 | mal_Mlym | 3.19 | 4.13 | 4.76 | tel_Telu | 2.87 | 3.78 | 3.98 | | cuk_Latn | 2.20 | 2.87 | 3.09 | mam_Latn | 1.84 | 2.20 | 2.22 | teo_Latn | 3.37 | 4.18 | 4.29 | | cym_Latn | 3.11 | 3.78 | 3.85 | mar_Deva | 3.87 | 5.13 | 5.65 | tgk_Cyrl | 2.63 | 3.29 | 6.11 | | dan_Latn | 4.06 | 5.03 | 6.94 | mau_Latn | 1.60 | 1.78 | 1.12 | tgl_Latn | 3.22 | 3.35 | 5.16 | | deu_Latn | 4.85 | 5.19 | 7.28 | mbb_Latn | 2.25 | 2.56 | 3.51 | tha_Thai | 1.50 | 2.72 | 4.10 | | djk_Latn | 2.07 | 2.46 | 3.53 | mck_Latn | 3.34 | 4.06 | 5.09 | tih_Latn | 2.21 | 2.89 | 4.57 | | dln_Latn | 3.89 | 4.89 | 5.23 | mcn_Latn | 3.74 | 4.42 | 5.60 | tir_Ethi | 1.90 | 1.93 | 4.03 | | dtp_Latn | 2.05 | 2.28 | 3.04 | mco_Latn | 1.42 | 1.63 | 1.69 | tlh_Latn | 3.02 | 3.52 | 5.71 | | dyu_Latn | 2.75 | 3.32 | 5.29 | mdy_Ethi | 1.36 | 1.26 | 2.89 | tob_Latn | 1.42 | 1.84 | 2.00 | | dzo_Tibt | 0.39 | 2.51 | 2.03 | meu_Latn | 3.26 | 3.79 | 5.10 | toh_Latn | 2.17 | 2.90 | 4.41 | Table 21: Accuracy of XLM-R-B, XLM-R-L, and Glot500-m on Round Trip Alignment (Part I). efi_Latn 2.55 3.25 **6.23** mfe_Latn 3.61 4.19 **6.26** toi_Latn 3.19 4.10 **4.31** ell_Grek 2.79 3.38 **4.77** mgh_Latn 2.78 3.28 **3.48** toj_Latn 1.43 1.84 **2.25** eng_Latn 4.02 4.49 **6.39** mgr_Latn 3.32 4.06 **6.39** ton_Latn 2.01 2.64 **3.63** enm_Latn 3.77 4.60 **7.19** mhr_Cyrl 2.75 3.28 **5.32** top_Latn 1.56 2.16 **2.19** epo_Latn 4.01 4.83 **5.88** min_Latn 2.62 3.05 **3.78** tpi_Latn 2.44 2.71 **5.96** est_Latn 4.34 5.24 **8.21** miq_Latn 2.23 3.13 **4.12** tpm_Latn 2.79 3.39 **4.67** eus_Latn 3.12 3.80 **4.19** mkd_Cyrl 3.99 4.54 **7.37** tsn_Latn 2.82 3.12 **4.63** ewe_Latn 2.22 2.67 **4.74** mlg_Latn 3.34 3.81 **6.33** tso_Latn 2.40 3.05 **5.00** fao_Latn 3.85 4.62 **5.75** mlt_Latn 2.94 3.57 **4.87** tsz_Latn 2.68 3.14 **4.20** fas_Arab 4.54 4.48 **7.00** mos_Latn 2.71 3.24 **4.25** tuc_Latn 1.43 1.83 **2.36** fij_Latn 2.81 3.17 **4.94** mps_Latn 1.50 1.65 **3.05** tui_Latn 2.47 2.83 **4.53** fil_Latn 3.26 3.92 **4.80** mri_Latn 2.81 3.44 **5.49** tuk_Cyrl 2.74 3.68 **4.33** fin_Latn 4.06 5.19 **6.03** mrw_Latn 2.69 3.24 **4.58** tuk_Latn 2.43 3.23 **4.74** fon_Latn 1.63 1.89 **3.70** msa_Latn 3.17 3.50 **5.38** tum_Latn 3.41 4.13 **6.15** fra_Latn 3.19 3.97 **5.08** mwm_Latn 1.74 1.99 **3.20** tur_Latn 5.18 4.86 **7.45** fry_Latn 3.36 3.99 **4.52** mxv_Latn 1.75 2.11 **2.31** twi_Latn 3.05 4.06 **6.70** gaa_Latn 2.74 3.26 **6.01** mya_Mymr 1.54 1.53 **2.46** tyv_Cyrl 2.31 2.83 **3.33** gil_Latn 2.76 3.20 **4.50** myv_Cyrl 2.90 3.42 **4.46** tzh_Latn 2.16 2.50 **3.08** giz_Latn 3.00 3.43 **5.40** mzh_Latn 2.62 3.02 **4.10** tzo_Latn 2.01 2.29 **2.77** gkn_Latn 1.93 2.07 **3.31** nan_Latn 1.99 2.51 **2.56** udm_Cyrl 2.90 3.48 **4.72** gkp_Latn 1.88 2.25 **3.40** naq_Latn 2.42 3.15 **4.41** uig_Arab 2.58 3.11 **3.61** gla_Latn 2.90 3.48 **3.61** nav_Latn 1.75 2.10 **2.71** uig_Latn 2.26 2.76 **3.79** gle_Latn 3.52 4.24 **4.49** nbl_Latn 3.09 3.87 **4.85** ukr_Cyrl 5.71 5.96 **7.47** glv_Latn 2.76 3.38 **4.45** nch_Latn 2.18 2.74 **3.32** urd_Arab 1.88 2.88 **3.96** gom_Latn 3.05 3.59 **4.40** ncj_Latn 2.64 3.40 **3.69** urd_Latn 2.29 2.97 **3.03** gor_Latn 2.26 2.73 **3.71** ndc_Latn 3.32 3.85 **6.67** uzb_Cyrl 2.73 3.26 **7.24** grc_Grek 1.11 2.00 **2.93** nde_Latn 4.00 4.60 **6.05** uzb_Latn 3.32 3.98 **5.91** guc_Latn 1.46 1.80 **2.23** ndo_Latn 3.21 3.85 **5.61** uzn_Cyrl 2.61 3.06 **5.86** gug_Latn 2.60 3.23 **4.70** nds_Latn 2.98 3.69 **4.70** ven_Latn 2.96 3.64 **5.34** guj_Gujr 3.18 4.15 **4.38** nep_Deva 3.02 2.97 **6.31** vie_Latn 3.99 4.48 **6.69** gur_Latn 2.14 2.59 **3.22** ngu_Latn 1.86 2.34 **3.39** wal_Latn 2.87 3.65 **4.24** guw_Latn 2.18 2.54 **4.56** nia_Latn 2.75 **3.47** 3.24 war_Latn 3.04 3.74 **5.43** gya_Latn 1.94 2.25 **4.63** nld_Latn 2.81 3.63 **4.90** wbm_Latn 2.44 2.86 **6.53** gym_Latn 1.44 1.78 **2.63** nmf_Latn 3.30 4.27 **5.05** wol_Latn 3.47 4.48 **6.10** hat_Latn 3.21 3.64 **6.39** nnb_Latn 2.46 3.14 **4.08** xav_Latn 0.87 1.03 **1.12** hau_Latn 3.69 4.24 **6.31** nno_Latn 3.90 4.61 **7.41** xho_Latn 3.61 4.27 **5.90** haw_Latn 2.25 2.63 **3.55** nob_Latn 3.88 4.81 **5.83** yan_Latn 2.95 3.35 **5.59** heb_Hebr 1.85 2.41 **3.92** nor_Latn 3.31 4.14 **5.82** yao_Latn 2.01 2.66 **3.87** hif_Latn 2.90 3.43 **3.60** npi_Deva 3.29 3.30 **5.93** yap_Latn 2.86 3.41 **3.45** hil_Latn 2.92 3.48 **4.88** nse_Latn 3.29 4.06 **5.74** yom_Latn 3.25 4.00 **5.17** hin_Deva 3.39 3.80 **5.13** nso_Latn 3.06 3.92 **5.51** yor_Latn 2.24 2.68 **3.88** hin_Latn 2.94 3.20 **4.77** nya_Latn 2.76 **3.19 5.96** yua_Latn 2.04 2.26 **2.86** hmo_Latn 2.43 2.70 **6.12** nyn_Latn 2.77 3.50 **5.59** yue_Hani 2.37 **3.19** 2.95 hne_Deva 2.48 2.53 **4.95** nyy_Latn 2.21 2.74 **2.95** zai_Latn 3.22 3.76 **5.21** hnj_Latn 2.14 2.53 **4.28** nzi_Latn 2.09 2.70 **4.20** zho_Hani 2.77 4.38 **5.03** hra_Latn 3.32 3.86 **5.19** ori_Orya 2.73 2.77 **3.92** zlm_Latn 4.39 5.15 **7.54** hrv_Latn 4.14 5.24 **7.02** ory_Orya 3.27 3.20 **4.39** zom_Latn 3.65 4.45 **5.36** hui_Latn 1.84 2.10 **3.47** oss_Cyrl 2.20 2.52 **5.85** zsm_Latn 4.49 5.07 **8.83** hun_Latn 4.54 4.10 **5.62** ote_Latn 1.89 2.23 **2.66** zul_Latn 3.67 4.39 **5.44** hus_Latn 1.70 2.00 **2.42** pag_Latn 2.93 3.44 **4.56** srd_Latn 87.2 66.6 5.4 aka_Latn 86.7 74.1 **14.2** dyu_Latn 68.5 27.4 **10.2** ben_Beng 5.2 3.7 7.2 mon_Latn 288 282.4 **33.7** nyy_Latn 628.5 198.3 **18.0** ajp_Arab 74.6 **34.0** 44.8 gor_Latn 89.8 140.7 8.8 tzh_Latn 320.3 82.8 4.7 tdx_Latn 688.4 716.4 **16.0** kjb_Latn 110.8 81.1 **16.2** hne_Deva 80.1 60.3 9.1 tpm_Latn 99.9 90.2 **17.9** lhu_Latn 44.7 12.3 2.0 bel_Cyrl 3.4 2.5 5.3 grc_Grek 10.1 10.4 3.4 bos_Latn 6.1 3.4 7.9 szl_Latn 46.4 30.2 3.1 sxn_Latn 469.2 148.3 **14.5** lmo_Latn 48.4 25.9 6.1 ksh_Latn 340.3 227.6 **19.9** cos_Latn 52.1 22.8 **13.3** mwn_Latn 697.8 543.8 **30.7** pcd_Latn 61.2 40.8 **13.2** tlh_Latn 53.6 46.3 **11.1** aym_Latn 1084.6 727.8 **14.5** ada_Latn 100 78.5 9.5 sid_Latn 1003.6 782.3 **34.5** aoj_Latn 95.1 53.7 7.4 pxm_Latn 101.3 120.7 2.7 jam_Latn 213.3 195.2 **15.8** est_Latn 7.7 4.0 22.1 xho_Latn 32.5 9.4 16.7 ban_Latn 40.8 76.1 **16.1** bre_Latn 12.9 3.7 12.3 kaa_Cyrl 72.9 29.2 8.8 kin_Latn 544.1 203.2 6.6 bsb_Latn 74.5 45.1 7.6 kea_Latn 754.2 525.3 **13.4** rop_Latn 150.7 93.4 8.4 yua_Latn 246.8 55.1 4.6 teo_Latn 587.1 271.7 **62.0** alz_Latn 511.9 145.6 **47.7** hrv_Latn 7.4 4.9 9.7 tsc_Latn 726.3 501.1 **17.0** kwy_Latn 598.8 514.4 **30.5** jav_Latn 20.2 4.4 22 hin_Deva 7.4 3.1 10 yor_Latn 109.1 55.9 **11.0** mai_Deva 42.9 48.8 6.0 ekk_Latn 7 3.8 11.8 lao_Laoo 4.2 4.4 3.8 tyv_Cyrl 104.1 104.4 7.3 umb_Latn 920 838.8 **17.4** aze_Latn 5.6 3.6 5.4 afb_Arab 68.7 **44.4** 55.9 tam_Taml 7.2 2.3 9.8 mya_Mymr 6.9 2.7 6.3 twi_Latn 178.9 66.7 **17.9** toi_Latn 988.7 246.5 **20.9** ssw_Latn 345.7 108.4 **20.2** sme_Latn 293 368.2 6.5 kon_Latn 463.7 418.9 **16.3** lus_Latn 493.5 131.2 **16.4** yom_Latn 468 240.7 **43.1** che_Cyrl 266.4 127.6 5.7 krc_Cyrl 120.1 63.2 9.3 tob_Latn 115 78.8 7.2 gaa_Latn 109.3 33.3 **13.5** hbo_Hebr 6.3 3.6 5.6 mxv_Latn 69.8 29.7 5.0 tzo_Latn 246.5 54.3 7.0 mgr_Latn 737.8 254.2 **33.0** ron_Latn 4.4 2.9 10.4 mon_Cyrl 5.8 3.4 8.6 crh_Cyrl 138.6 86.3 5.2 ile_Latn 67.9 40.1 5.7 cuk_Latn 211.5 72.1 **32.0** ara_Arab 10.1 6.3 18.8 cce_Latn 468.3 123.5 **22.5** ces_Latn 4.4 3.1 11.6 mar_Deva 7.5 4.6 11.2 uzn_Cyrl 402.4 138.7 5.2 rmy_Latn 288.2 349.8 **25.0** nba_Latn 638.8 675.1 **14.6** ibg_Latn 897.3 807.3 **21.8** phm_Latn 914.5 678.5 **11.6** mny_Latn 568.9 492.5 **38.7** hat_Latn 228 113.3 **14.0** glv_Latn 240.2 182.3 9.4 run_Latn 817.5 218.5 **16.9** fij_Latn 377.3 96 **12.8** diq_Latn 256.6 120.5 **13.4** rus_Cyrl 3.3 2.3 4.5 kbp_Latn 34.6 24.5 7.1 poh_Latn 62.8 68.9 3.8 hbs_Latn 4.5 2.6 6 mlt_Latn 223 162.2 **10.3** oss_Cyrl 121.8 58.7 5.1 lug_Latn 489 197.5 **13.1** kjh_Cyrl 209.8 88.8 **16.4** san_Deva 20.5 **12.4** 15.5 pls_Latn 91.7 98.9 6.9 ndo_Latn 892.3 178.1 **21.1** ote_Latn 127.8 71.2 8.0 hif_Latn 21.6 46.7 **13.5** rar_Latn 458.1 50.2 **12.1** her_Latn 776 707.3 **31.6** tll_Latn 244.6 161 **24.3** ell_Grek 3.4 2.6 5.9 efi_Latn 256.8 47 **11.5** crs_Latn 782.2 146.5 7.4 tvl_Latn 634.1 378.5 7.1 idu_Latn 117.7 90.9 **12.0** rng_Latn 656.6 606.8 **11.7** toj_Latn 287.1 113.6 9.6 hye_Armn 3.6 4.4 3.8 cjk_Latn 530.8 419.6 **24.0** ikk_Latn 67.8 49.5 8.6 gcf_Latn 450.8 292.4 5.5 seh_Latn 917.8 230 **11.2** ory_Orya 6.1 2.8 6.3 pus_Arab 12.9 7.5 12.7 rug_Latn 260.9 214.2 5.4 nor_Latn 5 2.8 8.5 sgs_Latn 119.2 124.7 **10.5** hau_Latn 14.5 7.1 17.2 enm_Latn 43.1 **31.0** 36.6 mbb_Latn 177.1 138 4.2 uzb_Latn 5.6 3.6 5.8 arz_Arab 17.5 1.5 6.8 som_Arab 7.2 3.1 9.3 bim_Latn 142.2 97.3 **11.3** bem_Latn 706.9 219.9 **27.1** hsb_Latn 109.6 103.6 5.2 vep_Latn 218.1 111.5 6.1 gkp_Latn 33.1 30.2 **12.7** ary_Arab 32.7 4.6 26 slv_Latn 7.8 4.9 26.9 guj_Gujr 6.2 3.6 6.5 hmo_Latn 509.3 77.7 **10.9** azj_Latn 5.3 3.3 5.1 tbz_Latn 39.2 40.4 8.4 quw_Latn 177.8 157.7 **26.1** cac_Latn 51.4 39.3 7.0 ven_Latn 268.3 62 9.4 pag_Latn 923.5 232.4 **25.8** npi_Deva 8.6 4.9 7.3 crh_Latn 151 70.9 6.5 ber_Latn 639.1 981.4 **21.3** lin_Latn 377.3 96.6 **15.3** xmv_Latn 593.2 491.4 **19.4** chk_Latn 766.9 151.6 **19.1** zom_Latn 238.7 176.2 **22.8** slk_Latn 4 2.9 11.2 kan_Knda 7.2 2.8 8.9 kmr_Cyrl 140.6 56.7 4.1 zne_Latn 854.7 658.4 **48.8** loz_Latn 895 113.7 **27.8** acm_Arab 113.6 **74.0** 81 cgg_Latn 565.7 454.4 **12.4** tih_Latn 247.6 151.3 4.9 fin_Latn 4.2 3.1 21.7 vie_Latn 7.6 3.1 16.4 mfe_Latn 767.9 255.4 **10.1** rmn_Grek 108.9 76.8 3.3 amh_Ethi 8.9 5.3 7.5 tel_Telu 6.5 4.0 7.9 wls_Latn 334.9 207.9 4.0 nyu_Latn 926.2 479.2 9.3 ina_Latn 26.9 17.1 7.2 hun_Latn 5.1 3.3 25.1 suz_Deva 63.4 76.4 2.5 isl_Latn 7.9 4.9 16.7 lij_Latn 98.8 55.1 5.9 tuc_Latn 108.9 80.8 7.6 tsz_Latn 990.6 199.7 **14.2** quh_Latn 279 176.6 **16.5** lub_Latn 670.8 577.5 **23.8** ori_Orya 5.2 3.0 4.7 yap_Latn 507.3 195.9 **10.6** epo_Latn 10.8 5.2 21 tat_Latn 168.4 65.5 6.9 abk_Cyrl 122.6 89.5 **20.1** ksw_Mymr 16.6 7.5 4.6 arg_Latn 29.2 13.6 7.2 cmn_Hani 10.4 5.0 9.8 mwl_Latn 69.1 35.6 4.9 kia_Latn 132.4 126.8 **18.5** csb_Latn 112.8 59.4 6.1 cak_Latn 101.7 46.1 5.4 afr_Latn 12.2 7.8 19.2 nbl_Latn 137.7 19.6 **13.9** bar_Latn 124.7 108.9 **14.4** myv_Cyrl 97.7 153.3 8.5 ndc_Latn 1188.5 374.6 **19.4** asm_Beng 6 3.8 5 bik_Latn 170.4 60.3 **13.7** oci_Latn 41.2 24.4 8.3 grn_Latn 199.3 141.6 **10.3** ltz_Latn 39.7 165.1 **10.9** fao_Latn 84.2 35.6 5.5 tso_Latn 506.1 115.2 **13.2** iso_Latn 236.2 222.4 8.7 tui_Latn 126.1 127 **20.6** nso_Latn 656.3 153.4 9.1 ewe_Latn 198 54.6 **20.0** xav_Latn 21.4 15.9 5.7 bum_Latn 282.8 91.5 **22.1** als_Latn 7.6 2.5 6.4 Table 23: Perplexity of all languages covered by Glot500-m (Part I). swc_Latn 39.2 22.5 **13.2** top_Latn 589.2 89.6 **23.5** hin_Latn **11.1** 22.1 11.9 deu_Latn 4.4 3.6 10.2 bin_Latn 278.1 169.8 **13.3** eng_Latn 5.7 4.0 7.5 caq_Latn 185.9 129 **21.6** chw_Latn 778.9 645.8 **33.9** hus_Latn 134.6 68.2 5.3 ceb_Latn 63.1 53.1 2.1 hyw_Cyrl 268.5 233.5 6.3 urh_Latn 236.8 211.5 **11.4** nia_Latn 280.3 85.5 7.5 kor_Hang 7.2 2.6 11 mkd_Cyrl 4.3 3.1 6.2 urd_Arab 8.3 5.3 8.7 btx_Latn 463 163.1 **19.3** wbm_Latn 58.9 47.3 **13.6** niu_Latn 600.1 437.5 **10.1** srn_Latn 609.3 137.2 **12.6** kwn_Latn 1053.6 753.2 **32.0** mrw_Latn 320.8 174.9 7.6 llb_Latn 555.6 589.8 **41.1** guc_Latn 432.6 117.8 9.4 bul_Cyrl 3.9 3.6 6.8 cbk_Latn 129.5 60.4 **11.6** quc_Latn 270.7 83.9 5.6 pau_Latn 333.7 147.3 7.2 bcl_Latn 270 60.1 **12.5** nds_Latn 112.5 161.1 7.4 tha_Thai 10.8 2.9 14.6 csy_Latn 198.3 152.5 **21.7** ind_Latn 8.5 5.4 17.1 ilo_Latn 786.7 184.4 **13.8** ctd_Latn 249.2 166.1 **11.6** nde_Latn 56.7 21.5 **12.1** kss_Latn 90.4 13.2 **11.2** plt_Latn 10.8 3.6 5.7 kua_Latn 1104.8 191.2 **13.4** zai_Latn 719.4 212.5 **10.4** smo_Latn 235.7 55.6 7.0 nch_Latn 705.1 166.4 **11.2** guw_Latn 267.7 65.5 6.9 kab_Latn 744.5 203.5 **24.3** por_Latn 5.1 3.9 9.3 kbd_Cyrl 175.7 94.4 9.1 gom_Deva 82.8 48.4 9.0 jpn_Jpan 7.9 3.9 10 dln_Latn 238.8 207.8 7.5 ukr_Cyrl 3.1 2.9 5.9 spa_Latn 4.6 3.5 7.8 war_Latn 200.9 110.7 2.3 ast_Latn 27.5 18.6 4.8 knv_Latn 129 78.3 5.8 tca_Latn 70.4 49 6.0 lvs_Latn 4.8 2.7 5.7 agw_Latn 150.1 73.4 **16.3** iku_Cans 2.2 1.9 5.8 rmn_Cyrl 624.3 513.1 8.7 ige_Latn 181.1 105.2 **11.9** bjn_Latn 41.3 17.6 **11.4** kir_Cyrl 7.7 2.9 11.9 dua_Latn 232.8 152.2 **19.1** ngu_Latn 918 110.9 **13.4** pfl_Latn 152 101.3 **11.3** ogo_Latn 131.3 129.7 **31.1** kmr_Latn 68 4.6 10.6 bqc_Latn 102.7 71.1 **26.5** bas_Latn 410.4 437.7 **16.7** tgl_Latn 7.9 4.4 8.9 yid_Hebr 7.6 4.8 5.1 bpy_Beng 20 21.4 2.9 eus_Latn 10.7 6.2 37.3 fil_Latn 9.2 2.3 9.9 lfn_Latn 60.4 51 6.9 hra_Latn 212.1 177.7 **54.3** nap_Latn 81.7 39.6 **10.5** ton_Latn 116 65.2 2.8 lue_Latn 839.2 627.4 **19.8** heb_Hebr 6.7 4.9 13.5 lim_Latn 66.8 43.5 **11.4** pol_Latn 4.5 2.7 10.6 sba_Latn 75.7 81.8 6.0 lav_Latn 4.2 2.2 6.6 leh_Latn 476.5 253.9 **26.2** ifa_Latn 371.9 266.1 6.0 bih_Deva 27.6 16.1 5.0 lat_Latn 15.3 3.7 24.5 ami_Latn 1070.7 710.2 **29.2** gym_Latn 509.6 66.3 **17.0** div_Thaa 1.6 1.5 3.5 gil_Latn 763.5 161.3 **15.7** ish_Latn 144.9 134 **11.6** min_Latn 105 39.7 3.9 djk_Latn 360.4 93.4 **13.4** zea_Latn 69.6 27.5 8.7 ctu_Latn 177.4 37.9 4.5 new_Deva 36.1 29.8 4.5 aln_Latn 3.9 2.3 12.7 tur_Latn 9.1 4.1 29.5 bam_Latn 74.5 **23.7** 46.8 gcr_Latn 352.9 314.7 7.5 dhv_Latn 509 435.8 **11.8** wol_Latn 236.4 158.3 **32.0** kal_Latn 377.2 370.9 8.3 lua_Latn 706 784.5 **21.7** alt_Cyrl 140.7 50.9 9.3 dan_Latn 6 3.6 13.1 rmy_Cyrl 488.1 389.3 9.3 kri_Latn 87.6 35.8 8.6 tah_Latn 363 330.9 4.8 zpa_Latn 476.1 550.1 **13.6** kom_Cyrl 93.4 57 4.9 kik_Latn 205.8 55.5 **12.1** gom_Latn 405.7 282.9 **27.9** sah_Cyrl 99.9 91.1 4.5 vmw_Latn 828.8 434.8 **17.8** dtp_Latn 166.4 78.7 5.5 mzh_Latn 132.8 133.4 9.6 eml_Latn 283.4 144.9 6.6 fra_Latn 4.1 2.8 6.9 sna_Latn 316.6 331.1 **16.4** sco_Latn 28.1 15.5 9.8 cat_Latn 4.1 2.2 7.3 bzj_Latn 264.7 75.8 **10.9** kac_Latn 189.9 76.3 **17.9** xmf_Geor 71.2 72.3 3.8 nld_Latn 5.7 4.5 12 ttj_Latn 865.2 509.5 **15.5** ixl_Latn 53 29.6 4.2 gug_Latn 626.9 141.6 8.4 lun_Latn 720.1 565.6 **31.9** ckb_Arab 72.2 80.6 6.0 yue_Hani 17.8 **10.6** 10.8 sot_Latn 269.1 122.4 8.1 ahk_Latn 44.8 9.1 2.1 fry_Latn 16.1 **15.4** 17.2 mau_Latn 199.7 13.6 8.4 sag_Latn 491.4 68.7 **11.1** jbo_Latn 132.3 187.1 9.0 yan_Latn 134.4 108.4 **31.4** qug_Latn 505 135.2 **13.7** iba_Latn 529.3 87 **16.6** ido_Latn 79.8 24.2 7.1 nyn_Latn 834.8 236.9 **16.8** nya_Latn 319.6 256.8 **12.7** rmn_Latn 968.8 1062.8 **22.9** koo_Latn 481.3 321.6 **13.8** tat_Cyrl 99.8 116 4.1 sat_Olck 1.4 1.2 4.6 uig_Arab 8.1 2.4 5.5 nzi_Latn 113.7 47.4 **12.5** mad_Latn 132.7 90.2 7.9 kam_Latn 225.9 155.7 **10.3** wal_Latn 492.7 120.3 **18.1** hil_Latn 366 38.7 9.6 gkn_Latn 248 74.6 9.4 pdt_Latn 417.7 143 **13.3** khm_Khmr 4.8 3.2 4.5 twx_Latn 1209.8 978.2 **15.5** apc_Arab 74.8 42.2 **37.2** fon_Latn 71.8 27 **10.4** skg_Latn 665.4 624.1 **15.8** mdy_Ethi 65.7 68.4 5.4 ngl_Latn 664.9 518.3 **15.9** arb_Arab 4.1 2.1 6 rue_Cyrl 18.7 11.4 4.5 tcf_Latn 224.5 225.4 6.9 mco_Latn 295 37.6 4.6 azb_Arab 194.1 141.8 4.8 gur_Latn 86.2 39 **17.9** sqi_Latn 6.2 2.1 8.4 bci_Latn 129.6 95.6 8.7 qvi_Latn 863.4 91.5 **12.3** cnh_Latn 496 154.4 **16.3** kmm_Latn 193.3 164.9 **20.2** izz_Latn 95.5 78.5 5.5 sin_Sinh 7.5 5.4 9.8 bak_Cyrl 99 79 5.3 kur_Arab 90.3 76.3 5.7 kmb_Latn 564.8 465.8 **15.6** miq_Latn 347.4 198.9 **23.6** hbs_Cyrl 3.7 2.3 4.3 vol_Latn 78.4 67.7 2.4 kaa_Latn 94.2 100.6 7.3 ach_Latn 488.8 114.6 **77.3** msa_Latn 8.2 26.1 15 bod_Tibt 8.8 4.0 6.3 wuu_Hani 35.9 16.8 **11.7** bba_Latn 75.5 65.5 **16.3** glg_Latn 5.9 4.6 9.2 quz_Latn 804.5 269.4 **12.2** tgk_Latn 11.9 11.7 7.5 tum_Latn 516.4 168.3 **10.2** tok_Latn 592.4 423 **94.5** tiv_Latn 912.3 716.3 **29.3** bbc_Latn 787.9 203.7 **13.6** bis_Latn 727.1 47.7 **10.7** hmn_Latn 60.9 52.5 8.8 kek_Latn 126.4 40.6 4.3 fur_Latn 196.5 142.8 7.7 swh_Latn 12.6 5.8 24.4 ace_Latn 81.5 54 6.4 ium_Latn 36.6 33.1 7.2 pis_Latn 563.2 64.7 9.7 pam_Latn 59.6 276.7 **28.2** nse_Latn 771.7 292.3 **13.7** mzn_Arab 50 34.3 6.3 fas_Arab 8 4.1 14.1 zul_Latn 36.3 **10.1** 21.7 Table 24: Perplexity of all languages covered by Glot500-m (Part II). bts_Latn 205.7 204.5 8.8 tsn_Latn 264.7 137.8 **12.5** orm_Latn 23.4 8.6 16 gla_Latn 11.5 12.7 7.2 pon_Latn 928.4 181.9 **19.2** luo_Latn 699.4 258.5 **85.1** kat_Latn 36.4 24.8 **18.3** nmf_Latn 297.6 310.6 **44.9** pcm_Latn 38.3 169.6 3.6 uig_Latn 188.8 173.9 **15.2** ajg_Latn 147.1 149.5 **22.6** nnb_Latn 364.1 95 **28.6** kat_Geor 6 3.9 6.4 tir_Ethi 28.3 15.7 4.4 kaz_Cyrl 4.3 5.4 9.6 mlg_Latn 10.9 4.4 7.6 bhw_Latn 411.2 126.2 **21.6** dzo_Tibt 8.5 3.3 5.7 arn_Latn 382.7 96.7 **17.6** mhr_Cyrl 122.9 168.4 5.8 sun_Latn 23.6 **11.9** 17 tuk_Latn 456.7 197.8 5.8 swe_Latn 4.8 3.5 12.7 vec_Latn 40.6 21.1 9.2 vls_Latn 97.7 39.6 9.7 scn_Latn 117 64.9 7.8 ayr_Latn 261.1 237.6 **27.7** hyw_Armn 15.8 9.1 4.3 udm_Cyrl 356.7 224.9 6.7 oke_Latn 209.2 220.1 **13.0** que_Latn 447.9 536.1 **11.9** ifb_Latn 246.3 177.9 5.1 kur_Latn 14.2 6.8 10.3 snd_Arab 13.2 4.1 19.5 naq_Latn 136.8 60.2 **15.7** mgh_Latn 680 272.8 **23.7** giz_Latn 81.9 82.9 **37.7** zlm_Latn 5.6 3.3 4.6 tgk_Cyrl 181.3 153 4.5 ita_Latn 4.5 3.3 7.2 hrx_Latn 478.1 679.1 **14.9** sop_Latn 607.5 228.2 **29.5** qub_Latn 283.2 312.7 9.4 lzh_Hani 70 58 **21.8** mos_Latn 272.6 118.3 **13.2** nav_Latn 228.5 126.5 5.2 pap_Latn 674.4 149.3 **18.1** rap_Latn 36.1 31.1 2.8 kqn_Latn 825.9 686.6 **17.5** cfm_Latn 235.1 155 **14.0** prk_Latn 69.4 45.9 7.1 toh_Latn 758.3 216.6 **19.6** chv_Cyrl 122.5 73.8 5.4 uzb_Cyrl 236.2 138.4 4.9 mah_Latn 314.7 81.8 **17.3** tdt_Latn 641.9 78.6 9.7 tog_Latn 821.1 777.7 **13.4** wes_Latn 144.6 103.9 **14.3** pan_Guru 4.4 2.5 4.3 mal_Mlym 5 3.7 6.2 nob_Latn 6.8 4.0 9.5 pms_Latn 83.6 46.2 3.6 nyk_Latn 1182.6 914.2 **16.5** ext_Latn 68.3 38.2 8.1 roh_Latn 243.5 170 7.0 quy_Latn 949.7 320.2 **14.5** lam_Latn 233.7 160.8 **21.6** prs_Arab 6.8 3.5 4.8 abn_Latn 245.2 272.5 8.7 mwm_Latn 44.8 53.1 7.1 tuk_Cyrl 277.4 86.3 6.7 mcn_Latn 120.7 129.7 **43.6** kpg_Latn 165.9 122.6 **15.1** srm_Latn 257.5 74.5 **12.3** nep_Deva 8.8 6.3 10 hau_Arab 5.3 3.0 8.1 gsw_Latn 288.2 181.2 **22.3** gle_Latn 10.5 3.7 9.8 ksd_Latn 150 154.9 7.7 fat_Latn 192.3 149 **17.6** cab_Latn 1216.7 155.6 **15.4** zsm_Latn 12.2 2.9 22.7 ldi_Latn 394.8 107.1 **38.2** mps_Latn 75.2 55.2 **17.4** hui_Latn 209.9 177 **10.0** kos_Latn 470.7 485.7 **27.0** pnb_Arab 51.8 30.8 7.1 cym_Latn 8.2 4.8 11.2 acr_Latn 155.7 90.7 5.8 swa_Latn 11.4 6.4 20 srp_Latn 10.9 7.9 13.3 mri_Latn 63 59.5 8.7 hnj_Latn 88.3 92.5 **11.3** bak_Latn 347.1 211 7.5 frr_Latn 117.6 101 9.5 haw_Latn 63.5 66.7 7.4 zho_Hani 20.7 5.9 31.3 mck_Latn 369.3 164.8 **24.7** tpi_Latn 891.8 67.8 8.8 nno_Latn 9.9 12.7 10.4 pes_Arab 5.5 3.1 5.3 ncj_Latn 1019 136.2 **13.7** gya_Latn 31 24.3 **16.5** san_Latn 94.4 96.8 **12.0** som_Latn 14.1 6.9 22.2 ibo_Latn 77.1 90.1 8.5 yao_Latn 738.9 162.4 **13.8** mam_Latn 132.7 62.4 6.1 meu_Latn 380.2 158.5 **26.7** srp_Cyrl 7.4 4.5 8.4 lit_Latn 4.4 2.5 10.6 ncx_Latn 1084.7 948.5 **14.6** ful_Latn 104 105.6 **13.1** ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? section 'Limitation' ✓ A2. Did you discuss any potential risks of your work? section 'Ethics Statement' ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3.3, Section 4, Appendix C ✓ B1. Did you cite the creators of artifacts you used? section 3.3, section 4, appendix c ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? section 'Ethics Statement' ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? section 'Ethics Statement' ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Since our work deals with millions of sentences in hundreds of languages, it was impossible for us to check the content. We leave it as a future work ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? section 3.1, appendix a, appendix c ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. section 5 ## C ✓ **Did You Run Computational Experiments?** Section 4.2 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? section 5 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? section 5. For continued pretraining, it is a single run due to computational resource limitation. For downstream task evaluation, it is multilple runs across 5 seeds. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? section 3.3 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left Blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

feng-etal-2023-joint

Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction

https://aclanthology.org/2023.acl-long.62

Emotion-Cause Pair Extraction (ECPE) aims to identify the document{'}s emotion clauses and corresponding cause clauses. Like other relation extraction tasks, ECPE is closely associated with the relationship between sentences. Recent methods based on Graph Convolutional Networks focus on how to model the multiplex relations between clauses by constructing different edges. However, the data of emotions, causes, and pairs are extremely unbalanced, and current methods get their representation using the same graph structure. In this paper, we propose a **J**oint **C**onstrained Learning framework with **B**oundary-adjusting for Emotion-Cause Pair Extraction (**JCB**). Specifically, through constrained learning, we summarize the prior rules existing in the data and force the model to take them into consideration in optimization, which helps the model learn a better representation from unbalanced data. Furthermore, we adjust the decision boundary of classifiers according to the relations between subtasks, which have always been ignored. No longer working independently as in the previous framework, the classifiers corresponding to three subtasks cooperate under the relation constraints. Experimental results show that **JCB** obtains competitive results compared with state-of-the-art methods and prove its robustness on unbalanced data.

## Joint Constrained Learning With Boundary-Adjusting For Emotion-Cause Pair Extraction Huawen Feng, Junlong Liu, Junhao Zheng, Haibin Chen, Xichen Shang, **Qianli Ma**∗ School of Computer Science and Engineering, South China University of Technology, Guangzhou, China [email protected], [email protected] ## Abstract Emotion-Cause Pair Extraction (ECPE) aims to identify the document's emotion clauses and corresponding cause clauses. Like other relation extraction tasks, ECPE is closely associated with the relationship between sentences. Recent methods based on Graph Convolutional Networks focus on how to model the multiplex relations between clauses by constructing different edges. However, the data of emotions, causes, and pairs are extremely unbalanced, but current methods get their representation using the same graph structure. In this paper, we propose a Joint Constrained Learning framework with Boundary-adjusting for Emotion-Cause Pair Extraction (JCB). Specifically, through constrained learning, we summarize the prior rules existing in the data and force the model to take them into consideration in optimization, which helps the model learn a better representation from unbalanced data. Furthermore, we adjust the decision boundary of classifiers according to the relations between subtasks, which have always been ignored. No longer working independently as in the previous framework, the classifiers corresponding to three subtasks cooperate under the relation constraints. Experimental results show that JCB obtains competitive results compared with state-of-theart methods and prove its robustness on unbalanced data. ## 1 Introduction Emotion cause analysis aims to capture causal relationships between human emotions and their corresponding causes, which has drawn extensive scholarly attention in recent years (Russo et al., 2011; Neviarouskaya and Aono, 2013; Ghazi et al., 2015; Gui et al., 2018). Emotion cause extraction (ECE), first proposed by Lee et al. (2010), is a branch of emotion analysis tasks. ECE aims at extracting potential causes for given emotions. However, it requires emotions to be marked first, which limits the applications in real-world scenarios. Hence, Emotion-Cause Pair Extraction (ECPE) (Xia and Ding, 2019) aims to extract all potential pairs of emotions and corresponding causes simultaneously. Early methods for ECPE are two-stage models (Xia and Ding, 2019), which predict emotions and causes first and then filter out wrong pairs from all possible pairs. Unfortunately, error propagation happens frequently because the predictions in the first stage directly affect the set of possible pairs in the second stage. To this end, the previous work adopts end-to-end frameworks (Ding et al., 2020b; Cheng et al., 2020; Singh et al., 2021) instead of two-stage models. These methods get the representation of emotions and causes separately and then model the pair with them. The distance between the pair of causes is also taken into account because two distant clauses being an emotion-cause pair is usually impossible. With the rapid development of Graph Convolutional Networks (Kipf and Welling, 2016; Defferrard et al., 2016), many methods have started to use graph structures to model the relations between clauses. For instance, RANKCP (Wei et al., 2020) uses a fully-connected graph to propagate information among clauses. At the same time, integrating a variety of edges while constructing the graph also attracts scholarly attention. Currently, the main issue in the field is how to model complex relations with different edges. PairGCN (Chen et al., 2020), for example, demarcates the kinds of edges with the distance between clauses. Based on the diverse representation of nodes of pairs and clauses, PBJE (Liu et al., 2022) divides the edges (e.g., emotion-emotion edges, emotion-cause edges, emotion-pair edges, and so on) through different vertexes. Moreover, owing to the relevance between pair extraction, emotion extraction, and cause extrac- *Corresponding author 1118 ![1_image_0.png](1_image_0.png) tion, most studies adopt multi-task learning to help the model learn a better representation of pairs (Cheng et al., 2020; Wei et al., 2020; Chen et al., 2020; Liu et al., 2022). However, the data of emotions, causes, and pairs are extremely unbalanced, and current methods get their representation using the same graph structure. As shown in Figure 1, most pairs are wrong samples, and only a small number are real emotion-cause pairs. The model can only gain limited knowledge from true pairs because of the small amount, which makes the learning process of ECPE difficult. Meanwhile, there is a big difference between the amounts of emotions and causes. An emotion clause can have several causes, while one cause can only lead to one emotion. The data imbalance limits representation layers and the classifiers' learning process and is usually ignored. Nearly all of the existing methods regard ECPE as a simple binary classification task and use the same networks (the same encoder, the same graph structure, and so on) to deal with pairs, emotions, and causes, which makes the model unaware of the difference between emotions and causes anywhere except for the labels. Consequently, the imbalance has a tremendously adverse effect on the representation of clauses and classifiers' decision boundaries. To sum up, previous models have biased representation of clauses and decision boundaries because they neglect the imbalance of data, which motivated us to propose a Joint Constrained Learning framework with Boundary-adjusting for EmotionCause Pair Extraction (JCB). Following the latest study of long-tail data, we focus on the learning process of representation layers and the decision boundaries of classifiers because they prove to be the performance bottlenecks of unbalanced data (Kang et al., 2019). Specifically, we first design a joint constrained learning framework enforcing some constraints by converting them into differentiable learning objectives, which generates more useful and learnable samples and alleviates the problem of unbalanced data to some extent. Moreover, in order to adjust the narrow decision boundaries, we balance the predicting process by enhancing and correcting results. In summary, the contributions of this paper are as follows: (1) Through a detailed analysis of the existing methods, we point out the problems in previous frameworks of ECPE. (2) We propose a boundary-adjusted model with Joint Constrained Learning. To the best of our knowledge, it is the first time to solve the problem of unbalanced data for ECPE. (3) We conduct experiments on the ECPE benchmark corpus. Compared with those strong baselines, the results demonstrate the effectiveness of the boundary-adjusted model and the Joint Constrained Learning in improving the prediction performance. ## 2 Related Work 2.1 Unbalanced Data Effectively modeling the unbalanced data in NLP tasks remains challenging. Long-tail data, a typical example of unbalanced data, requires a deep network model to simultaneously cope with imbalanced annotations among the head and mediumsized classes and few-shot learning in the tail classes. Similarly, ECPE is also highly unbalanced, because of the small number of true pairs and the enormous gap between the numbers of emotions and causes. Early studies on re-balancing data distribution focus on re-sampling and reweighting (Shen et al., 2016; Cao et al., 2019; Buda et al., 2018; Chen et al., 2018; Liu et al., 2019; Wang et al., 2017), which achieve limited successes due to overfitting. Some recent works aim to decouple the learning process of representation and classifiers, which prove to be the performance bottlenecks (Kang et al., 2019; Menon et al., 2020; Tang et al., 2020; Wang et al., 2020b; Li et al., 2020). Still, such a two-stage strategy requires tedious hyper-parameter tuning to adjust the boundaries initially learned by the classifier. Accordingly, we attempt to get better representation with constrained learning and adjust the biased decision boundaries with classifiers, which are always ignored before. ## 2.2 Constrained Learning Although data-driven methods provide a general and tractable way for relation extraction, their performance is still restricted by unbalanced and limited annotated resources. Early works suggest relations should be constrained by their logical properties (e.g., transitivity, symmetry, consistency, and so on), which comply with by global inferences. However, directly converting the constraints to logical reasoning leads to error propagation. Motivated by the logic-driven framework (Li et al., 2019), Wang et al. (2020a) proposes the constrained learning framework, where the declarative logical constraints are converted into differentiable functions that can be incorporated into the learning objective for relation extraction tasks. It aims to regularize the model towards consistency with the logical constraints across the relations among data. ## 2.3 **Emotion Extraction And Cause Extraction** Emotion Extraction and Cause Extraction are the common auxiliary tasks for ECPE (Cheng et al., 2020; Wei et al., 2020; Chen et al., 2020; Liu et al., 2022). However, due to the imbalance of emotions and causes, the decision boundaries are easily turned to be biased. Consequently, there is a huge gap in the final performance of Emotion Extraction and Cause Extraction (the accuracy of Emotion Extraction is always much higher than Cause Extraction). In this paper, we adopt the results of auxiliary tasks to correct the biased decision boundaries. ## 3 Methodology 3.1 Task Definition Given a document D consisting of n clauses D = [s1, s2*, ..., s*n], ECPE aims to extract all the emotion-cause pairs from $D$: $$P=\{...,(s_{i},s_{j}),...\}\qquad i,j\in[1,n]\tag{1}$$ As for the auxiliary tasks, once an emotion-cause pair (si, sj ) is extracted, an emotion clause and its corresponding cause are confirmed: $$Y_{i}^{e}=\left\{\begin{array}{l}{{1}}\\ {{0}}\end{array}\right.$$ $${\mathrm{(2)}}$$ $$i f(s_{i},s_{j})\in P$$ $$o t h e r w i s e$$ $$Y_{j}^{c}=\left\{\begin{array}{l}{{1}}\\ {{0}}\end{array}\right.$$ $$(3)$$ $$i f(s_{i},s_{j})\in P$$ $$o t h e r w i s e$$ where Y e i = 1 means the clause siis predicted as an emotion clause. The prediction of Cause Extraction is the same as Emotion Extraction. ## 3.2 Clause Encoder Similar to RANKCP (Wei et al., 2020), we adopt BERT and GCN to encode the clauses. Specifically, we feed the whole document D into BERT and use the average pooling of the outputs corresponding to each token as the representation of clauses H = [h1, h2*, ..., h*n]. Then we construct fully-connected graphs for emotions and causes. The representation of clauses H is used to initialize the emotion and cause nodes. As for the pair nodes linking emotion and cause nodes, we concatenate the representation of their corresponding emotions and causes and feed them into a linear layer Linear*pair*. The output of Linear*pair* is then used to initialize pair nodes. $$\begin{array}{l}{{H_{E}^{(0)}=[h_{1}^{e(0)},h_{2}^{e(0)},...,h_{n}^{e(0)}]}}\\ {{H_{C}^{(0)}=[h_{1}^{c(0)},h_{2}^{c(0)},...,h_{n}^{c(0)}]}}\\ {{H_{P}^{(0)}=[h_{11}^{p(0)},h_{12}^{p(0)},...,h_{n n}^{p(0)}]}}\\ {{h_{i}^{e(0)}=h_{i}^{c(0)}=h_{i}}}\\ {{h_{i j}^{p(0)}=L i n e a r_{p a i r}([h_{i};h_{j}])}}\end{array}\qquad\mathrm{(4)}$$ where H (0) E , H (0) C , and H (0) Pindicate the initial representation of emotion nodes, cause nodes, and pair nodes. [.; .] is concatenation. Following the previous framework, we divide the edges R into the pair-clause edge, clause-clause edge, and global edge. The details about the construction of graphs are explained in Appendix A. Given a node v, the process of convolution is defined as: $$\begin{array}{l}{{h_{v}^{(t+1)}=(W^{(t)}h_{v}^{(t)}+b^{(t)})}}\\ {{\ +\frac{1}{|N(v)|}\sum_{r\in R}\sum_{z\in N(v)}(W_{r}^{(t)}h_{z}^{(t)}+b_{r}^{(t)})}}\end{array}\quad\mathbf{(5)}$$ ![3_image_0.png](3_image_0.png) where W(t), b (t), W (t) r , and b (t) r are learnable parameters. N(v) is the neighbors of v and h (t) v is the t-layer representation of node v. By stacking K layers of GCN, the output of the last layer H (K) E, H (K) C, and H (K) Pare finally used as the representation of emotions, causes, and pairs. $$\begin{array}{l}{{H_{E}^{(K)}=[e_{1},e_{2},...,e_{n}]}}\\ {{H_{C}^{(K)}=[c_{1},c_{2},...,c_{n}]}}\\ {{H_{P}^{(K)}=[p_{11},p_{12},...,p_{n n}]}}\\ {{e_{i}=h_{I}^{e(K)}\quad c_{i}=h_{I}^{c(K)}\quad p_{i j}=h_{i j}^{p(K)}}}\end{array}$$ ## 3.3 Joint Constrained Learning Given the properties of emotion-cause pairs from the document, we define several learning objectives to regularize the model with logical constraints. Inspired by Wang et al. (2020a), we specify three types of constraints: Annotation Constraint (unary constraint), Asymmetry Constraint (binary constraint), and Contrastive Constraint(triplet constraint). ## 3.3.1 Annotation Constraint Annotation Constraint is a unary constraint. For labeled pairs, we expect the model to predict what annotations specify. As shown in Figure 2, (s1, s2), (s1, s3), and (s5, s4) are labeled as emotion-cause pairs. If we feed their representations p12, p13, and p54 into the pair classifier FP , their corresponding probabilities y p 12, y p 13, and y p 54 should be predicted to be high. As a result, the annotation constraint loss LA is defined as: - $$\mathbb{U}$$ $$L_{Annotation}=\sum_{(s_i,s_j)\in\hat{P}}-log(y^p_{ij})\hspace{1cm}(7)$$ $$\mathbb{V}$$ $$1121$$. ## 3.3.2 Asymmetry Constraint Asymmetry Constraint is a binary constraint. Asymmetry is a basic property of ECPE because emotion-cause is a unidirectional relationship. For instance, (s5, s4) is an emotion-cause pair in Figure 2. Given that, s5 is an emotion clause, and (s5, s4) is the corresponding cause but not vice versa. In other words, once a sample (si, sj ) has an emotion-cause relation, the pair in its symmetric position (sj , si) will certainly not have the same relation, which is the asymmetry. Given that, the predictions of (si, sj ) and (sj , si) are expected to be quite different. Applying the transformation to the negative log space as before, we have the asymmetry loss: $$L_{A s y m m e t r y}=\sum_{(s_{i},s_{j})\in\hat{P}}l o g(y_{j i}^{p})-l o g(y_{i j}^{p})\quad\mathrm{(8)}$$ In previous works, models adopt the same structure to deal with emotions and causes, which makes the models unaware of the difference between emotions and causes anywhere except for the labels. Consequently, the probability of the pairs in symmetric positions is easily predicted to be high. In this paper, the asymmetry loss helps the model learn more knowledge from minimal true pairs. Specifically, the model can clearly distinguish the emotions and causes in optimization. Here we aim to make the distinction between emotions and causes more clearly, but not the distinction between true and false pairs. It is worth noting that there are some cases whose emotion and cause are the same clause. These samples are on the diagonal of the pairs matrix, where symmetric pairs are themselves. Therefore, they do not affect the calculation of the asymmetry loss. ## 3.3.3 Contrastive Constraint Contrastive Constraint is a triplet constraint. As shown in Figure 1 and Figure 2, for part of the samples, a one-to-many relationship exists between emotions and causes. Inspired by Clustering, we regard the representation of each pair as a cluster center. First, we initialize the cluster centers with the average pooling of the emotion-cause pairs with the same emotion. And then, we randomly sample the representation of the other pairs as the negative pairs, which means the negative pairs can come from either the wrong pairs or the emotioncause pairs with different emotions. Similar to Contrastive learning, the representation of true pairs is supposed to be close to their cluster centers and far away from the negative pairs. Considering the computing cost, we use the triplet margin loss instead of the standard loss functions in contrastive learning. The contrastive loss is defined as: $$L_{Contrastive}=\frac{1}{|\hat{P}|}\sum_{(s_{i},s_{j})\in\hat{P}}max(d(p_{ij},center_{i})\tag{9}$$ $$-d(p_{ij},x_{ij})+\gamma,0)$$ where d(*., .*) means the Euclidean distance between two representations. *center*iis the cluster center of emotion i. xij is the representation of the negative pair to sample (si, sj ). γ is the hyperparameter of the margin. ## 3.4 Boundary Adjusting Due to the unbalanced data and relationships, the emotion classifier usually behaves much better than the cause classifier. Inspired by the two-stage approach for the long-tail distribution, we design an alignment strategy to take advantage of the classifier output to favor a more balanced prediction. Such an alignment strategy exploits the prior class and data input for learning class decision boundary, which avoids tedious hyperparameter tuning. There is a dyadic relation between Emotion Extraction and Cause Extraction, for they hold informative clues to each other. For example, as demonstrated in Figure 2, s4 is the corresponding cause of s5, which means the cause s4 leads to the emotion s5 but not the other emotion s1. According to that, we expect the emotion-oriented features and the cause-oriented features to exchange helpful information. Taking Cause Extraction as an example, we define the semantic relation between H (K) Cand H (K) Eas: $$\begin{array}{l}{{m_{i j}=\left(c_{i}\right)^{T}\times e_{j}}}\\ {{c_{i}\in H_{C}^{(K)}\quad e_{j}\in H_{E}^{(K)}}}\\ {{M_{i j}^{E2C}=\frac{e x p(m_{i j})}{\sum_{k=1}^{n}e x p(m_{i k})}}}\end{array}\tag{10}$$ For ciin Cause Extraction, we can obtain the valuable clues U E2C from Emotion Extraction by applying a weighted sum of semantic relations to all ej in Emotion Extraction: $$\begin{array}{l}{{U^{E2C}=[u_{1}^{E2C},u_{2}^{E2C},...,u_{n}^{E2C}]}}\\ {{u_{i}^{E2C}=\sum_{j=1}^{n}(M_{i j}^{E2C}\cdot e_{j})}}\end{array}\qquad\qquad(11)$$ The clues U C2E can be obtained similarly. Based on the structure of the residual network, we add the useful clues U E2C from Emotion Extraction to the original cause-oriented features H (K) C as the final features for Cause Extraction. And then we feed them into the cause classifier FC to get the prediction Y C = [Y c 1 , Y c 2 , ..., Y c n]: $$\begin{array}{c}{{\overline{{{H_{C}}}}=H_{C}^{(K)}+R e L U(W_{e2c}U^{E2C}+b_{e2c})}}\\ {{Y^{C}=F_{C}(\overline{{{H_{C}}}})}}\end{array}\tag{12}$$ where We2c and be2c are learnable parameters. Similarly, we can get the prediction of Emotion Extraction Y E = [y e 1 , ye 2 , ..., yen]. As explained above, the performance of the emotion classifier is quite strong, which can be helpful in adjusting the decision boundary of the pair classifier FP . Having the emotion predictions, we train an embedding layer EMBe to encode the emotional information in Pair Extraction. Finally, we concatenate the emotion-aware representation of pairs and the corresponding representations of emotions and pairs as the features for FP : $$\begin{array}{l}{{Y^{P}=F_{P}(\overline{{{H_{P}}}})}}\\ {{\overline{{{H_{P}}}}=[\overline{{{p_{11}}}},\overline{{{p_{12}}}},...,\overline{{{p_{n n}}}}]}}\\ {{\overline{{{p_{i j}}}}=W_{p}R e L U(p_{i j}+E M B_{e}(Y_{i}^{e}))+b_{p}}}\\ {{p_{i j}\in H_{P}^{(K)}}}\end{array}\tag{13}$$ where Wp and bp are learnable weights and biases of the linear pair classifier FP . ## 3.5 Optimization The loss function for the input documents D consists of the loss of auxiliary tasks and the loss of constrained learning: L = Lemotion + Lcause + LAnnotation + αLAsymmetry + βLContrastive Lemotion = − 1 |D| X|D| i=1 Yˆe i log y e i (14) Lcause = − 1 |D| X|D| i=1 Yˆc i log y c i $\mathbf{x}=\hat{V}^e$ c. where α and β are hyperparameters. Yˆe iand Yˆc i are emotion and cause label of clause si. ## 4 Experiments We conduct extensive experiments to verify the effectiveness of our proposed model JCB. In this section, we attempt to answer the following questions: **RQ1:** Does JCB perform better than existing methods? **RQ2:** Are the constrained learning and boundary-adjusted mechanism the key factors affecting the performance? **RQ3:** How do they work in optimization? RQ4: How does JCB perform on more unbalanced data? ## 4.1 Datasets And Preprocessing To evaluate the effectiveness of our model, we conduct experiments on the Chinese benchmark dataset released by Xia and Ding (2019). The corpus consists of 1,945 Chinese documents from the SINA news website. As shown in Table 1, the data is extremely unbalanced. For example, emotioncause pairs account for about 0.4% of all the possible pairs. On the other hand, an emotion clause can have several causes, while one cause can only lead to one emotion. Following the preprocessing of previous works, we set a relative distance constraint |i−j| ≤ 3. Using the relative distance constraint directly affects the degree of data imbalance, and we discuss it in Section 4.6. To make a fair comparison, we use the 10-fold cross-validation and split the data as Xia and Ding (2019) did. As for the evaluation metrics, we adopt the precision, recall, and F-score on three tasks: Emotion Extraction, Cause Extraction, and Pair Extraction. ## 4.2 Experimental Settings We implement JCB based on Transformers (Wolf et al., 2020) and adopt BERT-base-Chinese (Devlin | Item | Number | Percentage(%) | |----------------|----------|-----------------| | documents | 1,945 | 100 | | -w/ 1 EC pair | 1,746 | 89.8 | | -w/ 2 EC pairs | 177 | 9.1 | | -w/ 3 EC pairs | 22 | 1.1 | | pairs | 490,367 | 100 | | -EC pairs | 2,167 | 0.4 | | -non EC pairs | 488,200 | 99.6 | Table 1: Detailed dataset statistics. | Config | Value | |-------------------|-------------------| | Device | GeForce RTX 3090 | | Platform | Pytorch 1.8.0 | | Backbone | BERT-base-Chinese | | Dimension | 768 | | Batch Size | 4 | | Epochs | 50 | | Learning Rate | 2e-5 | | Warmup Proportion | 0.1 | | Dropout | 0.2 | | K | 1 | | α | 0.15 | | β | 0.5 | Table 2: Detailed experimental configs. ![5_image_0.png](5_image_0.png) et al., 2018) as the backbone. Clauses in the same document are concatenated and fed into the clause encoder, while each document in a batch is encoded separately. The setups of our experiments are listed in Table 2. We set α and β to 0.15 and 0.5 and conduct experiments on GeForce RTX 3090. Some documents have too many clauses and words, so we set the batch size to 4 and use a sliding window to deal with words exceeding the limit, which helps reduce the demands for large GPU resources. We compare our models with current strong baselines, including:**ECPE-2D** (Ding et al., 2020a), TransECPE (Fan et al., 2020), **RankCP** (Wei et al., 2020), **PairGCN** (Chen et al., 2020), **ECPEMLL** (Ding et al., 2020b), **UTOS** (Cheng et al., 2021), **MTST-ECPE** (Fan et al., 2021), and PBJE (Liu et al., 2022). Among them, **RankCP**, PairGCN, and **PBJE** use BERT+GCN as the clause encoder, which is similar to ours. ECPEMLL, **UTOS**, and **MTST-ECPE** convert ECPE to a sequence labelling task or a multi-label classification task. Different from them, each task of our approach is a binary classification. More details about these methods are listed in Appendix B. ## 4.3 Rq1: Does Jcb Perform Better Than Existing Methods? Table 3 shows the experimental results of JCB compared with others on three tasks. The overall results indicate the effectiveness of JCB. We can find that the performance of JCB is excellent on all tasks, which almost exceeds all the existing methods, especially on the main task - Pair Extraction. The precision P and recall R may not be the best of all but are still quite competitive compared with state-of-the-art methods. It is noteworthy that the improvement of the main task mainly comes from the excellent performance of Cause Extraction. Compared with RankCP (whose clause encoder is similar to ours), the F1 of Emotion Extraction of our model is slightly less, but the results of Pair Extraction (the main task) and Cause Extraction are much higher, which proves the constrained learning and the guidance of the Emotion Extraction help the model get a better representation of causes. The performance of the emotion and cause classifiers is balanced to achieve better results. ## 4.4 Rq2: Are The Constrained Learning And Boundary-Adjusted Mechanism The Key Factors Affecting The Performance? The results of the ablation study are shown in Table 4. Apparently, constrained learning has a profound effect on performance. The performance of Pair Extraction dramatically drops when removing constrained learning. Meanwhile, the F1 of Emotion Extraction is stable whereas that of Cause Extraction decreases sharply. Therefore, we conclude that the degradation of performance of the main task is mainly due to the fall of Cause Extraction. It also proves that constrained learning helps the model better represent pairs and causes. In comparison, Asymmetry Constraint has a more significant impact on Cause Extraction, while Contrastive Constraint has a more remarkable effect on Pair Extraction. We assume that Asymmetry Constraint distinguishes between emotions and causes more clearly, which facilitates the performance on the sample-scarce tasks (Pair Extraction and Cause Extraction). On the other hand, Contrastive Constraint mines the information of the emotion-cause pairs with the same emotion, which is important for Emotion Extraction. Otherwise, boundary adjusting somewhat solves the problem of biased decision boundaries. All three tasks are affected while removing boundary adjustments, especially Pair Extraction. It should be noted that both emotion and cause clues play an essential role in clues alignment. Removing each of them may not cause considerable fluctuations in Emotion Extraction but will eventually lead to the bad performance of the main task. We speculate that unbalanced ablation makes the amounts of information flow to encoders in a different manner, so the performance imbalance is intensified. ## 4.5 Rq3: How Do The Constrained Learning And Boundary-Adjusted Mechanism Work In Optimization? We observe the final output and plot heat maps to verify how JCB achieves the anticipation. We make a comparison with PBJE - the strongest one of the previous models. PBJE uses the same graph structure to encode emotions and causes, so the distinction between the pairs symmetric along the diagonal of the matrix is not very clear. Consequently, PBJE is easily misled to extract the right ones from these symmetric pairs. However, due to Asymmetry Constraint, JCB has a more asymmetric output (Figure 4(a)). On the other hand, Contrastive Constraint enables JCB to distinguish the difference among pairs with different emotions. In this way, JCB can get more differentiated results when facing documents containing two or more true pairs (Figure 4(b)). Moreover, there are usually several possible emotion or cause clauses, and mismatches occur frequently among them. As shown in Figure 4(c), after boundary-adjusting (clues alignment and emotion guidance), JCB allocates higher scores for pairs with truly-matched emotions and causes. Relatively, the pairs on the wrong intersection of mismatched emotion lines and cause lines are as- | Models | Pair Extraction | Emotion Extraction | Cause Extraction | | | | | | | |-----------|-------------------|----------------------|--------------------|---------|---------|---------|---------|---------|---------| | P | R | F1 | P | R | F1 | P | R | F1 | | | ECPE-2D | 72.92 | 65.44 | 68.89 | 86.27 | 92.21#1 | 89.10 | 73.36 | 69.34 | 71.23 | | TransECPE | 77.08 | 65.32 | 70.72 | 88.79 | 83.15 | 85.88 | 78.74 | 66.89 | 72.33 | | PairGCN | 76.92 | 67.91 | 72.02 | 88.57 | 79.58 | 83.75 | 79.07 | 68.28 | 73.75 | | UTOS | 73.89 | 70.62 | 72.03 | 88.15 | 83.21 | 85.56 | 76.71 | 73.20 | 74.71 | | MTST-ECPE | 75.78 | 70.51 | 72.91 | 85.83 | 80.94 | 83.21 | 77.64 | 72.36 | 74.77 | | RankCP | 71.19 | 76.30#1 | 73.60 | 91.23#1 | 89.99 | 90.57#1 | 74.61 | 77.88#2 | 76.15 | | ECPE-MLL | 77.00 | 72.35 | 74.52 | 86.08 | 91.91#2 | 88.86 | 73.82 | 79.12#1 | 76.30 | | PBJE | 79.22#1 | 73.84 | 76.37#2 | 90.77#2 | 86.91 | 88.76 | 81.79#1 | 76.09 | 78.78#2 | | JCB | 79.10#2 | 75.84#2 | 77.37#1 | 90.77#2 | 87.91 | 89.30#2 | 81.41#2 | 77.47 | 79.34#1 | Table 3: Experimental results of on ECPE benchmarks. The best result is in red, and the second is in blue. | Models | Pair Extraction | Emotion Extraction | Cause Extraction | | | | | | | |-----------------------------|-------------------|----------------------|--------------------|-------|-------|-------|-------|-------|-------| | P | R | F1 | P | R | F1 | P | R | F1 | | | JCB | 79.10 | 75.84 | 77.37 | 90.77 | 87.91 | 89.30 | 81.41 | 77.47 | 79.34 | | -w/o Asymmetry Constraint | 78.82 | 74.13 | 76.34 | 90.91 | 87.20 | 88.99 | 80.71 | 75.79 | 78.11 | | -w/o Contrastive Constraint | 76.83 | 75.42 | 76.05 | 88.72 | 87.54 | 88.08 | 80.02 | 77.23 | 78.54 | | -w/o Constrained Learning | 76.31 | 74.37 | 75.26 | 90.45 | 88.71 | 89.53 | 79.58 | 76.34 | 77.88 | | -w/o Emotion Clues | 78.93 | 74.38 | 76.55 | 91.16 | 87.77 | 89.41 | 81.02 | 76.18 | 78.50 | | -w/o Cause Clues | 79.20 | 74.44 | 76.67 | 91.01 | 87.49 | 89.16 | 81.28 | 76.33 | 78.66 | | -w/o Clues Alignment | 79.64 | 73.46 | 76.38 | 91.30 | 86.62 | 88.87 | 81.45 | 75.25 | 78.19 | | -w/o Emotion Guidance | 78.20 | 75.50 | 76.76 | 90.80 | 88.29 | 89.50 | 80.67 | 76.98 | 78.74 | | -w/o Boundary Adjusting | 78.32 | 74.32 | 76.19 | 90.86 | 87.49 | 89.10 | 81.17 | 76.36 | 78.61 | | Clause Encoder (BERT+GCN) | 73.01 | 76.23 | 74.44 | 89.17 | 88.77 | 88.92 | 77.25 | 78.21 | 77.62 | Table 4: The results of the ablation study on the benchmark corpus for the main task and auxiliary tasks. ![7_image_0.png](7_image_0.png) signed with lower scores. More cases are listed in Appendix C. ## 4.6 Rq4: How Does Jcb Perform On More Unbalanced Data? Figure 3 shows the fluctuation of their performance when relative distance changes. The performance of Rankcp is sensitive to the relative distance, while PBJE and JCB remain stable. There is not a strictly negative correlation between the performance and the relative distance Z. A small relative distance means fewer pairs to classify. Still, it also might | Models | Pair Extraction | | | |----------|-------------------|--------------|--------------| | P | R | F1 | | | RankCP | 64.26(6.93↓) | 66.94(9.36↓) | 65.49(8.11↓) | | PBJE | 78.41(0.81↓) | 71.31(2.53↓) | 74.66(1.71↓) | | JCB | 78.93(0.17↓) | 71.68(4.16↓) | 75.09(2.28↓) | Table 5: The results of RankCP, PBJE, and JCB without the relative distance constraint. filter out some right ones. The value of Z affects the degree of data imbalance and the final results. To evaluate the performance of JCB on more unbalanced data, we remove the relative distance constraint (which makes the data more unbalanced for more false pairs). In Table 5, compared with RankCP, whose clause encoder is similar to ours (BERT+GCN), the performance of JCB is not significantly influenced when dealing with all the possible pairs without preprocessing. As for PBJE, it is less affected, and we conclude that it is because of balancing the information flow while constructing the graph. The experimental result proves the effect of imbalance on performance and the robustness of our model on more unbalanced data. ## 5 Conclusion This paper summarizes existing ECPE methods, indicating that almost all of them ignore the biased representation of clauses and decision boundaries due to data imbalance. We propose a Joint Constrained Learning framework with Boundaryadjusting and conduct massive experiments on the ECPE benchmark dataset. The remarkable performance demonstrates the effectiveness of our method for learning better representations of unbalanced samples and adjusting biased decision boundaries. We expect our work will direct more scholarly attention to solutions to the problem of unbalanced data in information extraction. ## Limitations In this paper, we conduct experiments only on the Chinese benchmark dataset due to the lack of English datasets and comparisons of related methods. Moreover, the model is based on BERTbase-Chinese, so the maximum input length is constrained to less than 512. However, the numbers of words in some long documents exceed the limit, so we use a sliding window to deal with the problem. Otherwise, some documents having too many clauses require large GPU resources after aligning and padding. 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Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3171–3181. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38–45. Rui Xia and Zixiang Ding. 2019. Emotion-cause pair extraction: A new task to emotion analysis in texts. arXiv preprint arXiv:1906.01267. ![10_image_0.png](10_image_0.png) ![10_image_1.png](10_image_1.png) Figure 6: Differentiated output of JCB (right graphs) compared with PBJE (left graphs). ![10_image_2.png](10_image_2.png) | Models | Pair Extraction P R F1 | | | |----------|--------------------------|-------|-------| | k = 1 | 79.10 | 75.84 | 77.37 | | k = 2 | 78.27 | 73.16 | 75.58 | | k = 3 | 76.99 | 72.67 | 74.7 | ![10_image_3.png](10_image_3.png) Table 6: The decrease of performance with the increase of k. ## A Details About The Construction Of Graphs. We divide the nodes V into emotion nodes, cause nodes, and pair nodes, which are initialized as the output of BERT (H (0) E , H (0) C , and H (0) P). Based on that, the edges R are divided into pair-clause edges and clause-clause edges. In experiments, we also use global edges. These edges connect the global node (initialized as the average of the output of BERT) and the other nodes, which helps preserve global information. The general form of k-layer GCN with the set of edges R is listed in Formula 5. However, after parametric searching, we set k to 1 because we find the performance tends to drop with the increase of k (as shown in Table 6). When k is bigger than 1, the features of nodes from different groups may be over-mixed and indistinguishable. Besides, it has more learnable parameters, which easily brings about over-fitting. ## B Details About The Current Ecpe Methods. In experiments, we compare our models with the current strong baselines, including: ECPE-2D (Ding et al., 2020a): Use 2D transformer to get 2D representation and model the interactions of different emotion-cause pairs. TransECPE (Fan et al., 2020): Based on transition, convert the task into a parsing-like directed graph construction procedure. RankCP (Wei et al., 2020): Utilize the fullyconnected graph to model the relationships between clauses and rank all the possible pairs in a document. PairGCN (Chen et al., 2020): Construct a graph with pair nodes and define different edges according to the relative distance. ECPE-MLL (Ding et al., 2020b): Employ two collaborative frameworks for emotions and causes and apply multi-label learning to them. UTOS (Cheng et al., 2021): Convert the task into sequence labelling, which tackles the error propagation. MTST-ECPE (Fan et al., 2021): Similar to UTOS, design a multi-task sequence tagging framework but refine the tag distribution. PBJE (Liu et al., 2022): Construct a graph for each task and balance the information flow among them. ## C Case Study. As mentioned in Section 4.5, JCB has a more asymmetric and differentiated output and behaves better when more than one true pair needs to be extracted. Given several possible emotions and causes, JCB can precisely match them. Figure 5, Figure 6, and Figure 7 show the comparison of PBJE and JCB in three scenarios. Asymmetry Constraint helps JCB get a more asymmetric output so that the model will not be confused facing symmetric pairs any longer. Contrastive Constraint enables JCB to distinguish the difference among pairs with different emotions and find the similarity between pairs with the same ones. This way, JCB behaves better in documents with multiple emotion-cause pairs. Moreover, the boundary-adjusting mechanism solves the problem of mismatch to some extent. The pairs on wrong intersections of mismatched emotion lines and cause lines are assigned with low scores, and the right ones are enhanced by emotions and given higher scores. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations ✗ A2. Did you discuss any potential risks of your work? The dataset we use is collected from the SINA news website. All of the corpora don't cover party politics or economics and contain any information that names or uniquely identifies individual people or offensive content. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract 1 Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4 Experiments ✓ B1. Did you cite the creators of artifacts you used? 4 Experiments ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 4 Experiments Appendix A ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 4 Experiments ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Ethics Statement ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 4 Experiments ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4 Experiments Appendix A ## C ✓ **Did You Run Computational Experiments?** 4 Experiments ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4 Experiments Appendix B The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4 Experiments Appendix B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4 Experiments ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 4 Experiments D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

zhuang-tu-2023-pretrained

Pretrained Bidirectional Distillation for Machine Translation

https://aclanthology.org/2023.acl-long.63

Knowledge transfer can boost neural machine translation (NMT), for example, by finetuning a pretrained masked language model (LM). However, it may suffer from the forgetting problem and the structural inconsistency between pretrained LMs and NMT models. Knowledge distillation (KD) may be a potential solution to alleviate these issues, but few studies have investigated language knowledge transfer from pretrained language models to NMT models through KD. In this paper, we propose Pretrained Bidirectional Distillation (PBD) for NMT, which aims to efficiently transfer bidirectional language knowledge from masked language pretraining to NMT models. Its advantages are reflected in efficiency and effectiveness through a globally defined and bidirectional context-aware distillation objective. Bidirectional language knowledge of the entire sequence is transferred to an NMT model concurrently during translation training. Specifically, we propose self-distilled masked language pretraining to obtain the PBD objective. We also design PBD losses to efficiently distill the language knowledge, in the form of token probabilities, to the encoder and decoder of an NMT model using the PBD objective. Extensive experiments reveal that pretrained bidirectional distillation can significantly improve machine translation performance and achieve competitive or even better results than previous pretrain-finetune or unified multilingual translation methods in supervised, unsupervised, and zero-shot scenarios. Empirically, it is concluded that pretrained bidirectional distillation is an effective and efficient method for transferring language knowledge from pretrained language models to NMT models.

# Pretrained Bidirectional Distillation For Machine Translation Yimeng Zhuang, Mei Tu Samsung Research China - Beijing (SRC-B) {ym.zhuang,mei.tu}@samsung.com ## Abstract Knowledge transfer can boost neural machine translation (NMT), for example, by finetuning a pretrained masked language model (LM). However, it may suffer from the forgetting problem and the structural inconsistency between pretrained LMs and NMT models. Knowledge distillation (KD) may be a potential solution to alleviate these issues, but few studies have investigated language knowledge transfer from pretrained language models to NMT models through KD. In this paper, we propose Pretrained Bidirectional Distillation (PBD) for NMT, which aims to efficiently transfer bidirectional language knowledge from masked language pretraining to NMT models. Its advantages are reflected in efficiency and effectiveness through a globally defined and bidirectional context-aware distillation objective. Bidirectional language knowledge of the entire sequence is transferred to an NMT model concurrently during translation training. Specifically, we propose self-distilled masked language pretraining to obtain the PBD objective. We also design PBD losses to efficiently distill the language knowledge, in the form of token probabilities, to the encoder and decoder of an NMT model using the PBD objective. Extensive experiments reveal that pretrained bidirectional distillation can significantly improve machine translation performance and achieve competitive or even better results than previous pretrain-finetune or unified multilingual translation methods in supervised, unsupervised, and zero-shot scenarios. Empirically, it is concluded that pretrained bidirectional distillation is an effective and efficient method for transferring language knowledge from pretrained language models to NMT models. ## 1 Introduction Initializing parameters by a pretrained masked language model (LM) (Kenton and Toutanova, 2019) is a knowledge transfer method widely applied to natural language processing tasks. Following its success, pretrained neural machine translation (NMT) models have attracted more and more research interest (Conneau and Lample, 2019; Song et al., 2019; Liu et al., 2020; Li et al., 2022). However, the pretrain-finetune paradigm may suffer from potential issues. As is pointed out in He et al. (2021), the finetuned model may forget some critical language generation skills learned from the pretraining phase. The catastrophic forgetting problem (Kirkpatrick et al., 2017; McCloskey and Cohen, 1989) commonly exists in transfer learning, leading to overfitting to target domains. Hu et al. (2022); Fang et al. (2022) also observe similar forgetting problems in pretrained NMT tasks. Besides, in the pretrain-finetune paradigm, model parameters are initialized by a pretrained model; this requires structure consistency (e.g., exact dimensions, layers, attention heads, etc.) between the pretrained LM and the NMT models to some extent. However, a powerful but structurally inconsistent pretrained LM may incorporate more language knowledge. Knowledge distillation (KD) (Hinton et al., 2015) may be a potential solution to alleviate these issues, but few studies investigate language knowledge transfer from pretrained language models to NMT models by KD. Previous works use KD for model compression (Gordon and Duh, 2020), or data complexity reduction (Gu and Kong, 2021; Zhou et al., 2019), or multilingual translation (Sun et al., 2020; Tan et al., 2019). Zhou et al. (2022) utilizes confidence-based knowledge distillation to incorporate bidirectional global context into NMT models. In this paper, we propose Pretrained Bidirectional Distillation (PBD) for NMT, which can alleviate the difference caused by pretraining (mask language modeling, perturbed sentences) and MT fine-tuning (full sentences) in the pretrain-finetune paradigm and boost large-scale translation training. In pretrained bidirectional distillation, language knowledge acquired from pretraining is continu1132 ![1_image_0.png](1_image_0.png) ously transferred to the NMT model. Knowledge transfer runs through the training process to address the forgetting problem. We deal with the pretrained language knowledge by pretrained bidirectional distillation objectives, which are the token probabilities generated by the pretrained LM about potential tokens matching a global context. The pretrained bidirectional distillation objectives are distilled to the encoder and decoder of an NMT model. Therefore, there is no need to require structure consistency between pretrained LMs and NMT models, and bidirectional distillation enriches the NMT decoder with bidirectional semantic information. To guarantee the effectiveness and efficiency of pretrained bidirectional distillation, we propose self-distilled masked language pretraining, which can generate globally defined and bidirectional context aware token probabilities and use them as the pretrained bidirectional distillation objectives. "Globally defined" lets us obtain the full probabilities of each token in a single forward pass, guaranteeing distillation effect and execution efficiency. "Bidirectional context aware" distillation objectives incorporate bidirectional language knowledge of the whole sequence, guaranteeing effectiveness. Extensive experiments are conducted on widely used benchmark datasets. In a supervised scenario, the proposed method achieves +2.7 and +8.5 absolute average BLEU improvement using the unified multilingual translation model and pretrainfinetune paradigm, respectively. And our model obtains 19.28 and 16.55 average BLEU in unsupervised and zero-shot scenarios, respectively, outper- Algorithm 1 Pretrained Bidirectional Distillation for NMT Require: language model LM, NMT model TM, unlabeled LM data DLM , parallel data DTM 1: Initialize LM by random 2: **for each** X ∈ DLM do 3: Get loss L ← λLΩ + LΘ ▷ Equ 1,4 4: Update LM ← BACKPROP(L*, LM*) 5: **end for** 6: Initialize TM by random or pretraining 7: **for each** (X, Y ) ∈ DTM do 8: Get translation loss Lce ← TM(*X, Y* ) 9: Forward pass PΩ ← LM({*X, Y* }) 10: Get loss *L ← L*ce + Le + Ld ▷ Equ 8,10 11: Update TM ← BACKPROP(L*, TM*) 12: **end for** 13: **return** TM forming previous models. To summarize, our contributions are as follows: - We propose pretrained bidirectional distillation to investigate language knowledge transfer from pretrained language models to NMT models. - We propose self-distilled masked language pretraining to support concurrently computing full token probabilities of the full sequence. - We conduct extensive experiments to verify the effectiveness of our methods and achieve competitive or even better performance than previous pretrain-finetune or unified multilingual translation methods in supervised, unsupervised, and zero-shot scenarios. ## 2 Pretrained Bidirectional Distillation Figure 1 and Algorithm 1 illustrate the overall flow of the proposed Pretrained Bidirectional Distillation (PBD) for machine translation. It consists of two processes: (1) Self-distilled masked language pretraining takes unlabeled LM training data as input and optimizes a token reconstruction loss and a self-distillation loss. The produced self-distilled LM has the advantage of generating the full probability prediction of all input tokens in one pass rather than only the masked tokens as in previous masked LMs. This ensures the efficiency of pretrained bidirectional distillation in the second process. (2) Translation training with PBD losses ![2_image_0.png](2_image_0.png) trains a standard Encoder-Decoder NMT model using parallel data but enhances it with extra PBD losses. The PBD losses are jointly optimized with the standard translation loss, and pretrained language knowledge in the form of full token probabilities generated by the pretrained LM is distilled to the encoder and decoder of the NMT model. We will introduce these two processes in detail in the following sections. ## 2.1 Self-Distilled Masked Language Pretraining This paper proposes self-distilled masked language pretraining to obtain the pretrained bidirectional distillation objective for NMT models. Pretrained masked language models predict a token probability distribution over the vocabulary for each masked position, and these token probabilities indicate the potential tokens matching the context. Our assumption is that these token probabilities contain specific language knowledge and can be transferred to NMT models. Thus, we consider these token probabilities as the distillation objective. However, in our preliminary experiments, we discovered that the token probabilities predicted in non-masked positions often tend to focus too much on real tokens, which fails to accurately reflect the long-tailed distribution of potential tokens. In standard masked language pretraining, only a small percentage (typically 15%) of tokens can be masked. This limitation prevents us from efficiently achieving the full distillation objective that reflects the long-tailed distribution for each position of an input sequence in a single forward pass. To obtain a globally defined distillation objective, we adopt self-distillation, in which the token probabilities in non-masked positions are learned from the corresponding masked positions. Figure 2 illustrates the overall architecture of the proposed self-distilled masked language model, which follows the widely used masked language model framework (Kenton and Toutanova, 2019; Conneau and Lample, 2019) with some modifications to its architecture: (1) The target tokens to be predicted have two types: masked tokens and real tokens. (2) The input sequence is partitioned into three parts to avoid exposing information between masked tokens and real tokens. (3) Masked and real tokens have different prediction heads and loss functions. The following subsections elaborate on the architecture of the self-distilled masked language model. ## 2.1.1 Input Representation Let S denote an input sequence, and it may be a monolingual text S = {X} = {x1, · · · , xn} or 1134 ![3_image_0.png](3_image_0.png) the concatenation of a pair of parallel sentences S = {X, Y } = {x1, · · · , xn, y1, · · · , ym}. According to the random masking scheme, the input sequence consists of non-masked positions and masked positions (typically 15%). Specifically, as is shown in Figure 2, a portion of positions (in this case, the 3rd, 7th, and 8th positions) have corresponding [MASK] tokens appended at the end of the sequence. Therefore, we split the complete input sequence into three parts: the context part Pcontext which is used as the known context; the masked part Pmask which is used to reconstruct the real tokens; and the target part Ptarget in which tokens are the real tokens corresponding the masked part, and they are pretended to be unknown when predicting token probabilities. The corresponding position embeddings, language type embeddings, and a special [MASK] token embedding are summed to form the input representations in Pmask. And, the input representations in Ptarget and Pcontext are the sum of the corresponding position embeddings, language type embeddings, and the real token embeddings. ## 2.1.2 Contextual Mask Matrix In the masked token reconstruction task, the real token should be kept unknown to the corresponding masked position. Besides, the hidden state at the masked position is also needed to be invisible to the corresponding target position in the forward pass because the predicted probability at the masked position is the learning objective of the corresponding target position (i.e., avoiding supervised information leaking). Since the backbone of the masked language model is an attention-based Transformer encoder, the visibility of tokens can be controlled by a contextual mask matrix. As is illustrated in Figure 3, the contextual mask matrix controls that each token can attend to itself and the tokens in Pcontext. It means that the context S˜ is set to S˜ = {wt|wt ∈ Pcontext} for all the three parts Pmask, Ptarget and Pcontext. ## 2.1.3 Pretraining Loss We adopt different loss functions for the masked part and the target part. In the masked part, the language model learns to reconstruct the masked tokens. At each position of the target part, our model pretends not to have known the real token and predicts the potential tokens matching the context. Specifically, the probabilities of the potential tokens are learned to approximate the token reconstruction probabilities at the corresponding masked positions. This is because the token reconstruction probabilities are the predicted probabilities of potential tokens at the masked positions. Let S˜ = {wi|wi ∈ Pcontext} denote the context token set, S¯ = {wi|wi ∈ Ptarget} denote the target token set, ti denote the token at position i. The masked token reconstruction task defines the pretraining objective LΘ as minimizing the negative log-likelihood of target tokens as below. $${\mathcal{L}}_{\Theta}=-\log P_{\Theta}(\bar{S}|\tilde{S})\approx-\sum_{w_{i}\in S}\log P_{\Theta}(t_{i}=w_{i}|\tilde{S})\ .\eqno(1)$$ $$(3)$$ in which the token reconstruction probability PΘ is defined in the masked part and is computed by a prediction head Θ. $P_{\Theta}(t_{i}=w_{i}|\tilde{S})=\frac{\exp(\mathbf{h}_{i}^{\Theta}T\mathbf{e}(w_{i}))}{\sum_{w\in V}\exp(\mathbf{h}_{i}^{\Theta}T\mathbf{e}(w))}$ $\mathbf{h}_{i}^{\Theta}=\text{gelu}(\mathbf{h}_{i}^{\prime T}\mathbf{W}_{\Theta}+\mathbf{b}_{\Theta})$ $${\mathrm{(2)}}$$ where we use h ′ i to represent the hidden state of the last layer of a Transformer encoder at the masked position i, WΘ ∈ R D×D and bΘ ∈ R D are learnable parameters of the prediction head Ω, D is the dimension, e(w) ∈ R D denotes the embedding of token w, and V represents the vocabulary. A self-distillation approach is adopted here to learn the potential tokens' probabilities. The loss LΩ is defined by optimizing the KL divergence 1135 between the probability distribution of token reconstruction and the probability distribution of potential tokens. It is equivalent to $$\mathcal{L}_{\Omega}=-\sum_{i\in P_{\text{\rm{uuget}}}}\sum_{w\in V}P_{\Theta}(t_{i}=w|\tilde{S})\log P_{\Omega}(t_{i}=w|\tilde{S})\tag{4}$$ in which the probability of potential tokens PΩ is defined in the non-masked positions and is computed by a prediction head Ω. $$P_{\Omega}(t_{i}=w|\tilde{S})=\frac{\exp(\mathbf{h}_{i}^{\Omega^{T}}\mathbf{e}(w))}{\sum_{w\in V}\exp(\mathbf{h}_{i}^{\Omega^{T}}\mathbf{e}(w))}$$ $$\mathbf{h}_{i}^{\Omega}=\mathrm{gelu}(\mathbf{h}_{i}^{T}\mathbf{W}_{\Omega}+\mathbf{b}_{\Omega})$$ $$\quad(5)$$ where hi denotes the hidden state at the nonmasked position i. The overall loss integrates LΩ and LΘ by weighted summation. $\text{es,the,hidden,state,at,the}$. $${\mathcal{L}}=\lambda{\mathcal{L}}_{\Omega}+{\mathcal{L}}_{\Theta}$$ in which $\lambda$ is a hyper-parameter. ## 2.1.4 Inference In inference, there is no masked position for the input sequence S, and the probabilities of any potential token w at each position i can be computed as PΩ(ti = w|S). We consider these probabilities as the pretrained bidirectional distillation objective for NMT models. ## 2.2 Pretrained Bidirectional Distillation Loss In this paper, the knowledge learned from the aforementioned self-distilled mask language model is transferred to an NMT model using the pretrained bidirectional distillation loss. Specifically, we concatenate the source and target sentence without masking to form an input sequence to the selfdistilled LM, and obtain the full probability prediction PΩ from the LM as the pretrained bidirectional distillation objective, which is distilled to a NMT model by optimizing the KL divergence between the pretrained bidirectional distillation objective PΩ and its corresponding predictions from an intermediate layer of the encoder or decoder. The distillation loss of the encoder is as follows. $$\mathcal{L}_{e}=-\sum_{t}\sum_{w}P_{\Omega}(x_{t}=w|X,Y)\log P_{e}(x_{t}=w|X)\tag{8}$$ $$\mathbf{P}_{e}=\text{softmax}(\mathbf{H}_{e}^{l}\cdot\mathbf{E}^{T})\tag{9}$$ $$\begin{array}{c}{{X)}}\\ {{}}\\ {{\mathrm{(8)}}}\\ {{\mathrm{(9)}}}\end{array}$$ Here, we use X and Y to denote the sentence in source and target language, respectively, and xt denotes the t-th position of X. w is a word in the vocabulary V . Hle ∈ R|X|×D represents the hidden states of an intermediate layer l of the encoder. E ∈ R|V |×D is the token embedding matrix. We reuse the token embedding matrix, therefore, the pretrained bidirectional distillation won't add any extra parameters. The t-th row and w-th column of the probability matrix Pe is the value of Pe(xt = w|X). Similar distillation loss is applied to the decoder. $$P_{d}=-\sum_{t}\sum_{w}P_{\Omega}(y_{t}=w|X,Y)\log P_{d}(y_{t}=w|X,Y_{<t})\tag{10}$$ $${\bf P}_{d}={\rm softmax}({\bf H}_{d}^{l}\cdot{\bf E}^{T})\tag{11}$$ $$(6)$$ $$\left(7\right)$$ where yt denotes the t-th position of the target sentence, and we use Hld to represent the hidden states of an intermediate layer l of the decoder. Note that these distillation losses are jointly optimized with the standard translation loss when the NMT training. The pretrained bidirectional distillation objective is not only globally defined but also bidirectional context aware (i.e., bidirectional language knowledge of the complete source and target sentence). Therefore, it is a challenging task to approximate the pretrained bidirectional distillation objective for the encoder and decoder given only a source sentence or given the source and partial target sentence, but it is reasonable since the source sentence has complete semantics information. On the other hand, the challenging task may force the NMT model to learn global language knowledge from the self-distilled LM. It can enrich the NMT decoder with bidirectional semantic information, as using future information is important for machine translation. ## 3 Experiments We primarily study the proposed pretrained bidirectional distillation by conducting experiments on supervised, unsupervised, and zero-shot multilingual machine translation scenarios. ## 3.1 Experimental Setup 3.1.1 Language Model Pretraining Datasets We use the parallel dataset PC32 (Lin et al., 2020) and the monolingual dataset MC24 provided by Pan et al. (2021). PC32 contains 32 1136 | En-Fr | En-Tr | En-Es | En-Ro | En-Fi | Avg | △ | | | | | | | |------------------------------------------------------------|---------|---------|---------|---------|-------|------|------|------|------|------|-------|-------| | wmt14 | wmt17 | wmt13 | wmt16 | wmt17 | | | | | | | | | | → | ← | → | ← | → | ← | → | ← | → | ← | | | | | bilingual Transformer-6 (Lin et al., 2020) | 43.2 | 39.8 | - | - | - | - | 34.3 | 34.0 | - | - | - | | | Transformer-12 (Liu et al., 2020) | 41.4 | - | 9.5 | 12.2 | 33.2 | - | 34.3 | 36.8 | 20.2 | 21.8 | - | | | unified multilingual Multi-Distillation (Tan et al., 2019) | - | - | - | - | - | - | 31.6 | 35.8 | 22.0 | 21.2 | - | | | m-Transformer (Pan et al., 2021) | 42.0 | 38.1 | 18.8 | 23.1 | 32.8 | 33.7 | 35.9 | 37.7 | 20.0 | 28.2 | 31.03 | | | mRASP w/o finetune (Lin et al., 2020) | 43.1 | 39.2 | 20.0 | 25.2 | 34.0 | 34.3 | 37.5 | 38.8 | 22.0 | 29.2 | 32.33 | +1.30 | | mRASP2 (Pan et al., 2021) | 43.5 | 39.3 | 21.4 | 25.8 | 34.5 | 35.0 | 38.0 | 39.1 | 23.4 | 30.1 | 33.01 | +1.98 | | PBD-MT (Ours) | 43.9 | 41.5 | 20.7 | 26.3 | 35.1 | 35.4 | 38.8 | 40.5 | 24.5 | 31.0 | 33.77 | +2.74 | Table 1: Performance of our model and competing approaches in the surprised translation scenario. We denote the pretrained bidirectional distillation MT model as PBD-MT. Tokenized BLEU is reported. For En→Ro direction, we report the BLEU score after removing Romanian dialects as in Pan et al. (2021). | Lang-Pairs | En-Kk | En-Tr | En-Et | En-Fi | En-Lv | En-Cs | En-De | En-Fr | Avg | | | | | | |-------------------------------|--------------------|-----------|---------------|---------------|--------------|-----------|----------------|----------------|-------|------|------|------|------|------| | Source | WMT19 | WMT17 | WMT18 | WMT17 | WMT17 | WMT19 | WMT19 | WMT14 | | | | | | | | Size | 91k(low) | 207k(low) | 1.94M(medium) | 2.66M(medium) | 4.5M(medium) | 11M(high) | 38M(extr-high) | 41M(extr-high) | | | | | | | | Direction | → | ← | → | ← | → | ← | → | ← | → | ← | → | → | → | | | Direct (Vaswani et al., 2017) | 0.2 | 0.8 | 9.5 | 12.2 | 17.9 | 22.6 | 20.2 | 21.8 | 12.9 | 15.6 | 16.5 | 30.9 | 41.4 | 17.1 | | mBART (Liu et al., 2020) | 2.5 | 7.4 | 17.8 | 22.5 | 21.4 | 27.8 | 22.4 | 28.5 | 15.9 | 19.3 | 18.0 | 30.5 | 41.0 | 21.2 | | mRASP (Lin et al., 2020) | 8.3 | 12.3 | 20.0 | 23.4 | 20.9 | 26.8 | 24.0 | 28.0 | 21.6 | 24.4 | 19.9 | 35.2 | 44.3 | 23.8 | | CeMAT (Li et al., 2022) | 8.8 12.9 23.9 23.6 | 22.2 | 28.5 | 25.4 | 28.7 | 22.0 | 24.3 | 21.5 | 39.2 | 43.7 | 25.0 | | | | | PBD-MT w/ finetune (Ours) | 8.4 15.9 23.4 24.5 | 22.5 | 29.4 | 24.2 | 29.7 | 22.2 | 26.1 | 21.8 | 40.4 | 44.3 | 25.6 | | | | English-centric language pairs1, and MC24 consists of monolingual text in 24 languages2. We follow the original data preprocessing, data sampling, tokenization, and vocabulary by directly downloading the datasets3released by Pan et al. (2021), thus we can have a relatively fair comparison to our primary baselines, such as mRASP (Lin et al., 2020), mRASP2 (Pan et al., 2021) and CeMAT (Li et al., 2022). When pretraining, the source and target sentences are concatenated, and substituted synonyms are not masked. The masking ratio is 20%. Settings We adopt a 12-layer Transformer-based language model with 768 dimensions and 12 attention heads. The language model is trained on 8 Nvidia A100 GPUs for 1M steps using Adam optimizer. On each GPU, the number of tokens in each batch is at most 32K. The learning rate is set to 0.0001, and polynomial decay scheduling is used with a warm-up step of 10000. The hyperparameter λ in Equ 7 is 0.5, and the dropout rate is set to 0.1. See appendix for more details. ## 3.1.2 Machine Translation Training Datasets For training multilingual translation models, we reuse the parallel dataset PC32 and monolingual dataset MC24, consistent with Pan et al. (2021). We follow the experimental settings in CeMAT (Li et al., 2022) for finetuning experiments. Language pairs of various data sizes from WMT are used for finetuning, and the dataset information is shown in Table 2. For evaluating unified multilingual models, we use the evaluation datasets from WMT, IWSLT, and OPUS-100 (Zhang et al., 2020) following mRASP2 (Pan et al., 2021). Settings We follow the model configurations used in CeMAT (Li et al., 2022) to train a Transformer-big (Vaswani et al., 2017) size NMT model, which will compare with models using the pretrain-finetune paradigm. And for a fair comparison, a larger NMT model with 12 encoder layers and 12 decoder layers is trained to compare with unified multilingual models. The contrastive loss is used in training a unified multilingual model due to its importance to zero-shot translation (Pan et al., 2021). Other training hyper-parameters are referred to from the open-source implementation of mRASP2. For pretrained bidirectional distillation losses, the intermediate layer to be distilled | Ar | Zh | NI | | | | | | |---------------|------|------|------------|------|------|------|-------| | X→Ar | Ar→X | X→Zh | Zh→X | X→NI | NI→X | | | | m-Transformer | 3.7 | 5.6 | 6.7 | 4.1 | 2.3 | 6.3 | | | mRASP2 | 5.3 | 17.3 | 29.0 | 14.5 | 5.3 | 6.1 | | | PBD-MT (Ours) | 5.8 | 18.9 | 32.7 | 13.2 | 5.1 | 6.4 | | | Fr | De | Ru | Avg of all | | | | | | X→Fr | Fr→X | X→De | De→X | X→Ru | Ru→X | | | | m-Transformer | 7.7 | 4.8 | 4.2 | 4.8 | 5.7 | 4.8 | 5.05 | | mRASP2 | 23.6 | 21.7 | 12.3 | 15.0 | 16.4 | 19.1 | 15.31 | | PBD-MT (Ours) | 26.3 | 25.2 | 11.6 | 16.4 | 16.9 | 20.1 | 16.55 | is set to the antepenultimate layer of the encoder and decoder. Note that global distillation doesn't introduce extra parameters, and our model has the same size as the major baselines. ## 3.2 Supervised Translation We trained a unified multilingual NMT model with pretrained bidirectional distillation. As is shown in Table 1, our proposed PBD-MT clearly outperforms previously published approaches and achieves new state-of-the-art performances in most translation directions. It achieves +0.76 average BLEU improvement over mRASP2, which validates the effectiveness of the proposed pretrained bidirectional distillation. In addition, we investigate the effect of pretrained bidirectional distillation on the pretrainfinetune paradigm. Specifically, we adopt PBD losses on the encoder and decoder when finetuning. As we can see in Table 2, PBD-MT achieves better or competitive performance compared to previous pretrain-finetune models. It is noteworthy that no matter the unified model or the pretrain-finetune model, the improvement in X→En directions is more significant than that of En→X directions. We conjecture that English sentences are much more than other languages, thus the pretrained LM has a better understanding of English language. ## 3.3 Unsupervised And Zero-Shot Translation Table 3 summarizes the performance of unified multilingual models on a zero-shot translation scenario. Although the training data only consists of Englishcentric parallel sentences, multilingual NMT models show promising performance on zero-shot translation. Compared with mRASP2, PBD-MT further boosts the translation quality in most zero-shot di- | En-Nl | En-Pt | En-Pl | Avg | | | | | |---------------|----------|---------|-------|------|-----|------|-------| | iwslt2014 | opus-100 | wmt20 | | | | | | | → | ← | → | ← | → | ← | | | | m-Transformer | 1.3 | 7.0 | 3.7 | 10.7 | 0.6 | 3.2 | 4.42 | | mRASP | 0.7 | 10.6 | 3.7 | 11.6 | 0.5 | 5.3 | 5.40 | | mRASP2 | 10.1 | 28.5 | 18.4 | 30.5 | 6.7 | 17.1 | 18.55 | | PBD-MT (Ours) | 10.7 | 29.6 | 18.1 | 31.4 | 7.0 | 18.9 | 19.28 | rections, achieving a +1.24 average gain. Besides, we evaluate the unified multilingual models in unsupervised translation directions, and the results are shown in Table 4. For PBD-MT, positive results are observed in all translation directions but one direction, and the average BLEU score increases by a +0.73 point. These results validate the positive effects of the proposed pretrained bidirectional distillation not only on supervised scenario but also zero-shot and unsupervised scenarios. ## 3.4 Non-Autoregressive Nmt This section contains additional results for nonautoregressive translation (NAT) experiments. Specifically, we use a *Transformer-big* size fully NAT (Gu and Kong, 2021) as the base model. The model is initialized by a pretrained multilingual PBD-MT model and trained using a CTC loss as in Gu and Kong (2021). Because the decoder in the NAT model has upsampled length, for simplicity, we only adopt the encoder PBD loss when NAT training. Table 5 shows the performance of our model and other pretrained NAT models. Consistent BLEU gains are obtained by our PBD-NAT, validating its effectiveness. | WMT14 | | | |-------------------------------------------|-------|------| | En→De | De→En | | | Transformer (Vaswani et al., 2017) | 28.0 | 32.7 | | Mask-Predict (Ghazvininejad et al., 2019) | 26.1 | 29.0 | | mRASP (Lin et al., 2020) | 26.7 | 29.8 | | Fully NAT (Gu and Kong, 2021) | 26.5 | 30.5 | | CeMAT (Li et al., 2022) | 27.2 | 29.9 | | PBD-NAT (Ours) | 27.7 | 31.2 | | Model | BLEU | △ | |------------------------------------|--------|------| | Transformer (Vaswani et al., 2017) | 27.3 | | | Multi-300k (Zhou et al., 2022) | 27.9 | +0.6 | | CBBGCA (Zhou et al., 2022) | 28.3 | +1.0 | | PBD-MT | 29.1 | +1.8 | | w/o Encoder PBD loss Le | 28.8 | +1.5 | | w/o Decoder PBD loss Ld | 28.3 | +1.0 | ## 3.5 Model Analysis 3.5.1 Ablation Study In order to evaluate the individual contribution of model components, we conduct an ablation study. We train a self-distilled LM and *Transformer-base* (Vaswani et al., 2017) size bilingual NMT models on the WMT14 English-German dataset, and report the results in Table 6. Compared with the standard bilingual Transformer and confidence-based KD (Zhou et al., 2022), PBD-MT significantly improves the performance, which verifies the effectiveness of pretrained bidirectional distillation on bilingual NMT. Without the PBD loss on the encoder or decoder, the BLEU scores degrade to some extent, and the decoder PBD loss has more impact than the encoder PBD loss. The results prove the necessity of both pretrained bidirectional distillation losses. ## 3.5.2 Quantitative Analysis To investigate the contribution of self-distillation on LM which generates globally defined distillation objectives in a single forward pass, a quantitative analysis is conducted here. Figure 4 illustrates the results. For execution efficiency, we compare marginalizing over multiple masks with the self- ![7_image_0.png](7_image_0.png) distillation on LM. For example, masking 10% tokens each time results in 10 LM forward passes to generate the full distillation objectives. As we can see, the design of self-distilled LM significantly accelerates the execution speed than multiple masks. For the distillation effect, we compare distillation on partial tokens with global distillation. The red lines show that 20% is a relatively reasonable proportion for partial distillation, and as the mask ratio increases, the performance degrades. Masking too many tokens increases the uncertainty for the LM. The best performance is achieved by global distillation, verifying the superiority of globally defined distillation objectives. ## 3.5.3 Visualization We conduct a behavior analysis to understand which tokens are considered more certain in contexts by the self-distilled language model. In this experiment, instead of softmax, we use sigmoid to compute a scalar probability in the prediction head Ω. Figure 5 visualizes the predicted self-distilled token probabilities on randomly sampled sentences. In this experiment, no token is masked; thus, the token probabilities represent the tokens' matching degree and certainty in the complete bidirectional context. As we can see, verbs, articles, conjunctions, and prepositions are roughly of higher probabilities, while nouns, adverbs, and adjectives are harder to be predicted. It can be concluded that the syntactic structure is more regular, and meaningful words are more changeable. ## 4 Related Works 4.1 Masked Language Pretraining Kenton and Toutanova (2019) propose BERT, a pre-trained masked language model (MLM), which succeeds in capturing the syntactic and semantic As of 2015 , the total student enrollment was 34 \#\#9 . The ethnic makeup of the school was 34 . 4 % White , 30 . 7 % Hispanic Eva wasn ' t sure what to do first . She thought about ![8_image_0.png](8_image_0.png) calling or text \#\#ing Julian , but she didn ' t know how to start the conversation Mandy lay sprawled on the floor on her scattered beach bag contents , the open container of milk g \#\#lug \#\#ging white across the ceramic tile . meaning of contextualized texts by large-scale selfsupervised pretraining. Recent researches explore and strengthen BERT. XLNet (Yang et al., 2019) addresses the issue of pretrain-finetune discrepancy simultaneously considering bidirectional contexts by a permutation language modeling objective. RoBERTa (Liu et al., 2019) exhaustively explores the pretraining setup, such as data processing, training task, hyper-parameters, etc., to boost the model. ELECTRA (Clark et al., 2019) trains a discriminator to detect replaced tokens, which are substituted by an MLM generator, and improve the model's efficiency. Due to space limitations, we can not elaborate on BERT variants. Sun et al. (2022); Naseem et al. (2021); Min et al. (2021) surveyed the pre-trained language models. ## 4.2 Pretrained Machine Translation As far as pretrained machine translation is concerned, a lot of powerful deep learning approaches have been introduced. For instance, XLM (Conneau and Lample, 2019) introduces the crosslingual language model pretraining and get significant improvements on unsupervised and supervised NMT. MASS (Song et al., 2019) adopts the encoder-decoder framework to reconstruct a sentence fragment. mBART (Liu et al., 2020) can be directly finetuned by pretraining a complete model. mRASP (Lin et al., 2020) and mRASP2 (Pan et al., 2021) improve NMT by using code-switching strategy and contrastive learning. CeMAT (Li et al., 2022) utilizes a bidirectional decoder to improve the representation capability. ## 4.3 Language Knowledge Distillation Knowledge distillation is an effective technique for model compression and was first proposed by Hinton et al. (2015), in which knowledge is transferred from a teacher model to a student model. Sanh et al. (2019) distill a BERT-base model (Kenton and Toutanova, 2019) into smaller models by defining loss on the pre-trained predictions, which results in a task-agnostic pretraining distillation. Turc et al. (2019) conduct exhaustive analyses about the initialization of students in a task-specific setting, they show that students initialized by pretraining are better than that initialized from a truncated teacher (Sun et al., 2019; Sanh et al., 2019). Jiao et al. (2020); Wang et al. (2020, 2021); Choi et al. (2022) make assumptions about the student and teacher architectures and investigate aligning layer representations as well as attention matrices. Zhou et al. (2022) utilizes confidence-based knowledge distillation to incorporate bidirectional global context into NMT models. ## 5 Conclusion In this paper, we proposed the pretrained bidirectional distillation to investigate language knowledge transfer from pretrained language models to NMT models by knowledge distillation. The proposed approach has the advantages of distillation effectiveness and efficiency, and achieves new stateof-the-art performance in supervised, unsupervised, and zero-shot multilingual translation experiments. The model analysis also shows that the proposed self-distilled language model is critical to generating globally defined distillation objectives. In the future, we will do more research on optimizing the self-distilled language model and pretrained bidirectional distillation losses. ## Limitations The pretrained bidirectional distillation transfers language knowledge through the NMT training process, a limitation of this method is that a computational overhead is introduced during training. Specifically, there is an extra language model forward pass to generate the pretrained bidirectional distillation objectives. Although we significantly reduce the computational overhead by designing a self-distilled language model, the overhead cannot be completely avoided. Fortunately, most computations stem from back-propagation when model training, and the introduced computational overhead only affects training time. Once the training is completed, the NMT has an identical inference cost as regular translation models. ## References Dongha Choi, HongSeok Choi, , and Hyunju Lee. 2022. 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In *International* Conference on Learning Representations. ## A Lm Pretraining Details We follow consistent pretraining configurations for bilingual and multilingual language models. Table 7 lists detailed hyper-parameters we used in pretraining. | Hyper-parameters | Value | |-----------------------|---------| | Number of layers | 12 | | Hidden size | 768 | | FFN inner hidden size | 3072 | | Attention heads | 12 | | Dropout | 0.1 | | Attention dropout | 0.1 | | Warmup steps | 10k | | Peak learning rate | 1e-4 | | Batch size | 256k | | Max sequence length | 512 | | Mask ratio | 20 | | Clip norm | 1.0 | | Weight decay | 0.01 | | Max steps | 1M | | Learning rate decay | Linear | | Adam ϵ | 1e-8 | | Adam β1 | 0.9 | | Adam β2 | 0.999 | | Weight of loss term λ | 0.5 | Table 7: Hyper-parameters used for pretraining. ## B Nmt Training Details Table 8 lists detailed hyper-parameters we used in NMT model training. | Hyper-parameters | Big | Big12 | |-----------------------|--------|---------| | Encoder layers | 6 | 12 | | Decoder layers | 6 | 12 | | Hidden size | 1024 | 1024 | | FFN inner hidden size | 4096 | 4096 | | Attention heads | 16 | 16 | | Embeddings | Shared | Shared | | Dropout | 0.1 | 0.1 | | Attention dropout | 0.1 | 0.1 | | Activation dropout | 0.1 | 0.1 | | Label smoothing | 0.1 | 0.1 | | Warmup steps | 3k | 3k | | Peak learning rate | 1e-3 | 1e-3 | | Max sentences | 512 | 512 | | Batch size | 8K | 8K | | Update frequency | 50 | 50 | | Number of workers | 8 | 8 | | Max sequence length | 256 | 256 | | Weight decay | 0.01 | 0.01 | | Clip norm | 10 | 10 | | Max steps | 300k | 300k | | Learning rate decay | Linear | Linear | | Adam ϵ | 1e-6 | 1e-6 | | Adam β1 | 0.9 | 0.9 | | Adam β2 | 0.98 | 0.98 | Table 8: Hyper-parameters used for NMT training. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations Section. A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? At the end of the Introduction Section. ✓ A4. Have you used AI writing assistants when working on this paper? Grammarly. Spell checking. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Experimental Setup Section. We use publicly available datasets and code bases. ✓ B1. Did you cite the creators of artifacts you used? Experimental Setup Section ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? It is free to use the data and code for research purposes, so we don't mention it explicitly. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Experimental Setup Section B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Experimental Setup Section ✗ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. In the Experimental Setup Section, we mention that we follow the original data preprocessing, data sampling, tokenization, and vocabulary by directly downloading the datasets released by previous papers. Thus, we give the reference and don't repeat this information. ## C ✓ **Did You Run Computational Experiments?** Experiments Section. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Experimental setup Section, Appendix A, and Appendix B. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Experimental setup Section, Appendix A, and Appendix B. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? From Section 3.2 to 3.4.2. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Experimental setup Section, Appendix A, and Appendix B. ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

shin-etal-2023-pivotal

Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning

https://aclanthology.org/2023.acl-long.64

Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users{'} behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.

# Pivotal Role Of Language Modeling In Recommender Systems: Enriching Task-Specific And Task-Agnostic Representation Learning Kyuyong Shin†‡§ Hanock Kwak†§ Wonjae Kim‡ **Jisu Jeong**†‡ Seungjae Jung† Kyung-Min Kim†‡ Jung-Woo Ha‡ **Sang-Woo Lee**‡ NAVER† NAVER AI Lab‡ ## Abstract Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can *language modeling* for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to *taskspecific user histories* achieves excellent results on diverse recommendation tasks. Also, leveraging additional *task-agnostic user histories* delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services. ## 1 Introduction Recent advances in user modeling have focused on constructing unified user models to be directly adapted to diverse applications. Many of them leverage natural language or plain text data, which enables general-purpose applicability among various domains and systems (Qiu et al., 2021; Gu et al., 2021; Geng et al., 2022; Cui et al., 2022; Hou et al., 2022; Shin et al., 2023). These strategies pave a much more efficient way for service owners to quickly adapt to various task scenarios by tuning one single model, bringing performance improvement across whole systems in parallel. Based on the recent explosions of sequence prediction models in many domains (Chen et al., 2020; Brown et al., 2020; Ramesh et al., 2021; Chen et al., 2021; Borsos et al., 2022), it is natural to ask §Both authors contributed equally to this research. Correspondence to: <[email protected]>. whether recommender systems can benefit from representation trained by token sequence prediction, i.e., *language modeling*. Moreover, several works have provided deep insights into why and how language models help address downstream classification tasks (Gururangan et al., 2020; Saunshi et al., 2021; Wei et al., 2021; Karouzos et al., 2021; Krishna et al., 2022). Some recent studies confirm that continued pretraining of language model on few task-specific data drawn from *the target task distribution*, or data similar to a target domain can provide significant benefits to solve downstream classification tasks (Gururangan et al., 2020; Lee et al., 2020; Karouzos et al., 2021). Interestingly, Krishna et al. (2022) go further and validate that language models *trained from scratch* on task-specific or task-agnostic data1—data from other downstream tasks—can rival standard webtext language models. Another line of research provides mathematical explanations of how language model pretraining can improve performances on downstream tasks (Saunshi et al., 2021; Wei et al., 2021). More specifically, Saunshi et al. (2021) reformulate classification tasks as sentence completion tasks, thus demonstrating that linear classification using output features from fixed GPT-2 (Radford et al., 2019), i.e., no finetuning, also guarantees to solve sentence classification tasks. Motivated by these works, we introduce a new method called **LMRec**, which jointly trains Language Model and Recommendation task objectives from user behavior histories transformed as plain text format. As illustrated in Figure 1, our approach is conceptually simple but practically effective. We first investigate if the recommender system jointly trained with the language modeling objec-1Other studies, such as Gururangan et al. (2020) and Krishna et al. (2022), use the term "domain-specific data" or "cross-data" to represent task-irrelevant corpus that is not webtext data. However, we use the term "task-agnostic data" to generally refer to data from other downstream tasks. 1146 ![1_image_0.png](1_image_0.png) tive on *task-specific data* can enrich the user/item representations, thus providing better generalization even for unseen downstream tasks (Table 4 and 7). We then further verify that additional *taskagnostic data* can help across the various recommendation tasks, especially when using the taskagnostic data as a user feature (Figure 3). As a result, our methods significantly outperform all the baselines on all tasks, including three public benchmarks and three real-world datasets from different application service domains, and online A/B experiments. Moreover, the pretrained LMRec shows a promising ability to perform downstream transfers flexibly with simple feature-based transfer learning. We also explore several aspects of how the language modeling regime affects the model quality under various conditions, including transfer learning, corpus ablation, and model sizes. Our major findings are as follows: Jointly training language modeling and recommendation task objectives improve recommender systems. Language modeling on the user history can produce rich user/item representations for diverse applications. These results are consistent with the effect of task-adaptive pretraining in the previous research (Gururangan et al., 2020; Karouzos et al., 2021; Krishna et al., 2022). Furthermore, our approach also boosts the transfer learning capability of the recommendation model. Extensive experimental results show the efficacy of our approach compared to training without language model objectives (Table 4 and 7). Language modeling on task-agnostic data provides strong results on user representation learning. Consistent with prior work (Gururangan et al., 2020; Krishna et al., 2022), language modeling on additional *task-agnostic data* alleviates overfitting to a specific history corpus and benefits the learning of robust text representations (Table 4 and 7). We explore how language model pretraining on the diverse task-agnostic data affects transfer learning performances, by comparing with models pretrained on different domain corpora (Figure 3). Virtues of more user data. Recent studies argue that increasing information on user data should be treated as a top priority for improving recommendation performances (Shin et al., 2021; Ardalani et al., 2022). We collect additional user data matched with downstream task users based on user IDs and incorporate them as an additional user feature. Table 7 verifies the data scaling strategy has shown to be beneficial to our models. ## 2 Approach 2.1 **Language Models Help With Classification** Tasks The empirical and theoretical analyses from the prior work imply that the learned features from the language models trained with appropriate behavior corpus could help predict user and item interactions in recommender systems (Gururangan et al., 2020; Saunshi et al., 2021; Krishna et al., 2022). It is also consistent with the results in Table 1 that language model pretraining with appropriate corpusrelated to the downstream task rather than other | Method | OBS | Scientific | | | |------------|---------|--------------|---------|--------| | Recall@10 | NDCG@10 | Recall@10 | NDCG@10 | | | LMwebtext | 0.3135 | 0.1766 | 0.0335 | 0.0131 | | LMagnostic | 0.3142 | 0.1747 | 0.0327 | 0.0126 | | LMspecific | 0.3769 | 0.2136 | 0.0417 | 0.0194 | corpora such as webtext—leads to performance improvement. It is worth mentioning that linear probe results of LMagnostic can achieve that of LMwebtext performance, although task-agnostic data are in a much smaller-scale than webtext data. This result strongly motivates our research. Given a sequence of text tokens of user history, u = {h1*, ..., h*n} and item text tokens i = {g1*, ..., g*m}, the language model objective L1 is to maximize the following negative log-likelihood: $$L_{1}=-\sum_{j=1}\log P(h_{j}|h_{j-k},\ldots,h_{j-1};{\mathcal{M}}),\tag{1}$$ where k is the context size, and the conditional probability P is modeled using language model M. Then for the downstream tasks, user and item representations zu, zi ∈ Rdare computed as follows: $$\begin{array}{l}{{z_{u}={\mathcal{M}}(h_{\mathrm{EOS}}|u)}}\\ {{z_{i}={\mathcal{M}}(g_{\mathrm{EOS}}|i),}}\end{array}$$ where EOS denotes the end of the history token. We use a vector that corresponds to [EOS] token at the last layer as a feature (Neelakantan et al., 2022). The downstream recommendation task loss, L2, of each user-item pair is defined as: $$\begin{array}{c}{{p_{u,i}=\frac{1}{1+\exp(-\langle W_{u}z_{u},W_{i}z_{i}\rangle)},}}\\ {{L_{2}=-y\mathrm{log}p_{u,i}-(1-y)\mathrm{log}(1-p_{u,i}),}}\end{array}\tag{4}$$ where y ∈ {0, 1} is the label denoting whether the user interacted with an item or not. We use ·, · for the dot product. The weight matrices Wu, Wi ∈ Rd×dlinearly transform the user and item representations, respectively. Several works have highlighted that jointly optimizing language modeling during finetuning benefits avoiding catastrophic forgetting (Chronopoulou | Method | OBS | Scientific | | | |--------------|---------|--------------|---------|--------| | Recall@10 | NDCG@10 | Recall@10 | NDCG@10 | | | SelfPretrain | 0.4742 | 0.2796 | 0.1068 | 0.0473 | | LMRec | 0.4867 | 0.2940 | 0.1264 | 0.0695 | et al., 2019; Karouzos et al., 2021). Inspired by the merits of this strategy, we adopt a joint optimization: $$L=L_{1}+\lambda L_{2},$$ $$(6)$$ where L is the final joint training loss. We impose weight λ on L2 loss to prevent the overfitting of recommendation tasks. As illustrated in Figure 1, a model that optimizes Equation (6) is denoted as "**LMRec**". The model trained without the language model objective (L1) is "LMRec-lm". The performance comparison between the pretrain-thenfinetune model and our approach are presented in Table 2. (2) (3) $\frac{1}{2}$ ## 2.2 **Enriching Task-Specific And Task-Agnostic** Representation Leveraging task-agnostic data. Optimizing performances solely on task-specific data would restrict the potential of a unified framework. Therefore, a recent trend in user modeling research is to leverage large quantities of pretraining (or additional) data that are not directly related to the target task (Hou et al., 2022; Shin et al., 2023). To this end, we introduce "LMRec**+agnostic**", which utilizes additional task-agnostic data for language model objectives. This approach increases the generality by mitigating overfitting to a specific history corpus. Consequently, it boosts the learning of robust text representations, thus making LMRec+agnostic universal across various tasks. As a result, additional task-agnostic data further boost the performance of our default LMRec model, which already produces state-of-the-art results in all tasks and metrics. Transfer learning. There are several difficulties in applying a unified model to real-world applications: (1) target applications are commonly unknown or undefined during pretraining, (2) user ID cannot be matched across different companies, (3) large-scale recommender systems usually contain millions of ![3_image_0.png](3_image_0.png) users and items, thus it is computationally expensive to finetune the large models to numerous applications directly. To overcome these obstacles, we propose a simple transfer learning framework that can easily and quickly adapt the model to diverse applications. As visualized in Figure 2, we simply plug the target task-specific inputs into the pretrained LMRec and compute user/item embeddings to perform a linear probe. We add superscript to the model as "**LMRec**TL" for the transfer learning framework. The LMRecTL model jointly pretrains multiple tasks, excluding the target downstream task. The final loss to pretrain is as follows: $$L=\sum_{t\in{\mathcal{T}}_{s},{\mathcal{T}}_{a}}L_{1}^{t}+\lambda\sum_{t\in{\mathcal{T}}_{s}}L_{2}^{t},\qquad\qquad(7)$$ where Ts denotes a set of pretraining recommendation tasks, and Ta for additional task-agnostic data. Note that linear layers of pretraining and featurebased transfer learning are separate modules. Task-agnostic user features. Leveraging crossdomain data of users for improving recommender systems has been widely discussed (Man et al., 2017; Yuan et al., 2019; Zhu et al., 2022; Shin et al., 2023). These strategies assume that the underlying user preference in the source and the target domains can be related, and thus learning a common user semantic enhances the recommender system. Hence, we utilize additional task-agnostic data, obtained from application services whose user IDs are shared in a company level, as a user feature for target downstream tasks. The difference between task-specific and task-agnostic data in Figure 2 is only which user features are used for transfer learning. For example, if the target downstream task is ECOMM, models are first pretrained with OBS and OTA, and then use task-specific data of ECOMM to produce task-specific user features. For leveraging the task-agnostic user feature, the pretrained model extracts user features from task-agnostic data, such as Search and News. Components other than user features, such as the pretrained model, downstream architecture (linear layer), and ground truth interacted items of users, are all the same. We can verify that the transfer learning approach benefits from leveraging additional task-agnostic data as user features, especially when it is recommending for new users (Table 7, 8 and Figure 3). Appendix A describes the training details of our methods. ## 3 Experiments 3.1 Datasets To make user behavioral corpora, we consider the behavior description as items, i.e., search queries of search logs, news titles of online news click logs, and content titles of social media click logs. As illustrated in Figure 1, we concatenate the behavior logs using the "→" token. This simple form of a prompt template can have behavior sequences that are very long. Furthermore, separating corpus among multiple services provides flexible transfer learning capabilities by enabling easy proliferation of behaviors and filtering out redundant representation to target applications. We use Byte-level BPE (Wang et al., 2020) to tokenize the textual description of each item in the behavior logs. Task-specific datasets. We use three in-house datasets in order to assess our approach on various applications and add three public datasets that are predominantly evaluated in recommendation communities. The in-house datasets are built from services of an online booking service (OBS), an online travel agency (OTA), and e-commerce platmform (ECOMM). For public datasets, we select two categories *"Industrial and Scientific"* (Scientific) and *"Prime Pantry"* (Pantry) from Amazon review datatsets (Ni et al., 2019) which are two completely different service domains. We further collect *"Online Retail"*2 dataset from an online retail platform to validate the cross-system transferability of our models. 2https://www.kaggle.com/carrie1/ecommerce-data | Contents | In-house | Public | | | | | | | |---------------------|------------|----------|-------------|--------------|---------|---------------|-------------|--------------| | OBS | OTA | ECOMM | Pretraining | Scientific | Pantry | Online Retail | Pretraining | | | # of Users | 300, 000 | 142, 051 | 72, 477 | 10, 156, 217 | 8, 442 | 13, 101 | 16, 520 | 1, 361, 408 | | # of Items | 42, 453 | 2, 485 | 229, 775 | N/A | 4, 385 | 4, 898 | 3, 469 | 446, 975 | | # of Interact. | 495, 992 | 177, 281 | 130, 859 | 94, 011, 305 | 59, 427 | 126, 962 | 519, 906 | 14, 029, 229 | | Avg. history | 1.5 | 2.3 | 5.5 | 128.7 | 4.5 | 8.5 | 25.6 | 9.6 | | Avg. history tokens | 10.3 | 17.1 | 116.4 | 1, 222.7 | 212.5 | 214.7 | 206.6 | 347.3 | Task-agnostic datasets. We construct sufficiently large-scale task-agnostic behavioral corpora for inhouse datasets. These datasets are collected over two years and from four behavioral corpora, a search engine (Search), e-commerce (E-comm.), social media platform (SNS), and news website (News). As a result, the in-house dataset contains 10 million users and 94 million user history logs, and 12 billion BBPE tokens. Following the experimental setup of UniSRec (Hou et al., 2022) for public benchmarks, we select the five categories "Grocery and Gourmet Food", *"Home and* Kitchen", "CDs and Vinyl", *"Kindle Store"*, and "Movies and TV" from Amazon review datasets. These datasets are used as pretraining datasets for pretrain-then-transfer models such as UserBERT (Wu et al., 2022), UniSRec (Hou et al., 2022), M6-Rec (Cui et al., 2022), and CLUE (Shin et al., 2023), while used as additional task-agnostic data for LMRec+agnostic model. The details of datasets are outlined in Table 3. ## 3.2 Experimental Settings In-house downstream tasks. The datasets consist of positive pairs (*u, i*) which means a user u interacted with an item i. The negative pairs are generated through random sampling during training. Evaluation metrics are Recall@k and top-k Normalized Discounted Cumulative Gain (NDCG@k), which are evaluated from ground truth items mixed with 100 randomly sampled negative items. To test the generalizability of user representations, we randomly split the user pool among the training (80%), validation (10%), and test sets (10%). Public downstream tasks. We filter out users and items with fewer than 5 interactions. Each user's interaction history was listed chronologically. We use item descriptions such as titles, categories, and brands for item information. The maximum token length of item text is set to 512. Following previous works (Kang and McAuley, 2018; Sun et al., 2019; Hou et al., 2022), we adopt the leaveone-out strategy, i.e., next item recommendation task. The last item, second last item, and other items are used as the test, validation, and training data respectively. The Recall@k and NDCG@k are computed by ranking the ground-truth item among all the other items. ## 3.3 Baselines We compare our models against six strong baselines. Behavior Sequence Transformer (BST) (Chen et al., 2019) and LightGCN (He et al., 2020) are primarily used baselines in various tasks and domains. To reflect the recent trend of user modeling research, which adopts pretrainthen-transfer strategies, we employ several models from these lines of work. UserBERT (Wu et al., 2022) and UniSRec (Hou et al., 2022) pretrain self-supervision objectives with language embeddings and then finetune the model to downstream tasks. The most comparable unified user models to our methods are M6-Rec (Cui et al., 2022) and CLUE (Shin et al., 2023). These two methods treat user history as plain text and construct a universal encoder that can be adapted to any domain and task. Note that all the pretrain-then-transfer models, excluding CLUE, utilize webtext language models. Please see Appendix B for more details of baselines. ## 4 Results 4.1 Performance On Various Tasks Table 4 presents the efficacy of our LMRec against baselines. Across the six datasets, LMRec trained only with the task-specific data achieves state-ofthe-art performances compared to all the baselines, even though some methods utilize additional task-agnostic data. For the in-house datasets, LMRec surpasses best performing baseline models by over 1.6 ∼ 3.2% in Recall@10. In the public datasets, LMRec shows around 5% average improvements compared to baselines. Since other | Downstream tasks | Metrics | Only trained on task-specific data | Use additional task-agnostic data | Improv. | | | | | | | | |--------------------|-----------|--------------------------------------|-------------------------------------|-----------|---------|--------|--------|----------------|--------|--------|-------| | BST | LightGCN | LMRec-lm | LMRec | UserBERT | UniSRec | CLUE | M6Rec | LMRec+agnostic | | | | | OBS | Recall@10 | 0.4675 | 0.4628 | 0.4654 | 0.4867 | 0.4600 | 0.4745 | 0.4580 | 0.4615 | 0.5060 | +6.6% | | NDCG@10 | 0.2780 | 0.2759 | 0.2762 | 0.2940 | 0.2738 | 0.2825 | 0.2691 | 0.2754 | 0.3048 | +7.9% | | | OTA | Recall@10 | 0.7160 | 0.7277 | 0.7190 | 0.7428 | 0.7199 | 0.7186 | 0.7225 | 0.7314 | 0.7458 | +2.0% | | NDCG@10 | 0.4092 | 0.4235 | 0.4151 | 0.4407 | 0.4145 | 0.4144 | 0.4219 | 0.4306 | 0.4431 | +2.9% | | | ECOMM | Recall@10 | 0.6611 | 0.5378 | 0.6667 | 0.7322 | 0.6934 | 0.6725 | 0.5500 | 0.7093 | 0.7715 | +8.8% | | NDCG@10 | 0.4846 | 0.4290 | 0.5081 | 0.5637 | 0.5202 | 0.5079 | 0.4282 | 0.5090 | 0.6009 | +15.5% | | | Scientific | Recall@10 | 0.0625 | 0.0540 | 0.0951 | 0.1264 | 0.1055 | 0.1188 | 0.0894 | 0.0945 | 0.1283 | +8.0% | | NDCG@10 | 0.0323 | 0.0276 | 0.0428 | 0.0695 | 0.0457 | 0.0641 | 0.0393 | 0.0413 | 0.0701 | +9.4% | | | Pantry | Recall@10 | 0.0388 | 0.0402 | 0.0626 | 0.0692 | 0.0630 | 0.0636 | 0.0602 | 0.0645 | 0.0683 | +7.3% | | NDCG@10 | 0.0203 | 0.0195 | 0.0298 | 0.0343 | 0.0312 | 0.0306 | 0.0288 | 0.0324 | 0.0330 | +5.7% | | | Online Retail | Recall@10 | 0.1460 | 0.1322 | 0.1373 | 0.1475 | 0.1438 | 0.1449 | 0.1258 | 0.1458 | 0.1502 | +3.0% | | NDCG@10 | 0.0685 | 0.0608 | 0.0659 | 0.0718 | 0.0654 | 0.0677 | 0.0585 | 0.0702 | 0.0732 | +4.3% | | | Method | OBS | | |----------------------------|---------|--------| | Recall@10 | NDCG@10 | | | LMRec+agnostic (0% : 100%) | 0.4703 | 0.2805 | | LMRec+agnostic (30% : 70%) | 0.4811 | 0.2932 | | LMRec+agnostic (50% : 50%) | 0.4905 | 0.2991 | | LMRec+agnostic (70% : 30%) | 0.4917 | 0.3003 | | LMRec (100% : 0%) | 0.4867 | 0.2940 | Table 6: Inference time and trainable weight comparison of the downstream models measured from the OBS task. We calculate the inference time of a single batch on A100 GPU. | Models | Inputs | Speedup | Parameters | |------------------------------------------------|-----------------------|-----------|--------------| | Transformer† | User history logs | 1 | 125M | | LightGCN | User history logs | ×34 | 2M | | LMRecTL | Pretrained user repr. | ×157 | 1.2M | | † All the models, excluding LightGCN and CLUE. | | | | pretrain-then-transfer models leverage additional data, we introduce LMRec+agnostic, a more robust representation learning method using additional corpus for language modeling. LMRec+agnostic remarkably outperforms the other models in all tasks by a significant margin (see improvement in Table 4). We further conduct an ablation study on combining task-specific and task-agnostic corpus when the computation resources are limited. Table 5 presents the results. LMRec+agnostic (0% : 100%), i.e., language modeling on task-agnostic data only, outperforms LMRec-lm in Table 4, but shows the worst performance in Table 5. Increasing the ratio of used task-specific data delivers performance benefits to some point (70%). However, leveraging task-specific data solely finally decreases the performance. Previous research provides a theoretical analysis of why language model pretraining guarantees effective representation learning for downstream tasks (Saunshi et al., 2021; Wei et al., 2021). The additional analysis in Appendix C may support these results. ## 4.2 Linear Probe We show the effectiveness of the language model pretraining then feature-based transfer strategy (Figure 2) across all tasks. Our approach empirically demonstrates the flexible generalizability of the pretrained features. Note that all the baselines, excluding CLUE, are pretrain-then-finetune methods, and the downstream computational cost (Table 6) is much more expensive than the linear probe. As shown in Table 7, the linear probe result of LMRecTL -lm that are trained only on recommendation tasks shows worst transfer learning performances. Unsurprisingly, a model trained without language modeling cannot guarantee generalizability to other language corpora. It is worth mentioning that LMRecTL, which jointly trains language model and recommendation tasks objectives, shows decent transfer learning capability for downstream tasks. This result provides that incorporating language model pretraining with recommender system profits strong adaptability and generality compared to the recommendation model, even on the linear | Downstream tasks | Metrics | Task-specific feature | Task-agnostic feature | Combine | | | | | | | | |-------------------------------------------------------------------------------------------------------------|-----------|-------------------------|-------------------------|-----------|---------------|---------------|--------|--------|---------------|---------------|--------| | LMRecTL -lm LMRecTL LMRecTL +agn. UniSRec CLUE M6Rec LMRecTL LMRecTL +agn. UniSRec CLUE M6Rec LMRecTL +agn. | | | | | | | | | | | | | OBS | Recall@10 | 0.3661 | 0.4687 | 0.4861 | 0.5133 | 0.5112 0.5451 | 0.4837 | 0.5675 | 0.5397 | 0.5416 0.5540 | 0.5952 | | NDCG@10 | 0.2039 | 0.2792 | 0.2886 | 0.3139 | 0.3204 0.3357 | 0.2874 | 0.3514 | 0.3305 | 0.3372 0.3391 | 0.3766 | | | OTA | Recall@10 | 0.5531 | 0.7196 | 0.7375 | 0.7121 | 0.7408 0.7285 | 0.7231 | 0.7410 | 0.7201 | 0.7436 0.7324 | 0.7521 | | NDCG@10 | 0.3014 | 0.4119 | 0.4368 | 0.4103 | 0.4414 0.4288 | 0.4185 | 0.4421 | 0.4166 | 0.4445 0.4297 | 0.4579 | | | ECOMM | Recall@10 | 0.3202 | 0.7134 | 0.7655 | 0.6068 | 0.5763 0.6233 | 0.6273 | 0.6653 | 0.6882 | 0.6370 0.7204 | 0.7803 | | NDCG@10 | 0.3547 | 0.5355 | 0.5878 | 0.4748 | 0.4558 0.4810 | 0.4485 | 0.4969 | 0.5204 | 0.4838 0.5122 | 0.6117 | | | Method | CTR | GMV | | | |-------------------|-------|-------|-------|-------| | New | Total | New | Total | | | GNN | 1.00 | 1.00 | 1.00 | 1.00 | | CLUE | ×1.52 | ×1.14 | ×1.08 | ×1.02 | | LMRecTL +agnostic | ×1.76 | ×1.24 | ×1.12 | ×1.04 | probe, i.e., not trained on downstream tasks directly. As previous research (Gururangan et al., 2020; Krishna et al., 2022) confirmed, it is reasonable to believe that leveraging large quantities of additional data for language model pretraining is strictly more powerful than using small task-specific data. LMRecTL +agnostic shows enhanced transferability on linear probe. Comparing results among Table 4, 6, and 7, we can see that LMRecTL +agnostic outperforms other baselines with much fast and easy adaptation. ## 4.3 Virtues Of More User Data A line of research that studies scaling law in recommender systems argues that parameter growth will not always offer performance improvement and has low return-on-investment (ROI) in resource efficiencies (Ardalani et al., 2022; Shin et al., 2023). Hence, the data scaling scheme should be treated as a top priority for improving model performances. To verify the efficacy of the data scaling approach, we evaluate our model on downstream tasks by using task-agnostic data as user feature. Results are presented in Table 7-(Taskagnostic feature/Combine). We simply concatenate task-specific and task-agnostic data to use as inputs for the Combine setup. Most baselines are not adequately reflecting the possibility of using additional user features due to their pretraining methods, but LMRecTL +agnostic properly considers the potential of using more user data. It is an enormous benefit to the models seeing that LMRecTL +agnostic (Combine) shows outstanding performance by combining all the user data. Interestingly, LMRecTL, which is trained without task-agnostic data, also achieves state-of-the-art or comparable performances to the baseline models. This result highlights the efficacy of our approach. We conducted an online A/B experiment for a product collection recommendation task (see Appendix D for more details) on our in-house ecommerce platform for two weeks in August 2022. Table 8 shows the consistent superiority of our method online. For user groups 'new', the user representation by LMRecTL +agnostic significantly improves CTR and GMV compared to GNN (Jeong et al., 2020). We conjecture that it may benefit from additional user data from other services, thus contributing to users with no recorded behavior. ## 4.4 Effect Of Pretraining Behavior Corpora For Transfer Learning We perform ablation studies on the relations between pretraining corpora and using task-agnostic data as user features. As shown in Figure 3, the model pretrained with the specific corpus provides general and robust representations of that corpus even on unseen tasks. Interestingly, tailoring a language model to diverse corpora may bridge the gap between pretraining and taskagnostic corpus domains. For example, even though LMRecTL +search leverages only Search corpus for language model pretraining, it consistently outperforms LMRecTL -lm and LMRecTL on all the 1152 ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) downstream tasks with other task-agnostic features. As it can be seen in Figure 3, the performance of LMRecTL -lm in OBS task is relatively low compared to other tasks. It is due to the strong contribution of task-agnostic features (Table 7 and Figure 4) for the OBS task. In other words, when the taskagnostic features are well-transferable to the target downstream tasks, the performance differences between not pretrained (LMRecTL -lm) and the rest can be substantial. ## 4.5 Effect Of Model Size Many recent reports in NLP and computer vision have empirically demonstrated the existence of a scaling law, where performance scales strongly with model capacity (Brown et al., 2020; Kaplan et al., 2020; Zhai et al., 2021; Bahri et al., 2021). Recently, Shin et al. (2023) found the power-law learning curve as a function of model size in recommender systems. Figure 4 shows that scaling up the model leads to a strict performance improvement on the downstream tasks, consistent with the results in the prior works. However, we can also find that models' performances have an upper limit. It is in harmony with the trend in Ardalani et al. (2022) that the recommendation performance follows a power law plus a *constant* relationship to the model size, which is an irreducible error on our side. Note that the performances of LMRecTL -lm do not vary according to the model sizes. We conjecture that the model trained without language modeling has no benefits from high model complexity, as its learning capacity is naturally limited. ## 5 Related Work Any model that trains a text-based user model to adapt to unseen domains/systems can be viewed as prior work of our research. This line of work has been recently explored since learning text representation has been rapidly developed in the decade. In this context, Qiu et al. (2021) and Gu et al. (2021) are the earliest work we are aware of. They train the model through critical word matching in user logs and then finetune models to the downstream tasks. First, the word (item) embeddings are precomputed using pretrained language models (PLMs). The sequence of item embeddings is then passed to the encoder to produce user representations. Recently, some researchers propose to use behavior history as plain text data (Geng et al., 2022; Cui et al., 2022; Hou et al., 2022; Shin et al., 2023). Hou et al. (2022) and Shin et al. (2023) introduce a contrastive learning framework on multiple service domains, and perform transfer learning across various downstream tasks. Another line of work (Geng et al., 2022; Cui et al., 2022) tries to construct personalized prompts for building versatile framework, i.e., "Here is the history of {gender} {age}: {history from all services}, The user is now recommended a {item}". This approach profits from the methods that utilize language models such as GPT-2 (Radford et al., 2019), T5 (Raffel et al., 2020), and M6 (Lin et al., 2021). Their PLM-based approach can be generalized to various applications, with the ability to perform zero-shot learning. Shin et al. (2023) is the only work that trained the whole encoder from scratch rather than using PLMs. We refer readers to Liu et al. (2023) and Yuan et al. (2023) for an overview of this line of work. A related idea to our work is the training language model on task-specific or task-agnostic corpora. It has been shown to be beneficial in a variety of works (Chronopoulou et al., 2019; Gururangan et al., 2020; Lee et al., 2020; Karouzos et al., 2021; Krishna et al., 2022). Gururangan et al. (2020) continue pretraining of LM on task-specific data and show it can improve the downstream performances of standard webtext language models. Krishna et al. (2022) point out that the effect of pretraining on standard webtext data may have been overestimated. They show that models trained only on task-specific data comparably perform to existing webtext language models. On the one hand, a line of research jointly trains language models on taskspecific data during finetuning to avoid catastrophic forgetting (Chronopoulou et al., 2019; Karouzos et al., 2021). Some of the works above also investigate if the models pretrained on task-agnostic data can be effective for downstream tasks. Gururangan et al. (2020) and Lee et al. (2020) show domain-adaptive pretraining further improves the performance of pretrained language models. Recently, Krishna et al. (2022) have observed that pretraining on task-agnostic data can provide a significant advantage compared to standard webtext data. These findings give huge insight into our research. Note that our work aims at extending the potential of language modeling that has been successfully used for diverse applications to recommender systems. ## 6 Conclusion Recent works have built text-based user models and demonstrated that the rich nature of text information in any domain or system could be a valuable foundation for user modeling. Our primary contribution is jointly optimizing the language modeling and recommendation task objectives and successfully tackling a broad spectrum of diverse recommendation tasks, including transfer learning for unseen domains and systems. Overall, our analysis sheds remarkable insights on user representation learning through user behavioral corpora. ## Considerations And Limitations LMRec is trained on user behavior text data that are collected from diverse service applications. These datasets are preprocessed to users' behavior sequences as detailed in Figure 1 and Section 3.1. However, in order to improve the quality of user representations, choosing the item information differently for each application may improve the effectiveness. As such, we can consider domain-specific information for each service rather than using general item information. For example, we may leverage additional domain-specific information such as news topics or categories, names of the press agency, and keywords for the news content rather than using only news titles for the News dataset. This issue is a promising extension for practitioners to successfully apply LMRec to real-world applications. The types of task-agnostic data will largely affect the performance gains of LMRec+agnostic and LMRecTL +agnostic. We fully utilize four types of taskagnostic data, i.e., Search, E-comm., SNS, and News, and achieve state-of-the-art results. However, this paper does not thoroughly explore their optimized combination or mixing ratio of the corpus due to the heavy computational costs, which most large LM studies suffer from. While prior work shows how the pretraining corpus sources and their combination affect diverse downstream tasks (Raffel et al., 2020; Gururangan et al., 2020; Lee et al., 2020; Krishna et al., 2022; Shin et al., 2022), there still remain limitations in finding the generic relation between downstream performance and corpus properties; measuring the effect of the pretraining corpus on the downstream task is still underexplored. We point out that more careful study is left for future research. Regarding reproducibility, it is difficult to open our in-house data due to legal issues caused by privacy and user agreement. Therefore, we tried our best to validate the efficacy of our LMRec with the experiments on benchmark datasets in addition to in-house data. ## Acknowledgements All authors thank NAVER Smart Machine Learning (NSML) platform team (Sung et al., 2017; Kim et al., 2018) for their critical work on the software and hardware infrastructure on which all the experiments were performed. ## References Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, and Adnan Aziz. 2022. Understanding scaling laws for recommendation models. *arXiv* preprint arXiv:2208.08489. Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma. 2021. Explaining neural scaling laws. *arXiv preprint arXiv:2102.06701*. 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In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pages 1507–1515. ## A Training Details We utilize separate data loaders to deal with different batch sizes between language modeling and recommendation tasks. Furthermore, the early stopping strategy is employed based on the validation loss of the recommendation task and patience of 100 steps. We use the AdamW (Loshchilov and Hutter, 2019) with β1 = 0.9, β2 = 0.98, ϵ = 10−6, and Zero Redundancy Optimizer (Rajbhandari et al., 2020). We update the model using linear warm-up of the learning rate over the first 1% steps, followed by cosine decay (Loshchilov and Hutter, 2017) to decrease the learning rate to 10% of its initial value. The cosine decay is also applied to the λ value. We leverage the automatic mixedprecision (Micikevicius et al., 2018) package in Pytorch (Paszke et al., 2019) to reduce training time and GPU memory usage. Gradient norm clipping (Pascanu et al., 2013) is used with the max norm set to 0.1 to stabilize training. Unless otherwise specified, all results are reported by 125M transformer decoder (Vaswani et al., 2017). All models use a vocabulary size of 50, 258 and a max sequence length of 2, 048. The hyperparameter values for different sizes of LMRec is presented in Table 9. All the results are averaged over the 20 runs. ## B Details Of Comparison Models Behavior Sequence Transformer (BST) (Chen et al., 2019) embeds user history logs as lowdimensional vectors and passes them to the transformer layers to model underlying user preferences. LightGCN (He et al., 2020) leverages Graph Convolution Network (Kipf and Welling, 2017) for enhancing collaborative filtering. It linearly propagates user and item embeddings of a bipartite interaction graph. The final embedding is computed by the sum of the embeddings propagated at each layer. UserBERT (Lu et al., 2020) incorporates two self-supervision tasks for pretraining. These pretext tasks effectively capture the relations between user behaviors and inherent user interests. It finally finetuned models on target tasks. UniSRec (Hou et al., 2022) proposes to combine parametric whitening and MoE adaptor ![12_image_0.png](12_image_0.png) for learning personalized representation. UniSRec pretrains user history by sequence-to-sequence contrastive learning and then finetunes the model to downstream tasks. M6Rec (Cui et al., 2022) employs prompt tuning of pretrained language models for building a unified framework. M6Rec fully utilizes text inputs to generalize to any domains/systems and has the ability to perform zero-shot learning. Since they did not release pretrained M6 (Lin et al., 2021), we used Huggingface RoBERTa (Liu et al., 2019) to implement it.3 CLUE (Shin et al., 2023) presents a plain text-based contrastive learning framework, considering heterogeneous services or applications as a modality and users as a common semantic. It then performs feature-based transfer learning for downstream tasks. ## C Effect Of Language Modeling On Local Curvature One of the most well-known criteria influencing neural network generalization is observing Hessian eigenvalues with respect to parameters. Since the Hessian is often treated as local curvature, the eigenvalues of Hessian determine the smoothness of loss landscapes. Many researchers have argued that the flat loss landscape leads to better generalization (Li et al., 2018; Foret et al., 2021; Chen et al., 2022; Park and Kim, 2022). We calculate and gather top-5 Hessian eigenvalues by PyHessian (Yao et al., 2020), and resulting max eigenvalues are visualized using kernel density estimation in Scikit-learn (Pedregosa et al., 2011). Results are presented in Figure 5. The language model 3https://huggingface.co/transformers/model_doc/roberta | Model Size | nlayers | demb | nheads | dffn | λ | Batch Size | Learning Rate | Weight Decay | |--------------|-----------|--------|----------|--------|----------|--------------|-----------------|----------------| | 1.7M | 4 | 32 | 4 | 128 | 1 × 10−2 | 256 | 5 × 10−3 | 1 × 10−2 | | 7M | 4 | 128 | 4 | 512 | 1 × 10−2 | 512 | 2 × 10−3 | 1 × 10−2 | | 20M | 8 | 256 | 8 | 1024 | 8 × 10−3 | 1024 | 1 × 10−3 | 5 × 10−2 | | 64M | 12 | 512 | 8 | 2048 | 8 × 10−3 | 1024 | 8 × 10−4 | 1 × 10−1 | | 125M | 12 | 768 | 12 | 2048 | 3 × 10−3 | 1024 | 2 × 10−4 | 1 × 10−1 | | 210M | 24 | 768 | 16 | 2048 | 3 × 10−3 | 1024 | 2 × 10−4 | 1 × 10−1 | only (LM) on the OBS task produces many negative eigenvalues, which means the loss landscape is non-convex and, thus, challenging to optimize. This result is natural since the loss of the target task computed without adaptation of models cannot bring good properties. On the other hand, eigenvalues of models (LMRec-lm and LMRec) trained with target objectives flocked together on the positive side. The magnitude of the eigenspectra of LMRecmodel is smaller than that of LMRec-lm model. It means that learning two objectives simultaneously improves the robustness and generality of model performance on downstream tasks. ## D Online A/B Experiment We run A/B experiments on product collection recommendation tasks using LMRecTL +agnostic user feature to verify the practical usage of our method online. The product collection is a collection of products allotted by merchandisers with a particular category such as "Plush robe coats for men", "Winter sale special offer", and "Best backpacks for high school students". This task is to recommend the product collection banner, linked to a page displaying a list of products. We pretrain LMRecTL +agnostic with OBS, OTA, and ECOMM and then transfer to the product collection recommendation (target task). The mean pooled task-specific and task-agnostic user features are used as the final user features. During the 14 days of online experimentation, we measured two important metrics for the online recommender system, CTR and GMV, to track user satisfaction with the platform. CTR represents the click/view rate of recommendation, and GMV is the total value of sold products through recommendation. All models take the same amount of user traffic. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Considerations and Limitations section A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? It is described in the last paragraph of the Introduction section. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 describes the scientific artifacts used in this paper. ✓ B1. Did you cite the creators of artifacts you used? Section 3 descrbies it. We used Amazon review dataset proposed in https://nijianmo.github.io/amazon/. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The license guidelines are already described at (https://s3.amazonaws.com/amazon-reviews-pds/readme.html). And we strictly followed the guides (purpose of academic research). ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? The scientific artifacts used in this work are consistent with their intended use. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No offensive content exists in our datasets; even if it exists, offensive content can not harm our research since the final outputs are recommendation results. It is already anonymized, and there is no identifying information like names, phone, credit card numbers, addresses, user names, etc. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3.1 outlines our datasets. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3 and Table 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** The computation costs of our models are shown in Table 6. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We described it in Table 6 ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix A describes the experimental details of our approach. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? At the end of Appendix A. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

zhao-etal-2023-improving

Improving Continual Relation Extraction by Distinguishing Analogous Semantics

https://aclanthology.org/2023.acl-long.65

Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting. However, repeatedly replaying these samples may cause the overfitting problem. We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations. Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting.

## Improving Continual Relation Extraction By Distinguishing Analogous Semantics Wenzheng Zhao† Yuanning Cui† **Wei Hu**†, ‡, ∗ † State Key Laboratory for Novel Software Technology, Nanjing University, China ‡ National Institute of Healthcare Data Science, Nanjing University, China [email protected], [email protected], [email protected] ## Abstract Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting. However, repeatedly replaying these samples may cause the overfitting problem. We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations. Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting. ## 1 Introduction Relation extraction (RE) aims to detect the relation between two given entities in texts. For instance, given a sentence "*Remixes of tracks from Persona 5* were supervised by Kozuka and original composer Shoji Meguro" and an entity pair (Persona 5, *Shoji* Meguro), the "*composer*" relation is expected to be identified by an RE model. Conventional RE task assumes all relations are observed at once, ignoring the fact that new relations continually emerge in the real world. To deal with emerging relations, some existing works (Wang et al., 2019; Han et al., 2020; Wu et al., 2021; Cui et al., 2021; Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022; Wang et al., 2022) study continual RE. In continual RE, new relations and their involved samples continually emerge, and the goal is to classify all observed relations. Therefore, a continual RE model is expected ∗Corresponding author | Models | Max sim. | FewRel | TACRED | | | |--------------|--------------|----------|----------|------|-----| | Accuracy | Drop | Accuracy | Drop | | | | [0.85, 1.00) | 71.1 | 9.7 | 64.8 | 11.4 | | | CRL | [0.70, 0.85) | 78.8 | 5.7 | 76.6 | 5.0 | | (0.00, 0.70) | 87.9 | 3.2 | 89.6 | 0.6 | | | [0.85, 1.00) | 60.4 | 18.9 | 60.7 | 13.9 | | | CRECL | [0.70, 0.85) | 78.4 | 6.8 | 70.0 | 8.4 | | (0.00, 0.70) | 83.0 | 5.1 | 79.9 | 4.3 | | Table 1: Results of our empirical study. We divide all relations into three groups according to their maximum similarity to other relations. "Accuracy" indicates the average *accuracy* (%) of relations after the model finishes learning. "Drop" indicates the average accuracy drop (%) from learning the relation for the first time to the learning process finished. to be able to learn new relations while retaining the performance on learned relations. Existing works primarily focus on storing and replaying samples to avoid catastrophic forgetting (Lange et al., 2022) of the learned relations. On one hand, considering the limited storage and computational resources, it is impractical to store all training samples and re-train the whole model when new relations emerge. On the other hand, replaying a small number of samples every time new relations emerge would make the model prone to overfit the stored samples (Verwimp et al., 2021; Lange et al., 2022). Moreover, existing works simply attribute catastrophic forgetting to the decay of previous knowledge as new relations come but seldom delve deeper into the real causation. We conduct an empirical study and find that the severe decay of knowledge among analogous relations is a key factor of catastrophic forgetting. Table 1 shows the accuracy and accuracy drop of two existing models on the FewRel (Han et al., 2018) and TACRED (Zhang et al., 2017) datasets. CRL (Zhao et al., 2022) and CRECL (Hu et al., 2022) are both state-of-the-art models for continual RE. All relations in the datasets are divided into three groups according to the maximum cosine similarity of their prototypes to other relation prototypes. A relation prototype is the overall representation of the relation. We can observe that the performance on relations with higher similarity is poorer, which is reflected in less accuracy and greater accuracy drop. Given that a relation pair with high similarity is often analogous to each other, the performance on a relation tends to suffer a significant decline, i.e., catastrophic forgetting, when its analogous relations appear. For example, the accuracy of the previously learned relation "location" drops from 0.98 to 0.6 after learning a new relation "country of origin". Therefore, it is important to maintain knowledge among analogous relations for alleviating catastrophic forgetting. See Appendix A for more details of our empirical study. To address the above issues, we propose a novel continual extraction model for analogous relations. Specifically, we introduce memory-insensitive relation prototypes and memory augmentation to reduce overfitting. The memory-insensitive relation prototypes are generated by combining static and dynamic representations, where the static representation is the average of all training samples after first learning a relation, and the dynamic representation is the average of stored samples. The memory augmentation replaces entities and concatenates sentences to generate more training samples for replay. Furthermore, we propose integrated training and focal knowledge distillation to alleviate knowledge forgetting of analogous relations. The integrated training combines the advantages of two widely-used training methods, which contribute to a more robust feature space and better distinguish analogous relations. One method uses contrastive learning for training and generates prototypes for relation classification, while the other trains a linear classifier. The focal knowledge distillation assigns high weights to analogous relations, making the model more focus on maintaining their knowledge. Our main contributions are summarized below: - We explicitly consider the overfitting problem in continual RE, which is often ignored by previous works. We propose memory-insensitive relation prototypes and memory augmentation to alleviate overfitting. - We conduct an empirical study and find that analogous relations are hard to distinguish and their involved knowledge is more easily to be forgotten. We propose integrated training and focal knowledge distillation to better distinguish analogous relations. - The experimental results on two benchmark datasets demonstrate that our model achieves state-of-the-art accuracy compared with existing works, and better distinguishes analogous relations and overcomes overfitting for continual RE. Our source code is available at https://github.com/nju-websoft/CEAR. ## 2 Related Work Continual learning studies the problem of learning from a continuous stream of data (Lange et al., 2022). The main challenge of continual learning is avoiding catastrophic forgetting of learned knowledge while learning new tasks. Existing continual learning models can be divided into three categories: regularization-based, dynamic architecture, and memory-based. The regularization-based models (Li and Hoiem, 2016; Kirkpatrick et al., 2016) impose constraints on the update of parameters important to previous tasks. The dynamic architecture models (Mallya and Lazebnik, 2018; Qin et al., 2021) dynamically extend the model architecture to learn new tasks and prevent forgetting previous tasks. The memory-based models (Lopez-Paz and Ranzato, 2017; Rebuffi et al., 2017; Chaudhry et al., 2019) store a limited subset of samples in previous tasks and replay them when learning new tasks. In continual RE, the memory-based models (Wang et al., 2019; Han et al., 2020; Wu et al., 2021; Cui et al., 2021; Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022) are the mainstream choice as they have shown better performance for continual RE than others. To alleviate catastrophic forgetting, previous works make full use of relation prototypes, contrastive learning, multi-head attention, knowledge distillation, etc. EA-EMR (Wang et al., 2019) introduces memory replay and the embedding aligned mechanism to mitigate the embedding distortion when training new tasks. CML (Wu et al., 2021) combines curriculum learning and meta-learning to tackle the order sensitivity in continual RE. RP-CRE (Cui et al., 2021) and KIP-Framework (Zhang et al., 2022) leverage relation prototypes to refine sample representations through multi-head attention-based memory networks. Additionally, KIP-Framework uses external knowledge to enhance the model through a knowledge-infused prompt to guide relation proto- ![2_image_0.png](2_image_0.png) type generation. EMAR (Han et al., 2020), CRL (Zhao et al., 2022), and CRECL (Hu et al., 2022) leverage contrastive learning for model training. Besides, knowledge distillation is employed by CRL to maintain previously learned knowledge. ACA (Wang et al., 2022) is the only work that considers the knowledge forgetting of analogous relations ignored by the above works and proposes an adversarial class augmentation strategy to enhance other continual RE models. All these models do not explicitly consider the overfitting problem (Lange et al., 2022; Verwimp et al., 2021), which widely exists in the memory-based models. As far as we know, a few works (Wang et al., 2021) in other continual learning fields have tried to reduce the overfitting problem and achieve good results. We address both the problems of distinguishing analogous relations and overfitting to stored samples, and propose an end-to-end model. ## 3 Task Definition A continual RE task consists of a sequence of tasks T = {T1, T2*, . . . , T*K}. Each individual task is a conventional RE task. Given a sentence, the RE task aims to find the relation between two entities in this sentence. The dataset and relation set of Tk ∈ T are denoted by Dk and Rk, respectively. Dk contains separated training, validation and test sets, denoted by Dtrain k, Dvalid kand Dtest k, respectively. Rk contains at least one relation. The relation sets of different tasks are disjoint. Continual RE aims to train a classification model that performs well on both current task Tk and previously accumulated tasks T˜k−1 =Sk−1 i=1 Ti. In other words, a continual RE model is expected to be capable of identifying all seen relations R˜k = Sk i=1 Ri and would be evaluated on all the test sets of seen tasks D˜test k =Sk i=1 Dtest i. ## 4 Methodology 4.1 Overall Framework The overall framework is shown in Figure 1. For a new task Tk, we first train the continual RE model on Dk to learn this new task. Then, we select and store a few typical samples for each relation r ∈ Rk. Next, we calculate the prototype pr of each relation r ∈ R˜k according to the static and dynamic representations of samples. We also conduct memory augmentation to provide more training data for memory replay. Note that the augmented data are not used for prototype generation. Finally, we perform memory replay consisting of integrated training and focal knowledge distillation to alleviate catastrophic forgetting. The parameters are updated in the first and last steps. After learning Tk, the model continually learns the next task Tk+1. ## 4.2 New Task Training When the new task Tk emerges, we first train the model on Dtrain k. We follow the works (Cui et al., 2021; Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022) to use the pre-trained language model BERT (Devlin et al., 2019) as the encoder. Given a sentence x as input, we first tokenize it and insert special tokens [E11]/[E12] and [E21]/[E22] to mark the start/end positions of head and tail entities, respectively. We use the hidden representations of [E11] and [E21] as the representations of head and tail entities. The representation of x is defined as $$\mathbf{h}_{x}=\mathrm{LayerNorm}\big(\mathbf{W}_{1}[\mathbf{h}_{x}^{11};\mathbf{h}_{x}^{21}]+\mathbf{b}\big),\tag{1}$$ where h 11 x, h 21 x ∈ R dare the hidden representations of head and tail entities, respectively. d is the dimension of the hidden layer in BERT. W1 ∈ R d×2dand b ∈ R dare two trainable parameters. Then, we use a linear softmax classifier to calculate the classification probability of x according to the representation hx: $$P(x;\theta_{k})=\mathrm{softmax}(\mathbf{W}_{2}\mathbf{h}_{x}),$$ where θk denotes the model when learning Tk. W2 ∈ R|R˜k|×dis the trainable parameter of the linear classifier. Finally, the classification loss of new task training is calculated as follows: $$\mathcal{L}_{\text{new}}=-\frac{1}{|D_{k}^{\text{min}}|}\sum_{x_{i}\in D_{k}^{\text{train}}}\sum_{r_{j}\in R_{k}}\delta_{y_{i},r_{j}}\log P(r_{j}\mid x_{i};\theta_{k}),\tag{3}$$ where P(rj | xi; θk) is the probability of input xi classified as relation rj by the current model θk. yi is the label of xi such that if yi = rj , δyi,rj = 1, and 0 otherwise. ## 4.3 Memory Sample Selection To preserve the learned knowledge from previous tasks, we select and store a few typical samples for memory replay. Inspired by the works (Han et al., 2020; Cui et al., 2021; Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022), we adopt the k-means algorithm to cluster the samples of each relation r ∈ Rk. The number of clusters is defined as the memory size m. For each cluster, we select the sample whose representation is closest to the medoid and store it in the memory space Mr. The accumulated memory space is M˜k =Sr∈R˜k Mr. ## 4.4 Memory-Insensitive Relation Prototype A relation prototype is the overall representation of the relation. Several previous works (Han et al., 2020; Zhao et al., 2022; Hu et al., 2022) directly use relation prototypes for classification and simply calculate the prototype of r using the average of the representations of its typical samples. But, such a relation prototype is sensitive to the typical samples, which may cause the overfitting problem. To reduce the sensitivity to typical samples, Zhang et al. (2022) propose a knowledge-infused relation prototype generation, which employs a knowledge-infused prompt to guide prototype generation. However, it relies on external knowledge and thus brings additional computation overhead. To alleviate the overfitting problem, we first calculate and store the average representation of all training samples after first learning a relation. This representation contains more comprehensive knowledge about the relation. However, as we cannot store all training samples, it is *static* and cannot be updated to adapt to the new feature space in the subsequent learning. In this paper, the *dynamic* representation of typical samples is used to finetune the *static* representation for adapting the new feature space. The memory-insensitive relation prototype of relation r is calculated as follows: $$(2)$$ $$\mathbf{p}_{r}=\left(1-\beta\right)\mathbf{p}_{r}^{\mathrm{static}}+{\frac{\beta}{|M^{r}|}}\sum_{x_{i}\in M^{r}}\mathbf{h}_{x_{i}},\quad\quad(4)$$ where p static ris the average representation of all training samples after learning relation r for the first time, and β is a hyperparameter. ## 4.5 Memory Augmentation The memory-based models (Wang et al., 2019; Han et al., 2020; Cui et al., 2021; Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022) select and store a small number of typical samples and replay them in the subsequent learning. Due to the limited memory space, these samples may be replayed many times during continual learning, resulting in overfitting. To address this issue, we propose a memory augmentation strategy to provide more training samples for memory replay. For a sample x r i of relation r in Mr, we randomly select another sample x r j̸= x r i from Mr. Then, the head and tail entities of x r i are replaced by the corresponding entities of x r j and the new sample, denoted by x r ij , can be seen as an additional sample of relation r. Also, we use sentence concatenation to generate training samples. Specifically, we randomly select another two samples xm and xn from M˜k \ Mrand append them to the end of x r i and x r ij , respectively. Note that xm and xn 1165 are not the typical samples of relation r. Then, we obtain two new samples of relation r, denoted by x r i−m and x r ij−n . The model is expected to still identify the relation r though there is an irrelevant sentence contained in the whole input. We conduct this augmentation strategy on all typical samples in M˜k, but the augmented data are only used for training, not for prototype generation, as they are not accurate enough. Finally, the overall augmented memory space is Mˆk, and |Mˆk| = 4|M˜k|. ## 4.6 Memory Replay 4.6.1 Integrated Training There are two widely-used training methods for continual RE: Han et al. (2020); Zhao et al. (2022); Hu et al. (2022) use contrastive learning for training and make predictions via relation prototypes; Cui et al. (2021); Zhang et al. (2022) leverage the cross entropy loss to train the encoder and linear classifier. We call these two methods the *contrastive* method and the *linear* method, respectively. The contrastive method contributes to a better feature space because it pulls the representations of samples from the same relation and pushes away those from different relations, which improves the alignment and uniformity (Wang and Isola, 2020). However, its prediction process is sensitive to the relation prototypes, especially those of analogous relations that are highly similar to each other. The linear classifier decouples the representation and classification processes, which ensures a more taskspecific decision boundary. We adopt both contrastive and linear methods to combine their merits: $${\mathcal{L}}_{\mathrm{cls}}={\mathcal{L}}_{\mathrm{c\_cls}}+{\mathcal{L}}_{\mathrm{l\_cls}},$$ where Lc_cls and Ll_cls denote the losses of the contrastive and linear methods, respectively. In the contrastive method, we first leverage twolayer MLP to reduce dimension: $$\mathbf{z}_{x}=\operatorname{Norm}\bigl(\operatorname{MLP}(\mathbf{h}_{x})\bigr).$$ . (6) Then, we use the InfoNCE loss (van den Oord et al., 2018) and the triplet loss (Schroff et al., 2015) in contrastive learning: $$\begin{split}\mathcal{L}_{\text{c\_cls}}&=-\frac{1}{|\hat{M}_k|}\sum_{x_i\in\hat{M}_k}\log\frac{\exp(\mathbf{z}_{x_i}\cdot\mathbf{z}_{y_i}/\tau_1)}{\sum_{r\in\hat{R}_k}\exp(\mathbf{z}_{x_i}\cdot\mathbf{z}_{r}/\tau_1)}\\ &\quad+\frac{\mu}{|\hat{M}_k|}\sum_{x_i\in\hat{M}_k}\max(\omega-\mathbf{z}_{x_i}\mathbf{z}_{y_i}+\mathbf{z}_{x_i}\mathbf{z}_{y_i^{\prime}},0)\end{split},\tag{7}$$ where zr is the low-dimensional prototype of relation r. y′i = arg maxy′i∈R˜k\{yi} zxi·zy′i is the most similar negative relation label of sample xi. τ1 is the temperature parameter. µ and ω are hyperparameters. At last, the relation probability is computed through the similarity between the representations of test sample and relation prototypes: $$P_{c}(x_{i};\theta_{k})=\mathrm{softmax}(\mathbf{z}_{x_{i}}\cdot\mathbf{Z}_{\tilde{R}_{k}}),\qquad(8)$$ where ZR˜k denotes the matrix of prototypes of all seen relations. In the linear method, a linear classifier obtains the relation probability similar to that in the new task training step. The loss function is $$\mathcal{L}_{\rm l\_cls}=-\frac{1}{|\hat{M}_{k}|}\sum_{x_{i}\in\hat{M}_{k}}\sum_{r_{j}\in\hat{R}_{k}}\delta_{y_{i},r_{j}}\log P(r_{j}\,|\,x_{i};\theta_{k}).\tag{9}$$ ## 4.6.2 Focal Knowledge Distillation During the continual training process, some emerging relations are similar to other learned relations and are difficult to distinguish. Inspired by the focal loss (Lin et al., 2020), we propose the focal knowledge distillation, which forces the model to focus more on analogous relations. Specifically, we assign a unique weight for each sample-relation pair, according to the classification probability of the sample and the similarity between the representations of sample and relation prototype. Difficult samples and analogous sample-relation pairs are assigned high weights. The weight wi,j for sample xi and relation rj is $$s_{x_{i},r_{j}}=\frac{\exp\left(\text{sim}(\mathbf{h}_{x_{i}},\mathbf{p}_{r_{j}})/\tau_{2}\right)}{\sum_{r_{m}\in\tilde{R}_{k-1}}\exp\left(\text{sim}(\mathbf{h}_{x_{i}},\mathbf{p}_{r_{m}})/\tau_{2}\right)},\tag{10}$$ $$w_{x_{i},r_{j}}=s_{x_{i},r_{j}}\big{(}1-P(y_{i}\,|\,x_{i};\theta_{k})\big{)}^{\gamma},\tag{11}$$ where prj is the prototype of relation rj . sim(·) is the similarity function, e.g., cosine. τ2 is the temperature parameter and γ is a hyperparameter. With wxi,rj , the focal knowledge distillation loss is calculated as follows: $$a_{x_{i},r_{j}}=w_{x_{i},r_{j}}P(r_{j}\,|\,x_{i};\theta_{k-1}),\tag{12}$$ $$\mathcal{L}_{\text{fkd}}=-\frac{1}{|M_{k}|}\sum_{x_{i}\in\tilde{M}_{k}}\sum_{r_{j}\in\tilde{R}_{k-1}}a_{x_{i},r_{j}}\log P(r_{j}\,|\,x_{i};\theta_{k}),\tag{13}$$ $$\begin{array}{c}{{(12)}}\\ {{;\theta_{k}),}}\end{array}$$ $$\begin{array}{c}{{(13)}}\end{array}$$ where P(rj | xi; θk−1) denotes the probability of sample xi predicted to relation rj by the previous model θk−1. The focal knowledge distillation loss is combined with the training losses of contrastive and linear methods. The overall loss is defined as $${\cal L}_{\mathrm{replay}}={\cal L}_{\mathrm{cls}}+\lambda_{1}{\cal L}_{\mathrm{c\_fkd}}+\lambda_{2}{\cal L}_{\mathrm{l\_fkd}},\tag{14}$$ where Lc_fkd and Ll_fkd are the focal knowledge distillation losses of contrastive and linear methods, respectively. λ1 and λ2 are hyperparameters. ## 4.7 Relation Prediction After learning task Tk, the contrastive and linear methods are combined to predict the relation label of the given test sample x∗ i : $$y_{i}^{*}=\arg\max\left((1-\alpha)P_{c}(x_{i}^{*};\theta_{k})+\alpha P_{l}(x_{i}^{*};\theta_{k})\right),$$ $$y_{i}^{*}\in\bar{R}_{k}\tag{15}$$ (15) where Pc(x∗ i ; θk) and Pl(x∗ i ; θk) are the probabilities calculated by the contrastive and linear methods, respectively. α is a hyperparameter. ## 5 Experiments And Results In this section, we report the experimental results of our model. The source code is accessible online. ## 5.1 Datasets We conduct our experiments on two widely-used benchmark datasets: - **FewRel** (Han et al., 2018) is a popular RE dataset originally built for few-shot learning. It contains 100 relations and 70,000 samples in total. To be in accord with previous works (Cui et al., 2021; Zhao et al., 2022), we use 80 relations each with 700 samples (i.e., in the training and validation sets), and split them into 10 subsets to simulate 10 disjoint tasks. - **TACRED** (Zhang et al., 2017) is a large-scale RE dataset having 42 relations and 106,264 samples. Following the experiment setting of previous works, we remove "*no_relation*" and divide other relations into 10 tasks. ## 5.2 Experiment Setting And Baseline Models RP-CRE (Cui et al., 2021) proposes a completelyrandom strategy to split all relations into 10 subsets corresponding to 10 tasks, and *accuracy* on all observed relations is chosen as the evaluation metric, which is defined as the proportion of correctly predicted samples in the whole test set. This setting is widely followed by existing works (Zhao et al., 2022; Zhang et al., 2022; Hu et al., 2022). For a fair comparison, we employ the same setting and obtain the divided data from the open-source code of RP-CRE to guarantee exactly the same task sequence. Again, following existing works, we carry out the main experiment with a memory size of 10 and report the average result of five different task sequences. See Appendix B for the details of the hyperparameter setting. For comparison, we consider the following baseline models: EA-EMR (Wang et al., 2019), EMAR (Han et al., 2020), CML (Wu et al., 2021), RP-CRE (Cui et al., 2021), CRL (Zhao et al., 2022), CRECL (Hu et al., 2022) and KIP-Framework (Zhang et al., 2022). See Section 2 for their details. ## 5.3 Results And Analyses 5.3.1 Main Results Table 2 shows the results of all compared baselines in the main experiment. The results of EA-EMR, EMAR, CML, and RP-CRE are obtained from the RP-CRE's original paper, and the results of other baselines are directly cited from their original papers. We additionally report the standard deviations of our model. Based on the results, the following observations can be drawn: Our proposed model achieves an overall state-ofthe-art performance on the two different datasets for the reason that our model can reduce overfitting to typical samples and better maintain knowledge among analogous relations. Thus, we can conclude that our model effectively alleviates catastrophic forgetting in continual RE. As new tasks continually emerge, the performance of all compared models declines, which indicates that catastrophic forgetting is still a major challenge to continual RE. EA-EMR and CML do not use BERT as the encoder, so they suffer the most performance decay. This demonstrates that BERT has strong stability for continual RE. All models perform relatively poorer on TACRED and the standard deviations of our model on TACRED are also higher than those on FewRel. The primary reason is that TACRED is classimbalanced and contains fewer training samples for each relation. Therefore, it is more difficult and leads to greater randomness in the task division. | FewRel | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | |----------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------| | EA-EMR | 89.0 | 69.0 | 59.1 | 54.2 | 47.8 | 46.1 | 43.1 | 40.7 | 38.6 | 35.2 | | EMAR (BERT) | 98.8 | 89.1 | 89.5 | 85.7 | 83.6 | 84.8 | 79.3 | 80.0 | 77.1 | 73.8 | | CML | 91.2 | 74.8 | 68.2 | 58.2 | 53.7 | 50.4 | 47.8 | 44.4 | 43.1 | 39.7 | | RP-CRE | 97.9 | 92.7 | 91.6 | 89.2 | 88.4 | 86.8 | 85.1 | 84.1 | 82.2 | 81.5 | | CRL | 98.2 | 94.6 | 92.5 | 90.5 | 89.4 | 87.9 | 86.9 | 85.6 | 84.5 | 83.1 | | CRECL | 97.8 | 94.9 | 92.7 | 90.9 | 89.4 | 87.5 | 85.7 | 84.6 | 83.6 | 82.7 | | KIP-Framework△ | 98.4 | 93.5 | 92.0 | 91.2 | 90.0 | 88.2 | 86.9 | 85.6 | 84.1 | 82.5 | | Ours | 98.1 ±0.6 | 95.8 ±1.7 | 93.6 ±2.1 | 91.9 ±2.0 | 91.1 ±1.5 | 89.4 ±2.0 | 88.1 ±0.7 | 86.9 ±1.3 | 85.6 ±0.8 | 84.2 ±0.4 | | TACRED | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | | EA-EMR | 47.5 | 40.1 | 38.3 | 29.9 | 28.4 | 27.3 | 26.9 | 25.8 | 22.9 | 19.8 | | EMAR (BERT) | 96.6 | 85.7 | 81.0 | 78.6 | 73.9 | 72.3 | 71.7 | 72.2 | 72.6 | 71.0 | | CML | 57.2 | 51.4 | 41.3 | 39.3 | 35.9 | 28.9 | 27.3 | 26.9 | 24.8 | 23.4 | | RP-CRE | 97.6 | 90.6 | 86.1 | 82.4 | 79.8 | 77.2 | 75.1 | 73.7 | 72.4 | 72.4 | | CRL | 97.7 | 93.2 | 89.8 | 84.7 | 84.1 | 81.3 | 80.2 | 79.1 | 79.0 | 78.0 | | CRECL | 96.6 | 93.1 | 89.7 | 87.8 | 85.6 | 84.3 | 83.6 | 81.4 | 79.3 | 78.5 | | KIP-Framework△ | 98.3 | 95.0 | 90.8 | 87.5 | 85.3 | 84.3 | 82.1 | 80.2 | 79.6 | 78.6 | | Ours | 97.7 ±1.6 | 94.3 ±2.9 | 92.3 ±3.3 | 88.4 ±3.7 | 86.6 ±3.0 | 84.5 ±2.1 | 82.2 ±2.8 | 81.1 ±1.6 | 80.1 ±0.7 | 79.1 ±1.1 | ## 5.3.2 Ablation Study We conduct an ablation study to validate the effectiveness of individual modules in our model. Specifically, for "w/o FKD", we remove the focal knowledge distillation loss in memory replay; for "w/o LM" or "w/o CM", the model is only trained and evaluated with the contrastive or linear method; for "w/o MA", we only train the model with original typical samples in memory replay; and for "w/o DP" or "w/o SP", we directly generate relation prototypes based on the average of static or dynamic representations. The results are shown in Table 3. It is observed that our model has a performance decline without each component, which demonstrates that all modules are necessary. Furthermore, the proposed modules obtain greater improvement on the TACRED dataset. The reason is that TACRED is more difficult than FewRel, so the proposed modules are more effective in difficult cases. ## 5.3.3 Influence Of Memory Size Memory size is defined as the number of stored typical samples for each relation. For the memorybased models in continual RE, their performance is highly influenced by memory size. We conduct an experiment with different memory sizes to compare our model with CRL and CRECL for demonstrating that our model is less sensitive to memory size. We re-run the source code of CRL and CRECL with different memory sizes and show the results in Figure 2. Note that we do not compare with KIP- Intact Model **89.4 88.1 86.9 85.6 84.2** w/o FKD 89.3 88.0 86.8 85.5 84.0 w/o LM 89.0 87.5 86.5 85.1 83.6 w/o CM 89.3 87.5 86.8 **85.6** 84.0 w/o MA 88.4 87.4 86.4 85.4 83.7 w/o DP 89.2 87.9 86.6 85.3 83.8 w/o SP 89.3 87.8 86.6 85.2 83.5 Intact Model **84.5 82.2 81.1 80.1 79.1** w/o FKD 83.4 81.3 79.5 79.2 78.2 w/o LM 83.7 81.2 79.6 79.4 78.2 w/o CM 84.0 81.9 80.1 79.2 78.0 w/o MA 82.9 81.2 79.3 79.0 77.9 w/o DP 83.2 80.8 79.1 79.1 78.3 w/o SP 83.5 81.1 79.6 79.3 78.2 T6 T7 T8 T9 T10 | FewRel TACRED | |-----------------| Framework because it uses external knowledge to enhance performance, which is beyond our scope. In most cases, our model achieves state-ofthe-art performance with different memory sizes, which demonstrates the strong generalization of our model. However, our model does not obtain the best performance on TACRED with memory size 15 because the overfitting problem that we consider is not serious in this case. In fact, as the memory size becomes smaller, the overfitting problem is getting worse, and analogous relations are more difficult to distinguish due to the limited training data samples. From Figures 2(a), (b), (e), ![7_image_0.png](7_image_0.png) and (f), our model has greater advantages when the memory size is small, which indicates that our model can better deal with the overfitting problem in continual RE. We also observe that the performance of each model declines due to the decrease of memory size, which demonstrates that memory size is a key factor in the performance of continual RE models. From Figures 2(d) and (h), the performance difference between different memory sizes is smaller. Thus, we draw the conclusion that our model is more robust to the change of memory size. ## 5.3.4 Performance On Analogous Relations One strength of our model is to distinguish analogous relations for continual RE. We conduct an experiment to explore this point. Specifically, we select relations in the former five tasks which have analogous ones in the latter tasks, and report the accuracy and drop on them in Table 4. We consider that two relations are analogous if the similarity between their prototypes is greater than 0.85. As aforementioned, knowledge of the relations is more likely to be forgotten when their analogous relations emerge. Thus, all compared models are challenged by these relations. However, the performance of our model is superior and drops the least, which shows that our model succeeds in alleviating knowledge forgetting among analogous relations. ## 5.3.5 Case Study We conduct a case study to intuitively illustrate the advantages of our model. Figure 3 depicts the vi- ![7_image_1.png](7_image_1.png) sualization result. It is observed that the relations analogous in semantics (e.g., "*mouth of the watercourse*" and "*tributary*") have relatively similar relation prototypes, which reflects that our model learns a reasonable representation space. Moreover, we see that the discrimination between similar relation prototypes (e.g., "*director*" and "*screenwriter*") is still obvious, which reveals that our model can distinguish analogous relations. Please see Appendix C for the comparison with CRECL. ## 6 Conclusion In this paper, we study continual RE. Through an empirical study, we find that knowledge decay among analogous relations is a key reason for catastrophic forgetting in continual RE. Furthermore, the overfitting problem prevalent in memorybased models also lacks consideration. To this end, we introduce a novel memory-based model to address the above issues. Specifically, the proposed memory-insensitive relation prototypes and memory augmentation can reduce overfitting to typical ![8_image_0.png](8_image_0.png) Figure 3: Visualization of cosine similarity between relation prototypes generated by our model. We select 10 relations involving three highly-similar groups, i.e., [(1), (2)], [(3), (4), (5), (6)] and [(7), (8), (9), (10)]. samples. In memory replay, the integrated training and focal knowledge distillation help maintain the knowledge among analogous relations, so that the model can better distinguish them. The experimental results on the FewRel and TACRED datasets demonstrate that our model achieves stateof-the-art performance and effectively alleviates catastrophic forgetting and overfitting for continual RE. In future work, we plan to explore whether our model can be used in few-shot RE to help distinguish analogous relations. ## 7 Limitations Our model may have several limitations: (1) As a memory-based model, our model consumes additional space to store typical samples and static prototypes, which causes the performance to be influenced by the storage capacity. (2) Although we propose memory-insensitive relation prototypes and memory augmentation, our model still relies on the selection of typical samples. The selected samples of low quality may harm the performance of our model. (3) The recent progress in large language models may alleviate catastrophic forgetting and overfitting, which has not been explored in this paper yet. ## Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 62272219) and the Collaborative Innovation Center of Novel Software Technology & Industrialization. ## References Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. 2019. Efficient lifelong learning with A-GEM. In *ICLR*. Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjie Yi, and Yanghua Xiao. 2021. Refining sample embeddings with relation prototypes to enhance continual relation extraction. In ACL, pages 232–243. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In *NAACL*, pages 4171–4186. Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020. 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In CVPR, pages 5533–5542. Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. FaceNet: A unified embedding for face recognition and clustering. In *CVPR*, pages 815– 823. Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. *CoRR*, abs/1807.03748. Eli Verwimp, Matthias De Lange, and Tinne Tuytelaars. 2021. Rehearsal revealed: The limits and merits of revisiting samples in continual learning. In *ICCV*, pages 9365–9374. Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, and William Yang Wang. 2019. Sentence embedding alignment for lifelong relation extraction. In *NAACL*, pages 796–806. Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, and Zhifang Sui. 2022. Learning robust representations for continual relation extraction via adversarial class augmentation. *CoRR*, abs/2210.04497. Quanziang Wang, Yuexiang Li, Dong Wei, Renzhen Wang, Kai Ma, Yefeng Zheng, and Deyu Meng. 2021. 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In *Findings of ACL*, pages 3402– 3411. ## A More Results Of Empirical Study As mentioned in Section 1, we conduct an empirical study to explore the causation of catastrophic forgetting and find that the knowledge among analogous relations is more likely to be forgotten. As a supplement, we further report more results of our empirical study. Table 5 shows the average change of maximum similarity when the accuracy on relations suffers a sudden drop. Note that the number of relations greater than a 40% drop of CRECL on the TACRED dataset is quite small, thus the result may not be representative. It is observed that, if the maximum similarity of a relation to others obviously increases, its accuracy suddenly drops severely, which indicates that there tends to be a newly emerging relation analogous to it. In short, we can conclude that a relation may suffer catastrophic forgetting when its analogous relations appear. This also emphasizes the importance of maintaining knowledge among analogous relations. | Models | Sudden drop | Maximum similarity change FewRel TACRED | | |---------------|---------------|-------------------------------------------|---------------| | (0.0, 20.0) | 0.715 → 0.715 | 0.780 → 0.773 | | | [20.0, 40.0) | 0.700 → 0.888 | 0.798 → 0.899 | | | CRL | [40.0, 100.0) | 0.784 → 0.944 | 0.860 → 0.924 | | (0.0, 20.0) | 0.596 → 0.601 | 0.649 → 0.642 | | | CRECL | [20.0, 40.0) | 0.665 → 0.889 | 0.650 → 0.827 | | [40.0, 100.0) | 0.556 → 0.904 | 0.649 → 0.820 | | ## B Implementation Details We carry out all experiments on a single NVIDIA RTX A6000 GPU with 48GB memory. Our implementation is based on Python 3.9.7 and the version of PyTorch is 1.11.0. We find the best hyperparameter values through grid search with a step of 0.1 except 0.05 for ω and 0.25 for γ. The search spaces for various hyperparameters are α ∈ [0.2, 0.8], β ∈ [0.1, 0.5], µ ∈ [0.1, 1.0], ω ∈ [0.05, 0.25], γ ∈ [1.0, 2.0] and λ1, λ2 ∈ [0.5, 1.5]. Besides, we fix τ1 and τ2 to 0.1 and 0.5, respectively. The used hyperparameter values are listed below: - For FewRel, α = 0.5, β = 0.5, τ1 = 0.1, µ = 0.5, ω = 0.1, τ2 = 0.5, γ = 1.25, λ1 = 0.5, λ2 = 1.1. - For TACRED, α = 0.6, β = 0.2, τ1 = 0.1, µ = 0.8, ω = 0.15, τ2 = 0.5, γ = 2.0, λ1 = 0.5, λ2 = 0.7. ## C Case Study Of Our Model And Crecl To intuitively illustrate that our model can better distinguish analogous relations, we conduct a comparison to CRECL based on the case study in Section 5.3.5. As depicted in Figure 4, it is true for both our model and CRECL that if the relations are dissimilar in semantics, the similarity between their prototypes is low. However, we can observe that our model learns relatively dissimilar prototypes among analogous relations (e.g., lighter color between "*director*" and "*screenwriter*"), which demonstrates that our model can better distinguish analogous relations. ## D Comparison With Aca As aforementioned in Section 2, Wang et al. (2022) propose an adversarial class augmentation (ACA) strategy, aiming to learn robust representations to overcome the influence of analogous relations. Specifically, ACA utilizes two class augmentation methods, namely hybrid-class augmentation and reversed-class augmentation, to build hard negative classes for new tasks. When new tasks arrive, the model is jointly trained on new relations and adversarial augmented classes to learn robust initial representations for new relations. As a data augmentation strategy, ACA can be combined with other continual RE models. Therefore, we conduct an experiment to explore the performance of our model with ACA. We re-run the source code of ACA and report the results of RP-CRE + ACA, EMAR + ACA, and our model + ACA in Table 6. Compared with the original models, both EMAR and RP-CRE gain improvement, which demonstrates the effectiveness of ACA in learning robust representations for analogous relations. However, as we also explicitly consider the knowledge forgetting of analogous relations, there exist overlaps between ACA and our model. Thus, the performance of our model declines when combined with ACA. We leave the combination of our model and other augmentation methods in future work. | FewRel | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | |--------------|------|------|------|------|------|------|------|------|------|-------| | RP-CRE + ACA | 97.7 | 95.2 | 92.8 | 91.0 | 90.1 | 88.7 | 86.9 | 86.4 | 85.3 | 83.8 | | EMAR + ACA | 98.3 | 94.6 | 92.6 | 90.6 | 90.4 | 88.8 | 87.7 | 86.7 | 85.6 | 84.1 | | Ours | 98.1 | 95.8 | 93.6 | 91.9 | 91.1 | 89.4 | 88.1 | 86.9 | 85.6 | 84.2 | | Ours + ACA | 98.4 | 94.8 | 92.8 | 91.4 | 90.4 | 88.9 | 87.8 | 86.8 | 86.0 | 83.9 | | TACRED | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | | RP-CRE + ACA | 97.1 | 93.5 | 89.4 | 84.5 | 83.7 | 81.0 | 79.3 | 78.0 | 77.5 | 76.5 | | EMAR + ACA | 97.6 | 92.4 | 90.5 | 86.7 | 84.3 | 82.2 | 80.6 | 78.6 | 78.3 | 78.4 | | Ours | 97.7 | 94.3 | 92.3 | 88.4 | 86.6 | 84.5 | 82.2 | 81.1 | 80.1 | 79.1 | | Ours + ACA | 98.5 | 94.7 | 91.9 | 85.5 | 84.2 | 82.1 | 79.6 | 77.3 | 77.1 | 76.1 | ![11_image_0.png](11_image_0.png) ![11_image_1.png](11_image_1.png) ![11_image_2.png](11_image_2.png) ## E Performance On Dissimilar Relations We further conduct an experiment to explore the performance on dissimilar relations. We consider that relations with the highest similarity to other relations lower than 0.7 are dissimilar relations. As shown in Table 7, our model achieves the best accuracy on dissimilar relations. We attribute this to the better representations it learns through integrated training. However, our model does not always obtain the smallest drop as it focuses on alleviating the forgetting of analogous relations. Overall, from the results in Tables 4 and 7, we can conclude that our model achieves the best accuracy on both analogous and dissimilar relations as well as the least drop on analogous relations. | Models | FewRel | TACRED | | | |----------|----------|----------|------|-----| | Accuracy | Drop | Accuracy | Drop | | | CRL | 90.2 | 5.9 | 92.1 | 1.4 | | CRECL | 90.6 | 5.3 | 91.2 | 3.8 | | Ours | 92.4 | 4.1 | 93.7 | 2.3 | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7. ✗ A2. Did you discuss any potential risks of your work? No, our paper is a foundational research. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Sections 4 And 5. ✓ B1. Did you cite the creators of artifacts you used? Sections 4 and 5. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The artifacts that we use are all public. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 5. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The datasets that we use are all public ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? The artifacts that we use are all public. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 5. ## C ✓ **Did You Run Computational Experiments?** Section 5 And Appendix B. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix B. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 5 and Appendix B. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix B. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

das-etal-2023-improving

Improving Pretraining Techniques for Code-Switched {NLP}

https://aclanthology.org/2023.acl-long.66

Pretrained models are a mainstay in modern NLP applications. Pretraining requires access to large volumes of unlabeled text. While monolingual text is readily available for many of the world{'}s languages, access to large quantities of code-switched text (i.e., text with tokens of multiple languages interspersed within a sentence) is much more scarce. Given this resource constraint, the question of how pretraining using limited amounts of code-switched text could be altered to improve performance for code-switched NLP becomes important to tackle. In this paper, we explore different masked language modeling (MLM) pretraining techniques for code-switched text that are cognizant of language boundaries prior to masking. The language identity of the tokens can either come from human annotators, trained language classifiers, or simple relative frequency-based estimates. We also present an MLM variant by introducing a residual connection from an earlier layer in the pretrained model that uniformly boosts performance on downstream tasks. Experiments on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), involving four code-switched language pairs (Hindi-English, Spanish-English, Tamil-English, Malayalam-English) yield relative improvements of up to 5.8 and 2.7 F1 scores on QA (Hindi-English) and SA (Tamil-English), respectively, compared to standard pretraining techniques. To understand our task improvements better, we use a series of probes to study what additional information is encoded by our pretraining techniques and also introduce an auxiliary loss function that explicitly models language identification to further aid the residual MLM variants.

# Improving Pretraining Techniques For Code-Switched Nlp Richeek Das∗1, Sahasra Ranjan∗1, Shreya Pathak2**, Preethi Jyothi**1 1Indian Institute of Technology Bombay 2Deepmind ## Abstract Pretrained models are a mainstay in modern NLP applications. Pretraining requires access to large volumes of unlabeled text. While monolingual text is readily available for many of the world's languages, access to large quantities of code-switched text (i.e., text with tokens of multiple languages interspersed within a sentence) is much more scarce. Given this resource constraint, the question of how pretraining using limited amounts of code-switched text could be altered to improve performance for code-switched NLP becomes important to tackle. In this paper, we explore different masked language modeling (MLM) pretraining techniques for code-switched text that are cognizant of language boundaries prior to masking. The language identity of the tokens can either come from human annotators, trained language classifiers, or simple relative frequencybased estimates. We also present an MLM variant by introducing a residual connection from an earlier layer in the pretrained model that uniformly boosts performance on downstream tasks. Experiments on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), involving four code-switched language pairs (Hindi-English, Spanish-English, Tamil-English, Malayalam-English) yield relative improvements of up to 5.8 and 2.7 F1 scores on QA (Hindi-English) and SA (TamilEnglish), respectively, compared to standard pretraining techniques. To understand our task improvements better, we use a series of probes to study what additional information is encoded by our pretraining techniques and also introduce an auxiliary loss function that explicitly models language identification to further aid the residual MLM variants. ## 1 Introduction Multilingual speakers commonly switch between languages within the confines of a conversation or a sentence. This linguistic process is known as codeswitching or code-mixing. Building computational models for code-switched inputs is very important in order to cater to multilingual speakers across the world (Zhang et al., 2021). Multilingual pretrained models such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) appear to be a natural choice to handle code-switched inputs. However, prior work demonstrated that representations directly extracted from pretrained multilingual models are not very effective for code-switched tasks (Winata et al., 2019). Pretraining multilingual models using code-switched text as an intermediate task, prior to task-specific finetuning, was found to improve performance on various downstream code-switched tasks (Khanuja et al., 2020a; Prasad et al., 2021a). Such an intermediate pretraining step relies on access to unlabeled code-switched text, which is not easily available in large quantities for different language pairs. This prompts the question of how pretraining could be made more effective for code-switching within the constraints of limited amounts of code-switched text.1 In this work, we propose new pretraining techniques for code-switched text by focusing on two fronts: a) modified pretraining objectives that explicitly incorporate information about codeswitching (detailed in Section 2.1) and b) architectural changes that make pretraining with codeswitched text more effective (detailed in Section 2.2). Pretraining objectives. The predominant objective function used during pretraining is masked language modeling (MLM) that aims to reconstruct randomly masked tokens in a sentence. We will henceforth refer to this standard MLM objective as STDMLM. Instead of randomly masking tokens, we propose masking the tokens straddling language boundaries in a code-switched sentence; language boundaries in a sentence are characterized by two words of different languages. We refer to this objective as SWITCHMLM. A limitation of this technique is that it requires language identification (LID) of the tokens in a code-switched sentence. LID tags are not easily obtained, especially when dealing with transliterated (Romanized) forms of tokens in other languages. We propose a surrogate for SWITCHMLM called FREQMLM that infers LID tags using relative counts from large monolingual corpora in the component languages. Architectural changes. Inspired by prior work that showed how different layers of models like mBERT specifically encode lexical, syntactic and semantic information (Rogers et al., 2020), we introduce a regularized residual connection from an intermediate layer that feeds as input into the MLM head during pretraining. We hypothesize that creating a direct connection from a lower layer would allow for more language information to be encoded within the learned representations. To more explicitly encourage LID information to be encoded, we also introduce an auxiliary LID-based loss using representations from the intermediate layer where the residual connection is drawn. We empirically verify that our proposed architectural changes lead to representations that are more language-aware by using a set of probing techniques that measure the switching accuracy in a code-switched sentence. With our proposed MLM variants, we achieve consistent performance improvements on two natural language understanding tasks, factoidbased Question Answering (QA) in Hindi-English and Sentiment Analysis (SA) in four different language pairs, Hindi-English, Spanish-English, Tamil-English and Malayalam-English. Sections 3 and 4 elaborate on datasets, experimental setup and our main results, along with accompanying analyses including probing experiments. Our code and relevant datasets are available at the following link: https://github.com/ csalt-research/code-switched-mlm. ## 2 Methodology 2.1 Mlm Pretraining Objectives In the Standard MLM objective (Devlin et al., 2019) that we refer to as STDMLM, a fixed percentage (typically 15%) of tokens in a given sentence are marked using the [MASK] token and the objective is to predict the [MASK] tokens via an output softmax over the vocabulary. Consider an input sentence X = x1*, . . . , x*n with n tokens, a predetermined masking fraction f and an n-dimensional bit vector S = {0, 1} nthat indicates whether or not a token is allowed to be replaced with [MASK]. A masking function M takes X, f and S as its inputs and produces a new token sequence Xmlm as its output Xmlm = M(*X, S, f*) where Xmlm denotes the input sentence X with f% of the maskable tokens (as deemed by S) randomly replaced with [MASK]. For STDMLM, S = {1} n which means that any of the tokens in the sentence are allowed to be masked. In our proposed MLM techniques, we modify S to selectively choose a set of maskable tokens. ## 2.1.1 Switchmlm SWITCHMLM is informed by the transitions between languages in a code-switched sentence. Consider the following Hindi-English code-switched sentence and its corresponding LID tags: $$\begin{array}{l l l l l l}{{\mathrm{pp}}}&{{\mathrm{me}}}&{{\mathrm{bag}}}&{{\mathrm{me}}}&{{\mathrm{rakha}}}&{{\mathrm{hai}}}\\ {{\mathrm{HI}}}&{{\mathrm{EN}}}&{{\mathrm{HI}}}&{{\mathrm{HI}}}&{{\mathrm{HI}}}\end{array}$$ For SWITCHMLM, we are only interested in potentially masking those words that surround language transitions. S is determined using information about the underlying LID tags for all tokens. In the example above, these words would be "Laptop", "mere", "bag" and "me". Consequently, S for this example would be S = [1, 1, 1, 1, 0, 0]. LID information is not readily available for many language pairs. Next, in FREQMLM, we extract proxy LID tags using counts derived from monolingual corpora for the two component languages. 2.1.2 FREQMLM For a given language pair, one requires access to LID-tagged text or an existing LID tagger to implement SWITCHMLM. LID tags are hard to infer especially when dealing with transliterated or Romanized word forms. To get around this dependency, we try to assign LID tags to the tokens only based on relative frequencies obtained from monolingual corpora in the component languages. S = F(X, Cen, Clg) = {0, 1} n where F assigns 1 to those tokens that straddle language boundaries and LIDs are determined for each token based on their relative frequencies in a monolingual corpus of the embedded language (that we fix as English) Cen and a monolingual corpus of the matrix language Clg. For a given token x, we define nll_en and nll_- lg as negative log-likelihoods of the relative frequencies of x appearing in Cen and Clg, respectively. nll values are set to -1 if the word does not appear in the corpus or if the word has a very small count and yields very high nll values (greater than a fixed threshold that we arbitrarily set to ln 10). The subroutine to assign LIDs is defined as follows: def Assign_LID(nll_en, nll_lg): if nll_en == -1 and nll_lg == -1: return OTHER elif nll_en != -1 and nll_lg == -1: return EN elif nll_en == -1 and nll_lg != -1: return LG elif nll_lg + ln(10) < nll_en: return LG elif nll_en + ln(10) < nll_lg: return EN elif nll_lg <= nll_en : return AMB - LG elif nll_en < nll_lg : return AMB - EN else : return OTHER $$\begin{array}{r}{\bot\bot\vdash\bot}\\ {}\\ {\bot\bot\vdash\Box}\\ {}\\ {\bot\bot\vdash\Box}\\ {}\\ {\bot\bot\vdash\Box}\end{array}$$ Here, AMB-LG, AMB-EN refer to ambiguous tokens that have reasonable counts but are not sufficiently large enough to be confidently marked as either EN or LG tokens. Setting AMB-EN to EN and AMB-LG to LG yielded the best results and we use this mapping in all our FREQMLM experiments. (Additional experiments with other FREQMLM variants by treating the ambiguous tokens separately are described in Appendix C.2.) ## 2.2 Architectural Modifications In Section 2.1, we presented new MLM objectives that mask tokens around language transitions (or switch-points) in a code-switched sentence. The main intuition behind masking around switchpoints was to coerce the model to encode information about possible switch-point positions in a sentence. (Later, in Section 4.2, we empirically verify this claim using a probing classifier with representations from a SWITCHMLM model compared to an STDMLM model.) We suggest two architectural changes that could potentially help further exploit switch-point information in the code-switched text. ![2_image_0.png](2_image_0.png) Prior studies have carried out detailed investigations of how BERT works and what kind of information is encoded within representations in each of its layers (Jawahar et al., 2019; Liu et al., 2019; Rogers et al., 2020). These studies have found that lower layers encode information that is most taskinvariant, final layers are the most task-specific and the middle layers are most amenable to transfer. This suggests that language information could be encoded in any of the lower or middle layers. To act as a direct conduit to this potential source of language information during pretraining, we introduce a simple residual connection from an intermediate layer that is added to the output of the last Transformer layer in mBERT. We refer to this modified mBERT as RESBERT. We also apply dropout to the residual connection which acts as a regularizer and is important for performance improvements. We derive consistent performance improvements in downstream tasks with RESBERT when the residual connections are drawn from a lower layer for SWITCHMLM. With STDMLM, we see significant improvements when residual connections are drawn from the later layers. (We elaborate on this further using probing experiments.) ## 2.2.2 Auxiliary Lid Loss With RESBERT, we add a residual connection to a lower or middle layer with the hope of gaining more direct access to information about potential switch-point transitions. We can further encourage this intermediate layer to encode language information by imposing an auxiliary LID-based loss. Figure 1 shows how token representations of an intermediate layer, from which a residual connection is drawn, feed as input into a multi-layer perceptron MLP to predict the LID tags of each token. To ensure that this LID-based loss does not destroy other useful information that is already present in the layer embeddings, we also add an L2 regularization for representations from all the layers to avoid large departures from the original embeddings. Given a sentence x1*, . . . , x*n, we have a corresponding sequence of bits y1*, . . . , y*n where yi = 1 represents that xilies at a language boundary. Then the new loss Laux can be defined as: $${\mathcal{L}}_{\mathrm{aux}}=\alpha\sum_{i=1}^{n}-\log\mathrm{MLP}(x_{i})+\beta\sum_{j=1}^{L}||{\bar{\mathbf{W}}}^{j}-\mathbf{W}^{j}||^{2}$$ where MLP(xi) is the probability with which MLP labels xi as yi, W¯ jrefers to the original embedding matrix corresponding to layer j, Wj refers to the new embedding matrix and α, β are scaling hyperparameters for the LID prediction and L2-regularization loss terms, respectively. ## 3 Experimental Setup 3.1 Datasets We aggregate real code-switched text from multiple sources, described in Appendix B, to create pretraining corpora for Hindi-English, SpanishEnglish, Tamil-English and Malayalam-English consisting of 185K, 66K, 118K and 34K sentences, respectively. We also extract code-switched data from a very large, recent Hindi-English corpus L3CUBE (Nayak and Joshi, 2022) consisting of 52.9M sentences scraped from Twitter. More details about L3CUBE are in Appendix B. For FREQMLM described in Section 2.1.2, we require a monolingual corpus for English and one for each of the component languages in the four code-switched language pairs. Large monolingual corpora will provide coverage over a wider vocabulary and consequently lead to improved LID predictions for words in code-switched sentences. We use counts computed from the following monolingual corpora to implement FREQMLM. English. We use OPUS-100 (Zhang et al., 2020), which is a large English-centric translation dataset consisting of 55 million sentence pairs and comprising diverse corpora including movie subtitles, GNOME documentation and the Bible. Spanish. We use a large Spanish corpus released by (Cañete et al., 2020) that contains 26.5 million sentences accumulated from 15 unlabeled Spanish text datasets spanning Wikipedia articles and European parliament notes. Hindi, Tamil and Malayalam. The Dakshina corpus (Roark et al., 2020) is a collection of text in both Latin and native scripts for 12 South Asian languages including Hindi, Tamil and Malayalam. Samanantar (Ramesh et al., 2022) is a large publicly-available parallel corpus for Indic languages. We combined Dakshina and Samanatar 2 datasets to obtain roughly 10M, 5.9M and 5.2M sentences for Hindi, Malayalam and Tamil respectively. We used this combined corpus to perform NLL-based LID assignment in FREQMLM. The Malayalam monolingual corpus is quite noisy with many English words appearing in the text. To implement FREQMLM for ML-EN, we use an alternate monolingual source called Aksharantar (Madhani et al., 2022). It is a large publiclyavailable transliteration vocabulary-based dataset for 21 Indic languages with 4.1M words specifically in Malayalam. We further removed common English words3from Aksharantar's Malayalam vocabulary to improve the LID assignment for FRE-QMLM. We used this dataset with an alternate LID assignment technique that only checks if a word exists, without accumulating any counts. (This is described further in Section 4.1.) ## 3.2 Sa And Qa Tasks We use the GLUECOS benchmark (Khanuja et al., 2020a) to evaluate our models for Sentiment Analysis (SA) and Question Answering (QA). GLUECOS provides an SA task dataset for Hindi-English and Spanish-English. The Spanish-English SA dataset (Vilares et al., 2016) consists of 2100, 211 2Samanantar dataset contains native Indic language text, we use the Indic-trans transliteration tool (Bhat et al., 2015) to get the romanized sentences and then combine with the Dakshina dataset 3https://github.com/first20hours/ google-10000-english | QA HI-EN | SA | | | | | | | | |-----------------------------|-------------|-------------|-------------|-------------|-------------|-------------|-------------|----------| | TA-EN | HI-EN | ML-EN | ES-EN | | | | | | | Method | F1 | F1 | F1 | | | | | | | (20 epochs) | (30 epochs) | (40 epochs) | | | | | | | | Baseline | 62.1 ±1.5 | 63.4 ±2.0 | 62.9 ±2.0 | 69.8±2.6 | 67.3±0.3 | 76.4±0.3 | 60.8±1.1 | | | STDMLM | 64.8 ±2.0 | 65.4 ±2.5 | 64 ±3.3 | 74.9±1.5 | 67.7±0.6 | 76.7±0.1 | 62.2±1.5 | | | mBERT | SWITCHMLM | 69 ±3.7 | 68.9 ±4.2 | 67 ±2.5 | - | 68.4±0.5 | - | 63.5±0.6 | | FREQMLM | 68.6±4.5 | 66.7±3.5 | 67.1±3.2 | 77.1±0.3 | 67.8±0.4 | 76.5±0.2 | 62.5±1.0 | | | STDMLM + RESBERT | 66.89 ± 3.0 | 64.69 ± 1.7 | 64.49 ± 2.0 | 775 ± 0.3 | 68.49 ± 0.0 | 76.69 ± 0.2 | 63.19 ± 1.1 | | | SW/FREQMLM + RESBERT | 68.82 ± 3.1 | 68.92 ± 3.0 | 68.12 ± 3.0 | 77.42 ± 0.3 | 68.92 ± 0.4 | 77.12 ± 0.2 | 63.72 ± 1.8 | | | SW/FREQMLM + RESBERT + Laux | 682 ± 3.0 | 68.92 ± 3.2 | 69.82 ± 3.0 | 77.62 ± 0.2 | 69.12 ± 0.4 | 77.22 ± 0.4 | 63.72 ± 1.5 | | | XLM-R | Baseline | 63.2±3.0 | 63.1±2.3 | 62.7±2.5 | 74.1±0.3 | 69.2±0.9 | 72.5±0.7 | 63.9±2.5 | | STDMLM | 64.4±2.1 | 64.7±2.8 | 66.4±2.3 | 76.0±0.1 | 71.3±0.2 | 76.5±0.4 | 64.4±1.8 | | | SWITCHMLM | 65.3±3.3 | 65.7±2.3 | 69.2±3.2 | - | 71.7±0.1 | - | 64.8±0.2 | | | FREQMLM | 60.8±5.3 | 62.4±4.3 | 63.4±4.4 | 76.3±0.4 | 71.6±0.6 | 75.3±0.3 | 64.1±1.1 | | and 211 examples in the training, development and test sets, respectively. The Hindi-English SA dataset (Patra et al., 2018) consists of 15K, 1.5K and 3K code-switched tweets in the training, development and test sets, respectively. The Tamil-English (Chakravarthi et al., 2020a) and Malayalam-English (Chakravarthi et al., 2020b) SA datasets are extracted from YouTube comments comprising 9.6K/1K/2.7K and 3.9K/436/1.1K examples in the train/dev/test sets, respectively. The Question Answering Hindi-English factoid-based dataset (Chandu et al., 2018a) from GLUECOS consists of 295 training and 54 test question-answercontext triples. Because of the unavailability of the dev set, we report QA results on a fixed number of training epochs i.e., 20, 30, and 40 epochs. ## 3.3 Res**Bert And Auxiliary Loss:** Implementation Details We modified the mBERT architectures for the three main tasks of masked language modeling, question answering (QA), and sequence classification by incorporating residual connections as outlined in Section 2.2.1. The MLM objective was used during pretraining with residual connections drawn from layers x ∈ {1, *· · ·* , 10} and a dropout rate of p = 0.5. The best layer to add a residual connection was determined by validation performance on the downstream NLU tasks. Since we do not have a development set for QA, we choose the same layer as chosen by SA validation for the QA task. The training process and hyperparameter details can be found in Appendix A. ## 4 Results And Analysis 4.1 Main Results Table 1 shows our main results using all our proposed MLM techniques applied to the downstream tasks QA and SA. We use F1-scores as an evaluation metric for both QA and SA. For QA, we report the average scores from the top 8-performing (out of 10) seeds, and for SA, we report average F1-scores from the top 10-performing seeds (out of 12). We observed that the F1 scores were notably poorer for one seed, likely due to the small test-sets for QA (54 examples) and SA (211 for Spanish-English). To safeguard against such outlier seeds, we report average scores from the top-K runs. We show results for two multilingual pretrained models, mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020).4 Improvements with MLM pretraining objectives. From Table 1, we note that STDMLM is always better than the baseline model (sans pretraining). Among the three MLM pretraining objectives, SWITCHMLM consistently outperforms both STDMLM and FREQMLM across both tasks. We observe statistical significance at p < 0.05 (with p-values of 0.01 and lower for some language pairs) using the Wilcoxon Signed Rank test when comparing F1 scores across multiple seeds using SWITCHMLM compared to STDMLM on both QA and SA tasks. As expected, FREQMLM acts as a surrogate to SWITCHMLM trailing behind it in perfor-4Results using residual connections and the auxiliary LID loss during pretraining are shown only for mBERT since the main motivation to use intermediate layers was derived from BERTology (Rogers et al., 2020). We leave this investigation for XLMR as future work. mance while outperforming STDMLM. Since Tamil-English and Malayalam-English pretraining corpora were not LID-tagged, we do not show SWITCHMLM numbers for these two language pairs and only report FREQMLM-based scores. For QA, we observe that FREQMLM hurts XLM-R while significantly helps mBERT in performance compared to STDMLM. We hypothesize that this is largely caused by QA having a very small train set (of size 295), in conjunction with XLM-R being five times larger than mBERT and the noise inherent in LID tags from FREQMLM (compared to SWITCHMLM). We note here that using FRE-QMLM with XLM-R for SA does not exhibit this trend since Hindi-English SA has a larger train set with 15K sentences. Considerations specific to FREQ**MLM.** The influence of SWITCHMLM and FREQMLM on downstream tasks depends both on (1) the amount of code-switched pretraining text and (2) the LID tagging accuracy. Malayalam-English (ML-EN) is an interesting case where STDMLM does not yield significant improvements over the baseline. This could be attributed to the small amount of real code-switched text in the ML-EN pretraining corpus (34K). Furthermore, we observe that FRE-QMLM fails to surpass STDMLM. This could be due to the presence of many noisy English words in the Malayalam monolingual corpus. To tackle this, we devise an alternative to the NLL LID-tagging approach that we call X-HIT. X-HIT only considers vocabularies of English and the matrix language, and checks if a given word appears in the vocabulary of English or the matrix language to mark its LID. Unlike NLL which is count-based, X-HIT only checks for the existence of a word in a vocabulary. This approach is particularly useful for language pairs where the monolingual corpus is small and unreliable. Appendix C.1 provides more insights about when to choose X-HIT over NLL. We report a comparison between the NLL and X-HIT LID-tagging approaches for ML-EN sentences in Table 2. Since X-HIT uses a clean dictionary instead of a noisy monolingual corpus for LID assignment, we see improved performance with X-HIT compared to NLL. However, given the small pretraining corpus for ML-EN, FREQMLM still underperforms compared to STDMLM. To assess how much noise can be tolerated in the LID tags derived via NLL, Table 3 shows the label distribution across true and predicted labels using | Model | F1 (max) | F1 (avg) | Std. Dev. | |------------------|------------|------------|-------------| | Baseline (mBERT) | 77.29 | 76.42 | 0.42 | | STDMLM | 77.39 | 76.67 | 0.48 | | FREQMLM (NLL) | 76.61 | 76.20 | 0.43 | | FREQMLM (X-HIT) | 77.29 | 76.46 | 0.43 | the NLL LID-tagging approach for Hindi-English. We observe that while a majority of HI and EN tokens are correctly labeled as being HI and EN tags, respectively, a fairly sizable fraction of tags totaling 18% and 17% for HI and EN, respectively, are wrongly predicted. This shows that FREQMLM performs reasonably well even in the presence of noise in the predicted LID tags. | True/Pred | HI | AMB-HI | EN | AMB-EN | OTHER | |-------------|-------|----------|-------|----------|---------| | HI | 71.75 | 10.26 | 6.05 | 7.36 | 4.58 | | EN | 7.69 | 5.97 | 63.41 | 19.64 | 3.29 | | OTHER | 25.07 | 10.11 | 7.76 | 6.51 | 50.56 | Table 3: Distribution of predicted tags by the NLL approach for given true tags listed in the first column. Note: Here the distribution is shown as percentages. ## Improvements With Architectural Modifications. As shown in Table 1, we observe consistent improvements using RESBERT particularly for SA. STDMLM gains a huge boost in performance when a residual connection is introduced. The best layer to use for a residual connection in SA tasks is chosen on the basis of the results on the dev set. We do not have a dev set for the QA HI-EN task. In this case, we choose the same layers used for the SA task to report results on QA. While the benefits are not as clear as with STDMLM, even SWITCHMLM marginally benefits from a residual connection on examining QA and SA results. Since LID tags are not available for TA-EN and ML-EN, we use FREQMLM pretraining with residual connections. Given access to LID tags, both HI-EN and ES-EN use SWITCHMLM pretraining with residual connections. SW/FRE-QMLM in Table 1 refers to either SWITCHMLM or FREQMLM pretraining depending on the language pair. We observe an interesting trend as we change the layer x ∈ {1, *· · ·* , 10} from which the residual connection is drawn, depending on the MLM objective. When RESBERT is used in conjunction with STDMLM, we see a gradual performance | Model | F1 (max) | F1 (avg) | Std. Dev. | |-------------------------|------------|------------|-------------| | STDMLM | 69.01 | 68.18 | 0.56 | | SWITCHMLM | 70.71 | 69.19 | 1.06 | | FREQMLM | 69.41 | 68.81 | 0.58 | | STDMLM + RESBERT9 | 69.48 | 68.99 | 0.60 | | SWMLM + RESBERT2 | 69.76 | 69.23 | 0.64 | | SWMLM + RESBERT2 + Laux | 69.66 | 69.29 | 0.25 | | HINGMBERT | 72.36 | 71.42 | 0.70 | gain as we go deeper down the layers. Whereas we find a slightly fluctuating response in the case of SWITCHMLM— here, it peaks at some early layer. The complete trend is elaborated in Appendix D. The residual connections undoubtedly help. We see an overall jump in performance from STDMLM to RESBERT + STDMLM and from SWITCHMLM to RESBERT + SWITCHMLM. The auxiliary loss over switch-points described in Section 2.2.2 aims to help encode switch-point information more explicitly. As with RESBERT, we use the auxiliary loss with SWITCHMLM pretraining for HI-EN and ES-EN, and with FRE-QMLM pretraining for TA-EN and ML-EN. As shown in Table 1, SW/FREQMLM + RESBERT + Laux yields our best model for code-switched mBERT consistently across all SA tasks. ## Results On Alternate Pretraining Corpus. To assess the difference in performance when using pretraining corpora of varying quality, we extract roughly the same number of Hindi-English sentences from L3CUBE (185K) as is present in the Hindi-English pretraining corpus we used for Table 1. Roughly 45K of these 185K sentences have human-annotated LID tags. For the remaining sentences, we use the GLUECOS LID tagger (Khanuja et al., 2020a). Table 4 shows the max and mean F1-scores for HI-EN SA for all our MLM variants. These numbers exhibit the same trends observed in Table 1. Also, since the L3CUBE dataset is much cleaner than the 185K dataset we used previously for HindiEnglish, we see a notable performance gain in Table 4 for HI-EN compared to the numbers in Table 1. Nayak and Joshi (2022) further provide an mBERT model HINGMBERT pretrained on the entire L3CUBE dataset of 52.93M sentences. This model outperforms all the mBERT pretrained models, confirming that a very large amount of pretrain- ## 4.2 Probing Experiments We use probing classifiers to test our claim that the amount of switch-point information encoded in the neural representations from specific layers has increased with our proposed pretraining variants compared to STDMLM. Alain and Bengio (2016) first introduced the idea of using linear classifier probes for features at every model layer, and Kim et al. (2019) further developed new probing tasks to explore the effects of various pretraining objectives in sentence encoders. Linear Probing. We first adopt a standard linear probe to check for the amount of switch-point information encoded in neural representations of different model layers. For a sentence x1*, . . . , x*n, consider a sequence of bits y1*, . . . , y*n referring to switch-points where yi = 1 indicates that xi is at a language boundary. The linear probe is a simple feedforward network that takes layer-wise representations as its input and is trained to predict switch-points via a binary cross-entropy loss. We train the linear probe for around 5000 iterations. Conditional Probing. Linear probing cannot detect when representations are more predictive of switch-point information in comparison to a baseline. Hewitt et al. (2021) offer a simple extension of the theory of usable information to propose conditional probing. We adopt this method for our task and define performance in terms of predicting the switch-point sequence as: ## Perf(F[B(X), Φ(X)]) − Perf(F([B, 0])) where X is the input sequence of tokens, B is the STDMLM pretrained model, ϕ is the model trained with one of our new pretraining techniques, f is a linear probe, [·, ·] denotes concatenation of embeddings and Perf is any standard performance metric. We set Perf to be a soft Hamming Distance between the predicted switch-point sequence and the ground-truth bit sequence. To train f, we follow the same procedure outlined in Section 4.2, except we use concatenated representations from two models as its input instead of a single representation. ## 4.2.1 Probing Results Figure 2 shows four salient plots using linear probing and conditional probing. In Figure 2a, we observe that the concatenated representations from ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) models trained with STDMLM and SWITCHMLM carry more switch-point information than using STDMLM alone. This offers an explanation for the task-specific performance improvements we observe with SWITCHMLM. With greater amounts of switch-point information, SWITCHMLM models arguably tackle the code-switched downstream NLU tasks better. From Figure 2c, we observe that the intermediate layer (9) from which the residual connection is drawn carries a lot more switch-point information than the final layer in STDMLM. In contrast, from Figure 2d, we find this is not true for SWITCHMLM models, where there is a very small difference between switch-point information encoded by an intermediate and final layer. This might explain to some extent why we see larger improvements using a residual connection with STDMLM compared to SWITCHMLM (as discussed in Section 4.1). Figure 2b shows that adding a residual connection from layer 9 of an STDMLM-trained model, that is presumably rich in switch-point information, provides a boost to switch-point prediction accuracy compared to using STDMLM model alone. We note here that the probing experiments in this section offer a post-hoc analysis of the effectiveness of introducing a skip connection during pretraining. We do not actively use probing to choose the best layer to add a skip connection. ## 5 Related Work While not related to code-switching, there has been prior work on alternatives or modifications to pretraining objectives like MLM. Yamaguchi et al. (2021) is one of the first works to identify the lack of linguistically intuitive pretraining objectives. They propose new pretraining objectives which perform similarly to MLM given a similar pretrain duration. In contrast, Clark et al. (2020) sticks to the standard MLM objective, but questions whether masking only 15% of tokens in a sequence is sufficient to learn meaningful representations. Wettig et al. (2022) maintains that higher masking up to even 80% can preserve model performance on downstream tasks. All of the aforementioned methods are static and do not exploit a partially trained model to devise better masking strategies on the fly. Yang et al. (2022) suggests time-invariant masking strategies which adaptively tune the masking ratio and content in different training stages. Ours is the first work to offer both MLM modifications and architectural changes aimed specifically at codeswitched pretraining. Prior work on improving code-switched NLP has focused on generative models of code-switched text to use as augmentation (Gautam et al., 2021; Gupta et al., 2021; Tarunesh et al., 2021a), merging real and synthetic code-switched text for pretraining (Khanuja et al., 2020b; Santy et al., 2021b), intermediate task pretraining including MLM-style objectives (Prasad et al., 2021b). However, no prior work has provided an in-depth investigation into how pretraining using code-switched text can be altered to encode information about language transitions within a code-switched sentence. We show that switch-point information is more accurately preserved in models pretrained with our proposed techniques and this eventually leads to improved performance on code-switched downstream tasks. ## 6 Conclusion Pretraining multilingual models with codeswitched text prior to finetuning on task-specific data has been found to be very effective for code-switched NLP tasks. In this work, we focus on developing new pretraining techniques that are more language-aware and make effective use of limited amounts of real code-switched text to derive performance improvements on two downstream tasks across multiple language pairs. We design new pretraining objectives for code-switched text and suggest new architectural modifications that further boost performance with the new objectives in place. In future work, we will investigate how to make effective use of pretraining with synthetically generated code-switched text. ## Acknowledgements The last author would like to gratefully acknowledge a faculty grant from Google Research India supporting research on models for code-switching. The authors are thankful to the anonymous reviewers for constructive suggestions that helped improve the submission. ## Limitations Our current FREQMLM techniques tend to fail on LID predictions when the linguistic differences between languages are small. For example, English and Spanish are quite close: (1) they are written in the same script, (2) English and Spanish share a lot of common vocabulary. This can confound FREQMLM. The strategy to select the best layer for drawing residual connections in RESBERT is quite tedious. For a 12-layer mBERT, we train 10 RESBERT models with residual connections from some intermediate layer x ∈ {1, *· · ·* , 10} and choose the best layer based on validation performance. This is quite computationally prohibitive. We are considering parameterizing the layer choice using gating functions so that it can be learned without having to resort to a tedious grid search. If the embedded language in a code-switched sentence has a very low occurrence, we will have very few switch-points. This might reduce the number of maskable tokens to a point where even masking all the maskable tokens will not satisfy the overall 15% masking requirement. However, we never faced this issue. 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A corpus of english-hindi code-mixed tweets for sarcasm detection. Ishan Tarunesh, Syamantak Kumar, and Preethi Jyothi. 2021a. From machine translation to code-switching: Generating high-quality code-switched text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3154– 3169. Ishan Tarunesh, Syamantak Kumar, and Preethi Jyothi. 2021b. From machine translation to code-switching: Generating high-quality code-switched text. In *Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*, pages 3154–3169, Online. Association for Computational Linguistics. David Vilares, Miguel A. Alonso, and Carlos GómezRodríguez. 2016. EN-ES-CS: An English-Spanish code-switching Twitter corpus for multilingual sentiment analysis. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 4149–4153, Portorož, Slovenia. European Language Resources Association (ELRA). Alexander Wettig, Tianyu Gao, Zexuan Zhong, and Danqi Chen. 2022. Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, and Pascale Fung. 2019. Code-switched language models using neural based synthetic data from parallel sentences. In *Proceedings of the 23rd Conference on Computational Natural Language Learning* (CoNLL), pages 271–280, Hong Kong, China. Association for Computational Linguistics. Atsuki Yamaguchi, George Chrysostomou, Katerina Margatina, and Nikolaos Aletras. 2021. Frustratingly simple pretraining alternatives to masked language modeling. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3116–3125, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Dongjie Yang, Zhuosheng Zhang, and Hai Zhao. 2022. Learning better masking for better language model pre-training. Biao Zhang, Philip Williams, Ivan Titov, and Rico Sennrich. 2020. Improving massively multilingual neural machine translation and zero-shot translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1628– 1639, Online. Association for Computational Linguistics. Daniel Yue Zhang, Jonathan Hueser, Yao Li, and Sarah Campbell. 2021. Language-agnostic and languageaware multilingual natural language understanding for large-scale intelligent voice assistant application. In 2021 IEEE International Conference on Big Data (Big Data), pages 1523–1532. IEEE. ## A Training Details We employed the mBERT and XLM-R models for our experiments. The mBERT model has 178 million parameters and 12 transformer layers, while the XLM-R model has 278 million parameters and 24 transformer layers. AdamW optimizer (Loshchilov and Hutter, 2019) and a linear scheduler were used in all our experiments, which were conducted on a single NVIDIA A100 Tensor Core GPU. For the pretraining step, we utilized a batch size of 4, a gradient accumulation step of 20, and 4 epochs for the mBERT base model. For the XLM-R base model, we set the batch size to 8 and the gradient accumulation step to 4. For the Sentiment Analysis task, we used a batch size of 8, a learning rate of 5e-5, and a gradient accumulation step of 1 for the mBERT base model. Meanwhile, we set the batch size to 32 and the learning rate to 5e-6 for the XLMR base model. For the downstream task of Question Answering, we used the same hyperparameters for both mBERT and XLM-R: a batch size of 4 and a gradient accumulation step of 10. Results were reported for multiple epochs, as stated in Section 4.1. All the aforementioned hyperparameters were kept consistent for all language pairs. In the auxiliary LID loss-based experiments mentioned in Section 3.3, we did not perform a search for the best hyperparameters. Instead, we set α to 5e-2 and β to 5e-4, where α and β are defined in Section 2.2.2. ## B Pretraining Dataset We use the ALL-CS (Tarunesh et al., 2021b) corpus, which consists of 25K Hindi-English LID-tagged code-switched sentences. We combine this corpus with code-switched text data from prior work Singh et al. (2018); Swami et al. (2018); Chandu et al. (2018b); Patwa et al. (2020); Bhat et al. (2017); Patro et al. (2017) resulting in a total of 185K LID-tagged Hindi-English code-switched sentences. For Spanish-English code-switched text data, we pooled data from prior work Patwa et al. (2020); Solorio et al. (2014); AlGhamdi et al. (2016); Aguilar et al. (2018); Vilares et al. (2016) to get a total of 66K | CS Sentence: | Maduraraja | trailer | erangiyapo | veendum | kaanan | vannavar | undel | evide | likiko | |-----------------|--------------|-----------|--------------|-----------|----------|------------|---------|---------|----------| | NLL LID tags: | OTHER | EN | OTHER | ML | ML | OTHER | ML | ML | ML | | X-HIT LID tags: | ML | EN | ML | ML | ML | ML | ML | ML | ML | Table 5: LID assignment comparison for NLL and X-HIT sentences. These sentences have ground-truth LID tags associated with them. We pooled 118K Tamil-English code-switched sentences from Chakravarthi et al. (2020b, 2021); Banerjee et al. (2018); Mandl et al. (2021) and 34K Malayalam-English code-switched sentences from Chakravarthi et al. (2020a, 2021); Mandl et al. (2021). These datasets do not have ground-truth LID tags and high-quality LID tagger for TA-EN and MLEN are not available. Hence, we do not perform SWITCHMLM experiments for these language pairs. We will refer to the combined datasets for HindiEnglish, Spanish-English, Malayalam-English, and Tamil-English code-switched sentences as HI-EN COMBINED-CS , ES-HI COMBINED-CS , ML-HI COMBINED-CS , and TA-EN COMBINED-CS respectively. Nayak and Joshi (2022) released the L3CubeHingCorpus and HingLID Hindi-English codeswitched datasets. L3Cube-HingCorpus is a codeswitched Hindi-English dataset consisting of 52.93M sentences scraped from Twitter. L3Cube-HingLID is a Hindi-English code-switched language identification dataset which consists of 31756, 6420, and 6279 train, test, and validation samples, respectively. We extracted roughly 140k sentences from L3CubeHingCorpus with a similar average sentence length as the HI-EN COMBINED-CS dataset, assigned LID tags using the GLUECOS LID tagger (Khanuja et al., 2020a), and combined it with the 45k sentences of L3Cube-HingLID to get around 185K sentences in total. We use this L3CUBE -185k dataset in Section 4.1 to examine the effects of varying quality of pretraining corpora. ## C Freqmlm C.1 X-Hit **Lid Assignment** The Malayalam-English code-switched dataset (MLEN COMBINED-CS ) has fairly poor Roman transliterations of Malayalam words. This makes it difficult for the NLL approach to assign the correct LID to these words since it is based on the likelihood scores of the word in the monolingual dataset. Especially for rare Malayalam words in the sentence, the NLL approach fails to assign the correct LID and instead ends up assigning a high number of "OTHER" tags. The X-HIT approach described in Section 4.1 addresses this issue. X-HIT first checks the occurrence of the word in Malayalam vocabulary, then checks if it is an English word. Since we have a high-quality English monolingual dataset, we can be confident that the words that are left out are rare or poorly transliterated Malayalam words, and hence are tagged ML. As an illustration, we compare the LID tags assigned to the example Malayalam-English code-switched sentence *Maduraraja trailer erangiyapo veendum* kaanan vannavar undel evide likiko in Table 5 using NLL and X-HIT, with the latter being more accurate. ## C.2 Masking Strategies For Ambiguous Tokens In the NLL approach of FREQMLM described in Section 2.1.2, we assign ambiguous (AMB) LID tokens to words when it is difficult to differentiate between nll scores with confidence. To make use of AMB tokens, we introduce a probabilistic masking approach that classifies the words based on their ambiguity at the switch-points. - Type 0: If none of the words at the switch-point are marked ambiguous, mask them with prob. p0 - Type 1: If one of the words at the switch-point is marked ambiguous, mask it with prob. p1 - Type 2: If both the words are marked ambiguous, mask them with prob. p2 We try out different masking probabilities, which sum up to p = 0.15. Say we mask tokens of the words of Type 0, 1, and 2 in the ratio r0, r1, r2 and the counts of these words in the dataset are n0, n1, n2 respectively, then the masking probabilities p0, p1, p2 are determined by the following equation: ## P0N0 + P1N1 + P2N2 = P(N0 + N1 + N2) It is easy to see that the probabilities should be in the same proportion as our chosen masking ratios, i.e., p0 : p1 : p2 :: r0 : r1 : r2. We report the results we obtained for this experiment in Table 6. | r0 : r1 : r2 | F1 (max) | F1 (avg) | Std. Dev. | |----------------|------------|------------|-------------| | 1 : 1 : 1 | 72.22 | 67.09 | 3.43 | | 1 : 1.5 : 2 | 68.27 | 64.16 | 2.74 | | 2 : 1.5 : 1 | 65.1 | 61.71 | 2.23 | Table 6: FREQMLM QA scores (fine-tuned on 40 epochs) for experiments incorporating AMB tokens Test Results **Val Results** Method Max Avg Stdev **Avg Stdev** | layer 1 | 68.2 | 67.7 | 0.4 | 63.3 | 0.3 | | | |---------------------|--------|---------|-------|--------|-------|------|-----| | layer 2 | 68.5 | 67.9 | 0.8 | 63.6 | 0.3 | | | | layer 3 | 69.3 | 68.2 | 1 | 63.6 | 0.5 | | | | layer 4 | 68.8 | 68.2 | 0.6 | 63.6 | 0.4 | | | | layer 5 | 69.6 | 68.7 | 0.7 | 63.3 | 0.5 | | | | layer 6 | 68.9 | 68.3 | 0.5 | 63.6 | 0.2 | | | | layer 7 | 69.5 | 68.3 | 1.1 | 63.9 | 0.1 | | | | layer 8 | 69.5 | 68.5 | 0.7 | 63.8 | 0.2 | | | | layer 9 | 68.4 | 68.4 | 0 | 64.1 | 0.3 | | | | layer 10 | 69.4 | 68.8 | 0.4 | 64 | 0.2 | | | | STDMLM + RESBERT | | layer 1 | 68.8 | 68 | 0.6 | 63.2 | 0.4 | | layer 2 | 69.4 | 68.9 | 0.5 | 63.8 | 0.5 | | | | layer 3 | 69 | 68.4 | 0.4 | 63.4 | 0.3 | | | | layer 4 | 68.6 | 68.1 | 0.4 | 63.7 | 0.6 | | | | layer 5 | 68.6 | 68.2 | 0.3 | 63.8 | 0.4 | | | | layer 6 | 68.5 | 67.8 | 0.5 | 63.6 | 0.4 | | | | layer 7 | 69.9 | 68.1 | 1.3 | 63.6 | 0.5 | | | | layer 8 | 68.9 | 68.2 | 0.8 | 63.6 | 0.2 | | | | layer 9 | 69.5 | 68.6 | 0.7 | 62.9 | 0.1 | | | | layer 10 | 68.8 | 68 | 0.6 | 63.7 | 0.2 | | | | SWITCHMLM + RESBERT | | | | | | | | Table 7: RESBERT results for COMBINED-CS (HIEN language pair). We choose the best layer to draw a residual connection based on the results achieved on the Validation set of the SA Task. ## D Res**Bert Results** Table 7 presents our results for STDMLM and SWITCHMLM for RESBERT on all layers x ∈ {1, *· · ·* , 10} with a dropout rate of p = 0.5. The trend of results achieved with RESBERT clearly depends on the type of masking strategy used. In the case of STDMLM + RESBERT, we see a gradual improvement in test performance as we go down the residually connected layers, eventually peaking at layer 10. On the other hand, we do not see a clear trend in the case of SWITCHMLM + RESBERT. In both cases, we select the best layer to add a residual connection based on its performance on the SA validation set. We do a similar set of experiments for the TA-EN language pair to choose the best layer, which turns out to be layer 5 for STDMLM and layer 9 for SWITCHMLM pretraining. For the language pairs ES-EN, HI-EN (L3CUBE ), and ML-EN, we do not search for the best layer for RESBERT. As a general rule of thumb, we use layer 2 for SWITCHMLM and layer 9 for STDMLM pretraining of RESBERT for these language pairs. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? We discussed the limitations of work in section 7 of the paper. ✗ A2. Did you discuss any potential risks of your work? Our work does not have any immediate risks as it is related to improving pretraining techniques for code-switched NLU. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstraction and Introduction in Section 1 of the paper summarize the main paper's claim. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Yes, we use multiple datasets that we described in Section 3.1. Apart from the dataset, we use pretrained mBERT and XLMR models described in Section 1. In section 3, we cite the GLUECoS benchmark to test and evaluate our approach and the Indic-trans tool to transliterate the native Indic language sentences in the dataset. ✓ B1. Did you cite the creators of artifacts you used? We cite the pretrained models in section 1, the GLUECos benchmark, the Indic-trans tool, and the datasets in section 3 of the paper. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No, we used open-source code, models and datasets for all our experiments. Our new code will be made publicly available under the permissive MIT license. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Yes, the usage of the existing artifacts mentioned above was consistent with their intended use. We use the mBERT and XLMR pretrained models as the base model, the dataset mentioned to train and test our approach, GLUECoS as the fine-tuning testing benchmark, and Indic-trans for transliteration of the native Indic language sentences. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We used publicly available code-switched datasets containing content scraped from social media. We hope that the dataset creators have taken steps to check the data for offensive content. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No, we did not create any artifacts. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Yes, we report these relevant statistics for the dataset that we use in section 3. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Yes, we ran computational experiments to improve the pretraining approach for Code-Switched NLU. The description, setup, and results are described in sections 2, 3, and 4. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Yes, we reported all these details in Appendix section A. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Yes, we reported all these details in Appendix section A. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We report the average F1 scores for our major experiments over multiple seeds, which we mentioned in the result section 4. We report max, average, and standard deviation for various other experiments in section 4 over multiple seeds. Probing tasks described in sections 4.2 and 4.3 are reported on a single run as they involve training a small linear layer and not the full BERT/XLMR model. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? We used multiple existing packages, viz. GLUECoS, HuggingFace Transformers, and Indic-Trans. We report the parameter settings and models in Appendix section A. We plan to release the code after acceptance. ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left Blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

wang-etal-2023-theory

A Theory of Unsupervised Speech Recognition

https://aclanthology.org/2023.acl-long.67

Unsupervised speech recognition ({pasted macro {`}ASRU{'}}/) is the problem of learning automatic speech recognition (ASR) systems from \textit{unpaired} speech-only and text-only corpora. While various algorithms exist to solve this problem, a theoretical framework is missing to study their properties and address such issues as sensitivity to hyperparameters and training instability. In this paper, we proposed a general theoretical framework to study the properties of {pasted macro {`}ASRU{'}}/ systems based on random matrix theory and the theory of neural tangent kernels. Such a framework allows us to prove various learnability conditions and sample complexity bounds of {pasted macro {`}ASRU{'}}/. Extensive {pasted macro {`}ASRU{'}}/ experiments on synthetic languages with three classes of transition graphs provide strong empirical evidence for our theory (code available at \url{https://github.com/cactuswiththoughts/UnsupASRTheory.gitcactuswiththoughts/UnsupASRTheory.git}).

# A Theory Of Unsupervised Speech Recognition Liming Wang1**, Mark Hasegawa-Johnson**1and **Chang D. Yoo**2 1University of Illinois Urbana-Champaign 2Korea Advanced Institute of Science Technology {lwang114,jhasegaw}@illinois.edu, [email protected] ## Abstract Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from *unpaired* speech-only and text-only corpora. While various algorithms exist to solve this problem, a theoretical framework is missing to study their properties and address such issues as sensitivity to hyperparameters and training instability. In this paper, we proposed a general theoretical framework to study the properties of ASR-U systems based on random matrix theory and the theory of neural tangent kernels. Such a framework allows us to prove various learnability conditions and sample complexity bounds of ASR-U. Extensive ASR-U experiments on synthetic languages with three classes of transition graphs provide strong empirical evidence for our theory (code available at cactuswiththoughts/UnsupASRTheory.git). ## 1 Introduction Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from *unpaired* speech-only and textonly corpora. Such a system can not only significantly reduce the amount of annotation resources required for training state-of-the-art ASR system, but serve as a bridge between spoken and written language understanding tasks in the low-resource setting. Since its first proposal (Liu et al., 2018), it has seen remarkable progress and the current best system (Baevski et al., 2021) has achieved comparable performance to systems trained with paired data on various languages. However, there are several mysteries surrounding ASR-U, which potentially hinder the future development of such systems. In particular, prior experiments have found that training the current state-of-the-art ASR-U model, wav2vec-U (Baevski et al., 2021), requires careful tuning over the weights of various regularization losses to avoid converging to bad local optima and that even despite extensive regularization weight tuning, wav2vec-U may still fail to converge (Ni et al., 2022). Therefore, it remains a mystery whether or when unpaired speech and text data indeed provide sufficient information for learning an ASR system. Another mystery is whether the success of existing ASR-U models based on generative adversarial net (GAN) (Goodfellow et al., 2014) is sufficiently explained by the GAN objective function per se, or also requires other factors, such as randomness in training, quirks in the data used and careful domain-specific hyper-parameter settings, etc. In this paper, we provide a theoretical analysis of ASR-U to investigate the mysteries surrounding ASR-U. First, we prove learnability conditions and sample complexity bounds that crucially depend on the eigenvalue spacings of the transition probability matrix of the spoken language. Random matrix theory shows that such learnability conditions are achievable with high probability. Next, we study the gradient flow of GAN-based ASR-U and provide conditions under which the generator minimizing the GAN objective converges to the true generator. Finally, to verify our theory empirically, we perform GAN-based ASR-U experiments on three classes of synthetic languages. Not only do we observe phase transition phenomena predicted by our theory, but we achieve stable training with lower test word error rate by several modifications of the existing state-of-the-art ASR-U system inspired by our theory. ## 2 Problem Formulation General formulation The training data comprise a set of sequences of quantized speech vectors, and a set of sequences of phoneme labels. The data are unpaired: there is no label sequence that matches any one of the speech sequences. The data are, however, matched in distribution. Let PXi (x) and PYj (y) be the probability mass functions (pmfs) of the i th speech vector in a sequence, x ∈ X, and the 1192 ![1_image_0.png](1_image_0.png) j th phoneme in a sequence, y ∈ Y, respectively: the requirement that they are *matched in distribution* is the requirement that there exists some generator function O : (X, Y) → {0, 1} such that $$\sum_{x\in\mathbb{X}}P_{X_{i}}(x)O(x,y)=P_{Y_{i}}(y)\qquad\quad(1)$$ The problem of ASR-U is to find the generator function O. GAN-based ASR-U Eq. (1) leverages sequence information to remove ambiguity: O must be an optimal generator not only for the positionindependent distributions of X and Y , but also for their position-dependent distributions PXi , PYi∀i ∈ N 0. In reality we cannot observe every possible sequence of speech vectors, or every possible sequence of phonemes, but instead must estimate O from samples. To address this issue, a GAN can be used to reproduce the empirical distribution of the training dataset with minimum error, subject to the generator's inductive bias, e.g., subject to the constraint that the function O is a matrix of the form O ∈ {0, 1}|X|×|Y|, where |X| and |Y| are the sizes of the alphabets X and Y, respectively. As shown in Figure 1, a GAN achieves this goal by computing O as the output of a neural network, O = G(*x, y*; θ), and by requiring G to play a zerosum game with another neural network called the discriminator D with the following general utility function: $$\operatorname*{min}_{G}\operatorname*{max}_{D}J(G,D):=\mathbb{E}_{Y\sim P_{Y}}[a(D(Y))]-$$ $$\mathbb{E}_{X\sim P_{X}}[b(D(G(X)))].\quad(2)$$ For the original GAN (Goodfellow et al., 2014), a(D) = log(σ(D)) and b(D) = − log(1−σ(D)), where σ is the sigmoid function. For the Wasserstein GAN (Arjovsky et al., 2017), D(Y ) is a Lipschitz-continuous scalar function, and a(D) = b(D) = D. A maximum mean discrepancy (MMD) GAN (Li et al., 2017) minimizes the squred norm of Eq. (2), where D(Y ) is an embedding into a reproducing kernel Hilbert space (RKHS). In this paper we take the RKHS embedding to be the probability mass function of a scalar random variable D(Y ), and assume that the discriminator is trained well enough to maintain Eq. (2). In this situation, the MMD GAN minimizes Eq. (2) with a(D) = b(D) = Y . In practice, Eq. (2) is optimized by alternatively updating the parameters of the discriminator and the generator using gradient descent/ascent: $$\begin{array}{l c r}{{\phi_{i+1}=\phi_{i}+\eta\nabla_{\phi}J(G_{\theta_{i}},D_{\phi_{i}})}}&{{}}&{{(3)}}\\ {{\theta_{i+1}=\theta_{i}-\nu\nabla_{\theta}J(G_{\theta_{i}},D_{\phi_{i+1}}).}}&{{}}&{{(4)}}\end{array}$$ Theoretical questions of ASR-U The aforementioned formulation of ASR-U is ill-posed. Intuitively, the function O has finite degrees of freedom (O ∈ {0, 1}|X|×|Y|), while Eq. (1) must be valid for an infinite number of distributions (PXi and PYi for i ∈ N), so there is no guarantee that a solution exists. On the other hand, if the sequence is unimportant (PXi = PXj∀*i, j* ∈ N 0), then the solution may not be unique. One important question is then: what are the necessary and sufficient conditions for Eq. (1) to have a unique solution? Further, it is well-known that gradient-based training of GAN can be unstable and prior works on ASR-U (Yeh et al., 2019; Baevski et al., 2021) have used various regularization losses to stabilize training. Therefore, another question of practical significance is: what are the necessary and sufficient conditions for the alternate gradient method as described by Eq. (3)-(4) to converge to the true generator for ASR-U? In the subsequent sections, we set out to answer these questions. ## 3 Theoretical Analysis Of Asr-U 3.1 Learnability Of Asr-U: A Sufficient Condition A key assumption of our theory is that the distribution of the speech and the text units can be modeled by a single *hidden Markov model* whose hidden states are N-grams of speech units and whose outputs are N-grams of text units, as shown in Figure 1. The parameters of this HMM are its initial probability vector, π, which specifies the distribution of the first N speech vectors X0:(N−1) ∈ X N , its transition probability matrix A, which specifies the probability of any given sequence of N speech vectors given the preceding N speech vectors, and its observation probability matrix, which specifies the distribution of one phone symbol given one speech vector: $$\begin{array}{l}{{\pi:=P_{X_{0:N-1}}\in\Delta^{|\mathbb{X}|^{N}}}}\\ {{A:=P_{X_{N:2N-1}|X_{0:N-1}}\in\Delta^{|\mathbb{X}|^{N}\times|\mathbb{X}|^{N}}}}\\ {{O:=P_{Y|X}\in\Delta^{|\mathbb{X}|\times|\mathbb{Y}|},}}\end{array}$$ where ∆kis the k-dimensional probability simplex. The first-order Markov assumption is made plausible by the use of N-gram states, X0:N−1, rather than unigram states; with sufficiently long N, natural language may be considered to be approximately first-order Markov. The connection between the N-gram states and the unigram observations requires the use of a selector matrix, E = 1|X|N−1 ⊗ I|X|, where ⊗ denotes the Kronecker product, thus PXkN = π⊤AkE, and for multiples of N, Eq. (1) can be written PYkN = π⊤AkEO. It turns out that a crucial feature for a spoken language to be learnable in an unsupervised fashion is that it needs to be "complex" enough such that a simple, symmetric and repetitive graph is not sufficient to generate the language. This is captured by the following assumptions on the parameters A and π. Assumption 1. *There exists an invertible matrix* U ∈ R|X|N−1×|X|N−1= [U1|U2| · · · |UK]*, where* the columns of each matrix Uj = [uj1*| · · · |*ujNj ] are eigenvectors with the same eigenvalue and a diagonal matrix Λ = blkdiag(Λ1, · · · ,ΛK), where each matrix Λk is a diagonal matrix with all diagonal elements equal to the same scalar λk, such that A = UΛU−1 with |X| N ≥ K ≥ |X| nonzero eigenvalues λ1 > λ2 > · · · > λK. Assumption 2. For at least |X| values of j*, there* is at least one k *s.t.* π⊤ujk ̸= 0. With Assumptions 1 and 2, we can consider the following algorithm: First, we construct the following matrices $$P^{X}:=\begin{bmatrix}P^{\top}_{X0}\\ P^{\top}_{X_{N}}\\ \vdots\\ P^{\top}_{X_{(L-1)N}}\end{bmatrix},P^{Y}:=\begin{bmatrix}P^{\top}_{Y0}\\ P^{\top}_{Y_{N}}\\ \vdots\\ P^{\top}_{Y_{(L-1)N}}\end{bmatrix},\tag{5}$$ Then, O satisfies the following matrix equation $$P^{X}O=P^{Y}.$$ Y. (6) The binary matrix O in Eq. (6) is unique if and only if P X has full column rank. The following theorem proves that this is indeed the case under our assumptions. Theorem 1. Under Assumptions 1 and 2, P X has full column rank and perfect ASR-U is possible. Further, the true phoneme assignment function is O = P X+P Y*, where* P X+ = (P X⊤P X)−1P X⊤ is the left-inverse of P X. Further, if we measure how far the matrix P X is from being singular by its *smallest* singular value defined as $$\sigma_{\operatorname*{min}}(P^{X}):=\operatorname*{min}_{v\in\mathbb{R}^{|\mathbb{X}|}}{\frac{\|P^{X}v\|_{2}}{\|v\|_{2}}},$$ we can see that P X becomes further and further away from being singular as the sequence length L gets larger. An equivalent result for a different purpose has appeared in the Theorem 1 of (Bazán, 2000). Lemma 1. Under Assumptions 1 and 2 *and for simplicity assuming the number of distinct eigenvalues* K = |X| for T*, then we have* $$\begin{array}{c}{{\sigma_{\operatorname*{min}}(P^{X})\geq}}\\ {{\delta_{\underline{{{\mathrm{min}}}}}^{(|\mathbb{X}|-1)/2|\mathbb{X}|}\sum_{l=0}^{L-|\mathbb{X}|-1}\lambda_{\operatorname*{min}}^{2l}(A)}}\\ {{\kappa(V_{|\mathbb{X}|}(\lambda_{1:|\mathbb{X}|}))}}\end{array}\operatorname*{min}_{j}\|{\hat{r}}_{j}\|\quad(7)$$ where δmin := mini̸=j|λi(A) − λj (A)|, λmin(A) is the smallest eigenvalue of square matrix A, κ(V|X|(λ1:|X|)) is the condition number of the square Vandermonde matrix created from eigenvalues λ1(A)*, . . . , λ*|X|(A), rj = π TUjΩ⊤ j E*, and* Ω⊤ j is the set of rows of U−1corresponding to eigenvalue λj (A)*, after orthogonalizing them from every* other block of rows, i.e., U−1 = L[Ω1*| · · · |*ΩK] T such that L *is lower-triangular, and the blocks* Ωi and Ωj *are orthogonal.* Next, we will show that Assumption 1 can be easily met using random matrix arguments. ## 3.2 Finite-Sample Learnability Of Asr-U Matched setup Now we show that the requirement for distinct eigenvalues is a mild one as it can easily be satisfied with *random* transition matrices. According to such a result, ASR-U is feasible with high probability in the (empirically) *matched* setting commonly used in the ASR-U literature, where the *empirical* generated and true distributions can be matched exactly by some generator in the function class (Liu et al., 2018). Our proof relies crucially on the seminal work of (Nguyen et al., 2017) on eigenvalue gaps of symmetric random matrices with independent entries. In the context of ASR-U, it is of particular interest to study the eigenvalue gaps of a Markov random matrix, which unlike the symmetric case, is asymmetric with correlated entries. Fortunately, by modifying the proof for Theorem 2.6 of (Nguyen et al., 2017), we can show that if the language model belongs to a special but rather broad class of Markov random matrices defined below and the states are *non-overlapping* N-gram instead of the more common overlapping ones, it should have at least |X| distinct eigenvalues with minimum spacing depending on |X| and the N for the N-gram. Definition 1. (symmetric Markov random matrix) A symmetric Markov random matrix is a matrix of the form A := D−1W*, where the* adjacency matrix W is a real, symmetric random matrix with positive entries and bounded variance and D *is a diagonal* matrix with dii =Pj Wij > 0. Intuitively, a symmetric Markov random matrix is the transition matrix for a *reversible* Markov chain formed by normalizing edge weights of a weighted, undirected graph. Theorem 2. (simple spectrum of symmetric Markov random matrix) Let An = D−1 n Wn ∈ R n×n be a real symmetric Markov random matrix with adjacency matrix Wn*. Further, suppose* Wn = Fn + Xn, where Fn is a deterministic symmetric matrix with eigenvalues of order n γ and Xn is a symmetric random matrix of zeromean, unit variance sub-Gaussian random variables. Then we have for any C > 0*, there exists* **Theorem 1**.: _Let we have for any $\epsilon>0$, there exists $B>4\gamma^{\prime}C+7\gamma^{\prime}+1$ such that_ $$\max_{1\leq i\leq n-1}\Pr[|\lambda_{i}-\lambda_{i+1}|\leq n^{-B}]\leq n^{-C},$$ _with probability at least $1-O(\exp(-\alpha_{0}n))$ for some $\alpha_{0}>0$ dependent on $B$ and $\gamma^{\prime}=1$._ max{γ, 1/2}. Corollary 1. Suppose the speech feature transition probability is a symmetric Markov random matrix A := D−1W *with entries* Wij ∼ Uniform(0, 2 √3) and D *is a diagonal matrix with* dii =Pj Wij . Then for any ϵ > 0, there exists α0 > 0 *such that with probability at least* 1−O |X|−CN + exp −α0|X| N*, the transition* probability matrix A has |X| N distinct eigenvalues with minimum gap |X|−BN > 0. The proof of Theorem 2 and Corollary 1 are presented in detail in the Appendix A.2. Unmatched setup In the finite-sample, unmatched setup, the empirical distribution of the fake text data generated by the GAN does not necessarily match the empirical distribution of the true text data. Assuming the discriminator is perfect in the sense that it maintains Eq. (2) non-negative, and assuming D(Y ) is a scalar random variable, then minimizing Eq. (2) is equivalent to minimizing a divergence measure d(·, ·), between the empirical text distribution, P Y, and the text distribution generated by Ox(y) = PˆY |X(y|x): $$\operatorname*{min}_{O\in\Delta^{|\mathbb{X}|\times|\mathbb{Y}|}}d^{\gamma}(P^{Y},P^{X}O),\qquad\qquad(8)$$ where γ > 0. For example, for the original GAN, d(·, ·) is the Jensen-Shannon distance and for the MMD GAN, d(·, ·) is the Lγ distance between the expectations E[D(Y )] under the distributions P Y and P XO. In both cases, however, Eq. (8) can be minimized using a *decomposable* discriminator defined to be: $$\mathbb{E}_{P_{Y}}[a(D(Y))]=$$ $$\mathbb{E}_{P_{X}}[b(D(G(X)))]=$$ $$\begin{array}{l}{{\sum_{l=0}^{L-1}\mathbb{E}_{P_{Y_{l}}}[a(D_{l}(Y_{l}))]}}\quad(9)}\\ {{\sum_{l=0}^{L-1}\mathbb{E}_{P_{X_{l}}}[b(D_{l}(G_{l}(X)))],}}\\ {{\sum_{l=0}^{L-1}\mathbb{E}_{P_{X_{l}}}[b(D_{l}(G_{l}(X)))],}}\end{array}$$ with components Dl: |Y| 7→ R, l = 1, · · · , L. Under the assumption that D is decomposable and that the MMD GAN is used, we have the following sample complexity bound on perfect ASR-U. Theorem 3. *The empirical risk minimizer (ERM)* of Eq. (8) recovers the true assignment O perfectly from n X *speech frames and* n Ytext characters with probability 1 − 2δ if $$\begin{array}{r l}{{}}&{{}}\\ {{}}&{{\sigma_{\operatorname*{min}}(P^{X})\geq\sqrt{\frac{4L|\mathbb{Y}|(n^{X}+n^{Y})+L|\mathbb{X}|n^{X}}{n^{X}n^{Y}}}+}}\\ {{}}&{{}}\\ {{}}&{{10\sqrt{\frac{L\log{\frac{1}{\delta}}}{n^{X}\wedge n^{Y}}},}}\end{array}$$ *where $n^{X}\wedge n^{Y}:=\operatorname*{min}\{n^{X},n^{Y}\}$.* 1195 ![4_image_0.png](4_image_0.png) ## 3.3 Training Dynamic Of Gan-Based Asr-U So far, we have assumed the GAN training is able to find the optimal parameters for the discriminator and the generator. However, there is no guarantee that this is indeed the case with gradient updates such as Eq. (3). To analyze the behaviour of the GAN training dynamic for ASR-U, we follow prior works on neural tangent kernel (NTK) (Jacot et al., 2018) to focus on the *infinite-width, continuoustime* regime, or NTK regime, where the generator and the discriminator are assumed to be neural networks with an infinite number of hidden neurons trained with gradient descent at an infinitely small learning rate. Though highly idealized, studying such a regime is practically useful as results from this regime can often be converted to finite-width, discrete-time settings (See, e.g., (Du et al., 2019)). For simplicity, denote fτ := Dϕτ and gt:= Gθt and define Lt(f) := J(gt, f), then in the NTK regime, between each generator step, the training dynamic of the discriminator can be described by the following partial differential equation (PDE): $$\partial_{\tau}\phi_{\tau}=\nabla_{\phi_{\tau}}{\mathcal{L}}_{t}(f_{\tau}).$$ $\to\infty$ $f_{\tau}$ be the limit of Eq. (11). Let f∗ Pt = limτ→∞ fτ be the limit of Eq. (11). If the limit exists and is unique, the generator loss is well-defined as Ct(gt) := J(gt, f ∗ Pt ). Note that the output of the ASR-U generator is discrete, which is not a differentiable function per se, but we can instead directly parameterize the *generated text* distribution as Pgt:= PX ◦ Ot for some softmax posterior distribution Ot: $$O_{t,x}(y):=\prod_{l=1}^{L}\frac{\exp(h_{\theta,y_{l}}(x_{l}))}{\sum_{y_{l}^{\prime}}\exp(h_{\theta,y_{l}^{\prime}}(x_{l}))},\qquad(12)$$ where hθ is a neural network, and is assumed to be one layer in our analysis, though it can be extended to multiple layers with slight modifications using techniques similar to those in (Du et al., 2019). Using such a generator, the generator dynamic can be then described by the following PDE: $$\partial_{t}\theta_{t}=\sum_{y\in\mathbb{Y}^{L}}b(f_{g_{t}}^{*}(y))\nabla_{\theta_{t}}P_{g_{t}}(y),\quad\quad(13)$$ where the right-hand side is the term in the gradient of Ct with respect to θtignoring the dependency of the discriminator f∗ gt . Define the NTKs of the discriminator and the generator (distribution) as $$K_{f_{\tau}}(y,y^{\prime})=\mathbb{E}_{\phi_{0}\sim\mathcal{W}}\left[\frac{\partial f_{\tau}(y)}{\partial\phi_{\tau}}^{\top}\frac{\partial f_{\tau}(y^{\prime})}{\partial\phi_{\tau}}\right]\tag{14}$$ $$K_{g_{t}}(y,y^{\prime})=\mathbb{E}_{\theta_{0}\sim\mathcal{W}}\left[\frac{\partial P_{g_{t}}(y)}{\partial\theta_{t}}^{\top}\frac{\partial P_{g_{t}}(y^{\prime})}{\partial\theta_{t}}\right],\tag{15}$$ $$(11)$$ where W is the initialization distribution (usually Gaussian). Note that the NTKs are |Y| L *× |Y|* L matrices for ASR-U due to the discrete nature of the generator. A key result in (Jacot et al., 2018) states that as the widths of the hidden layers of the discriminator and generator go to infinity, Kfτ → KD, Kgt → KG stay constant during gradient descent/ascent and we have $$\partial_{\tau}f_{\tau}=K_{D}\left(\mathrm{diag}(P_{Y})\nabla_{f_{\tau}}a\right.\tag{16}$$ $$\left.-\mathrm{diag}(P_{gt})\nabla_{f_{\tau}}b\right),$$ (17) $$\partial_{t}P_{gt}=K_{G}\mathbf{b}_{f_{gt}},$$ where $\nabla_{f}\{a,b\}=\left[\frac{\partial\{a,b\}(f(y))}{\partial f(y)}\right]_{y\in\mathbb{Y}^{L}}$ and $\mathbf{b}_{f}=\mathbf{b}_{f}(b_{f}(y))_{y\in\mathbb{Y}^{L}}$. However, Eq. (16)-(17) is in general highly nonlinear and it remains an open problem as to their convergence properties. Instead, we focus on the case when the discriminator ftis decomposable with components ft,l, l = 1, · · · , L, and simplify 1196 Eq. (16) and Eq. (17) into PDEs involving only samples at a particular time step: $$\partial_{\tau}f_{\tau,l}=K_{D,l}\left(\text{diag}(P_{l}^{Y})\nabla_{f_{\tau,l}}\mathbf{a}_{f_{\tau,l}}\right.$$ $$\left.-\text{diag}(P_{l}^{g_{l}})\nabla_{f_{\tau,l}}\mathbf{b}_{f_{\tau,l}}\right),\tag{18}$$ $$\partial_{t}O_{t,x}^{\top}=\sum_{l=1}^{L}P_{l}^{X}(x)K_{O_{t,x}}\mathbf{b}_{f_{g_{l},l}},\tag{19}$$ for all l = 1, · · · *, L, x* ∈ X in terms of the *stepwise* NTKs defined as: $$K_{D,l}(y,y^{\prime}):=\mathbb{E}_{\phi_{0}\sim\mathcal{W}}\left[\frac{\partial f_{\tau}(y)}{\partial\phi_{\tau}}^{\top}\frac{\partial f_{\tau}(y^{\prime})}{\partial\phi_{\tau}}\right]$$ $$K_{O_{t,x}}(y,y^{\prime}):=\mathbb{E}_{\theta_{0}\sim\mathcal{W}}\left[\frac{\partial O_{t,x}(y)}{\partial\theta_{\tau}}^{\top}\frac{\partial O_{t,x}(y^{\prime})}{\partial\theta_{\tau}}\right].$$ We further focus on the special case that fτ,l is parameterized by a two-layer neural network with ReLU activation, though the framework can be extended to network of arbitrary depths: $$f_{\tau,l}(y)=\operatorname*{lim}_{m\to\infty}\frac{1}{\sqrt{m}}\sum_{r=1}^{m}v_{r}^{l}\operatorname*{max}\{W_{r y}^{l},0\}.\tag{20}$$ In this case, under mild regularity conditions, we can show that the generator trained with the alternate gradient method minimizes Eq. (8), which under the same condition as in Section 3.2, implies ASR-U is feasible. Theorem 4. Suppose the following assumptions hold: 1. *The discriminator is decomposable and parameterized by Eq. (20), whose parameters* are all initialized by standard Gaussian variables; 2. The generator is linear before the softmax layer; 3. *The GAN objective is MMD;* 4. The linear equation P XO = P Y has at least one solution. Then we have for any solution Ot *of Eq. (19),* limt→∞ P XOt = P Y. ## 4 Experiments Synthetic language dataset To allow easy control of the eigenvalue spacings of the transition matrix T and thus observe the phase transition phenomena predicted by our theory, we design six synthetic languages with HMM language models as follows. First, we create the HMM transition graph by treating non-overlapping *bigrams* as hidden states of the HMM. The hidden state of the HMM will henceforth be referred to as the "speech unit", while the observation emitted by the HMM will be referred to as the "text unit". For the asymptotic ASR-U, we control the number of eigenvalues of the Markov transition graph by varying the number of disjoint, identical subgraphs. The number of distinct eigenvalues of the whole graph will then be equal to the number of eigenvalues of each subgraph. For the finite sample setting, we instead select only Hamiltonian graphs and either gradually decrease the degrees of the original graph to its Hamiltonian cycle or interpolate between the graph adjacency matrix and that of its Hamiltonian cycle. Thus, we can increase σmin(P X) by increasing w. For both the subgraph in the former case and the Hamiltonian graph in the latter, we experiments with circulant, de Bruijn graphs (de Bruijn, 1946) and hypercubes, as illustrated in Figure 2. Next, we randomly permute the hidden state symbols to form the true generator mapping from the speech units to text units. To create matched speech-text data, we simply sample matched speech and text unit sequences using a single HMM. For unmatched datasets, we sample the speech and text data independently with two HMMs with the same parameters. Please refer to Appendix B for more details. Model architecture For finite-sample ASR-U, we use wav2vec-U (Baevski et al., 2021) with several modifications. In particular, we experiment with various training objectives other than the Jensen-Shannon (JS) GAN used in the original wav2vec-U, including the Wasserstein GAN (Liu et al., 2018) and the MMD GAN. All additional regularization losses are *disabled*. Moreover, we experimentally manipulate two hyperparameters: (1) the averaging strategy used by the generator, and (2) whether to *reset* the discriminator weights to zero at the beginning of each discriminator training loop. More details can be found in Appendix B. Phase transition of PER vs. eigenvalue gaps: asymptotic case The phoneme error rate (PER) as a function of the number of eigenvalues of A for the asymptotic ASR-U on the synthetic datasets are shown in Figure 3. For all three graphs, we observe ![6_image_0.png](6_image_0.png) ![6_image_1.png](6_image_1.png) ![6_image_2.png](6_image_2.png) clear phase transitions as the number of eigenvalues exceeds the number of speech units, and an increase of the number of distinct, nonzero eigenvalues required for perfect ASR-U as the number of speech units increases. Phase transition of PER vs. eigenvalue gaps: finite-sample case The PER as a function of the least singular value σ min ( P X ) for the finite-sample ASR-U on the synthetic datasets are shown in Figure 4. As we can see, the ASR-U exhibit the phase transition phenomena in all three graphs, albeit with differences in the critical point and their rate of approaching the perfect ASR-U regime. While the PER generally decreases as σ min ( P X ) gets larger, we found a dip in PER in the circulant graph case as σ min ( P X ) moves from 10− 31 to 10− 15 . Though unexpected, this observation is not contradictory to our theory since our theory does not make explicit predictions about the rate of phase transition for ASR-U. Across different GAN models, we found that JSD generally approaches perfect ASR- U at a faster rate than MMD in all three graphs, suggesting the use of nonlinear dynamic may be beneficial. Nevertheless, the overall trends for different GANs remain in large part homogeneous. Between Wasserstein and MMD, we observe very little difference in performance, suggesting the regularization effect of NTK is sufficient to control the Lipschitz coefficient of the network. Finally, for the MMD GAN in the matched setting, we found the network is able to achieve perfect ASR-U regardless of the spectral properties of the Markov transition graphs, which confirms our theory that a symmetric Markov random matrix tends to have simple eigenvalue spectrum suitable for ASR-U. Effect of discriminator reset As pointed out by (Franceschi et al., 2021), a discriminator may suffer from residual noise from previous updates and fail to approximate the target divergence measure. We analyze such effects for MMD and JSD as shown in Figure 5. We observed consistent trends that models whose weights are reset to the initial weights every discriminator loop outperform those without resetting. The effect is more pronounced for JSD GAN than MMD GAN and for smaller σmin(P X). Effect of generator averaging strategy The original wav2vec-U (Baevski et al., 2021) directly feeds the text posterior probabilities O into the discriminator, which we refer to as the *"soft input"* approach. Alternatively, we can instead calculate a weighted average of the gradient form over the samples y ∈ Y L as in Eq. (13), which we refer to as the "outside cost" approach. The comparison between the two approaches are shown in Figure 6. We observed mixed results: for MMD GANs, the softinput approach outperforms the outside-cost approach and performs best among the models in the high-σmin(P X) setting; for JSD GANs, we found that the outside-cost approach performs slightly better than the soft-input approach. Such inconsistencies may be another consequence of the regularization effect predicted by the GANTK. We leave the theoretical explanation as future work. ## 5 Related Works (Glass, 2012) first proposed the challenging task of ASR-U as a key step toward unsupervised speech processing, and framed it as a decipherment problem. (Liu et al., 2018) takes on the challenge by developing the first ASR-U system with groundtruth phoneme boundaries and quantized speech features as inputs, by training a GAN to match the speech-generated and real text distributions. (Chen et al., 2019) later replaced the ground truth boundaries with unsupervised ones refined iteratively by an HMM, which also incorporates language model information into the system. (Yeh et al., 2019) explored the cross entropy loss for matching the generated and real text distribution, but it is prone to mode collapse and needs the help of additional regularization losses such as smoothness weight. More recently, (Baevski et al., 2021; Liu et al., 2022) proposed another GAN-based model using continuous features from the last hidden layer of the wav2vec 2.0 (Baevski et al., 2020) model and additional regularization losses to stabilize training. Their approach achieves ASR error rates comparable to the supervised system on multiple languages, making it the current state-of-the-art system. To better understand the learning behavior of ASR-U systems, (Lin et al., 2022) analyze the robustness of wav2vec-U against empirical distribution mismatch between the speech and text, and found that N-gram language model is predictive of the success of ASR-U. Inspired by the original framework in (Glass, 2012), (Klejch et al., 2022) proposed a decipher-based cross-lingual ASR system by mapping IPA symbols extracted from a small amount of speech data with unpaired phonetic transcripts in the target language. Our analysis on the sufficient condition of ASRU is based on previous work on the asymptotic behaviour of GAN objective functions (Goodfellow et al., 2014; Arjovsky et al., 2017). Our finitesample analysis takes inspiration from later work extending the asymptotic analysis to the finitesample regimes (Arora et al., 2017; Bai et al., 2019). Such frameworks, however, do not account for the alternate gradient optimization method of GANs and inevitably lead to various inconsistencies between the theory and empirical observations of GAN training (Franceschi et al., 2021). Building upon prior works (Mescheder et al., 2017, 2018; Domingo-Enrich et al., 2020; Mroueh and Nguyen, 2021; Balaji et al., 2021), (Franceschi et al., 2021) proposed a unified framework called GANTK based on NTK (Jacot et al., 2018) to describe the training dynamic of any GAN objectives and network architectures. Our analysis on the training dynamic of ASR-U adopts and extends the GANTK framework to handle *discrete, sequential* data such as natural languages. ## 6 Conclusion In this paper, we develop a theoretical framework to study the fundamental limits of ASR-U as well as the convergence properties of GAN-based ASRU algorithms. In doing so, our theory sheds light on the underlying causes of training instability for such algorithms, as well as several new directions for more reliable ASR-U training. ## 7 Limitations Our theory currently assumes that input speech features are quantized into discrete units, as in (Chen et al., 2019), while preserving all the linguistic information in the speech. As a result, our theory does not account for the loss of linguistic information during the quantization process, as often occurred in realistic speech datasets. Further, more recent works (Baevski et al., 2021; Liu et al., 2022) have shown that continuous features, with the help of additional regularization losses, can achieve almost perfect ASR-U. Such phenomena is beyond explanations based on our current theory and require generalizing our theory to continuous speech features. Further, our model assumes that sufficiently reliable phoneme boundaries are fed to the ASR-U system, and kept fixed during training. It will be interesting to extend our framework to systems with trainable phoneme boundaries, such as the wav2vec-U systems, to better understand its effect on training stability. ## Acknowledgements This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2022-0-00184, Development and Study of AI Technologies to Inexpensively Conform to Evolving Policy on Ethics) ## References G. Anderson, A. Guionnet, and O. Zeitouni. 2009. 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(Theorem 1) For simplicity, we assume that the eigenvalues of A are real though a similar argument applies to complex eigenvalues as well. By Assumptions 1 and 2, it can be verified that $$\begin{array}{c}{{P_{X_{k N}}=\pi^{\top}A^{k}E}}\\ {{=\pi^{\top}U\Lambda^{k}U^{-1}E,}}\end{array}$$ where E = 1|X|N−2 ⊗ I|X|, where ⊗ denotes the Kronecker product. Define cjk = π⊤ujk. Define r⊤ jk to be the k th row of the j th block of the matrix U−1E, i.e., UU −1E =PK j=1 PNj k=1 ujkr⊤ jk. Define the matrix RK as RK = [r1, · · · , rK], where rj =PNj k=1 cjkrjk. Then we have: $$P_{X_{k N}}^{\top}=\sum_{j=1}^{K}\lambda_{j}^{k}r_{j}^{\top}$$ $$P^{X}=V_{L}(\lambda_{1:K})^{\top}R_{K}^{\top},$$ where VL(λ1:K) is the Vandermonde matrix formed by nonzero eigenvalues λ1, · · · , λK and with L columns, K ≥ |X| by Assumption 1. RK has full column rank of K ≥ |X| by Assumption 2, therefore it is possible to write RK = RˆKL, where RˆK = ˆr1*, . . . ,* rˆK] is a matrix with orthogonal columns, and L is lower-triangular. As a result, we have P X is full rank iff VL(λ1:K) has full row rank of at least |X|, which holds by Assumption 1. ## Proof. (Lemma 1) Use the Rayleigh-characterization of eigenvalues of the matrix P X⊤P X, we have σmin(P X) $$\begin{split}&\sigma_{\min}(P^{X})\\ &=\sqrt{\lambda_{\min}(P^{X\top}P^{X})}\\ &=\sqrt{\min_{\|w\|=1}w^{\top}P^{X\top}P^{X}w}\\ &=\sqrt{\min_{\|w\|=1}w^{\top}R_{K}V_{L}V_{L}^{\top}R_{K}^{\top}w}\\ &\geq\sqrt{\sum_{l=0}^{L-|\mathbb{X}|-1}\lambda_{\min}^{2l}\min_{\|w\|=1}w^{\top}R_{K}V_{|\mathbb{X}|}V_{|\mathbb{X}|}^{\top}R_{K}^{\top}w}\\ &=\sigma_{\min}(P_{1:|\mathbb{X}|}^{X})\sqrt{\sum_{l=0}^{L-|\mathbb{X}|-1}\lambda_{\min}^{2l}},\end{split}$$ where λmin is the eigenvalue of A with minimum absolute value, and P X 1:|X| is the first |X| rows of P X. Therefore, to lower bound σmin(P X), it suffices to lower bound σmin(P X 1:|X| ). But note that σmin(P X 1:|X| ) = min ∥w∥=1 ∥V T |X|R T Kw∥ ≥σmin(V T |X| ) min ∥w∥=1 ∥R T Kw∥ ≥ σmax(V|X|) κ(V|X|) min j∥rˆj∥ ≥ | det(V|X|)| 1/|X| κ(V|X|) min j∥rˆj∥ = |Q1≤i<j≤|X||λi − λj | 1/|X| κ(V|X|) min j∥rˆj∥ ≥ δ (|X|−1)/2|X| min κ(V|X|) min j∥rˆj∥ where the last equality uses the closed-form formula of the determinant of a square Vandermonde matrix, and where the behaviour of κ(V|X|), the condition number of the Vandermonde matrix, has been studied in depth in (Bazán, 2000). ## A.2 Finite-Sample Learnability Of Asr-U: Matched Setup Theory of small ball probability The proof of Theorem 2 makes extensive use of the theory of small ball probability. Therefore, we briefly provide some background on the subject. First, we define the *small ball probability* of a vector x as follows. Definition 2. (Small ball probability) Given a fixed vector x = (x1, · · · , xn)*, and i.i.d random variables* ξ = (ξi, · · · , ξn)*, the small ball probability* is defined as $$\rho_{\delta}(x):=\operatorname*{sup}_{a\in\mathbb{R}}\operatorname*{Pr}[|\xi^{\top}x-a|\leq\delta].$$ Intuitively, small ball probability is the amount of "*additive* structure" in x: for example, if the coordinates of x are integer multiples of each other and ξi's are symmetric Bernoulli variables, the product ξ⊤x tends to have small magnitude as terms cancel out each other very often. Since sparser vectors tend to have less additive structure, small ball probability can also be used to measure how *sparse* the weights of x are. Another way to look at this is that, if the L2 norm of x is fixed and most of the weight of x is gathered in a few coordinates, the product ξ⊤x has higher variance and is thus less likely to settle in any fixed-length intervals. This is quantitatively captured by the celebrated Offord-Littlewood-Erdös (OLE) anti-concentration inequality (and its inverse) for general subgaussian random variables: Lemma 2. (Erdös, 1945; Rudelson and Vershynin, 2008; Tao and Vu, 2009) Let ϵ > 0 *be fixed, let* δ > 0*, and let* v ∈ R m *be a unit vector with* $$\rho_{\delta}(v)\geq m^{-\frac{1}{2}+\epsilon}.$$ Then all but at most ϵm of the coefficients of v *have* magnitude at most δ. Note that here we use a slight generalization of the notion of sparsity called *compressibility* defined as follows. Definition 3. ((α, δ)*-compressible) A vector* v ∈ R nis (α, δ)-compressible if at most ⌊αn⌋ of its coefficients have magnitude above δ. Note that a sparse vector with a support of size at most ⌊αn⌋ is (α, 0)-compressible. A more generally applicable anti-concentration inequality requires the following definition of generalized arithmetic progression, which is used to quantify the amount of additive structure of a vector. Definition 4. *(Generalized arithmetic progression)* A generalized arithmetic progression (GAP) is a set of the form $$Q=\{a^{\top}w:a\in\mathbb{Z}^{r},|a_{i}|\leq N_{i},1\leq i\leq r\},$$ where r ≥ 0 *is called the* rank *of the GAP and* w1, · · · , wr ∈ R *are called* generators of the GAP. Further, the quantity $$\operatorname{vol}(Q):=\prod_{i=1}^{r}(2N_{i}+1)$$ is called the volume *of the GAP.* Lemma 3. (Continuous inverse Littlewood-Offord theorem, Theorem 2.9 of (Nguyen and Vu, 2011)) Let ϵ > 0 be fixed, let δ > 0 *and let* v ∈ R n *be a* unit vector whose small ball probability ρ := ρδ(v) obeys the lower bound $$\rho\gg n^{-O(1)}.$$ Then there exists a generalized arithmetic progression Q *of volume* $$v o l(Q)\leq\operatorname*{max}\left(O\left({\frac{1}{\rho{\sqrt{\alpha n}}}}\right),1\right)$$ such that all but at most αn *of the coefficients* v1, · · · , vn of v lie within δ *of Q. Furthermore, if* r denotes the rank of Q*, then* r = O(1) *and all* the generators w1, · · · , wr of Q *have magnitude* O(1). While applicable for any ρ ≫ n−ϵrather than only those with ρδ(v) ≥ n−1/2+ϵas required by Lemma 2, Lemma 3 is *weaker* than Lemma 2 in the sense that rather than showing that the vector is compressible with high probability and thus covered by the set of compressible vectors, it proves that the vector is covered by a small set with high probability. A related notion that is often more convenient for our analysis is the *segmental* small ball probability, which is simply small ball probability computed on a segment of the vector: $$\rho_{\delta,\alpha}(x)=\operatorname*{inf}_{I\subseteq\{1,\cdots,n\}:|I|=\lfloor\alpha n\rfloor}\rho_{\delta}(x_{I}),$$ From the definition, it is not hard to see that ρδ,α(x) ≥ ρδ(x). Eigen-gaps of symmetric Markov random matrix Armed with tools from the theory of small ball probability, we will establish guarantees of eigenvalue gaps for a symmetric Markov random matrix. First, we shall show that Theorem 2 implies Corollary 1. Proof. (Proof of Corollary 1) Using Theorem 2 and union bound, the probability that a symmetric Markov random matrix has at least |X| distinct eigenvalues can be bounded as $$\begin{array}{l}{{\mathrm{Pr}\left[\operatorname*{min}_{1\leq i\leq|\mathbb{X}|}|\lambda_{i}-\lambda_{i+1}|\leq|\mathbb{X}|^{-B N}\right]\leq}}\\ {{\mathrm{}}}\\ {{|\mathbb{X}|\operatorname*{max}_{i}\mathrm{Pr}\left[|\lambda_{i}-\lambda_{i+1}|\leq|\mathbb{X}|^{-B N}\right]}}\\ {{\mathrm{}}}\\ {{\mathrm{}=O(|\mathbb{X}|^{-C N}),}}\end{array}$$ $$=O(|\mathbb{X}|^{-C N}),$$ with probability at least $1-O(\exp(-\alpha_{0}|\mathbb{X}|^{N}))$. It turns out that a symmetric Markov random matrix enjoys various properties analogous to a symmetric matrix. First, we can show that its eigenvalues are real. This can be proved by noting that for a symmetric Markov random matrix An := D−1 n Wn and for any of its eigenvalues λ with eigenvector v, $$D_{n}^{-1}W_{n}v=\lambda v$$ $$\Longleftrightarrow D_{n}^{-1/2}W_{n}D_{n}^{-1/2}(D_{n}^{1/2}v)=\lambda D_{n}^{1/2}v,\tag{21}$$ which implies An has the same spectrum as D −1/2 n WnD −1/2 n , which is symmetric and thus has a real spectrum. Further, we can prove a variant of Cauchy's interlace theorem for symmetric Markov random matrix. Lemma 4. *Suppose* An = D−1 n Wn ∈ R n×n*is a* symmetric Markov random matrix with adjacency matrix Wn and eigenvalues λ1 ≥ · · · ≥ λn and Am = D−1 m Wm with adjacency matrix Wm−1 and eigenvalues ν1 ≥ · · · ≥ νm, m < n is formed by successively deleting i-rows and i*-columns, then* λi ≤ νi ≤ λi+n−m. Proof. Using the previous observation in Eq. 21, we can apply the standard Cauchy's interlacing theorem on A′n:= D −1/2 n WnD −1/2 n and A′m := D −1/2 m WmD −1/2 m , then we have $$\lambda_{i}(A_{n})=\lambda_{i}(A_{n}^{\prime})\leq\lambda_{i}(A_{m}^{\prime})=\lambda_{i}(A_{m})$$ $$\leq\lambda_{i+n-m}(A_{n}^{\prime})=\lambda_{i+n-m}(A_{n}).$$ $\square$ . Next, we can show that the eigenvalues of a symmetric Markov random matrix and its adjacency matrix are simultaneously distributed within the bounded intervals [−10n γ−1, 10n γ−1] and [−10n γ, 10n γ] with high probability. For this and subsequent proofs, we will assume γ′ = γ > 1/2. Lemma 5. Let An = D−1 n Wn be a symmetric Markov random matrix with adjacency matrix Wn and properties defined in Theorem *2, then we have* with probability at least 1 − O(exp(−α0n)), $$\begin{array}{l}{{\lambda_{i}(A_{n})\in[-10n^{\gamma-1},10n^{\gamma-1}],}}\\ {{\lambda_{i}(W_{n})\in[-10n^{\gamma},10n^{\gamma}],}}\end{array}$$ _for any $1\leq i\leq n$ and some $\alpha_{0}>0$._ Proof. First, by definition, we can let Wn = Fn + Xn, where Fn is a deterministic matrix with eigenvalues of order n γand Xn is a symmetric matrix whose elements are independent zero-mean unitvariance subgaussian random variables. Using standard results from random matrix theory (Anderson et al., 2009), we have $$\{\lambda_{1}(X_{n}),\cdots,\lambda_{n}(X_{n})\}\subset[-10n^{\gamma-1},10n^{\gamma-1}],$$ with probability at least 1−O(exp(−α0n)). Therefore, Weyl's matrix perturbation inequality then ensures that $$\{\lambda_{1}(W_{n}),\cdots,\lambda_{n}(W_{n})\}\in[-10n^{\gamma},10n^{\gamma}],$$ with probability at least 1 − O(exp(−α1n)). Suppose this event occurs and use Lemma 4 and the variational characterization of eigenvalues, we have $$\begin{array}{r l}{\lambda_{i}(A_{n})=\operatorname*{min}_{V_{i-1}}\operatorname*{max}_{v\in V_{i-1}^{\perp}}v^{\top}D_{n}^{-1/2}W_{n}D_{n}^{-1/2}v}\\ {=\operatorname*{min}_{V_{i-1}}\operatorname*{max}_{v\in V_{i-1}^{\perp}}v^{\top}W_{n}v}\\ {=\operatorname*{min}_{V_{i-1}}\operatorname*{max}_{v\in V_{i-1}^{\perp}}{\frac{v^{\top}W_{n}v}{v^{\top}D_{n}v}},}\end{array}$$ where Vi−1 is a subspace of dimension i − 1. Combining the two results, we have with probability at least 1 − O(exp(−α1n)), $$\operatorname*{max}_{v\in V_{i-1}^{\perp}}\left|{\frac{v^{\top}W_{n}v}{v^{\top}D_{n}v}}\right|\leq{\frac{\operatorname*{max}_{v:\|v\|=1}|v^{\top}W_{n}v|}{\operatorname*{min}_{v:\|v\|=1}|v^{\top}D_{n}v|}}$$ $$={\frac{\lambda_{1}(W)}{\operatorname*{min}_{i}|d_{i i}|}}$$ Recall that dii =Pn j=1 wij =Pn j=1(fij + xij ), where wij , fij , and xij are the (*i, j*) th elements of Wn, Fn, and Xn respectively. Since An = D−1 n Wn is a Markov matrix we assume that fij and the distribution of xij are selected to guarantee that wij ≥ 0, e.g., it must be true that fij ≥ 0. We also know that xij is a zero-mean unit-variance sub-Gaussian random variable, therefore $$\begin{array}{r}{\operatorname*{Pr}\left\{w_{i j}<\delta\right\}=\operatorname*{Pr}\left\{x_{i j}<-f_{i j}+\delta\right\}}\\ {\qquad\qquad\leq2\exp\left(-{\frac{1}{2}}(f_{i j}-\delta)^{2}\right)}\end{array}$$ $$\begin{array}{r}{\operatorname*{Pr}\left\{d_{i i}<n\delta\right\}=\operatorname*{Pr}\left\{\sum_{j=1}^{n}w_{i j}<n\delta\right\}}\\ {\qquad\qquad\leq2\exp\left(-\alpha_{2}n\right)}\end{array}$$ where α2 = − 1 2 ( ¯fi − δ) 2, and ¯fi = 1 n Pj fij . Therefore, with probability at least 1 − O(exp(−α0n)) where α0 = α1 + α2, $\lambda_{i}(A_{n})\in[-10n^{\gamma-1},10n^{\gamma-1}]$, $1\leq i\leq n$ (22) Remark. Lemma 5 ensures that for any symmetric Markov random matrix An = D−1 n Wn with properties defined in Theorem 2, we can focus our attention on any eigenvector v whose eigenvalue is no greater than O(n γ−1) and whose ∥Wnv∥2 is of order n γ with high probability. Therefore, we will assume such conditions in later proofs. Using Lemmas 4-5, we can reduce Theorem 2 to the following statement on small ball probability of the *eigenvectors* of Xn, analogous to the arguments for symmetric random matrices in (Nguyen et al., 2017). Lemma 6. Let An = D−1 n Wn ∈ R n×n be a symmetric Markov random matrix with adjacency matrix Wn. Let λi(An) and w = [u⊤, b]⊤ ∈ R n be the i*-th eigenvalue and eigenvector of the matrix* An*, respectively, where* u ∈ R n−1 and b ∈ R. Then we have Pr[|λi(An) − λi+1(An)| ≤ δ] ≤ nPr[ρδnγ+1 (v) ≥ c0n γ+1δ] + c0n γ+2δ + O(exp(−α0n)), $$f o r\,s o m e\;c_{0},\alpha_{0}>0.$$ Proof. Let Wn−1 and Dn−1 be the (n − 1)- dimensional minors of Wn and Dn, respectively, then $$\begin{bmatrix}W_{n-1}&w_{n}\\ w_{n}^{\top}&w_{n n}\end{bmatrix}\begin{bmatrix}u\\ b\end{bmatrix}=\lambda\begin{bmatrix}D_{n-1}&\mathbf{0}_{n}\\ \mathbf{0}_{n}^{\top}&d_{n n}\end{bmatrix}\begin{bmatrix}u\\ b\end{bmatrix},$$ 1204 where wn is the last column of Wn. Let v be the i-th eigenvector of matrix An−1 := D −1 n−1Wn−1, we have $$v^{\top}W_{n-1}u+v^{\top}Wb=\lambda_{i}(A_{n})v^{\top}D_{n-1}u$$ $$\Longrightarrow|(\lambda_{i}(X_{n-1})-\lambda_{i}(X_{n}))|\max_{1\leq i\leq n}d_{ii}\geq$$ $$|(\lambda_{i}(A_{n-1})-\lambda_{i}(A_{n}))v^{\top}D_{n-1}u|=|v^{\top}w_{n}b|.$$ Therefore, $$\Pr[|\lambda_{i}(A_{n})-\lambda_{i}(A_{n-1})|\leq\delta]$$ $$\leq\Pr\left[\frac{|v^{\top}w_{n}|}{\max_{1\leq i\leq n}d_{ii}}\leq\frac{\delta}{b}\right].$$ By Lemma 4, $\lambda_{i+1}(A_{n})\leq\lambda_{i}(A_{n-1})\leq\lambda_{i}(A_{n})$ and we have $$\begin{array}{c}{{\operatorname*{Pr}[|\lambda_{i}(A_{n})-\lambda_{i+1}(A_{n})|\leq\delta]\leq}}\\ {{\operatorname*{Pr}[|\lambda_{i}(A_{n-1})-\lambda_{i}(A_{n})|\leq\delta]\leq}}\\ {{\operatorname*{Pr}\left[\frac{|v^{\top}w_{n}|}{\operatorname*{max}_{1\leq i\leq n}d_{i i}}\leq\frac{\delta}{b}\right].}}\end{array}$$ dii is typically O(n), but we have been unable to prove that it is necessarily O(n). Consider that wij = fij + xij , where Fn is a symmetric matrix with eigenvalues λi(Fn) = O(n γ), therefore $$\sum_{j=1}^{n}f_{ij}=(F_{n}\mathbf{1}_{n})_{i}\leq\|F_{n}\mathbf{1}_{n}\|_{2}=\|F_{n}\|_{1}$$ $$\leq n^{1/2}\|F_{n}\|_{2}=O\left(n^{\gamma+\frac{1}{2}}\right).$$ $W_{n}=F_{n}+X_{n}$, therefore $$\Pr\left\{d_{ii}\neq O\left(n^{\gamma+\frac{1}{2}}\right)\right\}$$ $$\leq\Pr\left\{\sum_{j=1}^{n}x_{ij}>\sum_{j=1}^{n}f_{ij}-n\delta\right\}$$ $$\leq O(\exp(-\alpha_{2}n))$$ $\mathbf{1}$\(\mathbf Now, by the law of total probability, Now, by the law of four probability, $\begin{array}{ll}\Pr\left[\dfrac{|v^{\top}w_{n}|}{\max_{1\leq i\leq n}d_{ii}}\leq\dfrac{\delta}{b}\right]&\text{P}\\ \leq\Pr\left[\dfrac{|v^{\top}w_{n}|}{\max_{1\leq i\leq n}d_{ii}}\leq\dfrac{\delta}{b},\max_{1\leq i\leq n}d_{ii}\leq O(n^{\gamma+\frac{1}{2}})\right]&\text{P}\\ +\Pr\left[\max_{1\leq i\leq n}d_{ii}\neq O\left(n^{\gamma+\frac{1}{2}}\right)\right]&\text{N}\\ \leq\Pr\left[|v^{\top}w_{n}|=O\left(\dfrac{\delta n^{\gamma+\frac{1}{2}}}{b}\right)\right]+O(\exp(-\alpha_{2}n)).&\text{C}\\ &\text{1205}\end{array}$ By symmetry, we can choose any row and the corresponding column to split the matrix and derive inequality of the same form. Further, suppose for some b1 > 0, with probability at least 1 − exp(−c1n), there are at least nT coordinates of w that are at least b1 and suppose we choose the split index J uniformly at random. Let the J-th column of Wn be W and the J-th coefficient of the eigenvector of Wn be wJ , then we have $$\Pr[|\lambda_{i}(A_{n})-\lambda_{i+1}(A_{n})|\leq\delta]$$ $$\leq\Pr\left[|v^{\top}W|\neq O\left(\frac{\delta n^{\gamma+\frac{1}{2}}}{w_{J}}\right)|N_{b}\geq n_{b}\right]$$ $$+O(\exp(-c_{1}n))+O(\exp(-\alpha_{2}n))$$ $$\leq\frac{n}{n_{T}}\Pr\left[|v^{\top}W|\neq O\left(\frac{\delta n^{\gamma+\frac{1}{2}}}{b_{1}}\right)|N_{b}\geq n_{b}\right]$$ $$+O(\exp(-c_{1}n))+O(\exp(-\alpha_{2}n)),$$ $\cdot$\(\cdot where the second inequality can be proved as follows. Define $\mathcal{E}=\left\{N_b\geq n_b\right\},$ $\mathcal{F}=\left\{w_J\geq b_1\right\},$ $\mathcal{G}=\left\{|v^\top W|\neq O\left(\dfrac{\delta n^{\gamma+1/2}}{w_J}\right)\right\},$ $\mathcal{H}=\left\{|v^\top W|\neq O\left(\dfrac{\delta n^{\gamma+1/2}}{b_1}\right)\right\}.$ $\vdots$ use the above definitions and the fact that $\mathcal{F}$ is a $\mathcal{H}$-invariant. Then use the above definitions and the fact that F and G are conditionally independent given Nb, we have $$\Pr\left[|v^{\top}W|\neq O\left(\frac{\delta n^{\gamma+\frac{1}{2}}}{b_{1}}\right)|N_{b}\geq n_{b}\right]$$ $$=\Pr(\mathcal{H}|\mathcal{E})\geq\Pr(\mathcal{F}\cap\mathcal{G}|\mathcal{E})\geq\frac{n_{T}}{n}\Pr(\mathcal{G}|\mathcal{E})$$ $$=\frac{n_{T}}{n}\Pr\left[|v^{\top}W|\neq O\left(\frac{\delta n^{\gamma+1/2}}{w_{J}}\right)|N_{b}\geq n_{b}\right].$$ **For the $\Gamma$-norm, the above expression is $\Gamma$-norm.** Further, to remove the dependency on Nb, notice that $$\operatorname*{Pr}({\mathcal{H}}|{\mathcal{E}})\leq{\frac{\operatorname*{Pr}({\mathcal{H}})}{\operatorname*{Pr}({\mathcal{E}})}}=\operatorname*{Pr}({\mathcal{H}})+O(\exp(-c_{1}n)).$$ Next, by the pigeonhole principle, at least one coordinate of the unit eigenvector w is at least n−1/2, and thus we can let c1 = ∞, nb = 1 and b1 = n−1/2and arrive at $$\Pr\left[|\lambda_{i}(A_{n})-\lambda_{i+1}(A_{n})|\leq\delta\right]$$ $$\leq n\Pr\left[|v^{\top}W|\neq O\left(\delta n^{\gamma+1}\right)\right]+O(e^{-\alpha_{0}n})$$ $$\leq n\rho_{\delta O(1)n^{\gamma+1}}(v)+O(\exp(-\alpha_{0}n)),\tag{23}$$ where α0 = c1 + α2. Finally, recall the definition of small ball probability, we have $$\Pr\left[|v^{\top}W|\leq\delta\right]\leq\Pr\left[|v^{\top}W|\leq\delta|\rho_{\delta}(v)\leq\epsilon\right]$$ $$+\Pr[\rho_{\delta}(v)>\epsilon]$$ $$\leq\Pr[\rho_{\delta}(v)>\epsilon]+\epsilon,$$ and thus applying this inequality with δ := c0δnγ+1 on Eq. (23) yields the result. Remark. We can sharpen the bound in Lemma 6 by extending the delocalization theorem for a symmetric Wigner matrix (see Theorem 4.2 of (Nguyen et al., 2017)) to a symmetric Markov random matrix and using it to choose a larger nb in the proof. This will be left as future work. With the help of Lemma 6, we can reduce Theorem 2 to the following theorem. Theorem 5. Let An ∈ R n×n *be a symmetric* Markov random matrix matrix and v *be an eigenvector with eigenvalue* λ = O(n γ−1)*, then for any* fixed C > 0, there exists some B > max{4γC + 3γ, 4γ + 1} *such that* $$\rho_{n^{-B}}(v)\leq n^{-C},$$ with probability at least 1 − O(exp(−α0n)) for some α0 *depending on* B. Similar to the proof for the perturbed symmetric matrices in (Nguyen et al., 2017), we reduce Theorem 5 to the following. Theorem 6. Let v *be the eigenvector and* B be the constant defined in Theorem *5. Then for any* n−B ≤ δ ≤ n−B/2*, we have with probability* O(exp(−α0n)), $$n^{-C}\leq\rho$$ −C ≤ ρnγδ(v) ≤ n 0.49ρδ(v). (24) To show that Theorem 6 implies Theorem 5, we prove the contrapositive of the statement, that is, if ρn−B (v) > n−C, then there exists n−B ≤ δ ≤ n−B/2such that Eq. 24 holds with probability at least 1 − O(exp(−α0n)). To construct such δ, let $$\delta_{0}:=n^{-B}$$ $$\delta_{j+1}:=n^{\gamma}\delta_{j},$$ for j = 0, · · · , J − 1 with J = ⌊B/2γ⌋. By construction, we have $$\begin{array}{l}{{n^{-B}=\delta_{0}\leq\delta_{j}\leq\delta_{J}\leq n^{-B/2}}}\\ {{\rho_{\delta_{j}}(v)\geq\rho_{\delta_{0}}(v)\geq n^{-C}.}}\end{array}$$ Suppose Eq. 24 does not hold for any δ := δj , or otherwise the result follows, we have ρδJ (v) ≥ n 0.49Jρn−B (v) ≥ n 0.49⌊B/2γ⌋−C > 1, if B ≥ 4γC + 3γ, which contradicts the fact that ρδJ (v) ≤ 1. As a result, there has to exist some j such that Eq. 24 holds. Again similar to the perturbed symmetric matrix case in (Nguyen et al., 2017), we divide the proof of Theorem 6 into the compressible case and the non-compressible case. For the compressible case, we first prove the following lemma. Lemma 7. Suppose v *is an eigenvector of a symmetric Markov random matrix* An := D−1 n Wn with adjacency matrix Wn and the same properties defined in Theorem *2, and suppose there exists* δ ∈ [n−B, n−B/2] such that ρδ,α(v) ≥ (αn)−1/2+ϵ*, we* have with probability O(exp(−α0n)), $$n^{-C}\leq\rho_{n^{\gamma}\delta}(v)\leq n^{0}$$ −C ≤ ρnγδ(v) ≤ n 0.49ρδ(v). Proof. Using concentration inequalities, we have with probability at least 1 − O(exp(−α2n)) for some α2 > 0, $$d_{i i}=O\left(n^{\gamma+{\frac{1}{2}}}\right),\;1\leq i\leq n\qquad(25)$$ Further, since ρδ,α(v) ≥ (αn)−1/2+ϵ, by Lemma 2, we have v is (O(α), δ) compressible, and thus there exists I of of size O(αn) such that vi > δ only if i ∈ I. Without loss of generality, let I = {n − k, · · · , n} for k = O(αn) and E[Aij ] = 1. Further, split v = [v′⊤, v′′⊤]⊤, then by definition of eigenvalues and eigenvectors, $\begin{bmatrix}W_{n-k}&F\\ F^{\top}&W_{k}\end{bmatrix}\begin{bmatrix}v^{\prime}\\ v^{\prime\prime}\end{bmatrix}=\lambda\begin{bmatrix}D_{n-k}&\mathbf{0}\\ \mathbf{0}^{\top}&D_{k}\end{bmatrix}\begin{bmatrix}v^{\prime}\\ v^{\prime\prime}\end{bmatrix}.$ $\begin{bmatrix}W_{n-k}&F\\ F^{\top}&W_{k}\end{bmatrix}\begin{bmatrix}v^{\prime}\\ v^{\prime\prime}\end{bmatrix}=\lambda\begin{bmatrix}D_{n-k}&\mathbf{0}\\ \mathbf{0}^{\top}&D_{k}\end{bmatrix}\begin{bmatrix}v^{\prime}\\ v^{\prime\prime}\end{bmatrix}.$ $${}^{9}\rho_{\delta}(v).$$ Reading off the first line of the matrix equation, we have $$\begin{array}{c}{{\|F v^{\prime\prime}\|_{2}=\|(W_{n-k}-\lambda D_{n-k})v^{\prime}\|_{2}}}\\ {{\leq\|W_{n-k}v^{\prime}\|_{2}+\|\lambda D_{n-k}v^{\prime}\|_{2}.}}\end{array}$$ Notice that assuming Eq. 25 and Eq. 22 occur, we have that all elements v′i of v′ have |v′i| < δ, therefore ∥v′∥2 ≤ δn−1/2, therefore $$\begin{array}{c}{{\|W_{n-k}v^{\prime}\|_{2}\leq\delta n^{1/2}\;\operatorname*{max}_{v:\|v\|_{2}=1}\|W v\|_{2}}}\\ {{=O(n^{-B/2+1/2+\gamma})}}\end{array}$$ Furthermore, if we assume that Eq. (22) and Eq. (25) occur, then $$\begin{array}{c}{{\|\lambda D_{n-k}v^{\prime}\|_{2}=O(n^{\gamma-1}\cdot n^{\gamma+1/2}\cdot\delta n^{1/2})}}\\ {{=O(n^{-B/2+2\gamma}).}}\end{array}$$ Thus, using the fact that B ≥ 4γ + 1, On the other hand, using a standard epsilonnet argument, with probability at least 1 − O(exp(−α3n)), $$\operatorname*{inf}_{w\in\mathbb{R}^{k}:\|w\|=1}\|F w\|_{2}\geq n^{-1/2}.$$ Now, define the events E := {v is an eigenvector of A} $$\begin{array}{l}{{{\mathcal{E}}_{\alpha,\delta}:=\{v\mathrm{~is~}(O(\alpha),\delta)\mathrm{-compresible}\},}}\\ {{{\mathcal{E}}_{I}:=\{\|W_{I^{c},I}v_{I}\|_{2}\gg O(n^{-1/2})\},}}\end{array}$$ then by the previous discussion, we have Pr(EI |E ∩ Eα,δ) = O(exp(−α2n)) Pr(E c I|E) = O(exp(−α3n)). Note that to prove the lemma, it suffices to show that the eigenvector v is not (O(α), δ)- compressible with high probability, or Pr(Eα,δ|E) is small, since that will lead to ρδ,α(v) < (αn)−1/2+ϵ with high probability and thus a contradiction with high probability. Indeed, we have $$\Pr({\cal E}_{\alpha,\delta}|{\cal E})\leq\Pr({\cal E}_{\alpha,\delta}\cap{\cal E}_{I}|{\cal E})+\Pr({\cal E}_{\alpha,\delta}\cap{\cal E}_{I}^{c}|{\cal E})$$ $$\leq\Pr({\cal E}_{I}|{\cal E}\cap{\cal E}_{\alpha,\delta})+\Pr({\cal E}_{I}^{c}|{\cal E})$$ $$=O(\exp(-\alpha_{0}n))$$ for some α0 > 0. For the incompressible case, we apply the continuous inverse Offord-Littlewood theorem to discretize the set of eigenvectors, and prove the following result analogous to the symmetric case in (Nguyen and Vu, 2011). Lemma 8. Suppose v *is an eigenvector of a symmetric Markov random matrix* An := D−1 n Wn with adjacency matrix Wn and the same properties defined in Theorem 2, and suppose there exists δ ∈ [n−B, n−B/2] *such that* q := ρδ,α(v) < (αn)−1/2+ϵ*, we have with probability* O(exp(−α0n)), To prove this result, we need the following useful lemmas. Lemma 9. *For any eigenvector-eigenvalue pair* (v, λ) and α > 0 *with* |λ| = O(n γ−1), suppose n−C < ρδ,α(v) =: q ≤ (αn)−1/2+ϵ, then with probability at least 1 − O(exp(−α0n)) there exists a subset N of R n × R *of size* O(n−n/2+O(αn)q−n+O(αn)) *such that, there exists* (˜v, λ˜) ∈ N *with the properties:* $$\begin{array}{l}{{I.\ |v_{j}-\tilde{v}_{j}|\leq\delta\,f o r\,1\leq j\leq n;}}\\ {{}}\\ {{2.\ |\lambda-\tilde{\lambda}|\leq n^{\gamma}\delta.}}\end{array}$$ Proof. Split {1, · · · , n} into sets of length differing by at most 1, I1, · · · , Im, m = 1α + 1, then we have the length of each set is greater than or equal to ⌊αn⌋, and its small ball probability is $$\rho_{\delta}(v_{I_{i}})\geq\rho_{\delta,\alpha}(v)=q,1\leq i\leq m.$$ Therefore, since q ≤ (αn)− 12 +ϵand n−C < q, there exists a GAP $Q_i=\left\{\sum_{j=1}^{r_i}a_{ij}w_{ij}:\begin{array}{l}a_j\in\mathbb{Z},\\ |a_{ij}|\leq N_{ij},\\ 1\leq j\leq r_i\end{array}\right\}$ that ... such that $$\operatorname*{sup}_{j\in I_{i}\setminus S}\operatorname*{inf}_{{\tilde{v}}_{j}\in Q_{i}}|v_{j}-{\tilde{v}}_{j}|\leq\delta,$$ $$/2{+}\epsilon/q),1\leq i\leq n$$ with volume vol(Qi) ≤ O((αn) −1/2+ϵ/q), 1 ≤ i ≤ m, for all except at most O(α 2n) indices from some exceptional set S. Further, for each Qi, we can quantize its generators wi1, · · · , wiri to the closest multiple of qδ, w˜i1, *· · ·* , w˜iri . This introduces an additional approximation error of at most $$\left|\sum_{j=1}^{r_{i}}a_{i j}w_{i j}-\sum_{j=1}^{r_{i}}a_{i j}\tilde{w}_{i j}\right|$$ $$\leq\operatorname{vol}(Q_{i})\cdot q\delta\leq(\alpha n)^{-1/2+\epsilon}/q\cdot q\delta$$ $$=(\alpha n)^{-1/2+\epsilon}\delta=O(\delta).$$ Next, for the coefficients from the exceptional set S, we also round them to the closest multiple of qδ and let the set of such values be R, which ensures that $$n^{-C}\leq\rho_{n\gamma\delta}(v)\leq n^{0.49}\rho_{\delta}(v).$$ $$\operatorname*{sup}_{j\in S}\operatorname*{inf}_{v^{\prime}\in R}|v_{j}-v^{\prime}|=O(\delta).$$ $$1207$$ $$\mathrm{t}\ B\geq4\gamma+1,$$ $$\|F v^{\prime\prime}\|_{2}=$$ $$2^{\gamma})=C$$ ∥F v′′∥2 = O(n −B/2+2γ) = O(n −1/2). Therefore, for fixed generators wij 's and a given S, we can construct a finite set of vectors $$\{{\tilde{v}}:{\tilde{v}}_{j}\in\cup_{i=1}^{m}Q_{i},\,\forall j\not\in S{\mathrm{~and~}}v_{j}^{\prime}\in R,\,\forall j\in S\}$$ of size at most $$\begin{array}{l}{{\left(m\operatorname*{sup}_{i}\operatorname{vol}(Q_{i})\right)^{n-|S|}|R|^{|S|}}}\\ {{\leq}O\left(\frac{1}{\alpha}\frac{(\alpha n)^{-1/2+\epsilon}}{q}\right)^{n}\cdot O((1/q\delta)^{O(\alpha n)})}\\ {{\leq}O\left(n^{-\frac{n}{2}+\epsilon n}q^{-n+O(\alpha n)}\right)O\left(n^{B\alpha n}\right)}\\ {{=}O(n^{-n/2+O(\alpha n)}q^{-n+O(\alpha n)}),}\\ {{=}O(n^{-n/2+O(\alpha n)}q^{-n}),}\end{array}$$ that approximates v within O(δ) for every coefficients. The third line uses *δ > n*−B and α = O(1); the fourth line assumes ϵ = O(α). Further, if we allow the generators to be variable and assume S to be unknown, the quantization mentioned previously and the crude bound of the number of possible S by 2 nenlarges the set of vectors by a factor of $$\begin{array}{l}{{O\left((1/q\delta)^{\sum_{i=1}^{m}r_{i}}\right)\cdot O(2^{n})=O(n^{O(m)})\cdot O(2^{n})}}\\ {{\ \ \ \ =O(n^{O(1/\alpha)})\cdot O(2^{n})=O(n^{O(\alpha n)}).}}\end{array}$$ For the eigenvalue, we also have there exists a set that covers its domain to be within δnγ with a set of size $$O\left(\frac{n^{\gamma-1}}{n^{\gamma}\delta}\right)=O(n^{B-1})\leq O(n^{O(\alpha n)}).$$ with probability at least 1 − O(exp(−α0n)). Composing the sets, we find the set N has size O(n−n/2+O(αn)q−n+O(αn)). Lemma 10. *For any eigenvector-eigenvalue pair* (v, λ) *of an symmetric Markov random matrix* An = D−1 n Wn with adjacency matrix Wn and the same properties defined in Theorem 2 *and let* (˜v, λ˜) ∈ N *be the tuple that well approximates it* as defined in Lemma *9, we have* $$\|A_{I^{c},I}\tilde{v}_{I}-u\|_{2}=O(\delta n^{\gamma}),$$ where AI,J *is the matrix formed by row indices* from I and column indices from J and u := (λ˜ − AI c,Ic )˜vI c . Proof. By symmetry, we can let I = {1, · · · , k} for k = ⌊αn⌋. Notice by definition we can split A as Ak G F⊤ An−k w v′ = λ w v′ , where v = [w⊤, v′⊤]⊤, and as a result, ∥F ⊤v˜I − (λ˜ − An−k)˜vI c ∥2 ≤∥F ⊤w − (λ − An−k)v ′∥2+ ∥F ⊤(˜vI − w)∥2 + ∥(λ˜ − λ)˜vI c ∥2+ ∥(λ − An−k)(˜vI c − v ′)∥2 =∥F ⊤(˜vI − w)∥2 + |(λ˜ − λ)˜vI c ∥2 + ∥(λ − An−k)(˜vI c − v ′)∥2 =O(n γ−1· δn1/2) + O(n γδ) + O(n γ−1· δn1/2) = O(n γδ). Now we are ready to prove Lemma 8. Proof. Let E be the event that there exists some δ ∈ [n−B, n−B/2] such that $$n^{-C}\leq\rho_{n^{\gamma}\delta}(v)\leq n^{0.49}\rho_{\delta}(v)=:n^{0.49}q$$ with q := ρδ(v) and G be the event that $$\|A_{I^{e},I}\tilde{v}_{I}-u\|_{2}=O(\delta n^{\gamma}),$$ where u := (λ˜ − AI c,Ic )˜vI c and (˜*v, λ*) well approximates (*v, λ*) as defined in Lemma 10. Let k := |I| = O(αn), from Lemma 9, we have Pr(G c) = O(exp(−α0n)). On the other hand, if E occurs, define AI c,I = [ak+1*, . . . , a*n]⊤, u = [uk+1*, . . . , u*n]⊤, then we have $$\begin{array}{r l}{{\mathrm{Pr}({\mathcal{G}}|{\mathcal{E}})\leq}}&{{\sum_{(w^{\prime},{\bar{v}},{\bar{\lambda}})\in{\mathcal{N}}}}}\\ {{}}&{{\mathrm{Pr}\left[\sum_{i=k+1}^{n}|a_{i}^{\top}w^{\prime}-u_{i}|^{2}=O(\delta^{2}n^{2\gamma+1})\right]}}\\ {{}}&{{\leq|{\mathcal{N}}|(\rho_{n^{\gamma}\delta}(v))^{n-k}\leq|{\mathcal{N}}|(n^{0.49}q)^{n-k}}}\\ {{}}&{{=O(n^{-0.01n+O(\alpha n)}),}}\end{array}$$ which is O(exp(−α0n)) if α is chosen small enough. As a result, we have Pr(E) ≤ Pr(G|E) + Pr(G c) = O(exp(−α0n)). ## A.3 Finite-Sample Learnability Of Asr-U: Unmatched Setup Proof. (Theorem 3) Under the assumptions that the discriminator is perfect and decomposable and the GAN objective is MMD with a linear kernel over the embeddings D(Y ) = PˆY, Eq. (8) becomes the following least squares regression problem $$\operatorname*{min}_{O^{\prime}\in\mathbb{R}^{|\mathbb{X}|\times|\mathbb{Y}|}}\|\hat{P}^{X}O^{\prime}-\hat{P}^{Y}\|_{F}^{2}.\qquad\mathrm{(26)}$$ Let Oˆ be the ERM of Eq. (26) and O be the true assignment matrix, by definition and triangle inequality, $$\begin{array}{l}{{\|{\hat{P}}^{X}{\hat{O}}-{\hat{P}}^{Y}\|_{F}}}\\ {{\leq\|{\hat{P}}^{X}O-{\hat{P}}^{Y}\|_{F}}}\\ {{\leq\|{\hat{P}}^{X}O-{P}^{Y}\|_{F}+\|{\hat{P}}^{Y}-{P}^{Y}\|_{F}.}}\end{array}$$ Apply the triangle inequality again, we have $$\begin{array}{l}{{\|\hat{P}^{X}(\hat{O}-O)\|_{F}}}\\ {{\leq\|\hat{P}^{X}\hat{O}-\hat{P}^{Y}\|_{F}+\|\hat{P}^{X}O-\hat{P}^{Y}\|_{F}}}\\ {{\leq2\|\hat{P}^{X}O-P^{Y}\|_{F}+2\|\hat{P}^{Y}-P^{Y}\|_{F}}}\end{array}$$ Note that if we replace any X(i) → X(i)′and let the resulting empirical distribution be PˆX′, $$\begin{array}{l}{{\left\|\|{\hat{P}}^{X}O-P^{Y}\|_{F}-\|{\hat{P}}^{X^{\prime}}O-P^{Y}\|_{F}\right\|}}\\ {{\leq\|({\hat{P}}^{X}-{\hat{P}}^{X^{\prime}})O\|_{F}\leq\frac{\sqrt{2L}}{n^{X}},}}\end{array}$$ $$\mathrm{and~similarly~for~}\hat{P}^{X}\mathrm{~and~}\hat{P}^{Y},$$ $$\left|\left\|\hat{P}^{X}-P^{X}\right\|_{F}-\left\|\hat{P}^{X\prime}-P^{X}\right\|_{F}\right|\leq\quad\frac{\sqrt{2L}}{n^{X}}$$ $$\left|\left\|\hat{P}^{Y}-P^{Y}\right\|_{F}-\left\|\hat{P}^{Y\prime}-P^{Y}\right\|_{F}\right|\leq\quad\frac{\sqrt{2L}}{n^{Y}}.$$ Therefore, we can apply Moivreid's inequality. Therefore, we can apply McDiarmid's inequality to obtain Pr "∥PˆX − P X∥F ≥ pL|X| √nX+ ϵ # ≤ e − nXϵ 2 L Pr "∥PˆXO − P Y∥F ≥ pL|Y| √nX+ ϵ # ≤ e − nXϵ 2 L Pr "∥PˆY − P Y∥F ≥ pL|Y| √nY+ ϵ # ≤ e − n Y ϵ 2 L . Moreover, let ϵ XX := √L|X| √nX +ϵ, ϵ Y X := √L|Y| √nX + ϵ, ϵ Y Y = √L|Y| √nY + ϵ, then by a union bound, we have $\begin{array}{c}\Pr\left[\|\hat{P}^X(\hat{O}-O)\|_F\geq\epsilon^{YX}+\epsilon^{YY}\right]\leq\\ \Pr\left[\|\hat{P}^X\hat{O}-P^Y\|_F+\|\hat{P}^Y-P^Y\|_F\geq\\ \frac{\epsilon^{YX}+\epsilon^{YY}}{2}\right]\\ \\ \leq\Pr\left[\|\hat{P}^{YX}\hat{O}-P^{YY}\|_F\geq\frac{\epsilon^{YX}}{2}\right]+\\ \Pr\left[\|\hat{P}^Y-P^Y\|_F\geq\frac{\epsilon^{YY}}{2}\right]\leq e^{-\frac{n^{X_{\epsilon}2}}{4L}}+e^{-\frac{n^{Y_{\epsilon}2}}{4L}}.\end{array}$ Therefore, we have with probability at least 1 − 1. Therefore, we have with probability at least 1 − e− nXϵ 2 4L − e− n Y ϵ 2 4L , $$\epsilon^{Y X}+\epsilon^{Y Y}\geq\|\hat{P}^{X}(\hat{O}-O)\|_{F}$$ $$\geq\|P^{X}(\hat{O}-O)\|_{F}-\|\hat{P}^{X}-P^{X}\|_{F}\|\hat{O}-O\|_{F}$$ $$\geq(\sigma_{\min}(P^{X})-\|\hat{P}^{X}-P^{X}\|_{F})\|\hat{O}-O\|_{F},$$ and combined with the bound on $\|\hat{P}^{X}-P^{X}\|_{F}$ we obtain with probability at least $(1-e^{-\frac{n^{X}\epsilon^{2}}{4L}}-e^{-\frac{n^{Y}\epsilon^{2}}{4L}})(1-e^{-\frac{n^{X}\epsilon^{2}}{4L}})$, $$\|\hat{O}-O\|_{F}\leq\frac{\epsilon^{YX}+\epsilon^{YY}}{\sigma_{\min}(P^{X})-\epsilon^{XX}}.$$ Assume the correct mapping is deterministic, so that Oxy ∈ {0, 1} and each row has only one nonzero element, then to achieve perfect ASR-U, we need for any x ∈ X and y ̸= G(x), $|\hat{O}_{xG(x)}-\hat{O}_{xy}|>0$ $\Longleftarrow1-|\hat{O}_{xG(x)}-O_{xG(x)}|-|\hat{O}_{xy}-O_{xy}|>0$ $\Longleftarrow1-2\|\hat{O}-O\|_\infty>0\Longleftarrow\|\hat{O}-O\|_F<\dfrac{1}{2},$ which occurs if $$\sigma_{\operatorname*{min}}(P^{X})>\epsilon^{X X}+2\epsilon^{Y X}+2\epsilon^{Y Y}.$$ $$\begin{array}{l}{\lceil\bot}\end{array}$$ ## A.4 Training Dynamic Of Asr-U To prove Theorem 4, we need the following lemma on the properties of the gradient of the softmax function based on (Gao and Pavel, 2017). Lemma 11. Let H(x) be the Jacobian matrix of the softmax function σ : R d7→ R d *with* σi(x) = e xi Pd j=1 e xj , then we have H(x) = diag(σ(x)) − σ(x)σ(x)⊤ and H(x) *is positive semi-definite* (PSD) with the null space span{1d}. 1209 Proof. Apply product rule of calculus, we have $$H_{i j}(x)=\frac{\partial\sigma_{i}(x)}{\partial x_{j}}$$ $$=\delta_{i j}\sigma_{i}(x)-\frac{e^{x_{i}}e^{x_{j}}}{(\sum_{j=1}^{d}e^{x_{j}})^{2}}$$ $$=\delta_{i j}\sigma_{i}(x)-\sigma_{i}(x)\sigma_{j}(x),$$ $$\operatorname*{g}(\sigma(x))-\sigma(x)\sigma(x)^{\top}.$$ and therefore H(x) = diag(σ(x)) − σ(x)σ(x)⊤. To show that H(x) is PSD, notice that $$\begin{array}{r l}{v^{\top}H(x)v=v^{\top}\mathrm{diag}(\sigma(x))v-(v^{\top}\sigma(x))^{2}}\\ {=\mathbb{E}_{I\sim\sigma(x)}[v_{I}^{2}]-\mathbb{E}_{I\sim\sigma(x)}^{2}[v_{I}]}\\ {=\mathrm{Var}(v_{I})\geq0,}\end{array}$$ where by Jensen's inequality, achieves "=" if and only if vi = σ⊤v = C, ∀i for some constant C. Next, we shall establish explicit formula for NTKs of the discriminator and the generator. For clarity, we will copy the formula for the discriminator and the generator used in our analysis: $$f_{\tau,l}(y)=\operatorname*{lim}_{m\rightarrow\infty}\frac{1}{\sqrt{m}}\sum_{r=1}^{m}v_{r}^{\tau,l}\operatorname*{max}\{W_{r y}^{\tau,l},0\},\tag{27}$$ $$P_{\tau,l}^{g_{t}}(y)=\mathbb{E}_{\tau,\tau,\tau,\tau}\left[Q_{\tau,l}(y|X)\right],$$ $$P_{l}^{g_{t}}(y)=\mathbb{E}_{X\sim P_{l}^{X}}\left[O_{t}(y|X)\right]$$ $$:=\mathbb{E}_{X\sim P_{l}^{X}}\left[\frac{\exp(U_{y}^{t\top}x)}{\sum_{y^{\prime}\in\mathbb{Y}}\exp(U_{y^{\prime}}^{t\top}x)}\right].\tag{28}$$ Lemma 12. For the NTKs of the discriminators defined by Eq. (27), we have KD,l ≡ KD,1, 1 ≤ l ≤ L and 1|Y|is an eigenvector of KD,1. Proof. For simplicity, we ignore the dependency on τ for the terms in the proof. First, by definition, we have $$\begin{array}{l l}{{\frac{\partial f_{l}(y)}{\partial W_{r}^{l}}=\operatorname*{lim}_{m\to\infty}\frac{1}{\sqrt{m}}\sum_{r=1}^{m}v_{r}^{l}e_{y}\mathbb{1}[W_{r y}^{l}\geq0],}}\\ {{\frac{\partial f_{l}(y)}{\partial v_{r}^{l}}=\operatorname*{lim}_{m\to\infty}=\frac{1}{\sqrt{m}}\operatorname*{max}\{W_{r y}^{l},0\}}}\end{array}$$ and therefore $ \mathbb{E}_{v^{l},W^{l}\sim\mathcal{N}(0,I)}\left[\frac{\partial f_{l}(y)}{\partial W^{l}_{r}}\overset{\top}{\longrightarrow}\frac{\partial f_{l}(y)}{\partial W^{l}_{r}}\right]=$ $ \lim_{m\to\infty}\frac{1}{m}\mathbb{E}_{v^{l},W^{l}\sim\mathcal{N}(0,I)}\sum_{r=1}^{m}\delta_{yy'}v^{2}_{r}1\left[W^{l}_{ry}\geq0\right]$ $ =\delta_{yy'}\frac{1}{m}\sum_{r=1}^{m}\mathbb{E}_{W^{l}_{ry}\sim\mathcal{N}(0,1)}[1[W^{l}_{ry}\geq0]]$ $ =\frac{1}{2}\delta_{yy'}$. On the other hand, $$\begin{split}&\mathbb{E}_{v^{l},W^{l}\sim\mathcal{N}(0,I)}\left[\frac{\partial f_{l}(y)}{\partial v^{l}}^{\top}\frac{\partial f_{l}(y^{\prime})}{\partial v^{l}}\right]\\ &=\frac{1}{m}\mathbb{E}_{v^{l},W^{l}}\left[\sum_{r=1}^{m}\max\{W_{r y}^{l},0\}\max\{W_{r y^{\prime}}^{l},0\}\right]\\ &=\begin{cases}\mathbb{E}_{v_{1}^{1},W_{1}^{1}}^{1}\left[\max\{W_{11}^{1},0\}^{2}\right]&\text{if}y=y^{\prime},\\ \mathbb{E}_{v_{1}^{1},W_{1}^{1}}^{1}\left[\max\{W_{11}^{1},0\}\right]^{2}&\text{otherwise.}\end{cases}\end{split}$$ Therefore, KD,l(*y, y*′) = $$\begin{array}{l l}{{}}&{{}}\\ {{}}&{{\{\mathrm{H}_{D,l}(y,y^{\prime})=}}\\ {{}}&{{}}\\ {{}}&{{\left\{\left(\frac{1}{2}+\mathbb{E}_{v_{1}^{1},W_{1}^{1}}\left[\operatorname*{max}\{W_{11}^{1},0\}^{2}\right]\right)\quad\mathrm{if~y=y^{\prime},}\right.}}\\ {{}}&{{}}\\ {{}}&{{\left.\mathbb{E}_{v_{1}^{1},W_{1}^{1}}\left[\operatorname*{max}\{W_{11}^{1},0\}\right]^{2}\quad\mathrm{~otherwise.}\right.}}\end{array}$$ Notice that the sum of every row in KD,l is $$\left(\frac{1}{2}+\mathbb{E}_{v_{1}^{1},W_{1}^{1}}\left[\max\{W_{11}^{1},0\}^{2}\right]\right)+$$ $$(|\mathbb{Y}|-1)\mathbb{E}_{v_{1}^{1},W_{1}^{1}}\left[\max\{W_{11}^{1},0\}\right]^{2},$$ and thus $\mathbf{1}_{|\mathbb{Y}|}$ is an eigenvector of $K_{D,l}$. Lemma 13. For the generator defined by Eq. (28), we have $$\begin{array}{c}K_{O_{t,x}}=\\ \mathbb{E}_{U_{1:|\mathbb{Y}|}\sim\mathcal{N}(0,I)}\left[(\mathrm{diag}(O_{x})-O_{x}O_{x}^{\top})^{2}\right].\end{array}\tag{29}$$ _Further, the null space of $K_{O_{t,x}}$ is $\mathrm{span}\{\mathbf{1}_{|\mathbb{Y}|}\}$._ Proof. For simplicity, we ignore the dependency on t for the terms in the proof. By chain rule, $$\begin{array}{l}{{\frac{\partial O_{x}(y)}{\partial U_{x^{\prime}y^{\prime}}}=\frac{\partial h_{y^{\prime}}(x)}{\partial U_{x y^{\prime}}}\frac{\partial O_{x}(y)}{\partial h_{y^{\prime}}(x)}}}\\ {{=\delta_{x x^{\prime}}(O(y|x)\delta_{y y^{\prime}}-O(y|x)O(y^{\prime}|x))}}\end{array}$$ $$1210$$ As a result, $$\begin{array}{l}{{\sum_{d,y^{\prime}}\frac{\partial O_{x}(y)}{\partial U_{d y^{\prime}}}^{\top}\frac{\partial O_{x}(y^{\prime\prime})}{\partial U_{d y^{\prime}}}}}\\ {{=\sum_{y^{\prime}}(O_{x}(y)\delta_{y y^{\prime}}-O_{x}(y)O_{x}(y^{\prime}))}}\\ {{(O_{x}(y^{\prime\prime})\delta_{y^{\prime\prime}y^{\prime}}-O_{x}(y^{\prime\prime})O_{x}(y^{\prime})).}}\\ {{=((\mathrm{diag}(O_{x})-O_{x}O_{x}^{\top})^{2})_{y y^{\prime\prime}}}}\end{array}$$ Take the expectation over U and put everything in matrix form, we obtain $$K_{O_{x}}=\mathbb{E}_{U\sim{\mathcal{N}}(0,I)}\left[(\mathrm{diag}(O_{x})-O_{x}O_{x}^{\top})^{2}\right].$$ Next we shall study the null space of KOx . From Lemma 11, we have Hx := diag(Ox) − OxO⊤ x is PSD with null space span{1|Y|}, and thus $$v^{\top}K_{O_{x}}v=\mathbb{E}_{U\sim{\mathcal{N}}(0,I)}\left[\|H_{x}v\|^{2}\right]\geq0,$$ with equality achieved if and only if $$H_{x}v=0,\forall x\in\mathbb{X}\Leftrightarrow v\in\operatorname{span}(\mathbf{1}_{\mid\mathbb{Y}\mid}).$$ We are now ready to prove Theorem 4. Proof. (Theorem 4) When the objective is MMD, the discriminator can be decomposed as $$a_{f_{\tau}}(y)=f_{\tau}(y)=\sum_{l=1}^{L}f_{\tau,l}(y_{l}),$$ we have $$\mathcal{L}_{t}(f)=\sum_{l=1}^{L}\mathbb{E}_{Y_{l}\sim P_{l}^{Y}}[f_{l}(Y_{l})]-\mathbb{E}_{Y_{l}^{\prime}\sim P_{l}^{X}O_{t}}[f_{l}(Y_{l}^{\prime})],\tag{30}$$ and the discriminator dynamic PDE Eq. (18) becomes: $$\partial_{\tau}f_{\tau,l}=K_{D,l}(P_{l}^{Y}-P_{l}^{X}O_{t})^{\top}.$$ Without much loss of generality, suppose we initialize f0,l(y) ≡ 0 and stop training the discriminator after τmax steps. The solution for the discriminator PDE is then simply $$f_{g_{t},l}=\tau_{\mathrm{max}}K_{D,l}(P_{l}^{Y}-P^{X}O_{t})^{\top}.\tag{31}$$ Plug this expression into the generator loss and apply Lemma 12, we obtain $$\begin{array}{c}{{{\mathcal{C}}_{t}(g_{t}):=\tau_{\operatorname*{max}}\sum_{l=1}^{L}\|P_{l}^{Y}-P_{l}^{X}O_{t}\|_{K_{D,l}}^{2}}}\\ {{=\tau_{\operatorname*{max}}\|P^{Y}-P^{X}O_{t}\|_{K_{D,1}}^{2},}}\end{array}$$ where $\|A\|_{K}=\sqrt{\mbox{Tr}(AKA^{\top})}$ is the kernelized norm of $A$ by kernel $K$. norm of A by kernel K. Further, plug Eq. (31) into the generator PDE Eq. (19), we obtain $$\begin{array}{c}{{\partial_{t}{\cal O}_{t,x}^{\top}=K_{{\cal O}_{t,x}}\sum_{l=1}^{L}P_{l}^{X}(x)K_{{\cal D},l}(P_{l}^{Y}-P_{l}^{X}{\cal O}_{t})^{\top}}}\\ {{=K_{{\cal O}_{t,x}}K_{{\cal D},1}(P^{Y}-P^{X}{\cal O})^{\top}\tilde{P}_{x}^{X},}}\\ {{=\sum_{x,y}K_{{\cal D},1}(P^{Y}-P^{X}{\cal O})^{\top}\tilde{P}_{x}^{X},}}\end{array}$$ where P˜X xis the x-th column of P X. Next, notice that $$\begin{array}{l}{{\frac{\partial\mathcal{C}_{t}}{\partial{O_{t,x y}}}}}\\ {{=}2\tau_{\operatorname*{max}}K_{D,1}(y,\cdot)(P^{X}O-P^{Y})^{\top}\tilde{P}_{x}^{X}}}\\ {{\Longrightarrow\frac{\partial\mathcal{C}_{t}}{\partial{O_{t}}}=P^{X\top}(P^{X}O-P^{Y})K_{D,1}.}}\end{array}$$ Then apply the chain rule, $$\begin{array}{l}{\square}\end{array}$$ $$\begin{array}{c}{{\partial_{t}\mathcal{C}_{t}=\mathrm{Tr}\left(\frac{\partial\mathcal{C}_{t}}{\partial O_{t}}^{\top}\frac{\partial O_{t}}{\partial t}\right)}}\\ {{=\sum_{x\in\mathbb{X}}\mathrm{Tr}\left(\frac{\partial\mathcal{C}_{t}}{\partial O_{t,x}}\frac{\partial O_{t,x}}{\partial t}^{\top}\right)=}}\\ {{-\tau_{\operatorname*{max}}\sum_{x\in\mathbb{X}}\|\hat{P}_{x}^{X\top}(P^{Y}-P^{X}O_{t})\|_{K_{D,l}K_{G,l}K_{D,l}}^{2}.}}\end{array}$$ Now, apply Lemma 12, we have $$\begin{array}{c}{{\partial_{\tau}f_{\tau,l}^{\top}{\bf1}_{|\mathbb{Y}|}}}\\ {{=(P_{l}^{Y}-P_{l}^{X}O_{t})K_{D,l}{\bf1}_{|\mathbb{Y}|}}}\\ {{=\lambda(P_{l}^{Y}-P_{l}^{X}O_{t}){\bf1}_{|\mathbb{Y}|}=1-1=0}}\\ {{\Longrightarrow{\bf1}_{|\mathbb{Y}|}\perp K_{D,l}(P_{l}^{Y}-P_{l}^{X}O_{t})^{\top},}}\end{array}$$ where λ is the eigenvalue of KD,l associated with 1|Y|, and thus $$K_{D,l}(P^{Y}-P^{X}O_{t})^{\top}\tilde{P}_{x}^{X}\perp\mathbf{1}_{|\mathbf{Y}|}.$$ As a result, using Lemma 13, we conclude that the kernelized residual vector ∂τ fτ,l is always perpendicular to the null space of the stepwise generator 1211 NTK $K_{O_{t,x}}$ for all $1\leq l\leq L$, $x\in\mathbb{X}$, and thus . $$\begin{array}{l}{{\quad\|K_{D,l}(P^{Y}-P^{X}O_{t})^{\top}\tilde{P}_{x}^{X}\|_{K_{G,l}}}}\\ {{\quad\geq\lambda_{G}\|K_{D,l}(P^{Y}-P^{X}O_{t})^{\top}\tilde{P}_{x}^{X}\|_{2}}}\\ {{\quad\geq\lambda_{G}\lambda_{D}\|P^{Y}-P^{X}O_{t}\|_{K_{D,1}},}}\end{array}$$ where $$\begin{array}{l}{{\lambda_{G}\geq\operatorname*{min}_{1\leq l\leq L}\lambda_{|\mathbb{Y}|-2}(K_{G,l})>0,}}\\ {{\lambda_{D}\geq\lambda_{\operatorname*{min}}(K_{D,1})>0.}}\end{array}$$ Summing over x, we obtain $$\partial_{t}{\mathcal{C}}_{t}\leq-\tau_{\operatorname*{max}}\lambda_{G}\lambda_{D}\|P^{X\top}(P^{Y}-P^{X}{\cal O}_{t})\|_{K_{D,1}}^{2}.$$ Under the assumption that P XO = P Y has at least one solution, we have P Y − P XO is in the range space of P X, which implies $$\begin{array}{c}{{\|P^{X\top}(P^{Y}-P^{X}O_{t})\|_{K_{D,1}}^{2}\geq}}\\ {{\lambda_{X}\|P^{Y}-P^{X}O_{t}\|_{K_{D,1}}^{2},}}\end{array}$$ for some λX > 0. Put together the results, we can bound the convergence rate of the generator loss by $$\begin{array}{c}{{\partial_{t}\mathcal{C}_{t}\leq-\tau_{\operatorname*{max}}\lambda_{G}\lambda_{D}\lambda_{X}\mathcal{C}_{t}}}\\ {{\Longrightarrow\mathcal{C}_{t}\leq\mathcal{C}_{0}e^{-\tau_{\operatorname*{max}}\lambda_{G}\lambda_{D}\lambda_{X}t}\xrightarrow{t\to\infty}0,}}\end{array}$$ which implies that $\lim_{t\to\infty}P^{X}O_{t}=P^{Y}$. ## B Reproducibility Checklist Synthetic language creation To create a synthetic HMM language, we need to specify the initial probability vector π, the transition probability matrix T, the generator matrix O and the maximal length of the utterances L. Initial probability: we create π by first uniformly randomly sampling each coefficient between [0, 1] and then normalizing the resulting vector by its sum. Transition probability: for the asymptotic setting, for all three languages, we control the number of eigenvalues m of its transition matrix using a disjoint union of identical sub-graphs with m eigenvalues, with the remainder of the nodes being self-loops. The parameters and the procedure used to determine them are as follows: - *Circulant graph*: only undirected cycles or equivalently, circulant graph with the action set {−1, 1}, are used. Since the distinct eigenvalues of an undirected n-cycle Cn are − cos 2πk n , k = 0, *· · ·* , ⌊ n−1 2⌋ + 1, we can create a Markov graph with |X| N nodes and n ± 1 eigenvalues by a disjoint union of ⌊ |X|N 2n−1⌋ C2n−1 graphs. In our phase transition experiment, we fix N = 2 and vary 10 *≤ |X| ≤* 14 and 2 ≤ n ≤ 20; - *De Bruijn graph*: an undirected de Bruijn graph DB(*k, m*) is a graph with k m nodes such that node i connects to any node j whose k-ary numerals v(i) and v(j) satisfies v2:m(i) = v1:m−1(j). Clearly, m is the in/out-degree of the graph. The eigenvalues of DB(*k, m*) are known to be cos iπ j , 0 ≤ i < j ≤ m + 1 (Delorme and Tillich, 1998). Therefore, we can create a Markov graph with |X| N nodes and at most n, n ≤ (⌊logk|X| N ⌋ + 1)2/2 distinct eigenvalues by a disjoint union of |X|N k √2m−1 DB(k, √2n − 1) graphs. For the phase transition experiment, we set the in/out-degree of the de Bruijn subgraphs to be 2 and the N-gram size N = 3, and we vary 8 *≤ |X| ≤* 11 and 2 ≤ n ≤ 32 with a step size of 2 for the latter. $$\lceil\!\!\!\perp\!\!\!\perp\!\!\!\perp$$ - *Hypercube*: an n-cube Qn is a graph with 2 n nodes such that node i connects to any node j with Hamming distance between their binary numerals dH(b(i), b(j)) = 1. The eigenvalues of the adjacency matrix of Qn is 1 − 2k n , k = 0, · · · , n. Therefore, we can create a Markov graph with |X| N nodes and n ≤ ⌊N log2*|X|⌋* eigenvalues by a disjoint union of ⌊ |X|N 2n ⌋ n-cubes. For the phase transition experiment, we fix N = 4, and vary 5 ≤ |X| ≤ 8 and 2 ≤ n ≤ 9. In the finite-sample setting, we create transition matrices for phase transition experiments using two different setups: - For the circulant graph, we vary its action set to be {1, · · · , d}, where d takes values from 2 to 81 with a step size of 8; - For the other two graphs, we linearly interpolate between the underlying graph TG and its Hamiltonian cycle TC as $$T=(1-w)T_{G}+w T_{C},\qquad(3)$$ with a weight w ∈ [0, 1]. In particular, for the de Bruijn graph, the weight for the cycle w takes 10 different values equally spaced between [0, 1]; for the n-cube, the weight w takes 10 different values equally spaced between [0.98, 1]. Generator matrix O: set by assuming |X| = |Y| and randomly permuting the rows of the |X*| × |X|* identity matrix. Sampling: in the asymptotic case, no sampling is needed and we simply set maximal length L = 20 for cycle graph and 10 for the other two graphs. For the finite-sample case, the synthetic speech and text datasets are created independently by sampling from the same HMM twice. For all three graphs, we sample n X = n Y = 2560 utterances for both text and speech with L = 40 for the de Bruijn graph and L = 80 for the other two graphs. Model architecture We use a one-layer linear generator with |X| input nodes and |Y| output nodes, with no bias. Next, for all experiments except the experiment on different generator averaging strategies, we use a one-layer CNN with |Y| input channels, 1 output channel and a 1 × L kernel with no bias. For the experiment on different averaging strategies, we use instead a sequence of 2-layer MLPs with 128 hidden nodes and ReLU activation function, one at each time step, as the discriminators. For all experiments, we disable the logits for special tokens and silences during training and testing. Training setting SGD with a learning rate of 1.0 is used to train the discriminator, while Adam with a learning rate of 0.005 is used to train the generator. The dataset is used as a single batch for all experiments, though we do not observe any significant drop in performance using smaller batch sizes. No weight decays or dropouts is used. Further, we alternatively train the generator and discriminator 1 epoch each, and reset the discriminator weight to 0 for the linear case and to random Gaussian weights using Xavier initialization in the nonlinear case. All experiments are conducted on a single 12GB NVIDIA GeForce GTX 1080Ti GPU. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✗ A2. Did you discuss any potential risks of your work? It is theoretical paper and has no significant risks per se as far as the authors concern ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 1,2,3,4 ✓ B1. Did you cite the creators of artifacts you used? Section 5, Appendix B ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? It uses open source and publicly available toolkit/data and the license and terms are listed in their websites ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We believe we use all the artifacts with their intended purposes ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Our data are synthetic and do not contain personal information ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? The documentations are available on the official websites of the artifacts ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4, Appendix B ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix B The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4, Appendix B ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4, Appendix B D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ozturkler-etal-2023-thinksum

{T}hink{S}um: Probabilistic reasoning over sets using large language models

https://aclanthology.org/2023.acl-long.68

Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evaluation) or in the provided context (few-shot in-context learning). However, recent studies show that even the more advanced LLMs fail in scenarios that require reasoning over multiple objects or facts and making sequences of logical deductions. We propose a two-stage probabilistic inference paradigm, ThinkSum, which reasons over sets of objects or facts in a structured manner. In the first stage (Think {--} retrieval of associations), a LLM is queried in parallel over a set of phrases extracted from the prompt or an auxiliary model call. In the second stage (Sum {--} probabilistic inference or reasoning), the results of these queries are aggregated to make the final prediction. We demonstrate the possibilities and advantages of ThinkSum on the BIG-bench suite of LLM evaluation tasks, achieving improvements over the state of the art using GPT-family models on thirteen difficult tasks, often with far smaller model variants. We also compare and contrast ThinkSum with other proposed modifications to direct prompting of LLMs, such as variants of chain-of-thought prompting. Our results suggest that because the probabilistic inference in ThinkSum is performed outside of calls to the LLM, ThinkSum is less sensitive to prompt design, yields more interpretable predictions, and can be flexibly combined with latent variable models to extract structured knowledge from LLMs. Overall, our proposed paradigm represents a promising approach for enhancing the reasoning capabilities of LLMs.

ThinkSum: Probabilistic reasoning over sets using large language models Batu Ozturkler Stanford University Stanford, California, USA [email protected] Zhen Wang Ohio State University Columbus, Ohio, USA [email protected] ## Abstract Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evaluation) or in the provided context (few-shot in-context learning). However, recent studies show that even the more advanced LLMs fail in scenarios that require reasoning over multiple objects or facts and making sequences of logical deductions. We propose a two-stage probabilistic inference paradigm, **ThinkSum**, which reasons over sets of objects or facts in a structured manner. In the first stage (**Think** - retrieval of associations), a LLM is queried in parallel over a set of phrases extracted from the prompt or an auxiliary model call. In the second stage (Sum - probabilistic inference or reasoning), the results of these queries are aggregated to make the final prediction. We demonstrate the possibilities and advantages of **ThinkSum** on the BIG-bench suite of LLM evaluation tasks, achieving improvements over the state of the art using GPT-family models on thirteen difficult tasks, often with far smaller model variants. We also compare and contrast ThinkSum with other proposed modifications to direct prompting of LLMs, such as variants of chain-of-thought prompting. Our results suggest that because the probabilistic inference in ThinkSum is performed outside of calls to the LLM, **ThinkSum** is less sensitive to prompt design, yields more interpretable predictions, and can be flexibly combined with latent variable models to extract structured knowledge from LLMs. Overall, our proposed paradigm represents a promising approach for enhancing the reasoning capabilities of LLMs. ## 1 Introduction Large language models (LLMs; Brown et al., 2020; Rae et al., 2021; Chowdhery et al., 2022) can recall a broad range of basic facts, recognize and mimic Nikolay Malkin Mila, Université de Montréal Montréal, Québec, Canada [email protected] ## Nebojsa Jojic Microsoft Research Redmond, Washington, USA [email protected] various forms in language, and efficiently extrapolate analogies in structure and meaning. These abilities allow LLMs to excel in zero-shot and few-shot tasks formulated as the generation or selection of a likely completion to a prompt. This formulation requires LLMs to perform **fast associative thinking**, in which each token of text in the sequence making up the answer is generated or scored in one pass through the model and, other than that, no intermediate information is created or retained. This fast thinking is made possible by the compression of information that is repeated in a variety of ways in large training datasets, within the LLM's weights. However, it is increasingly evident that when reasoning, or slow thinking, is required, failure modes of LLMs are revealed. In our usage, reasoning refers to the sequential manipulation of concepts that can be expressed in language. Tasks that require iterative retrieval of rarely stated knowledge, uncertainties over multiple objects or facts, or multiple steps of deduction are difficult even for the most advanced LLMs (Suzgun et al., 2022). In a recently designed suite of evaluations, BIG-bench (Srivastava et al., 2022), some of the tasks where the gap between machine and human performance is large involve inference sequences with nested counterfactuals (LOGICAL DEDUCTION), concepts introduced through definitions (CONCEPTUAL COMBINATIONS), etc. (see Fig. B.1). These are tasks where a human solver's intuitive feeling of '(in)coherence' is insufficient to produce the right answer, and a sequence of thoughts, along with the use of intermediate results, may be necessary to arrive at the solution, particularly when working memory is insufficient. We show several tasks in BIG-bench that can be addressed by a two-component mechanism, which we name **ThinkSum**1: 1**ThinkSum** is named by analogy with other algorithms 1216 A binne is any furry four-legged creature, and a bam is a simple dwelling. ## Direct P**Rompting** A binne bam is a place for people *(55%)* **animals** *(44%)* birds *(0.87%)* researchers *(0.022%)* CHAIN OF THOUGHT / AUXILIARY K**NOWLEDGE** A binne is any furry four-legged creature, and a bam is a simple dwelling. Examples of binnes: cat, mink, ferret, guinea pig, rabbit. Examples of bams: hut, cabin, cottage, shelter, shack. A binne bam is a place for people *(51%)* **animals** *(48%)* birds *(0.76%)* researchers *(0.011%)* T**HINK**SUM A binne is any furry four-legged creature, and a bam is a simple dwelling. binne = {cat, mink, ferret, guinea pig, rabbit} bam = {hut, cabin, cottage, shelter, shack} ⌉⌋ THINK (auxiliary LM calls to define sets) A cat cottage is a place for A rabbit cabin is a place for A mink shelter is a place for · · · X ⌉ ⌋ SUM (aggregate LM likelihoods) A binne bam is a place for animals (65%) people (34%) birds (1.5%) researchers (0.056%) Figure 1: An example adapted from the CONCEPTUAL COMBINATIONS (INVENTED WORDS) task, in which models must select the most likely completion of a phrase that includes nonce words whose definitions are given. **Top:** Direct prompting evaluates completion likelihoods normalized over the four answer choices ('people', 'animals', 'birds', 'researchers'). **Middle: Chain-of-thought**-like or **auxiliary knowledge** approaches would query a LLM or knowledge base for additional context. This example shows the brittleness entrusting all 'reasoning' to self-attention in linear text, especially in smaller models, which have stronger recency bias (Malkin et al., 2022): if we simply list generated examples as the additional context in the prompt, the recency bias causes the LLM to still give a higher probability to 'people' than to 'animals', simply because 'bam' (simple dwelling) examples are given after the 'binne' examples. **Bottom:** Our **ThinkSum** approach to this task queries a LLM (GPT-2 XL) to produce sets of examples defining the nonce words, then marginalizes over substitutions of these examples into the target phrase. - **Think** (fast thinking / association / knowledge retrieval step): creating an association of text spans with sets of strings. This process may involve generation from a language model, as is the case in Fig. 1, where the novel word 'binne' is associated with the set of strings {'cat', 'mink'*, . . .* } by prompting GPT-3 with the definition and asking for examples. Alternatively, it may consist solely of a scoring mechanism, resulting in the formation of a matrix of probabilities on which probabilistic inference is performed. - Sum (slow thinking / Summarization / reasoning step): probabilistic inference that aggregates generated strings or probabilities to produce the final answer. Summarization typically involves, and often entirely consists of, summing of probabilities of strings (computed in the **Think** step), as in Fig. 1, where the final word is assumed to be sampled from a mixture of possible substitutions of 'binne' and 'bam' words into the input. We discuss different ways to **Think** and to Sum in section §2, but we start with one example, illuswith 'expand' and 'aggregate' steps, such as MapReduce in distributed computing and sum-product in graphical models. trated in Fig. 1 (bottom), motivated by the CON-CEPTUAL COMBINATIONS (INVENTED WORDS) ![1_image_0.png](1_image_0.png) task in BIG-bench. In this task, the LLM is provided with the definitions of two invented words and asked to infer the most plausible sentence that uses a combination of the invented words. As the words are not common or consistently used in the training set, the LLM needs to understand and combine the definitions of the invented words to reason about the meaning of the combination. The LLM is queried to produce example instances of the invented words with the help of the definitions. These example instances can be substituted into the query in place of the invented words. By mapping individual spans of the text of interest to sets, we arrive at a mixture model (in this example, a mixture with 25 components for 5 possible replacements of each word), which can be used in the same manner the original LLM is used, either to score text or to generate it token by token. When we score all candidate completions using this mixture model and normalize over the four choices, the correct answer - that 'binne bams' are for animals and not people – becomes the most likely. An important difference between our **ThinkSum** and existing chain-of-thought-like prompt engineering methods (Wei et al., 2022; Kojima et al., 2022), is that our reasoning step is not reduced to a generation problem for the LLM, but is performed as a probabilistic inference external to the LLM. This reduces vulnerability to features of the prompt, such as accidental distraction of the LLM by spurious patterns (see Fig. 1, middle). Instead, we engineer the slow thinking process to make parallel calls to the LLM to query for intermediate information, then possibly perform programmatic recombination of strings (**Think**). The final reasoning step - in which likelihoods obtained from the LLM for the recombinations derived from earlier steps of the reasoning process are combined to make the final prediction - is left to classical probabilistic reasoning (Sum). In a sense, Sum replaces the self-attention mechanism over linear text, which is used as the sole 'reasoning' mechanism in chain-ofthought-like approaches that expect the intermediate 'thoughts' to take the form of generated tokens intervening between the input and output. Imposing an alternative reasoning system over an associative "knee-jerk reaction" system has an analogy with models of human cognitive processes (Tversky and Kahneman, 1974; Kahneman, 2011) that separate System 1 (fast thinking) and System 2 (slow thinking). System 2 acts as a 'controller' that can prime System 1 to appropriately bias its fast thinking. In the context of reasoning with deep learning models, System 2 has been interpreted as operating with sparse concepts that can be described in language (Bengio, 2017; Goyal and Bengio, 2020). Through repeated usage, the functions of System 2 become compressed into System 1 intuitions, in the same manner that iterative 'reasoning' functions of which smaller LLMs are not capable become zero-shot generation capacities for large LLMs. As is the case with humans, there is always the next frontier of problems where a trained model with remarkable 'intuition' needs to be slowed down. The main claim of this paper is that more is possible with LLMs of existing scale when they are used in concert with a wise controller that allows for probabilistic inference. ## 2 Thinksum 2.1 How To Think Here we list examples of the "fast thinking" that precedes the summarization stage. Elementary string manipulations. Standard ways to turn a question into a prompt that can be given to a LLM for generation or scoring involve choices (e.g., of the prompt format) that can be seen as being made by a controlling agent. The default approach to multiple-choice questions is to write them as Cloze tasks. However, there are nontrivial operations used in inference procedures that sometimes work better, such as: - **Order inversion**: Exchanging the order of the question and answers, as in Min et al. (2022). - **Premise erasure**: Deleting a part of the question. Removing a premise with which the answer is expected to have high mutual information is a step in inference procedures that aim to correct for bias towards answers with high unconditional likelihood (Zhao et al., 2021; Holtzman et al., 2021; Malkin et al., 2022). Substitution and normalization. An example is shown in Fig. 1. Elements from a set may be substituted in place of 'slot' words in a prompt, such as 'cat' substituted for 'binne' in the prompt "A binne bam is a place for". This operation can be combined with syntax-normalization steps that are reliably achieved by standard NLP tools, such as ensuring subject-verb agreement. Example and list generation. A LLM can be prompted to generate or score lists of words or phrases. We suggest and experiment with three instances of this: - **Example generation**: In Fig. 1, the LLM is prompted to turn a definition or characterizing property, such as 'simple dwelling', into a list of examples. This can be achieved with a prompt such as "A bam is a simple dwelling. Examples: 1.". The generated completion can be parsed into a set to be used later in the inference procedure. - **List extension**: A similar approach can also be used to hallucinate additional possible answers to questions, as we will show in some of the experiments. - **List of words**: Similar prompts provide an even simpler **Think** method that we use for scoring – but not generation - in several tasks. Just prompting a LLM with "List of words: , ", where and are words or phrases, and computing the likelihood of conditioned on "List of words: ," is a good measure of semantic relatedness of and . Fact generation. This way of **Think**ing associates an input word with a set of phrases in a similar manner to generating examples from a definition. It can be achieved with prompts such as "List facts about cats. 1." The generated facts are good targets for substitutions of other concepts ('dogs', 'galaxies') in place of the concept ('cats') about which facts are generated. A variation on this asks the LLM to generate differences between two concepts, as shown in Fig. 2 (right). Translation. The LLM can be prompted to convert between different forms of representing the same concept as a sequence of tokens. We use two basic examples of this in experiments: - Translation between languages by prompting the LLM in formats such as "French: J'adore les chats noirs. English:". A very similar approach can be used to convert non-alphabetic symbols, such as emoji, into words with similar meanings. - Converting text to formal (symbolic) structures, like turning a word problem into a collection of mathematical equations. ## 2.2 How To Sum Elementary inference. As above, we begin by listing existing standard ways of turning LLM outputs into answers, which we see as trivial cases of aggregation (Sum). - **Majority/minority vote (argmin/argmax)**: a component of most answer selection procedures. - **Ratio of likelihoods**: Likelihoods from different variants of the same prompt can be combined by considering their ratio or more general loglinear or other mixture. For example, this can be done to correct the likelihood of an answer conditioned on a question by its unconditional likelihood, in combination with the **Premise erasure** operation described above. Mixture (average) aggregation. A collection of prompts can be treated as the components of a mixture model over completions. An example is shown in Fig. 1, where substitutions of a set of words yield 25 different prompts. Likelihoods of the completion over these 25 prompts are averaged. Product aggregation. We use products of likelihoods in two different ways: - In a similar way as mixtures, but when the more natural probabilistic model has all elements of a set (of prompts) generating the answer, such as when a description or definition must be satisfied by all concepts in a set. - In a task where we are to determine whether a statement or its negation ′is true, we can compute the likelihood of both and ′ being true (as posterior over the tokens 'True' and 'False' in an appropriate prompt), then compare (True|)(False|′) ( is true and ′is false) with (False|)(True|′) ( is false and ′is true). ## 3 Experiments In this section, we perform case studies on three tasks from the BIG-bench suite to demonstrate the possibilities of the inference approaches discussed in §2. We also experiment with ten other tasks from BIG-bench; the best results are summarized in Table 1 and the methods, grouped by the style of **Think**ing and Summing, are described in Appendix (§A). All details of the tasks can be found in the Appendix (§C). Comparisons to direct prompting and algorithms that append retrieved or generated tokens to the prompt are given in §3.4. ## 3.1 **Conceptual Combinations: Invented Words** In INVENTED WORDS, two nonce words 1, 2 are defined and the correct statement must be chosen out of a set of statements = { } that begin with (possibly inflected forms of) "1 2" (Fig. 1). We use an **Example generation** prompt to obtain a set of example words fitting the definitions of 1 and 2. We thus obtain sets 1 and 2 of words that can be substituted for 1 and 2, respectively. We treat each statement as a template into which words 1 ∈ 1 and 2 ∈ 2 can be substituted by replacing with and normalizing the syntax to ensure subject-verb agreement. Denoting by ⟨1, 2⟩ such a substitution, we form a vector of probabilities by scoring the **Substitution** of each possible pair of words into each statement and performing **Mixture aggregation** and considering the **Ratio of likelihoods** with the template without substitution: $p_{j}=\frac{1}{|S_{1}||S_{2}|}\sum_{w_{1}\in S_{1},w_{2}\in S_{2}}p_{\text{LLm}}(s_{j}\langle w_{1},w_{2}\rangle)$, $p_{\text{LLm}}(s_{j})$ LLM() . The statement with highest likelihood under this normalized mixture, arg max , is selected. ## 3.2 Odd One Out We examine possible **Think** and Sum approaches in depth on the ODD ONE OUT task, in which the | GPT-3 (davinci) 𝑛-shot | ThinkSum | | | | | | | | |---------------------------------------|------------|--------|------|------|------|-------|-------------|----------| | Task | Avg. H | 𝑛 = 0 | 1 | 2 | 3 | GPT-3 | InstructGPT | GPT-2 XL | | INVENTED WORDS (§3.1) | N/A | 0.29 | 0.14 | 0.14 | 0.21 | 0.64 | 0.71 | 0.29 | | ODD ONE OUT (§3.2) | 0.80 | 0.27 | 0.20 | 0.23 | 0.23 | 0.80 | 0.84 | 0.71 | | FIVE OBJECTS (§3.3) | N/A | 0.23 | 0.29 | 0.28 | 0.32 | - | 0.77 | - | | SPORTS UNDERSTANDING (§A.1) | 0.71 | 0.50 | 0.50 | 0.50 | 0.50 | 0.71 | 0.74 | 0.54 | | KNOWN UNKNOWNS (§A.1) | 0.80 | 0.61 | 0.52 | 0.48 | 0.50 | 0.54 | 0.76 | - | | MISCONCEPTIONS RUSSIAN (§A.2) | 0.65 | 0.33 | 0.33 | 0.41 | 0.35 | 0.70 | 0.61 | - | | EMOJI MOVIE (§A.2) | 0.93 | 0.12 | 0.18 | 0.12 | 0.19 | 0.80 | 0.75 | - | | PARSINLU READING COMPREHENSION (§A.2) | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | - | 0.02 | - | | PHRASE RELATEDNESS (§A.3) | 0.74 | 0.37 | 0.42 | 0.52 | 0.59 | 0.85 | 0.87 | 0.79 | | CODENAMES (§A.3) | 0.18 | 0.01 | 0.11 | 0.16 | 0.19 | 0.37 | 0.41 | 0.36 | | NOVEL CONCEPTS (§A.4) | 0.67 | 0.47 | 0.47 | 0.56 | 0.56 | 0.72 | 0.75 | 0.50 | | CODE LINE DESCRIPTION (§A.4) | 0.60 | 0.32 | 0.32 | 0.28 | 0.32 | 0.83 | 0.90 | 0.77 | | LANGUAGE IDENTIFICATION (§A.5) | 0.16 | 0.16 | 0.12 | 0.13 | 0.11 | 0.57 | - | 0.30 | ![4_image_0.png](4_image_0.png) word in a set = {} that is *least* semantically related to the others must be chosen (e.g., Pick the odd word out: glass, head, arm, leg, hand, foot). List of words. We form a semantic relatedness matrix by querying the LLM with a **List of** words **Think** prompt for each pair of indices , : $P_{ij}=$ PLLM($w_{j}$ | "List of words: $w_{i}$,") This matrix is aggregated by averaging over (in log domain) and selecting the with lowest average, i.e., least likelihood of being generated by a product mixture of all words in the set: = arg min Î . This is a case of **Product aggregation**. Because this approach is the most successful with all model sizes we experimented with, its performance is reported in Table 1. Remarkably, near-average-human accuracy is maintained for all model sizes from GPT-2 Small to the largest GPT-3 model (Fig. 2 (left)). Fact generation. As an alternative approach, we use a **Fact generation** prompt. An effective way to mine facts for semantic relatedness tasks is to consider two items in the same context in order to get relevant facts regarding how items are related to each other (prompt in Fig. 2 (right)). The demonstration used in the prompt ensures that the LLM generates statements in an expected format, which can be parsed and used for probability computation later. Using this prompt, we obtain a collection of statements = {} about items . We treat each generated as a template into which different words can be substituted and denote by ⟨⟩ the **Substitution** of word into template . We then form a || × || matrix , defined 1220 by = LLM(⟨⟩). Then, we can perform Minority voting: we take argmin over and pick as the answer the most frequently occurring value, i.e., the item that is most often the least likely to fit a generated statement. Comparison with auxiliary knowledge approaches. We compare our method with a knowledge-based prompting method, herein referred to as auxiliary knowledge. In auxiliary knowledge, we prepend generated facts in the prompt before the question. Details of the prompt for auxiliary knowledge are provided in §D.3. In Figure 2 (middle), we show that the accuracy of Fact generation-based **ThinkSum** rises as the number of generated facts is increased, while the auxiliary knowledge technique peaks and then degrades as the prompt lengthens. Fig. 2 (left) shows how performance varies with the size of the LLM used for GPT-3, auxiliary knowledge and **ThinkSum** on ODD ONE OUT. Even with GPT-2 Small, **ThinkSum** dramatically improves over much larger largest zero- or few-shot models with or without auxiliary knowledge. A finetuned iteration of the largest GPT-3 model, textdavinci-002, is the only model variant that, with the help of auxiliary knowledge, achieves competitive performance with **ThinkSum**. This result provides experimental evidence for our claim that while new models may create qualitative jumps, **ThinkSum** can push the performance limits of smaller models. Latent variable models. As we have shown, the detection of the odd item can be performed with simple inference operations on items, facts, and their joint likelihoods. However, it is also possible to assume a latent structure in the items and facts, consisting of two or more clusters such that the facts and items belonging to a cluster can be freely interchanged. We describe a problem-specific latent variable model that enables selecting the facts that characterize the majority class, thus explaining why the minority item is ruled as the odd one out and helping interpret the decisions of the system. We model items ∈ and facts ∈ as being generated from a latent class ∈ {0, 1}. The distribution is modeled as: $$P(i,f)=\sum_{c}P(c)P(i|c)P(f|c)$$ where (, ) is a matrix of likelihoods from the LLM and the semantic components, groupings (|) and ( |), are derived from the matrix using a standard iterative expectation-maximization | Model | LoW | LVM | MV | |------------------|-------|-------|------| | text-davinci-002 | 0.84 | 0.67 | 0.70 | | text-davinci-001 | 0.74 | 0.77 | 0.70 | (EM; Dempster et al., 1977) inference procedure (see §E). Then, the score for an item belonging to a cluster and all other items ∈ , { ≠ } belonging to another cluster can be found as = Í,′≠ (|)() Î≠ (|′)(′). We show the effectiveness of the latent variable models in Table 2, where we analyze different methods for solving ODD ONE OUT using the InstructGPT variants text-davinci-001 and textdavinci-002. For the 'latent variable model' and 'minority voting' methods, we use number of differences = 5. The latent variable model is trained for 200 EM iterations. All probabilistic reasoning methods perform well, outperforming previous baselines reported in Table 1. Inference using EM, as well as the other approaches, can be seen as a Sum (inference) operation and can be applicable in other tasks of similar structure. ## 3.3 Logical Deduction In the LOGICAL DEDUCTION task, different types of items and clues regarding their order are provided (Fig. 3(a)). The goal is to select the correct statement from a set of statements about their placements. The ordering problems involve different types of objects (cars, birds, etc.) and orderings (by size, price, contest ranking, etc.). The task creators emphasize that this task requires parsing information about multiple objects and their relationships, understanding rules regarding ordered objects in various scenarios, and iteratively applying these rules. The LLM calls in the **Think** stage of **ThinkSum** can perform mappings required to parse information and understand rules, and the Sum stage can integrate mappings of objects to the placements under these rules. Here, we use a Translation prompt to map the given problem into a set of mathematical (in)equalities (Fig. 3(c)). The **Translation** prompt in Fig. 3(b), containing generic ordering statements and object names that are not used in the task as an in-context demonstration, is sufficient to perform the translation from natural language to equations. By prepending this ![6_image_0.png](6_image_0.png) demonstration prompt to a problem statement, we induce the LLM to map the objects in the problem to the set of strings corresponding to numbers from 1 to N , where N is the number of objects, and to produce a set of inequalities (Fig. 3 (c)). Once a translation of the problem into a set of inequalities is obtained, the Sum stage considers all possible mappings of items to indices to determine the mapping compatible with the discovered set of (in)equalities. This can be done by an external algorithm or by the LLM itself, as an LLM may be capable of understanding that, for example, "2>3" is a less likely string than "2>1" (see §D.2). Finally, the probability of each of the candidate statements, like "yellow_book=2", can thus be obtained by: $p($"yellow book=2"$|$$T$) $\infty$$\sum_{\bf b\in\{1,...,N\}^{N}}$$p_{\bf LLm}(\{T_{t}\langle{\bf b}\rangle:T_{t}\in T\}$ (1) $\cup\{$"yellow book=2"$\langle{\bf b}\rangle\})$ where b denotes the vector of positions for the N items (e.g., (5, 2, 3, 4, 1)), T = { T t } t = 1 is the set of inequalities obtained from the Translation prompt as a set of strings (e.g., "black_book<purple book"), and s ⟨ b ⟩ denotes the substitution of the corresponding entry in b in place of the object name in the string s (e.g., "4<5"). The term inside the sum is a case of Product aggregation : the LLM likelihoods of all strings in the set are multiplied. In summary, our solution to this task involves composition of two Think operations - a Translation into a set of equations and then Substitution of numbers in place of item names - and two Sum operations - a Product aggregation followed by a Mixture aggregation . (Other options are discussed below.) Results and discussion. For the 500 L OGI - CAL DEDUCTION problems with N = 5 objects, ThinkSum yields an accuracy of 77% (see Table 1 ), besting the average human performance. When the necessary summations become large, it becomes very unlikely that pure prompt engineering can be competitive, as even humans need paper and pencil to create and attend to many alternative solutions, and would likely translate the premises into a simpler notation using a single letter (representing a variable to which a numeric value can be assigned) to represent each object, rather than directly attending to the words in the problem statement. We also test an auxiliary knowledge method akin to chain-of-thought reasoning, where the information obtained with the prompt in Fig. 3 is appended to the LLM input. In particular, the problem, together with its translation into inequalities, is used as a prompt to each of the answer options, and then the option with the highest likelihood is chosen for the answer. This approach does improve over straightforward zero-shot GPT-3 scoring, but only raises the accuracy to 50% (see § 3.4 and Table 3 ). Optimizations, failure modes, and extensions. We have seen that InstructGPT is able both to translate logical deduction problems into (in)equalities (Fig. 3) and to evaluate each of them after replacement of items with position numbers (§D.2). We conclude that the Sum stage is there simply to search over all possible mappings, the way a human might. But, just as a human might use shortcuts in the search, the Sum stage of **ThinkSum** could be implemented in more or less efficient ways. For example, instead of summing over all possible assignments of the five items, we can avoid the ones that are not permutations of {1, 2, 3, 4, 5}. Furthermore, instead of using LLM from Fig. D.1 in (1), we can simply evaluate each inequality externally, giving a high constant probability for each inequality ⟨b⟩ that is true and a low probability when it is false, or the summing can be aborted whenever an incorrect statement is detected in a particular assignment b of positions to items. The prompt in Fig. 3(b) instructs the LLM to assign positive integers depending on the language used (e.g., the smallest object gets 1), but a common behaviour of the LLM is to generalize to assigning negative numbers, such as using −2 to represent 'second from the end' (or second-largest, etc.). To remain robust to such a behavior of the Think stage, we can convert negative position numbers into + + 1 before evaluating statements. However, a persistent failure mode of this kind of ThinkSum is that the LLM may translate inequality statements inconsistently with equality statements (e.g., by coding the leftmost item as 1 and being consistent with this choice for other equality constraints, but translating inequality constraints consistently with the reverse order, with 'left of' meaning >). Such failures can be addressed by careful engineering in the Sum stage, such as by summing out a binary latent variable indicating whether inequalities should be reversed. This increases the number of model evaluations, but also allows for robust auto-correction by the Sum stage of inconsistencies in the **Think** stage. ## 3.4 Comparisons With Chain-Of-Thought And Auxiliary Knowledge Approaches ThinkSum vs. auxiliary knowledge. Table 3 shows the comparison of **ThinkSum** with algorithms that append auxiliary knowledge as an oracle 'reasoning chain'. For PHRASE RELATED-NESS, auxiliary knowledge was generated using the "list differences" prompt shown in Fig. 2 (right). For both auxiliary knowledge and **ThinkSum**, 6 generated differences were used, as that was the ![7_image_0.png](7_image_0.png) best for auxiliary knowledge (see Fig. 2 (middle)). ThinkSum ODD ONE OUT and PHRASE RELAT-EDNESS are solved with the "list of words" prompt. For LOGICAL DEDUCTION, the **Think** prompt shown in Fig. 3 was included before the question in the prompt. In all cases, **ThinkSum** outperforms auxiliary knowledge. ThinkSum vs. chain of thought. Following Wei et al. (2022), we use "chain-of-thought (CoT) methods" to mean LLM scoring approaches that use insertion of generated tokens between the prompt and the target answer. The model is taught, using fewshot demonstrations, how to generate these intermediate tokens. Above we have compared **ThinkSum** with approaches that add *extracted* (from an auxiliary LM call), not *generated* (within the LM's linear workspace) token sequences after the prompt, for the ODD ONE OUT, PHRASE RELATEDNESS, and LOGICAL DEDUCTION tasks (see Table 3). With suitable examples, it may be possible for a CoT approach to replace the **Think** phase, by learning from demonstrations to generate the appropriate knowledge, and parts of the Sum phase, although inference over parallel evaluations of the LLM is no longer possible. Our auxiliary knowledge baselines make precisely that generous assumption and focus the comparisons on the need for parallel calls and reasoning over possibilities using probabilistic inference (instead of leaving it to the LLM to make the right conclusions from the list of extracted alternatives). Although we expect that appending facts in a standard format to the prompt would help the model more than teaching the model to generate these facts, we experimented with CoT approaches on several tasks. Table A.1 shows example demonstrations and prompt formats used for each task, and Table 4 shows the results using two variants of the largest GPT-3 model. As expected, **ThinkSum** outperforms CoT prompting on all tasks with all variants except KNOWN UNKNOWNS with the davinci variant, where direct prompting already performs well. (We did not evaluate **ThinkSum** with davinci on LOG-ICAL DEDUCTION because prompts like the one | GPT-3 (davinci) | GPT-3 (davinci-002) | | | | | |--------------------|-----------------------|------------------|----------|------|------| | Task | Direct | CoT ThinkSum CoT | ThinkSum | | | | ODD ONE OUT | 0.27 | 0.33 | 0.80 | 0.64 | 0.84 | | PHRASE RELATEDNESS | 0.59 | 0.55 | 0.85 | 0.79 | 0.87 | | LOGICAL DEDUCTION | 0.32 | 0.25 | - | 0.39 | 0.77 | | KNOWN UNKNOWNS | 0.61 | 0.70 | 0.54 | 0.74 | 0.76 | | INVENTED WORDS | 0.29 | 0.50 | 0.64 | 0.64 | 0.71 | in Figure 3 did not reliably produce outputs in the correct format; notice that CoT is barely better than random guessing (20%).) When interpreting these results, it is important to note that only one prompt format was evaluated for both CoT and **ThinkSum**, and the format of prompts and demonstrations can have a strong and often unpredictable effect on the LLM. We observed that CoT approaches are highly sensitive to minor changes in the prompt format or the construction of in-context examples, consistent with the known biases of in-context learning (Lu et al., 2022; Zhao et al., 2021). On the other hand, using structured, shorter components is more reliable, as demonstrated by the efficacy of the **Think** prompts used in **ThinkSum**. ## 4 Related Work Improvements to LLM inference. After the discovery of the in-context learning abilities of LLMs, there has been an explosion of interest in improving inference with LLMs in the zero-shot and few-shot setting (Brown et al., 2020; Chowdhery et al., 2022; Rae et al., 2021). One approach to improving the reasoning abilities of LLMs involves appending, or learning to generate, auxiliary knowledge within the prompt (Shwartz et al., 2020; Zelikman et al., 2022; Nye et al., 2021a). Recently, more general auxiliary knowledge or chain-of-thought prompting methods have been proposed (Wei et al., 2022; Wang et al., 2022b; Zhou et al., 2022a; Creswell et al., 2022; Wang et al., 2022a; Liu et al., 2022b), including those that allow a control flow external to the main LLM (Khot et al., 2022). Later, Kojima et al. (2022) showed zero-shot chain-of-thought prompting can improve performance on a variety of reasoning tasks. This method does not require any hand-crafted few-shot examples, which is a shared property with **ThinkSum**. (Nye et al., 2021b) observed that a dual-system approach where an associative "System 1" and a logical "System 2" can increase coherence of LLMs in tasks such as robust story generation and grounded instruction following. The two-step paradigm in **ThinkSum** is similar, where "System 1" is the (querying of the LLM for) fast thinking, and "System 2" is the probabilistic inference step. Brittleness of chain-of-thought prompting. Despite the recent success of chain-of-thought approaches, recent studies have raised concerns regarding the limitations of chain-of-thought approaches. Webson and Pavlick (2022) observed that instructive prompts perform similarly with misleading or intentionally irrelevant prompts. Additionally, Ye and Durrett (2022) showed improvements due to few-shot chain-of-thought are not observed in question answering, or natural language inference. More critically, few-shot prompts are highly sensitive to the order in which the samples are provided, the prompt format, and the selection of in-context examples, (Lu et al., 2022; Zhao et al., 2021). Thus, it is crucial to design techniques that are robust to such changes in the prompt. Inference as reasoning. Iterative inference over LLM outputs has been proposed for tackling true/false question answering and commonsense question answering (Jung et al., 2022; Liu et al., 2022a). Xie et al. (2021) presents a Bayesian inference perspective on in-context learning, and Dohan et al. (2022) formalizes and unifies existing prompting techniques in a probabilistic framework. Our work generalizes such approaches to perform arbitrary probabilistic inference outside of the LLM. ## 5 Conclusion In this paper we presented **ThinkSum**, a two-step probabilistic inference paradigm that reasons over sets in a structured manner. The fast thinking stage of **ThinkSum** allows elementary string manipulations as well as natural language prompting, which may enable numerous approaches to solve a natural language task. Even with far smaller model variants, **ThinkSum** achieves state-of-the-art results on ten difficult tasks in BIG-bench using GPT-family models. The two-step paradigm allows operating over sets instead of manipulating the prompt itself, preventing sensitivity to prompt format during the probabilistic inference in **ThinkSum**, which is performed outside of calls to the LLM. As a result, **ThinkSum** is more robust to prompt design, yields more interpretable predictions, and can be combined with many probabilistic inference approaches to tackle a diverse set of tasks. ## Acknowledgments The authors thank Alexandros Graikos, Sudha Rao, and Alessandro Sordoni for valuable discussions. ## Limitations Our proposed **ThinkSum** has demonstrated strong performance on thirteen challenging BIG-bench tasks. However, it is important to acknowledge certain limitations of the system. Firstly, as the number of objects or facts that are reasoned over increases, the computation cost will also rise. However, increasing the number of objects will also make the task harder, and direct prompting may cease to work at all (as we indeed observe in BIG-bench results, such as LOGICAL DEDUCTION with more than five objects), while ThinkSum offers a generalizable methodology, as the atomic **Think** operations do not increase in complexity as the number of objects grows. Secondly, when solving a new task, it is necessary to expend human effort to select specific operations in each step, as outlined in §2. This limitation is shared with prompt engineering of all kinds, including direct or chain-of-thought prompting: finding a prompt for a new task requires an often-cumbersome prompt engineering procedure. We have described **ThinkSum** as a general twostage paradigm, with an external inference step. This generality aims to facilitate the adaptation of ThinkSum to new tasks, with minimal modifications to the **Think** and Sum steps. Work on automating the prompt engineering procedure (Zhou et al., 2022b) is a promising path towards overcoming this limitation. An alternative to prompt engineering that does not require such human effort is tuning (i.e., differentiable end-to-end learning) of prompts or model parameters; however, this remains impractical for GPT-3-scale models, and attempts to tune models directly on symbolic reasoning chains have met with limited success (Kassner et al., 2020). Last but not least, **ThinkSum** has mainly been evaluated with GPT-3 (davinci) and InstructGPT (text-davinci-002) models. To further improve performance, it may be beneficial to apply **ThinkSum** to more recent instruction-tuned models such as Flan-PaLM (Chowdhery et al., 2022; Chung et al., 2022), text-davinci-003, ChatGPT, and GPT-4, which seem more capable of robustly performing Think steps. ## Ethics And Impact Statement We foresee no direct or immediate societal impacts arising from this work. However, we would like to emphasize that relying solely on LLMs' associative reactions to prompts can lead to undesired bias in the behaviour of systems. 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In *Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*, pages 1393–1404, Online. Association for Computational Linguistics. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, and Ed Chi. 2022a. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022b. Large language models are human-level prompt engineers. *arXiv preprint arXiv:2211.01910*. ## A Additional Tasks Descriptions of all the tasks studied here can be found in §C. ## A.1 Uncertainty And Hallucination Detection LLMs are prone to generating hallucinations that contain incorrect statements. The likelihoods of these statements are often dominated by short plausible patterns, which also makes it difficult for LLMs to evaluate their own uncertainty about a fact. Thus, detection (Liu et al., 2021; Zhou et al., 2021) and reduction of such hallucinations is crucial for widespread use of LLMs in real applications (Dziri et al., 2021; Shuster et al., 2021). ## A.1.1 Sports Understanding ![12_Image_0.Png](12_Image_0.Png) Figure A.1: Example posterior probabilities generated from text-davinci-002 for SPORTS UNDERSTANDING with the description *"threw a touchdown"*. The basketball player given in the question *Draymond Green* has a much lower posterior probability than the generated football players, from which we conclude the sentence *"Draymond* Green threw a touchdown." is implausible. Questions in SPORTS UNDERSTANDING ask to determine whether it is 'plausible' or 'implausible' that a professional sports player (e.g., 'Draymond Green', a basketball player) performed an action associated with a sport (e.g., 'threw a touchdown', an action in American football). It is implied that the combination of and is plausible if the sport with which player is associated coincides with the sport in which action is performed. We consider an approach that does not rely on identifying the latent variable (sport) as an intermediate step and is thus more generalizable to other domains. We use an Example generation **Think** prompt to produce a set of players who perform action , then do **Posterior computation** by normalizing the likelihood assigned by the LLM to each player in , as well as , performing action : $$\forall y\in S\cup\{x\}\quad p(y|a)={\frac{p_{\mathrm{LLM}}(``y\ a")}{\sum_{y^{\prime}\in S\cup\{x\}}p_{\mathrm{LLM}}(``y^{\prime}\ a")}}$$ The statement is considered to be implausible if the posterior on is sufficiently low (**Thresholding**) – see Fig. A.1. ## A.1.2 Known Unknowns Questions in the KNOWN UNKNOWNS task ask to determine whether the answer to a question is a certain precise concept or 'unknown'. Given a question (e.g., "What was the temperature in Cuzco on the day of the Emperor Vespasian's birth") and the candidate precise answer (e.g., 25◦C), we use a **List extension** prompt to generate a set of other possible answers to . We then do a **Posterior computation** over and the original answer , similar to that used for SPORTS UNDERSTANDING: $\forall y\in S\cup\{a\}\quad p(y|q)=\frac{P_{\text{LLM}}(``q?\ y")}{\sum_{y^{\prime}\in S\cup\{a\}}P_{\text{LLM}}(``q?\ y")}$. The answer is chosen if the posterior on is sufficiently high (**Thresholding**), and otherwise 'unknown' is chosen. ## A.2 Translation Between Languages And Writing Systems This extends the results on LOGICAL DEDUCTION in §3.3. ## A.2.1 Russian Misconceptions. In the MISCONCEPTIONS RUSSIAN task, the true statement must be chosen out of a pair of Russian sentences: a statement and its negation . We first describe an approach that does not use translation and already performs better than random guessing - and better than baseline methods that simply select the more likely of the two statements - using the largest GPT-3 model, which has sufficient knowledge of Russian. We compute the posterior over the two hypotheses " is true, is false" and " is false, is true": $\parallel\;\;1\;\;0\!\!\!\!\!\perp\;\;1$ . LLM("T" | "T or F? . Answer: ")LLM("F" | "T or F? . Answer: "), ## 1 Introduction The _Chandra_ satellite ([http://www.chandra.org/](http://www.chandra.org/)) is a very powerful instrument for studying the properties of the atmosphere. The _Chandra_ satellite is a very powerful instrument for studying the properties of the atmosphere. LLM("F" | "T or F? . Answer: ")LLM("T" | "T or F? . Answer: "). where T denotes True and F False in the actual prompt. This is a kind of **Product aggregation**. If the posterior on the first option is higher, is chosen as the true statement; otherwise, is chosen. This approach can be combined with a **Translation** prompt that produces translations of and into English, then uses these translations in place of and in the above computations. The approach can be further extended by sampling a set of translations and performing **Mixture aggregation** over the translations. Our reported result uses 10 generated translation for each statement, but it is only 2% higher than the result using one generated translation. ## A.2.2 Emoji Movie The multiple-choice EMOJI MOVIE task requires selecting the name of a movie from a list {} that is best described by a sequence of emoji symbols = (1 *. . .* ). An **Order inversion** prompt performs best on this task using the Davinci variant of GPT-3: choosing the answer arg max LLM( | "Emoji describing the movie "). We also attempt to use a **Translation** prompt to obtain a single-word English description of each emoji in , then score using arg max LLM(1 *. . .* | "Words describing the movie "). This approach performs slightly better than **Order inversion** alone using InstructGPT. However, it does not work with the base GPT-3 models, which do not as reliably translate emoji to English. ## A.2.3 Persian Qa We solve this standard extractive question answering task by simply translating the passage and question from Persian to English using a **Translation** prompt, generating English text, up to the first period or line break, following the concatenation of the translated prompt and question, and translating the result back to Persian using another **Translation** prompt. No few-shot algorithms have above zero accuracy on this task, indicating models' knowledge is sufficient to translate between languages (probably due to the presence of paired data in the training corpus), but insufficient to reason in the source language without passing through an intermediate latent variable, the translation. Finally, note that the accuracy is evaluated by exact string match, which contributes to the very low scores. We observed that the answers generated by **ThinkSum** are often paraphrases or terms related to the correct answers, which suggests that the result could be improved by using the knowledge that the target string always appears verbatim as a substring of the prompt. ## A.3 Semantic Relatedness This extends the results on ODD ONE OUT in §3.2. ## A.3.1 Phrase Relatedness Each question in the multiple-choice PHRASE RELATEDNESS task requires to determine which of a given set of words or phrases {} is related to a query phrase . We query the LLM for the likelihood of following a **List of words** prompt to form a vector of likelihoods: = LLM( | "List of words: , "). The answer selected is the one with highest likelihood, arg max (a trivial Sum operation). We note that this is also an instance of **Order inversion**: the query is scored following a prompt in which each of the candidate answers is substituted. ## A.3.2 Codenames Each question in CODENAMES requires selecting the words from a set {} that are most closely related to a query word . We form a vector in the same way as for PHRASE RELATEDNESS, then select the top entries in to produce the output.2 ## A.4 Substitution And Aggregation We give two other example of substitution and aggregation operations complementing the experiments on INVENTED WORDS (§3.1) and ODD ONE OUT (§3.2). ## A.4.1 Novel Concepts In the multiple-choice NOVEL CONCEPTS task, a set of words or phrases = {} and a set of statements = { } with third-person plural pronoun subjects ('They all...') are given, and the statement which is true for all items in must be determined. We treat each statement as a *template*, into which words can be substituted by replacing 'They all' with . Denoting by ⟨⟩ the substitution of into , we form a || × || matrix by scoring the **Substitution** of each word into each statement and considering the **Ratio of likelihoods** with the template without substitution: = LLM ( ⟨ ⟩) LLM ().We then perform **Product aggregation** to select the statement which is most likely to be generated by all words in the set. To be precise, the selected statement is arg max Î . ## A.4.2 Code Line Description We solve the CODE LINE DESCRIPTION task, in which a correct comment for a code snippet is to be chosen, using **Order inversion** and **Substitution** techniques. The greatest gain - amounting for all but 1% of the improvement relative to direct prompting - arises from **Order inversion**. Instead of ranking the candidate comments by their likelihood following the given code (i.e., (|)), we score each candidate comment by the likelihood of the code to follow formatted as a Python comment ((|"\# ")). We also experimented with **Substitution** and **Product aggregation**, which yielded an additional small accuracy gain. The code snippets are written in Python, which requires code to be formatted using an arbitrary but consistent number of spaces for line indentation. Using the knowledge that the correct comment should be most likely to generate the program in any of its equivalent representations, we scored comments in the manner described in the preceding paragraph, but with reformatted with different number of indentation spaces . The resulting scores were then multiplied over = 1, 2*, . . . ,* 6 and the highest-scoring comment selected. ![15_image_0.png](15_image_0.png) ## A.5 Other Tasks A.5.1 Language Identification The multiple choice LANGUAGE IDENTIFICATION task is similar in form and solution to CODE LINE DESCRIPTION and we include it for completeness to show the large difference that can be made by **Order** inversion. Rather than scoring all candidate language names ℓ following the given sentence (i.e., (|ℓ)), we instead score each language name ℓ by (|"The following is a sentence in ℓ:") and select the highest-scoring ℓ as the answer. ## B Big-Bench Lite Figure B.1 shows the performance margin between an average human and zero-shot GPT-3 on tasks in BIG-bench Lite, a select subset of tasks chosen by the authors of the benchmark to showcase the most important aspects of LLMs that need improvement. The vertical black bar separates the dataset into tasks where GPT-3 is already within the margin of just 10% compared to the average human accuracy, and the harder tasks (on the left). We show in the main text that some of these harder tasks, in particular EMOJI MOVIE, CONCEPTUAL COMBINATIONS,KNOWN UNKNOWNS, NOVEL CONCEPTS, MISCONCEPTIONS RUSSIAN and LOGICAL DEDUCTION, the margins are shrunk considerably, often exceeding average human performance. Other tasks in BIG-bench lite such as LOGIC GRID PUZZLE and SYMBOL INTERPRETATION share a similar structure to the addressed by **ThinkSum**, and thus could be investigated as part of future work. Another example where **ThinkSum** can be applied is the CODE LINE DESCRIPTION task, where we observe in our preliminary experiments that a simple order inversion can significantly outperform average human accuracy. ## C Task Descriptions C.1 Hallucination Detection Known unknowns. In this task, the aim is to measure the ability of LLMs to identify whether the answer to a question is known, or unknown. If the answer to the question cannot be known, an imaginary answer is generated, and the model is asked to choose between the imaginary answer and the word "Unknown". If the question has a correct answer, the correct answer and "Unknown" are provided as options. An example question from this task is *"How often did Abraham Lincoln cut his toenails?"*, and the options are *"Every Saturday night","Unknown"*. Similar to the sports understanding task, we can use an LLM to generate other plausible answers with the following prompt: *How often did Abraham Lincoln cut his* toenails? A possible answer is: \n 1. Every Saturday night. List 4 other possible answers in the same format as the first: \n 2. Then, the answer given in the question is predicted to be "known" if its posterior is higher by the second most likely option by some margin. In our experiments, we chose this value to be 1/ where is the number of examples, including the original option. Sports understanding. In this task, artificial sentences are constructed using the name of professional sports players and actions from particular sports. The model is then asked to identify whether the sentence is plausible, where a sentence is considered plausible if the sport of the player matches the sport of the action described in the sentence. An example from the task is *"Statement: Draymond Green threw a* touchdown. Plausible/implausible?" For **ThinkSum** to be able to parse outputs, GPT-3 generations need to be in a pre-determined format. While larger models can obey a format without demonstrations, for smaller models it is helpful to demonstrate the format with an example. Thus, we use the following prompt: "*List 4 examples of players* who scored a rabona goal.\n 1. Cristiano Ronaldo\n 2. Erik Lamela\n 3. Mario Balotelli\n 4. Angel Di Maria\n List 4 examples of players who threw a touchdown.\n1.". ## C.2 Translation Emoji movie. In this task, the LLM is presented with emojis that describe the plot of a movie, and the model is asked to guess the correct movie from the given options. The task evaluates the LLMs understanding of emojis, and its knowledge of popular movies. An example from the task is "What movie does this emoji describe? {lying-face-emoji} \n, pinocchio \n heat \n the big sleep, \n inception \n lilo & stitch Misconceptions Russian. In this task, the LLM is presented with sentences in Russian that contain misconceptions, and their true counterparts. The task evaluates if the LLM believes common misconceptions. One example of a target pair from the task, translated to English is as follows: "Vaccines do not cause autism. \n Vaccines cause autism." ParsiNLU reading comprehension. This is a standard extractive QA task in which a short passage and question are provided in Persian and the model must generate the answer, also in Persian. ## C.3 Semantic Relatedness Phrase relatedness. In this task, an input phrase, and four target phrases are presented to the language model. The language model is asked to identify the most related choice from the listed target options. An example from the task is "For each word or phrase, identify the most related choice from the listed options. \n Input: home town \n Option: town center \n Option: location \n Option: native city \n Option: home run" Codenames. In this task, the language model is asked to identify words associated with a given word. An example from the task is *"Try to identify the 2 words best associated with the word WHITE from the* following list: \n book, anchor, rainbow, shoulder, tunnel, sack, drum, pacific, page, mark, gear, glacier. Give your answer in alphabetical order." Odd one out. This task is aimed at evaluating the capability of LLMs in semantic relatedness. This task presents the model with four to six words, where all words except one word are semantically or grammatically related to each other. The goal for the language model is to identify the odd word. An example question from the task is *"Pick the odd word out: glass, head, arm, leg, hand, foot"*. ## C.4 Concept Understanding In the following tasks, the shared goal is to test the ability of LLMs on concepts over entities that have likely not been observed during training. Conceptual combinations: Invented words. In this task, the LLM is provided with two invented words, and their definitions in the input. The LLM is then asked to infer the most plausible meaning resulting from the combination of the invented words. As the words are invented, they are not present in the training set, and the LLM needs to understand and combine the definitions of the invented words to reason about the meaning of the combination. An example is: *"The word 'binne' means any animal* that is furry and has four legs, and the word 'bam' means a simple sort of dwelling. Question: Which of the following sentences best characterizes binne bams?". Similar to SPORTS UNDERSTANDING, we can use the following prompt to force the LLM to obey a fixed format: *"List synonyms of binne, separate* synonyms by comma:" Novel concepts. In this task, the LLM is presented with two to four disparate entities that typically would not co-occur frequently, but share an underlying conceptual or linguistic concept. The aim is to test the ability of the LLM to reason about entities that are unlikely to have been observed in the same context during training. In a multiple-choice setting, the LLM is given concepts relating to the entities, and is asked to generate the intended concepts against carefully chosen tempting distractors. The choices are not presented in the prompt. An example question from the task is as follows: *"What do the following have in* common? 1) bumble bees 2) 01010101 3) race cars", and the answer options are They all make noise, "They all are yellow, They all are binary, They all go fast, They all have stripes". ## C.5 Other Tasks Two multiple-choice tasks test the LLM's knowledge of specific domains, such as uncommon languages and programs. Code line description. This task requires the LLM to select the appropriate text description, out of four choices, for a short snippet of Python code, that could act as a comment describing the behaviour of a function. ## C.5.1 Language Identification. This task requires the LLM to select, out of eleven choices, the language in which a text is written. The languages represent a diversity of language families and writing systems and most are very infrequent in text found on the Internet. ## D Additional Experimental Details Our experiments are performed using four different sizes of GPT-2 (Small, Medium, Large, and XL) (Radford et al., 2019), GPT-3 with four different model sizes (ada,babbage,curie,davinci) (Brown et al., 2020), and InstructGPT (Ouyang et al., 2022). All GPT-3 experiments are run between August 2022 and September 2022 by using the OpenAI API. Our GPT-2 experiments were run in PyTorch (Paszke et al., 2019) and the Hugging Face Transformers library with a Tesla K80 GPU. ## D.1 Hyperparameters Maximum generation length. For tasks that require **example and list generation**, such as CONCEP-TUAL COMBINATIONS, KNOWN UNKNOWNS, and SPORTS UNDERSTANDING, we use max_tokens = 100. For **fact generation** in ODD ONE OUT with auxiliary knowledge and **ThinkSum**, we use max_tokens = 1000. Temperature. All GPT-2 experiments used temperature = 0.5. For SPORTS UNDERSTANDING and translation tasks, we used temperature = 0.5 to promote diversity of generated plausible options. All other experiments used temperature = 0 (greedy decoding). Number of examples (). For CONCEPTUAL COMBINATIONS we used = 2, and for KNOWN UNKNOWNS and SPORTS UNDERSTANDING we used = 4. Threshold. A threshold of 0.01 was used for SPORTS UNDERSTANDING. ![18_image_0.png](18_image_0.png) ![18_image_1.png](18_image_1.png) ## D.2 Using An Llm To Evaluate Inequalities. Using GPT-3 or external algorithms to evaluate inequalities. We show how a LLM can be used to find the truth values of inequalities involving small numbers, rather than resorting to calls to an external system that is aware of arithmetic. Fig. D.1 shows the matrix of posterior probabilities evaluated using InstructGPT (text-davinci-002) for strings of form "=", "<", ">" for , ∈ {1*, ..,* 9}. The probabilities are computed using prompts of the form "True or false: <? The answer is:" and normalizing the probability of the first token over the two options "true" and "false". These are the probabilities evaluated in (1). ## D.3 Knowledge Generation Details Post-processing. In our knowledge generation experiments for both **ThinkSum** and the auxiliary knowledge approach, we post-process the generated knowledge statements, to ensure formatting does not harm the predictions of each method. We first remove the extra spaces and the numbers and punctuation generated by the LLM before each fact while enumerating the items of the list. Later, we only keep sentences that contain only one of the objects of interest from the task, to make sure each sentence contains a knowledge statement into which any of the objects can be substituted. Finally, sentences with less than 3 words are removed as these are not likely to contain informative statements. Auxiliary knowledge. For auxiliary knowledge experiments, we prepend the generated and postprocessed knowledge statements before the question in the task. An example is illustrated in Figure D.2. ## D.4 Inference Cost For Thinksum The inference cost for ThinkSum scales with the number of parallel calls to the LLM, which is determined for each task by the number of **Think** prompts used and the number of objects for which likelihood computations are required at the Sum stage. For the tasks that we considered, as the number of **Think** prompts is not typically high and the prompts are short, the inference cost increase is marginal. In some cases, **ThinkSum** is faster than chain-of-thought prompting due to its ability to perform parallel calls to the LLM. For instance, **ThinkSum** is 23% faster for PHRASE RELATEDNESS compared to chain-of-thought approaches with 5 facts generated using InstructGPT. ## E Expectation Maximization We model items ∈ and facts ∈ as being generated from a latent class ∈ {0, 1}. The distribution is modeled as: $P(i,f\mid c)=P(i\mid c)P(f\mid c)\quad P(i,f)=\sum_{c}P(c)P(i,f\mid c)$ where (, ) is a matrix of likelihoods from the LLM and the semantic components, groupings ( | ) and ( | ). The iterative expectation-maximization (EM; Dempster et al., 1977) algorithm to derive ( | ) and ( | ) has the following updates: $$\begin{array}{c}{{Q(c\mid i,f)\propto P(i\mid c)P(f\mid c)P(c)}}\\ {{P(i\mid c)\propto\sum_{f}P(i,f)Q(c\mid i,f)}}\\ {{P(f\mid c)\propto\sum_{i}P(i,f)Q(c\mid i,f)}}\\ {{P(c)\propto\sum_{i,f}P(i,f)Q(c\mid i,f)}}\end{array}$$ where ( | , ) is the posterior distribution over the latent class that we maintain for each pair (, ). EM is run for 200 iterations, which is more than sufficient for convergence. | Words: blue, pink, magenta, banana All words are colors except banana. The odd one out is banana. | | |-----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ODD ONE OUT | Words: pencil, eraser, baby, rule, notebook All words are office supplies except baby. The odd one out is baby. For each word or phrase, identify the most related choice from the listed options. Input: Ice Cream Option: Antarctica Option: Titanic Option: Dessert Option: Sour Cream Ice cream is a type of dessert. Therefore, ice cream and dessert are the most related. Answer: Dessert | | PHRASE RELATEDNESS | What was the population of San Francisco in 2018? Option: 879,676 Option: Unknown The question asks the population of San Francisco in 2018, for which data can be collected. Population data for cities on a yearly basis is available, and thus the answer is known, and it is 879,676. Answer: 879,676 What was the population of San Francisco yesterday? Option: 891,402 Option: Unknown The question asks the population of San Francisco yesterday. As it is not possible to know the exact population of a city on a daily basis, the answer for this question is unknown. Answer: Unknown | | KNOWN UNKNOWNS | On a table, there are five plates: a black plate, a white plate, a green plate, a blue plate, and a red plate. The white plate is bigger than the green plate. The red plate is the biggest. The black plate is bigger than the blue plate. The black plate is smaller than the green plate. Which plate is the smallest? Option: The red plate is the smallest. Option: The black plate is the smallest. Option: The white plate is the smallest. Option: The green plate is the smallest. Option: The blue plate is the smallest. The black plate is bigger than the blue plate. The black plate is smaller than the green plate, as a result the green plate is bigger than the blue plate as well. The white plate is bigger than the green plate, which is bigger than the blue plate. As a result, the green plate is bigger than the blue plate. The red plate is the biggest, so it is bigger than the blue plate. Since all other plates are bigger than the blue plate, the blue plate is smallest. Answer: The blue plate is the smallest. | | LOGICAL DEDUCTION | The word 'borger' are animals who bite specific things for fun, and the word 'folpt' is a type of a chewy toy. Question: Which of the following sentences best characterizes borger folpts? Option: Borger folpts are leashes for animals. Option: Borger folpts are toys for infants. Option: Borger folpts are hard to swallow. Option: Borger folpts are pet toys. Borgers are animals, and folpts are chewy toys. Therefore, borger folpts are chewy toys that animals, or pets, can play with. Therefore, the answer is borger folpts are pet toys. Answer: Borger folpts are pet toys. | | INVENTED WORDS Table A.1: Few-shot demonstrations used for chain of thought (Table 4). | | Task: PHRASE RELATEDNESS Input: For each word or phrase, identify the most related choice from the listed options. Input: home town Option: town center Option: location Option: native city Option: home run Task: ODD ONE OUT Input: Pick the odd word out: glass, head, arm, leg, hand, foot Task: CONCEPTUAL COMBINATIONS: INVENTED WORDS Input: The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling. Question: Which of the following sentences best characterizes binne bams? Option: Binne bams are for pets. Option: Binne bams are typically furnished with sofas and televisions. Option: Binne bams are luxurious apartments. Option: Binne bams are places where people live Task: NOVEL CONCEPTS Input: What do the following have in common? 1) bumble bees 2) 01010101 3) race cars Option: They all make noise. Option: They all are yellow. Option: They all are binary. Option: They all go fast. Option: They all have stripes. Task: LOGICAL DEDUCTION: FIVE OBJECTS Input: On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book. The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right. Option: The {gray, red, purple, blue, black} book is second from the right. Task: CODENAMES Input: Try to identify the 2 words best associated with the word WHITE from the following list: book, anchor, rainbow, shoulder, tunnel, sack, drum, pacific, page, mark, gear, glacier. Give your answer in alphabetical order. Task: KNOWN UNKNOWNS Input: How often did Abraham Lincoln cut his toenails? Option: Every Saturday night Option: Unknown Task: SPORTS UNDERSTANDING Input: Statement: Draymond Green threw a touchdown. Plausible/implausible? Task: EMOJI MOVIE Input: What movie does this emoji describe? {lying-face-emoji} Option: pinocchio Option: heat Option: the big sleep Option: inception Option: lilo & stitch Task: MISCONCEPTIONS RUSSIAN Input: Vaccines cause autism. / Vaccines do not cause autism. *[in Russian]* Task: CODE LINE DESCRIPTION Input: for i in range(23): print(i) Option: prints values from 0 to 22, Option: computes first 10 prime numbers, Option: prints values from 1 to 10, Option: prints 'hello world' to the terminal Task: PARSINLU READING COMPREHENSION Input: To reduce fever, use over-the-counter medications such as acetaminophen and ibuprofen. Note the appropriate dosage and do not use them alongside other fever-reducing medications. You should not give aspirin to your baby without consulting a doctor. Babies under 6 months of age should not be given ibuprofen. What brings down fever? [in Persian] Task: LANGUAGE IDENTIFICATION Input: Given a sentence, select the correct language among the choices. Mi texaas o a mu vipin simi ri xavil ina vipin si Krais xa. E mi lamon o ne taa siak a xavil ina vipin si Krais e faxuvule xuvul pana vipin sina tefin aava lisan xolane, piau paaliu! Options: Assamese, Nandi, Patamona, Chavacano, Kapingamarangi, Turkish, Kara, Bribri, Gofa, Pali, Shatt ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? See "limitations" section on p.9. ✗ A2. Did you discuss any potential risks of your work? We see no risks beyond those already inherent in large language models, but we include Limitations and Ethics sections before the references (p.9). ✓ A3. Do the abstract and introduction summarize the paper's main claims? See the abstract and introduction. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** We use existing models and datasets. See following answers. ✓ B1. Did you cite the creators of artifacts you used? See the introduction, where we cite the BIG-bench suite. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Note that the BIG-bench benchmark, which we use, is licensed for use in academic work such as ours. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We use the BIG-bench suite. In the introduction, we describe it and summarize its motivations. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We used an existing large-scale benchmark to evaluate pretrained language models. We believe the data for the specific tasks we studied is very unlikely to contain such content, which should be clear from the task examples (last page of the paper), although this may not be true of all tasks in the BIG-bench suite. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? See the task descriptions in Appendix D. ✗ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. We used existing benchmarks (BIG-bench) for which extensive documentation exists. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** See Section 3 And The Appendix. ✗ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We use the OpenAI API to run experiments with GPT-3-family models, which accounts for the bulk of the computational cost. However, the exact cost is unknown. On the order of 250k queries were made to the API to obtain the results in the paper. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? See Appendix E. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Most of the experiments are deterministic. A few experiments use sampled decoding of large language models (at low temperature), and we describe the settings in Appendix E. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? See Appendix E. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

nimah-etal-2023-nlg

{NLG} Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist

https://aclanthology.org/2023.acl-long.69

In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks, yet they have a weak correlation with human. Human-aligned metrics (CTC, CtrlEval, UniEval) improves correlation level by incorporating desirable human-like qualities as training objective. However, their effectiveness at discerning system-level performance and quality of system outputs remain unclear. We present metric preference checklist as a framework to assess the effectiveness of automatic metrics in three NLG tasks: Text Summarization, Dialogue Response Generation, and Controlled Generation. Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation. We show that automatic metrics provide a better guidance than human on discriminating system-level performance in Text Summarization and Controlled Generation tasks. We also show that multi-aspect human-aligned metric (UniEval) is not necessarily dominant over single-aspect human-aligned metrics (CTC, CtrlEval) and task-agnostic metrics (BLEU, BERTScore), particularly in Controlled Generation tasks.

# Nlg Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist Iftitahu Ni'mah♣,♠ Meng Fang♦ Vlado Menkovski♣ **Mykola Pechenizkiy**♣ ♣ Eindhoven University of Technology ♦ University of Liverpool ♠ BRIN Indonesia {i.nimah, v.menkovski, m.pechenizkiy}@tue.nl, [email protected] ## Abstract In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks, yet they have a weak correlation with human. Human-aligned metrics (CTC, CtrlEval, UniEval) improves correlation level by incorporating desirable human-like qualities as training objective. However, their effectiveness at discerning system-level performance and quality of system outputs remain unclear. We present metric preference checklist as a framework to assess the effectiveness of automatic metrics in three NLG tasks: Text Summarization, Dialogue Response Generation, and Controlled Generation. Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation. We show that automatic metrics provide a better guidance than human on discriminating system-level performance in Text Summarization and Controlled Generation tasks. We also show that multi-aspect human-aligned metric (UniEval) is not necessarily dominant over single-aspect human-aligned metrics (CTC, CtrlEval) and task-agnostic metrics (BLEU, BERTScore), particularly in Controlled Generation tasks. 1 ## 1 Introduction Natural Language Generation (NLG) refers to an automatic process to generate texts in one or more language categories that satisfy multiple desirable human-like qualities. For example, in Text Summarization (Novikova et al., 2017; Maynez et al., 2020; Bhandari et al., 2020; Fabbri et al., 2021), NLG 1Our code is available at https://github.com/inimah/metricpreference-checklist. systems are expected to produce *coherent, consistent, fluent,* and *relevant* summarization outputs. In Dialogue Response Generation (See et al., 2019), the system outputs are mainly assessed based on aspects that are important in a typical human conversation, such as *naturalness* and *engagingness*. In Controlled Generation (Dathathri et al., 2020), the generation outputs are evaluated based on its relevance to the predefined topic category or sentiment category as control attributes. A standard evaluation protocol in NLG for assessing the above human-like qualities involves conducting a human evaluation study or running an automatic evaluation, or both ways. A human evaluation study improves the reliability of evaluation process, particularly when the assessment is done by experts. It is also often infeasible to translate human evaluation aspects into an automatic statistical metric formulation due to its multi-dimensional abstractive properties (Birch et al., 2013; Hashimoto et al., 2019). However, human evaluation is known to be more costly and does not scale well (Howcroft et al., 2020; Freitag et al., 2021). Utilizing automatic metrics, on the other hand, is cost-effective and more feasible for large-scale evaluation data. Recent works on automatic NLG evaluation metrics, such as CTRLEval (Ke et al., 2022), CTC (Deng et al., 2021), and UniEval (Zhong et al., 2022), have made progress in improving the correlation between automatic metrics and human up to 43% by developing human-aligned automatic metrics. Despite the advancements, there is a need for a standardized framework to assess the utility of these metrics in the context of discerning systemlevel performance. The reason is that an overall correlation score to human does not necessarily represents the metric effectiveness as an evaluation tool, as demonstrated by previous analysis studies on NLG automatic metrics (Caglayan et al., 2020; Hanna and Bojar, 2021; Sai et al., 2021, 2022). However, none of these works connect the correla- | Assessment Type | Description | Research Question | | | |---------------------------------------|-----------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|---------|----| | Transfer experiment | Correlation | analysis | between | au | | tomatic | metrics | and | human | judg | | ments in In-Domain (ID) and Out-ofDomain (OOD) use cases. | Is correlation level to human judgments consistent across ID and OOD use cases? | | | | | Aspect-level evaluation | Evaluating metric's effectiveness at identifying different levels of humanlike quality. | Is human-aligned metric better at distinguishing between different levels of human-like quality of system outputs? | | | | Aspect-level preference | Preference similarity between human and automatic metrics on identifying different levels of human-like quality | Do human and automatic metrics rank the quality of system outputs similarly? | | | | System-level evaluation | Evaluating the metric effectiveness at | Is human-aligned metric better at discerning performance of independent | | | | discerning system-level performance | NLG systems? | | | | | System-level preference | Preference similarity between human and automatic metrics on identifying the performance rank of the systems. | Do human and automatic metrics rank systems similarly? | | | | Table 1: Metric preference checklist. | | | | | tion analysis to the metric effectiveness at addressing the main objective of NLG benchmarking. That is, for distinguishing system-level performance. Our study addresses the above research gap by designing a metric preference checklist for measuring the effectiveness of automatic metrics in three NLG tasks: Text Summarization (TextSumm), Dialogue Response Generation (DiagGen), and Controlled Generation (CtrlGen). We introduce three types of assessment for evaluating NLG automatic metrics: Transfer experiment, Aspect-level evaluation, and System-level evaluation. The implications of our study are threefold: - Verifying the faithfulness of automatic metrics to human preference is a necessary component for a more accurate interpretation of evaluation outcomes (section §6.1). - Automatic metrics can be more discriminating than human (section §6.2). - Benchmarking NLG systems via pairwise comparison provides more insights into the strengths and limitations of the systems w.r.t. desirable human-like qualities (section §6.3). ## 2 Related Work Existing automatic metrics in NLG are mainly dominated by task-agnostic metrics - metrics that assess the quality of generation outputs without considering human evaluation aspects as context or objective of the evaluation task (Sai et al., 2022). Task-agnostic metrics are highly adaptable across NLG tasks because the adaptation does not require task-specific design. For example, BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004a), which represent string-based metrics, are largely adopted in Neural Machine Translation (NMT) and Text Summarization. Perplexity (Jelinek et al., 1977; Brown et al., 1992) - a reference-less metric, is a standard evaluation metric in a Language Modeling-based NLG tasks, including Controlled Generation (Keskar et al., 2019; Dathathri et al., 2020). BERTScore (Zhang* et al., 2020) has been largely adopted in diverse NLG tasks, including NMT (Colombo et al., 2022), Text Summarization (Deutsch and Roth, 2021), and Dialogue System (Yeh et al., 2021). Nevertheless, currently available task-agnostic metrics are weakly correlated to human judgment (Novikova et al., 2017; Sai et al., 2021, 2022). A low correlation score introduces a criticism on the capability of automatic metrics at identifying the different quality of system outputs and their potential usage to substitute a costly human evaluation study. Recent works (Deng et al., 2021; Ke et al., 2022; Zhong et al., 2022) have demonstrated that incorporating desirable human-like qualities as a training objective or contextual knowledge is the best-fit solution for improving the correlation level between automatic metrics and human. However, verifying whether a higher correlation represents a higher human preference for ranking the quality of system outputs and ranking system performance, and vice versa, remains an underexplored query. Compared to the recent analysis studies that focus on validating the robustness (Caglayan et al., 2020; Hanna and Bojar, 2021; Chen et al., 2021; Vu et al., 2022), explainability (Kaster et al., 2021), reproducibility (Chen et al., 2022), and fairness (Sun et al., 2022) of the NLG evaluation metrics, our study focuses more on a systematic assessment by connecting the link between correlation score to the practical use of the metrics in NLG evaluation. That is, (i) for discriminating the system outputs based on desirable human-like qualities; and (ii) for ranking system performance. ## 3 Metric Preference Checklist A standard evaluation protocol in NLG involves validating automatic metrics based on their correlation to human. Intuitively, a high correlation suggests a high agreement on discerning the quality of system outputs because low-quality outputs are penalized with lower scores, while high-quality outputs are rewarded with higher scores. However, currently available metrics are known to have a poor correlation to human. So, it is unclear to what extend current automatic metrics are capable of (i) identifying human-like quality of system outputs and (ii) discriminating performance between independent NLG systems. To further investigate the above questions, we pose several relevant research questions as a metric preference checklist, as presented in Table 1. We define the assessment tasks for evaluating NLG automatic metrics into five (5) fine-grained aspects, as follows: ## 3.1 Transfer Experiment (Zero-Shot) The assessment is designed to investigate whether the correlations between automatic metrics and human are consistent across NLG use cases. For measuring the adaptability of automatic metrics in new target domain, we define In-Domain (ID) and Out-of-Domain (OOD) use cases as follows 2: In-Domain (ID) For learnable or tunable automatic metrics, we define ID data as the dataset in which the metrics are introduced. For example, UniEval (Zhong et al., 2022) is introduced with a subset of data from SummEval (Fabbri et al., 2021) 2We follow the categorization of OOD that is discussed in previous work by Arora et al. (2021). and Topical-Chat (Mehri and Eskenazi, 2020). For task-agnostic metrics, such as Perplexity, BLEU, ROUGE, and BERTScore; the categorization of ID and OOD data is rather unknown. So, we define ID data based on a common sense perspective on how close a domain is to the NLG domain where the metric is introduced. For example, BLEU is originally introduced for a Neural Machine Translation (NMT) task (Papineni et al., 2002), yet the metric is widely adopted in Text Summarization (TextSumm). Thus, datasets in Text Summarization domain are considered to be ID samples for BLEU metric. Semantic-Shift OOD Samples are drawn from the same domain or NLG task where the metric is introduced, but they do not necessarily contain overlapped semantic features with ID samples. For example, let consider ID samples {*x, y*} are drawn from a subset of SummEval and TopicalChat datasets introduced in UniEval benchmarking (Zhong et al., 2022). Semantic-Shift OOD samples are the superset {*X, Y* }, which are drawn from the original benchmark datasets of SummEval by Fabbri et al. (2021) and Topical-Chat by Mehri and Eskenazi (2020). Domain-Shift OOD Samples are drawn from a new domain where the human evaluation aspects overlap with ID domain, but the background features are different. For example, CTRLEval (Ke et al., 2022) is firstly introduced and evaluated in a Controlled Generation task. Thus, samples from different NLG use cases, such as Text Summarization and Dialogue Response Generation are considered to be a Domain-Shift OOD samples. ## 3.2 System-Level Evaluation The task's objective is to evaluate the effectiveness of the evaluation metrics at discerning the performance difference between independent NLG systems. For quantifying the degree to which the scores produced by automatic metrics are able to discern the performance between two different NLG systems, we utilize **Kolmogorov-Smirnov** (KS) as a statistical distance metric D: $$P_{1},P_{2})=\operatorname*{sup}_{n}|P_{1}|$$ s|P1(s) − P2(s)|,(1) where P1 and P2 denote the empirical cumulative density function (cdfs) of scores based on metric M for system A and system B, where D ∈ [0, 1]. s denotes the evaluation scores as random variables | NLG Task | Benchmark | Data Abbreviation | #Samples | Human-like Aspects | | | | |-----------------------------------|--------------------------------------------|---------------------|--------------------|----------------------|----------|------|-----------------------------| | UBER-PPLM | (Dathathri | UBER-Topic | 14626 | Fluency, Relevance | | | | | et al., 2020) | | | | | | | | | CtrlGen | CTRL (Keskar et al., 2019) | CTRL-Topic | 3120 | Fluency, Relevance | | | | | CTRL-Eval UBER (Ke et al., | CtrlEval-Topic | 960 | Coherence, Consistency, Fluency, Relevance | | | | | | 2022) USR Persona chat (Mehri and | USR-PC | 900 | Understandable, | Natural, | | | | | MaintainsContext, Engaging, | | | | | | | | | Eskenazi, 2020) | UsesKnowledge, Overall | | | | | | | | DiagGen | USR Topical chat (Mehri and | USR-TC | 1080 | Understandable, | Natural, | | | | MaintainsContext, Engaging, | | | | | | | | | Eskenazi, 2020) | UsesKnowledge, Overall | | | | | | | | UniEval Topical chat (Zhong | UniEval-TC | 360 | Understandability, | Natural | | | | | ness, Coherence, Engagingness, Groundedness, Overall | | | | | | | | | et al., 2022) SummEval (Fabbri | et | al., | summEval | 5100 | Coherence, Consistency, Fluency, Relevance, Overall | | | | 2021) | | | | | | | | | TextSumm | Newsroom | (Grusky | et | al., | Newsroom | 1260 | Coherence, Informativeness, | | 2018) | Fluency, Relevance, Overall | | | | | | | | UniEval SummEval (Zhong | Unieval-summ | 1600 | Coherence, Consistency, Fluency, Relevance, Overall | | | | | | et al., 2022) | Table 2: Benchmark datasets in this study. | | | | | | | Boolean QA **UniEval** (Zhong et al., 2022) UniEval-summ, UniEval-TC summEval, Newsroom, USR-TC, USR-PC Category Metric ID Semantic-Shift Domain-Shift Human-aligned Surface-level **BLEU** UniEval-summ, summEval, NewsroomUniEval-TC, USRTC, USR-PC- - ROUGE UniEval-summ, summEval, NewsroomUniEval-TC, USRTC, USR-PC- - Semantic similarity **BERTScore** UniEval-summ, summEval, NewsroomUniEval-TC, USRTC, USR-PCUBER-Topic, CtrlEval-Topic - Language Model **Perplexity** UniEval-TC, USRTC, USR-PCUBER-Topic, CtrlEval-Topic UniEval-summ, summEval, Newsroom- Information alignment CTC (Deng et al., 2021) CTC-TC, summEval, Newsroom USR-TC, USR-PC UBER-Topic, CtrlEval-Topic ✓ Text Infilling **CTRLEval** (Ke et al., 2022) CtrlEval-Topic UBER-Topic, summEval, NewsroomUSR-TC, USR-PC ✓ Table 3: Automatic metrics and the corresponding datasets for transfer experiment. | ROUGE | UniEval-summ, summEval, Newsroom | UniEval-TC, | USR | |--------------------------|----------------|---------------|---| | TC, USR-PC | - | - - | | | CtrlEval-Topic UniEval-summ, summEval, Newsroom | - | | | | CtrlEval-Topic | | | | | CTC-TC, summEval, | USR-TC, USR-PC | UBER-Topic, | ✓ | | Newsroom | CtrlEval-Topic | | | | CtrlEval-Topic | UBER-Topic, | sum | | | mEval, Newsroom | USR-TC, USR-PC | ✓ | | | summEval, | News | | | | room, | USR-TC, | | | | UniEval-summ, UniEval-TC | USR-PC | UBER-Topic, | ✓ | | CtrlEval-Topic | | | | of metric M. D(.) = 0 indicates the two distributions are identical. ## 3.3 System-Level Preference The standard evaluation protocol in NLG consists of comparing the ranking of the systems based on the averaged evaluation scores. In many use cases, human and automatic metrics are in agreement about the system ranking. However, a prior study in Controlled Generation (Dathathri et al., 2020) shows that the assumption does not necessarily hold. Therefore, we design a task to compare the system ranking between automatic metrics and human as a similarity measure. Definition 1. System-level preference Let a and b denote two independent NLG systems. We adopt the concept of utility function in human evaluation (Ethayarajh and Jurafsky, 2022) to measure systemlevel preference. The relation a ≺ b means that b is strictly preferred than a if and only if the utility of a < the utility of b: $\tau\;\;\;u$. a ≺ b ⇐⇒ u(a) < u(b). (2) a ≻ b means that a is preferred than b, while a ∼ b means that a and b are indiscernible. In this study, the utility function u(.) is the averaged evaluation scores for a particular NLG system. Distance Measure To compute preference similarity between two metrics, we adopt Levenshtein distance, which calculates the minimum number of insertions, deletions, and substitutions required to change one sequence into the other sequence. $$d_{i}(\hat{P},P)=\mathrm{{Lev}}(\hat{P},P),$$ where P and Pˆ can be expressed as two sequential orders of system-level preference. For example, let consider P = a ≺ b and Pˆ = b ≺ a. Then, Levenshtein distance between Pˆ and P is 2. One of the limitations of Levenshtein distance is that the metric mainly calculates number of operations and does not take into account the sequence length differences. For example, the distance between two pairs P1 = {*cdabe, abcde*} and P2 = {*cbed, abcde*} are same, 4, even though the two pairs are composed of different sequences. To tackle this issue, we extend the distance metric formulation into a similarity measure by incorporating the total length of both sequences. Definition 2. Preference similarity The similarity S between the two sequences P1 and P2 can be defined as: $$S=\frac{(L_{1}+L_{2})-2*\mathrm{{Lev}}(P_{1},P_{2})}{(L_{1}+L_{2})},\qquad(4)$$ where S denotes the similarity score; L1 and L2 are the length of P1 and P2 respectively. Using the above formula, the similarity between the first example pair P1 = {*cdabe, abcde*} is 0.2, while the similarity of the second pair P2 = {*cbed, abcde*} is 0.11. ## 3.4 Aspect-Level Evaluation NLG evaluation involves addressing qualitative questions, such as "Can the automatic metrics identify aspect-specific quality that is inferred in the generated texts?" For example, a dialogue system that uses the preceding conversation as a context when generating a new question or response can be considered more engaging and more faithful to the context than the system that outputs repetitive responses. Thus, an automatic metric can be considered adequately *good* if it can discern between low and high-quality system outputs. For measuring the capability of metrics on discerning aspect-level qualities, we utilize **Kolmogorov-Smirnov (KS)**, as described in Eq. 1. ## 4 Experiment 4.1 Datasets And Metrics 3 $$({\mathfrak{I}})$$ We consider publicly available author-annotated benchmark datasets in three NLG tasks, as listed in Table 2. For automatic metrics, we consider commonly used task-agnostic automatic metrics in NLG evaluation and the recent proposal of humanaligned automatic metrics, as listed in Table 3. ## 4.2 Evaluation Setup ID vs OOD samples We classify benchmark datasets as target evaluation data into In-Domain (ID) and Out-of-Domain (OOD) categories, as shown in Table 3. The configuration of the data split is explained in section § 3.1. Level of quality We split samples in each benchmark dataset into three categories (if applicable) based on their corresponding human ratings: low quality (rating < 3); **moderate** (rating = 3); and high quality (rating > 3). The split is disjointly applied to each human evaluation aspect. Easy vs. Hard samples We split samples in each benchmark dataset into two categories: **Easy** and Hard. First, The rank of systems is obtained by averaging their corresponding human scores. **Easy** pair is composed of two systems with the large performance difference (e.g. systems with the lowest vs. highest human scores), while **Hard** pair contains systems with a close performance score. ## 5 Results, Analysis, And Discussion 5.1 Transfer Experiment Figure 1 shows the correlation level between automatic metrics and human ratings across NLG domains (ID and OOD). The result is summarized below. ## Low Correlation In Transfer Experiment. We observe that the correlation level of automatic metrics deteriorates sharply on target datasets with Semantic-Shift OOD and Domain-Shift OOD, particularly for tunable metrics, such as LM-based Perplexity, BERTScore, and human-aligned metrics (CTC, CtrlEval, UniEval). In general, the notably low correlation is observed in Controlled Generation (CtrlGen) task. **UniEval**'s correlation scores to human are considered moderately high in TextSumm (**0.341**) and DiagGen (**0.298**), but 3Details are provided in Appendix. ![5_image_0.png](5_image_0.png) the metric does not correlate well with human in CtrlGen (**0.006**). The result suggests the remaining challenges of adapting human-aligned automatic metrics to a new task or domain, regardless whether the target task has similar dimensions of desirable human-like qualities. ## 5.2 Aspect-Level Evaluation Figure 2-3 shows aspect-level evaluation of automatic metrics in Text Summarization (TextSumm) and Controlled Generation (CtrlGen). Our main observations are as follows: UniEval performs best in TextSumm Multiaspect human-aligned metric (**UniEval**) is observed to have superior performance (up to **0.579**) at distinguishing between different levels of quality in UniEval-summ and summ-Eval. However, the discriminative power of the metric is less visible in Newsroom and Controlled Generation (CtrlGen) task. In Newsroom, both **BLEU** and **BERTScore** are more discriminative than human-aligned metrics (CTC, CTRlEval, UniEval). BERTScore is comparably good in TextSumm BERTScore has an adequately good discriminative property (KS=**0.557**) in UniEval-summ, comparable to multi-aspect human-aligned metric (**UniEval**) with KS=**0.579**. In Newsroom, BERTScore consistently has a higher performance score in three sample categories (Lo-Hi, Lo-Mod, Hi-Mod) than human-aligned metrics (CTC, CtrlEval, UniEval). The finding suggests that the characteristics of datasets in Text Summarization domain adequately fit with automatic metrics based on semantic similarity of text embeddings. Higher KS is not necessarily highly agreeable Perplexity has the highest KS score for distinguishing between low and high quality outputs in UBER data. In contrast, the metric's aspect-level preference is not in alignment with human. ## 5.3 System-Level Evaluation Figure 4-6 show the effectiveness of the metrics at discerning system-level performance. Our main observations are as follows: BLEU is more discriminative in Newsroom In general, apart from BLEU in **Newsroom**, the remaining metrics' KS scores across three NLG tasks are considered low-to-moderate (≤ 0.6). We further inspect the reason why **BLEU** performs considerably well in Newsroom and discover that the data is mainly composed of outputs from two types of NLG systems: extractive vs. abstractive summarization systems. We also observe that in the Newsroom dataset, abstractive systems are often voted lower (averaged score = 2.5) than extractive systems (averaged score =**3.85**). Such characteristic of human ratings in Newsroom is a good fit for surface-level metric (BLEU), because the metric is more likely to penalize abstractive systems with zero score (0.0) and extractive systems with a higher score (e.g. 1.0). Automatic metrics are more discriminating than human When human struggles to distinguish between different system-level performances, automatic metrics are observed to be more discriminative. For example, in UniEval-summ (Hard), human has a considerably low score (KS =**0.145**), while **UniEval** has a higher KS score (KS =**0.269**). ![6_image_1.png](6_image_1.png) ![6_image_0.png](6_image_0.png) ![6_image_2.png](6_image_2.png) ![6_image_3.png](6_image_3.png) In Newsroom (*Hard*), BLEU, **BERTScore**, and UniEval are more discriminative (KS > 0.4) than human (KS=**0.163**). The possible reason for this particular use case is that *Hard* sample pairs are mainly composed of systems from a similar source or origin. For example, in Persona-Chat (USR-PC), the *Hard* sample category is composed of a pair of human reference systems: **Original Ground** Truth, **New Human Generated**. In Newsroom, Hard sample pairs consist of models from the same category (e.g. extractive-based systems). In UBERTopic, where low KS scores are more visible across human and automatic metrics, both *Easy* and *Hard* pairs consist of systems that are derived from one pretrained Language Model. Multi-aspect human-aligned metric is not always dominant In Persona-Chat (USR-PC), a single aspect human-aligned metric (CTC) has a higher KS score (**0.386**) and higher preference similarity (**0.888**) than a multi-aspect metric (**UniEval**), in which KS =**0.218** and similarity=**0.833**. In UBER-Topic, UniEval has the lowest KS score (**0.025** for Easy pairs, **0.027** for Hard pairs). We find that the less distinctiveness of **UniEval** is mainly due to a high alignment between **UniEval** and multi-dimensional human evaluation aspects. For example, in Persona-Chat (USR-PC), the agreement between human evaluation aspects is low. The three aspects (*Understandable, Natural, and Engaging*) yield a different system rank than the remaining aspects. Thus, a high alignment to interaspect disagreement may necessarily introduce a lower KS. ## 5.4 Visualizing Pairwise System Ranking We compare pairwise win fractions of NLG systems based on human ratings and automatic metrics in this study. The objectives are: (i) to better reason on why automatic metrics are more discriminating than human and (ii) to inspect the agreement level between metrics on system ranking. Notice that the results of pairing evaluation, as ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) ![7_image_2.png](7_image_2.png) shown in Figure 7, are consistent with our empirical findings in Figure 4-6, particularly for preference similarity with human. The system rankings based on BERTScore F1 and single-aspect CTC metrics are more similar to human on *Relevance*. Perplexity is more discriminating than human, but its similarity to human (*Fluency*) is lower. We also observe that although automatic metrics are more discriminating than human ratings in general, human voting on *Relevance* aspect can discern system-level performance more effectively than BERTScore and CTC-E Relevance. The result suggests that although a binary voting scheme in a human evaluation study may be less insightful than rating or error correcting protocol, the approach is cost-effective for performance selection based on a particular evaluation aspect. ![8_image_0.png](8_image_0.png) ![8_image_1.png](8_image_1.png) ![8_image_2.png](8_image_2.png) ## 6 Implications 6.1 Faithfulness To Human Preference We show that both low correlation scores and low discriminative power (KS scores) do not represent low faithfulness to human preference. In Controlled Generation, we observe that metrics with lower correlation and lower KS score, such as BERTScore-F1 and single-aspect CTC, on the contrary have a higher similarity with human on system-level preference and ranking. The result suggests the importance of verifying the metric's correlation score to its faithfulness to human preference, particularly for NLG use cases with poor correlation score (e.g. ρ < 0.2) and low agreement on system ranking. ## 6.2 Discriminating System-Level Performance We show that automatic metrics can be more discriminating than human, particularly when NLG systems are derived from the same training objective or encoding scheme. In contrast, for human evaluation aspect that is measured based on a binary voting scheme, such as *Relevance* in Controlled Generation, we observe that the scores based on the corresponding aspect are more distinctive than automatic metrics. ## 6.3 Guidance To System Selection We show that benchmarking NLG systems and evaluation metrics via pairwise comparison provides more insights into the agreement level for selecting the best-performed system. Low agreement between metrics on ranking system-level performance suggests at least two scenarios. **First**, the automatic metrics are not able to capture the human-like qualities inferred in texts as key factors for discriminating system outputs. **Second**, each metric focuses on a particular evaluation aspect among multi-dimensional human-like qualities. For example, *Fluency* focuses on penalizing repetition and grammatical errors, while *Relevance* focuses on measuring the closeness between the generation outputs and the given control attribute (e.g. topic category). For guiding the selection of the best-performed system, the second scenario allows a fine-grained assessment to scrutinize both strengths and limitations of the system based on desirable human-like qualities. ## 7 Conclusion We introduce the metric preference checklist as a framework for analyzing the effectiveness of currently available NLG automatic metrics. We show the importance of verifying the preference similarity between automatic metrics and human, regardless of their correlation scores. We also find that automatic metrics are more discriminating than human for discerning system-level performance, except for human evaluation aspect with a binary voting protocol. Lastly, we show the implication of current work on guiding the selection of the best-performed system based on pairwise system ranking. ## Limitations Robustness to perturbations Our empirical study does not explore the connection between the discriminative power of automatic metrics based on the proposed metric preference checklist and their robustness to simple perturbations or other natural language phenomena that may occur in texts or NLG use cases. Metric Fairness (Social Bias) Our study does not include an investigation of metric fairness or social bias issues that may be introduced by Language Model-based NLG evaluation Metrics. Single-aspect vs. Multi-aspect Our current empirical experiments mainly explore the discriminative power of evaluation metrics in single-aspect experiment setup (section §5.2). It may also be interesting to inspect to what extend the metrics can identify multi-aspect levels of quality, particularly when there exists disagreement between human evaluation aspects. For example, instead of disjointly splitting samples into {low *Engagingness*, moderate *Engagingness*, high *Coherence*}, samples can be divided based on the joint aspects, such as {low *Engagingness* and low *Coherence*}. Universal input-output structure Our experiments are mainly carried on publicly available author-annotated human evaluation benchmark datasets. Thus, we do not guarantee the universal input-output structure and a uniform naming system across datasets or tasks. For example, UniEval - Topical Chat data (UniEval-TC) (Zhong et al., 2022) and USR - Topical Chat (USR-TC) (Mehri and Eskenazi, 2020) use a different naming system for human evaluation aspects, yet the aspects refer to the same dimension of human-like qualities. Dependency of NLG Systems When comparing outputs from two different NLG systems, the systems are presumably independent. However, in many NLG use cases, this assumption is not fully accurate. For example, in Controlled Generation task, the systems originate from one pretrained Language Model as an encoder model. In inference or decoding stage, the encoder's probability outputs are used as inputs for multiple decoding schemes, such as the use of Log-Likelihood ranking, distance scoring as filter, etc (Dathathri et al., 2020), yielding n-systems to compare with. As a result of this setup, the generation outputs from these n-systems are often less diverse and less distinguishable than the outputs from two independent systems that do not share the same encoding scheme or training objective. ## Ethics Statement The purpose of this study is not to provide an immutable checklist to define what makes a good NLG evaluation metrics. Instead, the main objective is to introduce an extended perspective on how to assess metric-level performance beyond a correlation analysis. Our empirical experiments are carried on previously reported human evaluation data and NLG use cases under ACL Ethics Policy. Human evaluation datasets are extracted from peerreviewed scientific publications by Mehri and Eskenazi (2020) in ACL 2020; Dathathri et al. (2020) in ICRL 2020; Ke et al. (2022) in ACL 2022; and Zhong et al. (2022) in EMNLP 2022, as we have listed in our Experiment section. Our empirical findings are not necessarily representative for NLG use cases and datasets that are not covered in this study. However, our metric preference checklist can be easily adopted as fine-grained analysis to measure the effectiveness of new NLG automatic evaluation metrics, regardless of their overall correlation scores to human judgments. ## Acknowledgment We thank the anonymous reviewers for the constructive feedback, which has greatly improved the final version of the paper. This research has been partially supported by the Dutch Research Council (NWO) and Indonesian Endowment Fund for Education (LPDP) Scholarship under Beasiswa Pendidikan Indonesia (BPI) - ID Number 0003194/SC/D/9/LPDP2016. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. ## References Udit Arora, William Huang, and He He. 2021. Types of out-of-distribution texts and how to detect them. 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We list the modification of the paper based on reviewers' suggestion as follows. - We add the visualization of pairwise system ranking (section §5.4) to accomodate the reviewers' suggestion on linking the current work to the objectives of NLG evaluation, particularly for reasoning and guiding model selection, - We add **Implications** (§6) to improve the clarity of the paper, - We add **Related Work** in the main page (section §2) to clarify the positioning of current proposed framework, - We add **Background** in Appendix for providing detail information on NLG tasks and automatic metrics used in this study. - We improve the presentation of the paper by highlighting the core points and the implications of the study for future works. We also correct the grammatical errors found in the manuscript. The revision is particularly done for Abstract, Introduction, **Related Work**, and **Conclusion** section. ## A.2 Background A.2.1 Nlg Tasks Our empirical study is mainly carried on three (3) NLG tasks: Controlled Generation, Dialogue Response Generation, and Text Summarization. 1. Controlled Generation (CtrlGen) (Dathathri et al., 2020) is firstly introduced as Conditional Language Modeling (Keskar et al., 2019). In a general setup of CTRLGen, NLG systems are mainly trained based on a language modeling objective where the task is to predict next token or word given the preceding sequence of tokens. During inference stage, the trained system is perturbed with an external control attribute (e.g. topics, sentiment labels, aspects of sentiment) to generate texts that are semantically linked to the control attribute. All tasks in CtrlGen can be categorized as open-ended NLG tasks because ground truth human references are not provided by default. The quality of NLG system outputs is defined based on how semantically close the generation outputs to the corresponding control attribute, which can be aligned to several human-likeness aspects, such as *coherence,* consistency, fluency, and *relevance*. End-to-End NLG Systems We measure the performance of the following systems based on previous work on in Controlled Generation task (Dathathri et al., 2020): B: Baseline, unchanged pretrained GPT-2 Language Model. BR: Sampling B r times based on Log Likelihood ranking and distance-based ranking. BC: For each decoding step, update latent representation H˜t based on attribute model log likelihood loss. **BCR:** Combine approach from BC (updating H˜t) and BR (sampling and output ranking). 2. Dialogue Response Generation (DiagGen) is NLG use case in neural conversational domain, which can be further divided into an investigation of multi-turn dialogue response generation in a Persona Chat domain (See et al., 2019); or single response generation in Topical Chat and Persona Chat domains (Mehri and Eskenazi, 2020; Zhong et al., 2022). In this study, we focus on the evaluation of the latter category, where the quality of NLG system outputs is mainly assessed based on how good the machine responses to the preceding conversation. The *goodness* is mainly defined based on several aspects of human-likeness, such as *understandability, naturalness, coherence, engagingness,* and *groundedness*. End-to-End NLG Systems For Persona-Chat dialogue response generation (USR-PC), we compare the performance of the following systems based on (Mehri and Eskenazi, 2020; Zhong et al., 2022): Systems based on pretrained models in ParlAI 4for CONVAI2 competition (Colombo et al., 2022), i.e. Seq2Seq - a Sequence-to-Sequence model trained on Persona Chat, **KV-MemNN** - Key Value Profile Memory Network, **Language Model** - LSTMbased Language Model, **Seq2Seq**, and human annotated references - **Human Generated Old**, and Human Generated New. For Topical-Chat (USRTC and UniEval-TC), the systems are: Human annotations - Human Generated Old, **Human** Generated New, and four systems that origin from Transformers with different decoding systems, such as **Nucleus Decoding** p = 0.3, **Nucleus Decoding** p = 0.5, **Nucleus Decoding** p = 0.7, **Argmax** Decoding - greedy decoding. 3. Neural Text Summarization (TextSumm) (Grusky et al., 2018; Fabbri et al., 2021) focuses on a compression type of NLG where the main objective is to generate a concise version of texts, yet maintaining the salient information expressed in the document sources. The quality of system outputs is mainly assessed based on human evaluation aspects that fit into the objective of the task, such as *coherence, consistency, fluency,* and *relevance*. End-to-End NLG Systems In **Newsroom** dataset (Grusky et al., 2018), the systems are divided into **Extractive** approach: - **TextRank** (Mihalcea and Tarau, 2004) - unsupervisedly rank sentences in document to form a summary with an approach similar to Google PageRank (); - **Extractive Oracle (Fragments)** - Fragments F(*A, S*) are sets of shared sequences of tokens in A = ⟨a1, a2*, . . . , a*n⟩ and S = ⟨s1, s2*, . . . , s*m⟩ 4https://github.com/facebookresearch/ParlAI/ tree/main/projects/convai2 Abstractive approach: - **Sequence-to-Sequence (Seq2Seq) / Attention**, Tensorflow implementation of (Rush et al., 2015) 5 ## And **Mixed** Approach: - **Pointer Generator** (See et al., 2017) with copying (Vinyals et al., 2015; Gulcehre et al., 2016) and coverage (Tu et al., 2016) mechanism; - **Lower Bound (Lede-3)** - baseline approach, by copying the first sentence, first paragraph, or first k words as the summary In **summEval** dataset, systems are divided into Extractive: - **M1, NEUSUM** (Zhou et al., 2018) - scoring and selecting sentences based on hierarchical representation of a document; - **M2, BanditSum** (Dong et al., 2018) - contextual bandit approach of summarization where the document is seen as context and the sequence of sentences to be included in the summary as action; - **M3, LATENT** (Zhang et al., 2018) - views sentences in document as relevance binary labels of latent variables; - **M4, REFRESH** (Narayan et al., 2018) - a reinforcement approach by focusing on combining individually high-scoring sentences; - **M5, RNES** (Wu and Hu, 2018) - improving REINFORCE network by combining coherence model and ROUGE scores as a reward; - **M6, JECS** (Xu and Durrett, 2019) - scoring possible constituency-based compressed units; - **M7, STRASS** (Bouscarrat et al., 2019) - selecting sentences based on the closest embeddings to the document embedding; ## And **Abstractive**: - **M8, Pointer Generator** (See et al., 2017) – encoder decoder model where the decoder can generate samples based on the log-likelihood of words in vocabulary or copy words from the sentence source; 5https://modelzoo.co/model/textsum - **M9, Fast-abs-rl** (Chen and Bansal, 2018) – improves Pointer Networks with ROUGE-L reward of REINFORCE; - **M10, Bottom-up** (Gehrmann et al., 2018) - decoding method with content selection model to restrict the copy attention distribution of pretrained Pointer Generation Network during inference; - **M11, Improve-abs** (Krysci ´ nski et al. ´ , 2018) - augments the decoder with external LSTMbased Language Model and RL-based objective; - **M12, Unified-ext-abs** (Hsu et al., 2018) – aligns word-level attention scores of abstractive model with sentence level attention based on the probability outputs of extractive model; - **M13, ROUGESal** (Pasunuru and Bansal, 2018) - improves reinforcement approach by using three types of rewards: keyphrase-based salience, entailment-based, and ROUGEbased reward; - **M14, Multi-task (Ent+QG)** (Guo et al., 2018) - a multi-task learning approach with question and entailment generation as auxiliary tasks; - **M15, Closed book decoder** (Jiang and Bansal, 2018) - introduces copy-less and attention-less decoder on Pointer Generator Network; - **M16, SENECA** (Sharma et al., 2019) - combines entity-aware content selection module and abstractive generation module; - **M17, T5** (Raffel et al., 2022) - improves Transformers-based architecture by exploring the limitation of various transfer learning approaches; - **M18, NeuralTD** (Böhm et al., 2019) - define RL-based reward function based on 2500 human evaluation outcomes ; - **M19, BertSum-abs** (Liu and Lapata, 2019) – extend BERT with document-level encoder; - **M20, GPT-2** (Ziegler et al., 2019) - finetune GPT-2 on human summaries with a reinforcement learning framework; ![16_image_0.png](16_image_0.png) - **M21, UniLM** (Dong et al., 2019) - use three language model tasks as pretrianing objective: unidirectional, bidirectional, and sequence-tosequence prediction; - **M22, BART** (Lewis et al., 2020) - use denoising autoencoder for pretraining sequence-tosequence task; - **M23, Pegasus** (Zhang et al., 2020) - model is trained on documents after removing important sentences. ## A.2.2 Types Of Automatic Metrics Figure 8 shows the classification of metrics based on whether they are task-agnostic or humanaligned. We briefly discuss the categorization as follows: Task-agnostic metrics Task-agnostic metric refers to a category of NLG evaluation metric that does not need task-specific design or contextual knowledge prior to its utilization in a new NLG task. - **Surface-level** refers to automatic metrics that mainly assess the quality of system outputs based on word-overlapping or string-based matching techniques between the generation outputs and human-generated references. Our study specifically focuses on two surfacelevel-based similarity metrics: Bilingual Evaluation Understudy (**BLEU**) (Papineni et al., 2002) - computes n-gram precision of the generation outputs w.r.t. the corresponding ground truth references; Recall-Oriented Understudy for Gisting Evaluation (**ROUGE**) (Lin, 2004b) - measures how good the system at recalling n-grams from human text references; - **Semantic similarity** refers to metrics that measure the similarity between system outputs and text references based on the distance of textual features X in an embedding space X ∈ R. In many cases, the mapping from texts to the corresponding vector representations R requires a Deep Neural Network as an encoder, such as by utilizing pretrained Language Models (BERT) (Devlin et al., 2019) or word embeddings (Bengio et al., 2003; Mikolov et al., 2013a,b). In this study, we focus on investigating **BERTScore** (Zhang* et al., 2020) to assess to what degree the generation outputs are similar to the given contexts (e.g. text sources, reference summaries, contextual knowledge, or control attributes); - **Language Model-based metric** refers to evaluation metric that define the quality of generation outputs by linking the outputs to the surprisal score of an independent pre-trained Language Model - where the surprisal of a word is mainly described as the negative logarithm of the word probability given preceding context words. **Perplexity** (Brown et al., 1992) is an example of automatic evalution metric that is defined based on the entropy of Language Model. Given machine-generated texts as the inputs of a pretrained LM (e.g. GPT-2), **Perplexity** scores are the exponents of Negative Log-Likelihood (NLL) of the inputs; Human-aligned metrics refers to automatic metrics that translate multi-dimensional explainable human evaluation aspects (e.g. Coherence, Consistency) into measureable statistical features of an evaluation metric. We further classify humanaligned automatic metrics into two categories as follows: - **Single-aspect** views multi-dimensional human-like aspects or qualities as independent entities. - CTC (Deng et al., 2021) - is an automatic metric that the main objective is to align information between input, context, and output texts in **Compression**based NLG (Summarization), **Transduction**-based NLG (Style Transfer), and **Creation**-based NLG (Dialogue Response Generation). The alignment function is estimated by Embedding Matching (E**), Discriminative Model (**D), and Aggregated Regression (R). For example, in a compression task, **Consistency** aspect is described as the average of the alignment score (fE(.), fD(.), or fR(.)) between the summarization outputs y and the source x. Although CTC metric assesses the quality of system outputs based on multiple human evaluation aspects, the aspects are measured independently. Recent report () also discloses that CTC scores are bias to particular human-like aspect or quality. For example, **CTC-E Consistency** is highly correlated to consistency score based on human ratings, but it cannot explain the other human evaluation aspects. Therefore, our study classifies the metric as single-aspect human-aligned metric; - **CtrlEval** (Ke et al., 2022) - is unsupervised reference-less metric in Controlled Generation (Dathathri et al., 2020). The metric translates three human evaluation aspects: Consistency, Coherence, Relevance into a **Text Infilling** objective. That is, given the input I = (*X, a, Y* ) consisting of prefix sentence X, control attribute a, and the generation output Y , the score is calculated by projecting pair of sequences from I to N-number of pattern evaluators, where each pattern evaluator's score is estimated by the log probability outputs of pretrained model.; - **Multi-aspect** introduces a unifying perspective of multi-aspect human-like qualities via multi-task and continual learning objectives. - **UniEval** (Zhong et al., 2022) - re-frames evaluation aspect as a Boolean Question Answering (QA) objective. For example, for a **Coherence** aspect, given a summarization output and the corresponding document source, the metric calculates the performance score by modeling a binary classification task (Yes/No) for a question "*Is this a coherent summary of* the document?". Given n-multi dimensional aspects d = (d1*, . . . , d*n), the generation outputs x, reference texts y (if applicable), and context c, the quality of the system outputs is measured based on the probability of the system generating words that can be either classified as positive and negative samples for addressing question qi: $$\frac{P(\mathrm{``Yes''}|x,y,c,q_{i})}{P(\mathrm{``Yes''}|x,y,c,q_{i})+P(\mathrm{``No''}|x,y,c,q_{i})}$$ (5) A.3 Assessment setups Data Preprocessing - **summEval, Newsroom, UniEval-summ** (**TextSumm**) - We use standard data preprocessing: we remove punctuation and nontextual (i.e. numeric and abbreviation) features; we also substitute latin abbreviation, such as i.e. to *id est* and e.g. to *exempli gratia*; prior to using the data to calculate the scores based on **Perplexity, CTC, CtrlEval,** and **UniEval** metrics. Specific to **CtrlEval**, we mainly utilize tf-idf weights in (Ke et al., 2022) 6, but we additionally generate relevant prompt and verbal dictionary for the summarization task. as shown in Table 4. - USR-PC, USR-TC, UniEval-TC (**DiagGen**) - Specific to CTC-based evaluator, the format of references (list of personas) as relevancebased attribute is adjusted accordingly to follow the input-output structure of the pretrained evaluator. That is by transforming lineseparable personas into a single line of text input separated by a character "||". - **UBER-Topic, CTRL-Topic, CtrlEval-Topic** (**CtrlGen**) - Data preprocessing follows the procedur in Text Summarization task. Since the nature of benchmark datasets in Controlled Generation is reference-less and openendedness - no human-generated texts as 6https://github.com/thu-coai/CTRLEval ground truth references, we use the concatenation between control attribute (topic category, such as "Science") and its corresponding list of relevant keywords as a means of reference. References and Human-like Aspects Our study uses the following frame of references, which are dependent to the target NLG evaluation task or benchmark dataset and the characteristic of automatic metrics: - summEval (**TextSumm**) - The dataset uses n-references (n = 11) as ground truth humangenerated summaries. For each system output and the corresponding references, the score based on BLEU, ROUGE, **BERTScore**, and human ratings (**Coherence, Consistency, Fluency, Relevance**) are already included in dataset. For BLEU, **ROUGE**, and BERTScore, we average the metric scores based on 1-reference and 11-references. Our work additionally compute the scores based on **Perplexity, CTC, CtrlEval,** and UniEval metrics. **Perplexity** mainly uses the system's outputs as the input x of the metric. For CTC, we use 1-reference only as the ground truth target and average the scores based on embedding-based CTC (CTCE), discriminator-based CTC (CTC-D), and regressor-based CTC (CTC-R) w.r.t. the two aspects of evaluation: **"Consistency"** and "Relevance". The inputs for CTC metric are x = {*docs, hypos, refs*} - where *docs* denotes document source to be summarized, *hypos* denotes the system's generation outputs, and *refs* is ground truth human-generated summaries. For **CtrlEval** and **UniEval**, we use 11references as evaluation target for the metrics. For **CtrlEval**, the performance score is computed based on **"Coherence"** aspect by solely utilizing the system outputs as the input sources for pretrained GPT-2. For **UniEval**, the evaluator is pretrained on summarization task for assessing four aspects: "Coherence", "Consistency", **"Fluency"**, and **"Relevance"**. For assessing "Coherence" and "Consistency" aspects, UniEval uses document source and the system outputs as the inputs for pretrained evaluator. The system outputs is used solely as inputs for measuring "Fluency", while the generation outputs and ground truth references are compared for measuring "Relevance" aspect. - Newsroom (**TextSumm**) - The evaluation setup for Newsroom dataset is similar to summEval, except that Newsroom does not include ground truth human references. Instead, the title of articles is used as a means of reference for assessing the quality of system outputs. - UniEval-summ (**TextSumm**) - is a subset of summEval. Therefore, the evaluation setup follows the configuration in summEval data. - USR-PC (**DiagGen**) - is composed of three source of textual inputs for the evaluation metrics: persona of the model (NLG system) and human evaluators as a background knowledge (fact), the preceding dialogue as a context, and the system responses (generation outputs). BLEU, ROUGE are computed by comparing between the system responses and the concatenation of document source and factual or contextual knowledge (i.e. list of personas in USR-PC and document title in USR-TC). While, **BERTScore**is computed by comparing between system's responses and document sources. CTC scores are measured based on "Engagingness" and "Groundedness" (Use Knowledge) aspects, two aspects out of total five aspects based on human ratings (Understandable, Natural, Maintains Context, Engaging, Use Knowledge). CTC-based engagingness is measured by utilizing (i) the concatenated version of factual knowledge (personas) and dialogue history, and (ii) system responses as inputs to be compared. While, CTC-based groundedness measures the relevance of information by inspecting how the system responses comply with the predefined factual knowledge. CtrlEval scores are measured based on "Coherence", "Consistency", and "Relevance" aspects. CtrlEval-Coherence uses the concatenation of dialogue history and system response as input. CtrlEval-Consistency measures how consistent the system response w.r.t. the prefix or dialogue history. While, CtrlEvalRelevance compares the degree of relevance | NLG Task | Benchmark dataset | Prompts | Verbal Dict. | |------------------------------------------------------------------------------------------------|--------------------------------------------|----------------------------------------|-------------------------------------------------| | TextSumm | summEval, Newsroom | ⟨ gen_result ⟩ Article: ⟨ mask_token ⟩ | N/A | | ⟨ gen_result ⟩ Summary: ⟨ mask_token ⟩ | N/A | | | | ⟨ gen_result ⟩ It was about ⟨ mask_token ⟩ | N/A | | | | DiagGen | USR-PC | ⟨ gen_result ⟩ Persona: ⟨ mask_token ⟩ | list of system's and human evaluator's personas | | The persona of ⟨ gen_result ⟩ is ⟨ mask_token ⟩ ⟨ gen_result ⟩ contains ⟨ mask_token ⟩ persona | | | | | USR-TC, UniEval-TC | ⟨ gen_result ⟩ It was about ⟨ mask_token ⟩ | context | | | ⟨ gen_result ⟩ It was related to ⟨ mask_token ⟩ | | | | | CtrlGen | UBER-Topic, CTRL-Topic | ⟨ gen_result ⟩ News: ⟨ mask_token ⟩ | computers, politics, religion, | | ⟨ gen_result ⟩ It was about ⟨ mask_token ⟩ | science, | legal, | clickbait, | | space, military | | | | Table 4: Examples of prompts and verbal dictionary as auxiliary inputs for CtrlEval metric. between the generated responses and the predefined personas. UniEval scores are computed based on human evaluation aspects included in **USR-PC** data: UnieEval-Understandability, UniEvalNaturalness, UniEval-Coherence, UniEvalEngagingness, UniEval-Groundedness, and UniEVal-Overall; given dialogue histories as source, list of personas as contextual knowledge, and the system responses as output to be evaluated. ## - Usr-Tc, Unieval-Tc (**Diaggen**) - The main difference between USR-TC and USRPC is that the two benchmarks use different factual knowledge as a means of reference for model or metric. In USR-PC, the reference is the predefined list of model and human personas as multi-turn agents in a dialogue system. While, in USR-TC, the predefined knowledge-grounded conversation is used as a means of reference for evaluating systems and metrics in this study. ## - **Uber-Topic, Ctrl-Topic, Ctrleval-Topic** (**CtrlGen**) - are mainly composed of prefixes, the perturbed version of generation outputs, and control attributes (i.e. topic categories) as textual inputs for the evaluation metrics. The contextual knowledge is constructed by concatenating topic category as control attribute for each prefix sample and the corresponding list of keywords as a pointer to particular topic or domain. BERTScore is defined based on the comparison between the system's generated outputs and the control attributes as contextual knowledge. For each system output, we construct the context by concatenating topic category (e.g. "Science") and its corresponding list of relevant keywords. While, **Perplexity** is measured by projecting the system outputs as inputs for pretrained GPT-2. CTC measures two aspects: Consistency and Relevance. We specifically use "SummarizationScorer" of CTC for assessing the quality of system outputs in Controlled Generation task because the task share more similar characteristic to Text Summarization than task in Dialogue Response Generation. The setup follows the configuration of Summarizationbased CTC evaluation. CtrlEval measures three evaluation aspects: Coherence, Consistency, and Relevance. CtrlEval-Coherence outputs the pattern evaluator score by pairing sentences in the generation outputs as a text infilling task. CtrlEvalConsistency uses prefixes and system outputs as the inputs of the metric. While, CtrlEvalRelevance measures whether the generation outputs are relevant to the given control attributes (topic categories). UniEval measures four aspects: Coherence, Consistency, Fluency, and Relevance. The setup follows the configuration of summarization-based UniEval evaluation, but the reference list is defined based on the concatenation between control attribute (topic category) and its corresponding pointer words (keywords). ## A.4 Experiment Results A.4.1 Transfer Experiment Table 5- 6 shows the correlation score between automatic metrics and human ratings across NLG tasks (ID and OOD). | Automatic metrics | ID | Semantic-Shift | Domain-Shift | |------------------------------|-------|------------------|----------------| | LM-Perplexity | 0.170 | 0.022 | -0.116 | | Surface-level (BLEU & ROUGE) | 0.215 | 0.193 | 0.000 | | Semantic (BERTScore) | 0.213 | 0.075 | 0.054 | | Single-CTC | 0.259 | 0.091 | 0.024 | | Single-CTRLEval | 0.145 | 0.156 | 0.058 | | Multi-UniEval | 0.445 | 0.257 | 0.006 | Table 5: Correlation level to human scores across ID and OOD samples Table 6: Correlation level to human scores across NLG tasks | Automatic metrics | TextSumm | DiagGen | CtrlGen | |------------------------------|------------|-----------|-----------| | LM-Perplexity | -0.116 | 0.170 | 0.022 | | Surface-level (BLEU & ROUGE) | 0.215 | 0.193 | 0.000 | | Semantic (BERTScore) | 0.213 | 0.074 | 0.054 | | Single-CTC | 0.026 | 0.147 | 0.024 | | Single-CTRLEval | 0.156 | 0.074 | 0.086 | | Multi-UniEval | 0.341 | 0.298 | 0.006 | Sample Analysis In this section, we sample data in In-Domain (ID) and Out-of-Domain subsets to further analyze the contexts in which automatic metrics are not in alignment with human judgments. The samples are mainly grouped based on the agreement-level of multi-aspect human ratings (low vs. high) across ID and OOD subsets (Figure 1a) and NLG use cases (Figure 1b). A.4.2 Aspect-level Evaluation Figure 9 shows Kolmogorov-Smirnov (KS) scores for aspect-level evaluation in Dialogue Response Generation (DiagGen) and the corresponding similarity score to human preference. ## A.4.3 System-Level Evaluation Table 17-19 show Kolmogorov-Smirnov (KS) scores of both human and automatic metrics as a measure of metric's capability at distinguishing performance differences between independent NLG systems. Table 20-22 show the preference similarity between human and automatic metrics at deciding the performance rank of the systems. ## A.5 Packages We use publicly available Python Packages for running the experiments, as listed in Table 9. The prerequisite installation is provided in the shared implementation code. ## A.6 Hyperparameters BLEU Package: evaluate, https://huggingface. co/spaces/evaluate-metric/bleu. **Parameters**: 'brevity_penalty': 1.0 (default). ROUGE Package: evaluate, https: //huggingface.co/spaces/evaluate-metric/rouge. BERTScore Package: evaluate, https: //huggingface.co/spaces/evaluate-metric/ bertscore. **Model**: "roberta-large_L17_noidf_version=0.3.12(hug_trans=4.25.1)". Perplexity Package: evaluate, https:// huggingface.co/spaces/evaluate-metric/perplexity. Model: "gpt2". CTC Package: CTC. For Embedding-based alignment (CTC-E), we use BERTAligner/BERT embedding (default). For discriminative alignment (CTC-D), we use "roberta-large". For regressive alignment (CTC-R), we use BLEURTAligner. CtrlEval Package: CtrlEval. **Model**: "google/pegasus-large". We use default configuration in https://github.com/thu-coai/CTRLEval. We reuse the TfIdf features of the original work. For the other required external knowledge (prompt and verbal list), we adjust accordingly to the objective of target NLG task. The prompt and verbal files are provided in the shared data and code implementation. UniEval Package: UniEval. We use two types of pretrained evaluators in https://github.com/ maszhongming/UniEval: UniEval-sum and UniEvaldialog. We re-use the multi-dimensional human evaluation aspects of the corresponding pretrained evaluators. We adjust the configuration of inputsoutputs of the evaluators based on the target NLG tasks. ## A.7 Computing Resources Experiments were done in computing nodes of a HPC cluster with specifications of 4 GPUs Nvidia Tesla V100 (16GB RAM, 2560 tensor cores, 10480 CUDA cores, compute capability 7.0). 1 CPU Intel Xeon E5-2698v4 @ 2.2GHz (40 hyperthreads, RAM: 256GB). ![21_image_0.png](21_image_0.png) Table 7: The system outputs in **summEval** with high agreement level between multiple human-like aspects for high human ratings (N-sample = 1987(39%)) and low human ratings (N-sample = 43(0.8%)). BLEU score is by default represented as percentage rather than decimal in benchmark dataset. Both BLEU and ROUGE scores are based on an averaged between 1-reference score and 11-references score. ![21_image_1.png](21_image_1.png) | System | System Outputs | Human Rating | Metric Score | | | | | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|----------------|----------------|-------|-------|---------------------------------------------------------------------------------------------------------------------------------------|------------|--------| | Perplexity ↓ BLEU (%) ↑ ROUGE ↑ BERTScore ↑ CTC ↑ | CtrlEval ↑ | UniEval ↑ | | | | | | | | M20 | Varvara traveled 14,000 miles across Coherence: 4, Consistency: 2, Fluency: 5, Relevance: the Pacific Ocean. (Hat tip: The Daily Beast) 1, Average: 3 | 35.68 | 4.17 | 0.204 | 0.285 | E-Consistency: 0.848, E-Relevance: 0.518, D-Consistency: 0.766, D-Relevance: 0.348, R-Consistency: 0.645, R-Relevance: 0.322 (-)4.464 | Coherence: | 0.113, | | Coherence: | Consistency: 0.721, Fluency: 0.945, Relevance: 0.789 | | | | | | | | | M8 | the whale , named varvara , swam nearly 14,000 miles ( 22,500 kilometers ) . it said the previous record was set by a humpback whale that swam a mere 10,190-mile round trip between the " warm breeding waters of the arctic and antarctic regions " . Coherence: 2, Consistency: 4, Fluency: 5, Relevance: 2, Average: 3.25 | 50.71 | 28.74 | 0.443 | 0.613 | E-Consistency: 0.908, E-Relevance: 0.571, D-Consistency: 0.951, D-Relevance: 0.627, R-Consistency: 0.970, R-Relevance: 0.653 (-)3.228 | Coherence: | 0.682, | | Coherence: | Consistency: 0.957, Fluency: 0.690, Relevance: 0.112 | | | | | | | | | Source: (CNN)A North Pacific gray whale has earned a spot in the record books after completing the longest migration of a mammal ever recorded. The whale, named Varvara, swam nearly 14,000 miles (22,500 | | | | | | | | | Source: (CNN)A North Pacific gray whale has earned a spot in the record books after completing the longest migration of a mammal ever recorded. The whale, named Varvara, swam nearly 14,000 miles (22,500 kilometers), according to a release from Oregon State University, whose scientists helped conduct the whale-tracking study. Varvara, which is Russian for "Barbara," left her primary feeding ground off Russia's Sakhalin Island to cross the Pacific Ocean and down the West Coast of the United States to Baja, Mexico. Varvara's journey surpassed a record listed on the Guinness Worlds Records website. It said the previous record was set by a humpback whale that swam a mere 10,190-mile round trip between the "warm breeding waters near the equator and the colder food-rich waters of the Arctic and Antarctic regions." Records are nice, but Bruce Mate, the lead author of the study, thinks the long trip might say more about the whale than just its ability to swim. During her 14,000-mile journey, Varvara visited "three major breeding areas for eastern gray whales," which was a surprise to Mate, who is also the director of the Marine Mammal Institute at Oregon State University. "For her to go to Mexico," Mate said, "It's pretty strong evidence that it's where she's from." Varvara was thought to be an endangered western whale, but her ability to "navigate across open water over tremendously long distances is impressive," he said in the release, which could mean that some western gray whales are actually eastern grays. With only 150 western gray whales believed to be in existence, that number might be even lower. "Past studies have indicated genetic differentiation between the species, but this suggests we may need to take a closer look," Mate said. Fourth baby orca born this season 1 st **Reference:** The whale, Varvara, swam a round trip from Russia to Mexico, nearly 14,000 miles. The previous record was set by a humpback whale that migrated more than 10,000 miles. 2 nd **Reference:** A record for the longest distance migration of a mammal was shattered recently by a north pacific gray whale. The whale made a trip of 14,000 miles. 3 rd **Reference:** The longest mammalian migration was just recorded by a pacific gray whale. It swam over 14,000 miles in the process. There are only about 150 gray whales known. M11 jordan henderson is set to sign a new long-term contract at anfield . the club s vice-captain had 14 months remaining ´ on his current contract . henderson is the third major player in liverpool s fa cup . ´ the fa cup fourth round . raheem sterling is expected to return to liverpool in the summer . | 3 rd Reference: The longest mammalian migration was just recorded by a pacific gray whale. It swam over 14,000 miles in the process. There are only about 150 gray whales known. M11 jordan henderson is set to sign a new long-term contract at anfield . the club s vice-captain had 14 months remaining ´ on his current contract . henderson is the third major player in liverpool s fa cup . ´ the fa cup fourth round . raheem sterling is expected to return to liverpool in the summer . 45.03 28.72 0.410 0.589 E-Consistency: 0.868, E-Relevance: 0.546, D-Consistency: 0.803, D-Relevance: 0.538, R-Consistency: 0.834, R-Relevance: 0.517 Coherence: 1, Consistency: 4, Fluency: 1, Relevance: 4, Average: 2.5 (-)2.635 | Coherence: | 0.018, | | | | | | | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|----------|-------|-------|-------|---------------------------------------------------------------------------------------------------------------------------------------|------------|--------| | Coherence: | Consistency: 0.637, Fluency: 0.675, Relevance: 0.011 | | | | | | | | | M8 | jordan henderson has provided liverpool with a lift after their fa cup heartache . the club s vice-captain had 14 months ´ remaining on his current contract . his advisors had been in talks with liverpool since the beginning of this season . Coherence: 1, Consistency: 5, Fluency: 5, Relevance: 2, Average: 3.25 | 68.84 | 21.68 | 0.403 | 0.498 | E-Consistency: 0.922, E-Relevance: 0.581, D-Consistency: 0.983, D-Relevance: 0.642, R-Consistency: 1.066, R-Relevance: 0.622 (-)4.360 | Coherence: | 0.973, | | Coherence: | Consistency: 0.939, Fluency: 0.639, Relevance: 0.711 | | | | | | | | | Source: Jordan Henderson has provided Liverpool with a lift after their FA Cup heartache by agreeing a new long-term contract. The club's vice-captain had 14 months remaining on his current contract and his advisors had | | | | | | | | | Source: Jordan Henderson has provided Liverpool with a lift after their FA Cup heartache by agreeing a new long-term contract. The club's vice-captain had 14 months remaining on his current contract and his advisors had been in talks with Liverpool since the beginning of this season. They have now reached a resolution and Henderson is expected to put pen-to-paper on improved terms that are likely be worth in the region of £100,000. His new deal will run to 2020. Liverpool midfielder Jordan Henderson is set to sign a new long-term contract at Anfield Henderson chases down Aston Villa's Jack Grealish during Liverpool's FA Cup semi-final defeat at Wembley Henderson's new deal is worth around £100,000-a-week and will run until the summer of 2020 Henderson, 24, is the third big player in Brendan Rodgers' squad to agree a contract extension, following on from Daniel Sturridge and Philippe Coutinho. The England international, who was signed by Kenny Dalglish in June 2011 for £16million from Sunderland, has been one of the most improved players under Rodgers' watch. His form this season has been excellent and he has contributed 13 assists as well as seven goals from midfield; he will be considered for the role of club captain when Steven Gerrard moves to LA Galaxy. Talks with Raheem Sterling are not expected to resume until the end of the season but Ian Ayre, Liverpool's Chief Executive, last week said he expected the England forward to be at Anfield for 'a long time'. Henderson could replace Steven Gerrard as Liverpool captain when the 34-year-old departs this summer Liverpool boss Brendan Rodgers (right) is keen to tie-down Henderson with up to 10 players set to leave Raheem Sterling has rejected a new deal at Liverpool but talks are expected to resume in the summer 1 st **Reference:** Jordan Henderson is set to sign an improved deal with Liverpool. The 24-year-old midfielder has 14 months left on his current contract. Henderson could replace Steven Gerrard as club captain this summer. Liverpool will resume talks with Raheem Sterling at the end of the season. 2 nd **Reference:** A player has signed onto a new contract with another team which is set to start in 2020. The player has shown to be quite impressive over the years and replaced a veteran last year. 3 rd **Reference:** Jordan Henderson was heroic for Liverpool with a newly-signed contract. He has improved immensely over the years. He could very well replace Gerrard as team captain soon. Table 8: The system outputs in **summEval** with low agreement level between multiple human-like aspects. Package name Version Link Python 3.7.12 conda install Numpy 1.21.6 pip install Pandas 1.3.5 pip install Matplotlib 3.5.2 pip install NLTK 3.7 pip install Pytorch 1.11.0+cu102 conda install Transformers 4.25.1 pip install Evaluate 0.2.2 https://github.com/huggingface/ evaluate.git CTC N/A https://github.com/tanyuqian/ ctc-gen-eval.git CtrlEval N/A https://github.com/thu-coai/ CTRLEval.git UniEval N/A https://github.com/maszhongming/ UniEval.git Table 9: Python packages used in this study. Benchmark Easy pair Hard pair UBER-Topic ('BR', 'BCR') ('BC', 'BCR') ('BC', 'BR') ('B', 'BR') CTRL-Topic ('BCR', 'CTRL') ('CTRL', 'WD') ('BCR', 'WD') Table 10: System pairs in CtrlGen. Benchmark Easy pair Hard pair UniEval-summ ('M11', 'M22') ('M11', 'M9') ('M11', 'M23') ('M13', 'M12') ('M9', 'M22') ('M23', 'M22') ('M9', 'M23') ('M11', 'M20') ('M11', 'M2') ('M17', 'M15') ('M11', 'M0') ('M0', 'M2') ('M20', 'M2') ('M2', 'M12') ('M20', 'M0') ('M17', 'M0') ('M11', 'M17') ('M1', 'M13') ('M20', 'M17') ('M22', 'M23') ('M20', 'M23') ('M0', 'M22') ('M20', 'M22') Table 11: System pairs in TextSumm (UniEval-Summ). Benchmark Easy pair Hard pair summEval ('M11', 'M22') ('M11', 'M9') ('M11', 'M23') ('M13', 'M12') ('M9', 'M22') ('M23', 'M22') ('M9', 'M23') ('M11', 'M20') ('M11', 'M2') ('M23', 'M17') ('M11', 'M0') ('M0', 'M2') ('M20', 'M2') ('M5', 'M2') ('M20', 'M0') ('M17', 'M0') ('M11', 'M17') ('M1', 'M13') ('M20', 'M17') ('M23', 'M23_dynamicmix') ('M11', 'M23_dynamicmix') ('M20', 'M23_dynamicmix') ('M20', 'M23') ('M20', 'M22') Table 12: System pairs in TextSumm (summEval). Table 13: System pairs in TextSumm (Newsroom). Table 14: System pairs in DiagGen (UniEval-TC). Table 15: System pairs in DiagGen (USR-TC). ![23_image_0.png](23_image_0.png) Table 16: System pairs in DiagGen (USR-PC). | Benchmark | Easy pair | Hard pair | |-------------|------------------------------------|-------------| | UniEval-TC | ('Nucleus Decoding (p = ('Original | Ground | | 0.5)', 'New Human Generated') | Truth', | 'New Human | | Generated') | | | | ('Nucleus Decoding (p = 0.5)', 'Original Ground Truth') ('Nucleus Decoding (p = 0.5)', 'Nucleus Decoding (p = 0.7)') ('Nucleus Decoding (p = 0.3)', 'New Human Generated') ('Nucleus Decoding (p = 0.3)', 'Original Ground Truth') ('Nucleus Decoding (p = 0.7)', 'New Human Generated') ('Nucleus Decoding (p = 0.7)', 'Original Ground Truth') | | | | Benchmark | Easy pair | Hard pair | |-------------|------------------------------------|-------------| | USR-TC | ('Nucleus Decoding (p = ('Original | Ground | | 0.5)', 'New Human Generated') | Truth', | 'New Human | | Generated') | | | | ('Nucleus Decoding (p = 0.5)', 'Original Ground Truth') ('Nucleus Decoding (p = 0.5)', 'Nucleus Decoding (p = 0.7)') ('Nucleus Decoding (p = 0.3)', 'New Human Generated') ('Nucleus Decoding (p = 0.3)', 'Original Ground Truth') ('Nucleus Decoding (p = 0.7)', 'New Human Generated') ('Nucleus Decoding (p = 0.7)', 'Original Ground Truth') | | | | Benchmark | Easy pair | Hard pair | | |----------------|-------------|--------------|--------| | USR-PC | ('Seq2Seq', 'New Human Generated') | ('Original | Ground | | Truth', | 'New Human | | | | Generated') | | | | | ('Seq2Seq', | 'Original | ('KV-MemNN', | | | Ground Truth') | 'Seq2Seq') | | | | ('KV-MemNN', | 'New | | | | Human Generated') ('KV-MemNN', 'Original Ground Truth') ('Language Model', 'New Human Generated') ('Language Model', 'Original Ground Truth') | | | | | Benchmark | Easy pair | Hard pair | |------------------------------------------------------------------------------------------------------|---------------------------|-----------------------------| | Newsroom | ('abstractive','lede3') | ('abstractive','fragments') | | ('abstractive','textrank') | ('pointer_n','pointer_s') | | | ('fragments','lede3') | ('textrank','lede3') | | | ('fragments','textrank') | ('pointer_c','textrank') | | | ('abstractive','pointer_s') ('pointer_s','lede3') ('fragments','pointer_s') ('pointer_n','textrank') | | | Data Difficulty Human Perplexity BLEU ROUGE BERTScore Single-CTC Single-CtrlEval Multi-UniEval UniEval-summ Easy 0.535 0.356 0.532 0.367 0.508 0.513 0.296 **0.596** Hard 0.145 0.295 **0.325** 0.155 0.306 0.296 0.232 0.269 summEval Easy 0.441 0.403 0.365 0.324 0.344 0.479 0.199 0.6 Hard 0.100 **0.266** 0.188 0.173 0.159 0.257 0.180 0.262 Newsroom Easy 0.396 0.333 **0.808** 0.506 0.700 0.596 0.553 0.584 Hard 0.163 0.286 0.527 0.278 0.478 0.383 0.358 **0.528** Table 17: Kolmogorov-Smirnov (KS) Scores on system-level performance in TextSumm. Table 18: Kolmogorov-Smirnov (KS) Scores on system-level performance in DiagGen. Table 19: Kolmogorov-Smirnov (KS) Scores on system-level performance in CtrlGen. | Data | Difficulty | Human | Perplexity | BLEU | ROUGE | BERTScore | Single-CTC | Single-CtrlEval | Multi-UniEval | |------------|--------------|---------|--------------|--------|---------|-------------|--------------|-------------------|-----------------| | UniEval-TC | Easy | 0.686 | 0.283 | 0.194 | 0.303 | 0.261 | 0.375 | 0.144 | 0.565 | | Hard | 0.203 | 0.225 | 0.158 | 0.200 | 0.133 | 0.226 | 0.125 | 0.317 | | | USR-TC | Easy | 0.562 | 0.336 | 0.194 | 0.303 | 0.253 | 0.416 | 0.197 | 0.486 | | Hard | 0.121 | 0.242 | 0.158 | 0.200 | 0.125 | 0.232 | 0.144 | 0.283 | | | USR-PC | Easy | 0.347 | 0.394 | 0.236 | 0.300 | 0.353 | 0.481 | 0.144 | 0.386 | | Hard | 0.156 | 0.433 | 0.258 | 0.375 | 0.275 | 0.390 | 0.147 | 0.218 | | Table 20: Preference similarity in TextSumm. Table 21: Preference similarity in DiagGen. Table 22: Preference similarity in CtrlGen. | Data | Difficulty | Human | Perplexity | BERTScore | Single-CTC | Single-CtrlEval | Multi-UniEval | |------------|--------------|---------|--------------|-------------|--------------|-------------------|-----------------| | UBER-Topic | Easy | 0.213 | 0.316 | 0.132 | 0.173 | 0.144 | 0.025 | | Hard | 0.048 | 0.134 | 0.105 | 0.074 | 0.073 | 0.027 | | | CTRL-Topic | Easy | 0.106 | 0.101 | 0.304 | 0.165 | 0.249 | 0.136 | | Hard | 0.079 | 0.113 | 0.097 | 0.075 | 0.092 | 0.096 | | | Data | Difficulty | Perplexity | BLEU | ROUGE | BERTScore | Single-CTC | Single-CtrlEval | Multi-UniEval | |--------------|--------------|--------------|--------|---------|-------------|--------------|-------------------|-----------------| | UniEval-summ | Easy | 0.711 | 0.933 | 0.989 | 0.989 | 0.924 | 0.622 | 0.989 | | Hard | 0.648 | 0.758 | 0.612 | 0.709 | 0.688 | 0.685 | 0.803 | | | summEval | Easy | 0.752 | 0.919 | 0.776 | 0.943 | 0.943 | 0.752 | 0.983 | | Hard | 0.707 | 0.647 | 0.673 | 0.613 | 0.762 | 0.693 | 0.730 | | | Newsroom | Easy | 0.444 | 1.000 | 0.889 | 1.000 | 0.963 | 1.000 | 0.833 | | Hard | 0.555 | 0.889 | 0.889 | 0.889 | 0.870 | 0.889 | 0.833 | | | Data | Difficulty | Perplexity | BLEU | ROUGE | BERTScore | Single-CTC | Single-CtrlEval | Multi-UniEval | |--------------|--------------|--------------|--------|---------|-------------|--------------|-------------------|-----------------| | UniEval-summ | Easy | 0.889 | 1.000 | 1.000 | 0.667 | 1.000 | 0.444 | 1.000 | | Hard | 0.611 | 0.722 | 0.944 | 0.833 | 0.722 | 0.388 | 0.722 | | | summEval | Easy | 0.778 | 1.000 | 1.000 | 0.667 | 1.000 | 0.629 | 0.926 | | Hard | 0.500 | 0.833 | 0.944 | 0.833 | 0.722 | 0.593 | 0.796 | | | Newsroom | Easy | 1.000 | 1.000 | 0.778 | 0.667 | 1.000 | 0.741 | 0.944 | | Hard | 0.611 | 0.722 | 0.833 | 0.667 | 0.833 | 0.889 | 0.833 | | | Data | Difficulty | Perplexity | BERTScore | Single-CTC | Single-CtrlEval | Multi-UniEval | |------------|--------------|--------------|-------------|--------------|-------------------|-----------------| | UBER-Topic | Easy | 0.667 | 0.667 | 0.667 | 0.667 | 0.667 | | Hard | 0.333 | 1.000 | 0.778 | 0.555 | 0.417 | | | CTRL-Topic | Easy | 0.333 | 1.000 | 0.611 | 0.555 | 0.417 | | Hard | 0.333 | 1.000 | 0.666 | 0.555 | 0.333 | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitation section is after Conclusion (Section 7) and before Reference list ✓ A2. Did you discuss any potential risks of your work? Yes, The potential risks are included in Limitations and Ethics Statement. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract. Introduction (Section 1) ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** section 4.1. Datasets are listed and accompanied by the citation of the original paper in Table 2. ✓ B1. Did you cite the creators of artifacts you used? section 4.1 and Table 2. ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Limitations, Ethics Statement, and Appendix section A.2 (Background) ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Limitations, Ethics Statement, and Appendix section A.2 (Background) ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Limitations, Ethics Statement, and Appendix section A.2 (Background), Appendix A.3 ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appendix section A.2 (Background) ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Table 2 ## C ✗ **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Not applicable. Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Not applicable. Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. Left blank. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section A.5 Appendix: Packages. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

lv-etal-2023-dialogps

{D}ialo{GPS}: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations

https://aclanthology.org/2023.acl-long.70

In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts. Without such patterns, models poorly generalize and prefer responding safely. Many attempts have been made in either multi-turn settings from a one-to-many perspective or in a many-to-many perspective but limited to single-turn settings. The major challenge to many-to-many augment multi-turn dialogues is that discretely replacing each turn with semantic similarity breaks fragile context coherence. In this paper, we propose DialoGue Path Sampling (DialoGPS) method in continuous semantic space, the first many-to-many augmentation method for multi-turn dialogues. Specifically, we map a dialogue to our extended Brownian Bridge, a special Gaussian process. We sample latent variables to form coherent dialogue paths in the continuous space. A dialogue path corresponds to a new multi-turn dialogue and is used as augmented training data. We show the effect of DialoGPS with both automatic and human evaluation.

DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations Ang Lv1∗ , Jinpeng Li2∗ , Yuhan Chen1, Xing Gao3, Ji Zhang3**, Rui Yan**1,4† 1Gaoling School of Artifical Intelligence, Renmin University of China 2Wangxuan Institute of Computer Technology, Peking University 3Alibaba DAMO Academy 4Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education {anglv, yhchen, ruiyan}@ruc.edu.cn, [email protected], {gaoxing.gx,zj122146}@alibaba-inc.com ## Abstract In open-domain dialogue generation tasks, contexts and responses in most datasets are oneto-one mapped, violating an important manyto-many characteristic: a context leads to various responses, and a response answers multiple contexts. Without such patterns, models poorly generalize and prefer responding safely. Many attempts have been made in either multiturn settings from a one-to-many perspective or in a many-to-many perspective but limited to single-turn settings. The major challenge to many-to-many augment multi-turn dialogues is that discretely replacing each turn with semantic similarity breaks fragile context coherence. In this paper, we propose DialoGue Path Sampling (DialoGPS) method in continuous semantic space, the first many-to-many augmentation method for multi-turn dialogues. Specifically, we map a dialogue to our extended Brownian Bridge, a special Gaussian process. We sample latent variables to form coherent dialogue paths in the continuous space. A dialogue path corresponds to a new multi-turn dialogue and is used as augmented training data. We show the effect of DialoGPS with both automatic and human evaluation. ## 1 Introduction Open-domain dialogue generation has received significant attention and has made notable advancements (Zhang et al., 2020b; Shuster et al., 2022; OpenAI, 2022). However, it still faces challenges due to the nature of the data. One specific challenge is the many-to-many relationship between contexts and responses in open-domain conversations. A context can lead to various responses, and a response can be relevant to multiple contexts. Unfortunately, most datasets only provide one-to-one ![0_image_0.png](0_image_0.png) ![0_image_1.png](0_image_1.png) Figure 1: (a) When replacing each utterance in the original conversation by semantic similarity, the modified dialogue is incoherent. (b) We map dialogues into a continuous semantic space where latent distributions of utterances correlate with each other, and sample dialogue paths for training. Each path corresponds to a discrete multi-turn conversation. mappings between contexts and responses. This limitation results in models being poorly generalized when they rely on learned one-to-one patterns, making them prone to generating safe yet uninteresting responses (Jiang and de Rijke, 2018; Jiang et al., 2019). To address this limitation, many attempts (Sai et al., 2020; Qiu et al., 2019; Xie et al., 2022) have been made from a one-to-many perspective which involves constructing multiple responses for a context. Furthermore, some works are proposed from a many-to-many perspective but are limited to singleturn settings. To construct new dialogue sentence pairs, they either replace sentences based on se1267 mantic similarity (Zhang et al., 2020a) or sample new sentences from probabilistic models (Li et al., 2019). Next, they adopt BERT (Devlin et al., 2019) or GAN (Goodfellow et al., 2014) discriminators to filter incoherent sentence pairs. These methods cannot be trivially extended to multi-turn settings. Considering T utterances in a dialogue and K candidates for each utterance, they need to (1) prepare a large sentence set as candidates for replacement or a strong generative model, and (2) check the coherence of the modified conversation at least KT −1times, which is impractical. Figure 1(a) shows a case in which we replace each utterance in a conversation following Zhang et al. (2020a). The modified conversation is still incoherent across turns. Therefore, to enhance multi-turn dialogue generation from a many-to-many perspective, we resort to a continuous semantic space that satisfies two requirements. First, it describes semantic distributions of utterances, allowing for sampling semantic neighbors of each utterance. Second, latent variables sampled from any two distributions should be temporally correlated, contributing to a new coherent dialogue path in the latent space without requiring post-checks. This path can be utilized as a new training sample to augment the model. Our motivation is illustrated in Figure 1(b). Driven by this motivation, we propose a novel method for augmenting open-domain dialogues from a many-to-many perspective, called DialoGue Path Sampling (DialoGPS), aiming to enhance generalization and improve the quality of generated responses. Specifically, our approach involves the following steps: (1) We map each utterance in a multi-turn dialogue to a special Gaussian process in a continuous semantic space known as the Brownian Bridge (Revuz and Yor, 2013). (2) For each utterance xi, we sample K latent variables z j i , j ∈ [1, K], establishing K different dialogue paths in the bridge. Each path corresponds to a new multi-turn conversation in the discrete space. (3) DialoGPS utilizes an encoder-decoder architecture. To construct augmented data, we mix the latent variable zi with representations of xiin the encoder if xiis part of the context, and in the decoder if it is the response. (4) Finally, we train the model using the augmented data. To ensure the effectiveness of DialoGPS, we address several key issues. First, traditional Brownian Bridges have deterministic endpoints, which prevent response sampling and lead our method degenerating into a many-to-one paradigm, further impairing generalization. To overcome this limitation, we derive the formula of endpoint distributions. Second, since augmented data that lacks discrete utterance labels makes the optimization challenging, we propose a self-distillation framework where the model first learns from the ground truth and then distills its knowledge to guide itself in utilizing augmented data. We evaluate DialoGPS on two multi-turn opendomain datasets. Both automatic and human evaluation show that DialoGPS performs better than strong baselines and even outperforms the model trained on manually denoted multi-reference data, which demonstrates the benefit of the many-tomany augmentation paradigm. Because DialoGPS is plug-and-play, we add it to BART (Lewis et al., 2020) and achieve competitive results with the state-of-the-art model, DialoFlow (Li et al., 2021). Our contributions are as follows: - DialoGPS is the first work to augment multiturn dialogues from a many-to-many perspective. - To ensure the effectiveness of DialoGPS, we have introduced dialogue-specific designs, including endpoint sampling of Brownian Bridges and self-distillation for model optimization. - Experiments conducted on both non-pretrained and pre-trained models show that our DialoGPS method outperforms all baselines. ## 2 Related Work: Dialogue Generation Augmentation In general, dialogue generation can be categorized into two groups: task-oriented and open-domain. Open-domain generation is a context-aware process that lasts for turns. The model learns to generate a proper but open response from the preceding utterances (i.e., contexts). Task-oriented dialogues progress for specific purposes and are limited to specific domains, such as obtaining knowledge (Zhao et al., 2020; Tao et al., 2021). However, due to the specific domains in task-oriented dialogues, the many-to-many relationship is not as apparent compared to open-domain dialogues. In this paper, we focus on open-domain dialogue generation augmentation from an X-to-many perspective. From a one-to-many perspective, Sai et al. (2020) manually denoted multiple responses for a dialogue context. Based on such multi-reference datasets, Qiu et al. (2019) proposed to capture the common feature in feasible responses and then add the specific feature to obtain the final output, which augments the utility of the data and improves the generalization. Xie et al. (2022) proposed that with only one-to-one data, models can construct pseudotarget data in the decoder and improve the model by bootstrapping. From a many-to-many perspective, existing methods work in single-turn settings. Li et al. (2019) generated multiple context or responses with CVAE (Zhao et al., 2017) and introduced a GAN (Goodfellow et al., 2014) discriminator to filter incoherent sentence pairs. Zhang et al. (2020a) augmented a one-to-one dialogue dataset Dp with an unpaired sentence set Du. They sample sentences from Du and replace the most similar sentences in Dp. They use BERT (Devlin et al., 2019) and knowledge distillation to filter noise in incoherent sentence pairs. Until now, manyto-many augmentation in multi-turn settings are understudied. ## 3 Method We first present some preliminaries (§ 3.1). Then, we introduce mapping dialogue texts to the desired latent space (§ 3.2), augmented data construction (§ 3.3), augmented data utilization (§ 3.4), and inference details (§ 3.5). Figure 2 shows the overview of DialoGPS. ## 3.1 Preliminary In open-domain dialogue generation, given a multiturn dialogue X = [x0, x1*, ..., x*T ], the goal is to predict the response xT based on the context X0:T −1. The number of tokens in xtis denoted as |xt|, t ∈ {0, 1*, . . . , T*}. The i-th token in the xt is denoted as x it . A Brownian Bridge B defined on time range [0, T] is a special Gaussian process established on deterministic endpoints µ0 and µT . At time t, the latent variable zt follows a Gaussian distribution B(t|µ0, µT ): $$z_{t}\sim{\cal B}(t|\mu_{0},\mu_{T})={\cal N}(\mu_{0}+\frac{t}{T}(\mu_{T}-\mu_{0}),\frac{t(T-t)}{T}),\tag{1}$$ ## 3.2 Extended Brownian Bridge In DialoGPS, given X, a non-linear function fθ maps each xtto µt, the expectations of the corresponding semantic distribution. Based on µ0 and µT , we can establish a Brownian Bridge, and from which we sample the latent variable zt as the semantic neighbor of xt. Meanwhile, z0, z1*, ..., z*T compose a coherent dialogue path because in a Brownian Bridge, the covariance between t1 and t2, with 0 < t1 < t2 < T is t1(T −t2) T, where the constant positive covariance guarantees that B(t1|µ0, µT ) and B(t2|µ0, µT ) are temporally correlated. However, as defined in Eq. 1, a conventional Brownian Bridge B has deterministic endpoints, which prevents us from sampling for xT , the response, and x0, the first utterance in the context. To avoid degenerating to a many-to-one mode that impairs the generalization, we derive an extended Brownian Bridge β with samplable endpoints. Take the derivation of β(T|µ0, µT ) as example: given a B, both the distance dδ between µT and zT −δ and the summation of dδ and zT −δ follow the Gaussian distribution, we can derive the distribution of zT as follows: $$z_{T-\delta}\sim{\cal N}(\frac{T-\delta}{T}\mu_{T}+\frac{\delta}{T}\mu_{0},\frac{\delta(T-\delta)}{T})\Bigg{\}}\Rightarrow$$ $$d_{\delta}=\mu_{T}-z_{T-\delta}\sim{\cal N}(\frac{\delta}{T}\mu_{T}-\frac{\delta}{T}\mu_{0},\frac{\delta(T-\delta)}{T})\Bigg{\}}\Rightarrow$$ $$z_{T}=d_{\delta}+z_{T-\delta}\sim{\cal N}(\mu_{T},\frac{2\delta(T-\delta)}{T}).\tag{2}$$ Due to the symmetry, $z_{0}$ follows ${\cal N}(\mu_{0},\frac{2\delta(T-\delta)}{T})$. Here, $\delta$ serves as a hyper N (µ0, T). Here, δ serves as a hyperparameter. To sum up, we define the extended Brownian Bridge β as: $$\beta(t|\mu_{0},\mu_{T})=\begin{cases}\mathcal{N}(\mu_{t},\dfrac{2\delta(T-\delta)}{T}),\,\text{t}=0\text{or T},\\ \mathcal{N}(\mu_{0}+\dfrac{t}{T}(\mu_{T}-\mu_{0}),\dfrac{t(T-t)}{T}),\,\text{otherwise}.\end{cases}\tag{3}$$ To optimize the mapping function $f_{\theta}$, we follow To optimize the mapping function fθ, we follow (Wang et al., 2022) to adopt a contrastive learning framework where positive samples are ordered sentence triplets from the same conversation (xt0 , xt1 , xt2 , t0 < t1 < t2) and negative samples are constructed by randomly replacing the middle point xt1 with other sentences xt ′ 1 from the mini-batch B. The objective is as below: $$\mathcal{L}_{\beta}=\mathbb{E}_{X}\left[\log\left(1+\frac{\sum\limits_{(x_{t_{0}},x_{t_{1}}^{\prime},x_{t_{2}})\in\mathbb{B}}\exp(d(x_{t_{0}},x_{t_{1}}^{\prime},x_{t_{2}};f_{\theta}))}{\exp(d(x_{t_{0}},x_{t_{1}},x_{t_{2}};f_{\theta}))}\right)\right],\tag{4}$$ where $d(x_{t_{0}},x_{t_{1}},x_{t_{2}};f_{\theta})=-\frac{1}{2\sigma_{t_{1}}^{2}}\|f_{\theta}(x_{t_{1}})-(1-\frac{t_{1}}{t_{2}})f_{\theta}(x_{t_{0}})-\frac{t_{1}}{t_{2}}f_{\theta}(x_{t_{2}})\|_{2}^{2}$. The essence of Eq. 4 t2 ) − t2 )∥ is to optimize the outputs of fθ, i.e., µt0 , µt1 , and µt2 to the linear relationship as defined in Eq. 1. In DialoGPS, a 4-layer MLP serves as fθ. To embed utterance as inputs of fθ, there are many choices such as averaging token embeddings or encoding ![3_image_0.png](3_image_0.png) ## 3.3 Augmented Data Construction As shown in Figure 2(a), we take Transformer (Vaswani et al., 2017) as the bone architecture. With fθ, an extended Brownian Bridge β is established. We sample latent variables zt ∼ β(t|µ0, µT ) and mix them with representations of corresponding xt. In the encoder, for each utterance xtin the context X0:T −1, we conduct: $$\begin{array}{l}{{e_{t}^{1},e_{t}^{2},...e_{t}^{|x_{t}|}=\mathrm{Encoder}(x_{t}),}}\\ {{\hat{e}_{t}^{i}=W_{x}^{e n c}\cdot e_{t}^{i}+W_{z}^{e n c}\cdot z_{t},}}\end{array}\qquad(5)$$ where e it is the output corresponding to the i-th token in xt from the encoder, i ∈ [1, |xt|]. Wenc z and Wenc xare trainable vectors of the same dimension as e and z. Finally, eˆ is sent to the decoder for cross-attention. We conduct the mixup every decoder layer: $$\begin{array}{l}{{\hat{d}_{j}^{i}=W_{x}^{d e c_{j}}\cdot d_{j}^{i}+W_{z}^{d e c_{j}}\cdot z_{T},}}\\ {{i\in\left[1,\left|x_{T}\right|\right],j\in\left[1,N\right],}}\end{array}\qquad\qquad(6)$$ where N is the number of decoder layers, d i j is the self-attention output at position i in layer j. Also, W decj z and W decj x are trainable vectors. ˆdj is used as *Query*, and eˆ are used as both Key and *Value* in the cross-attention. For a dialogue text X, we conduct sampling and mixup K times, which is equivalent to providing K extra discrete dialogues Xˆ k = -xˆ k 0 , xˆ k 1 , ..., xˆ k T , k ∈ [1, K] for training. Figure 2(b) shows mixup details. ## 3.4 Utilizing Augmented Data By Self-Distillation In general, given X to a dialogue generation model, parameters ϕ of model are optimized by minimizing the negative log-likelihood: $$\phi=\arg\min\left(\mathbb{E}x\,\left[-\log(P_{\phi}(x_{T}|X_{0:T-11}))\right]\right).\tag{7}$$ However, as aforementioned, what we obtain are continuous representations of Xˆ whereas the corresponding discrete sentences are inaccessible, which makes Eq. 7 intractable. Hence, to utilize the augmented data, we make an assumption that: There is an inaccessible many-to-many dialogue dataset DM toM . P*M toM* describes the conditional distribution of responses given contexts in this dataset. The accessible one-to-one dataset D1to1 is collected by sampling from D*M toM* uniformly, and thus P1to1 can be viewed as an approximation of P*M toM* . Based on this assumption, we propose a selfdistillation framework consisting of two steps: (1) It optimizes the model with the original discrete data following Eq. 7. (2) During training, as Pϕ fits P1to1, which is an approximation of P*M toM* , the model can use its output given X to teach itself when presented with augmented data, i.e., the representations of Xˆ: ϕ = argmin DKL hPϕ(xT |X0:T −1)||Pϕ(ˆxT |Xˆ0:T −1) i , (8) where DKL[*·||·*] is the KL-divergence (Kullback and Leibler, 1951). In Eq. 8, to remove the gap between utilizing the original discrete data X and the augmented continuous data Xˆ in the same architecture, we mix each utterance in X with the expectations µ0:T . Formally, the overall training objective is to minimize: ![4_image_0.png](4_image_0.png) ## 3.5 Inference The inference goal is to predict xT based on context X0:T −1. First, fθ takes X0:T −1 and outputs corresponding µt for sampling and mixup in the encoder, where t ∈ {0, 1*, . . . , T* − 1}. Next, the decoder receives the encoder output and an inferred µT to decode the response in an autoregressive manner. To obtain the value of µT , we do not require additional prediction networks. Instead, we can directly derive its value based on the property of Brownian Bridge. Specifically, given the context, we know that for any t: $$\mu_{t}=\mu_{0}+\frac{t}{T-1}(\mu_{T-1}-\mu_{0}).\tag{10}$$ If µT is already known, a Brownian bridge established on µT and µ0 would yield the same µt values. Consequently, we can establish an equality and derive the value of µT as follows: $$\mu_{t}=\mu_{0}+\frac{t}{T}(\mu_{T}-\mu_{0})=\mu_{0}+\frac{t}{T-1}(\mu_{T-1}-\mu_{0})$$ $$\Rightarrow\mu_{T}=\frac{T}{T-1}\mu_{T-1}-\frac{1}{T-1}\mu_{0}.\tag{11}$$ We find that the $\mu_{T}$ is locally $\mu_{T}$. We find that there is hardly a difference in evaluation results when conducting mixup operations with either expectations µ or sampled variables z. To reduce randomness for easier analyses, experiments in below use expectations µ to mixup. Nonetheless, sampling variables gives DialoGPS the ability to generate diverse responses to an arbitrary context and we will discuss it in § 5.4. ## 4 Experimental Settings Datasets We conduct multi-turn dialogue generation experiments on two public datasets: DailyDialog (Li et al., 2017) and PersonaChat (Zhang et al., 2018a). DailyDialog contains high-quality multi-turn dialogues collected from daily conversations, and it has many multi-reference versions (Sai et al., 2020; Gupta et al., 2019) denoted by humans, which makes it possible for us to compare DialoGPS with human annotators. Besides, it is more reliable to evaluate the generalization and performance with multiple references. PersonaChat collects dialogues based on chatters' profiles. Profiles are not shown to models, so it is more challenging and open to generate proper responses, measuring generalization capacity better. Baselines and Parameters We compare DialoGPS with (1) Transformer (Vaswani et al., 2017). (2)DD++ (Sai et al., 2020): it is a variant of DailyDialog in which each context has five manually denoted responses. We train a vanilla Transformer on it. (3) TSA (Xie et al., 2022): it is an unsupervised augmentation method in the decoder side. It uses its decoder's output to construct pseudo-target data which is used to train the model for another round. From a dialogue generation viewpoint, it is a one-to-many method that bootstraps based on one-to-one data. (4) M&D-D (Zhang et al., 2020a): it uses a pre-trained model and BM-25 algorithm to construct new context-response pairs from unpaired sentences. Since it is a single-turn augmentation, given a multi-turn dialogue, we only apply this method to the last two turns. (5) ResBag (Qiu et al., 2019): an augmented VAE-based model. It captures the common feature in the bag of plausible responses and then adds the specific feature to obtain the final output, which utilizes the multiple references better. Because DialoGPS is a plug-and-play method, we add it to a BARTLarge (Lewis et al., 2020) and compare with DialoFlowLarge (Li et al., 2021). DialoFlow is one of the state-of-the-art pre-trained models in open-domain dialogue generation. It augments the model by modeling the dialogue flow. More details on the implementation and hyperparameters are in Appendix A.1. Evaluation Metrics We consider three automatic evaluation metrics: BLEU (Papineni et al., 2002), Distinct (DIST) (Li et al., 2016), and BLEURT (Sellam et al., 2020). BLEU measures the word overlap between generated responses and the ground truth. DIST measures the ratio of unique n-grams in the generated responses. Because these two metrics are only sensitive to lexical variation, we evaluate BLEURT, an advanced learned semanticsensitive evaluation metric based on BERT (Devlin et al., 2019). On the evaluation of fine-tuning pre-trained models, we follow (Li et al., 2021) to report METEOR (Lavie and Agarwal, 2007) and Models BLEU-1 BLEU-2 BLEU-3 BLEU-4 DIST-1 DIST-2. BLEURT Transformer 17.79[0.14] 6.93[0.06] 3.03[0.08] 1.41[0.06] 0.82[0.01] 6.60[0.05] 30.16[0.05] ResBag 17.82[0.17] 6.88[0.12] 3.04[0.09] 1.37[0.11] 0.85[0.02] 6.83[0.02] 30.25[0.17] TSA 17.76[0.19] 6.92[0.16] 2.97[0.15] 1.35[0.10] 0.85[0.02] 6.56[0.01] 30.66[0.09] M&D-D 18.42[0.13] 7.25[0.09] 3.23[0.11] 1.44[0.07] 0.80[0.01] 6.55[0.01] 30.46[0.13] DialoGPSK=1 18.29[0.08] 7.21[0.05] 3.14[0.03] 1.44[0.05] **1.05**[0.01] **7.97**[0.07] 30.54[0.06] DialoGPSK=2 18.96[0.15] 7.61[0.09] 3.32[0.04] 1.54[0.02] 0.84[0.00] 7.10[0.04] **30.77**[0.14] DialoGPSK=4 **19.05**[0.18] **7.70**[0.16] **3.41**[0.09] **1.61**[0.07] 0.91[0.01] 7.45[0.09] 30.29[0.12] DialoGPSK=8 19.04[0.08] 7.64[0.11] 3.40[0.10] 1.60[0.08] 0.93[0.01] 7.64[0.06] 30.39[0.14] Multi-reference DailyDialog Dataset Transformer 33.93[0.26] 12.32[0.25] 4.93[0.23] 2.14[0.14] 2.59[0.03] 20.62[0.12] 35.79[0.15] ResBag 34.10[0.27] 12.61[0.18] 4.82[0.17] 2.13[0.13] 2.98[0.06] 24.44[0.17] 35.22[0.15] TSA 36.14[0.11] 13.21[0.15] 5.43[0.14] 2.46[0.13] 3.56[0.04] 26.89[0.21] 35.37[0.13] DD++ 36.87[0.32] 14.09[0.24] 6.13[0.23] 2.91[0.17] 3.84[0.03] 28.58[0.38] 37.04[0.14] M&D-D 36.97[0.12] 14.28[0.09] 6.50[0.19] 3.28[0.17] 3.65[0.03] 25.35[0.21] 36.02[0.15] DialoGPSK=1 37.21[0.12] 14.72[0.14] 6.65[0.12] 3.29[0.11] 4.25[0.05] 28.39[0.14] 36.14[0.08] DialoGPSK=2 38.01[0.13] 14.79[0.07] 6.52[0.06] 3.20[0.04] 4.34[0.06] 29.04[0.25] 36.15[0.16] DialoGPSK=4 38.27[0.20] 14.77[0.13] 6.62[0.15] **3.33**[0.20] **4.53**[0.07] **30.18**[0.17] 36.09[0.08] DialoGPSK=8 **38.46**[0.18] **15.05**[0.23] **6.70**[0.24] 3.30[0.14] 4.32[0.06] 28.35[0.14] 35.82[0.16] DialoGPSK=16 38.38[0.14] 14.89[0.06] 6.62[0.13] 3.30[0.15] 4.41[0.05] 29.84[0.08] 35.81[0.05] Component Ablation on Multi-reference DailyDialog (K=4) –M.E. 38.04[0.17] 15.00[0.12] 6.63[0.12] 3.21[0.11] 4.22[0.03] 28.05[0.10] 35.96[0.09] –M.D. 34.62[0.12] 12.71[0.13] 5.20[0.08] 2.33[0.08] 3.19[0.04] 24.65[0.16] 35.14[0.13] –Brown. 38.05[0.22] 14.68[0.05] 6.36[0.04] 3.01[0.10] 4.05[0.09] 27.58[0.18] 35.52[0.11] –M.E. –Brown. 38.42[0.13] 14.76[0.15] 6.55[0.05] 3.17[0.12] 4.11[0.03] 27.64[0.16] 36.12[0.12] –M.D. –Brown. 34.49[0.31] 12.68[0.28] 5.15[0.23] 2.29[0.17] 2.97[0.45] 24.46[0.15] 35.11[0.12] –M.E. –M.D. 33.93[0.26] 12.32[0.25] 4.93[0.23] 2.14[0.14] 2.59[0.03] 20.62[0.12] 35.79[0.15] | PersonaChat Dataset | |---------------------------------------------------------| | Multi-reference DailyDialog Dataset | | Component Ablation on Multi-reference DailyDialog (K=4) | Entropy (Zhang et al., 2018b). For human evaluation, we recruit five evaluators to manually judge 200 samples from each experiment in blind testing, where we set three metrics to comprehensively evaluate the generation quality: whether a response is *readable* (**Read.**), *coherent* (**Coh.**), and *informative* (**Info.**). For each aspect, evaluators can score at 'bad', 'borderline' and 'good'. ## 5 Results Table 1 shows the automatic evaluation results. On PersonaChat, without access to chatters' profiles, conversations are so open that there is so much noise in data for models to learn. Therefore, models prefer safe responses and thus DISTs are relatively low. However, DialoGPS still improves by about 20% in DISTs than the best-performing baseline. Also, BLEU and BLEURT scores imply that DialoGPS matches references more lexically and more semantically. On the multi-reference DailyDialog dataset, DialoGPS gains improvement by a large margin than other strong baselines. Also, most baselines suffer a trade-off between matching the references and diversifying responses. By contrast, DialoGPS performs evenly well on all metrics. DialoGPS also wins 6 out of all 7 metrics compared with the model trained on DD++, the human-written multi-reference training set. Our | Models | DailyDialog | PersonaChat | | | | | | | |-----------------|---------------|---------------|---------|--------|--------|--------|---------|------| | BLEU-2 | BLEU-4 | METEOR | Entropy | BLEU-2 | BLEU-4 | METEOR | Entropy | | | BART | 27.87 | 10.85 | 14.69 | 9.29 | 9.95 | 3.38 | 8.69 | 6.55 | | DialoFlow | 28.02 | 11.57 | 16.40 | 9.46 | 10.46 | 3.03 | 9.32 | 6.89 | | BART + DialoGPS | 29.18 | 12.05 | 15.30 | 9.73 | 10.97 | 4.08 | 9.26 | 6.70 | Table 2: Automatic evaluation results on fine-tuning pre-trained models (beam search with width 5). | Models | DailyDialog | PersonaChat | | | | | |-------------|---------------|---------------|-------|-------|-------|-------| | Read. | Coh. | Info. | Read. | Coh. | Info. | | | Transformer | 70/8 | 69/9 | 73/12 | 53/14 | 51/11 | 52/9 | | ResBag | 58/13 | 60/11 | 64/14 | 51/14 | 50/19 | 51/16 | | TSA | 59/15 | 57/16 | 60/16 | 48/20 | 47/22 | 43/20 | | DD++ | 53/24 | 55/20 | 51/17 | - | - | - | | M&D-D | 56/19 | 47/20 | 52/16 | 44/21 | 46/18 | 45/17 | | BART | 40/34 | 42/23 | 44/26 | 39/31 | 41/26 | 34/20 | | DialoFlow | 36/32 | 40/29 | 43/27 | 39/34 | 35/28 | 35/25 | results in bold pass the significance test p < 0.01. In Table 2, when adding DialoGPSK=2 to a pretrained BART and fine-tuning on two datasets, it achieves competitive performance as one of the SOTA dialogue generation pre-trained models, DialoFlow. DialoFlow augments the generation with the help of 'flow', i.e., the difference of adjacent utterances in continuous space. Their flows are not as flexible as paths sampled from the Brownian Bridge, which is one of the reasons that DialoGPS outperforms DialoFlow in five out of all eight metrics. Table 3 shows human evaluation results. In three metrics, DialoGPS achieves the top rank with solid agreement among evaluators. More evaluation details are in Appendix A.2. ## 5.1 Study On Dialogue Paths We conduct an ablation study on the number of sampled dialogue paths K, results are shown in Table 1. On both datasets, with the increase of K, various metrics increase and then reach the bottleneck or slightly decrease. This phenomenon mainly dues to that different from discrete data, sampled paths in continuous space have a information bottleneck, i.e., if K is big enough to cover the most samplable area in the Brownian Bridge, then increasing K further may cause little improvement or even de- ![6_image_0.png](6_image_0.png) crease due to more noise. We visualize the sampled paths of a conversation with 5 utterances during training in Figure 3. A sample at each time step is denoted as a point and paths are depicted. We can see that the Brownian Bridge area covered by paths is significantly increased when K increases from 1 to 8, but there is a slight difference when K further increases to 16. The visualization confirms automatic evaluation results in Table 1. ## 5.2 Component Ablation We study the effect on the performance of the following components in DialoGPS: mixup in the encoder (M.E.), mixup in the decoder (M.D.), and constraints from Eq. 4 that is the optimization of the mapping function (Brown.). The results are reported at the bottom of Table 1. Removing mixup in the decoder (–M.D.) degenerates DialoGPS to a many-to-one mode and thus the performance degrades much, confirming the intuition mentioned in §1. Removing mixup in the encoder(–M.E.) degenerates DialoGPS to a one-to-many pattern which is insufficient compared with the many-tomany pattern, and DIST drops while the BLEU maintains. Nonetheless, the performance is still | Method | BLEU-2 | BLEU-4 | DIST-1 | DIST-2 | |-------------|----------|----------|----------|----------| | Avg. | 14.77 | 3.33 | 4.53 | 30.18 | | Avg. + Pos. | 14.41 | 2.89 | 4.19 | 29.22 | | GPT-2 | 15.13 | 3.28 | 4.23 | 29.55 | competitive with the best one-to-many baseline. Without constraints from Eq. 4 (–Brown.), there is no context-wise correlation among sampled latent variables and the mixup turns to introduce noise. This variant resembles sampling each utterance with a VAE (Bowman et al., 2016; Miao et al., 2016). However, Eq. 11 does not hold anymore so there exist gaps between the inference and the training, and results drop compared to the variant with Eq. 4. Overall, this variant still plays a positive role because adding noise during training is proved to be effective in improving the robustness and generalization of the model (Srivastava et al., 2014; Gao et al., 2021). When there is neither M.D. nor M.E., the method becomes a vanilla transformer. ## 5.3 Study On Utterance Representation In §3.3, we defer details on obtaining utterance representations of each turn in a dialogue. We study three variants of encoding an utterance: (1) average embeddings of each token in an utterance (Avg.), (2) average embeddings of each token in an utterance along with position embeddings (Avg. + Pos.), and (3) encode utterances by a GPT-2 (Radford et al., 2019). We conduct this study on the multireference DailyDialog dataset and the results are in Table 4. The simplest method (Avg.) achieves first place. With extra positional information, the performance drops a little, and in this experiment, we observed that the Lβ term in the overall training objective Eq. 9 maintains steadily, but other terms increase a little. An explanation is that features to be mixed with latent variables (e and d) have included positional information and positional information in latent variables introduces redundancy. For (GPT-2), we add a special token '<eou>' at the end of an utterance and view its corresponding output as the utterance representation. (GPT-2) costs much more training time and only beat (Avg.) in one metric. We guess there is an expression capacity gap so we try to (1) train a 4-layer language model to replace the GPT-2 and (2) apply GPT-2 in pre-trained experiments. In both experiments, we do not observe improvement than (Avg.). To sum | X0:2 | |-------------| | x3 DialoGPS | | A: Excuse me, sir. Is there a barber near here? B: Yes, the nearest one is at the third cross of this road. A: I'm a stranger here. How can I get there, please? B: ________________________ | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ResBag | Two stops at the next door. | | TSA | Let me see. It's about ten minutes. | | M&D-D | You can take the subway to get there. You have to go to the next stop. (×2) You get off at the next stop. (×2) You have to change. (×2) You have to go to the hotel. (×1) It's not easy. You have to go. (×1) You have to go to the airport. (×1) Then, you have to go to the hotel. (×1) | up, the simplest (Avg.) achieves the best trade-off between performance and costs so in DialoGPS, we adopt this scheme by default. ## 5.4 What Does The Model Learn From Augmented Data? If we mixup with sampled variables instead of expectations during inference, the model obtains the ability to generate diverse responses. Although we do not know what discrete labels augmented data have, to some extent the diverse outputs during inference reflect semantics that augmented data have during training. We provide a case in Table 5. Transformer and ResBag generates incoherent responses, and TSA answers the arrival time but not the way. DD++ reply to the context but does not leads to the follow-up dialogue. M&D-D responds properly but can only provide one answer. We let DialoGPS generate 10 times and report all the outputs along with their respective frequency. The frequency, the semantics, and lexical features of responses resemble a Gaussian distribution. In this case, 'you have to go to (get off at) the next stop' is close to the expectation. As the semantics get farther away, the frequency of other responses are lower. Overall, DialoGPS provides diverse choices to arrive at the barber. This case shows that continuous augmented data do have open dialogue knowledge which is conducive to model generalization. ## 6 Conclusion We propose DialoGPS that first augments opendomain and multi-turn dialogue generation from a many-to-many perspective. Specifically, We map dialogues into the continuous semantic space which is modeled by our extended Brownian Bridge and sample dialogue paths to augment training. We propose a self-distillation framework to utilize augmented data despite the inaccessible discrete labels. Empirically, we prove the effect of DialoGPS and study its characteristics. DialoGPS could be a general method that suits seq2seq tasks where the source has multiple sentences and the target is different from the source in semantics, like summarization. However, DialoGPS should be modified according to the unique properties of the task, which is left to study in the future. ## Limitations Similar to other augmentation methods, DialoGPS demands high requirements for computing resources. The training is performed on up to 8 V100 GPUs. On DailyDialog: a vanilla transformer only needs 50 minutes while a non-pretrained DialoGPS takes about 80 minutes when K = 1. Other baselines take about the same amount of time as DialoGPS K = 1. But when DialoGPS achieves its performance peak (K = 16), the training takes 4 hours. Most of time cost comes from sampling which is difficult to be accelerated by GPUs. ## Acknowledgement This work was supported by National Natural Science Foundation of China (NSFC Grant No. 62122089), Beijing Outstanding Young Scientist Program NO. 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Association for Computational Linguistics. ## A Appendix A.1 Model Implements DailyDialog 44050 4176 6740(Multi-ref) PersonaChat 68859 8593 8239 Datasets Train Valid Test In pre-process, we truncate the original long conversations in the dataset with the window size 5. Table 6 shows the dataset statistics. Table 6: Dataset statistics. | Method | LR(DD) | Batch size(DD) | LR(PS) | Batch size(PS) | |--------------|----------|------------------|----------|------------------| | Transformer | 1e-4 | 112 | 1e-4 | 32 | | ResBag | 8e-5 | 160 | 1e-4 | 64 | | TSA | 8e-5 | 160 | 1.5e-4 | 32 | | DD++ | 8e-5 | 112 | - | - | | M&D-D | 1e-4 | 112 | 1e-4 | 64 | | DialoGPSK=1 | 1.5e-4 | 160 | 1.5e-4 | 64 | | DialoGPSK=2 | 1.5e-4 | 160 | 1e-4 | 64 | | DialoGPSK=4 | 1.5e-4 | 112 | 1.2e-4 | 64 | | DialoGPSK=8 | 1.5e-4 | 160 | 1.2e-4 | 64 | | DialoGPSK=16 | 8e-5 | 160 | - | - | For non-pretrained experiments, our code is based on fairseq (Ott et al., 2019). We adopt grid search to tune hyper-parameters. On the DailyDialog dataset, the search ranges for learning rate and batch size are {0.00008, 0.00010, 0.00012, 0.00015} and {112, 160}, respectively. On the PersonaChat dataset, the search ranges for learning rate and batch size are {0.00010, 0.00012, 0.00015} and {32, 64}, respectively. We choose the parameter combination with the lowest perplexity in the validation set. Table 7 shows the searched results for each experiment. Table 7: Learning rate and batch size in each experiment. Except for batch size and learning rate, the following important settings: the warmup steps are 4000. We use Adam optimizer with β = (0.9, 0.98). Both attention dropout and activation dropout are 0.1. For models trained from scratch, δ on Dailydialog is 12 and 13 on PersonaChat. For fine-tuned models, δ is 12 on two datasets. We select the best checkpoint based on the perplexity in the validation set. Early stop patience is 10 epochs. For pre-trained experiments, on both datasets, the batch size is 64 and learning rate is 0.00002. The training is performed on Nvidia V100 GPU. On DailyDialog: our method takes about 80 minutes when K = 1, 4 hours when K = 16, and 8 hours | Method | PersonaChat | DailyDialog | |---------------|---------------|---------------| | Transformer | 2.93 | 3.08 | | ResBag | 2.93 | 3.12 | | TSA | 2.92 | 3.13 | | DD++ | - | 3.24 | | M&D-D | 2.96 | 3.13 | | DialoGPS(K=4) | 3.03 | 3.24 | Table 8: QuantiDCE results on two datasets. ## To Finetune A Bartlarge. Because M&D-D does not suit multi-turn settings, we only use it to modify the last two turns with Okapi BM25 algorithm and we finetune BERT on DailyDialog and PersonaChat respectively to measure the fluency between the last two utterances and the fluency between the penultimate sentence and the above as filtration. In our experiments, on two datasets, the paired sentence set Dp is same as the original training set and the unpaired sentence set Du is constructed from all sentences in DD++. On DailyDialog, we use multiple references in DD++ as the response bag of ResBag, and on PersonaChat, we use constructed data from M&D-D as its response bag. ## A.2 Evaluation Details Because some evaluation script links of DialoFlow (Li et al., 2021) are out of date, we can not reproduce NIST (Lin and Och, 2004) scores so we do not report it. This issue was also reported by the community 1. Also, METEOR and Entropy are reproduced. Our reproduced BLEU scores are close to the original paper so we directly quote their results. Our human evaluators are recruited from Amazon Mturk. In terms of human evaluation, all generated responses are re-capitalized and de-tokenized fairly. The salary for each evaluator is 1 dollar per 10 samples. To give a fair salary, we first evaluate 50 samples by ourselves, calculate the time and effort, and set this amount (samples evaluated by ourselves are just for evaluating the salary, which is not given to evaluators and not reported in the final results). ## A.3 Quantidce In addition to the metrics mentioned in the main paper, we further supplement our evaluation with the dialogue-specific metric QuantiDCE (Ye et al., 2021), which measures the coherence between the 1https://github.com/microsoft/DialoGPT/issues/ 72 response and the context. The results show that our proposed DialoGPS outperforms all baseline models. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Following instructions, we add Limitations after Conclusion. ✓ A2. Did you discuss any potential risks of your work? In Limitations. ✓ A3. Do the abstract and introduction summarize the paper's main claims? The main claims in the paper are stated in the abstract and in the introduction. ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** We use public datasets and open pre-trained models. These are mentioned in many places in the paper such as Introduction and Experiments. ✓ B1. Did you cite the creators of artifacts you used? We have cited all datasets we use. We have cited open pre-trained models. For example, in Section.1 Introduction and Section.4 Experiments, etc. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? All open code we use are from github where code is licensed under MIT by default. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. In appendix A1, we report the dataset statistics. ## C ✓ **Did You Run Computational Experiments?** In Section 4. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? In terms of parameters, we report model structure, e.g., 4-layer transformer, BART large... which have certain parameters. In appendix A1, we report computational budget and GPU version. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? In 4.1.2 and appendix A1, we discuss experimental setup, including hyperparameter search and best-found hyperparameter values. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We report standard deviation across 5 runs if there's randomness. We report p-value in t-test and kappa value of human evaluation agreement. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? In 4.1.3, we report evaluation metrics. In 4.1.2, 4.1.3, and 4.5, we report pre-trained models we use. ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** In 4.1.3 And 4.2. ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? In 4.1.3, we summarized three aspects of evaluation instructions. Also, in appendix A2, before human evaluation, we have de-tokenized and re-capitalized the outputs for a fair and solid evaluation, and thus the instructions are relatively concise. ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? We discuss these In appendix A2, D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

lin-etal-2023-techs

{TECHS}: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning

https://aclanthology.org/2023.acl-long.71

Extrapolation reasoning on temporal knowledge graphs (TKGs) aims to forecast future facts based on past counterparts. There are two main challenges: (1) incorporating the complex information, including structural dependencies, temporal dynamics, and hidden logical rules; (2) implementing differentiable logical rule learning and reasoning for explainability. To this end, we propose an explainable extrapolation reasoning framework TEemporal logiCal grapH networkS (TECHS), which mainly contains a temporal graph encoder and a logical decoder. The former employs a graph convolutional network with temporal encoding and heterogeneous attention to embed topological structures and temporal dynamics. The latter integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. A forward message-passing mechanism is also proposed to update node representations, and their propositional and first-order attention scores. Experimental results demonstrate that it outperforms state-of-the-art baselines.

# Techs: Temporal Logical Graph Networks For Explainable Extrapolation Reasoning Qika Lin1,2, Jun Liu1,3∗, Rui Mao4, Fangzhi Xu1,2**, Erik Cambria**4 1School of Computer Science and Technology, Xi'an Jiaotong University 2Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering 3National Engineering Lab for Big Data Analytics 4School of Computer Science and Engineering, Nanyang Technological University [email protected], [email protected], [email protected], [email protected], [email protected] ## Abstract ![0_Image_0.Png](0_Image_0.Png) Extrapolation reasoning on temporal knowledge graphs (TKGs) aims to forecast future facts based on past counterparts. There are two main challenges: (1) incorporating the complex information, including structural dependencies, temporal dynamics, and hidden logical rules; (2) implementing differentiable logical rule learning and reasoning for explainability. To this end, we propose an explainable extrapolation reasoning framework TEemporal logiCal grapH networkS (TECHS), which mainly contains a temporal graph encoder and a logical decoder. The former employs a graph convolutional network with temporal encoding and heterogeneous attention to embed topological structures and temporal dynamics. The latter integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. A forward message-passing mechanism is also proposed to update node representations, and their propositional and first-order attention scores. Experimental results demonstrate that it outperforms state-of-the-art baselines. ## 1 Introduction Knowledge Graphs (KGs) are widely used in intelligent systems (Ji et al., 2022; Mao et al., 2022; Zhu et al., 2023), where knowledge is commonly represented by triplets in the form of (s, r, o). The limit of conventional KGs is that real-world knowledge usually evolves over time. For example, a fact (*Donald Trump, presidentOf, USA*) is incorrect now because *Joe Biden* has been the new president of the USA since 2021. For more comprehensive representations of knowledge, Temporal Knowledge Graphs (TKGs) (Liang et al., 2022) are proposed by introducing time information (time point or interval) via quadruplets, i.e., (s, r, o, t). Then, the former example is defined as (*Donald Trump, presidentOf, USA, 2017/01/20-2021/01/20*). ∗ Corresponding author. TKGs are usually incomplete (Cai et al., 2022; Liang et al., 2022). Many studies predicted future facts, based on past facts, namely TKG forecasting or extrapolation reasoning. Figure 1a shows the task that predicts facts at time ti with the facts at ti−2 and ti−1. A model should not only learn topology dependencies, i.e., the neighbor information of an entity (like *Barack Obama* at ti−2), but also learn temporal dynamics, i.e., the variations of properties of an entity over time (e.g., *Angela Merkel* evolves during ti−2 to ti−1). Thus, temporal embedding methods, e.g., TNTComplEx (Lacroix et al., 2020) and CyGNet (Zhu et al., 2021) were proposed. However, these blackbox methods fail to explain their predictions. An explainable method, xERTE (Han et al., 2021) conducted instanced propositional reasoning. However, the model is not scalable, as the evidence is entity-dependent, e.g., related to *Barack Obama* and other entities in Figure 1a. If we can learn the entity-independent rule in Figure 1b for the query (Barack Obama, *makeStatement*, ?, ti) in Figure 1a, the correct answer *South Korea* will be easily obtained after rule grounding. Motivated by the fact that TKGs have many hidden logical rules to achieve explainable and accurate predictions, TLogic (Liu et al., 2022) searched first-order logical rules and used them for reasoning. However, this two-step pipeline method may cause error propagation issues. Generally, there are two main challenges for explainable extrapolation reasoning on TKGs: (1) TKGs contain diverse information, e.g., structural dependencies, temporal dynamics, and hidden logical rules that are difficult to incorporate together and achieve full coverage; (2) Logical rule representations are discrete and symbolic, resulting in the natural gap between logical rules and the continuous computation of neural networks. Thus, implementing differentiable logical rule learning and reasoning is not directly achievable (Yang et al., 2017). To address above issues, we propose a unified framework TEemporal logiCal grapH networkS (TECHS). It first utilizes a graph convolutional network (GCN) to embed topological structures and temporal dynamics. To determine the weights of different edges between entities, a generic time encoding and a heterogeneous attention mechanism is introduced. Then, a logical decoder is proposed to integrate propositional and first-order reasoning to find the answer. A reasoning graph that contains both query entity and entity-time pair nodes is used to constantly expand over iterations. We update propositional and first-order attention weights as well as node representations via a novel forward message-passing mechanism. Finally, nodes' attention weights with the same entity are aggregated as the answer indicator. Besides, first-order logical rules can be induced by a novel Forward Attentive Rule Induction (FARI) algorithm using learned first-order attention weights. Our contributions are summarized as follows: (1) A unified framework TECHS is proposed to conduct explainable extrapolation reasoning on TKGs. To our best knowledge, this is the first study to jointly model structural dependencies, temporal dynamics, and propositional and first-order reasoning. (2) We integrate propositional and first-order reasoning in a logical decoder, where a forward message-passing is proposed to update their attention weights and node representations to achieve explainability. First-order logical rules are induced by a novel FARI algorithm. (3) Extensive experiments verify the effectiveness of each module and the superiority over state-of-the-art baselines. ## 2 Related Work The studies of extrapolation reasoning can be categorized into the following three trends. Static Embedding. By omitting time information in fact quadruplets, general KG embedding methods can be utilized for TKGs, such as TransE (Bordes et al., 2013), DistMult (Yang et al., 2015) and ComplEx (Trouillon et al., 2016). However, these methods simply consider the structural dependency in TKGs and ignore the temporal dynamics. Temporal Embedding. TTransE (Leblay and Chekol, 2018) expanded TransE to the temporal setting by fusing temporal information in relation embeddings. Similarly, TA-DistMult and TATransE (García-Durán et al., 2018) learned relation representations with time information and calculated quadruplet plausibility by DistMult and TransE. Differently, DE-SimplE (Goel et al., 2020) proposed diachronic entity embedding which contained static segment and time-varying segment. Upon ComplEx, TNTComplEx (Lacroix et al., 2020) learned complex-valued embeddings for the entity, relation and time. RE-Net (Jin et al., 2020) learned the global representations of the time subgraph and the local representations of nodes on it. CyGNet (Zhu et al., 2021) introduced a timeaware copy-generation mechanism to model the probability of existing facts, occurring in the future and predicted whether new facts would emerge. However, the aforementioned methods are all in black-box fashion and lack of explainability. Explainable Reasoning. xERTE (Han et al., 2021) proposed a human-understandable reasoning strategy, introducing an expanding query-relevant subgraph to achieve explainability. TITer (Sun et al., 2021) conducted reasoning from a query node and sequentially transferred to a new node related to the prior on TKGs until the answer was founded. Upon AnyBURL (Meilicke et al., 2019) that sampled paths to learn first-order rules in static KGs, TLogic (Liu et al., 2022) learned temporal logical rules with confidences via a temporal random walk. The candidate scores were obtained by rule applications in TKGs. However, xERTE and TITer conducted propositional reasoning by an end-toend framework that had limited scalability, as its reasoning process was query-specific. Although TLogic learned query-independent first-order logical rules, its pipeline method might cause error propagation and performance degradation. ## 3 Preliminaries A TKG can be represented as G = {E, R, T , F}, where E, R and T denote the set of entity, relation and time, respectively. *F ⊂ E ×R×E ×T* is the fact collection. Each fact is a quadruplet, such as (*s, r, o, t*) where s, o ∈ E, r ∈ R and t ∈ T . For a query (˜s, r, ˜ ?,t˜) in testing, the model needs to predict an answer entity o˜, based on the facts that occur earlier than t˜, i.e., t > max ˜ (T*train*). Logical reasoning in KGs can be categorized as: propositional and first-order. Propositional reasoning, generally known as multi-hop reasoning (Ren and Leskovec, 2020; Zhang et al., 2021, 2022a), is entity-dependent that usually reasons over queryrelated paths to obtain an answer. First-order reasoning is entity-independent, using first-order logical (FOL) rules for different entities (Zhang et al., 2022b), describing causal knowledge in the form of body to *head*, e.g., premise⇒*conclusion*, where new facts can be deduced, given observed ones. For efficient and explainable reasoning on TKGs, we define the FOTH rule and the reasoning graph. Definition 1. First-order Temporal Horn (FOTH) Rule: Based on Horn rules (Lin et al., 2022) on static KGs, atoms in FOTH rule body are connected transitively by shared variables. Meanwhile, rule body and rule head have the same start and end variables. Time growth also needs to be satisfied, i.e., time sequence is increasing and the time in the rule head is the maximum. For example, the following rule ϵ, ∃X,Y,Z r1(X, Y ):t1∧r2(Y,Z): t2 ⇒ r(X,Z) : t is a FOTH rule with length 2 if t1 ⩽ t2 < t. X, Y and Z are variables that can be instantiated as entities of TKGs by rule grounding. Noticeably, for rule learning and reasoning, t1, t2 and t are virtual time variables that are only used to satisfy the time growth and do not have to be instantiated. To represent the rule certainty, each rule is assigned with a confidence value ϵ ∈ [0, 1]. Definition 2. Reasoning Graph: For a query (˜s, r, ˜ ?,t˜), we introduce a reasoning graph Ge = {O, R, F}e for propositional and first-order reasoning. O is a node set that consists of nodes in different iteration steps, i.e., O = O0 ∪ O1 *∪ · · · ∪ O*L. O0 only contains a query entity s˜ and others consist of nodes in the form of entity-time pairs. (n l i , *r, n* ¯ l+1 j) ∈ Fe is an edge that links nodes at two neighbor steps, i.e., n l i ∈Ol, n l+1 j ∈Ol+1 and r¯∈R. The reasoning graph is constantly expanded by searching for posterior neighbor nodes. For start node n 0 = ˜s, its posterior neighbors are N (n 0) = {(ei, ti)|(˜s, r, e ¯ i, ti) *∈ F ∧*ti < t˜}. For a node in following steps n l i = (ei, ti) ∈ Ol, its posterior neighbors are N (n l i ) = {(ej , tj )|(ei, r, e ¯ j , tj ) ∈ F ∧ti ⩽ tj∧tj <t˜}. Its prior parents are Ne(n l i )= {(n l−1 j, r¯)|n l−1 j ∈ Ol−1 ∧(n l−1 j, *r, n* ¯ l i ) ∈ F}e . An example reasoning graph with two steps is shown in Figure 1c. To take prior nodes into account at the current step, an extra relation *self* is added. Then, n l i = (ei, ti) can be obtained at the next step as n l+1 i = (ei, ti) (tiis the minimum time if l = 0). ## 4 Methodology There are three key technical parts in TECHS: temporal graph encoder, logical decoder, and extrapolation prediction. Figure 2 shows its architecture. ## 4.1 Temporal Graph Encoder Generally, GCNs follow an iterative messagepassing strategy to continuously aggregate information from neighbor nodes. As conventional GCNs cannot model time information, we propose a temporal graph encoder. The generic time encoding (Xu et al., 2020) is introduced to embed times in TKGs as it is fully compatible with attention to capture temporal dynamics, which is defined as: et = q 1 dt [cos(w1t + b1), *· · ·* , cos(wdt t + bdt )]. [w1, · · · , wdt ] and [b1, · · · , bdt ] are trainable parameters for transformation weights and biases. dt is the dimension of time embedding. Based on it, a temporal GCN is proposed by fusing neighbor information with the heterogeneous attention: $$\mathbf{h}_{o}^{k+1}\!=\!\mathbf{W}_{h1}^{k}\mathbf{h}_{o}^{k}+\sum_{\begin{array}{c}{{(s,r,t)\!\in\!{\hat{\cal N}}(o)}}\end{array}}\alpha_{s,r,o,t}^{k}\mathbf{W}_{h2}^{k}\mathbf{m}_{s,r,t}^{k},\quad(1)$$ where W denotes a transformation matrix. Nb is the neighbor set. mk s,r,t is the message information of neighbors that contains subject, relation and time representations, which is given by: $${\bf m}_{s,r,t}^{k}\!=\!{\bf W}_{m1}^{k}\left[\left({\bf h}_{s}^{k}+{\bf e}_{t}\right)\odot\left({\bf g}_{r}^{k}+{\bf e}_{t}\right)\right].\tag{2}$$ h and g are the entity and relation embeddings, respectively. ⊙ is the element-wise product of two embedding vectors. α k s,r,o,t is a heterogeneous attention value to determine the importance of a current temporal edge. It is obtained by the correlation ![3_image_0.png](3_image_0.png) between time, relation and the current entities: $$a^{k}_{s,r,o,t}=\sigma\big{(}(\alpha^{k})^{\top}\mathbf{W}^{k}_{a}[\mathbf{e}_{t}||\mathbf{g}^{k}_{r}||(\mathbf{h}^{k}_{s}-\mathbf{h}^{k}_{o})]\big{)},\tag{3}$$ $$\alpha^{k}_{s,r,o,t}=\frac{\exp(a^{k}_{s,r,o,t})}{\sum_{(s^{\prime},r^{\prime},t^{\prime})\in\widehat{\mathcal{N}}(o)}\exp(a^{k}_{s^{\prime},r^{\prime},o,t^{\prime}})},$$ where $\sigma$ is _LeakyReLU_ (Xu et al., 2015). $||$ is con where σ is *LeakyReLU* (Xu et al., 2015). ∥ is concatenation. αkis the attention vector to be learned. Finally, the relation embedding is updated by g k+1 r = Wk rg k r . At the last layer K, the representation matrix H, G and E of entity, relation and time are obtained, then feeding into the logical decoder. ## 4.2 Logical Decoder For decoding the answer for query (s˜,r˜,?,t˜), we introduce an iterative forward message-passing mechanism in a continuously expanding reasoning graph, regulated by propositional and first-order reasoning. In the reasoning graph, we set three learnable parameters for each node n l i to guide the computation: node embedding n l i , hidden FOTH embedding on l i and reasoning attention βn l i . The start node n 0=s˜ is initialized as its embedding hs˜. A hidden FOTH representation on0 for n 0is initialized as a query relation embedding gr˜ . The attention weight βn0 for n 0is initialized as 1. The node ni=(ei, ti) are firstly represented by the linear transformation of GCN embeddings: ni=Wn[hei∥eti ]. Constant forward computation is required in the reasoning sequence of the target, whether conducting multi-hop propositional reasoning or first-order logic reasoning. Thus, forward message-passing is proposed to pass information (i.e., representations and attention weights) from the prior nodes to their posterior neighbor nodes. The computation of each node is contextualized with prior information that contains both entity-dependent and entity-independent parts, reflecting the continuous accumulation of knowledge and credibility in the reasoning process. Specifically, to update node embeddings in step l+1, its own feature and the information from its priors are integrated: $$\begin{array}{c}{{{\bf n}_{j}^{l+1}{=}{\bf W}_{n1}^{l}{\bf n}_{j}+\sum\beta_{n_{i}^{l},\bar{r},n_{j}^{l+1}}{\bf W}_{n2}^{l}{\bf m}_{n_{i}^{l},\bar{r},n_{j}^{l+1}},}}\\ {{(n_{i}^{l},\bar{r}){\in}\tilde{N}(n_{j}^{l+1})}}\end{array}$$ where mn l i ,r,n¯ l+1 j is the message from a prior node to its posterior node, which is given by the node and relation representations: $${\bf m}_{n_{i}^{l},\bar{r},n_{j}^{l+1}}\!=\!{\bf W}_{m2}^{l}[{\bf n}_{i}^{l}\|{\bf g}_{\bar{r}}\|{\bf n}_{j}].$$ $$({\boldsymbol{S}})$$ i∥gr¯∥nj ]. (5) This updating form superficially seems similar to the general message-passing in GCNs. However, they are actually different as ours is in a one-way and hierarchical manner, which is tailored for the tree-like structure of the reasoning graph. The attention weight βn l i ,r,n¯ l+1 j for each edge in a reasoning graph contains two parts: propositional and first-order attention. As propositional attention is entity-dependent, we compute it by the semantic association of entity-dependent embeddings between the message and the query: $$e_{n_{i}^{l},\bar{r},n_{j}^{l+1}}^{1}=\mathrm{SIGMOID}(\mathbf{W}_{p}^{l}[\mathbf{m}_{n_{i}^{l},\bar{r},n_{j}^{l+1}}\|\mathbf{q}]),\tag{6}$$ where q = Wq[hs˜∥gr˜∥et˜] is the query embedding. As first-order reasoning focuses on the interaction among entity-independent relations, we first obtain the hidden FOTH embedding of an edge by fusing the hidden FOTH embedding of the prior node and current relation representation via a gated recurrent unit (GRU) (Chung et al., 2014). Then, the firstorder attention is given by: $$\begin{array}{l}{{{\bf0}_{n_{i}^{l},\bar{r},n_{j}^{l+1}}=\mathrm{GRU}({\bf g}_{\bar{r}},{\bf0}_{n_{i}^{l}}),}}\\ {{{e}_{n_{i}^{l},\bar{r},n_{j}^{l+1}}=\mathrm{SIGMOID}({\bf W}_{f}^{l}{\bf0}_{n_{i}^{l},\bar{r},n_{j}^{l+1}}).}}\end{array}\tag{7}$$ Furthermore, the overall reasoning attention can be obtained by incorporating propositional and firstorder parts to realize the complementarity of these two reasoning methods. Since the prior node with high credibility leads to faithful subsequent nodes, the attention of the prior flows to the current edge. Then, the softmax normalization is utilized to scale edge attentions on this iteration to [0,1]: en l i ,r,n¯ l+1 j=βn l i (e 1 n l i ,r,n¯ l+1 j +λe2n l i ,r,n¯ l+1 j ), βn l i ,r,n¯ l+1 j= exp(en l i ,r,n¯ l+1 j) P(n l i′ ,r¯′)∈Ne(n l+1 j) exp(en l i′ ,r¯′,n l+1 j) , (8) where λ is the weight for balancing the two reasoning types. Finally, the FOTH representation and attention of a new node n l+1 jare aggregated from edges for the next iteration: $$\begin{array}{c}{{{\bf0}_{n_{j}^{l+1}}=\sum_{\begin{array}{c}{{(n_{i}^{l},\bar{r})\in\bar{\mathcal{N}}(n_{j}^{l+1})}}\\ {{}}\end{array}}}\\ {{\beta_{n_{j}^{l+1}}=\sum_{\begin{array}{c}{{(n_{i}^{l},\bar{r})\in\bar{\mathcal{N}}(n_{j}^{l+1})}}\\ {{}}\end{array}}}\\ {{(n_{i}^{l},\bar{r})\in\bar{\mathcal{N}}(n_{j}^{l+1})}}\end{array}}}\tag{9}$$ Insights of FOTH Rule Learning and Reasoning. In general, the learning and reasoning of first-order logical rules on KGs or TKGs are usually in twostep fashion (Galárraga et al., 2013, 2015; Qu and Tang, 2019; Zhang et al., 2019; Qu et al., 2021; Vardhan et al., 2020; Liu et al., 2022; Cheng et al., 2022; Lin et al., 2023). First, it searches over whole data to mine rules and their confidences. Second, for a query, the model instantiates all variables to find all groundings of learned rules and then aggregates all confidences of eligible rules. For example, for a target entity o, its score can be the sum of learned rules with valid groundings and rule confidences can be modeled by a GRU. However, this is apparently not differentiable and cannot be optimized by an end-to-end manner. Thus, our model conducts the transformation of merging multiple ![4_image_0.png](4_image_0.png) rules by merging possible relations at each step, using first-order attention as: $$\begin{split}S_{o}&=\sum_{\gamma\in\Gamma}\beta_{\gamma}\\ &=\sum_{\gamma\in\Gamma}f\big{[}\text{GRU}(\mathbf{g}_{\gamma,h},\mathbf{g}_{\gamma,b^{1}},\cdots,\mathbf{g}_{\gamma,b^{|\gamma|}})\big{]}\\ &\approx\prod_{l=1}^{L}\sum_{n_{j}\in\mathcal{O}_{l}}\bar{f}_{l}\big{[}\text{GRU}(\mathbf{g}_{\bar{r}},\mathbf{o}_{n_{j}}^{l}))\big{]}.\end{split}\tag{10}$$ βγ is the confidence of rule γ. gγ,h and gγ,bi are the relation embeddings of head h and i-th body b i of this rule. ¯flis for the attention calculation. In this way, the differentiable process is achieved. This is an extension and progression of Neural-LP (Yang et al., 2017) and DURM (Sadeghian et al., 2019) on TKGs. Figure 3 intuitively illustrates such transformation. Finally, the real FOTH rules can be easily induced to constantly perform attention calculation over the reasoning graph, which is summarized as FARI in Algorithm 1. ## 4.3 Extrapolation Prediction After attention weights for nodes in the last decoding step L have been obtained, we can aggregate node attentions with the same entity to get the entity score: So =Pn L i =(o,ti) βn L i . All entity scores can be normalized into [0,1] by yˆo = P So p Sp . Compared with the true label yo, the model can be optimized by a binary cross-entropy loss: $${\mathcal{L}}=-\sum_{o}y_{o}\log({\hat{y}}_{o})+(1-y_{o})(1-\log({\hat{y}}_{o})).\tag{11}$$ The number of nodes may explode in the logical decoder as it shows an exponential increase to ![5_image_1.png](5_image_1.png) ![5_image_2.png](5_image_2.png) 13 Normalize e 2 j,l of n l 14 for n L iin OL do 15 e 2 i,L, B(n L i ) = DL[n L j] ; 16 for ϵ, γb in B(n L i ) do 17 Γ.add([*ϵ, γ*b[1](*X, Y*1) : t1 *∧ · · · ∧* γb[L](YL−1, Z) : tL ⇒ *r˜(X, Z*) : t]) 18 **Return** rule set Γ. reach |N (ni)| L by iterations. For computational efficiency, posterior neighbors of each node are sampled with a maximum of M nodes in each iteration. For sampling M node in the reasoning graph, we follow a time-aware weighted sampling strategy, considering that recent events may have a greater impact on the forecast target. Specifically, for a posterior neighbor node with time t′, we compute its sampling weight by exp(t P ′−t˜) t¯ exp(t¯−t˜) for the query (s˜,r˜,?,t˜), where t¯ denotes the time of all possible posterior neighbor nodes for a prior node. After computing attention weights for each edge in the same iteration, we select top-N among them with larger attention weights and prune others. As we add an extra *self* relation in the reasoning graph, the FARI algorithm can obtain all possible rules (no longer than length L) by deleting existing atoms with the *self* relation in induced FOTH rules. ## 5 Experiments And Results 5.1 Datasets And Experiment Setup We conduct experiments on five common TKG datasets for extrapolation reasoning, i.e., ICEWS14, ICEWS18, ICEWS0515, WIKI (Leblay and Chekol, 2018) and YAGO (Mahdisoltani et al., 2015), which are the union ones of model xERTE, TITer and TLogic. The first three are all the ![5_image_0.png](5_image_0.png) subsets of Integrated Crisis Early Warning System (O'brien, 2010). The last two contain massive real facts that are distinguished by years. The statistics of these five datasets are detailed in Table 1. For training and testing, we add an inverse relation for each relation in TKGs. Thus, for the head entity prediction of query (?, r, ˜ o, ˜ t˜), we can predict results by its variant (˜o, r˜−1, ?,t˜). For testing, *time-filter* setting is used in which all correct entities at the query time except for the true query object are filtered out from answers. For entities out of the final iteration of the reasoning graph, we set their scores as 0. Mean reciprocal rank (MRR) and Hits@k (H@k for abbreviation, k is 1, 3 or 10) are selected as evaluation metrics, where larger values denote better performance. The above settings are all in line with baselines for equal comparison. We introduce fourteen baselines in three technical trends: (1) **Static Embedding:** TransE (Bordes et al., 2013), DistMult (Yang et al., 2015) and ComplEx (Trouillon et al., 2016). (2) Temporal Embedding: TTransE (Leblay and Chekol, 2018), TA-DistMult (García-Durán et al., 2018), TA-TransE (García-Durán et al., 2018), DESimplE (Goel et al., 2020), TNTComplEx (Lacroix et al., 2020), RE-Net (Jin et al., 2020) and CyGNet (Zhu et al., 2021). (3) **Explainable Reasoning:** xERTE (Han et al., 2021), TITer (Sun et al., 2021), AnyBURL (Meilicke et al., 2019) and TLogic (Liu et al., 2022). When conducting experiments, the default max number of sampled nodes and selected edges are 600 and 100, respectively. The learning rate, GCN layers, GCN dimensions, iteration steps, decoder dimensions and first-order weight λ are set to 0.001, 2, 200, 3, 50 and 0.65 by default. Adam algorithm (Kingma and Ba, 2015) is utilized to optimize the model parameters. When conducting experiments, out model is implemented in DGL (Wang et al., 2019) and PyTorch (Paszke et al., 2019), and trained on a single GPU of NVIDIA Tesla V100 with 32G memory. ## 5.2 Comparison Results In each dataset, we run five times with different random seeds and report their mean results in Ta- Model ICEWS14 ICEWS0515 ICEWS18 MRR H@1 H@3 H@10 MRR H@1 H@3 H@10 MRR H@1 H@3 H@10 TransE 22.48 13.36 25.63 41.23 22.55 13.05 25.61 42.05 12.24 5.84 12.81 25.10 DistMult 27.67 18.16 31.15 46.96 28.73 19.33 32.19 47.54 10.17 4.52 10.33 21.25 ComplEx 30.84 21.51 34.48 49.58 31.69 21.44 35.74 52.04 21.01 11.87 23.47 39.87 TTransE 13.43 3.11 17.32 34.55 15.71 5.00 19.72 38.02 8.31 1.92 8.56 21.89 TA-DistMult 26.47 17.09 30.22 45.41 24.31 14.58 27.92 44.21 16.75 8.61 18.41 33.59 TA-TransE 17.41 0.00 29.19 47.41 19.37 1.81 31.34 50.33 12.59 0.01 17.92 37.38 DE-SimplE 32.67 24.43 35.69 49.11 35.02 25.91 38.99 52.75 19.30 11.53 21.86 34.80 TNTComplEx 32.12 23.35 36.03 49.13 27.54 19.52 30.80 42.86 21.23 13.28 24.02 36.91 RE-Net 38.28 28.68 41.34 54.52 42.97 31.26 46.85 63.47 28.81 19.05 32.44 47.51 CyGNet 32.73 23.69 36.31 50.67 34.97 25.67 39.09 52.94 24.93 15.90 28.28 42.61 xERTE†40.79 32.70 45.67 57.30 46.62 37.84 52.31 63.92 29.31 21.03 33.51 46.48 TITer†41.73 32.74 46.46 58.44 - – - – 29.98 **22.05** 33.46 44.83 AnyBURL‡29.67 21.26 33.33 46.73 32.05 23.72 35.45 50.46 22.77 15.10 25.44 38.91 TLogic†43.04 33.56 48.27 61.23 46.97 36.21 53.13 67.43 29.82 20.54 33.95 48.53 TECHS 43.88 34.59 49.36 61.95 48.38 38.34 54.69 68.92 **30.85** 21.81 **35.39 49.82** Model WIKI YAGO MRR H@10 MRR H@10 TTransE 29.27 42.39 31.19 51.21 TA-DistMult 44.53 51.71 54.92 66.71 DE-SimplE 45.43 49.55 54.91 60.17 TNTComplEx 45.03 52.03 57.98 66.69 CyGNet 33.89 41.86 52.07 63.77 RE-Net 49.66 53.48 58.02 66.29 xERTE 71.14 79.01 84.19 89.78 TITer 75.50 79.02 87.47 90.27 TECHS 75.98 82.39 **89.24 92.39** Table 3: The experiment results (%) in WIKI and YAGO. The baseline results are from Sun et al. (2021). ble 2 and Table 3. As shown, our TECHS has achieved advanced performance. Compared with static embedding and temporal embedding models, e.g., the strongest RE-Net, our metrics have been greatly improved by 5.6%, 5.91%, 8.02% and 7.43% in ICEWS14. The performance of TECHS is also competitive with the explainable reasoning methods. It outperforms xERTE, TITer and AnyBURL by 3.09%, 2.15% and 14.21% MRR in ICEWS14, respectively. It demonstrates TECHS makes up for the shortcomings of simply using propositional reasoning or static first-order logical rules on TKGs. Finally, compared with the state-of-the-art TLogic, TECHS also shows certain improvements, i.e., achieving better performance on all twelve metrics of ICEWS14, ICEWS0515 and ICEWS18 datasets. TECHS has an average improvement of 0.92%, 1.65% and 1.26% on these three datasets. Besides, TECHS yields 0.48%, 3.37%, 1.77% and 2.12% improvements in MRR and Hits@10 metrics in WIKI and YAGO datasets, compared with the state-of-the-art TITer. In summary, the results show the superiority of our model that conducts temporal graph embedding as well as integrates propositional and first-order reasoning. ## 5.3 Ablation Studies To verify the effectiveness of each module in TECHS, ablation studies are carried out in Table 4. For "w/o time", we remove the time embedding in the GCN. "w/o emd" means we remove the whole GCN encoder module and perform random initialization for embeddings. For the logical decoder, "w/o PR" or "w/o FO" means that we remove propositional or first-order attention in Eq. 8 when computing nodes' attention for the ablation of the corresponding reasoning pattern. We analyze the results from the following two aspects: First, both topology structures and time dynamics in GCN embeddings contribute to extrapolation reasoning. When only removing time information, the metrics decrease slightly compared with the whole GCN ablation, e.g., 0.44% vs. 1.43% MRR drops in ICEWS14. Second, for logical reasoning, both propositional and first-order logic reasoning | Ablation | ICEWS14 | ICEWS0515 | ICEWS18 | | | | |------------|-----------|-------------|-----------|----------|-------------|------| | MRR | H@10 | MRR | H@10 | MRR H@10 | | | | TECHS | 43.88 | 61.95 | 48.38 | 68.92 | 30.85 49.82 | | | w/o time | 43.44 | 60.74 | 47.61 | 67.16 | 30.11 48.96 | | | ∆ | 0.44 | 1.21 | 0.77 | 1.76 | 0.74 | 0.86 | | w/o emd | 42.45 | 60.21 | 46.57 | 66.68 | 29.87 48.34 | | | ∆ | 1.43 | 1.74 | 1.81 | 2.24 | 0.98 | 1.48 | | w/o PR | 42.57 | 58.41 | 46.1 | 65.36 | 28.84 46.93 | | | ∆ | 1.31 | 3.54 | 2.28 | 3.56 | 2.01 | 2.89 | | w/o FO | 42.84 | 60.06 | 46.27 | 65.49 | 29.78 47.59 | | | ∆ | 1.04 | 1.89 | 2.11 | 3.43 | 1.07 | 2.23 | is important. Propositional reasoning has a bigger impact in ICEWS14 than first-order reasoning (3.54% vs. 1.89% Hits@10 drops), while they have roughly the same effect in ICEWS0515 and ICEWS18 (3.56% vs. 3.43%, 2.89% vs. 2.23% Hits@10 drops). This may be due to the different topology structures of different datasets, resulting in different logical reasoning patterns. In summary, ablation studies show that structural dependencies and temporal dynamics as well as propositional and first-order reasoning all bring positive gains. ## 5.4 Hyperparameter Analysis We run our model with different hyperparameters to explore weight impacts in Figure 4. Figure 4a shows the changes in the performance of models with different sampling hyperparameters M and N, where small values would lead to great performance decline. This is because fewer nodes and edges lead to insufficient and unstable training, respectively. When increasing M and N, the GPU memory of the model will increase rapidly in Figure 4b, especially for M. We also record the average training time of one epoch with different M and N in Figure 4c. Its overall trend is consistent with Figures 4a and 4b. In general, TECHS is time efficient as the running time is between 0.2 and 1 hour. Figure 4d shows the impact of different weights when using first-order reasoning, where smaller weights show worse results, generally. Thus, the FOTH rule is functional for extrapolation reasoning on TKGs. Different contextualized, e.g., vanilla RNN, GRU, LSTM (Hochreiter and Schmidhuber, 1997) for FOTH rule learning and reasoning are compared in Figure 4e, where GRU outperforms the other two competitors. RNN performs worst, showing that simple models are not competent enough for discrete structures of FOTH rules. To explore the effects of decoder iterations on model performance, we carry out experiments with iteration L=1, 2, 3, 4 in ICEWS14, ICEWS0515 and ICEWS18. As Figure 4f shows, the performance generally improves with the iteration increasing. The metrics of L=3 and L=4 are similar, which shows that the answer is usually in the adjacent hops of the target entity. Larger hops bring more candidates, which may affect model performance, e.g., Hits@10 values drop when L=4 in ICEWS14 and ICEWS18. Therefore, L=3 is selected as the default setting in our experiments. ![7_image_0.png](7_image_0.png) ## 5.5 Case Study For Explainable Reasoning Figure 5 visualizes two reasoning graphs on ICEWS14 and ICEWS0515, showing the extrapolation reasoning process of TECHS. The propositional attention weights of nodes are listed nearby them, which represent the propositional reasoning score of each node at the current step. For example, the uppermost propositional reasoning path from Massoud Barzani to *Iran: 2014-08-26* in case B learned a large attention score for the correct answer *Iran*. Generally, nodes with more prior neighbors or larger prior attention weights significantly impact subsequent steps and the prediction of final entity scores. From both reasoning cases, we induce several FOTH rules using the FARI algorithm. Some typical ones with their confidence scores are shown in Table 5. For example, the rule [7] with lower confidence is learned for the prediction of the false candidate *Iraq* in case B. These attentions and FOTH rules demonstrate the explainability of our model. Besides, we observe that propositional and first-order reasoning have an No. ϵ premise ⇒ *conclusion* case A [1] 0.22 makeAppeal(X,Y1):t1∧consult−1(Y1,Y2):t2∧makeStatement(Y2,Z):t3⇒*appealCooperation*(X,Z):t [2] 0.13 hostVisit−1(X,Y1):t1∧signAgreement(Y1,Y2):t2∧praise(Y2,Z):t3⇒*appealCooperation*(X,Z):t [3] 0.06 expressIntentTo(X,Y1):t1∧expressIntentTo(Y1,Y2):t2∧makeStatement(Y2,Z):t3⇒*appealCooperation*(X,Z):t case B [4] 0.17 demand(X,Y1):t1∧makeStatement(Y1,Y2):t2∧engageCooperation−1(Y2,Z): t3⇒*makeStatement*(X,Z):t [5] 0.16 consult(X,Y1):t1∧expressIntentTo−1(Y1,Y2):t2∧consult−1(Y2,Z):t3⇒*makeStatement*(X,Z):t [6] 0.10 demand(X,Y1):t1∧consult(Y1,Y2):t2∧makeStatement(Y2,Z):t3⇒*makeStatement*(X,Z):t [7] 0.04 praise(X,Y):t1∧makeStatement(Y,Z):t2⇒*makeStatement*(X,Z):t ![8_image_0.png](8_image_0.png) incompletely consistent effect. Thus, they can be integrated to jointly guide the reasoning process, leading to more accurate reasoning results. ## 6 Conclusion To effectively integrate complex information on TKGs and implement differentiable logical reasoning, this work proposes TECHS which mainly contains a temporal graph encoder and a logical decoder. The former utilizes the temporal encoding and heterogeneous attention to embed structural dependencies and temporal dynamics. The latter realizes differentiable rule learning and reasoning by continuously conducting forward message-passing in the proposed reasoning graph. Finally, FOTH rules can be easily induced by a novel FARI algorithm. In the future, we will explore mining more types of rules on TKGs, such as numerical rules (Wang et al., 2020), and expand to the scenario of inductive reasoning (Pan et al., 2022). ## 7 Limitations Due to the massive combination of relations and times on TKGs, balancing the model performance and efficiency is challenging. Our model TECHS performs well as Section 5.2 and 5.4 discussed. However, there is also a limitation. TECHS is a two-step approach that can be further improved if we can fuse logical reasoning in the graph encoder like ConGLR (Lin et al., 2022). The model will be more efficient for computational space and time. ## Acknowledgments This work was supported by National Key Research and Development Program of China (2022YFC3303600), National Natural Science Foundation of China (62137002, 62293553, 62250066, 62176207, 62192781, and 62250009), Innovative Research Group of the National Natural Science Foundation of China (61721002), "LENOVO-XJTU" Intelligent Industry Joint Laboratory Project, Foundation of Key National Defense Science and Technology Laboratory (6142101210201), Project of China Knowledge Centre for Engineering Science and Technology, Natural Science Basic Research Program of Shaanxi (2023-JC-YB-293), the Youth Innovation Team of Shaanxi Universities, XJTU Teaching Reform Research Project "Acquisition Learning Based on Knowledge Forest". ## Ethical Statement We honor the ethical code set out in the ACL Code of Ethics. ## References Antoine Bordes, Nicolas Usunier, Alberto GarcíaDurán, Jason Weston, and Oksana Yakhnenko. 2013. 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Recommending learning objects through attentive heterogeneous graph convolution and operationaware neural network. *IEEE Transactions on Knowledge and Data Engineering (TKDE)*, 35(4):4178– 4189. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 7 ✓ A2. Did you discuss any potential risks of your work? 4.3 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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yin-etal-2023-consistency

Consistency Regularization Training for Compositional Generalization

https://aclanthology.org/2023.acl-long.72

Existing neural models have difficulty generalizing to unseen combinations of seen components. To achieve compositional generalization, models are required to consistently interpret (sub)expressions across contexts. Without modifying model architectures, we improve the capability of Transformer on compositional generalization through consistency regularization training, which promotes representation consistency across samples and prediction consistency for a single sample. Experimental results on semantic parsing and machine translation benchmarks empirically demonstrate the effectiveness and generality of our method. In addition, we find that the prediction consistency scores on in-distribution validation sets can be an alternative for evaluating models during training, when commonly-used metrics are not informative.

Consistency Regularization Training for Compositional Generalization Yongjing Yin1,2∗ , Jiali Zeng3, Yafu Li1,2, Fandong Meng3, Jie Zhou3**, Yue Zhang**2,4† 1 Zhejiang University 2 School of Engineering, Westlake University 3 Pattern Recognition Center, WeChat AI, Tencent Inc 4Institute of Advanced Technology, Westlake Institute for Advanced Study {yinyongjing,liyafu}@westlake.edu.cn {lemonzeng,fandongmeng,withtomzhou}@tencent.com [email protected] ## Abstract Existing neural models have difficulty generalizing to unseen combinations of seen components. To achieve compositional generalization, models are required to consistently interpret (sub)expressions across contexts. Without modifying model architectures, we improve the capability of Transformer on compositional generalization through consistency regularization training, which promotes representation consistency across samples and prediction consistency for a single sample. Experimental results on semantic parsing and machine translation benchmarks empirically demonstrate the effectiveness and generality of our method. In addition, we find that the prediction consistency scores on in-distribution validation sets can be an alternative for evaluating models during training, when commonly-used metrics are not informative. ## 1 Introduction Compositional (systematic) generalization refers to the ability to understand and produce a potentially infinite number of novel combinations of known atoms (Chomsky, 2009; Janssen and Partee, 1997). Humans exhibit exceptional compositional generalization capability, easily producing and understanding unseen linguistic expressions by recombining the learned rules (Montague and Thomason, 1975). Therefore, it is also regarded as a desired property for neural networks. Despite the impressive progress in language modeling (Vaswani et al., 2017; Liu et al., 2019; Raffel et al., 2020), the sequence-to-sequence (seq2seq) models have been demonstrated inefficient in capturing the compositional rules, thus failing to generalize to novel compositions (Lake and Baroni, 2018; Keysers et al., 2020a; Kim and Linzen, 2020; Li et al., 2021). ∗This work was done as an intern at Pattern Recognition Center, WeChat AI, Tencent Inc, China. †Corresponding author Achieving compositional generalization requires a model to perform *consistently* in the interpretation assigned to a (sub)expression across contexts (Janssen and Partee, 1997; Dankers et al., 2022). For example, the interpretation of a phrase "the book" is consistent whether it is described by a modifier "he likes", in both semantic parsing and machine translation domains (Kim and Linzen, 2020; Li et al., 2021). To improve the consistency, most existing work considers a change of neural architecture to suit particular composition or generalization test sets (Chen et al., 2020b; Guo et al., 2020b; Yin et al., 2022; Zheng and Lapata, 2022), which limits their potentials in real world applications. Recently, the Transformer architecture has become the standard for natural language processing (NLP), particularly in supporting large pretrained language models (PLMs) such as T5 and GPT-3 (Raffel et al., 2020; Brown et al., 2020). The Transformer-based PLMs have significantly improved few-shot fine-tuning and even made efficient zero-shot learning possible. As a result, there has been a trend towards developing data-centric AI (Koch et al., 2021; Jakubik et al., 2022), where the focus is on data preparation and training strategies rather than on the model architecture. However, it has recently been shown that the standard Transformer is underestimated in its ability to handle compositionality (Csordás et al., 2021; Ontanon et al., 2022), and there has been relatively little research done on how to improve this capability through training. We observe that limitation of compositional generalization in Transformer can arise from the internal inconsistency under the standard training paradigm. First, Transformer token representations have been shown to reside within a narrow range of the embedding space (Gao et al., 2019; Cai et al., 2021), which can easily be affected by context variations, especially from novel compositions (Zheng ![1_image_0.png](1_image_0.png) and Lapata, 2022). Second, internal uncertainties like dropout can lead to prediction variations of a single sample (Sajjadi et al., 2016; Liang et al., 2021). Such prediction inconsistency can limit the efficiency of learning patterns in training data (Ghiasi et al., 2018). During inference, this defect is not significant when the models process in-distribution data; however, unseen compositions can magnify the negative influence, which degrades the final performance on compositional generalization. Without modifying model architectures, we improve compositionality of Transformer with consistency regularization training in terms of representation and prediction. For representation, we encourage the representations of the same token across contexts to be more consistent with each other, and the representations of different tokens to be separated, which can be achieved by contrastive learning (Khosla et al., 2020; Chen et al., 2020a). As shown in the right part of Figure 1, when combined with the modifier "he likes", the representation of "book" is pulled to be consistent with those in other contexts. Such representations tolerate context changes better and meanwhile capture discriminative semantics. For prediction consistency, we feed each instance to the model multiple times and force the output distributions of a specific token to be close. In this way, the negative influence of internal uncertainties can be mitigated, which decreases fluctuation in output distributions while maintaining task-specific features. We conduct experiments on standard benchmarks for compositional generalization, including representative semantic parsing datasets (COGS (Kim and Linzen, 2020) and CFQ (Keysers et al., 2020a)), and machine translation datasets (CoGnition (Li et al., 2021) and OPUS En-Nl (Dankers et al., 2022)). Our method consistently improves upon standard Transformer or pre-trained language models, achieving state-of-the-art performance on COGS, CoGnition, and OPUS En-Nl, and competitive performance on CFQ. Specifically, we explore a consistency-based metric for model selection on COGS, as commonly-used metrics (e.g., accuracy) on the validation set are often not informative. The analysis of learning efficiency shows that our regularization enables the model to achieve an accuracy score of 18% with only 1.2k samples on CFQ MCD1, which the baseline fails to learn. In addition, our analyses of representation variance and robustness to input noise demonstrate that our method delivers better consistency.1 ## 2 Related Work Compositional Generalization has attracted increasing attention with dedicated datasets (Lake and Baroni, 2018; Keysers et al., 2020a; Kim and Linzen, 2020; Li et al., 2021; Shaw et al., 2021; Dankers et al., 2022). One line of research considers dedicated model architectures (Chen et al., 2020b; Gordon et al., 2020; Kim, 2021), which perform well on small scaled data but can face difficulties scaling to large or practical data. For example, Chen et al. (2020b) propose a differentiable neural network to operate a symbolic stack machine. Another line of research enhances the compositionality of standard architectures (i.e., Transformer) by introducing new modules (Bergen et al., 2021; Yin 1The code is available at https://github.com/ARIESLM/CSR4CG.git. et al., 2022; Zheng and Lapata, 2022). However, significant architecture changes can bring about extra training cost or decoding latency. For example, Edge Transformer (Bergen et al., 2021) uses vectorbased attention weights, and Dangle Transformer (Zheng and Lapata, 2022) re-encodes source representations at each decoding step, which increase model complexity to O(n 3). Proto-Transformer (Yin et al., 2022) uses an additional attention module to incorporate prototype vectors obtained by clustering algorithms (e.g., K-Means). Different from them, we improve Transformer from the perspective of regularization training without any architecture changes. Recently, Csordás et al. (2021) and Ontanon et al. (2022) empirically make slight changes of Transformer components, and find its capability of compositionality is underestimated. Meta-learning (Conklin et al., 2021) and data augmentation (Andreas, 2020; Guo et al., 2020a) are also introduced to improve the base models, but the experiment results are limited. Along the line of compositional generalization studies without modifying the model architectures, our method focuses on the internal consistency of Transformer, and achieves better performance. Regularization training has been shown effective in semi-supervised training (Sajjadi et al., 2016; Tarvainen and Valpola, 2017), robust training (Cheng et al., 2018; Liang et al., 2021), continual training (Kirkpatrick et al., 2016; Lopez-Paz and Ranzato, 2017), etc. To encourage compositional behavior, Guo et al. (2020a) softly combine source/target sequence embeddings during training, and Conklin et al. (2021) introduce gradient based meta learning to simulate distribution shift. In addition, contrastive learning serving as regularization has achieved success in various NLP tasks (Chi et al., 2021; Su et al., 2022; Zhang et al., 2022). Different form them, we explore the effectiveness of the regularization training on the two different tasks in compositional generalization. ## 3 Method We propose to regularize the model training in two aspects, as illustrated in Figure 1: representation consistency of tokens across different contexts (§3.1), and consistency of model prediction for a single sample (§3.2). ## 3.1 Representation Consistency The representation consistency encourages the contextualized representations of the same token across contexts to be more consistent in the embedding space. To this end, we introduce the popular contrastive learning (Chen et al., 2020a; He et al., 2020), especially the supervised variant (Khosla et al., 2020). Specifically, we collect representations that belong to the same token as *positive* samples, and representations of different tokens in the mini-batch as *negative* samples. For example, in Figure 1, for the token "book" in the sequence Y1, the positive sample is h2 in Y2, and the negatives include the representations of other tokens. Following (Gao et al., 2021), the dropout augmentation is also considered as positive samples. For construction of positive samples, we can use a data sampling strategy which groups minibatches according to token types. When building a mini-batch, we first randomly sample a token from the vocabulary, then retrieve several sentence pairs (e.g., 8) containing the token. We repeat this process until reaching the batch size, and the sentence pairs that have been chosen will not be retrieved again in that training epoch. In practice, since the current focus on compositional generalization is the composition of high-frequency atoms, a relatively large batch size is able to ensure reasonable co-occurrence of positive samples. Formally, given a mini-batch of input pairs {(*X, Y* )}, we define the contrastive objective as Lr = − 1 N X N i=1 X p∈P(i) log e s(hi,hp)/τ PN j=1 1i̸=je s(hi,hj )/τ , (1) where N is the number of the total tokens that are chosen for regularization, considering that some tokens can be excluded from the consistency regularization, e.g., the token used for padding. P(i) is the set of indices of all the positive samples for hi, τ is a temperature hyper-parameter2. Moreover, s(·) denotes the cosine similarity between representations to: $$\mathrm{s}(h_{i},h_{p})={\frac{h_{i}^{T}h_{p}}{\|h_{i}\|\|h_{p}\|}},\qquad\qquad(2)$$ where hiis the representations of the top layer in the encoder or the decoder, projected by a multilayer perceptron with ReLU activation. 2We set τ to 0.07 in the experiments. ## 3.2 Prediction Consistency Due to the training mechanism of neural models, predictions of the same instance can vary across forward passes. The internal stochastic perturbations in the model components accumulate layerby-layer, negatively affecting the efficiency of invariance learning (Ghiasi et al., 2018). To enforce the sample-level consistency, we feed the instance (*X, Y* ) to the model M multiple times during training, and obtain the final output distributions derived from different dropout perturbations. We minimize the difference between the output distributions for each target token: $$L_{p}=\frac{1}{|Y|}\sum_{y_{i}\in Y}d(p^{1}(y_{i}|X,y_{<i}),...,p^{M}(y_{i}|X,y_{<i})),\tag{3}$$ where |Y | is the number of tokens in the target sequence Y , d(·) is a metric function measuring the difference, and M denotes the number of perturbations. Empirical results show that Jensen-Shannon divergence between two perturbations are effective enough while maintaining efficiency We also experimented with more than two perturbations and other metrics such as sample variance, and found that it possibly lead to better performance but also more training cost. Therefore, we set M as 2 in all the experiments. By explicitly encouraging the model to generate consistent output during training, the model is able to capture global compositional patterns with more confidence. ## 3.3 Training And Inference. The overall loss function is defined as: $$L=L_{c e}+\alpha L_{r}+\beta L_{p},$$ L = Lce + αLr + βLp, (4) where Lce denotes cross-entropy loss for baseline models, and α and *beta* are the coefficients of the two regularization losses, respectively. Notably, our proposed regularization terms guide the model training from the aspects of representation and prediction, without changing the inference process, which means no additional decoding latency. ## 4 Experiments: Semantic Parsing This section demonstrates empirical results on representative semantic parsing benchmarks for compositional generalization: COGS and CFQ. | Model | ACC | |---------------------|-------| | MAML-Transformer | 66.7 | | Rela-Transformer | 81.0 | | Lex-LSTM | 82.1 | | Dangle-Transformer* | 85.9 | | Transformer | 80.8 | | Transformer + CReg | 84.5 | | Transformer* + CReg | 86.2 | ## 4.1 Cogs Setting. All of our models are implemented based on Fairseq3. The embedding and feedforward dimension of Transformer are 512 and the number of model layers is 2. We use the Adam optimizer with learning rate 1e-4, warmup steps 4,000, and a batch size of 4,096 tokens. For our regularization, we set α and β to 0.01 and 1.0, respectively, and we apply the representation consistency on the target side. Following the previous work (Csordás et al., 2021; Zheng and Lapata, 2022), we use dropout with probability of 0.1. We report the mean accuracy over three runs. More details about the dataset are shown in Appendix A. $$(4)$$ Results. The baselines models used for comparison on COGS includes MAML-Transformer (Conklin et al., 2021), Lex-LSTM (Akyurek and Andreas, 2021), Rela-Transformer (Csordás et al., 2021), and Dangle-Transformer (Zheng and Lapata, 2022). The results in Table 1 show that, enhanced with the proposed regularization, the Transformer model is improved by 3.7% and achieves an overall 84.5% generalization accuracy. Rela-Transformer achieves good performance with several modifications to Transformer (e.g., initialization, relative positional encoding), and ours performs better than it. In comparison to MAML-Transformer trained using meta-learning, our method is more effective and conceptually simpler, requiring no meta-gradients or construction of meta-datasets. In particular, using the same initialization (i.e., Glove (Pennington et al., 2014)), our regularized Transformer outperforms Dangle-Transformer without architecture modifications and additional decoding latency. Consistency-based Metric for Model Selection. A general and important problem in compositional 3https://github.com/facebookresearch/fairseq generalization is the lack of effective validation sets that are representative of the generalization distribution, particularly on the popular benchmark COGS (Conklin et al., 2021; Csordás et al., 2021; Zheng and Lapata, 2022). Concretely, the only provided IID validation set in COGS is easy to achieve 100% or almost 100% accuracy, which is difficult for model selection and testing novel ideas. Previous studies have resorted to sampling a small subset from the generalization test set, which can potentially lead to overfitting to the test set. We hypothesize that consistency on the IID validation set can be used as a metric to predict their generalization ability. To verify it, we conduct a preliminary experiment on COGS. We use three configurations for training Transformer4: (1) M1, which has two layers with 128 embedding dimension and 256 feedforward dimension, (2) M2, which has four layers with 128 embedding dimension and 256 feedforward dimension, and (3) M3, which has two layers with 512 embedding and feedforward dimensions. Each model is run five times with different random seeds for 50,000 training steps. We record the validation loss (*w/ Loss*), accuracy (*w/ Acc*), and prediction consistency score of each checkpoint every 1000 training steps, after they pass the period of drastic changes (i.e., 15,000 steps). In order to reduce the impact of random fluctuations on the correlation calculation, we only save the adjacent checkpoints if the performance difference exceeding 0.5. For the consistency score, we feed each instance into the model twice with dropout retained, and calculate the sample variance (*w/ Pvar*) and JS divergence (*w/ Js*) over the output token distributions. The results are shown in Table 2. Although all of the models can achieve 99.9% accuracy on the validation set5, their oracle generalization performances are different. Overall, the consistency scores exhibit a higher correlation to the generalization performance than the validation loss and accuracy. For example, the *w/ Acc* of M2 achieves a 0.533 spearman's correlation while *w/ Js* achieves 0.805. According to the consistency score, we can select the M3 checkpoint with 81.0 test accuracy, which is equal to the oracle, while only obtaining a model with 79.7 test accuracy according to the validation accuracy. Additionally, we display the 4We use the code released by Csordás et al. (2021) 5The accuracy score is reported 100% in (Csordás et al., 2021) and the minor difference possibly results form the differences in software and hardware. | Model | M1 | M2 | M3 | |--------------|--------------|--------------|--------------| | w/ Loss | 74.4 / 0.228 | 79.8 / 0.085 | 79.7 / 0.033 | | w/ Acc | 79.5 / 0.669 | 80.7 / 0.533 | 79.7 / 0.223 | | w/ Js | 78.3 / 0.793 | 81.0 / 0.805 | 81.0 / 0.292 | | w/ Pvar | 78.3 / 0.801 | 81.0 / 0.803 | 80.4 / 0.468 | | Valid | 99.9 | 99.9 | 99.9 | | Test(oracle) | 79.7 | 81.4 | 81.0 | ![4_image_0.png](4_image_0.png) relationship between the test accuracy and consistency scores of M2 during training in Figure 2. As the training progresses, it can be seen that the consistency score, especially the one calculated via variance, decreases as the test accuracy increases. ## 4.2 Cfq Setting. We use the Universal Transformer architecture (Uni-TF) (Bergen et al., 2021; Csordás et al., 2021) as the base model, and encoder and decoder are 6 layers with 256 embedding dimension. Moreover, pre-trained language models are critical for achieving good performance on CFQ (Furrer et al., 2020; Zheng and Lapata, 2022). Following Zheng and Lapata (2022), we use RoBERTa-Base as the encoder and combine it with a Transformer decoder initialized randomly. The encoder has 12 | Model | MCD1 | MCD2 | MCD3 | AVE | |---------------------|--------|--------|--------|-------| | HPD | 72.0 | 66.1 | 63.9 | 67.3 | | Uni-Transformer | 44.0 | 11.0 | 14.0 | 23.0 | | Evolved-Transformer | 42.4 | 9.3 | 10.8 | 20.8 | | Edge-Transformer | 47.7 | 13.1 | 13.2 | 24.7 | | Uni-TF+CReg | 57.5 | 28.8 | 31.5 | 39.2 | | T5-11B-mod | 61.6 | 31.3 | 33.3 | 42.1 | | RoBERTa-Dangle | 78.3 | 59.5 | 60.4 | 66.1 | | RoBERTa | 60.6 | 33.6 | 36.0 | 43.4 | | RoBERTa+CReg | 74.8 | 53.3 | 58.3 | 62.1 | | Model | BLEU | Instance | Aggregate | |--------------------|--------|------------|-------------| | Transformer | 59.5 | 28.4 | 62.9 | | Seq-Mixup | - | 28.6 | 60.6 | | Proto-Transformer | 60.1 | 21.7 | 51.8 | | Dangle-Transformer | 60.6 | 22.8 | 50.6 | | Transformer+CReg | 61.3 | 20.2 | 48.3 | | Table 4: Compound translation error rate (CTER) on CoGnition. Instance and Aggregate denote the instancelevel and aggregate-level CTER, respectively. | | | | layers with the embedding dimension 756, and the decoder has 2 layers of which the embedding dimension is 256. We set the learning rate to 1e-4 and the warmup steps to 4,000. The α and β are set to 0.3 and 1.0, respectively. We apply the representation consistency on the encoder side for the RoBERTa-based model and decoder side for the Universal Transformer. The dropout probability is set to 0.1. We report the mean accuracy over three runs. We use exact matching accuracy to measuring model performance, and run each experiment three times and report the mean accuracy. Results. For models trained from scratch, we compare our method with Evolved-Transformer (Furrer et al., 2020), Uni-Transformer (Csordás et al., 2021), Edge-Transformer (Bergen et al., 2021) and HPD (Guo et al., 2020b). The pretrained language models include T5-11B-MOD (Furrer et al., 2020), RoBERTa-Dangle (Zheng and Lapata, 2022), and RoBERTa (Zheng and Lapata, 2022). Note that HPD is a not a seq2seq model and is a hierarchical decoding structure dedicated for CFQ. As shown in Table 3, it is highly challenging to train a Transformer, especially on the MCD2 and MCD3 splits, whether pre-trained models are used or not. Although deep contextualized representations are useful, they still lag behind HPD, suggesting that more efficient methods of achieving compositional generalization by exploiting proper inductive biases exist. Specifically, RoBERTa+dec achieves an average test accuracy of 43.4%. When trained with consistency regularization, it is further improved to an average of 62.1%. DangleRoBERTa re-encodes the concatenation of the source sequence and target history at each decoding step, leading to large computational overhead especially for long sequences. Despite the minor performance gap (4%), our model requires no modifications to model architecture and decoding, resulting in a much lower decoding latency. ## 5 Experiments: Machine Translation Unlike semantic parsing, the target of MT is also natural language and compositionality in natural domains is far more intricate. we further validate the effectiveness of our method on two dedicated machine translation datasets: CoGnition (Li et al., 2021) and OPUS En-Nl (Dankers et al., 2022). ## 5.1 Cognition Setting. We use the Transformer iwslt_de_en setting in Fairseq with 4 layers. The batch size is 4,096 tokens, and we stop training if a model does not improve on the validation for 10 epochs. We set α and β to 0.5 and 3.0, respectively. The dropout is set to 0.3, and we apply the representation consistency on the target side. We use beam search with width 5 for inference. We use compound translation error rate (CTER; (Li et al., 2021)) to measure model performance. Specifically, *instance-level* CTER denotes the ratio of the instances in which the novel compounds are translated incorrectly to the total instances, and *aggregate-level* CTER denotes the ratio of the compound types which are translated wrong at least once in the corresponding contexts. We also report BLEU score (Papineni et al., 2002), which evaluates the quality of whole translations. Results. We compare our method to Seq-Mixup (Yin et al., 2022), which trains Transformer with sequence-level mixup regularization (Guo et al., 2020a); Dangle-Transformer (Zheng and Lapata, 2022); and Proto-Transformer (Yin et al., 2022), which applies K-Means during training to categorize the representations for each source token, and | Model | Small | Medium | | | | |----------------------|-----------|----------|---------|------|---------| | Data | Condition | TF | TF+CReg | TF | TF+CReg | | S -> NP VP synthetic | NP | .72 | .78 | .84 | .82 | | synthetic | VP | .79 | .87 | .87 | .91 | | semi-natural | NP | .56 | .70 | .66 | .70 | | S-> S CONJ S | ′ | | | | | | synthetic | S 1 | .87 | .91 | .90 | .95 | | synthetic | S3 | .68 | .75 | .76 | .89 | | ′ | | | | | | | semi-natural | S 1 | .70 | .78 | .73 | .79 | | semi-natural | S3 | .40 | .56 | .49 | .54 | | natural | S ′ 1 | .60 | .72 | .67 | .75 | | natural | S3 | .28 | .45 | .39 | .51 | | Average | - | .62 | .72 | .70 | .76 | | BLEU | - | 22.6 | 23.4 | 25.1 | 25.8 | integrates the cluster representations to the encoding to reduce representation sparsity.. The main results are shown in Table 4. The Transformer gives instance-level and aggregatelevel CTERs of 29.4% and 63.8%, respectively, while the regularized Transformer achieves 19.9% and 48.8%, respectively. Our model obtains a substantial improvement of 8.3% and 11.2% without changing the model architecture. Particularly, the CG-test set requires NMT models to put more emphasis on the invariance of atom translation under context variations, and the result demonstrates that the encouragement of consistency helps the model learn it better . Besides, compared to SeqMix regularization, the improvement of our method is more significant, possibly due to the inconsistency introduced by the stochastically interpolated samples in SeqMix. Moreover, the regularized Transformer performs better than Dangle-Transformer and Proto-Transformer. This indicates that through training regularization, the generalization ability of the Transformer can be significantly improved with scalability to various tasks maintained. ## 5.2 Opus Setting. We use Tranformer_Base configuration in Fairseq following Dankers et al. (2022). We use a learning rate of 5e-4 with 4,000 warmup steps, and a batch size of 4,096 tokens on 4 GPUs. We stop training if the model does not show improvement on the validation set for 10 consecutive epochs. The regularization coefficients α and β are set to 0.2 and 1.0, respectively, The dropout is | Model | COGS | CFQ | CoGnition | |----------|--------|-------|-------------| | (*)+CReg | 84.5 | 62.1 | 20.2/48.3 | | w/o Lr | 81.9 | 52.5 | 22.3/51.8 | | w/o Lp | 83.4 | 59.0 | 24.3/57.7 | Table 6: Results of ablation study. set to 0.3, and lower probabilities lead to worse consistency scores. For our regularization, the representation consistency is used on the target side. The evaluation metric is the translation consistency score, which measures the consistency of the model's translations for a sample when the context changes. Specifically, in the **S -> NP VP** setup, two translations are considered consistent if they differ by only one word. In the **S-> S CONJ S** setup, the consistency is measured for the translations of the second conjunct. For more details, please refer to Appendix A and the paper (Dankers et al., 2022). Result. The overall result is presented in Table 5. In both small and medium settings, our consistency regularization can enhance the learning of systematicity of Transformer, and makes the model less prone to changing their translations after small adaptations to source sentences. Specifically, when trained on small size corpus (1.1M), the consistency score of the NMT model is improved significantly from 0.62 to 0.72 in average. In addition, increasing training data can intuitively improve the model's systematicity ability since the model sees more compositions during training. The proposed regularized model trained on medium size corpus (8.6M) achieves 0.76 consistency score, outperforming the baseline by 0.6 in average. In particular, it performs better than the model trained on the full data (0.73 reported in (Dankers et al., 2022)). Finally, the BLEU scores on the general test set is also improved due to the amelioration in compositionality learning. ## 6 Analysis In this section, we aim to provide a deeper understanding of how our consistency regularization improves compositional generalization by analyzing various aspects of the model's performance. ## 6.1 Ablation Study To present the influence of different regularization terms, we conduct an ablation study on CFQ, ![7_image_0.png](7_image_0.png) COGS, and CoGnition. The results are shown in Table 6. We can see that using either of the two regularization methods alone can also improve the generalization performance. Specifically, the contrastive loss Lr has a greater impact on COGS and CFQ, indicating that the structure generalization can benefit from more consistent atom representations across samples. On the other hand, the prediction consistency loss Lp has a more significant effect on CoGnition, since the evaluation metric requires the NMT model to generate coherent translations of each atom in different contexts. Finally, further improvement can be achieved by leveraging the training regularization of both the representation and prediction consistency. ## 6.2 Learning Efficiency We argue that the inconsistency can negatively affect the efficiency of learning invariance and composition patterns from the training data, which can be mitigated by our consistency training. To verify it, we train the models with different training sizes and report the test performance in Figure 3. For CFQ, we randomly sample four different sizes of training corpora containing 1.2k, 2.5k, 5k, and 10k sentence pairs, respectively. For CoGnition, we train the models using 1/2, 1/3, 1/4, and 1/5 of the total sentence pairs in the training set, respectively. We can observe that consistency regularization enables the Transformer model to learn the generalizable composition patterns with less training data. On CFQ, the Transformer enhanced by RoBERTa fails to learn when there only exists 1.2k training instances, while the regularization enables the model to achieve almost 20% accuracy on the generalization test set. ## 6.3 Intra-Class Variance In this part, we calculate the intra-class variance to perform quantitative study of the improvement ![7_image_1.png](7_image_1.png) ![7_image_2.png](7_image_2.png) of representation invariance to context changes (Zheng and Lapata, 2022). For each token, we perform a forward pass over the training set with the trained model to collect all of its contextualized representations. The intra-class variance is defined as the weighted average of all tokens' variances by their frequency: $$\frac{1}{d}\sum_{i=1}^{d}E_{y}v a r(h_{i}^{y}),\qquad\qquad\qquad(5)$$ where d is the dimension of representations and y denotes a token type. A lower intra-class variance indicates more disentangled features, which are more robust to variations in input composition. As shown in Figure 4, the representations of the regularized model have lower variance, and this phenomenon can be explained by the influence of the contrastive loss, which pulls the representations belonging to the same token closer together. ## 6.4 Input Noise Input noise can be regard as a special case of compositional generalization, which possibly destroy semantics of sentences and is common in real applications (Michel and Neubig, 2018; Wang et al., 2021). In this experiment, we investigate whether our method can lead to a more robust model to input noise. We chose CoGniton as the test bed, since the novel compounds and the contexts are clearly divided. For each source sentence in the CG-test set, we keep the compound unchanged and randomly replace K tokens in the context part with the other tokens in the vocabulary. For each K, we sample 10 times and the violin plot is shown in Figure 5. The vertical axis represents the average of instance and aggregate CTER. Under the input noise of different extents, the performances of TF+CReg consistently outperform TF. Even though the contexts are destroyed seriously (K=5), TF+CReg can give a performance comparable to the baseline, indicating the regularized model learns the invariant translation patterns better. The figures with the other values of K are put in Appendix B. ## 7 Conclusion We presented a regularization method to enhance compositional generalization, jointly encouraging the consistency of token representations across samples and sample-level prediction consistency. Experiments on four dedicated datasets show the effectiveness of our method. The regularized Transformer can be a strong baseline for future investigate of compositional generalization. ## Limitations For representation consistency, we apply the regularization to all the tokens and do not distinguish between the different roles the tokens play. Adaptive determination of which tokens or chunks require to be consistent in the representation space is an intriguing research question, which we leave as future work. More effective data sampling strategies can also be explored. ## Acknowledgements This work is funded by the Ministry of Science and Technology of China (grant No. 2022YFE0204900). 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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In *NeurIPS 2017*, pages 1195–1204. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUSMT - building open translation services for the world. In *EAMT 2020*, pages 479–480. European Association for Machine Translation. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In *NeurIPS2017*, pages 5998–6008. Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, and Bo Li. 2021. Adversarial GLUE: A multitask benchmark for robustness evaluation of language models. In *NeurIPS Datasets and Benchmarks 2021,*. Yongjing Yin, Yafu Li, Fandong Meng, Jie Zhou, and Yue Zhang. 2022. Categorizing semantic representations for neural machine translation. In *Proceedings of the 29th International Conference on Computational Linguistics*, pages 5227–5239, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. Tong Zhang, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, and Wen Zhao. 2022. Frequency-aware contrastive learning for neural machine translation. In *AAAI2022*, pages 11712–11720. Hao Zheng and Mirella Lapata. 2022. Disentangled sequence to sequence learning for compositional generalization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4256–4268, Dublin, Ireland. Association for Computational Linguistics. ## A Data And Settings In this section, we describe the datasets and the model configurations in detail. Statistics of all the datasets can be found in Table 7. COGS COGS is a dataset that maps English sentences to logical forms, consisting of a training set with 24,155 examples and a generalization testing set with 21,000 examples. The generalizaiton types include novel combination of familiar primitives and grammatical roles, novel combination modified phrases and grammatical roles, verb argument structure alternation, verb class, deeper recursion, etc. In particular, Conklin et al. (2021) and Zheng and Lapata (2022) construct a generalization validation set sampled from the test set, which contains 2,100 instances and used for tuning hyper-parameters. The chosen hyper-parameters are used to rerun the model with the other different random seeds for reporting final results on the test set. CFQ The task of interest of CFQ is to semantic parsing from a natural language question (e.g., 'Which art director of [Stepping Sisters 1932] was a parent of [Imre Sándorházi]?') to a Freebase SPARQL query. With a principle of maximizing compound divergence (MCD) (Keysers et al., 2020b), the authors construct three splits (i.e., MCD1, MCD2, and MCD3), which are used to test structural generalization, i.e., the syntax patterns in the test set are greatly different from those in the training set. A number of studies have shown that the prediction difficulty can be mitigated by normalizing the target sequence (Guo et al., 2020b; Zheng and Lapata, 2022) or using the intermediate representation (Herzig et al., 2021), and we follow Zheng and Lapata (2022) to preprocess the data. CoGnition CoGnition is an English→Chinese (En→Zh) story translation dataset, consisting of 196,246 training sentence pairs and a validation set with 10,000 sentence pairs. The compositional generalization test set (CG-test set) has 10,800 sentences containing three types of novel compounds (i.e., NP, VP, and PP). All the tokens are high frequent to eliminate the influence of low-frequency words on translation quality. OPUS En-Nl Dankers et al. (2022) use English→Dutch data in OPUS (Tiedemann and Thottingal, 2020) as the training set, containing 69M sentences pairs in total. They conduct evaluation on three settings: using the full dataset, using 1/8 of the data (medium), and using one million pairs in the small setup. We conduct the experiments with the small and medium settings since using the full data only gives a slight improvement (Dankers et al., 2022). The validation and test sets for BLEU evaluation are from FLORES101 (Goyal et al., 2022). To evaluate systematicity, Dankers et al. (2022) construct a large number of test sets with two settings: (1) **S -> NP VP**, which investigates the recombinations of noun and verb phrases; and (2) **S-> S CONJ S**, which uses sentences joined by "and" to see whether the translation of the second sentence depends on the first one. Additionally, the source sentences used for evaluation are divided into three categories: synthetic, semi-natural, and natural data. The number of sentences to translate in the generalization test sets is 45,000. ## B Input Noise The performances of input noise on CoGnition with all the values of K are shown in Figure 6. ## C Dropout For the benchmarks we used, the hyper-parameters of the Transformer baselines, such as dropout and model sizes, are well-tuned by the previous studies. Dropout probabilities are 0.1 on COGS and CFQ, and 0.3 on CoGnition and OPUS En-Nl. Disabling or minimizing dropout can lead to worse performances. Concretely, when disabling dropout, the baseline performances drop from 80.8 to 78.5 on COGS, and from 60.6 to 56.0 on CFQ-MCD1, respectively. On CoGnition, the translation error rate increases significantly from 20.2/48.3 to 45.4/76.7 when using dropout probability 0.1. On the Small scale of OPUS En-Nl, the average consistency score deceases significantly from 0.72 to 0.51 when using dropout probability 0.1. | Dataset | #Train | #Valid | #Test | Voc | |--------------------|-----------|----------|---------|-----------| | COGS | 24,155 | 3,000 | 21,000 | 752/672 | | CFQ | 95,743 | 1,968 | 1,968 | 104/104 | | CoGnition | 196,246 | 10,000 | 10,800 | 5504/2208 | | OPUS En-Nl(Small) | 1,072,851 | 997 | 45,000 | 41,296 | | OPUS En-Nl(Medium) | 8,582,811 | 997 | 45,000 | 4,681 | ![12_image_0.png](12_image_0.png)

yin-etal-2023-nuwa

{NUWA}-{XL}: Diffusion over Diffusion for e{X}tremely Long Video Generation

https://aclanthology.org/2023.acl-long.73

In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a {``}coarse-to-fine{''} process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26{\%}) at the same hardware setting when generating 1024 frames. The homepage link is [NUWA-XL](\url{https://msra-nuwa.azurewebsites.net})

## Nuwa-Xl: Diffusion Over Diffusion For Extremely Long Video Generation Shengming Yin1∗ , Chenfei Wu2∗ , Huan Yang2, Jianfeng Wang3**, Xiaodong Wang**2 Minheng Ni2, Zhengyuan Yang3, Linjie Li3, Shuguang Liu2, **Fan Yang**2 Jianlong Fu2, Gong Ming2, Lijuan Wang3, Zicheng Liu3, Houqiang Li1, **Nan Duan**2† 1University of Science and Technology of China 2Microsoft Research Asia 3Microsoft Azure AI {sheyin@mail., lihq@}ustc.edu.cn, {chewu, huan.yang, jianfw, v-xiaodwang, t-mni, zhengyang, lindsey.li, shuguanl, fanyang, jianf, migon, lijuanw, zliu, nanduan}@microsoft.com ## Abstract In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a "coarse-to-fine" process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26%) at the same hardware setting when generating 1024 frames. The homepage link is https://msra-nuwa.azurewebsites.net/ ## 1 Introduction Recently, visual synthesis has attracted a great deal of interest in the field of generative models. Existing works have demonstrated the ability to generate high-quality images (Ramesh et al., 2021; Saharia et al., 2022; Rombach et al., 2022) and short videos (e.g., 4 seconds (Wu et al., 2022b), 5 seconds (Singer et al., 2022), 5.3 seconds (Ho et al., 2022a)). However, videos in real applications are often much longer than 5 seconds. A film typically lasts more than 90 minutes. A cartoon is usually 30 minutes long. Even for "short" video applications like TikTok, the recommended video length is 21 to 34 seconds. Longer video generation is becoming increasingly important as the demand for engaging visual content continues to grow. However, scaling to generate long videos has a significant challenge as it requires a large amount of computation resources. To overcome this challenge, most current approaches use the "Autoregressive over X" architecture, where "X" denotes any generative models capable of generating short video clips, including Autoregressive Models like Phenaki (Villegas et al., 2022), TATS (Ge et al., 2022), NUWA-Infinity (Wu et al., 2022a); Diffusion Models like MCVD (Voleti et al., 2022), FDM (Harvey et al., 2022), LVDM (He et al., 2022). The main idea behind these approaches is to train the model on short video clips and then use it to generate long videos by a sliding window during inference. "Autoregressive over X" architecture not only greatly reduces the computational burden, but also relaxes the data requirements for long videos, as only short videos are needed for training. Unfortunately, the "Autoregressive over X" architecture, while being a resource-sufficient solution to generate long videos, also introduces new challenges: 1) Firstly, training on short videos but forcing it to infer long videos leads to an enormous training-inference gap. It can result in unrealistic shot change and long-term incoherence in generated long videos, since the model has no opportunity to learn such patterns from long videos. For example, Phenaki (Villegas et al., 2022) and TATS (Ge et al., 2022) are trained on less than 16 frames, while generating as many as 1024 frames when applied to long video generation. 2) Secondly, due to the dependency limitation of the sliding window, the inference process can not be done in parallel and thus takes a much longer time. For example, TATS (Ge et al., 2022) takes 7.5 minutes to generate 1024 frames, while Phenaki (Villegas et al., 2022) takes 4.1 minutes. ∗Both authors contributed equally to this research. † Corresponding author. ![1_image_0.png](1_image_0.png) To address the above issues, we propose NUWAXL, a "Diffusion over Diffusion" architecture to generate long videos in a "coarse-to-fine" process, as shown in Fig. 1. In detail, a global diffusion model first generates L keyframes based on L prompts which forms a "coarse" storyline of the video. The first local diffusion model is then applied to L prompts and the adjacent keyframes, treated as the first and the last frames, to complete the middle L − 2 frames resulting in L + (L − 1) × (L − 2) ≈ L 2"fine" frames in total. By iteratively applying the local diffusion to fill in the middle frames, the length of the video will increase exponentially, leading to an extremely long video. For example, NUWA-XL with m depth and L local diffusion length is capable of generating a long video with the size of O(L m). The advantages of such a "coarse-to-fine" scheme are three-fold: 1) Firstly, such a hierarchical architecture enables the model to train directly on long videos and thus eliminating the training-inference gap; 2) Secondly, it naturally supports parallel inference and thereby can significantly speed up long video generation; 3) Thirdly, as the length of the video can be extended exponentially w.r.t. the depth m, our model can be easily extended to longer videos. Our key contributions are listed in the following: - We propose NUWA-XL, a "Diffusion over Diffusion" architecture by viewing long video generation as a novel "coarse-to-fine" process. - To the best of our knowledge, NUWA-XL is the first model directly trained on long videos (3376 frames), which closes the traininginference gap in long video generation. - NUWA-XL enables parallel inference, which significantly speeds up long video generation. Concretely, NUWA-XL speeds up inference by 94.26% when generating 1024 frames. - We build FlintstonesHD, a new dataset to validate the effectiveness of our model and provide a benchmark for long video generation. ## 2 Related Work Image and Short Video Generation Image Generation has made many progresses, auto-regressive methods (Ramesh et al., 2021; Ding et al., 2021; Yu et al., 2022; Ding et al., 2022) leverage VQVAE to tokenize the images into discrete tokens and use Transformers (Vaswani et al., 2017) to model the dependency between tokens. DDPM (Ho et al., 2020) presents high-quality image synthesis results. LDM (Rombach et al., 2022) performs a diffusion process on latent space, showing significant efficiency and quality improvements. Similar advances have been witnessed in video generation, (Vondrick et al., 2016; Saito et al., 2017; Pan et al., 2017; Li et al., 2018; Tulyakov et al., 2018) extend GAN to video generation. Syncdraw (Mittal et al., 2017) uses a recurrent VAE to automatically generate videos. GODIVA (Wu et al., 2021) proposes a three-dimensional sparse attention to map text tokens to video tokens. VideoGPT (Yan et al., 2021) adapts Transformerbased image generation models to video generation with minimal modifications. NUWA (Wu et al., 2022b) with 3D Nearby Attention extends GODIVA (Wu et al., 2021) to various generation tasks in a unified representation. Cogvideo (Hong et al., 2022) leverages a frozen T2I model (Ding et al., 2022) by adding additional temporal attention modules. More recently, diffusion methods (Ho et al., 2022b; Singer et al., 2022; Ho et al., 2022a) have also been applied to video generation. Among them, VDM (Ho et al., 2022b) replaces the typical 2D U-Net for modeling images with a 3D U-Net. Make-a-video (Singer et al., 2022) successfully extends a diffusion-based T2I model to T2V without text-video pairs. Imagen Video (Ho et al., 2022a) leverages a cascade of video diffusion models to text-conditional video generation. Different from these works, which concentrate on short video generation, we aim to address the challenges associated with long video generation. Long Video Generation To address the high computational demand in long video generation, most existing works leverage the "Autoregressive over X" architecture, where "X" denotes any generative models capable of generating short video clips. With "X" being an autoregressive model, NUWA-Infinity (Wu et al., 2022a) introduces autoregressive over auto-regressive model, with a local autoregressive to generate patches and a global autoregressive to model the consistency between different patches. TATS (Ge et al., 2022) presents a time-agnostic VQGAN and time-sensitive transformer model, trained only on clips with tens of frames but can infer thousands of frames using a sliding window mechanism. Phenaki (Villegas et al., 2022) with C-ViViT as encoder and MaskGiT (Chang et al., 2022) as backbone generates variable-length videos conditioned on a sequence of open domain text prompts. With "X" being diffusion models, MCVD (Voleti et al., 2022) trains the model to solve multiple video generation tasks by randomly and independently masking all the past or future frames. FDM (Harvey et al., 2022) presents a DDPMs-based framework that produces long-duration video completions in a variety of realistic environments. Different from existing "Autoregressive over X" models trained on short clips, we propose NUWAXL, a Diffusion over Diffusion model directly trained on long videos to eliminate the traininginference gap. Besides, NUWA-XL enables parallel inference to speed up long video generation ## 3 Method 3.1 Temporal Klvae (T-Klvae) Training and sampling diffusion models directly on pixels are computationally costly, KLVAE (Rombach et al., 2022) compresses an original image into a low-dimensional latent representation where the diffusion process can be performed to alleviate this issue. To leverage external knowledge from the pretrained image KLVAE and transfer it to videos, we propose Temporal KLVAE(T-KLVAE) by adding external temporal convolution and attention layers while keeping the original spatial modules intact. Given a batch of video v ∈ R b×L×C×H×W with b batch size, L frames, C channels, H height, W width, we first view it as L independent images and encode them with the pre-trained KLVAE spatial convolution. To further model temporal information, we add a temporal convolution after each spatial convolution. To keep the original pretrained knowledge intact, the temporal convolution is initialized as an identity function which guarantees the output to be exactly the same as the original KLVAE. Concretely, the convolution weight W*conv*1d ∈ R cout×cin×kis first set to zero where cout denotes the out channel, cin denotes the in channel and is equal to cout, k denotes the temporal kernel size. Then, for each output channel i, the middle of the kernel size (k − 1)//2 of the corresponding input channel i is set to 1: $$W^{c o n v1d}[i,i,(k-1)//2]=1\qquad\qquad(1)$$ Similarly, we add a temporal attention after the original spatial attention, and initialize the weights Watt_out in the out projection layer into zero: $$W^{a t t\_o u t}=0$$ $$(2)$$ Watt_out = 0 (2) For the T-KLVAE decoder D, we use the same initialization strategy. The training objective of TKLVAE is the same as the image KLVAE. Finally , we get a latent code x0 ∈ R b×L×c×h×w, a compact representation of the original video v. ![3_image_0.png](3_image_0.png) ## 3.2 Mask Temporal Diffusion (Mtd) fined diffusion process: q (xt|xt−1) = N (xt; √αt xt−1, (1 − αt) I) (3) In this section, we introduce Mask Temporal Diffusion (MTD) as a basic diffusion model for our proposed Diffusion over Diffusion architecture. For global diffusion, only L prompts are used as inputs which form a "coarse" storyline of the video, however, for the local diffusion, the inputs consist of not only L prompts but also the first and last frames. Our proposed MTD which can accept input conditions with or without first and last frames, supports both global diffusion and local diffusion. In the following, we first introduce the overall pipeline of MTD and then dive into an UpBlock as an example to introduce how we fuse different input conditions. Input L prompts, we first encode them by a CLIP Text Encoder to get the prompt embedding p ∈ R b×L×lp×dp where b is batch size, lp is the number of tokens, dp is the prompt embedding dimension. The randomly sampled diffusion timestep t ∼ U(1, T) is embedded to timestep embedding t ∈ R c. The video v0 ∈ R b×L×C×H×W with L frames is encoded by T-KLVAE to get a representation x0 ∈ R b×L×c×h×w. According to the predex0 is corrupted by: xt = √α¯t x0 + p(1 − α¯t)ϵ ϵ ∼ N (0, I) (4) where ϵ ∈ R b×L×c×h×w is noise, xt ∈ R b×L×c×h×w is the t-th intermediate state in diffusion process, αt, α¯tis hyperparameters in diffusion model. For the global diffusion model, the visual conditions v c0 are all-zero. However, for the local diffusion models, v c0 ∈ R b×L×C×H×W are obtained by masking the middle L − 2 frames in v0. v c0 is also encoded by T-KLVAE to get a representation x c0 ∈ R b×L×c×h×w. Finally, the xt, p, t, x c0 are fed into a Mask 3D-UNet ϵθ (·). Then, the model is trained to minimize the distance between the output of the Mask 3D-UNet ϵθ (xt*, p, t, x*c0 ) ∈ R b×L×c×h×w and ϵ. $${\mathcal{L}}_{\theta}=||\epsilon-\epsilon_{\theta}\left(x_{t},p,t,x_{0}^{c}\right)||_{2}^{2}\qquad\qquad(5)$$ The Mask 3D-UNet is composed of multi-Scale DownBlocks and UpBlocks with skip connection, ![4_image_0.png](4_image_0.png) while the x c0 is downsampled to the corresponding resolution with a cascade of convolution layers and fed to the corresponding DownBlock and UpBlock. To better understand how Mask 3D-UNet works, we dive into the last UpBlock and show the details in Fig. 3. The UpBlock takes hidden states hin, skip connection s, timestep embedding t, visual condition x c0 and prompts embedding p as inputs and output hidden state hout. It is noteworthy that for global diffusion, x c0 does not contain valid information as there are no frames provided as conditions, however, for local diffusion, x c0 contains encoded information from the first and last frames. The input skip connection s ∈ R b×L×c*skip*×h×w is first concatenated to the input hidden state hin ∈ R b×L×cin×h×w. $$h:=[s;h_{i n}]$$ where the hidden state h ∈ R b×L×(c*skip*+cin)×h×w is then convoluted to target number of channels h ∈ R b×L×c×h×w. The timestep embedding t ∈ R cis then added to h in channel dimension c. $$h:=h+t$$ h := h + t (7) Similar to Sec. 3.1, to leverage external knowledge from the pre-trained text-to-image model, factorized convolution and attention are introduced with spatial layers initialized from pre-trained weights and temporal layers initialized as an identity function. For spatial convolution, the length dimension L here is treated as batch-size h ∈ R (b×L)×c×h×w. For temporal convolution, the hidden state is reshaped to h ∈ R (b×hw)×c×L with spatial axis hw treated as batch-size. $$h:=S p a t i a l C o n v\left(h\right)\qquad\qquad\left(8\right)$$ $$h:=T e m p o r a l C o n v\left(h\right)\qquad\qquad\left(9\right)$$ Then, h is conditioned on x c0 ∈ R b×L×c×h×w and x m 0 ∈ R b×L×1×h×w where x m 0is a binary mask to indicate which frames are treated as conditions. They are first transferred to scale w c, wm and shift b c, bm via zero-initialized convolution layers and then injected to h via linear projection. $$h:=w^{c}\cdot h+b^{c}+h\qquad\qquad(10)$$ $$h:=w^{m}\cdot h+b^{m}+h\qquad\qquad(11)$$ After that, a stack of Spatial Self-Attention (SA), Prompt Cross-Attention (PA), and Temporal SelfAttention (TA) are applied to h. For the Spatial Self-Attention (SA), the hidden state h ∈ R b×L×c×h×w is reshaped to h ∈ R (b×L)×hw×c with length dimension L treated as batch-size. $$Q^{S A}=h W_{Q}^{S A};K^{S A}=h W_{K}^{S A};V^{S A}=h W_{V}^{S A}\tag{12}$$ $$\widetilde{Q}^{S A}=S e l f a t t n(Q^{S A},K^{S A},V^{S A})\tag{13}$$ where $W_{Q}^{SA},W_{K}^{SA},W_{V}^{SA}\in\mathbb{R}^{c\times d_{in}}$ are parameterized by $\alpha$. c×din are parameters to be learned. For the Prompt Cross-Attention (PA), the prompt embedding p ∈ R b×L×lp×dpis reshaped to p ∈ R (b×L)×lp×dp with length dimension L treated as batch-size. $$Q^{PA}=hW^{PA}_{Q};K^{PA}=pW^{PA}_{K};V^{PA}=pW^{PA}_{V}\tag{14}$$ $$\tilde{Q}^{PA}=Crossattn(Q^{PA},K^{PA},V^{PA})\tag{15}$$ where $Q^{PA}\in\mathbb{R}^{(b\times L)\times hw\times d_{in}}$, $K^{PA}\in\mathbb{R}^{(b\times L)\times l_{p}\times d_{in}}$, $V^{PA}\in\mathbb{R}^{(b\times L)\times l_{p}\times d_{in}}$, $V^{PA}\in\mathbb{R}^{(b\times L)\times l_{p}\times d_{in}}$ are query. V (14) $$(6)$$ $$(15)$$ $$(7)$$ P A ∈ R key and value, respectively. WP A Q ∈ R c×din , WP A K ∈ R dp×din and WP A V ∈ R dp×din are parameters to be learned. The Temporal Self-Attention (TA) is exactly the same as Spatial Self-Attention (SA) except that spatial axis hw is treated as batch-size and temporal length L is treated as sequence length. Finally, the hidden state h is upsampled to target resolution hout ∈ R b×L×c×hout×wout via spatial convolution. Similarly, other blocks in Mask 3DUNet leverage the same structure to deal with the corresponding inputs. ## 3.3 Diffusion Over Diffusion Architecture In the following, we first introduce the inference process of MTD, then we illustrate how to generate a long video via Diffusion over Diffusion Architecture in a novel "coarse-to-fine" process. In inference phase, given the L prompts p and visual condition v c0 , x0 is sampled from a pure noise xT by MTD. Concretely, for each timestep t = T, T − 1*, . . . ,* 1, the intermediate state xtin diffusion process is updated by $$x_{t-1}=\frac{1}{\sqrt{\alpha_{t}}}\left(x_{t}-\frac{1-\alpha_{t}}{\sqrt{(1-\bar{\alpha}_{t})}}\epsilon_{\theta}\left(x_{t},p,t,x_{0}^{c}\right)\right)$$ $$+\frac{\left(1-\bar{\alpha}_{t-1}\right)\beta_{t}}{1-\bar{\alpha}_{t}}\cdot\epsilon\tag{16}$$ where ϵ ∼ N (0, I), p and t are embedded prompts and timestep, x c0 is encoded v c0 . αt, α¯t, βt are hyperparameters in MTD. Finally, the sampled latent code x0 will be decoded to video pixels v0 by T-KLVAE. For simplicity, the iterative generation process of MTD is noted as $$v_{0}=D i f f u s i o n(p,v_{0}^{c})$$ $$(17)$$ When generating long videos, given the L prompts p1 with large intervals, the L keyframes are first generated through a global diffusion model. $$v_{01}=G l o b a l D i f f u s i o n(p_{1},v_{01}^{c})$$ where v c01 is all-zero as there are no frames provided as visual conditions. The temporally sparse keyframes v01 form the "coarse" storyline of the video. Then, the adjacent keyframes in v01 are treated as the first and the last frames in visual condition v c02. The middle L − 2 frames are generated by feeding p2, v c02 into the first local diffusion model where p2 are L prompts with smaller time intervals. $$v_{02}=L o c a l D i f f u s i o n(p_{2},v_{02}^{c})$$ Similarly, v c03 is obtained from adjacent frames in v02, p3 are L prompts with even smaller time intervals. The p3 and v c03 are fed into the second local diffusion model. $$v_{03}=L o c a l D i f f u s i o n(p_{3},v_{03}^{c})$$ Compared to frames in v01, the frames in v02 and v03 are increasingly "fine" with stronger consistency and more details. By iteratively applying the local diffusion to complete the middle frames, our model with m depth is capable of generating extremely long video with the length of O(L m). Meanwhile, such a hierarchical architecture enables us to directly train on temporally sparsely sampled frames in long videos (3376 frames) to eliminate the training-inference gap. After sampling the L keyframes by global diffusion, the local diffusions can be performed in parallel to accelerate the inference speed. ## 4 Experiments 4.1 Flintstoneshd Dataset Existing annotated video datasets have greatly promoted the development of video generation. However, the current video datasets still pose a great challenge to long video generation. First, the length of these videos is relatively short, and there is an enormous distribution gap between short videos and long videos such as shot change and long-term dependency. Second, the relatively low resolution limits the quality of the generated video. Third, most of the annotations are coarse descriptions of the content of the video clips, and it is difficult to illustrate the details of the movement. To address the above issues, we build FlintstonesHD dataset, a densely annotated long video dataset, providing a benchmark for long video generation. We first obtain the original *Flintstones* cartoon which contains 166 episodes with an average of 38000 frames of 1440×1080 resolution. To support long video generation based on the story and capture the details of the movement, we leverage the image captioning model GIT2 (Wang et al., 2022) to generate dense captions for each frame in the dataset first and manually filter some errors in the generated results. $$(18)$$ ## 4.2 Metrics $$\begin{array}{r l}{{}}&{{}{\mathrm{Inception}}}\\ {\mathbf{a}=\mathbf{b}=\mathbf{a}\mathbf{a}\mathbf{b}\mathbf{a}\mathbf{b}}\end{array}$$ Avg-FID Fréchet Inception Distance(FID) (Heusel et al., 2017), a metric used to evaluate image generation, is introduced to calculate the average quality of generated frames. $$(20)$$ Block-FVD Fréchet Video Distance (FVD) (Unterthiner et al., 2018) is widely used to evaluate the quality of the generated video. In this paper, we propose Block FVD for long video generation, which splits a long video into several short clips to calculate the average FVD of all clips. For simplicity, we name it B-FVD-X where X denotes the length of the short clips. | Method | Phenaki (Villegas | FDM* | (Harvey | NUWAXL/128 | NUWAXL/256 | |-------------------|---------------------|--------------|----------------|----------------|--------| | et al., 2022)/128 | et al., 2022)/128 | | | | | | Arch | AR over AR | AR over Diff | Diff over Diff | Diff over Diff | | | Avg-FID↓ | 40.14 | 34.47 | 35.95 | 32.66 | | | 16f | B-FVD-16↓ | 544.72 | 532.94 | 520.19 | 580.21 | | Time↓ | 4s | 7s | 7s | 15s | | | Avg-FID↓ | 43.13 | 38.28 | 35.68 | 32.05 | | | 256f | B-FVD-16↓ | 573.55 | 561.75 | 542.26 | 609.32 | | Time↓ | 65s | 114s | 17s (85.09%↓) | 32s | | | Avg-FID↓ | 48.56 | 43.24 | 35.79 | 32.07 | | | 1024f | B-FVD-16↓ | 622.06 | 618.42 | 572.86 | 642.87 | | Time↓ | 259s | 453s | 26s (94.26%↓) | 51s | | Table 1: Quantitative comparison with the state-of-the-art models for long video generation on FlintstonesHD dataset. 128 and 256 denote the resolutions of the generated videos. *Note that the original FDM model does not support text input. For a fair comparison, we implement an FDM with text input. | Model | Temporal Layers | FID↓ | FVD↓ | Model | MI | SI | FID↓ | FVD↓ | |---------------------------------------------|-----------------------------------------------------|----------------------|--------------|------------|----------------------|------|--------------|--------| | KLVAE | - | 4.71 | 28.07 | | | | | | | T-KLVAE-R | random init | 5.44 | 12.75 | | | | | | | T-KLVAE | identity init | 4.35 | 11.88 | MTD w/o MS | × | × | 39.28 548.90 | | | MTD w/o S | ✓ | × | 36.04 526.36 | | | | | | | MTD | ✓ | ✓ | 35.95 520.19 | | | | | | | (a) Comparison of different KLVAE settings. | (b) Comparison of different MTD settings. | | | | | | | | | Model | depth 16f | 256f | 1024f | Model | L | 16f | 256f | 1024f | | NUWA-XL-D1 | 1 | 527.44 697.20 719.23 | | | | | | | | NUWA-XL-D2 | 2 | 516.05 536.98 684.57 | | | | | | | | NUWA-XL-D3 | 3 | 520.19 542.26 572.86 | NUWA-XL-L8 | 8 | 569.43 673.87 727.22 | | | | | NUWA-XL-L16 | 16 | 520.19 542.26 572.86 | | | | | | | | NUWA-XL-L32 | 32 | OOM | OOM | OOM | | | | | | (c) Comparison of different NUWA-XL depth. | (d) Comparison of different local diffusion length. | | | | | | | | Table 2: Ablation experiments for long video generation on FlintstonesHD (OOM stands for Out Of Memory). ## 4.3 Quantitative Results 4.3.1 Comparison With The State-Of-The-Arts We compare NUWA-XL on FlintstonesHD with the state-of-the-art models in Tab. 1. Here, we report FID, B-FVD-16, and inference time. For "Autoregressive over X (AR over X)" architecture, due to error accumulation, the average quality of generated frames (Avg-FID) declines as the video length increases. However, for NUWA-XL, where the frames are not generated sequentially, the quality does not decline with video length. Meanwhile, compared to "AR over X" which is trained only on short videos, NUWA-XL is capable of generating higher quality long videos. As the video length grows, the quality of generated segments (BFVD-16) of NUWA-XL declines more slowly as NUWA-XL has learned the patterns of long videos. Besides, because of parallelization, NUWA-XL significantly improves the inference speed by 85.09% when generating 256 frames and by 94.26% when generating 1024 frames. ## 4.3.2 Ablation Study KLVAE Tab. 2a shows the comparison of different KLVAE settings. KLVAE means treating the video as independent images and reconstructing them independently. T-KLVAE-R means the introduced temporal layers are randomly initialized. Compared to KLVAE, we find the newly introduced temporal layers can significantly increase the ability of video reconstruction. Compared to T-KLVAE-R, the slightly better FID and FVD in T-KLVAE illustrate the effectiveness of identity initialization. ![7_image_0.png](7_image_0.png) MTD Tab. 2b shows the comparison of different global/local diffusion settings. MI (Multi-scale Injection) means whether visual conditions are injected to multi-scale DownBlocks and UpBlocks in Mask 3D-UNet or only injected to the Downblock and UpBlock with the highest scale. SI (Symmetry Injection) means whether the visual condition is injected into both DownBlocks and UpBlocks or it is only injected into UpBlocks. Comparing MTD w/o MS and MTD w/o S, multi-scale injection is significant for long video generation. Compared to MTD w/o S, the slightly better FID and FVD in MTD show the effectiveness of symmetry injection. ## Depth Of Diffusion Over Diffusion Tab. 2C shows the comparison of B-FVD-16 of different NUWA-XL depth m with local diffusion length L fixed to 16. When generating 16 frames, NUWAXL with different depths achieves comparable results. However, as the depth increases, NUWA-XL can produce videos that are increasingly longer while still maintaining relatively high quality. Length in Diffusion over Diffusion Tab. 2d shows the comparison of B-FVD-16 of diffusion local length L with NUWA-XL depth m fixed to 3. In comparison, when generating videos with the same length, as the local diffusion length increases, NUWA-XL can generate higher-quality videos. ## 4.4 Qualitative Results Fig. 4 provides a qualitative comparison between AR over Diffusion and Diffusion over Diffusion for long video generation on FlintstonesHD. As introduced in Sec. 1, when generating long videos, "Autoregressive over X" architecture trained only on short videos will lead to long-term incoherence (between frame 22 and frame 1688) and unrealistic shot change (from frame 17 to frame 20) since the model has no opportunity to learn the distribution of long videos. However, by training directly on long videos, NUWA-XL successfully models the distribution of long videos and generates long videos with long-term coherence and realistic shot change. 5 Conclusion We propose NUWA-XL, a "Diffusion over Diffusion" architecture by viewing long video generation as a novel "coarse-to-fine" process. To the best of our knowledge, NUWA-XL is the first model directly trained on long videos (3376 frames), closing the training-inference gap in long video generation. Additionally, NUWA-XL allows for parallel inference, greatly increasing the speed of long video generation by 94.26% when generating 1024 frames. We further build FlintstonesHD, a new dataset to validate the effectiveness of our model and provide a benchmark for long video generation. ## Limitations Although our proposed NUWA-XL improves the quality of long video generation and accelerates the inference speed, there are still several limitations: First, due to the unavailability of open-domain long videos (such as movies, and TV shows), we only validate the effectiveness of NUWA-XL on public available cartoon Flintstones. We are actively building an open-domain long video dataset and have achieved some phased results, we plan to extend NUWA-XL to open-domain in future work. Second, direct training on long videos reduces the training-inference gap but poses a great challenge to data. Third, although NUWA-XL can accelerate the inference speed, this part of the gain requires reasonable GPU resources to support parallel inference. ## Ethics Statement This research is done in alignment with Microsoft's responsible AI principles. ## Acknowledgements We'd like to thank Yu Liu, Jieyu Xiao, and Scarlett Li for the discussion of the potential cartoon scenarios. We'd also like to thank Yang Ou and Bella Guo for the design of the homepage. We'd also like to thank Yan Xia, Ting Song, and Tiantian Xue for the implementation of the homepage. ## References Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. 2022. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11315–11325. Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, and Hongxia Yang. 2021. Cogview: Mastering text-to-image generation via transformers. 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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \ Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. 30. Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. 2022. Phenaki: Variable length video generation from open domain textual description. Vikram Voleti, Alexia Jolicoeur-Martineau, and Christopher Pal. 2022. Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation. Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba. 2016. Generating Videos with Scene Dynamics. 29. Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang. 2022. GIT: A Generative Image-to-text Transformer for Vision and Language. Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. 2021. GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions. Chenfei Wu, Jian Liang, Xiaowei Hu, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, and Nan Duan. 2022a. NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis. Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. 2022b. N\"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion. In Proceedings of the European Conference on Computer Vision (ECCV). Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. 2021. VideoGPT: Video Generation using VQ-VAE and Transformers. Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, and Burcu Karagol Ayan. 2022. Scaling Autoregressive Models for ContentRich Text-to-Image Generation. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? line 531 limitations ✓ A2. Did you discuss any potential risks of your work? line 547 Ethics Statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? abstract line 001; introduction line 107 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✗ **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? No response. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? No response. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? No response. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? No response. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

yue-etal-2023-synthetic

Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

https://aclanthology.org/2023.acl-long.74

Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pretrained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.

# Synthetic Text Generation With Differential Privacy: A Simple And Practical Recipe Xiang Yue1,∗ , Huseyin A. Inan2, Xuechen Li3, Girish Kumar5, Julia McAnallen4, Hoda Shajari4, Huan Sun1, David Levitan4, and Robert Sim2 1The Ohio State University, 2Microsoft Research, 3Stanford University, 4Microsoft, 5UC Davis {yue.149,sun.397}@osu.edu [email protected] [email protected] {Huseyin.Inan,Julia.McAnallen,hodashajari,David.Levitan,rsim}@microsoft.com ## Abstract Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pre-trained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.1 ## 1 Introduction The issue of privacy has gained increasing attention in natural language processing (NLP). Privacy attacks against common NLP pipelines have demonstrated that models trained without formal privacy guarantees can reveal membership information and enable training data reconstruction (Shokri et al., 2017; Carlini et al., 2021). Privacy concerns manifested through tightening legislation (e.g., GDPR (Art. 29 WP, 2014)) and growing discussions on policy and ethics call for improved approaches for privacy-preserving machine learning. ∗Most of the work was done when Xiang, Xuechen, and Girish interned at Microsoft (Research). 1Our code is available at https://github.com/ microsoft/dp-transformers Among different approaches for learning with private data, learning with differential privacy (DP) (Dwork et al., 2006) has become the gold standard as its formal guarantee enables reasoning about the privacy loss in a principled manner and makes the approach resilient to strong privacy attacks (Carlini et al., 2019). Recent developments have substantially improved the computational efficiency and privacy-utility trade-off of DP machine learning (Subramani et al., 2021; Li et al., 2022b; Yu et al., 2022; De et al., 2022; Bu et al., 2022; Li et al., 2022a; Mehta et al., 2022, *inter alia*), demonstrating gains for learning models that perform specific downstream tasks. In contrast to the above works, we study *synthetic text generation by building generative text* models with DP training algorithms (Figure 1). The goal of this approach is to learn a generative model that faithfully captures distributional properties of the training data (and the underlying distribution), as opposed to learning task-oriented models with specific functions. Compared to directly learning models for target tasks, this paradigm has several advantages: (1) DP-trained generative models can be used to draw synthetic data for learning an expanding set of task models without incurring any additional privacy loss (due to the post-processing property of DP); (2) Dataset debugging is made easy as synthetic text generated from DP-trained models can be shared more freely, and inspecting its samples poses less of a privacy concern compared to examining the original private data (Augenstein et al., 2020); (3) Synthetic data generated from DP-trained models can be retained for a longer time under certain existing policies (e.g., right to be forgotten) thanks to the fact that DP implies some degree of approximate machine unlearn1321 ![1_image_0.png](1_image_0.png) ing (Bourtoule et al., 2021; Sekhari et al., 2021). In this work, we initiate a systematic empirical study of the problem and show that DP language model (LM) fine-tuning can be an effective solution to synthetic text generation with privacy. In particular, we show that simply fine-tuning progressively larger autoregressively pre-trained language models on (private) data leads to models that generate increasingly useful synthetic text. For instance, we fine-tune a GPT-2 Large model (Radford et al., 2019) on a review dataset with DP at ϵ = 4 and then use it to generate synthetic text to build downstream classifiers. The classification models achieve comparable performance (only 2-4% in accuracy away) to the classifiers trained on the original dataset. Furthermore, we demonstrate that generating a small amount of synthetic data with DP is sufficient to create classification models that are on par with those trained directly on the entire original dataset with DP. One of the advantages of the synthetic data approach is that the privacy loss is fixed, and an unlimited number of downstream models can be built without incurring additional leakage. In contrast, training additional downstream models on the original data with DP accumulates privacy loss. Distributional similarity evaluation additionally confirms that the synthetic text distribution resembles the original data distribution. We also uncover a novel phenomenon in DP-trained LMs that is of independent interest. Specifically, we observe a length truncation effect in text generation with DPtrained models, resulting in completions that are generally shorter than their non-DP counterparts and instances in the original dataset. We further extensively study learning dynamics with DP by injecting specially-crafted *canaries* (Carlini et al., 2019) in the training data. This allows for (i) stress-testing the extent to which DP fine-tuning limits the *leakage of private information* and (ii) understanding the conditions under which a *subject of interest* would appear in synthetic generations. Finally, we conclude our studies on an industriallevel private customer feedback dataset to show the feasibility of our approach in real-world scenarios. ## 2 Background 2.1 Differential Privacy Definition 2.1 (Differential Privacy (DP) (Dwork et al., 2006)). A randomized algorithm M : D → S is (*ϵ, δ*)-differentially private if for any two neighboring datasets D, D′ ∈ D that differ exactly in a single data sample, and for all sets S ⊆ S: ## P[M(D) ∈ S] ≤ E Εp[M(D′) ∈ S] + Δ. This definition provides a rigorous privacy guarantee by theoretically bounding the effect of a single data sample in the dataset. For a differentially private algorithm, the output distribution is statistically similar whether any individual data sample appears in the input dataset or not. The privacy parameter ϵ quantifies the maximum allowable impact of a single individual's data on the outcome. δ specifies the maximum probability that the privacy guarantee may fail. An algorithm can typically be made (*ϵ, δ*)-DP by bounding the contribution of a single data sample and adding controlled noise from a predetermined distribution (e.g., Gaussian) (Dwork and Roth, 2014). Setting ϵ and δ in practice often requires careful consideration of the specific use case and the acceptable trade-off between privacy and utility. We discuss our choice of ϵ and δ in Section 4.1. An appealing property of DP crucial to this work is *robustness to post-processing*. This property ensures that if the algorithm M satisfies (*ϵ, δ*)-DP, then so does F ◦ M for any deterministic or randomized function F (which is independent of M). Namely, one can perform arbitrary post-processing without incurring additional privacy loss. ## 2.2 Dp Stochastic Gradient Descent Deep learning models can be trained with DP via a modification of the stochastic gradient descent (SGD) algorithm (Song et al., 2013; Bassily et al., 2014; Abadi et al., 2016). The modified algorithm clips *per-sample gradients* to bound the contribution of individual examples. Noise from a Gaussian distribution is sampled and added to the sum of the clipped gradients in a batch to obfuscate the gradient update. The resulting algorithm, called Differentially Private Stochastic Gradient Descent (DP-SGD), can be shown to be DP for some (*ϵ, δ*) for each update of the model. Privacy parameters at the end of training can be computed via privacy composition algorithms (Abadi et al., 2016; Gopi et al., 2021a). In the next section, we will utilize DP-SGD to train a language model with privacy for synthetic text generation. ## 3 Method In this section, we formally state the problem and present our method (see Figure 1 for an illustration) that produces a synthetic version of private text data with differential privacy. ## 3.1 Problem Statement Let D be a database representing the collection of token sequences from a fixed dictionary V. We define a (randomized) mapping M : *D → D* such that for a given dataset D ∈ D, the goal is to generate a synthetic version M(D) = D˜ with privacy constraints and utility desiderata. Regarding privacy constraints, we require that M be (*ϵ, δ*)-DP with domain D. This requirement provides strong protection for the participants in the input dataset as this participation will be statistically indistinguishable to a certain degree through any adversary accessing the model or synthetic version of the dataset in the output. For the case of utility, ideally, the synthetic version D˜ should be able to replace D in providing a training resource for models on relevant downstream applications. In other words, on target downstream tasks, models trained on the synthetic dataset D˜ are expected to have performance similar to the models trained on the original dataset D. More generally, distributional properties of the dataset D should be captured as much as possible in the synthetic version D˜ without violating the aforementioned privacy requirement. These will be extensively explored in Section 4. ## 3.2 Synthetic Text Generation With Dp Conventionally, to generate synthetic text, an autoregressive language model (e.g. GPT-2 (Radford et al., 2019)) is trained on the original dataset and subsequently sampled using a sampling mechanism (e.g., beam search, top-k sampling (Fan et al., 2018), nucleus sampling (Holtzman et al., 2020), etc.) to produce synthetic sequences. To make this operation differentially private, we adopt DP-SGD to fine-tune a pre-trained generative LM. The post-processing property of DP ensures that once the LM has been fine-tuned with DP, sampling from the model incurs no extra privacy loss. It would be desirable to synthesize examples with labels. We achieve this by building a conditional generator introduced in (Keskar et al., 2019) to provide more explicit control over text generation. By using so-called control codes (Keskar et al., 2019), the probability distribution of a text sequence x = (x1, x2*, . . . , x*n) is conditioned on a control code c and decomposed as: $$\mathbb{P}\left(x|c\right)=\prod_{i=1}^{n}\mathbb{P}\left(x_{i}|x_{1},x_{2},\ldots,x_{i-1},c\right).$$ A neural network pθ(·) is then trained to model each conditional distribution. The model can later be used to generate new samples conditioned on a control code c by sequentially sampling pθ(x1|c), pθ(x2|x˜1, c), . . . , pθ(xm|x˜1, . . . x˜m−1, c). The advantage of this approach is that it provides flexibility in the text generation of the model by allowing the conditional control codes to specify a particular style, domain, sentiment, or category. For example, feedback data collected from users on a set of products may contain product types and review scores associated with each data sample. Control codes can be constructed as cp,r = "Product type: p *| Review score:* r" for different product type (p) and review score (r) pairs. In our method, we utilize control codes to prepend each sample with its corresponding categories as a simple preprocessing step. During the text generation, this allows us to use the control codes to generate as many samples as the original categorical distribution is preserved. We point out that the categorical distribution in the original dataset may also be a piece of private information itself. However, its estimation could easily be privatized (Dwork and Roth, 2014) and for simplicity, we ignore the low-cost privacy loss of this step and use the exact categorical distribution of the original dataset in this paper. ## 4 Analyses On A Public Review Dataset In this section, we extensively analyze our method with experiments on a public benchmark dataset: Yelp Open Dataset,2 which has been widely adopted for language modeling and text classification tasks. We then apply our method to an internal private customer feedback dataset in Section 5. ## 4.1 Experimental Setup Dataset. The Yelp dataset contains review text data on businesses that can be studied for academic purposes. We select two attributes for the conditional generation as well as the downstream task applications: review stars (1-5) and business category. We sample 10 frequent business categories and remove the reviews that do not have ratings (Details can be found in Appendix A.1). This results in a dataset that has 1.9M reviews for training, 5000 for validation, and 5000 for testing. Implementation Details. We utilize the public repository (Inan et al., 2022), which is based on Huggingface (Wolf et al., 2019) and Opacus (Yousefpour et al., 2021), for fine-tuning language models with DP. Specifically, we fine-tune three language models: GPT2 (Radford et al., 2019), GPT2-Medium, and GPT2-Large, for synthetic text generation. Additionally, we fine-tune the RoBERTa-base model (Liu et al., 2019) for downstream text classification tasks. Control codes are constructed based on attributes such as *"Business Type: Bar | Review Stars: 5.0"* 2https://www.yelp.com/dataset | Data Type | Data Generator | ϵ | Rating | Category | |-------------|------------------|--------|----------|------------| | Original | - | - | 0.7334 | 0.7752 | | GPT2 | ∞ | 0.6892 | 0.7584 | | | 4 | 0.6656 | 0.7478 | | | | Synthetic | GPT2-Medium | ∞ | 0.6878 | 0.7550 | | 4 | 0.6756 | 0.7486 | | | | GPT2-Large | ∞ | 0.7090 | 0.7576 | | | 4 | 0.6936 | 0.7568 | | | and are prepended to each sample. Hyperparameters are specified in Appendix A. For both synthetic text generation and classification, we set the maximum sequence length to 128, unless otherwise specified. During training, we evaluate the models on the dev dataset and select the checkpoint that achieves the best validation performance for the final evaluation on the test set. We set the privacy parameter ϵ to 4, which is supported by prior work (Yu et al., 2021a; Li et al., 2022b; Yu et al., 2022; De et al., 2022; Mehta et al., 2022) and real-world applications. For instance, the release of US population data uses ϵ = 13.64 (Bureau, 2020), and the development of a nextword prediction model uses ϵ = 6.92 (Google, 2022). Our ϵ = 4 is smaller and provides stronger privacy protection. As recommended by (Hsu et al., 2014; De et al., 2022), δ should be smaller than the inverse of the dataset size N, and we set δ = 1/(N· log N). The additive noise scale is calculated using the numerical composition algorithm (Gopi et al., 2021b), given the batch size and epochs for each setting mentioned in Appendix A for DP training. To generate synthetic text samples, we employ top-k sampling (Fan et al., 2018) and nucleus sampling (top-p) (Holtzman et al., 2020), with k = 50 and p = 0.9. To produce synthetic datasets that preserve categorical distributions (e.g., business category), we generate 100K samples from the finetuned models using the appropriate control codes. ## 4.2 Downstream Tasks On Synthetic Data One way to evaluate the quality of the synthetic dataset is by examining the performance of downstream task models trained on it. We fine-tune RoBERTa-base models for classifying review ratings and business categories using the synthetic | Data | Data | DP | Task Accuracy | | |-----------|--------|----------------|-----------------|----------| | Type | Size | Position | Rating | Category | | Original | 1.9M | Task modeling | 0.7014 | 0.7644 | | Original | 100K | Task modeling | 0.6689 | 0.7552 | | Synthetic | 100K | Data Generator | 0.6936 | 0.7568 | dataset. We further compare their performance with models trained on the original dataset. All models are evaluated on the same original test set. The results are summarized in Table 1. The downstream task models trained on the synthetic data generated by GPT2 with DP (ϵ = 4) achieve comparable performance to the models trained on the synthetic data generated without DP (ϵ = ∞) and the models trained on the original dataset. Additionally, we observe that the quality of the synthetic generations improves when larger pre-trained language models are used (sampled generations can be found in Appendix F), and the performance gap between private and non-private generations diminishes. Surprisingly, models trained on synthetic data generated by GPT2-Large with DP exhibit similar or even better performance compared to models trained on synthetic data generated by GPT2 without DP. These results highlight the significant potential of our method for generating synthetic data across various downstream applications. ## 4.3 Synthetic Data Generation With Dp V.S. Downstream Task Modeling With Dp It is natural to compare how downstream task models built on synthetic text generated by a DP-trained LM fare against models directly trained on the original data with DP. The results of this comparison are presented in Table 2. We observe that by using the same privacy parameter (ϵ = 4), both approaches achieve comparable performances. However, it is important to note that training two task models on the private dataset with DP will result in a higher overall privacy loss than ϵ = 4, and this loss will accumulate with additional downstream tasks. In contrast, the postprocessing property of DP allows us to train any number of models for different downstream tasks on the synthetic data generated by a DP-trained LM without incurring additional privacy loss. An interesting observation is that once the syn- | Generator | ϵ | F1↑ | FID↓ | MAUVE↑ | |-------------|--------|--------|--------|----------| | GPT2 | ∞ | 0.5199 | 3.2368 | 0.7158 | | 4 | 0.4786 | 4.7998 | 0.5579 | | | GPT2-Medium | ∞ | 0.5446 | 3.1464 | 0.7222 | | 4 | 0.5076 | 4.1880 | 0.6085 | | | GPT2-Large | ∞ | 0.5852 | 3.0978 | 0.7238 | | 4 | 0.5140 | 4.1352 | 0.6093 | | thetic data is generated with DP, a smaller dataset size (100K instead of 1.9M) is sufficient to produce superior downstream models compared to models directly trained with DP on the original data of the same size (as seen in the second row of Table 2). ## 4.4 Similarity Between Synth. And Real Data To further assess the quality of the synthetic generations, we evaluate the similarity between the synthetic dataset and the original dataset. Unlike typical natural language generation tasks like machine translation or summarization, where gold references can be used for evaluation, it is challenging to directly compare synthetic generations with the original dataset when there is no one-toone mapping between them. In our evaluation, we measure the "similarity" from three different perspectives: Embedding Distribution Distance, Topic Difference, and Text Length Distribution. Embedding Distribution Distance. To measure the embedding distribution distance between the synthetic and original data, we use sentencetransformers (Reimers and Gurevych, 2019) to embed both datasets. We calculate the distance between the two distributions using three metrics: 1) F1 Score: the harmonic mean of Precision and Recall (Kynkäänniemi et al., 2019). Precision estimates the average sample quality, while Recall measures the coverage of the sample distribution. 2) Fréchet Inception Distance (FID): FID calculates the feature-wise mean and covariance matrices of the embedding vectors and then measures the Fréchet distance between the two sets (Heusel et al., 2017). 3) MAUVE: MAUVE compares the distributions of the synthetic and original data using divergence frontiers (Pillutla et al., 2021). We note that the absolute scale of these metrics may vary depending on the specific embedding models used. To account for this, we conduct the evaluations with five different pre-trained sentence ![5_image_1.png](5_image_1.png) transformers (details provided in Appendix A.6), and then compute the average for each metric. Table 3 shows the distribution distances between the synthetic data and the original data based on the metrics introduced above. We observe that the quality of the synthetic data improves as we use larger pre-trained models for private fine-tuning. Similar to the results of the previous section, we observe that the F1 score of the GPT2-Large model with DP (the last row) matches the F1 score of GPT2 model without privacy (the first row). On the other hand, there remains a gap between synthetic generations with and without DP for FID and MAUVE. Topic Difference. Another approach to measuring the similarity between the synthetic and original data is to analyze their topic distributions. Topic modeling is a commonly used technique to uncover hidden semantic structures or abstract "topics" within a collection of documents. To compare the distributions of topics in the synthetic and original data, we combine them into a single collection and utilize an unsupervised topic model called BERTopic (Grootendorst, 2022) to extract the top 10 most frequent topics. The distributions of these topics for both the synthetic data and the original data are plotted in Figure 2. From the results, we observe that the topic distributions of the synthetic data, both with and without DP, are highly similar to those of the original data. This further demonstrates the high quality of the synthetic data generated using our approach. Text Length Distribution. Lastly, we examine the distribution of sequence lengths in the synthetic data and compare them to the original data. To investigate whether the maximum sequence length or truncation during the pre-processing phase has a significant impact on the generations, we train two sets of generative models with maximum sequence ![5_image_0.png](5_image_0.png) ## Lengths Of 128 And 512. We plot the density of the sequence lengths in Figure 3. We observe that, in general, the synthetic data generated with or without privacy tends to be shorter than the original data (*length truncation* effect). Furthermore, we notice that the synthetic data generated with DP has a higher concentration of shorter sequences compared to the data generated without DP. Although the issue is somewhat mitigated with larger model sizes, it is not fully resolved, and we can still observe that the generations with DP are slightly shorter than their non-private counterparts using the same decoding strategy (e.g., average length of 84.5 vs. 89.4 for GPT2-Large). ## 4.5 Learning Dynamics With Dp In this section, we examine the learning dynamics with DP from two perspectives: (i) the preservation of *private information* specific to individuals; (ii) the generation of information that is common to many individuals (i.e., the *subject of interest*). To analyze these dynamics, we extend the approach introduced in (Carlini et al., 2019). We construct "canary" samples that represent private information and the subject of interest respectively. These canary samples are then injected into the original training data to assess the extent to which they can be reconstructed in the synthetic generations. This allows us to evaluate how effectively private information is protected and how well the subject of interest is captured in the generations. Leakage of Private Information. The objective of this experiment is to evaluate whether any private information, such as Personally Identifiable Information (PII), leaks in the generated text. We | Repetition | ϵ | Perplexity Rank | Leaked Canaries | Original Data | Synthetic Data | | | |--------------|------------|-------------------|-------------------|-----------------|------------------|--------------|------------| | 1 | ∞ | 1017/10000 | 0% | | | | | | 4 | 3926/10000 | 0% | ϵ | # of samples | percentage | # of samples | percentage | | ∞ | 100 | 0.005% | 80 | 0.004% | | | | | ∞ | 1000 | 0.053% | 3678 | 0.194% | | | | | ∞ | 10000 | 0.526% | 57040 | 3.002% | | | | | 4 | 100 | 0.005% | 0 | 0.000% | | | | | 4 | 1000 | 0.053% | 10 | 0.001% | | | | | 4 | 10000 | 0.526% | 32271 | 1.698% | | | | | 10 | ∞ | 1/10000 | 0% | | | | | | 4 | 3320/10000 | 0% | | | | | | | 100 | ∞ | 1/10000 | 80% | | | | | | 4 | 969/10000 | 0% | | | | | | focus on measuring the leakage of PIIs, as they are direct identifiers of individuals and highly sensitive data governed by privacy regulations like GDPR. We construct 5 artificial review-style canary sequences, each containing specific types of private information (e.g., "The food took literally 6 hours to arrive at *1940W State St Boise."*; please refer to Appendix B for the full list).3 We conduct experiments by injecting these 5 canary sequences with varying repetition rates into the original dataset. The purpose of repeating the private information is to account for worst-case scenarios regarding privacy, as previous studies (Lee et al., 2022; Kandpal et al., 2022; Carlini et al., 2022) have demonstrated that data duplication is a major contributing factor to model memorization. After generating the synthetic data, we examine whether the private information (underlined text in the example) from the canary sequences appears in the generations. The results are presented in Table 4. We observe that even with a repetition rate as high as 100, the private information from the canary sequences does not appear in the synthetic data when the model is trained with DP. In contrast, without DP, 4 out of 5 canary sequences verbatim appear in the synthetic data at this repetition rate. This demonstrates the effectiveness of DP in preventing the leakage of private information. We note that the appearance of the canaries in the synthetic dataset is tied to the way we generate text. As such, our evaluation is not exhaustive, and we Table 5: Injection of a subject of interest in the original data and the appearance of it in the synthetic data. cannot completely rule out the possibility that canaries could be extracted from DP-trained models using alternative decoding methods and hyperparameters. To address this limitation, we directly examine the rank of the private information within a canary sequence (e.g., "*1940W State St Boise*") based on its perplexity compared to 10,000 similar candidates.4 The details of how we construct similar candidates are included in Appendix B. We present the average rank of the private information in the canary sequences in Table 4. Additionally, the perplexity distributions of all similar candidates for each canary type can be found in Figure 5 in Appendix C. Based on our investigation, we draw the following notable findings: For all repetition levels, training the language model with DP effectively eliminates the risk of privacy leakage. The private information in the canary sequences does not achieve low ranks and is not distinguishable among similar candidates. When the canary sequence appears only once in the training set, the risk of extraction during generation is relatively low. However, some canaries (e.g., Address and Plate in Figure 5) still obtain top ranks. This indicates that even if certain private information appears only once in the training set, models may still memorize it, potentially leading to leakage in synthetic generations. Additionally, when we repeat the canary sequences 10 or 100 times, they consistently achieve top ranks without DP. In contrast, models trained with DP consistently exhibit much higher ranks for the inserted sequences, with a leakage percentage of 0. Appearance of a Subject of Interest. In this experiment, we aim to investigate whether a spe-4The rank refers to the position of the private information in terms of perplexity compared to the set of similar candidates. In our evaluation, we aim for private information to have a higher perplexity rank among similar candidates. This indicates that the model has difficulty distinguishing private information from other similar entities, making it less likely to be extracted or identified in the synthetic generations. cific "subject of interest" can be extracted from fine-tuned models when it appears in multiple distinct instances in the training data. This evaluation allows us to assess the extent to which our DP guarantee (ϵ = 4) permits the generation of information that is common to many individuals. First, we select the subject of interest "beautiful paintings by Van Gogh in a restaurant" that we want to be present in the synthetic generations.5 However, instead of replicating the subject, we simulate the scenario where different people may express this subject in different ways. To achieve this, we utilize a variant of GPT-3 (Brown et al., 2020) to generate a number of reviews (100, 1,000, and 10,000) that include this subject (more details can be found in Appendix D). Next, we inject different numbers of canary reviews into the original training dataset. After generating the synthetic dataset, we examine whether the subject of interest (including its substrings or paraphrases) appears in the synthetic data. The results are presented in Table 5. Interestingly, we observe that without DP, when 100 canary samples are injected, the subject appears as frequently as it does in the original data. However, with 1,000 and 10,000 injected samples, the subject tends to be over-represented in the synthetic data. Conversely, when DP is applied, the subject is not present in the synthetic data even with 100 injected samples, and only appears in a few generations even with 1,000 injected samples. This indicates that while DP protects the privacy of individual samples, it also has a detrimental effect on learning and generating the tail of the data distribution. And with 10,000 injections, although over-generation of the subject still occurs, it happens to a lesser degree compared to the case without privacy protection. ## 5 Results On Private Customer Feedback To demonstrate the effectiveness of our method in safeguarding utility and privacy in practical scenarios, we evaluate its performance using a Microsoft private feedback dataset obtained from customers. Background. Industrial applications often receive a significant volume of customer feedback regarding their products. Customer feedback is valuable as it provides insights into product performance, user satisfaction, and areas for improvement. While customer feedback may not typically 5We randomly select this subject during brainstorming. | Data Type | ϵ | A1 | A2 | A3 | |-------------|-----|-------|-------|-------| | Original | - | 0.690 | 0.716 | 0.563 | | Synthetic | ∞ | 0.664 | 0.558 | 0.555 | | Synthetic | 4 | 0.642 | 0.536 | 0.552 | contain personally identifiable information, it may still include sensitive details that could potentially disclose the customer's identity. For example, customers might mention specific job titles, company names, or locations in their feedback. When combined with other publicly available information, these details could potentially be used to identify the customer and compromise their privacy. Protecting the privacy of this information is crucial to comply with privacy regulations such as the GDPR (Art. 29 WP, 2014), build trust with customers, and mitigate the risk of unauthorized access or misuse. Dataset. In our scenario, 1M customer feedback is collected on a set of Microsoft products. For downstream tasks, we are interested in three attributes of the feedback, which we call A(ttribute)1, A2 and A3. Attributes can be a number of product characteristics including, but not limited to, user satisfaction scores, date and time range, product name, product type, location, etc. Using the attributes (A1, A2, A3) together with a particular combination of their respective values, such as (VA1, VA2, VA3), the conditional text generation prompt becomes: "A1: VA1 | A2: VA2 | A3: VA3". We use the GPT2-Large model with the settings described in Section 4.1 in our scenario. Downstream Task Performance. Similar to Section 4.2, to measure the quality of synthetic data, we evaluate the performance of classification models trained on them. We train three classification models, to predict three attributes A1, A2, and A3 with 5, 45, and 5 classes respectively. We present the results in Table 6. We observe that the downstream task models trained on the synthetic data generated by GPT2-Large with DP (ϵ = 4) achieve comparable performance to the ones trained on the synthetic data generated without DP (ϵ = ∞). However, especially for A2, the performance gap between models trained on the synthetic data and the original data is more pronounced in this scenario. This is primarily due to the dataset size, which is roughly half of the one adopted in Section 4 and A2 having a much larger set of classes compared to the other attributes. This highlights the importance of collecting data sufficiently representing each class in scenarios where data contains a high number of sub-classes. Text Length Distribution. We further compare the sequence lengths of the synthetic data generated with and without DP to the original dataset. The results are shown in Figure 4 of Appendix E. We notice a similar phenomenon that the data generated with DP exhibits a length truncation effect compared to the data generated without DP. ## 6 Related Work Synthetic Data Generation with DP. The problem of DP synthetic data generation has been widely studied for tabular and image data in machine learning. Notable works in the literature on DP tabular data generation address the privacyutility trade-off problem by building Bayesian networks (Zhang et al., 2014), by preserving marginals (McKenna et al., 2021), or through training generative adversarial networks with DPSGD (Kunar et al., 2021; Xie et al., 2018; Jordon et al., 2019; Tao et al., 2021). The literature on DP image generation has so far mostly focused on GAN-based methods (Augenstein et al., 2020; Xie et al., 2018; Neunhoeffer et al., 2021). To the best of our knowledge, there are only a few works on DP synthetic text generation. Bommasani et al. (2019) preliminarily outlined potential approaches without going in depth. A concurrent work (Mattern et al., 2022) generates synthetic data by fine-tuning pre-trained LMs with DP on a very small number of training samples (e.g., 25-5K). However, there are significant disparities in terms of methodology and experiment design. In terms of methodology, our approach offers simplicity and practicality for real-world use. We avoid the need to construct templates for different task instructions, and we do not introduce additional prompt-mismatch loss during the fine-tuning of LMs. Regarding evaluations, we not only assess downstream classification but also consider text distribution similarity using various metrics (Section 4.4). Moreover, we include a private Customer Feedback dataset obtained from real practice, alongside the publicly available review datasets (e.g., Yelp). We point out that other one-to-one mapping approaches including both token-level (Weggenmann and Kerschbaum, 2018; Feyisetan et al., 2019, 2020; Xu et al., 2021a,b; Bo et al., 2021; Qu et al., 2021; Yue et al., 2021) and sentence-level (Krishna et al., 2021; Habernal, 2021; Meehan et al., 2022; Weggenmann et al., 2022) perturbations fail to satisfy our privacy requirement outlined in Section 3.1 even though they possess certain DP guarantees themselves. This is because we require that the procedure of synthetic text generation should be statistically similar whether a data sample appears in the original dataset or not. These one-to-one mapping methods focus on producing a perturbed version of a single data sample, therefore, cannot fulfill this requirement. Besides, such one-to-one perturbations cannot meet the requirement of GDPR (Art. 29 WP, 2014) with regard to "linkability" since the data owner can always link the perturbed text to a specific user as long as they keep the user meta record. However, our method can fulfill the requirement as the data owner cannot link any of the generated sequences to a specific user. DP Fine-tuning of Language Models. DP finetuning has been recently demonstrated to be an effective privacy-preserving approach for solving a variety of NLP tasks including text classification, table-to-text generation, dialog generation, and semantic parsing (Li et al., 2022b; Yu et al., 2022; Mireshghallah et al., 2022; Du et al., 2023). However, past works have not studied these techniques for the problem of synthetic text generation. Unlike the above works, we initiate a careful empirical study of private fine-tuning for building synthetic text generation models, measure the different aspects of the approach, and demonstrate its general effectiveness as well as its unique limitations. ## 7 Conclusion In this paper, we present a simple and practical recipe for generating synthetic text data with privacy guarantees. Our method is built upon pretrained language models and differential privacy, where the former enables us to generate highquality synthetic text data and the latter provides formal privacy guarantees that no single example in the training dataset can influence the trained model by a substantial amount probabilistically. We conduct comprehensive experiments evaluating both utility and privacy risks of the synthetic data. The results demonstrate that our method can generate high-quality text while mitigating privacy risks. ## 8 Limitations Through extensive empirical analyses, we demonstrated that our proposed method can produce highutility synthetic text with strong privacy protection. However, we acknowledge there are limitations. Our method captures general statistical properties of the original text but is not able to perfectly replicate all details. DP protects the privacy of individual samples in the original training text, but this means that DP also limits the model in learning the tail of the training distribution (Suriyakumar et al., 2021). Overall, strong DP guarantees render the generation of rare patterns in the original data unlikely. This means that the synthetic text generated from a DP-trained model may potentially miss valuable information conveyed in the outliers of the training text. We observed in our conditional generation studies that DP disproportionally affects classes (corresponding to control codes) with different sample sizes. In particular, tight DP guarantees most negatively impact learning the distribution of small-size classes. Future work may study approaches that mitigate this negative impact for minority populations in private synthetic data generation. We selected values for privacy parameters ϵ = 4 and δ = 1/(N · log N) based on prior privacyutility trade-off studies for text classification and table-to-text generation (Li et al., 2022b; Yu et al., 2021b). We leave it to future work for a more extensive privacy-utility trade-off analysis for general synthetic text generation. Our canary extraction experiments demonstrated that strong DP guarantees lead to strong empirical privacy even for "private" information (the subject) that appears across multiple training instances. However, we note that DP guarantees generally translate into strong empirical privacy guarantees only when individual samples have low or no correlation (Kifer and Machanavajjhala, 2011). It is therefore crucial that DP machine learning be applied in conjunction with other modes of privacypreserving techniques (e.g., data deduplication and redaction (Zhao et al., 2022)) for optimal protection. For deployments of DP synthetic text generation, one should also consider meaningful example boundaries. ## 9 Ethics Statement In this work, we focus on the problem of synthetic text generation with formal privacy guarantees. Our goal is to generate synthetic text that preserves the statistical properties of the original text while also protecting the privacy of individuals. We take the issue of privacy very seriously and have designed our method to ensure that it meets the highest ethical standards. In particular, we have incorporated differential privacy, which is the gold-standard privacy mitigation technique employed in industry and by the US census bureau, to ensure that the synthetic generations do not compromise the privacy of individuals present in the original data. We also recognize that synthetic text generated by our model has the potential to be misused, and we encourage responsible and ethical use of our model. We encourage researchers and practitioners to consider the ethical implications of the method and to follow best practices in data privacy. ## Acknowledgements The authors would thank all the anonymous reviewers for their valuable and constructive comments. The authors would also thank Microsoft and OSU NLP group colleagues for providing suggestions and feedback at different stages of the project. ## References Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. 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In *Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational* Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pages 943–955. ## A Implementation Details And Hyperparameters A.1 Details Of Yelp Dataset We sample 10 frequent business categories and remove the reviews that do not have ratings. 10 categories are: Restaurants, Bars, Shopping, Event Planning & Services, Beauty & Spas, Arts & Entertainment, Hotels & Travel, Health & Medical, Grocery, Home & Garden. ## A.2 Models Trained Without Dp We specify the hyperparameters for the models trained without DP in Table 7. We train all the models without DP on the Yelp dataset with 16 Tesla V100 GPUs and models on the internal feedback data with 2 Tesla A100 GPUs. Model Epochs LR Batch size GPT2 5 5e-5 32 GPT2-M 5 5e-5 32 GPT2-L 5 2e-5 32 Table 7: Hyperparameter setting for models trained without DP. ## A.3 Models Trained With Dp We specify the hyperparameters for the models trained with DP in Table 8. We train all the models with DP on the Yelp dataset with 16 Tesla V100 GPUs and models on the internal feedback data with 2 Tesla A100 GPUs. Table 8: Hyperparameter setting for models trained with DP. ## A.4 **Models For Downstream Text Classification** Tasks | Model | Epochs | LR | Batch size | Clip norm | |---------|----------|------|--------------|-------------| | GPT2 | 50 | 1e-4 | 4096 | 1.0 | | GPT2-M | 25 | 1e-4 | 4096 | 1.0 | | GPT2-L | 20 | 1e-4 | 4096 | 1.0 | We use Roberta-base model for all downstream text classification tasks. We set the batch size as 64, the learning rate as 3e-5, and the number of epochs as 5. ## A.5 Embedding Distance Metrics For Similarity Between Synthetic And Real Data 1) F1 Score (Harmonic mean of Precision and Recall) (Kynkäänniemi et al., 2019). The Precision and Recall estimate the average sample quality and the coverage of the sample distribution by checking whether a generation falls within the surroundings (e.g., k = 3 nearest neighbors) of any original samples (measured by the Euclidean distances) and whether an original sample falls within the surroundings of any generations. 2) Fréchet Inception Distance (FID) (Heusel et al., 2017). The FID score is originally proposed to measure the quality of synthetic images in computer vision. Here we re-purpose it for synthetic text evaluation. It first calculates feature-wise mean and covariance matrices of the embedding vectors and then measures the distance of two sets based on Fréchet distance (Wasserstein-2 distance). 3) MAUVE (Pillutla et al., 2021) compares the distributions of the synthetic data and the original data using divergence frontiers. Specifically, after embedding the text into embedding vectors, it first groups them into several clusters and then counts the cluster assignments to form histograms. Finally, a divergence curve built upon the histograms is plotted and the area under the curve is reported as the metric to measure the gap between the two distributions. ## A.6 Embedding Models For Similarity Between Synthetic And Real Data We run 5 sentence-transformers from huggingface.co: "all-MiniLM-L6-v2", "paraphraseMiniLM-L6-v2", "all-mpnet-base-v2", "stsbroberta-base-v2", "distilbert-base-nli-stsb-meantokens" and take the average for each metric. ## B Canary Sequences We construct 5 types of canary sequences shown in Table 9. To calculate the perplexity rank in Table 4, for each canary type, we construct 10,000 similar candidates by replacing the canary placeholder with another randomly sampled named entity within the same category. The named entity lists are either obtained from the web (e.g., names and addresses) or generated randomly based on patterns (e.g., numbers, emails, license plates). Type Canary Sequence Table 9: The 5 canary sequences inserted into the Yelp training set for the privacy leakage analysis | Name | My name is Chelsey Ann. I had a hard time finding a good restaurant like this. | |---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Address | The food took 6 hours to arrive to 1940 W State St Boise! Like literally! | | Number | Driver couldn't find my house, even tho I told him my number is 208-344-2163 My issue is not with the driver 99% of the time however I just discovered they often send me phishing email to my personal account [email protected] | | Plate | I get my vehicle registered and they give me a new plate D76HTH | ## C Distributions Of Perplexities Of Private Information Of Injected Canary Sequences Figure 5 plots the distributions of perplexities of private information of injected canary sequences among their similar set of candidates measured by GPT2 models trained with and without DP. ## D Synthesize Canary Reviews With Gpt-3 We use the model text-davinci-003 with the prompt *"Write a review talking about beautiful* paintings by Van Gogh in a restaurant" to synthesize canary reviews. To increase the diversity, we try different values of hyperparameters (e.g., top-k/p) and filter duplicates. ## E Sequence Length Distribution Of The Original And Synthetic Data Generated With And Without Dp Figure 4 plots sequence length distributions of the synthetic data generated with and without DP and the original customer feedback data. ## F Sampled Synthetic Data In this section, we randomly sample 15 synthetic examples generated by GPT2, GPT2-Medium, and GPT2-Large in Table 10, Table 11, and Table 12 respectively. ![15_image_0.png](15_image_0.png) ![16_image_0.png](16_image_0.png) | Generated Reviews | Business | Review | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|-----| | Category | Stars | | | | I love sushi! I've never tried a dish on a different menu. We're not going to bother ordering anything else. The only reason I give it 4 stars is that it's not a divey bar where you can't order food, which is not very good. The food is good, especially with the addition of the spicy tuna. It may be good to get some of that spicy stuff when you order in. I'm not the kind of person who likes to eat raw tuna when I can. | Restaurants | 4.0 | | | Great food, atmosphere, and service. This is my go to spot for happy hour and specials. We were | Restaurants | 5.0 | | | given free take out. Everything was delicious and fresh. My boyfriend and I both have high hopes for this place. First, we wanted to order some of the burgers here. We didn't really need to ask. Our waiter suggested we check out the menu because it was pretty close. He said he liked it. My husband and i also ordered their burgers for him. So, my burger was cold, the side fries were undercooked and they just didn I got a side burger, it's like I've been drinking so long to get a second burger and it barely came out. | Restaurants | 2.0 | | | I don't want to waste a review on a place I love and can't stand, but the restaurant is very nice and the food is good. I will be back. The food was very good, although the portions were a little high, and it didn't take long to get the bowl of gumbo, the chicken queso, some meat. However, as for the drinks, they were decent, however I'm a sucker for a big bottle of water with a straw. We got the shrimp tartar and my husband got a cocktail | Event | Plan | | | ning & Services | 4.0 | | | | If you are looking for a place to sit outside at night, I would highly recommend this place. The drinks are good and the atmosphere is chill and fun. I have been coming here for years for the best wine at an affordable price. | Arts & Entertainment | 5.0 | | | After a few years, my family and I decided to try this property. The staff was friendly and accommodating. We rented a room on a whim (which wasn't super exciting since we already had it) and the hotel was ready for our new home. What can I say? So we were not only greeted and greeted at the door, but also told how much we love them. My daughter and her boyfriend both agreed to check them out on our own and left feeling satisfied. | Hotels | & | 5.0 | | Travel | | | | | Horrible hotel. The hotel was built in 1914. It's a complete lie. I stayed on a Sunday morning. Two people were on the first floor, and the second floor was locked and was not accessible. When we were finally allowed to get a seat on my two couches, we got kicked by one of the front desk. The staff here are very rude. This hotel is on fire. Even the owners are rude and don't know what they're doing. My husband stayed at the hotel for 3 months with his friend. We have NEVER | Hotels | & | 1.0 | | Travel | | | | | So glad we took our Yelp search into the realm of authentic Italian food. I went here for the first time today and ordered a Caesar salad. The Caesar dressing was fresh and a tasty addition to the salad and also very good. Definitely recommend the meatloaf as well. My only complaint would be the price, it was very over priced. For the amount of meat I was eating I'd expect the same amount. For my $50+ Caesar Salad I had to give them a try! Good quality food, good prices and good service. | Restaurants | 4.0 | | | This place is great. The gel manicure is super friendly and all the staff is very helpful. I would | Beauty | & | 5.0 | | definitely go back here and recommend it to anyone! | Spas | | | | I'm going to give five stars because this place is BYOB. It's a little over two blocks from my house. Food is awesome, service is outstanding, drinks are decent. I've never had a bad meal here. They have a very reasonable price point for an authentic Chinese food. | Restaurants | 5.0 | | | Service was slow but the customer service was awful! The room was filthy, there was no shower | Hotels | & | 1.0 | | and there wasn't even a lamp on the wall, it was in a dirty room with dirty sheets. | Travel | | | | I ordered a cheesesteak and it had a mild flavor to it but nothing amazing. I also ordered the | Restaurants | 2.0 | | | blackberry and bacon and I didn't get much flavor either. I had a great time and the service was great. Very friendly. I will def come back here again! | Restaurants | 4.0 | | | Just bought a car and we were looking for something different to eat there. I don't recommend anything on this menu unless your in the mood for a decent meal. My order was prepared ahead of time. The food was well done, with the right amount of flavor. For comparison, this might be better than a burger: it's $7 and you'll need a few extras. | Restaurants | 3.0 | | | Delicious! A perfect brunch spot for lunch, brunch or dinner. Try the shrimp and grits. | Restaurants | 5.0 | | | Table 10: Randomly sampled synthetic reviews generated by the GPT2 model trained with DP. | | | | | Generated Reviews | Business | Review | | |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|-----| | Category | Stars | | | | I've tried a few burgers and it's ok. I don't eat fries (I never do) so don: put them on your salad | Restaurants | 3.0 | | | or whatever else you have on hand. I have been here many times for brunch and dinner. This place is one of the best BBQ spots around! They also have many amazing burgers on the | Bars | 5.0 | | | menu. The food is always hot and always tasty. One of the best concert venues in Reno. Great space and the sound and lighting is amazing. The | Arts & Entertainment | 5.0 | | | sound guys in the stadium really help to get you into the atmosphere with your music and sound. We love this place. It has a variety of options in the menu, but I always get the fried chicken which is definitely a better option. If you don't like fried food, there is a decent selection of regular chicken. You could also choose to get their bbq, which I am not a fan of, and get a burger. | Restaurants | 3.0 | | | Love the new decor. The new tables are all wood. You don't feel like sitting on an old bed anymore. They even put their old fireplace on the inside. Food was OK - I like the steak house. I liked that you can customize the menu to your taste. The drinks are better too - especially the gin martinis. | Restaurants | 4.0 | | | Ordered a bunch of items, then received confirmation from my Santa that she had already shipped the items. She did that as I was in the middle of a drive-thru. When I got home I immediately called the store and asked what the order was for. They said that they had ordered a lot of stuff (which is nice) and they wanted to be sure. I said, "Well, what's in it for me?" They told me it would take a little bit to get out, but when I left they said they would send me another box. | Shopping | 4.0 | | | This place is a perfect addition to the community. You get a chance to enjoy some outdoor fun and enjoy all the outdoor activities that you'll find in the surrounding area. The staff is attentive and professional. It's a great place to hang out while having a blast. | Arts & Entertainment | 4.0 | | | I ate here today. My wife and I were in the area. I ordered the "Gumbo Sushi". This was a good value considering the size of the bowl. It was cooked perfectly and the rice was fresh. This place is very well run, friendly and has a great variety of sushi! | Restaurants | 5.0 | | | We went here to be checked out. I had gone in about 1 1/2 months before. We asked about getting an appointment and were told they had no one there that could help us and we just had to go to the front desk and ask. They took care of us right away. Their nurse was super nice and helped us with our appointment. She even made sure that we made it into the room without us knowing, and the COG were there to keep me calm during my appointment which was awesome! I would highly recommend this place. The room is | Health | & | 5.0 | | Medical | | | | | The food was awesome and friendly. Our server was excellent. I loved that the server wasn't intrusive with my order. The restaurant was clean and a lot of fun. If I could make it back here, I would. We will be back next time I'm in Tucson | Restaurants | 5.0 | | | I'm not a fan of Italian cuisine but this was very good. We had the spaghetti and meatballs, but they were also very tasty. Also had a meatball with bacon on top. The food is very inexpensive and very authentic, and the atmosphere is fun and intimate. We will definitely be back! | Restaurants | 5.0 | | | Was expecting a classy place for a casual date night and was disappointed. The drinks are not | Bars | 1.0 | | | worth it. And the service was horrible! We had a really good time with the team. They were friendly and the service was great. I had the shrimp tacos which were a total keeper. My boyfriend had his "Tacos" and he said they were delicious. The chips and salsa were good too. If your looking for some great local eats in Indy, I highly recommend this place. | Restaurants | 5.0 | | | I was looking for a spot to meet friends and I came across this beautiful place! Very quaint and intimate and the service was great as well. Our table was very small but it was fine as the chairs were just the right height to comfortably recline. I highly recommend this place. Will definitely be back! | Arts & Entertainment | 5.0 | | | I love the food here. It's a bit pricey. My wife and I had an amazing experience there. The place is a great size, it was busy, and we ordered take out. There was also a server who was kind enough to come over, take our order, etc. After about 5 minutes, the waitress came back and said she would make our food for us. This is our first time there, so I think we should make sure we do not order wrong. We asked for the pork and the rice and she said they were out of rice | Restaurants | 2.0 | | | Table 11: Randomly sampled synthetic reviews generated by the GPT2-Medium model trained with DP. | | | | | Generated Reviews | Business | Review | | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|----------|-----| | Category | Stars | | | | Pleasant experience. Great food and great service. Good music and the live music really helped | Bars | 4.0 | | | bring out the crowd. Nice, clean place to grab a bite. My boyfriend and I both order the chicken quesadilla, which comes with 3 pieces of chicken, 2 fried tortillas, sour cream, rice, and a guacamole. It comes out in about 5 minutes, the tacos are pretty good and the quinoa is a bit sweet for my taste. Our server was pretty nice, but was not very friendly or helpful. We're all pretty tired by the time we get to our table so we didn't want to spend the extra money. I don't know if my boyfriend got a bad batch of food | Restaurants | 2.0 | | | The dentist office at DDS was great. They were very professional and gave a great service. I've had numerous dental problems over the years, so I was happy to see that the dentists they employ are so professional. The only reason I gave them three stars is that there is no phone calling service to call for follow-up, and their website is so poor that I couldn't call and they'd have the call placed over an hour later. | Health | & | 3.0 | | Medical | | | | | One of the best sushi places in the city! I usually get the chicken and fish roll! It is so fresh and has so much flavor! The service is excellent. They have a nice selection of beer and drinks. I highly recommend this place to everyone who loves sushi. | Restaurants | 5.0 | | | The food is phenomenal. The portions are generous. And the service is excellent. | Restaurants | 5.0 | | | I'm so glad I tried The Little Noodle. I've had the chicken curry and the pad thai. It's so good. | Restaurants | 5.0 | | | There was a small part of me that wanted to try the curry but I was too full. My first time at this spot. They were very friendly and accommodating. The place was clean and | Bars | 5.0 | | | the service was excellent. I will be coming back! I had a burger and fries. Food was really good! I wish they had a more modern menu but the food is so fresh it would | Restaurants | 4.0 | | | take a long time for me to go back. Great prices too. This place should be called Hotdog King because of the price. The food wasn't the best, the burgers were ok, but the whole menu was way too much to consume in one meal. My friend went with her boyfriend and ordered two different burgers. We ordered the cheesesteak medium rare. We waited another 5 minutes before the waiter came to take our food. He took our order and then asked if we wanted our drinks and food brought out. I didn't realize they only have a microwave and microwave oven. It wasnt even hot | Hotels | & | 1.0 | | Travel | | | | | This place is an awesome experience! The owner and manager were so friendly, friendly and knowledgeable. There were plenty of great options to choose from and I loved every single meal I had! I will definitely be returning to this wonderful spot. | Event | Plan | | | ning & Services | 5.0 | | | | Food and service was great. Food was just average and very mediocre. The place was pretty | Restaurants | 3.0 | | | empty, so if you go to check it out be prepared to wait. Just ordered the "special" platter of 6 shrimp, 5 wings, and a small drink. The platters are big | Restaurants | 5.0 | | | enough to share, which is a nice touch for two people. I'm not sure what happened to these girls, but every time I walk in and ask for a gel manicure I'm treated with indifference. I have gone in 3 times and never been offered gel or cuticles or anything of the kind. It's just a horrible experience that can leave you feeling very unorganized and unappreciated. I had the worst experience with two different ladies, both of whom are very nice and have done a great job with my nails. The third time was very disappointing. Both ladies seemed to be very frustrated | Beauty | & | 1.0 | | Spas | | | | | If you want a good Cuban, get the ones in West Chester. It's always the same thing. Great | Restaurants | 4.0 | | | service, delicious food and a great price. I've been there twice and can't say enough good things about it. The food was absolutely delicious. We ordered the "Biscuits" and "Mac & cheese". I am not sure why the mac and cheese is a biscuit but it was AMAZING! I would recommend coming here and eating it as your meal. This is the first time I've tried out this restaurant and it's definitely my new spot to stop in. | Bars | 5.0 | | | Table 12: Randomly sampled synthetic reviews generated by the GPT2-Large model trained with DP. | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 9 ✓ A2. Did you discuss any potential risks of your work? 10 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** 4,5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix A The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix A ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 4.1 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

chen-etal-2023-close

A Close Look into the Calibration of Pre-trained Language Models

https://aclanthology.org/2023.acl-long.75

Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. We take a close look into this problem, aiming to answer two questions: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? For the first question, we conduct fine-grained control experiments to study the dynamic change in PLMs{'} calibration performance in training. We consider six factors as control variables, including dataset difficulty, available training samples, training steps, the number of tunable parameters, model scale, and pretraining. We observe a consistent change in calibration performance across six factors. We find that PLMs don{'}t learn to become calibrated in training, evidenced by the continual increase in confidence, no matter whether the predictions are correct or not. We highlight that our finding somewhat contradicts two established conclusions: (a) Larger PLMs are more calibrated; (b) Pretraining improves model calibration. Next, we study the effectiveness of existing calibration methods in mitigating the overconfidence issue. Besides unlearnable calibration methods (e.g., label smoothing), we adapt and extend two recently proposed learnable methods that directly collect data to train models to have reasonable confidence estimations. Experimental results show that learnable methods significantly reduce PLMs{'} confidence in wrong predictions.

# A Close Look Into The Calibration Of Pre-Trained Language Models ## Abstract Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. We take a close look into this problem, aiming to answer two questions: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? For the first question, we conduct fine-grained control experiments to study the dynamic change in PLMs' calibration performance in training. We consider six factors as control variables, including dataset difficulty, available training samples, training steps, the number of tunable parameters, model scale, and pretraining. We observe a consistent change in calibration performance across six factors. We find that PLMs don't learn to become calibrated in training, evidenced by the continual increase in confidence, no matter whether the predictions are correct or not. We highlight that our finding somewhat contradicts two established conclusions: (a) Larger PLMs are more calibrated; (b) Pretraining improves model calibration. Next, we study the effectiveness of existing calibration methods in mitigating the overconfidence issue. Besides unlearnable calibration methods (e.g., label smoothing), we adapt and extend two recently proposed learnable methods that directly collect data to train models to have reasonable confidence estimations. Experimental results show that learnable methods significantly reduce PLMs' confidence in wrong predictions. The code is available at https://github. com/lifan-yuan/PLMCalibration. ## 1 Introduction Pre-trained language models (PLMs) are successful in many downstream tasks regarding performance (Wang et al., 2019). In high-stake applications, it's equally essential for PLMs to possess a sense of calibration (Vaicenavicius et al., ∗Equal contribution # Yangyi Chen∗ Uiuc Lifan Yuan∗ Hust Ganqu Cui, Zhiyuan Liu Tsinghua University Heng Ji Uiuc [email protected] [email protected] ![0_image_0.png](0_image_0.png) Figure 1: The demonstration of the under-fitted and over-fitted states in the training process with RoBERTa on SST-2. 2019). However, the confidence scores (a.k.a, predictive probability) of existing deep neural networks cannot serve as reliable estimates of their uncertainty (Guo et al., 2017), and a deep understanding of PLMs calibration is lacking. In this paper, we give a systematical analysis of PLMs calibration. We consider two questions about PLMs calibration: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? We first introduce the metrics we adopt for calibration performance evaluation. The most widely used calibration metric ECE (Expected Calibration Error (Naeini et al., 2015)) is considered. It measures the difference between confidence and accuracy by portioning samples into various confidence zones. To give a more comprehensive and practical calibration evaluation, we provide an application-driven perspective, describing two undesirable situations in practice: (1) Correct predictions (positive) are rejected due to low confidence; (2) Wrong predictions (negative) are accepted due to high confidence. We propose to measure the average confidence scores on correct and wrong predictions respectively to characterize undesirable situations. Two kinds of calibration errors are measured, denoted as CErrpos and CErrneg. For the first question, we consider the influ1343 ence of six factors on PLMs' calibration performance, including dataset difficulty, available training samples, training steps, the number of tunable parameters, model scale, and pretraining. Some of them are overlooked in previous empirical studies (Snoek et al., 2019; Nixon et al., 2019; Minderer et al., 2021). We motivate to conduct finegrained control experiments to study the dynamic change in PLMs' calibration performance in training through manipulating control variables. We empirically observe an overall consistent change in calibration performance across six factors. All six factors influence PLMs' fitness on the training distribution. This results in two states of PLMs considering calibration performance, namely under-fitted and over-fitted states (see Fig.1). In the under-fitted state, PLMs' performance and confidence increase at different speeds when more fitted on the training distribution. In the over-fitted state, PLMs' confidence continues to increase steadily with little change in performance. **We find evidence that PLMs don't learn** to become calibrated in training: PLMs' confidence in their predictions continues to increase when more fitted on the distribution (e.g., more tunable parameters, training longer). This results in two miscalibration behaviors: (1) Increasing ECE in the latter over-fitted state, and (2) Continually increasing confidence in wrong predictions, indicating that PLMs mostly don't know "what they don't know". We highlight our finding presents contradictory views with the two established conclusions: (a) Larger PLMs show better calibration (Srivastava et al., 2022); (b) Pretraining improves model calibration (Hendrycks et al., 2019b). We identify that the inconsistency lies in: (1) The difficulty of evaluation datasets: the performance doesn't saturate in the considered datasets (e.g., BIG-bench (Srivastava et al., 2022)). Thus, the evaluation is on the under-fitted state, leaving the miscalibration behavior in the over-fitted state unobserved; (2) Evaluation metrics: previous work doesn't measure the confidence in wrong predictions, overlooking the fact that models are becoming more confident in wrong predictions when scaling larger and employing pretraining. Thus, we find that the main issue of PLMs calibration lies in their overconfidence in wrong predictions, which cannot be trivially solved by increasing the model scale. So we consider the effectiveness of existing calibration methods in mitigating the overconfidence issue. We partition existing calibration methods into unlearnable and learnable groups. Unlearnable methods heuristically manipulate the original confidence in predictions (e.g., label smoothing). Learnable methods directly collect data and train models to give reasonable confidence scores in their predictions. Namely, an extra calibration task is introduced, which aims to extract features from samples and models' preceding performance to predict whether models' predictions are correct or not. In our experiments, we identify the superiority of learnable methods compared to unlearnable ones, considering both in-distribution (ID) and out-of-distribution (OOD) settings. This is characterized by a sharp decrease in their confidence in wrong predictions when using learnable methods, indicating that they significantly mitigate the overconfidence issue. Moreover, learnable methods can maintain a reasonable increase in CErrpos, holding consistent correlations between the drop in confidence and performance under distribution shifts. This shows the difference from unlearnable methods, which take effect by roughly imposing confidence regularization on models' predictions (e.g., label smoothing), resulting in almost the same amount of increase in CErrpos with the decrease in CErrneg. To further understand learnable calibration methods, we consider the influence of more data and larger model scales for the calibration task, the adopted model for the calibration task, and the data distribution, on PLMs' calibration performance. We highlight three findings: (1) More data and larger model scales for the calibration task both play significant positive roles in PLMs' calibration performance; (2) PLMs can be trained to give their uncertainty. This finding is consistent with the concurrent work (Lin et al., 2022). Further, we provide an extension to this conclusion. We find that using an extrinsic predictive model can achieve comparable results, given the same calibration training data. Thus, we identify that the success of this paradigm essentially lies in the learnable attribute of the calibration task, instead of the PLMs' self-checking process; (3) PLMs' calibration performance under distribution shifts depends on the evaluation datasets chosen. Previous work shows that PLMs exhibit degraded calibration performance under distribution shifts (Desai and Durrett, 2020). We find that this conclusion is reversed when the ID datasets are harder and PLMs achieve better performance on OOD datasets. The concrete arguments and explanations are detailed in Appendix E. ## 2 Background Calibration measure. We can visualize model calibration through reliability diagram (DeGroot and Fienberg, 1983). Based on the diagram, we can measure the ECE (Naeini et al., 2015) by partitioning samples into different confidence zones. The central idea is to measure the absolute difference between models' predictive confidence and accuracy. Although alternative theoretic-motivated metrics have been proposed (Vaicenavicius et al., 2019; Gupta et al., 2021), we still employ ECE in our experiments due to its simplicity and popularity. Benchmark & Analysis. Given appropriate evaluation metrics, large-scale benchmarks have been conducted to analyze model calibration under different settings, spanning model architectures (Guo et al., 2017; Minderer et al., 2021), model scales (Dan and Roth, 2021), modalities (Desai and Durrett, 2020; Minderer et al., 2021; Kadavath et al., 2022), calibration methods (Guo et al., 2017; Desai and Durrett, 2020), and distribution shifts (Nixon et al., 2019; Kong et al., 2020). Our work is closely related to Xiao et al. (2022) that quantifies the uncertainty of PLMs. However, previous benchmarks follow the fixed training and evaluation paradigms. In this paper, we instead conduct a fine-grained and more comprehensive empirical evaluation to take a close look into PLMs calibration from multiple dimensions that have often been overlooked. Also, we consider and conduct a detailed analysis of the recently proposed learnable calibration methods (Lin et al., 2022; Kadavath et al., 2022). Method. Calibration is essential for out-ofdistribution detection (Hendrycks et al., 2019a), selective prediction (Varshney et al., 2022), robustness (Kumar et al., 2022), and pseudolabeling (Rizve et al., 2021). Existing calibration methods can be partitioned into unlearnable and learnable groups. For unlearnable methods, there are mainly four categories. Post-hoc calibration intends to readjust the output logits referring to the performance on a held-out validation set (Platt et al., 1999; Guo et al., 2017). Regularization methods aim to prevent models from being over-confident on predictions (Szegedy et al., 2016; Pereyra et al., 2017). Data augmentation (Hendrycks et al., 2020; Wang et al., 2021) and model ensemble (Gal and Ghahramani, 2016; Lakshminarayanan et al., 2017) have also been empirically proven to improve model calibration. For learnable methods, the typical way is to first collect data for the calibration task, and then train a model to predict whether the given answer is correct. The model can be a multi-layer perceptron, and the features can be hand-engineered (Ye and Durrett, 2022; Zhang et al., 2021b; Si et al., 2022) or the last hidden states of PLMs (Kadavath et al., 2022). PLMs can also be directly trained to output their uncertainty by words (Lin et al., 2022). ## 3 Evaluation Metrics For basic evaluation, we report accuracy (Acc) and average confidence score (Conf) on the testing set. For calibration evaluation, we report ECE using equal-mass binning and 100 bins following Minderer et al. (2021). Besides, we provide an application-driven perspective to evaluate model calibration, aiming to quantify two unsatisfied scenarios due to miscalibration in practice: (1) Correct predictions (positive) are rejected due to low confidence; (2) Wrong predictions (negative) are accepted due to high confidence. Specifically, we consider the average confidence in correct predictions **Conf**pos and wrong predictions **Conf**neg respectively. For unified comparison, we report two calibration error (CErr) cases, CErrpos = 1 − Confpos and CErrneg = Confneg. In principle, we expect calibrated models to have both low CErrpos and CErrneg, indicating that they reasonably assign high confidence in correction predictions and low confidence in wrong predictions. ## 4 Do Plms Learn To Become Calibrated? 4.1 Experimental Setting For model architectures, we choose RoBERTabase (Liu et al., 2019) and T5-base (Raffel et al., 2020), since they represent two classic types of PLMs, namely encoder-only and encoder-decoder models. We experiment with four representative tasks in NLP, including sentiment analysis, natural language inference, news classification, and topic classification. For datasets, we choose SST2 (Socher et al., 2013a), MNLI (Williams et al., ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) 2018a), AG-News (Zhang et al., 2015), and Yahoo (Zhang et al., 2015) respectively. We employ the prompt-based learning paradigm (Liu et al., 2021) since its superior performance compared to traditional fine-tuning, especially in the few-shot setting. Specifically, we inherit the masked language modeling task in the pre-training stage and use templates to wrap samples into prompts. We fine-tune the whole PLMs to fill in the [mask] position in the prompt. The manual template and verbalizer for each dataset are listed in Appendix A. ## 4.2 Experimental Results We conduct a fine-grained control study to explore the influence of six factors, including dataset difficulty, available training samples (Fig.2), training steps (Fig.3), number of tunable parameters (Fig.4 and Fig.10), pretraining (Fig.6), and model scale (Fig.5). Due to space limits, we show the corresponding results of RoBERTa and results of T5 on AG-News in Appendix B. We summarize the overall conclusions and leave the detailed experimental settings and findings in Appendix B. We note that all six factors dynamically influence PLMs' fitness on the training distribution, which we identify as the decisive factor of PLMs' calibration performance. We observe an overall consistent change in calibration performance across six factors, resulting in two PLMs' states (see Fig.1) in training: Under-fitted state. In this state, PLMs' performance and confidence increase at different speeds when more fitted on the training distribution. The ECE score fluctuates during this process. In principle, miscalibration is due to the mismatch between performance and confidence. However, we look closely into some critical points where ECE changes sharply (e.g., Fig.2), and empirically find that the increase or decrease in ECE can be estimated by comparing the increasing rates of PLMs' performance and confidence. We observe that a larger (smaller) increasing rate in performance reduces (increases) ECE. Thus, high ECE can be partially attributed to PLMs' relatively rapid growth in confidence with performance lagging behind. Over-fitted state. In this state, PLMs' performance doesn't have a substantial difference due to their generalization ability (Zhang et al., 2021a). However, PLMs' confidence continues to increase in this state, resulting in increasing ECE. This is especially obvious when more training steps and tunable parameters are introduced (see Fig.3 and Fig.4). Thus, being more fitted on the training dis- ![4_image_0.png](4_image_0.png) ![4_image_1.png](4_image_1.png) Figure 5: Results of increasing PLMs scales with T5. tribution may bring a negative effect on PLMs calibration. In addition, due to the increase of ECE in this state, the evaluation of calibration performance may be sensitive to the training paradigm. This indicates that previous conclusions drawn from empirical studies should be carefully examined since the training paradigms may be different in model architectures and calibration methods. Given the two states observed, we conclude that **PLMs don't learn to become calibrated in** training, evidenced by the continually increasing confidence in predictions, no matter correct or not, in the fitting process. Specifically, this results in two miscalibration behaviors: (1) Increasing ECE in the over-fitted state; (2) The consistent increase in CErrneg throughout the whole training process. This is an undesirable property in practice since users may accept wrong predictions due to their high confidence, and indicates that PLMs mostly don't know "what they don't know". We highlight two of the considered factors, namely pretraining and model scales (Fig.5 and Fig.6), which are examined in previous work. Our findings present some contradictory views with the established conclusions: (1) Larger PLMs show better calibration (Srivastava et al., 2022); (2) Pretraining improves model calibration (Hendrycks et al., 2019b). Actually, scaling larger and employing pretraining are both strategies to increase PLMs capacity, making them more fitted on the training distribution. Our general conclusion can also be applied. We highlight two observations: (1) Essentially, the influence of scaling larger and pretraining on PLMs calibration is dynamically determined by the relative increase in performance and confidence, which is highly relevant to the chosen evaluation datasets. For example, the original scaling experiments are conducted on BIGbench (Srivastava et al., 2022), in which the performance is far from saturation and increasing the model scale brings substantial improvement to PLMs performance. This shows consistency with the identified under-fitted state. However, when the performance score saturates on evaluation datasets given the certain scale of PLM, scaling larger will only bring up confidence. This results in increasing ECE due to the mismatch between two trends (e.g., T5 and RoBERTa on Yahoo); (2) Scaling larger and employing pretraining consistently bring CErrneg higher. This indicates that these two strategies don't enable PLMs to learn to become calibrated in the training process. Random LSTM TF-IDF BoW Figure 6: Results of the pretraining influence with T5. ![5_image_0.png](5_image_0.png) ![5_image_1.png](5_image_1.png) ## 5 How Effective Are Existing Methods? 5.1 Calibration Methods We choose representative calibration methods from each category summarized in Sec. 2. For unlearnable methods, we consider vanilla finetuning (Vanilla), temperature scaling (TS) (Guo et al., 2017), label smoothing (LS) (Szegedy et al., 2016), easy data augmentation (EDA) (Wei and Zou, 2019), and deep-ensemble (Ensemble) (Lakshminarayanan et al., 2017). For learnable methods, an extra calibration task is introduced, aiming to train a model to predict whether the original predictions are correct or not. Each sample in the dataset of the calibration task consists of the original input, the model's original prediction, and the label indicating whether the original prediction is correct or not. We adopt the validation set to generate the training set for the calibration task. We describe the specially designed training paradigms of different methods in the following paragraph and leave the detailed construction process of the calibration training dataset in Appendix C. For better clarification, we use the main task to denote the original task. The predictive model for the calibration task can be a separate extrinsic model that we use "E-" for denotation. Specifically, we adapt the method proposed in Kadavath et al. (2022) that uses MLP as the extrinsic model (E-MLP) and the inputs are the hidden states of the main task model. Based on a similar intuition, we extend this method by using an extra T5 as the extrinsic model (E-T5). An example of the template to wrap the sample into an input prompt is: "<original input>, the model's prediction is <prediction>, is the prediction True or False? It's <mask>." The probability of the "True" class in the calibration task is deemed as PLMs' confidence in their predictions. The concrete manual template and verbalizer of the calibration task for each dataset are listed in Table 11. Besides, the main task model can also be directly employed to perform the calibration task. We deem this paradigm as the intrinsic one, denoted as "I-". Lin et al. (2022) show that GPT3 (Brown et al., 2020) can be trained to output the uncertainty by words. We adapt this method by first training the model using the main task data, and then continuing the training by using the calibration task data (I-Vanilla). However, this continual learning paradigm may result in degraded performance in the main task according to our results. To tackle this, we propose two more practical intrinsic calibration methods through modifying the training paradigm. Specifically, we train PLMs iteratively (I-Iter) or simultaneously (I-Simul) on the original task and the calibration task. The latter can be achieved due to the unified text-to-text training paradigm. The input is the same as E-T5. ## 5.2 Experimental Setting PLMs are expected to tackle out-of-distribution (OOD) samples in practice, particularly in the presence of adversarial attacks (Chen et al., 2022). Thus, we experiment with both in-distribution (ID) and OOD settings. We consider natural language inference, sentiment analysis, and hate-speech detection tasks due to their wellestablished OOD datasets in NLP. Specifically, we choose MNLI (HANS, ANLI), Amazon (SST-5, SemEval), and Civil (Hate Speech, Implicit Hate) as the ID (OOD) datasets. The references and detailed descriptions of chosen datasets for ID and OOD evaluation are in Appendix A. ## 5.3 Experimental Results The results are listed in Table 1 (T5) and Table 4 (RoBERTa). We summarize the overall conclu- ![6_image_0.png](6_image_0.png) Dataset Amazon SST-5 SemEval Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Vanilla 86.50 94.85 8.35 3.47 84.12 55.06 92.36 37.30 5.96 90.30 31.31 85.58 54.27 16.22 86.41 TS 86.50 89.22 **2.75** 8.44 74.22 55.06 83.99 28.93 14.36 81.97 31.31 75.48 44.17 26.87 76.56 LS 86.19 85.53 3.41 13.06 76.74 56.94 83.74 **26.80** 16.19 83.64 30.50 77.71 47.21 23.77 78.36 EDA 86.29 95.44 9.15 **3.06** 86.01 52.73 92.24 39.50 **4.61** 88.72 30.34 87.45 57.11 **13.86** 88.03 Ensemble **86.54** 94.82 8.28 3.53 84.22 56.52 91.90 35.38 6.72 90.15 **31.41** 85.49 54.09 16.49 86.40 E-MLP 86.50 89.28 5.52 10.69 89.10 55.06 87.38 32.34 12.59 87.34 31.31 81.65 50.74 18.39 81.66 E-T5 (ours) 86.50 79.43 12.24 15.35 45.84 55.06 78.74 35.30 19.11 75.97 31.31 41.67 38.68 65.84 45.11 I-Vanilla 85.58 78.40 12.45 15.69 43.33 53.55 68.34 33.38 27.48 63.53 **31.41** 40.92 38.30 65.43 43.82 I-Iter (ours) 86.30 70.86 15.49 24.07 38.95 57.12 74.92 28.39 22.16 71.02 30.69 37.02 **28.37** 68.84 **39.62** I-Simul (ours) 86.53 76.50 17.65 17.15 35.64 **57.15** 80.26 38.64 15.85 75.08 30.66 38.65 46.06 68.40 41.76 Vanilla 91.00 95.65 4.86 2.97 82.05 **69.73** 82.78 13.52 12.30 71.72 55.03 76.83 21.75 17.54 69.94 TS 91.00 90.50 **1.39** 7.74 73.20 **69.73** 71.98 **4.94** 23.01 60.69 55.03 65.45 **10.37** 29.14 58.83 LS 91.25 85.75 6.78 13.14 74.09 70.67 73.50 5.55 22.53 63.95 53.57 69.79 16.23 25.65 64.53 EDA 92.00 96.29 4.29 **2.51** 82.46 67.67 87.58 20.20 **7.97** 78.27 **57.27** 83.11 25.96 **11.87** 76.40 Ensemble 91.57 95.78 4.21 2.88 81.14 69.35 83.00 13.66 12.13 72.00 56.34 77.81 21.47 16.52 70.49 E-MLP 91.00 91.34 5.13 8.66 91.31 **69.73** 84.06 14.73 16.04 84.28 55.03 75.87 20.83 24.17 75.91 E-T5 (ours) 91.00 70.36 20.65 23.02 3.40 **69.73** 35.23 38.72 57.70 18.95 55.03 27.61 28.30 58.42 10.50 I-Vanilla 89.14 70.03 19.11 21.79 **2.91** 68.23 32.70 38.85 58.35 **13.49** 42.52 21.53 21.80 55.84 **4.79** I-Iter (ours) **92.20** 72.66 19.54 21.66 5.58 70.67 33.17 38.49 60.59 18.13 55.38 26.91 28.86 59.90 10.52 I-Simul (ours) 91.87 71.72 20.15 22.38 5.09 69.54 31.45 38.26 61.73 15.88 55.28 26.35 29.37 60.57 10.17 Dataset Civil Hate Speech Implicit Hate ![6_image_6.png](6_image_6.png) Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg ![6_image_7.png](6_image_7.png) Vanilla 86.08 94.23 7.74 3.88 82.12 75.52 92.54 17.23 5.88 87.72 60.64 89.68 28.83 8.62 87.04 TS 86.08 89.65 **3.16** 7.79 73.27 75.52 86.29 11.13 11.60 79.84 60.64 82.24 21.38 15.49 78.71 LS 86.30 84.93 5.29 13.62 75.78 74.48 83.51 **9.03** 14.65 78.15 60.64 81.19 **20.55** 17.36 78.95 EDA 86.87 95.46 8.59 **3.09** 85.83 73.64 95.20 21.56 **3.57** 91.75 **61.95** 92.92 30.97 **5.78** 90.80 Ensemble 86.04 94.51 8.46 3.65 83.10 75.36 93.57 18.80 5.04 89.35 60.83 90.98 30.14 7.50 88.62 ![6_image_8.png](6_image_8.png) E-MLP 86.08 90.61 4.52 9.40 90.62 75.52 88.93 13.41 11.13 89.10 60.64 87.41 26.78 12.59 87.42 E-T5 (ours) 86.08 66.22 19.87 23.24 0.99 75.52 41.80 46.42 55.51 33.51 60.64 25.28 40.27 64.82 10.02 I-Vanilla 75.31 63.39 11.92 15.95 0.35 **75.73** 39.32 48.19 57.19 **28.43** 56.39 22.68 38.30 65.48 **7.38** I-Iter (ours) 86.58 69.04 17.53 20.50 1.61 74.06 45.69 44.92 52.14 39.52 61.29 29.05 38.67 60.89 13.11 I-Simul (ours) **87.06** 70.69 16.55 19.04 1.62 73.01 46.63 46.34 50.30 38.31 61.14 30.50 40.17 58.65 13.44 Dataset MNLI HANS ANLI ![6_image_1.png](6_image_1.png) ![6_image_2.png](6_image_2.png) ![6_image_3.png](6_image_3.png) ![6_image_4.png](6_image_4.png) Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg sions as follows: All calibration methods have negligible influence on PLMs' performance in the ID and OOD settings except I-Vanilla. However, PLMs are significantly less calibrated under considered distribution shifts, especially on challenging datasets due to the severe mismatch between performance and confidence. For example, the vanilla T5 achieves only 30.53% accuracy on ANLI, but its average confidence is up to 93.77%. For ID evaluation, we observe lower ECE, consistent with Desai and Durrett (2020). However, the conclusion that PLMs are calibrated on ID data (Desai and Durrett, 2020) is questionable given our answer to the first question (see Sec. 4). The low ECE can be attributed to their high performance on ID datasets and consistently assigning high confidence scores to their predictions. We further show the conclusion that PLMs calibration degrades under distribution shifts is one-sided and heavily depends on the evaluation datasets chosen in Appendix E. Unlearnable methods. We summarize the findings as follows: (1) Data augmentation and model ensemble don't bring substantial benefits to PLMs calibration, considering the three calibration metrics spanning all evaluation datasets and two PLMs. The reason lies in their inability ![6_image_5.png](6_image_5.png) ![6_image_9.png](6_image_9.png) ![6_image_10.png](6_image_10.png) ![6_image_11.png](6_image_11.png) ![6_image_12.png](6_image_12.png) ![6_image_13.png](6_image_13.png) ![6_image_14.png](6_image_14.png) ![6_image_15.png](6_image_15.png) to relieve the overconfident issue, resulting in the same Cerrneg with the vanilla fine-tuning; (2) TS achieves overall better ECE, maintaining a strong baseline method, with LS being the second effective method for the unlearnable category. This is consistent with previous empirical studies (Nixon et al., 2019). However, we can observe almost the same amount of increase in CErrpos with the decrease in CErrneg. The reason is that these two methods directly impose confidence regularization on predictions, which don't actually make PLMs have clear confidence estimations. Learnable methods. Compared to unlearnable methods, learnable ones significantly mitigate the overconfidence issue, reflected in the sharp decrease in CErrneg, indicating that learnable methods output very low confidence in wrong predictions. But we also observe that learnable methods lower the confidence in correct predictions, resulting in increasing CErrpos and ECE. However, we highlight two observations indicating that learnable methods essentially teach models to have clearer confidence estimations, instead of roughly reducing the confidence like LS: (1) Compared to the vanilla version, the Dataset Size Dataset Amazon SST-5 SemEval | Small Middle Large | |----------------------| Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 91.00 90.41 **1.71 9.59** 90.39 **69.73** 87.81 18.08 **12.16** 87.73 **55.03** 86.86 31.83 **13.11** 86.83 E-T5 (ours) 91.00 68.92 22.08 28.16 39.44 **69.73** 55.95 15.12 41.71 50.58 **55.03** 50.99 **8.54** 43.17 43.84 I-Vanilla 89.06 68.45 20.61 28.01 39.62 63.92 56.49 **10.66** 39.82 49.96 51.48 49.47 9.12 44.10 42.64 I-Iter (ours) 90.58 68.96 21.62 28.08 40.47 69.63 56.69 12.95 41.27 52.00 53.72 53.89 10.24 43.31 50.64 I-Simul (ours) **91.37** 80.44 15.44 15.05 **32.78** 71.13 66.28 26.97 25.58 **46.23** 54.08 **37.51** 34.94 53.82 **27.30** Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 91.00 90.44 **4.35 9.56** 90.41 **69.73** 85.18 **15.45 14.69** 84.87 55.03 78.39 23.36 **21.63** 78.42 E-T5 (ours) 91.00 71.03 19.97 22.40 4.63 **69.73** 31.73 38.80 61.80 16.83 55.03 29.72 26.28 56.23 12.54 I-Vanilla 88.25 70.91 17.34 20.16 **3.86** 63.07 29.81 34.08 59.42 **11.42** 48.08 25.32 23.69 55.53 **7.59** I-Iter (ours) **91.69** 71.76 19.93 22.23 5.43 68.23 33.46 36.87 59.79 18.96 **56.23** 35.21 **21.42** 50.98 17.48 I-Simul (ours) 91.38 70.92 20.47 22.80 4.30 70.29 32.03 42.12 60.65 14.72 54.75 26.18 30.70 59.34 8.67 Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 91.00 91.34 **5.13 8.66** 91.31 69.73 84.06 **14.73 16.04** 84.28 55.03 75.87 **20.83 24.17** 75.91 E-T5 (ours) 91.00 70.36 20.65 23.02 3.40 69.73 35.23 38.72 57.70 18.95 55.03 27.61 28.30 58.42 10.50 I-Vanilla 89.14 70.03 19.11 21.79 **2.91** 68.23 32.70 38.85 58.35 **13.49** 42.52 21.53 21.80 55.84 **4.79** I-Iter (ours) **92.20** 72.66 19.54 21.66 5.58 **70.67** 33.17 38.49 60.59 18.13 **55.38** 26.91 28.86 59.90 10.52 I-Simul (ours) 91.87 71.72 20.15 22.38 5.09 69.54 31.45 38.26 61.73 15.88 55.28 26.35 29.37 60.57 10.17 increase in CErrpos is significantly lower than the decrease in CErrneg, especially on ID samples; (2) Learnable methods give obviously lower confidence in OOD samples, and the average confidence drop is highly relevant to the performance drop under distribution shifts. Thus, the low confidence and relatively higher CErrpos and ECE on OOD samples may be reasonable. Further, we give a detailed analysis of extrinsic and intrinsic learnable methods and also compare our extended calibration methods with previous methods: (1) For extrinsic methods, the extended E-T5 exhibits significantly better calibration performance compared to the adapted E-MLP considering the mitigation of the overconfidence issue. The essential difference mainly lies in the extrinsic model for the calibration task. We find that using the larger capacity model as the extrinsic calibrator shows the same trend with shifting from the vanilla fine-tuning to learnable methods. We further study this scaling effect in Sec. 5.4; (2) For intrinsic methods, the three different training paradigms don't show substantial differences considering the calibration performance, and none of them consistently achieves the best performance on all datasets. As a comparison, our methods (I-Iter and I-Simul) address the degraded performance issue of I-Vanilla and make the main task performance match with the vanilla fine-tuning; (3) Interestingly, there doesn't exist a substantial difference between the extrinsic E-T5 method and other intrinsic methods, given the same base architecture (e.g., T5). This finding leads us to reconsider the conclusion in Lin et al. (2022) that PLMs can be trained to give their uncertainty by words. Given the comparable performance between intrinsic and extrinsic methods, we provide an extension to this conclusion. We identify that the success of this paradigm essentially lies in the learnable attribute of the calibration task, instead of the self-checking process of PLMs. Namely, the findings in previous work may not only be attributed to the capability of PLMs but also the "learnable" property of the calibration task. ## 5.4 Emergent Calibration In Sec. 5.3, we identify the potential in learnable methods. However, a detailed exploration of learnable calibration methods is lacking. We conduct experiments to study the influence of two important factors, namely the dataset size and the model scale for the calibration task, on PLMs calibration. Note that the model scale in this section considers the model adopted for the calibration task, instead of the main task. Dataset size. Table 2 shows the results of different sizes of the calibration dataset. Two basic findings are: (1) The five learnable methods show a consistent trend when increasing the dataset size, indicating that the essence of these methods is the same; (2) The size of datasets for training the calibration task doesn't have a substantial influence on PLMs performance on the main task. Beyond these, we observe that there is a sharp difference in calibration performance when increasing the dataset size from small to middle. The trend is overall consistent with the one observed when shifting from vanilla fine-tuning to learnable calibration methods. The trend can be summarized as: (1) For ID samples, we can observe a sharp decrease in CErrneg with relatively less negative influence on ECE and CErrpos; (2) For OOD samples, the CErrpos and ECE increase significantly along with increasing the dataset size. However, given the arguments in Sec. 5.3, we identify that PLMs' calibration performance improves when trained on larger calibration datasets. Besides, we don't observe further improvement in calibration performance when increasing the dataset size from middle to large. This is consistent with normal task training, where increasing the dataset size doesn't increase performance after a critical point. Model scale. Table 5 shows the results of various model scales. Two basic findings are: (1) The five learnable methods still show a consistent trend when scaling larger; (2) We observe a consistent confidence increase when scaling larger, which is similar to the trend observed in Sec. 4, where increasing capacity makes PLMs more confident. Surprisingly, although the confidence continues to increase, for ID samples, we observe a consistent decrease in CErrpos with neglectable influence on ECE and CErrneg when scaling larger. Note that the dataset for the calibration task is collected from ID. Thus, if provided enough ID samples for the calibration task training, scaling larger enables models to better learn the calibration task, ensuring better calibration performance on ID samples. For OOD samples, we don't observe a consistent trend due to the influence of various factors. Specifically, when using out-of-the-box to tackle OOD samples, the problem of distribution shifts appears in the introduced calibration task. Whether scaling the calibration-task model larger improves calibration performance under distribution shifts is determined by many factors (e.g., the dataset difficulty, the overconfidence issue in the calibration task). We leave it for future exploration. ## 6 Conclusion We take a close look into PLMs calibration, motivating to answer two central questions: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? We present a comprehensive empirical study, including the analysis of various decisive factors and concrete calibration methods. Besides the findings that support existing conclusions, we also provide extensions or contradictory arguments to some established conclusions. ## Limitations And Future Work We identify two limitations in our work that necessitate further investigation and improvement. First, only empirical results are presented in our work. A theoretical understanding of PLMs calibration is still lacking. Going forward, we are motivated to investigate this problem from the standpoint of feature learning. We see great potential in unifying several problems in AI safety (Houben et al., 2021) from a feature-learning perspective, including spurious correlations (Gu et al., 2019; Wang et al., 2022), robustness (Yuan et al., 2021; Zhang et al., 2022), backdoor learning (Sheng et al., 2022; Cui et al., 2022), and calibration (Ulmer et al., 2022). Second, we propose three simple extended calibration methods based on existing ones. In our experiments, we evaluate the calibration performance of existing and our calibration methods. We make an assumption that we have a large held-out validation set that can be employed as the training dataset for the calibration task. We demonstrate the effectiveness of learnable calibration methods in this ideal situation. 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SST-5 contains the complete corpus with all five labels, while **SST-2** discards the label *neutral* and polarizes the remaining 4 classes, i.e., negative or somewhat negative vs. somewhat positive or positive. **Amazon Fine Foods** (McAuley and Leskovec, 2013), denoted as **Amazon** for simplicity throughout the paper, is a sentiment analysis dataset of reviews on fine foods from Amazon. Due to the enormous dataset size in the dataset, we sample 10k samples per class from the dataset. SemEval 2016 Task 4 (Nakov et al., 2013) is the sentiment analysis in the Twitter task. We consider Subtask A, where all Twitter texts are labeled as negative, neutral, or positive. **Dynasent** (Potts et al., 2021) is a challenging and dynamically evolved dataset, adopting human-in-the-loop efforts in dataset construction. We merge the data of round 1 and round 2 in our experiments. Natural language inference. MNLI (Williams et al., 2018b) consists of 10 types of written and spoken English data and has two versions called matched and mismatched respectively, according to whether the domain of the train set and dev/test set is matched. We use the matched version in our experiment. **HANS** (McCoy et al., 2019) is a heuristic analysis dataset for NLI systems, based on the specific hypotheses about invalid heuristics that may be captured by the NLI model. **ANLI** (Nie et al., 2020) is an adversarial NLI dataset, created by an iterative (three rounds in total), humanand-model-in-the-loop procedure. We merge the data from all three rounds in our experiments. Topic classification. Yahoo Topic Answers (Zhang et al., 2015) contains 10 categories of questions and their corresponding answers from the Yahoo! Webscope program. For each sample, the title and content of the question are concatenated as one text, and the best answer to the question is used as a label. Since the original training dataset is extremely large (1.4 million samples for each category), we randomly sample 140,000 samples for simplicity. **AG News** (Zhang et al., 2015) is a corpus of news articles consisting of 4 classes: World, Sports, Business, and Science/Technology. For each article, we construct the text by concatenating the title and description. Toxic detection. Civil Comments1is collected from the Civil Comments platform. Each comment is annotated with a float toxicity score, scaling from 0 to 1. We follow the official instructions to set samples with a toxicity score smaller than 0.5 as label 0 and vice versa. **Hate Speech** (de Gibert et al., 2018), the arguably most popular dataset in toxic detection, is collected from Stormfront, a large forum of white nationalists. The test set we use is sampled by the author in the official Github repository. **Implicit Hate** (ElSherief et al., 2021) consists of hate tweets from extremist groups in the US. Notably, a part of the hate tweets is implicit, which contains some subtle tricks to conceal the toxicity and evade keyword detection. Plain text. BookCorpus (Zhu et al., 2015) collects a tremendous number of free novel books and thus is used in the pre-training stage of pre-trained language models. We sample 10k texts for evaluation. **Random Words** contains 1k meaningless texts, each synthesized by concatenating 20 random words. ## B Additional Results Of Control Experiments For the empirical control study in the influence of six factors on PLMs calibration, we provide additional experimental results. The results of T5-base on AG News are shown in Fig.7, Fig.8, Fig.9, and Fig.10. The results of RoBERTa-base are shown in Fig.11, Fig.12, Fig.13, Fig.14, Fig.15, and Fig.16. We discuss detailed experimental settings and conclusions for each considered factor. Available training samples. We adopt K-shot learning, where K is the number of samples per class. We experiment with each K five times on each dataset and report the average performance due to the potential variance in the fewshot setting. In this dimension, we additionally find that the trends in average confidence are different in the two model architectures. While 1https://www.kaggle.com/competitions/ jigsaw-unintended-bias-in-toxicity-\ classification ![14_image_0.png](14_image_0.png) ![14_image_1.png](14_image_1.png) ![14_image_2.png](14_image_2.png) ![14_image_3.png](14_image_3.png) ![14_image_4.png](14_image_4.png) ![14_image_5.png](14_image_5.png) ![14_image_6.png](14_image_6.png) Acc Conf ECE CErrpos **CErrneg** ![14_image_7.png](14_image_7.png) Figure 10: Results of tunable parameters with T5 (Soft-prompt). T5 has an obvious confidence drop in the early stage, the confidence of RoBERTa seems to continually increase along with the number of available training samples. This can be partially ex- ![15_image_0.png](15_image_0.png) Acc Conf ECE CErrpos **CErrneg** ![15_image_1.png](15_image_1.png) Acc Conf ECE CErrpos **CErrneg** Training dynamics. We decompose the whole training process into steps, and measure five metrics during some fixed intervals. In this dimension, Number of tunable parameters. To quantitatively explore the influence of the number of tunable parameters on PLMs calibration, we employ the parameter efficient tuning methods in NLP (Houlsby et al., 2019; Zaken et al., 2022; Ding et al., 2022). We adopt Soft-prompt (Lester ![16_image_0.png](16_image_0.png) Tiny Mini Small Medium Base **Large** ![16_image_1.png](16_image_1.png) ![16_image_2.png](16_image_2.png) Random LSTM TF-IDF BoW Figure 14: Results of the pretraining influence with RoBERTa. et al., 2021) and Adapter (Houlsby et al., 2019) tuning due to their simplicity, stability, and practicality. We experiment with various numbers of soft tokens and bottleneck dimensions of the inserted adapter modules. Only the parameters in the soft tokens and adapter module are tunable. We summarize the extra findings as follows: (1) Soft-prompt and Adapter tuning show different trends spanning four datasets; (2) For Soft-prompt tuning, the model performance and confidence increase continually with more tunable parameters. We can observe that the increasing rates are nearly matched, thus decreasing ECE continually. The negative effect is also the increase in CErrneg due to the overconfidence in wrong predictions. This is consistent with the trend we observed in the underfitted state; (3) The world in Adapter tuning is different, where increasing capacity cannot bring ![17_image_0.png](17_image_0.png) ![17_image_1.png](17_image_1.png) ![17_image_2.png](17_image_2.png) Acc Conf ECE CErrpos **CErrneg** may negatively impact PLMs calibration, especially at the critical point when current capacity is sufficient to solve the task well. Model scale. We consider the scaling law and experiment with various model sizes. For T5, we choose models with small, base, large, and 3b sizes. For RoBERTa, we choose models with tiny, mini, small, medium, base, and large sizes. ![18_image_0.png](18_image_0.png) Dataset Dynasent Amazon DSC Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Vanilla 78.45 86.83 8.38 9.94 75.07 86.57 95.28 8.71 3.44 87.02 **90.00** 94.40 4.48 4.10 80.85 TS 78.45 79.10 **1.02** 17.37 66.27 86.57 89.92 3.36 8.59 80.31 **90.00** 89.26 **0.78** 8.90 72.68 LS **78.47** 78.22 3.64 18.89 67.69 86.55 85.48 3.42 13.35 77.91 89.75 84.61 5.31 13.95 72.02 EDA 76.30 89.20 12.91 **7.76** 79.44 **87.19** 97.07 9.88 **1.75** 89.04 88.05 95.50 7.45 **2.81** 83.03 Ensemble 78.18 86.76 8.58 9.89 74.75 86.37 95.02 8.66 3.71 86.99 89.74 94.27 4.56 4.17 80.67 E-MLP 78.45 78.99 4.45 21.05 79.11 86.57 83.15 **2.92** 16.85 83.14 **90.00** 82.53 7.17 17.48 82.63 E-T5 (ours) 78.45 61.63 18.26 33.00 42.07 86.57 89.99 6.51 6.94 71.00 **90.00** 86.14 6.19 11.03 61.60 I-Vanilla **78.47** 61.95 17.91 32.77 42.72 84.44 89.89 6.52 6.18 68.52 88.84 86.15 5.76 10.77 61.69 I-Iter (ours) 77.92 61.45 16.47 33.26 42.78 86.03 86.92 2.99 9.99 **67.91** 89.45 84.72 4.88 12.54 61.55 I-Simul (ours) 78.13 66.36 24.59 25.51 **37.34** 85.67 91.26 13.29 5.28 70.59 88.61 87.83 12.46 8.41 **58.61** ![18_image_3.png](18_image_3.png) Dataset MNLI HANS ANLI Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Vanilla 85.90 96.24 9.50 2.40 87.36 54.17 95.09 39.68 2.71 92.36 29.78 90.94 61.14 11.28 91.90 TS 85.90 86.65 **0.90** 11.09 71.84 54.17 82.15 26.74 15.43 79.16 29.78 75.57 45.57 27.32 76.80 LS 86.28 86.88 4.43 11.92 79.31 55.59 86.96 31.37 11.47 85.00 29.25 81.59 52.37 20.23 82.34 EDA 85.99 97.07 11.09 **1.78** 90.05 **58.24** 96.87 38.63 **1.91** 95.16 **31.34** 92.00 60.66 **8.81** 92.38 Ensemble 86.60 96.32 9.74 2.37 87.90 56.09 96.44 40.35 2.00 94.45 30.06 90.47 60.46 11.38 91.26 E-MLP 85.90 85.82 13.73 14.16 85.67 54.17 81.92 29.36 17.87 81.66 29.78 81.49 51.71 18.88 81.65 E-T5 (ours) 85.90 74.37 18.51 18.93 33.58 54.17 74.47 28.79 10.10 56.23 29.78 35.21 45.46 74.72 39.43 I-Vanilla 85.76 75.23 18.25 18.32 36.45 57.28 77.14 32.26 13.23 64.23 28.63 37.14 44.78 71.91 40.77 I-Iter (ours) **86.63** 60.04 26.59 33.85 **20.41** 53.70 57.77 **21.70** 29.34 **42.82** 31.06 21.29 **31.88** 83.71 **23.55** I-Simul (ours) 86.46 74.81 18.91 18.49 32.01 56.65 75.84 33.83 13.79 62.28 29.16 38.67 45.44 66.86 40.95 Dataset Amazon SST-5 SemEval Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Vanilla 90.90 98.17 7.28 1.09 90.84 **70.29** 94.29 24.05 3.95 90.14 56.02 90.45 34.43 7.05 87.26 TS 90.90 89.66 **2.02** 8.73 73.58 **70.29** 78.15 **7.91** 18.42 70.04 56.02 70.34 **14.32** 25.98 65.65 LS 91.89 88.50 6.71 10.64 78.83 69.92 84.01 14.20 14.38 80.28 55.17 81.64 26.47 15.46 78.08 EDA **92.39** 98.34 5.95 **0.92** 89.46 66.64 93.98 27.34 **3.82** 89.57 **57.05** 93.45 36.43 **4.37** 90.56 Ensemble 91.69 98.19 6.50 1.06 89.93 69.56 93.67 24.22 4.24 88.93 55.94 90.14 34.23 7.19 86.76 E-MLP 90.90 95.08 9.14 4.94 95.34 **70.29** 83.57 22.22 16.18 82.99 56.02 77.12 25.42 22.49 76.63 E-T5 (ours) 90.90 71.97 19.27 21.20 3.72 **70.29** 32.10 45.94 61.74 17.53 56.02 23.64 36.13 64.58 8.63 I-Vanilla 88.00 71.60 17.13 19.18 3.97 64.85 26.74 46.32 65.75 **12.86** 44.43 17.51 31.05 66.92 **5.07** I-Iter (ours) 90.11 71.34 18.88 21.18 **3.24** 66.54 34.13 41.70 58.17 18.82 53.28 34.05 27.10 48.25 13.86 I-Simul (ours) 90.60 71.07 19.80 21.91 3.41 69.35 33.96 44.16 58.75 17.46 53.50 24.20 33.35 61.15 7.36 Dataset Civil Hate Speech Implicit Hate Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Vanilla 86.94 98.15 10.09 **1.14** 92.91 76.99 98.22 21.94 **1.22** 96.41 **62.88** 96.37 32.02 2.88 95.00 TS 86.94 90.94 **2.88** 7.29 77.87 76.99 89.70 13.34 8.58 84.16 **62.88** 85.50 **21.15** 12.72 82.28 LS **87.91** 87.73 9.52 11.79 84.24 **78.45** 88.31 **10.86** 11.48 87.54 62.58 86.79 24.21 12.82 86.13 EDA 83.61 97.01 13.40 2.08 92.35 77.82 97.28 19.65 2.30 95.82 61.53 96.68 35.14 **2.71** 95.70 Ensemble 86.45 97.96 11.52 1.29 93.16 76.32 97.58 21.28 1.75 95.41 62.77 96.19 33.42 3.08 94.97 ![18_image_11.png](18_image_11.png) E-MLP 86.94 91.93 12.24 8.09 92.01 76.99 88.52 19.66 11.62 88.98 **62.88** 83.08 25.45 17.15 83.47 E-T5 (ours) 86.94 70.97 15.99 18.62 1.68 76.99 46.28 48.83 52.25 41.37 **62.88** 30.90 41.57 59.84 15.20 I-Vanilla 77.92 69.06 8.92 11.60 **0.83** 76.99 45.25 49.59 53.24 **40.21** 58.12 29.51 38.32 58.58 **13.00** I-Iter (ours) 85.40 75.36 10.31 12.18 2.48 76.15 50.43 49.62 50.02 51.84 60.59 34.15 38.04 54.50 16.69 I-Simul (ours) 87.25 70.69 16.65 19.22 1.71 78.24 45.86 50.64 53.36 43.03 62.56 29.60 41.56 60.57 13.17 Table 3: Results T5's calibration performance under hard-to-easy distribution shifts. ![18_image_1.png](18_image_1.png) ![18_image_2.png](18_image_2.png) ![18_image_4.png](18_image_4.png) ![18_image_5.png](18_image_5.png) ![18_image_6.png](18_image_6.png) ![18_image_7.png](18_image_7.png) ![18_image_8.png](18_image_8.png) ![18_image_9.png](18_image_9.png) ![18_image_10.png](18_image_10.png) Our results support the "scaling improves calibration" conclusion in some cases. We observe that ECE decreases when larger capacity brings substantial improvement to PLMs' performance (e.g., T5 on SST-2 and MNLI). However, when the performance reaches a plateau value, increasing capacity only boosts PLMs' confidence (e.g., T5 and RoBERTa on Yahoo). In this case, the ECE increases when the PLM's scale keeps increasing. Pretraining. We choose the pre-trained RoBERTa-base and pre-trained T5-base (Pretrained), and compare them with several nonpretrained models, including random initialized RoBERTa-base and T5-base (Random), BiLSTM (LSTM) (Hochreiter and Schmidhuber, 1997), Term Frequency Inverse Document Frequency (TF-IDF) (Luhn, 1957), and Bag-of-word ![18_image_12.png](18_image_12.png) ![18_image_13.png](18_image_13.png) ![18_image_14.png](18_image_14.png) ![18_image_15.png](18_image_15.png) (BoW) (Harris, 1954). We find that pretraining only reduces ECE on relative simpler datasets, like SST-2 and AG-News, but bring negligible benefits on MNLI and Yahoo. This finding shares the same ground with scaling experiments. ## C Construction Of The Calibration Training Dataset In this paper, we consider the classification tasks. The construction process can be extended to the natural language generation tasks. We have an annotated dataset D = {(xi, yi) N i=1} for the standard training on the classification tasks. We typically Model Scale Dataset Amazon SST-5 SemEval Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 87.65 86.41 **4.78 13.59** 86.43 **65.14** 80.15 **15.23 19.86** 80.17 49.23 77.14 27.91 **22.89** 77.17 E-T5 (ours) 87.65 67.80 19.85 23.71 7.49 **65.14** 28.16 37.29 64.06 13.63 49.23 30.45 19.40 50.65 12.12 I-Vanilla 81.64 57.35 24.28 30.30 **2.45** 55.01 3.95 51.21 93.35 **0.66** 44.57 2.17 42.43 95.53 **0.32** I-Iter (ours) 87.54 68.20 19.33 22.89 5.66 64.10 28.81 36.99 62.99 14.16 48.52 32.05 **17.49** 47.86 13.13 I-Simul (ours) **87.66** 68.61 19.05 22.63 6.35 64.57 29.59 37.57 62.38 14.95 **50.38** 35.00 18.89 45.87 15.58 | T5-small T5-base T5-large | |-----------------------------| Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 91.00 90.44 **4.35 9.56** 90.41 69.73 85.18 **15.45 14.69** 84.87 55.03 78.39 23.36 **21.63** 78.42 E-T5 (ours) 91.00 71.03 19.97 22.40 4.63 69.73 31.73 38.80 61.80 16.83 55.03 29.72 26.28 56.23 12.54 I-Vanilla 88.25 70.91 17.34 20.16 **3.86** 63.07 29.81 34.08 59.42 **11.42** 48.08 25.32 23.69 55.53 **7.59** I-Iter (ours) **91.69** 71.76 19.93 22.23 5.43 68.23 33.46 36.87 59.79 18.96 **56.23** 35.21 **21.42** 50.98 17.48 I-Simul (ours) 91.38 70.92 20.47 22.80 4.30 **70.29** 32.03 42.12 60.65 14.72 54.75 26.18 30.70 59.34 8.67 Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg E-MLP 91.58 91.95 **4.70 8.04** 91.89 **73.85** 83.52 **10.24 16.52** 83.61 56.65 78.26 **21.61 21.74** 78.26 E-T5 (ours) 91.58 70.10 21.48 23.70 2.66 **73.85** 29.96 47.35 64.65 14.75 56.65 28.56 29.98 57.52 10.36 I-Vanilla 88.88 69.42 19.46 22.12 **1.81** 71.79 28.30 46.83 65.12 **11.55** 49.00 24.66 25.95 56.30 **6.37** I-Iter (ours) 92.96 88.26 10.48 8.74 48.71 72.45 70.35 30.29 25.22 58.71 **58.08** 84.26 35.21 12.77 80.14 I-Simul (ours) **93.34** 74.45 19.39 20.62 5.43 73.66 36.92 45.40 57.27 20.66 56.87 40.04 28.43 44.23 19.29 Table 5: Results of T5's calibration performance with increasing model scales. ID Dataset SST-2 Yahoo OOD Dataset SST-2 Bookcorpus Random Words Yahoo Bookcorpus Random Words Method Conf Entropy Conf Entropy Conf Entropy Conf Entropy Conf Entropy Conf Entropy Vanilla 98.04 5.01 93.38 15.97 84.46 34.95 82.76 51.94 47.62 152.43 56.95 126.54 TS 93.89 18.02 85.07 35.23 72.49 54.69 75.72 76.29 38.43 177.74 47.70 154.00 LS 88.64 33.90 83.65 40.46 72.31 55.30 74.35 93.81 44.29 168.14 54.08 145.94 EDA 98.27 4.33 93.73 15.45 83.00 37.15 83.68 46.75 50.59 141.92 69.03 92.58 Ensemble 97.96 5.20 93.21 16.47 82.75 37.87 82.41 53.01 48.29 150.39 55.87 130.57 E-MLP 88.62 35.37 86.94 38.69 85.04 42.17 74.93 - 61.80 - 67.57 - E-T5 (ours) 55.96 62.11 56.35 64.08 64.02 60.32 60.29 - 13.64 - 22.56 - I-Vanilla 56.31 62.13 57.72 63.99 66.47 59.90 60.51 - 13.71 - 22.78 - I-Iter (ours) 43.43 57.59 43.24 60.62 56.07 61.10 61.35 - 20.62 - 39.08 - I-Simul (ours) 63.24 10.50 65.74 2.25 77.68 0.01 60.52 - 6.44 - 14.67 - fit a model F on the training dataset by minimizing the pre-defined loss (e.g., cross-entropy loss). We denote the original task as the main task. Then for the newly introduced calibration task, we need to generate a calibration training dataset D∗for training. To do so, we first train the model on the main task using the training dataset, and employ the trained model to give predictions on samples from the validation set. Then the calibration training dataset D∗ = {(xi, y∗ i , ci)M i=1} can be generated from the validation set, where xiis the original sample in the validation set, y∗ i is model's original prediction, and ciis a binary value that indicates whether the original prediction is correct or not. Specifically, we perform downsampling to ensure a balanced label distribution. In this paper, we adopt the same process to generate the calibration training dataset. But different methods may adopt specially designed training paradigms to utilize the calibration training data. | ID Dataset | SST-2 | Yahoo | |-----------------------|---------|---------| | Unlearnable Learnable | | | We described the training details in Sec. 5.1. ## D Additional Results Of Calibration Methods For exploring the effectiveness of existing calibration methods, we provide results with RoBERTa in Table 4, Table 7, and Table 8 The results with the model scaling effect are in Table 5. ## E Further Analysis Of Distribution Shifts In Sec. 5.3, we show that PLMs are less calibrated under distribution shifts, consistent with previous work (Desai and Durrett, 2020; Minderer et al., 2021). However, can we safely conclude that distribution shifts degrade PLMs' calibration performance? We study **hard-to-easy distribution** shifts (see Appendix F for the detailed setting) to further investigate the essence of this problem. In this setting, models are trained on a difficult ID dataset and infer on easier OOD datasets. This ![20_image_0.png](20_image_0.png) Dataset Dynasent Amazon DSC Method Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg Acc Conf ECE CErrpos CErrneg ![20_image_1.png](20_image_1.png) Vanilla **78.61** 94.56 17.10 **3.56** 88.06 85.47 97.84 12.48 1.18 92.08 87.93 97.23 9.30 1.74 89.70 TS **78.61** 77.47 **0.95** 19.47 66.96 85.47 86.61 **2.54** 11.24 74.11 87.93 85.09 2.99 12.84 70.03 LS 76.48 85.95 9.46 12.37 80.47 **85.85** 89.34 7.39 9.53 82.53 **87.15** 88.19 5.46 10.71 80.75 EDA 76.97 95.65 18.74 2.92 90.85 84.12 97.92 13.81 1.08 92.64 85.53 97.13 11.62 1.64 89.87 Ensemble 77.67 94.85 17.22 3.44 88.89 85.37 97.88 12.52 **1.12** 92.11 86.69 97.11 10.43 **1.76** 89.77 E-MLP **78.61** 71.06 19.59 28.81 70.59 85.47 85.74 12.10 14.25 85.69 87.93 79.37 **14.46** 20.61 79.25 E-T5 (ours) **78.61** 64.94 23.76 26.76 **34.43** 85.47 85.53 13.23 9.45 **56.03** 87.93 81.72 14.91 13.50 **49.71** I-Vanilla 77.38 66.71 22.76 24.92 38.06 83.85 85.80 12.18 7.99 53.56 87.10 82.30 14.25 12.89 49.77 I-Iter (ours) 77.89 64.17 21.98 28.43 38.09 84.49 87.49 10.00 7.47 60.06 87.05 82.83 12.14 12.86 53.81 I-Simul (ours) 78.63 65.00 25.56 27.08 35.84 83.65 79.79 15.36 13.28 44.38 85.79 77.29 17.78 16.91 42.30 Table 7: Results of RoBERTa's calibration performance under hard-to-easy distribution shifts. comes with relatively lower ID and higher OOD performance. Specifically, we consider the sentiment analysis task and choose Dynasent (Amazon and DSC) as the ID (OOD) datasets. The details of the datasets are described in Appendix A. The results of T5 and RoBERTa are shown in Table 3 and Table 7 respectively. We observe completely different results with Sec. 5.3. Across all methods, the ECE and CErrpos decrease under the hard-to-easy distribution shifts, contradictory to the previous conclusion that PLMs are less calibrated on OOD samples. In hard-to-easy shifts, performance and confidence both increase due to the relative simpleness of the OOD samples. The indication is that PLMs' relative calibration performance on ID and OOD samples relies on the dataset difficulty, and the conclusion that PLMs are less calibrated under distribution shifts is onesided. This is consistent with our empirical study in Sec. 4 that emphasizes the influence of dataset difficulty on PLMs calibration. To further investigate the influence of dataset difficulty on PLMs' calibration performance, we evaluate **the calibration on task-irrelevant inputs** (see Appendix F for the detailed setting) of PLMs trained on ID datasets with different difficulty (e.g., SST-2 and Yahoo). The task-irrelevant inputs include plain texts (e.g., bookcorpus) and random words. Since no golden labels are provided, we measure the calibration performance through maximum confidence scores and predictive entropy. The results of T5 are shown in Table 6, and RoBERTa are shown in Table 8. We show that PLMs have unreasonable high confidence in taskirrelevant inputs, especially when trained on SST2. Comparing the results when trained on SST-2 or Yahoo, we find that the ID training dataset has significant influence on PLMs calibration. Still, this can be attributed to the dataset difficulty. We also observe the superior performance of learnable calibration methods. They produce lower confidence scores on plain text and random tokens compared to unlearnable ones. In summary, the influence of distribution shifts on PLMs calibration is dependent on the evaluation datasets chosen. The original conclusion that calibration performance degrades on OOD samples is based on two premises: (1) PLMs are overconfident in their wrong predictions, which is supported by our experiments; (2) The OOD datasets are harder so PLMs cannot achieve good | ID Dataset | SST-2 | Yahoo | | | | | | | | | | | | |----------------|---------|------------|--------------|---------|------------|--------------|-------|---------|-------|---------|-------|---------|----| | OOD Dataset | SST-2 | Bookcorpus | Random Words | Yahoo | Bookcorpus | Random Words | | | | | | | | | Method | Conf | Entropy | Conf | Entropy | Conf | Entropy | Conf | Entropy | Conf | Entropy | Conf | Entropy | | | Vanilla | 98.33 | 4.27 | 94.85 | 12.63 | 96.28 | 9.97 | 90.18 | 26.96 | 72.17 | 77.84 | 78.49 | 59.14 | | | TS | 93.43 | 19.62 | 86.41 | 32.66 | 87.50 | 32.46 | 71.73 | 90.13 | 44.01 | 163.43 | 50.51 | 148.65 | | | LS | 87.88 | 35.74 | 83.30 | 42.64 | 82.88 | 44.11 | 82.08 | 74.02 | 67.53 | 110.10 | 74.89 | 93.55 | | | EDA | 98.43 | 3.67 | 95.54 | 10.79 | 91.55 | 20.06 | 94.24 | 15.08 | 83.30 | 44.77 | 86.10 | 35.91 | | | Ensemble | 98.24 | 4.49 | 94.65 | 12.87 | 93.26 | 15.98 | 91.22 | 23.92 | 75.10 | 69.13 | 80.31 | 54.06 | | | Unlearnable | E-MLP | 94.48 | 15.99 | 80.75 | 36.41 | 63.81 | 59.36 | 74.15 | - | 41.87 | - | 42.31 | - | | E-T5 (ours) | 84.79 | 16.26 | 63.99 | 24.34 | 22.84 | 27.72 | 68.71 | - | 22.70 | - | 15.20 | - | | | I-Vanilla | 84.83 | 16.33 | 65.34 | 25.09 | 23.08 | 28.39 | 69.55 | - | 24.84 | - | 17.78 | - | | | I-Iter (ours) | 56.89 | 20.06 | 62.99 | 21.10 | 42.25 | 30.37 | 76.16 | - | 54.33 | - | 48.54 | - | | | I-Simul (ours) | 75.24 | 9.44 | 46.51 | 13.88 | 8.11 | 5.44 | 64.66 | - | 19.70 | - | 19.47 | - | | | Learnable | | | | | | | | | | | | | | | Task | Dataset | # Classes | Avg.Len | Train | Dev | Test | |----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|---------------------------------------------|----------------------|---------|-------|--------| | SST-2 | 2 | 19.23 | 6920 | 1821 | 872 | | | Sentiment | Amazon | 3 | 77.86 | 24000 | 78741 | 91606 | | Analysis | SST-5 | 3 | 18.75 | - | - | 1067 | | SemEval | 3 | 19.61 | - | - | 6000 | | | Natural Language Inference | MNLI | 3 | 19.36/10.06 | 373067 | 19635 | 9815 | | HANS | 2 | 9.15/5.61 | - | - | 30000 | | | ANLI | 3 | 54.40/10.34 | - | - | 3200 | | | Topic | Yahoo | 10 | 96.98 | 126000 | 14000 | 60000 | | AG | 4 | 38.5 | 10000 | - | 7600 | | | Classification | Civil | 2 | 52.86 | 48000 | 12000 | 97320 | | Toxic | Hate Speech | 2 | 21.55 | - | - | 478 | | Detection | Implicit Hate | 2 | 17.34 | - | - | 21479 | | Plain | Book Corpus | - | 13.39 | - | - | 10000 | | Text | Random Words | - | 20.28 | - | - | 1000 | | Table 9: Dataset Statistics. | | | | | | | | Task | Dataset | Template | Verbalizer | | | | | SST-2 | It was {"mask"} . {"placeholder": "text a"} | [bad, good] | | | | | | Sentiment | Amazon | It was {"mask"} . {"placeholder": "text a"} | [bad, good, neutral] | | | | | Analysis | SST-5 | It was {"mask"} . {"placeholder": "text a"} | [bad, good, neutral] | | | | | SemEval | It was {"mask"} . {"placeholder": "text a"} | [bad, good, neutral] | | | | | | Given the two sentences: (1) {"placeholder": "text a"}. | | | | | | | | MNLI | [No, Yes, Maybe] | | | | | | | (2) {"placeholder": "text b"}. Does the first sentence entails the second ? {"mask"}. Given the two sentences: | | | | | | | | Natural | (1) {"placeholder": "text a"}. | | | | | | | Language | HANS | [No, Yes, Maybe] | | | | | | (2) {"placeholder": "text b"}. | | | | | | | | Inference | Does the first sentence entails the second ? {"mask"}. Given the two sentences: (1) {"placeholder": "text a"}. | | | | | | | ANLI | [No, Yes, Maybe] | | | | | | | (2) {"placeholder": "text b"}. Does the first sentence entails the second ? {"mask"}. | [society, science, health, education, computers, sports, business, entertainment, relationships, politics] | | | | | | | Yahoo | A {"mask"} question : {"placeholder": "text a"} {"placeholder": "text b"} | | | | | | | Topic Classification | AG | A {"mask"} news : {"placeholder": "text a"} | [politics, sports, | | | | | {"placeholder": "text b"} | business, technology] | | | | | | | Civil | It was {"mask"} . {"placeholder": "text a"} | [benign, toxic] | | | | | | Toxic Detection | Hate Speech | It was {"mask"} . {"placeholder": "text a"} | [benign, toxic] | | | | | Implicit Hate | It was {"mask"} . {"placeholder": "text a"} | [benign, toxic] | | | | | | Table 10: The manual templates and verbalizers adopted for each dataset. | | | | | | | performance. The second premise has not always been satisfied, and we show that the relative dataset difficulty significantly influences PLMs' calibration performance on ID and OOD samples. | Task | Dataset | Template | Verbalizer | |------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|--------------| | SST-2 | Sentence: {"placeholder": "text a"} The predicted sentiment is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . | | | | Amazon | Sentence: {"placeholder": "text a"} The predicted sentiment is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . | | | | Sentiment Analysis | SST-5 | Sentence: {"placeholder": "text a"} The predicted sentiment is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . | | | SemEval | Sentence: {"placeholder": "text a"} The predicted sentiment is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . Given the two sentences: {"placeholder": "text a"} | | | | MNLI | The predicted relationship between the two sentences is {"placeholder": "text b"} Is the prediction True or False ? It's {"mask"} . | | | | Natural | Given the two sentences: {"placeholder": "text a"} | | | | HANS | The predicted relationship between the two sentences is {"placeholder": "text b"} | [False, True] | | | Language Inference | Is the prediction True or False ? It's {"mask"} . Given the two sentences: {"placeholder": "text a"} | | | | ANLI | The predicted relationship between the two sentences is {"placeholder": "text b"} Is the prediction True or False ? It's {"mask"} . | | | | Topic | Sentence: {"placeholder": "text a"} The predicted topic is {"placeholder": "text b"} | | | | Classification | Yahoo | Is the prediction True or False ? It's {"mask"} . | | | Civil | Sentence: {"placeholder": "text a"} The predicted toxicity is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . | | | | Toxic | Sentence: {"placeholder": "text a"} The predicted toxicity is {"placeholder": "text b"} . | | | | Detection | Hate Speech | Is the prediction True or False ? It's {"mask"} . | | | Implicite Hate | Sentence: {"placeholder": "text a"} The predicted toxicity is {"placeholder": "text b"} . Is the prediction True or False ? It's {"mask"} . | | | | Table 11: The manual templates and verbalizers of the calibration task for each dataset. | | | | ## F Details Of Evaluation Setting. Hard-to-easy shift. we choose Dynasent as the in-distribution dataset, and choose Amazon and DSC as the out-of-distribution datasets. The evaluation metrics are the same as the ones adopted in experiments on standard OOD shifts. This evaluation setting is expected to test the conclusion that PLMs' calibration performance degrades under distribution shifts. Calibration on task-irrelevant inputs We choose SST-2 and Yahoo as the in-distribution datasets, and choose Bookcorpus and a synthetic dataset as out-of-distribution datasets. Each sample in the synthetic dataset is constructed by composing random words. Well-calibrated PLMs should give very low confidence and high probability entropy in the task-irrelevant inputs. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The final section. A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✗ **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? No response. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response. ## C ✓ **Did You Run Computational Experiments?** 4, 5 C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Not applicable. Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Not applicable. Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. Left blank. ✗ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

li-etal-2023-dionysus

{DIONYSUS}: A Pre-trained Model for Low-Resource Dialogue Summarization

https://aclanthology.org/2023.acl-long.76

Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues have limitations because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains. In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain. To pre-train DIONYSUS, we create two pseudo summaries for each dialogue example: one from a fine-tuned summarization model and the other from important dialogue turns. We then choose one of these pseudo summaries based on information distribution differences in different types of dialogues. This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus. Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings

# Dionysus: A Pre-Trained Model For Low-Resource Dialogue Summarization Yu Li∗†, Baolin Peng‡, Pengcheng He‡, Michel Galley‡, Zhou Yu†**, Jianfeng Gao**‡ †Columbia University, New York, NY ‡Microsoft Research, Redmond, WA {yl5016, zy2461}@columbia.edu {bapeng,penhe,mgalley,jfgao}@microsoft.com ## Abstract ![0_Image_0.Png](0_Image_0.Png) Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues have limitations because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains. In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain. To pretrain DIONYSUS, we create two pseudo summaries for each dialogue example: one from a fine-tuned summarization model and the other from important dialogue turns. We then choose one of these pseudo summaries based on information distribution differences in different types of dialogues. This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus. Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings. ## 1 Introduction Text summarization aims to produce concise and accurate summaries of long texts. Recent research on pre-trained neural language models has shown success in summarizing monologues (Lewis et al., 2020; Raffel et al., 2022; Zhang et al., 2019; He et al., 2022), such as news articles (Lee et al., 2022; Ravaut et al., 2022) and scientific publications (Ibrahim Altmami and El Bachir Menai, 2022; Dong et al., 2021). However, dialogue summarization presents additional challenges due to the different information distribution in dialogues. Self-supervised text summarization models (Zhang et al., 2019; Wan and Bansal, 2022; Phang ∗Work was done when Yu Li was interning at MSR Figure 1: A summary of a dialogue in the SAMSum dataset, where the golden summary effectively compiles relevant information (in yellow) from the entire conversation. et al., 2022) are typically pre-trained on free-form text data, with selected sentences as the pre-training objective. While this approach can be effective for monologues such as news articles, it is less successful at summarizing semistructured and multiparticipant dialogues. As illustrated in Figure 1, in daily chats, dialogue information is often dispersed across various dialogue turns, making it difficult to extract all relevant information through a few selected turns. While a golden summary needs to accurately captures vital information throughout the entire conversation. Furthermore, real-world dialogue-summarization applications often have limited or even no labeled data, making it challenging to develop effective models. Therefore, it is crucial to develop dialogue summarization models that can perform well in zero-shot and few-shot 1368 settings for their practical use. To address these challenges, we propose DIONYSUS, a pre-trained sequence-to-sequence model designed to summarize dialogues in any domain, even with a lack of labeled data. It uses pseudo summaries as its pre-training objective, which can be dynamically selected from two sources. First, for daily chats where multiple dialogue turns are not sufficient to summarize the dialogue, we train a summary helper using high-quality dialogue summarization datasets to generate pseudo summaries for these types of dialogues. On the other hand, for dialogues like meeting minutes, interviews, and debates, which can be summarized through a selection of essential turns, we use a method inspired by the gap sentence generation (GSG) technique in PEGASUS to select these turns as pseudo summaries for training. For instance, choosing the final few turns in a conversation can effectively summarize meeting minutes. We have improved upon the GSG method by using the generated summaries from the summary helper as references during gap sentence selection, as they tend to have less noise compared to the full dialogue context. We refer to this source of pseudo summaries as "Principal" and refer to our improved method as GSG+. We find that our improved method outperforms previous methods in low-resource settings across different domains, such as daily chats, emails, and customer service dialogues. Additionally, we study different objective strategies for selecting the pseudo summary as a pre-training objective from the generated summary and the "Principal." We evaluate DIONYSUS on six dialogue summarization datasets. Our best model trained on 19 dialogue corpora surpasses PEGASUSLARGE in a zero-shot setting across all domains. We also found that the best performance is achieved by selecting the source with the highest ROUGE score as the objective strategy. Our main contributions are: - The development of DIONYSUS, a pretrained sequence-to-sequence model for summarizing dialogues in any domain in a zeroshot or few-shot setting. - The introduction of new self-supervised pretraining objectives for dialogue summarization using a summary helper and GSG+. - The demonstration that DIONYSUS outperforms baselines on six domains in low- resource settings, and can be fine-tuned with only 10 training examples to outperform vanilla T5 (Raffel et al., 2022) fine-tuning with 1, 000 examples. ## 2 Approach Figure 2 outlines the steps for constructing DIONYSUS: § 2.1 First, a summary helper is constructed using two high-quality dialogue summarization datasets. This helper generates a pseudo summary for each dialogue in our pre-training corpus. § 2.2 Next, the "Principal" is extracted using GSG+ as the other pseudo summary for the dialogue. § 2.3 Finally, various strategies are employed to select the best pseudo summaries from the first and second steps to serve as the objective for pre-training. ## 2.1 Summary Helper In certain types of dialogue, such as daily chats, it can be challenging to gather all necessary information from just a few dialogue turns due to the dispersed nature of dialogue information. To address this problem, we have created a summary helper model that generates pseudo summaries for each training example in our pre-training corpus. We build our summary helper upon the T5 (Raffel et al., 2022) model. To capture essential information in a dialogue, we have trained our helper on the MultiWoz dataset (Budzianowski et al., 2018; Eric et al., 2020) in DS2 (Shin et al., 2022), which contains summaries derived from dialogue states using templates. This allows us to capture essential information from each turn in the conversation. Additionally, we have continued training our helper on the DialogSum (Chen et al., 2021) dataset, a human-annotated dataset in the daily life domain. This allows us to overcome the fixed format of summaries introduced by templates in DS2 and produce more natural pseudo summaries. ## 2.2 Gap Sentence Generation Plus (Gsg+) Algorithm 1 Gsg+ 1: P ← ∅ 2: for j ← 1 to m do 3: si:= rouge(P ∪ {xi}, G), ∀*i s.t. x*i ∈/ P 4: k := argmax{si}n 5: P := P ∪ {xk} 6: **end for** Dialogues in certain settings, such as meetings and medical dialogues, often include summary ![2_image_0.png](2_image_0.png) turns that summarize the entire conversation. For example, a participant may summarize a meeting, or a doctor may explain the outcome. These summary turns can be used as a pre-training objective because they highlight the main points of the dialogue and provide a concise overview of the topic discussed. In order to make DIONYSUS more adaptable to these scenarios, we have improved the independent principal method in the GSG method (Zhang et al., 2019) by using it to select essential summary turns as pseudo summaries for training. Our new method, called Gap Sentence Selection Plus (GSG+), uses the ROUGE1-F1 score between each dialogue turn xi and the generated summary G from the helper in Section 2.1 rather than the remaining text D \ xito determine the importance of each turn. The generated summary eliminates much of the extraneous information from the dialogue and thus tends to have less noise than the full dialogue context, resulting in a less cluttered summary. This enables us to select the top-m-scored summary turns as the "Principal," which will provide a more comprehensive overview of the vital information in the dialogue. For instance, Using the summary helper to identify key points increases the likelihood of selecting the most important dialogue turns as the "Principal" summary when creating pseudo summaries for meeting minutes instead of randomly selecting dialogue turns. Specifically, given a dialogue D = {xi}n, we use Algorithm 1 to obtain the pseudo-summary "Principal" P. The input for our training example is the remainder of the dialogue D \ P. In Appendix C, we explore the impact of the dialogue turns order on the formation of the "Principal". Using GSG+ can effectively identify essential summary turns and generate more accurate pseudo-summaries than with the original GSG method. 1: S ← ∅ 2: sg := rouge(G, D \ {P}) 3: sp := rouge(P, D \ {P}) 4: if sg > sp then 5: S := G $\mathbf{M}$ 6: **else** 7: S := P 8: **end if** $${}^{\!\!-}S:=P$$ ## 2.3 Pre-Training Objectives Strategy To generate the final pseudo summary S for each specific dialogue training example, we consider three strategies. These strategies are based on the generated pseudo summary G and the extracted "Principal" P. These strategies serve as the pretrain objective for the dialogue training example. All G S = G: We always select the generated summary from the summary helper as the pretraining objective. All P S = P: We always select the "Principal" as the pre-training objective. Better ROUGE We use either G or P based on the recall of information from the dialogue to determine the pre-training objective. We utilize Algorithm 2 to get the pre-training objective by calculating the ROUGE1-F1 score for the pseudo summaries and the dialogue, excluding the "Principal" D \ P. It is important to note that we use the same reference to ensure a fair comparison. For pre-training with above strategies, if we choose G as the pseudo summary, we input the full dialogue. If we choose P, we input the dialogue, excluding the "Principal," D \ P to create an abstract summary. However, we also include the "Principal" with a probability, using a copying mechanism to create an extractive summary. More information about this copy mechanism can be found in Section 5.4. It is important to note that we do not combine these two pseudo summaries for a single training example. Each example in our pre-training corpus will have either G or P as its designated pseudo summary. ## 3 Training Corpus To train DIONYSUS, we utilized 19 conversational corpora that do not come with pre-defined dialogue summaries. We employed a self-supervised approach by using pseudo-summaries as the pretraining objective. Conversational Corpora We collect 19 available conversational corpora consisting of 1.7M examples after truncating for pre-training. Corpus information is listed in Table 1. We access these corpora through ConvoKit v2.5.31. This helps us to ensure that DIONYSUS is well-equipped to handle a variety of conversational scenarios. | Corpora | # Dialogues | |------------------------------------------|---------------| | CaSiNo (Chawla et al., 2021) | 1,030 | | Chromium (Meyers et al., 2018) | 163,675 | | Gone Awry (CMV) (Zhang et al., 2018) | 6,842 | | Gone Awry (Wiki) (Zhang et al., 2018) | 4,188 | | Diplomacy (Peskov et al., 2020) | 246 | | Friends (Zhou and Choi, 2018) | 1,301 | | GAP (Braley and Murray, 2018) | 28 | | IQ2 (Zhang et al., 2016) | 108 | | Cornell Movie Dialogs2 | 83,097 | | Parliament (Zhang et al., 2017b) | 216,894 | | PERSUASIONFORGOOD3 | 1,017 | | Reddit Coarse (Zhang et al., 2017a) | 9,483 | | Reddit Corpus (small) 4 | 8,286 | | Supreme Court 5 | 7,700 | | Switchboard (Stolcke et al., 2000) | 1,155 | | Tennis (Fu et al., 2016) | 81,974 | | Wiki Deletion (Mayfield and Black, 2019) | 383,918 | | Wiki Talk Pages6 | 125,292 | | Winning Arguments (Tan et al., 2016) | 3,051 | Table 1: Corpora we use to pre-train DIONYSUS. We train our objective summary helper with a rule-based dialogue summarization dataset (DS2) and an abstractive summarization dataset (DialogSum). DS2 This dataset (Shin et al., 2022) creates dialogue summaries for the MultiWOZ (Budzianowski et al., 2018; Eric et al., 2020) dataset by heuristic rules from the dialogue states. It includes 5 domains and 10, 000 dialogues. DialogSum This dataset (Chen et al., 2021) collects human annotated summaries for daily-life dialogues from three datasets: DailyDialog (Li et al., 2017), DREAM (Sun et al., 2019), and MuTual (Cui et al., 2020), as well as dialogues from an English-speaking practice website. It has 13,460 dialogues in total. ## 4 Experiments 4.1 Downstream Tasks And Metrics We evaluate our methods on three public dialogue summarization datasets or benchmarks: SAMSum (Gliwa et al., 2019), ConvoSumm (Fabbri et al., 2021), and TWEETSUMM (Feigenblat et al., 2021) SAMSum This dataset contains over 16k natural messenger-like dialogues with manually annotated summaries by language experts. ConvoSumm It is a benchmark of four domains: New York Times comment, StackExchange, W3C email, and Reddit. Dialogues are extracted from publicly available data, and each domain has 500 dialogues. They hire crowdsorce workers on Amazon Mechanical Turk to annotate dialogue summary. TweetSumm This dataset contains 1,100 reconstructed real-world customer support dialogues from Tweet. Each dialogue has human annotated abstractive summaries and extractive summaries. We only use abstractive summaries in the dataset as references in our experiments. We report ROUGE-1, ROUGE-2, and ROUGEL scores (Lin, 2004) to evaluate generated summaries against references. ## 4.2 Baselines We compare our methods with three competitive baselines. T5v1.1 It is an improved version of the original T5 model (Raffel et al., 2022). Since the original T5 model is pre-trained on downstream tasks in supervised learning, the test set of downstream tasks overlaps with the pre-training data. To make a fair comparison in a zero-shot setting, we choose T5v1.1 as it is pre-trained on C4 without mixing in the downstream tasks. PEGASUS Zhang et al. (2019) propose this pretrained model for abstractive summarization tasks. The pre-training objective is GSG, transforms any text into an abstractive summarization example by selecting important sentences as output summaries. We use the PEGASUSLARGE checkpoint7 as there is no publicly available PEGASUSBASE checkpoint. GSG* We use the independent principal strategy of GSG training objective in PEGASUS (Zhang et al., 2019) but pre-train DIONYSUS with our training corpora. We build this baseline to explore the performance gap between our pre-training objective and GSG. ## 5 Results And Analysis We focus on low-resource dialogue summarization settings because it is difficult to collect enough training examples. We evaluate DIONYSUS with "All G", "All P", and "Better ROUGE" strategies in zero-shot and few-shot settings and compare it to the baselines. ## 5.1 Zero-Shot Results In order to evaluate the effectiveness of DIONYSUS, we conduct a zero-shot test on DIONYSUSLARGE with all strategies and other baselines. We present the results in Table 2. The ROUGE1-F1, ROUGE2-F1, and ROUGEL-F1 scores are used as the standard evaluation measures for summarization tasks. Our models show impressive performance improvements over the baselines on all downstream datasets. Specifically, DIONYSUSLARGE with the "Better ROUGE" strategy performs the best overall across all downstream datasets (Average: ROUGE-1/2/L: 29.7/8.0/20.2), indicating that it benefits from both generated and extractive pseudo summaries and can adapt to various domains. The "All P" strategy performs better than the GSG* baseline on most datasets, indicating that our Gap Sentence Selection Plus method can effectively select dialogue turns that provide an accurate dialogue summary. Additionally, the DIONYSUSLARGE with "All G" and "Better ROUGE" strategies demonstrate significant improvement compared to T5v1.1LARGE (Average ROUGE2: +5.6/ + 6.1) and PEGASUSLARGE (Average ROUGE2: +2.2/ + 2.7), indicating that pre-training with our summary helper is 7https://huggingface.co/google/pegasus-large highly beneficial. However, the "All G" strategy only performs as well as the "Better ROUGE" strategy on the SAMSum dataset (ROUGE-1/2/L/: 41.3/16.1/30.6 → 41.3/16.2/30.9), suggesting that the improvement from the summary helper is more pronounced on this particular dataset. This may be due to the similarity between the datasets used to train the helper and the SAMSum dataset, which we discuss further in Sections 5.5 and 5.6. Overall, our models outperform previous methods, such as PEGASUS, in a zero-shot setting, demonstrating their effectiveness and potential for further development. ## 5.2 Few-Shot Results We investigated reducing annotation labor in dialogue summarization tasks by using few-shot dialogue summarization. We report ROUGE1-F1, ROUGE2-F1, ROUGEL-F1, and ROUGELSumF1 scores to evaluate model performance. Specifically, We fine-tune DIONYSUSLARGE, PEGASUSLARGE, and T5v1.1LARGE with the first 1/10/100/1K/10K training examples from the SAMSum dataset. We show the results of our experiments with varying training data sizes in Figure 3. We found that all models improved with more examples. Among these models, DIONYSUSLARGE consistently outperformes both PEGASUSLARGE and T5v1.1LARGE when trained with a dataset ranging from 0 to 10, 000 examples. This suggests that our pre-training process helps DIONYSUS adapt to downstream tasks more quickly. Additionally, we observed that PEGASUSLARGE outperformed T5v1.1LARGE due to its pre-training on summarization tasks. Figure 3 shows the gap between DIONYSUSLARGE and PEGASUSLARGE is particularly significant when using fewer than 100 training examples, indicating better recall capabilities in dialogue summarization for DIONYSUS. Even with only 10 training examples, DIONYSUSLARGE achieves higher ROUGE scores than the T5v1.1LARGE model trained with 1,000 examples, making it the best option for lowresource dialogue summarization. ## 5.3 Effect Of Compression Ratio In GSG+, we can choose a fixed number of turns in the dialogue as a training objective or select turns with a compression ratio. We investigate the compression ratio in a dialogue turn level as the number of selected turns over the number of totals turns in the dialogue (Nprincipal/N*dialogue*). A | Model | SAMSum | NYT | Reddit | Stack | Email | TweetSumm | Avg. | |----------|----------------|---------------|---------------|---------------|---------------|----------------|---------------| | T5v1.1 | 9.6/1.6/8.6 | 11.6/1.4/8.7 | 12.3/1.7/9.2 | 15.6/2.4/11.0 | 14.9/2.7/11.1 | 6.0/1.4/5.1 | 11.7/1.9/9.0 | | PEGASUS | 27.5/7.6/21.5 | 23.7/3.2/13.2 | 23.1/4.1/13.6 | 26.7/4.8/15.2 | 23.9/5.7/15.3 | 21.8/6.3/16.0 | 24.5/5.3/15.8 | | GSG* | 13.3/3.5/12.0 | 17.1/2.4/12.9 | 16.0/2.1/12.5 | 21.2/3.5/15.1 | 21.0/4.2/15.9 | 15.4/2.8/13.1 | 17.3/3.1/13.6 | | Ours: G | 41.3/16.1/30.6 | 21.7/3.7/14.8 | 23.5/4.3/15.7 | 26.3/5.4/16.8 | 26.4/7.1/17.2 | 29.4/8.4/22.1 | 28.1/7.5/19.5 | | Ours: P | 23.5/7.5/18.6 | 19.8/2.7/12.9 | 20.0/2.9/12.7 | 24.5/4.3/15.0 | 24.3/5.5/15.8 | 22.1/6.7/17.6 | 22.4/4.9/15.4 | | Ours: BR | 41.3/16.2/30.9 | 24.1/4.0/15.4 | 24.8/4.4/15.9 | 28.5/5.6/17.6 | 28.9/7.7/18.0 | 30.7/10.1/23.4 | 29.7/8.0/20.2 | Table 2: The ROUGE-1/ROUGE-2/ROUGE-L scores of the DIONYSUSLARGE with strategy P: "All P", G: "All ![5_image_0.png](5_image_0.png) G", and BR: "Better ROUGE" and compared to T5v1.1LARGE and PEGASUSLARGE in a zero-shot setting on three datasets: SAMSum, ConvoSumm, and TweetSumm. ![5_image_1.png](5_image_1.png) low compression ratio will select fewer turns in the dialogue as the objective, making pre-training less challenging. However, it tends to have a lower ROUGE1-F1 score with the remaining dialogue turns, meaning the "Better ROUGE" strategy selects more generated summaries as the objective. While choosing a high compression ratio will make the pre-training more challenging. Nevertheless, it has a higher ROUGE score compared to generated summaries, leading to more principal under the "Better ROUGE" strategy. We show the zero-shot performance on development sets of the SAMSum dataset and TweetSumm dataset with compression rates from 10% to 60% in Figure 4. It shows that the model with 15% compression ratio achieves the highest ROUGE-2 score. ## 5.4 Effect Of Copying Mechanism | ROUGE-1/2/L | All P | w/o copying | |---------------|---------------|---------------| | SAMSum | 25.8/8.5/19.7 | 17.7/5.7/15.7 | | NYT | 21.3/2.7/13.5 | 17.4/2.2/13.4 | | Reddit | 22.3/3.4/13.8 | 16.3/2.6/13.1 | | Stack | 25.9/4.5/15.8 | 20.3/3.4/15.1 | | Email | 26.6/6.1/16.8 | 20.0/3.5/14.7 | | TweetSumm | 24.1/8.5/19.0 | 19.4/3.8/16.3 | Table 3: ROUGE-1/2/L scores of zero-shot setting for DIONYSUSBASE with "All P" strategy and "All P" without copying mechanism on SAMSum, ConvoSumm, and TweetSum. The copying mechanism is important for dialogues like meetings and medical dialogues because it allows for summarization of entire dialogue through several turns. As shown in Table 3, we compare the performance of the "All P" strategy to a scenario where 50% of the selected dialogue turns are retained in the input rather than being removed. In this case, the input for each pre-training example includes the entire dialogue D, rather than D \ P. This leads the model to focus on extractive summarization. We observed that adding a random copy mechanism significantly improved the overall performance. Additionally, we ![6_image_0.png](6_image_0.png) also evaluate the "Better ROUGE" strategy with different copying probabilities ranging from 0.15 to 0.7. In these experiments, we choose top-2 dialogue turns as principal, which results in 51.9% of pre-training objectives being the principal, and the rest is the generated summary. Figure 5 shows that leaving 15% of dialogue turns in the principal best enhances the overall quality of dialogue summarization. | ROUGE-1/2/L | All G | Helper | |---------------|----------------|----------------| | SAMSum | 41.3/16.1/30.6 | 35.8/13.5/27.9 | | NYT | 21.7/3.7/14.8 | 21.2/4.0/15.2 | | Reddit | 23.5/4.3/15.7 | 20.2/3.5/14.4 | | Stack | 26.3/5.4/16.8 | 25.1/5.0/16.0 | | Email | 26.4/7.1/17.2 | 22.9/5.6/15.2 | | TweetSumm | 29.4/8.4/22.1 | 26.8/6.2/20.8 | ## 5.5 **Comparison Between All G And Summary** Helper Since the summary helper model provides the generated summary as an objective candidate and has shown strong capabilities in zero-shot dialogue summarization. As shown in Table 4, we compare the helper model to our "All G" model in a zeroshot setting. The difference is that we train the "All G" model on the pre-training corpora annotated by the helper. We found that the helper model is not on par with our model. While the helper model may have performed well on a particular task (NYT), its overall performance is not as strong as our model. This is because DIONYSUS has been extensively trained on various dialogue datasets, which makes it consistently perform well in a wide range of tasks and scenarios. 5.6 Test-Set Overlap with Pre-Training Corpora In order to ensure a fair comparison, we check for overlap between pre-training and downstream test datasets. This is done by calculating the similarity between all pairs of test set targets in the SAMSum dataset and pre-training documents using the ROUGE2-recall measure, which is calculated as the number of overlapping bigrams divided by the total number of bigrams in the test target. We then count the number of test set examples that have a similarity to any pre-training example above a certain threshold. As shown in Table 5, the overlap between the SAMSum dataset and the datasets used for training the helper and the pre-training datasets is low when the similarity threshold is set between 0.4 and 1.0. This suggests that there is not significant similarity between our test set and the pre-training datasets. It indicates that the improvement in DIONYSUS is due to the pre-training process rather than potential test data leakage. ## 5.7 Human Evaluation | Threshold | ConvoKit | DS2 | DialogSum | |-------------|------------|-------|-------------| | ≥ 1.0 | 0% | 0% | 0% | | ≥ 0.8 | 0% | 0% | 0% | | ≥ 0.6 | 0% | 0% | 1% | | ≥ 0.4 | 5% | 0% | 3% | | Ratings | | |---------------|--------| | T5v1.1LARGE | 3.54∗∗ | | PEGASUSLARGE | 3.90∗ | | DIONYSUSLARGE | 4.04 | | Human-written | 4.08 | We evaluate the performance of DIONYSUS by conducting human evaluation experiments on Amazon Mechanical Turk. We randomly select 100 examples from the SAMSum dataset to compare summaries generated by our model with those written by humans in the dataset. We choose DIONYSUS trained with the "Better ROUGE" strategy and generate summaries in a zero-shot setting. Participants are asked to rate the summaries on a scale of 1 to 5, with higher scores indicating better quality. We collect the scores from three participants for each example and report the average scores in Table 6. A paired t-test is conducted to determine if scores are significantly different between our model and other models. Our results show that DIONYSUS could generate summaries of similar quality to human-written summaries without any training data. DIONYSUS also gets better ratings than the vanilla T5 and PEGASUS models, which aligns with the results obtained from the automatic evaluation. More information on the human evaluation process can be found in Appendix F. ## 6 Related Work Dialogue summarization is a rapidly growing area of research that focuses on automatically generating concise and informative summaries of conversations (Feng et al., 2022). Unlike research on traditional documents like news articles (Fabbri et al., 2019; Ahuja et al., 2022) or scientific papers (Lu et al., 2020; Ibrahim Altmami and El Bachir Menai, 2022), dialogue summarization is particularly relevant in multi-party interactions, such as emails (Zhang et al., 2021), meetings (Carletta et al., 2005), medical dialogues (Zeng et al., 2020), and daily chats (Chen et al., 2021). However, many existing methods for dialogue summarization require a large training dataset with annotated summaries. This can be a major barrier to applying these methods in real-world scenarios, particularly in cases with limited or no annotated data available. Our study examines the use of dialogue summarization in low-resource settings to make the process more practical and effortless in various contexts. Pre-trained Transformer-based (Vaswani et al., 2017) language models (Devlin et al., 2019; Radford et al., 2019; Yang et al., 2019) have become increasingly popular in natural language processing tasks for tackling the data shortage problem. However, many of these models have limitations when it comes to dialogue summarization. Zhang et al. (2019) propose PEGASUS, which masks multiple whole sentences and pre-trains sequence-tosequence models to reconstruct the original text. Built on that, Wan and Bansal (2022) improve the sentence selection strategy and add modules for ensuring factuality during fine-tuning to address the problem of factuality in summarization. Phang et al. (2022) extend PEGASUS with a modified architecture and long-sequence pre-training to tackle long-input summarization. He et al. (2022) propose ZCode++, a pre-trained language model optimized for abstractive summarization with improved encoder. However, all these methods rely on the Gap Sentence Selection method, which has limitations for dialogue summarization. In contrast, our approach uses pseudo-summary construction as the pre-training objective, making it possible for zeroshot dialogue summarization. Another line of work focuses on pre-trained models for dialogues. DialoGPT (Zhang et al., 2020) and PLATO (Bao et al., 2020), which are pretrained on large-scale conversation datasets such as Reddit. For dialogue summarization, Jia et al. (2022) post-train pre-trained language models to rephrase dialogues into narratives and then finetunes them for summarization. In contrast, our approach follows the T5 model's unified text-to-text format for both pre-training and fine-tuning. Zhong et al. (2022) train UNILM (Dong et al., 2019) with a window-based denoising framework for long dialogue understanding and summarization but do not focus on low-resource settings. Zou et al. (2021) propose a pre-training paradigm that pre-trains the encoder and decoder separately in a supervised manner. While our method uses a self-supervised pre-training approach that applies to any dialogue dataset, making it easier to extend to larger pretraining corpora for further improvement. ## 7 Conclusion And Future Work We present DIONYSUS, a pre-trained encoderdecoder model for zero-shot dialogue summarization in any new domain. We pre-train using a self-supervised approach that generates pseudosummaries for large dialogue corpora as the pretraining objective. We investigate the impact of various pre-training objective strategies and model sizes on dialogue summarization performance. Our experiments show that DIONYSUS outperforms state-of-the-art models on six datasets in a zeroshot setting. Furthermore, DIONYSUS can be fine-tuned with only 10 examples to outperform vanilla T5 fine-tuning with 1,000 examples. This makes dialogue summarization more practical and easier to use in various contexts with minimal effort. We plan to extend this method to abstractive summarization tasks to develop a general zero-shot summarization model. ## 8 Limitations Training Data Our pre-training data is sourced from 19 existing dialogue datasets. However, it's important to note that these datasets may contain noise, such as harmful content, irrelevant file names, and URL links. Despite utilizing multiple automatic tools to filter out this content during preprocessing, there is still a chance that some noise may be present in our pre-training data. This could potentially impact the performance of DIONYSUS, making it important to monitor and improve the pre-processing steps continuously. We also know the potential drawbacks of constructing pseudo summaries using the GSG method, which may lead to unnatural summaries for dialogue data. To mitigate this, we introduced the Summary Helper in Section 2.1, which is specifically trained on two dialogue summarization datasets containing natural summaries. This approach enables more realistic pseudo-summaries and enhances zero-shot performance. Although we employ top-m turns as an additional source of pseudo summaries, Figure 4 illustrates that GSG+ contributes a minor portion of the pseudo summary, with a 0.7 to 0.3 ratio between generated and topm turns. Our method thus minimizes referent and pronoun confusion, ensuring better coherence than solely employing the standard GSG technique. Training Resource To improve our model's performance, we employ the "Better ROUGE" strategy, which calculates the ROUGE score for both candidates and selects the best one as the final training objective. This data pre-processing process can be pretty time-consuming, taking approximately one day to complete for our pre-training data when utilizing 100 threads. Additionally, we utilize 16 Nvidia V100 GPUs to train our models, which may not be accessible or reproducible for all researchers. This could present a significant obstacle for those looking to replicate or build upon our work. Test Data Another potential concern is the test datasets used to evaluate DIONYSUS. The test set size is relatively small, which may not fully represent the breadth of dialogue types that a general dialogue summarization model should be able to handle. This could lead to the model performing well on the test set but not generalizing to other unseen dialogue types. Further, our analysis did not include the assessment of long dialogue summarization, such as lengthy meetings (Carletta et al., 2005; Zhong et al., 2021; Janin et al., 2003) or screenplays (Chen et al., 2022). However, our study's approach has the potential to handle these scenarios, even though it was not specifically designed for them. By incorporating LongT5 (Guo et al., 2022) or DialogLM (Zhong et al., 2022), which are known for their ability to process extended input sequences, we expect that they could efficiently tackle this task. ## 9 Acknowledgement Our gratitude goes out to Microsoft Research for providing us with computational resources. We would also like to thank Kun Qian for valuable discussions and the Columbia NLP and Microsoft Deep Learning Group members for their feedback and discussions. Additionally, we thank the Mechanical Turk workers for conducting the human evaluation. ## References Ojas Ahuja, Jiacheng Xu, Akshay Gupta, Kevin Horecka, and Greg Durrett. 2022. ASPECTNEWS: Aspect-oriented summarization of news documents. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6494–6506, Dublin, Ireland. 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(2019) to save time and computation, we first conduct ablation experiments on a reduced-size T5v1.1BASE model with 250M parameters. Then we scale up with the best settings to the final T5v1.1LARGE model with 800M parameters. We use heuristics to clean up our pre-training corpora. First, we remove dialogues with less than two dialogue turns since they are too short to summarize. Then we remove URLs and emojis in the text. DIONYSUS is implemented with Huggingface Pytorch Transformers8(Wolf et al., 2020). We split dialogue turns with line breakers in pre-training input and add a "[Summary]" prefix. For pseudo summary creation, we use a compression ratio of 0.15 for the "Principal." This means that for a dialogue with l turns, we select 0.15l turns as "Principal." We explore the effect of different compression ratios in Section 5.3. We use Adam (Kingma and Ba, 2014) with weight decay for pre-training. We truncate dialogue training examples to ensure a maximum length of 512. Models are pre-trained with batch size 8 and learning rate 0.00001 on 16 Nvidia V100 GPUs until we observe no progress 8https://github.com/huggingface/transformers is licensed under the Apache License 2.0 on validation data or up to 5 epochs. For few-shot experiments in Section 5.2, we fine-tune models up to 20 epochs with batch size 8 and learning rate 0.00005, and pick the checkpoint with the best validation performance. ## B Additional Base Model Results Table 7 presents the results of DIONYSUSBASE in a zero-shot setting, and Figure 6 compares the few-shot results of DIONYSUSBASE with those of the T5 base model. These initial results demonstrate the potential for further analysis and optimization of DIONYSUS. Upon comparison with other baselines, it is clear that DIONYSUS performs better under both zero-shot and few-shot conditions, outperforming the GSG* model. These results provide valuable insight into the capabilities of DIONYSUS and can inform the development of larger models. ## C Effect Of The Dialogue Turns Order In Principal We could use two possible orders to align the dialogue turns in the principal. The first order is to align the text with the ROUGE1-F1 score. The second order is to align the principal with the order in the original dialogue. This means that the principal will be arranged in the same order as in the original dialogue, without rearrangement. This option helps preserve the original flow and structure of the dialogue. We compare these two orders of principal in the GSG* baseline. As shown in Table 8, the results suggest that keeping the order in the original dialogue helps improve zero-shot performance as it provides a more nuanced understanding of the dialogue. We choose this order for all our models. ## D Pre-Training Steps To evaluate the performance of DIONYSUS during pre-training, we measured the ROUGE1-F1, ROUGE2-F1, ROUGEL-F1, and ROUGELSumF1 scores on the SAMSum dataset in Figure 7. We keep track of the model's progress by logging its performance every 1,000 training steps. This allows us to monitor the model's improvements over time and confirm that it is learning effectively. ## E Example Model Outputs In order to evaluate the performance of DIONYSUS, we randomly selected model output examples | Model | SAMSum | NYT | Reddit | Stack | Email | TweetSumm | |--------------|----------------|---------------|---------------|---------------|---------------|---------------| | T5v1.1BASE | 9.7/1.2/8.6 | 5.8/0.7/4.9 | 8.9/1.2/7.3 | 11.5/1.7/8.9 | 8.4/1.6/7.2 | 6.8/1.0/6.2 | | GSG* | 13.7/4.0/12.6 | 17.9/2.4/13.9 | 15.8/2.2/12.7 | 20.7/3.4/15.5 | 20.8/3.8/15.9 | 17.0/3.2/14.5 | | All G | 39.2/15.2/29.5 | 20.0/3.1/13.7 | 21.4/3.6/14.7 | 24.1/4.9/16.0 | 24.1/6.5/16.0 | 28.3/9.0/22.1 | | All P | 25.8/8.5/19.7 | 21.3/2.7/13.5 | 22.3/3.4/13.8 | 25.9/4.5/15.8 | 26.6/6.1/16.8 | 24.1/8.5/19.0 | | Better ROUGE | 39.6/15.4/30.1 | 23.1/3.7/15.0 | 23.1/4.0/15.1 | 27.3/5.6/17.1 | 27.0/6.9/17.6 | 30.3/9.8/23.2 | Table 7: The ROUGE-1/ROUGE-2/ROUGE-L scores of the DIONYSUSBASE when implemented with different ![13_image_0.png](13_image_0.png) strategies and compared to T5v1.1BASE in a zero-shot setting on three datasets: SAMSum, ConvoSumm, and TweetSumm. | ROUGE-1/2/L | GSG* (Dialogue) | GSG* (ROUGE) | |---------------|-------------------|----------------| | SAMSum | 13.7/4.0/12.6 | 13.1/3.7/12.2 | | NYT | 17.9/2.4/13.9 | 17.6/2.2/13.7 | | Reddit | 15.8/2.2/12.7 | 15.3/2.2/12.5 | | Stack | 20.7/3.4/15.5 | 20.1/3.1/15.2 | | Email | 20.8/3.8/15.9 | 19.8/3.6/15.1 | | TweetSumm | 17.0/3.2/14.5 | 15.1/2.7/12.8 | from both the SAMSum dataset and the TweetSumm dataset. We report these examples with their corresponding gold summaries in Tables 9 and 10. The gold summaries served as a benchmark for our model's output, allowing us to compare and estimate the quality of the generated summaries. We found that DIONYSUS could generate zero-shot summaries on par with those written by humans. However, we also identified factual errors in the generated summaries, such as misunderstandings of the subject matter. These errors suggest room for improvement in DIONYSUS, and we plan to address this issue in future work. ## F Human Evaluation Details In our human evaluation experiments, we utilized the task template shown in Figure 8. Mechanical workers were instructed to rate four summaries for a given dialogue on a scale of 1 (poor) to 5 (excellent). To minimize bias, we provided a dialogue with its corresponding gold summary as an example of a high-quality summary. The summaries were presented in a randomized order for each task to prevent order bias. Three different workers independently completed each task, and the median score across all workers was retained for each summary. Participants were compensated with 0.3 USD per task, and we implemented the following qualifications for worker selection to ensure a high level of quality: (1) HIT approval rate for all requesters' HITs is greater than 90%. (2) Location is one of AU, NZ, GB, and US. (3) Number of HITs approved is greater than 100. ![14_image_0.png](14_image_0.png) | Example | SAMSum Dzuka: Until further notice, the staff meeting will be held at 8:30 instead of 8:00. Please change the calendar for everyone. Thanks. Anna: No problem. Why the change Dzuka: We had a few that never make it on time. I'm hoping this will encourage more participation. Anna: Could be just the opposite! Dzuka: We'll give it a try. Anna: Sure, no problem. Dzuka: I'll let you know if it changes again. Thanks. Anna: NP | |---------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dialogue#1 Gold | The stuff meeting is postponed from 8.00 to 8.30 to encourage more participation. Dzuka will inform Anna if it changes again. | | DIONYSUS | The staff meeting will be held at 8:30 instead of 8:00. Dzuka hopes this will encourage more participation and will let Anna know if it changes again. Jane: Hello Vegano Resto: Hello, how may I help you today? Jane: I would like to make a reservation for 6 people, tonight around 20:00 Vegano Resto: Let me just check. Ah, I'm afraid that there is no room at 20:00. However, I could offer you a table for six at 18:30 or at 21:00. Would either of those times suit you? Jane: Oh dear. Let me just ask my friends. Vegano Resto: No problem. Jane: 21:00 will be ok. Vegano Resto: Perfect. So tonight at 21:00 for six people under your name. Jane: great, thank you! | | Gold | Jane made a 9 PM reservation for 6 people tonight at Vegano Resto. | | Dialogue#2 DIONYSUS | The restaurant has no room for six people at 20:00 and offers Jane a table for six at 18:30 or 21:00. Jane asks her friends to make a reservation at 21:00. Mia: Hi Dad! I need a hand with repairing the bathroom door. William: Hi! What happened? Mia: Nothing. I can't open/close it properly. It's sort of sagging. William: I see. I'll drop by after work and take a look. Mia: Thank you so much! Love you! William: I love you too. | | Gold | Mia's dad William will come to her place after work to repair her bathroom door. | | DIONYSUS | The bathroom door is sagging. William will drop by after work and take a look. | | Dialogue#3 | Table 9: Example zero-shot outputs on the SAMSum dataset. | | Example | TWEETSUMM @549761: My BITS service resets it's Startup type from disabled to automatic. It leeches on to my bandwidth like crazy. Please provide a solution. @MicrosoftHelps: Hi. We hear you. We'd like to check what happened prior to this issue? What Windows version are you using? Let us know. @549761: I am using Windows 10 Home Single Language. Nothing specific happened prior to this issue. Just the service used to leech on to bandwidth (it claims to use idle network but doesn't). I want it to stop from resetting Startup type from disabled to automatic. @MicrosoftHelps: Thanks for the info. For us to isolate your concern, let's try the troubleshooting steps 1/2 https://t.co/3qcAsLFkaY listed in this link: https://t.co/IBZ1MaTm1l. Kindly check the post of Jesinta Rozario. @MicrosoftHelps: Hi, Pratik. How's it going? Please let us know if you need further assistance. We're here for you. @549761: Hi. The service still becomes running after disabling(after a few days). What can be the reason for the service switching it's startup type? @MicrosoftHelps: In that case, we suggest contacting Answer Desk: https://t.co/9Ouw33YVZI to further assist you with your concern. Let us know how it goes. @MicrosoftHelps: Hello, Pratik! Were we able to resolve your concern? If no, we're just one tweet away if you have other concerns. If yes, please send us your feedback about your experience with our support here: https://t.co/CczzJgTng1. | |------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dialogue#1 | Customer is complaining about the BITS service for resetting startup type from disabled mode to automatic. | | Gold | Agent suggests to try out some troubleshooting steps by following the shared URL and reach out Answer desk team for further assistance. The BITS service leeches on to the bandwidth like crazy. | | DIONYSUS | Pratik wants it to stop from resetting Startup type from disabled to automatic. MicrosoftHelps suggests checking the post of Jesinta Rozario. @471404: Please bring security back to the Hall Green store. @471404: The store is getting a more an more uncomfortable vibe, not alone on this either! @Tesco: Hi there, sorry to be a pain but can you confirm which Hall Green store this is? TY - Reece @471404: It's the Hall Green store right next to the train station. Hoping you haven't removed security from the others too now... @Tesco: Hi, can you please confirm what you mean by "uncomfortable vibe"? - Nick @471404: Well there's pretty obvious shop lifters regularly, and today we had a man clearly intoxicated screaming and randomly asking people things. @Tesco: Yes the express store! Thanks aswell. I'd review the CCTV from when security were removed. If customers can see the changes you will too! @Tesco: Hi there. I have spoken to the store. They have had a few problems recently and are looking into improving security. Thanks - Ian @471404: Thank you again. I often worry for the staff as it is becoming a hot spot for undesirables. The homeless aren't the issue to save confusion! @Tesco: Hi there, thank you for bringing this to our attention the last thing we want is our customers to feel unsafe. Thank you - Brooke @471404: No thank you for taking it seriously here's hoping the store gets back to normal soon! @Tesco: Hi there, I'm glad one of my colleagues has dealt with the issue. Enjoy the rest of your weekend - Rian | | Gold | The customer is complaining that he facing some uncomfortable vibe. The agent confronted the customer saying that they had a few problems recently and they are looking into improving security. | | Dialogue#2 | The store is getting a more an more uncomfortable vibe. Nick asks Tesco to bring security back to the Hall Green store and confirms the location. | | DIONYSUS | Nick also tells Tesco the Express store has had some problems recently and is looking into improving security. Table 10: Example zero-shot outputs on the TWEETSUMM dataset. | ![16_image_0.png](16_image_0.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 1 ✓ B1. Did you cite the creators of artifacts you used? Section 1 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix B ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 1 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. It is discussed in the original artifacts I use. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. It is discussed in the original artifacts I use. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix A ## C ✓ **Did You Run Computational Experiments?** Section 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix A The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix A ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix A D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 5.7 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix F ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Appendix F ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Appendix F D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. It is in the Amazon Mechanical Turk user agreement protocal. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. It is in the Amazon Mechanical Turk user agreement protocal.

jing-etal-2023-ms

{MS}-{DETR}: Natural Language Video Localization with Sampling Moment-Moment Interaction

https://aclanthology.org/2023.acl-long.77

Given a text query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that semantically matches the query. In this paper, we adopt a proposal-based solution that generates proposals (i.e. candidate moments) and then select the best matching proposal. On top of modeling the cross-modal interaction between candidate moments and the query, our proposed Moment Sampling DETR (MS-DETR) enables efficient moment-moment relation modeling. The core idea is to sample a subset of moments guided by the learnable templates with an adopted DETR framework. To achieve this, we design a multi-scale visual-linguistic encoder, and an anchor-guided moment decoder paired with a set of learnable templates. Experimental results on three public datasets demonstrate the superior performance of MS-DETR.

# Ms-Detr: Natural Language Video Localization With Sampling Moment-Moment Interaction Jing Wang1,3,4**, Aixin Sun**2∗ , Hao Zhang1**, Xiaoli Li**1,3,4∗ 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 S-Lab, Nanyang Technological University, Singapore 3Institute for Infocomm Research, A*STAR, Singapore 4 Centre for Frontier AI Research, A*STAR, Singapore {jing005@e.,axsun@,hao007@e.}ntu.edu.sg, [email protected] ## Abstract Given a query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that semantically matches the query. In this paper, we adopt a proposal-based solution that generates proposals (*i.e.,* candidate moments) and then select the best matching proposal. On top of modeling the cross-modal interaction between candidate moments and the query, our proposed Moment Sampling DETR (MS-DETR) enables efficient moment-moment relation modeling. The core idea is to sample a subset of moments guided by the learnable templates with an adopted DETR (DEtection TRansformer) framework. To achieve this, we design a multiscale visual-linguistic encoder, and an anchorguided moment decoder paired with a set of learnable templates. Experimental results on three public datasets demonstrate the superior performance of MS-DETR.1 ## 1 Introduction Natural language video localization (NLVL) aims to retrieve a temporal moment from an untrimmed video that semantically corresponds to a given language query, see Fig. 1 for an example. This task is also known as temporal sentence grounding in video, and video moment retrieval. As a fundamental video-language task, it has a wide range of applications, such as video question answering (Fan et al., 2019; Yu et al., 2018; Li et al., 2019), video retrieval (Gabeur et al., 2020; Liu et al., 2019; Chen et al., 2020), and video grounded dialogue (Le et al., 2019; Kim et al., 2021). Generally speaking, in NLVL models, a video is first split to a sequence of many small fixed-length segments. Video features are then extracted from these segments to interact with the text query. Conceptually, each video segment can be viewed as a ![0_image_0.png](0_image_0.png) Figure 1: An NLVL example with query and ground truth video moment. Two moment candidates with similar video features are also highlighted in light and dark green colors. form of "video token". There are mainly two genres of approaches to NLVL. *Proposal-free methods* directly model the interaction between video tokens and text, and aim to identify start/end boundaries along the video token sequence. Proposal-based methods generate candidate moments as proposals and then select the best matching proposal2as the answer. Each proposal is a continuous span of video tokens. To generate proposals, some methods enumerate all possible moment candidates via pre-defined anchors. Anchors are reference start/end positions along the video. Fig. 2 shows three 2D-Map examples. Each cell in a 2D-Map corresponds to a candidate moment defined by its start/end time along the two axes. Some other methods produce moment candidates with a proposal generator guided by text query and then refine them independently. The interaction between text and video is mainly modeled between text and video moments; each moment is characterized by the video segments that compose it. Very few studies have considered moment-moment interaction. Consequently, it is challenging to discriminate among moments if there are multiple moments that all demonstrate high level of semantic matching with the text query. For instance, the two candidate moments in Fig. 1 have very similar video content and share similar semantic correspondence with the query. In this paper, we adopt the proposal-based ap-2We use the terms *proposal* and *candidate moment* interchangeably, or even simply *moment* when the context is clear. ![1_image_0.png](1_image_0.png) Figure 2: Illustration of three strategies of moment-level interactions. Each cell represents a moment with start time i and end time j indicated on the two axes; only the upper triangular area is valid as i ≤ j. proach for its capability of cross-modal interaction at both segment level and moment level. We propose MS-DETR to facilitate effective text-moment alignment and efficient *moment-moment interaction*. For text-moment alignment, we devise a multi-scale vision-language transformer backbone to conduct segment-word and segment-segment interactions at different segment scales. For momentmoment interaction, our main focus is on which moments should be sampled for interaction, due to the large number of possible pairs. Recall that a moment is a span of segments. Let O(N) be the magnitude of segment space; the magnitude of moments is O(N2). Then moment-moment interaction has a space of O(N4). In practice, not every pair of moments are relevant to each other, and are needed to be discriminated for a given query. Existing methods (Zhang et al., 2020b, 2021b; Wang et al., 2021a) mainly rely on a strong assumption that only the overlapping or adjacent moments are more likely to be relevant, *i.e.,* moment locality. An example of moment locality is shown in Fig. 1, where two adjacent candidate moments share high level of visual similarity. The local interaction strategy is illustrated in Fig. 2, where the reference moment only interacts with the surrounding moments in the 2D-Map. However, not all relevant moments are overlapping or located close to each other. Following the example in Fig. 1, if the person plays saxophone again in the later part of the video (not showing for the sake of space), and the query becomes "He plays saxophone *again*", then there will be at least two highly relevant moments for playing saxophone, separated by his action of talking in between. To correctly locate the answer, the model needs to understand that "*again*" refers to the second moment of playing saxophone. This calls for a better way of sampling moments for efficient moment-moment interaction, to avoid the full global interaction as shown in Fig. 2. The proposed MS-DETR samples moments for interaction using learnable templates and anchors, illustrated in the third 2D-Map in Fig. 2. We design an anchor-guided moment decoder to interact and aggregate moment features from the encoder in an adaptive and progressive manner. A fixed number of learnable templates paired with dynamic anchors are used to match the moment content and its location. Here, the templates are used to match video content in a moment, and anchors specify the reference start/end positions of the moment because multiple moments may share similar visual features. We then revise the anchors based on the predictions from the last decoder block in an iterative manner. We remark that our method has no assumption on moment locality: the moments can be scattered in diverse locations of the video. Our key contributions are threefold. First, we propose a novel multi-scale visual-linguistic encoder (Section 4.1) to align textual and video features as well as to aggregate language-enhanced semantics of video frames, in a hierarchical manner. Second, we introduce a new anchor-guided moment decoder (Section 4.2) to decode learnable templates into moment candidates, in which we propose an anchor highlight mechanism to guide the decoding. Third, we conduct extensive experiments (Section 5) on three benchmark datasets: ActivityNet Captions, TACoS, and Charades-STA. Our results demonstrate the effectiveness of the proposed MS-DETR. ## 2 Related Work We first briefly review existing NLVL approaches and highlight the differences between our work and other proposal-based solutions. Next, we briefly introduce object detection to provide background for the concept of learnable templates. Natural Language Video Localization. NLVL was first introduced in Hendricks et al. (2017), and since then a good number of solutions have been proposed (Zhang et al., 2022c). As aforementioned, existing methods can be largely grouped into proposal-based and proposal-free methods. Proposals, or candidate moments, can be either predefined (Gao et al., 2017; Hendricks et al., 2017) or computed by proposal generator (Xiao et al., 2021a,b; Liu et al., 2021a). Proposal-free methods output time span (Zhang et al., 2020a, 2022b, 2021a; Liu et al., 2021b) or timestamps (Yuan et al., 2019; Ghosh et al., 2019; Li et al., 2021; Zhou et al., 2021) directly on top of video tokens, without considering the notion of candidate moments. Most proposal-based methods conduct multimodal interaction between video segments and text, then encode moments from the segment features. Typically there is no further interactions among moments. 2D-TAN (Zhang et al., 2020b) is the first to demonstrate the effectiveness of moment-level interaction. However, 2D-TAN assumes moment locality and only enables local interactions among moments as shown in Fig. 2. However, similar moments requiring careful discrimination may be scattered all over the video. This motivates us to go beyond the moment locality assumption and propose moment sampling for interaction, which is a key difference and also a contribution of our work. In this paper, we adapt the concept of learnable templates from DETR framework to achieve dynamic moment sampling. DETR was originally introduced for object detection in computer vision (CV), to be briefed shortly. Most similar to our work is Xiao et al. (2021a), which also uses learnable templates. However, their work directly adopts learnable templates without any adaption to the specific requirements of NLVL. For instance, the answer moment in NLVL needs to match the given text query, whereas in object detection, there is no such requirement. We bridge the gap between NLVL and object detection by introducing a hierarchical encoder and a decoder with an anchor highlight mechanism. These designs greatly improve performance and unveil the potential of DETR for NLVL. At the same time, these designs also make our model much different from the original DETR. ## Transformer-Based Object Detection. Object detection is a fundamental CV task. Transformerbased methods now set a new paradigm that uses learnable templates to sparsely localize objects in images. The core idea is to aggregate encoder features globally, by using (randomly initialized) learnable templates. To achieve end-to-end detection, object detection is reformulated as a set prediction problem, *e.g.,* certain template combinations can be used to identify some specific image objects. Early solutions match predictions with ground-truth one by one using bipartite matching, leading to unstable matching and slow convergence. Recent work alleviates this issue by designing many-to-one assignment (Chen et al., 2022; Jia et al., 2022) or the self-supervision task specifically for learnable templates (Li et al., 2022; Zhang et al., 2022a). Introducing learnable templates to NLVL poses two challenges: *supervision sparsity* and *scale mismatching*. An image typically contains multiple objects and these co-occurred objects all serve as detection objects for supervision. In NLVL, given a good number of candidate moments in a video, there is only one ground-truth. We refer to this phenomenon as supervision sparsity. The scale extremity in NLVL is more severe than that in object detection. The ground truth moments in videos, analogous to objects in images, vary from 3% to 90% in terms of video length. The diverse scales bring the issue of scale mismatching when the learned templates are decoded to cover all encoder features, i.e., the entire video. Hence in MS-DETR, we adapt learnable templates mainly for the purpose of sparsely sampling moments for interaction, rather than as the main backbone. ## 3 Problem Formulation We first present how to map video and text into features, and then define NLVL in feature space. Let V = [ft] t=T −1 t=0 be an untrimmed video with T frames; L = [wj ] j=M−1 j=0 be a natural language query with M words. We uniformly split the video V into N segments (*i.e.,* video tokens) and employ a pre-trained video feature extractor to encode these segments into visual features V = [vi] i=N−1 i=0 . The M words are encoded with pre-trained word embeddings as L = [wj ] j=M−1 j=0 . Given the video and text query in their encoded features (V,L), the task of NLVL is to localize the timestamp pair (ts, te), the start and end timestamp, of the video moment that matches the query. Note that, due to the uniform split to segments, there is a correspondence between ts and te of the original video and the segment Ids in the segment sequence. ## 4 Method The main architecture of the proposed MS-DETR is depicted in Fig. 3. Illustrated in the *feature extraction* part, given visual features V ∈ R dv×N and language query features L ∈ R dw×M, we first project them into a unified dimension d using single layer FFN and decorate them by adding positional encoding, respectively. The linearly projected visual features {v 0 i} i=N−1 i=0 and language query features {w0 j} j=M−1 j=0 are then concatenated and fed into multi-scale vision-language transformer. Next, we mainly detail two main components: multi-scale ![3_image_0.png](3_image_0.png) ## 4.1 Multi-Scale Visual-Language Encoder Many transformer-based methods for cross-modal interaction treat video and language tokens identically, in a unified sequence. However, video and text have completely different syntactic and semantic structures. It is more reasonable to use separate projections for the two modalities, similar to the idea of modality-specific expert Peng et al. (2022). In MS-DETR, we separate the projections by using specifically designed attention modules. Before we further modify the multi-modal attention modules to handle different video resolutions (*i.e.,* multi-scale), we present our attention designs in their base form. We design two sets of attentions: visual cross-modal attention and *linguistic crossmodal attention*, see the middle part of Fig. 3. The two sets are highly similar. For conciseness, we only introduce visual cross-modal attention, which contains language to video (L→V), and video to video (V→V) attentions. The visual cross-modal attention aggregates visual embeddings Vl ∈ RN×d and language embeddings L l ∈ RM×dinto new visual features as Vl+1: $$\mathbf{A}_{L V}^{l+1}={\frac{\mathrm{FFN}(\mathbf{V}^{l})\mathrm{FFN}(\mathbf{L}^{l})}{\sqrt{d_{h}}}}\qquad\qquad(1)$$ $$\mathbf{A}_{V V}^{l+1}={\frac{\mathrm{FFN}(\mathbf{V}^{l})\mathrm{FFN}(\mathbf{V}^{l})}{\sqrt{d_{h}}}}\qquad\qquad(2)$$ $$\mathbf{A}^{l+1}=\mathbf{A}_{L V}^{l+1}\oplus\mathbf{A}_{V V}^{l+1}\qquad\qquad(3)$$ $$\mathbf{V}^{l+1}=\mathrm{Softmax}(\mathbf{A}^{l+1})$$ $$\qquad\qquad\times\left(\mathrm{FFN}(\mathbf{L}^{l})\oplus\mathrm{FFN}(\mathbf{V}^{l})\right)\qquad(4)$$ The linguistic cross-modal attention uses a similar set of equations to model language to language Sequence-reduced Multi-modal Attention. Recall that relative lengths of ground truth range from 3% to 90% to their source videos. A fixed resolution for all moments becomes sub-optimal. To this end, we extend the aforementioned multi-modal attention and build a transformer that is capable of providing hierarchical text-enhanced video features, from high to low temporal resolutions. Our encoder design is motivated by the Pyramid Vision Transformer (PVT) (Wang et al., 2021b), which is a successful application of deploying transformer in segmentation problem. Handling high temporal resolution is a challenge. Directly applying multi-modal attention on high temporal resolution video features suffers from its quadratic complexity as in Eq. 2. Recall that the sequence lengths of key, query, and value in multihead attention (Vaswani et al., 2017) do not have to be the same. Its output has the same length as the query, and the key-value pair keep the same length. Thus, reducing sequence lengths of the key and value simultaneously is an effective way to save computation. Accordingly, we modify V→V attention in the *visual cross-modal attention* module to a sequence-reduced version as follows: $$\mathbf{V}_{r}^{l}=\text{Conv}1\mathbf{D}(\mathbf{V}^{l})$$ $$\mathbf{A}_{VV}^{l+1}=\frac{\text{FFN}(\mathbf{V}^{l})\text{FFN}(\mathbf{V}^{l})}{\sqrt{d_{h}}}\tag{5}$$ $$\mathbf{V}^{l+1}=\text{Softmax}(\mathbf{A}^{l+1})$$ $$\qquad\qquad\times\left(\text{FFN}(\mathbf{L}^{l})\oplus\text{FFN}(\mathbf{V}_{r}^{l})\right)\tag{6}$$ Here, Conv1D is a non-overlapping 1D convolutional Here, Conv1D is a non-overlapping 1D convolution with stride and kernel size set to R. Eq. 2 and Eq. 4 are respectively modified to their new versions in Eq. 5 and Eq. 6. Time complexity is reduced from O(N2) to O( N2 R ). We also apply sequence reduction to V→L attention in the *linguistic cross-modal attention*. Conceptually, this sequence reduction technique can be explained as decomposing the local and global interaction. The local interaction is achieved by convolution and the global interaction by attention. Next, we focus on how to merge high to low temporal resolutions. Temporal Merging To form a hierarchical architecture, a crucial step is a pooling-like step to shrink the temporal scale. We utilize an 1D convolution with overlapping to shrink representations from high to low temporal resolutions. The overlapped convolution allows information flow among convolutional windows, so that the interaction is not constrained locally within windows. With both sequence-reduced multi-modal attention and temporal merging, we form a hierarchical architecture. For the deeper layers in the encoder, which already have a low resolution, we turn off these two components and use the vanilla multi-modal attention. Auxiliary Supervision Losses We design two auxiliary losses: *span loss* and *masked word loss*. Span loss is to enhance the language-conditioned video representations from encoder. We use the video features V(Lenc−1) from the last layer of encoder to predict whether each video segment falls within the ground truth. This auxiliary supervision facilitates the model to distinguish relevant video segments from irrelevant ones. We predict span logits Ssp = F F N(VLenc−1) by passing forward encoder output V(Lenc−1) after a two-layer FFN. Span scores Psp are then calculated from Ssp with a sigmoid function. Then the span loss is computed in Eq. 7, where Ysp ∈ {0, 1}. $${\mathcal{L}}_{s p a n}=\left(\log\mathbf{P}_{s p}\times\mathbf{Y}_{s p}\right)\times\left(\mathbf{P}_{s p}-\mathbf{Y}_{s p}\right)^{2}\ \ (7)$$ Considering ground-truth can be a small portion of the source video, focal loss (Lin et al., 2020) is adopted here to alleviate the label imbalance issue. The masked word loss aims to better align text features and video features. We dynamically replace 15% words from language query during training with randomly initialized mask embedding. The model is then compelled to learn from both textual and video contexts to recover the missing tokens. Text features W(Lenc−1) from last layer of encoder are used to predict the original words before masking. Masked word score is predicted by Pwm = *Sof tmax*(Swm), where ![4_image_0.png](4_image_0.png) $\eqref{eq:walpha}$. Swm = F F N(W(Lenc−1)). We use the cross entropy loss for masked word prediction. $$\mathbf{m},\,\mathbf{Y}_{\mathrm{{wm}}})$$ $${\overset{\mathbf{2}}{-}}m a s k=$$ Lmask = *CrossEntropy*(Pwm, Ywm) (8) Multi-scale Text-enhanced Features After Lenc layers of encoder, we select C text-enhanced video features of different scales from intermediate layer outputs. We re-index the selected outputs {Vi0*· · ·* ViC−1 } into {V0 s*· · ·* VC−1 s } for future reference. ## 4.2 Anchor-Guided Moment Decoder After obtaining the multi-scale text-enhanced video features Vs = {Vcs} c=C−1 c=0 , our focus now is to decode the learnable templates with their corresponding anchors into moment timestamps. Recall that templates aim to match moment content and anchors are the reference start/end positions. Initially, the anchors are uniformly distributed along the video to guarantee at least one anchor falls within the range of ground truth. The moment decoder contains two parts: (i) Moment-moment Interaction, which is achieved by self-attention, and (ii) Anchor Highlighting, which aims to not only highlight the area that is relevant to the current moment but also be aware of the global context. The highlighting, or searching for relevant moments, is achieved through an Anchor Highlight Attention, an modification of the cross attention in DETR with RoI features, shown in Fig. 4. All attentions mentioned above follow the specification of multi-head scaled dot-product defined in Vaswani et al. (2017). Learnable Templates and Anchors In the original DETR (Carion et al., 2020) paper, the learnable templates can be seen as special positional embeddings, to provide a spatial prior of objects. However, the recent success of advanced DETRs (Liu et al., 2022; Meng et al., 2021) motivates us to separately model a moment anchor according to which the attention is constrained. Let k denote the index ![5_image_0.png](5_image_0.png) of templates among the total Nq templates. We define qk as the k th learnable template and (c 0 k , w0 k ) as its initial anchor. Here c and w stand for the center and width of the corresponding moment, which can be easily mapped to the start/end boundary. Anchors will be refined in the decoder, layer by layer. We use (c l k , wlk ) to denote the anchor after refinement of the l th decoder layer. Anchor Highlight Attention. One of our motivations is to discriminate the best matching moment among all candidate moments that share good matching to the text query. To highlight the areas that are similar to the current moment, we modify the attention query to adjust attention weight. Suppose the current anchor is (ck, wk), we can easily locate the corresponding area in the n th multi-scale feature from the encoder output. We use rc,k to denote the features in this area that are taken from the c th multi-scale video features Vcs . Let Rk be the collection of features from all scales. We then construct a function f to map Rk to a single vector Hk ∈ R dto guide the highlight in attention mechanism, illustrated in Fig. 4. Let HNq×d ∈ R be the collection of Hk and M be the moment features after self-attention module in decoder layer, we adjust the attention as follows: $$\begin{array}{c}{{\mathbf{A}_{A H}=\frac{F F N(\mathbf{M}+\mathbf{H})F F N(\mathbf{V}_{s}^{C-1})^{\mathsf{T}}}{\sqrt{d_{h}}}}}\\ {{\mathbf{M}_{A H}=\mathbf{A}_{A H}\times F F N(\mathbf{V}_{s}^{C-1})}}\end{array}\tag{9}$$ Here, AAH refers to the adjusted attention weight, and MAH is the output of the adjusted cross attention. Since H is sampled and transformed from the corresponding anchor areas in encoder outputs Vs, it is essentially the representation of moment content. Therefore, the term H(VC−1 s) T will be more responsive when a specific area from VC−1 s is similar to the moment content. Consequently, the attention above will highlight the areas similar to the current moment. We then refine the anchors based on these highlighted areas, through an offset prediction head as shown in Fig. 4. Anchor Refinement. Based on the predictions from the last decoder block, we revise the anchor with the predicted offsets. This is analogous to the eye skimming process of humans: focuses on a local area in the video and then decides where to move her sight at the next step. The anchors are refined iteratively as shown in Fig. 5. Specifically, we first project the center c l k and scale s l k of the k th anchor at the l th decoder level into logit space, using an inverse sigmoid operation. The offset (∆c l k , ∆w l k ) is added to their logits, then the modified logits are projected back using sigmoid. The whole process is described in Eq. 10. $$\begin{array}{l}{{c_{k}^{m+1}=\sigma\left(\Delta c_{k}^{m}+\sigma^{-1}(c_{k}^{m})\right)}}\\ {{w_{k}^{m+1}=\sigma\left(\Delta w_{k}^{m}+\sigma^{-1}(w_{k}^{m})\right)}}\end{array}\tag{10}$$ Here σ stands for sigmoid function, and σ−1for inverse sigmoid function. Boundary Modeling. After encoding moment candidate features, we pass them through two separate FFNs to predict anchor offset and scores, respectively. Depending on anchor positions, only a small portion of anchors may match with ground truth moments. Among them, we simply select the candidate moment with the largest IoU (intersection over union) with ground truth as our positive sample. A similar label assignment strategy has been used in early studies (Carion et al., 2020). After labeling predictions as positive or negative, we refer to the index of positive prediction as ip. Then we model the boundary with two losses: (i) IoU prediction loss, and (ii) L1 regression loss. Note that, L1 regression loss is only applied to the positive prediction. Let (t k s, tk e) be the timestamps predicted by k th anchor and (t g s, tg e) be the groundtruth timestamps, we calculate L1 regression loss and IoU prediction loss as follows: $$\begin{array}{l}{{{\mathcal L}_{L1}=\frac{1}{2}\left(|t_{s}^{i_{p}}-t_{s}^{g}|+|t_{e}^{i_{p}}-t_{e}^{g}|\right)}}\\ {{{\mathcal L}_{I o U}=\frac{1}{N_{q}}\sum_{k\in N_{q}}\mathrm{Focal}(\mathrm{TrIoU}_{k},o_{k})}}\end{array}\tag{11}$$ Here *T rIoU* truncates IoU between (t k s, tk e) and (t g s, tg e) below a threshold θ and set IoU of the assigned positive prediction to 1. Different from Carion et al. (2020), by using *T rIoU*, we not only calculate IoU loss for the positive prediction but also consider the hard negative predictions which have large overlapping with ground-truth. Note that, IoU prediction loss and L1 regression loss are calculated for all decoder layer outputs. ## 4.3 Training And Inference The overall training loss of MS-DETR contains three losses: $$\begin{array}{l}{{\cal L}=\lambda_{span}{\cal L}_{span}+\lambda_{mask}{\cal L}_{mask}}\\ {\phantom{\frac{}{}}+\sum_{m\in{\cal L}_{dec}}(\lambda_{IoU}{\cal L}_{IoU}+\lambda_{L1}{\cal L}_{L1})}\end{array}\tag{12}$$ To stabilize training, we introduce an extra denoising group of templates and pass them through the decoder, motivated by (Chen et al., 2022). The overall loss is averaged over losses calculated from two groups independently. During inference, we deprecate the denoising group and use the main group only. All moments are sorted by their scores and their anchors are converted from (*c, w*) to start/end format. We apply truncation to start/end timestamps to deal with out-of-range values, since no constraint is attached to (*c, w*) during training. ## 5 Experiments We evaluate MS-DETR against baselines on three public benchmarks: ActivityNet Captions (Krishna et al., 2017), TACoS (Regneri et al., 2013), and Charades-STA (Gao et al., 2017). The three datasets cover videos from different domains and lengths (see Appendix A.1 for video distributions and train/dev/test splits). Following prior work (Zhang et al., 2021a), we adopt "R@*n, IoU* = µ" and "mIoU" as evaluation metrics. R@*n, IoU* = µ is the percentage of testing samples that have at least one of top-n results hitting ground truth, where "hitting" means an overlapping with IoU ≥ µ. mIoU denotes the average IoU over all test samples. We set n = 1 and µ = {0.3, 0.5, 0.7}. In our comparison and discussion, we mainly focus on µ = 0.7 as large IoU means high-quality matching. ## 5.1 Comparison With The State-Of-The-Arts Results on the three datasets are compared in Tables 1, 2, and 3, respectively. Baseline results are mostly cited from (Zhang et al., 2021a). We also include GTR (Cao et al., 2021), LP-Net (Xiao et al., 2021a) and MMN (Wang et al., 2022) for a complete comparison. | Method | R@1, IoU = µ | mIoU | | | |----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | DEBUG | 55.91 | 39.72 | - | 39.51 | | ExCL | 63.00 | 43.60 | 24.10 | - | | SCDM | 54.80 | 36.75 | 19.86 | - | | CBP | 54.30 | 35.76 | 17.80 | - | | GDP | 56.17 | 39.27 | - | 39.80 | | 2D-TAN | 59.45 | 44.51 | 27.38 | - | | TSP-PRL | 56.08 | 38.76 | - | 39.21 | | TMLGA | 51.28 | 33.04 | 19.26 | - | | VSLNet | 63.16 | 43.22 | 26.16 | 43.19 | | DRN | - | 45.45 | 24.36 | - | | LGI | 58.52 | 41.51 | 23.07 | - | | SeqPAN | 61.65 | 45.50 | 28.37 | 45.11 | | GTR | - | 49.67 | 28.45 | - | | LP-Net | 64.29 | 45.92 | 25.39 | 44.72 | | MMN | 65.05 | 48.59 | 29.26 | - | | MS-DETR | 62.12 | 48.69 | 31.15 | 46.82 | Table 1: Results on ActivityNet Captions. The best results are in bold face and second best underlined. | Method | R@1, IoU = µ | mIoU | | | |----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | TGN | 21.77 | 18.90 | - | - | | ACL | 24.17 | 20.01 | - | - | | DEBUG | 23.45 | 11.72 | - | 16.03 | | SCDM | 26.11 | 21.17 | - | - | | CBP | 27.31 | 24.79 | 19.10 | 21.59 | | GDP | 24.14 | 13.90 | - | 16.18 | | TMLGA | 24.54 | 21.65 | 16.46 | - | | VSLNet | 29.61 | 24.27 | 20.03 | 24.11 | | DRN | - | 23.17 | - | - | | SeqPAN | 31.72 | 27.19 | 21.65 | 25.86 | | DRN | - | 23.17 | - | - | | CMIN | 24.64 | 18.05 | - | - | | 2D-TAN | 37.29 | 25.32 | - | - | | GTR | 40.39 | 30.22 | - | - | | MMN | 39.24 | 26.17 | - | - | | MS-DETR | 47.66 | 37.36 | 25.81 | 35.09 | MS-DETR achieves the best R@1, µ = 0.7 and mIoU on ActivityNet and TACos, and the second best on Charades-STA. Our model achieves reasonably good results on smaller µ's. However, large µ ensures high-quality matching. A possible reason for the results on Charades-STA is that the videos in this dataset are very short (30 seconds on average), making moment-level interaction less necessary. ## 5.2 Ablation Study We perform ablation studies on ActivityNet Captions for the effectiveness MS-DETR. Multi-scale Encoder. We evaluate four variants to study the effectiveness of multi-scale design in our transformer encoder. First, to evaluate whether | Method | R@1, IoU = µ | mIoU | | | |----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | DEBUG | 54.95 | 37.39 | 17.69 | 36.34 | | ExCL | 61.50 | 44.10 | 22.40 | - | | MAN | - | 46.53 | 22.72 | - | | SCDM | - | 54.44 | 33.43 | - | | CBP | - | 36.80 | 18.87 | - | | GDP | 54.54 | 39.47 | 18.49 | - | | 2D-TAN | - | 39.81 | 23.31 | - | | TSP-PRL | - | 45.30 | 24.73 | 40.93 | | MMN | 47.31 | 27.28 | - | - | | VSLNet | 70.46 | 54.19 | 35.22 | 50.02 | | LGI | 72.96 | 59.46 | 35.48 | - | | SeqPAN | 73.84 | 60.86 | 41.34 | 53.92 | | MS-DETR | 68.68 | 57.72 | 37.40 | 50.12 | Table 3: Results on Charades-STA, best results in bold face, and second best underlined. Table 4: Ablation study on multi-scale hierarchical encoder. hierarchical design benefits cross-modal interaction, the 'uni-scale' variant replaces all sequencereduced layers with normal layers without resolution shrinkage, and set the number of clips to 32. The multi-scale transformer now degrades to a uniscale cross-modal transformer. To study the contribution of encoding moment contents R for anchor highlighting in multiple scales, the 'single-scale' variant selects the output of the last encoder layer only and fuses it to attention query, while keeping encoder's hierarchical structure. Then, we study the effect of arranging sequence-reduced layers in different positions in the 5 encoder layers. We compare two arrangements "BBBRR" and "RBBBR" against MS-DETR's "RRBBB". Here 'R' means sequence-reduced and 'B' means the base version. Results in Table 4 suggest the effectiveness of multi-scale hierarchical encoder. Performance drops with the removal of the multi-scale mechanism, or the other arrangement of sequencereduced layers. Placing sequence-reduced version at shallow layers serves the purpose of reducing computational cost while benefiting performance. Anchor Highlight Attention is a variant of standard cross attention Vaswani et al. (2017). It is used to highlight similar content with correspond- Table 5: Anchor highlight attention versus standard cross attention without anchor highlighting. | Method | R@1, IoU = µ | mIoU | | | |--------------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | MS-DETR | 62.12 | 48.29 | 31.15 | 46.82 | | uni-scale | 61.08 | 47.85 | 30.69 | 45.62 | | single-scale | 61.57 | 47.86 | 30.91 | 45.86 | | BBBRR | 60.99 | 46.97 | 30.00 | 44.84 | | RBBBR | 61.42 | 47.14 | 30.05 | 45.48 | | Methods | R@1, IoU = µ | mIoU | | | |-------------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | MS-DETR | 62.12 | 48.29 | 31.15 | 46.82 | | CrossAtten. | 61.25 | 46.05 | 27.94 | 44.30 | Methods R@1, IoU = µ mIoU µ = 0.3 µ = 0.5 µ = 0.7 MS-DETR 62.12 48.29 31.15 **46.82** w/o L*span* 58.67 45.75 30.15 44.06 w/o L*mask* 62.04 47.9 30.17 45.40 w/o both 57.50 46.07 30.03 43.82 Table 6: The impact of auxiliary losses. ing moments across the video. We compare its design with the standard cross attention. Table 5 shows that anchor highlight attention outperforms standard cross attention, by a large margin. This justifies the advantage of using anchor highlight attention and dynamic anchor jointly, to narrow the range of attention to anchor areas. The Auxiliary Loss. We use two auxiliary supervision losses, span loss and word mask loss, in our encoder (see Section 4.1). Table 6 reports the results of removing either one or both auxiliary losses. Results suggest that both auxiliary losses benefit MS-DETR, and span loss contributes more to the effectiveness of MS-DETR. That is, supervising encoder to discriminate whether segments fall within the ground-truth area is important for vision-language alignment. Hyper-parameter Study. Results of the choices of the number of encoder/decoder blocks, and number of denoising groups for training stabilization are in Appendix A.3. ## 6 Conclusion In this paper, we adapt DETR framework from object detection to NLVL. With the proposed MSDETR, we are able to model moment-moment interaction in a dynamic manner. Specifically, we design a multi-scale visual-linguistic encoder to learn hierarchical text-enhanced video features, and an anchor-guided moment decoder to guide the attention with dynamic anchors for iterative anchor refinement. The promising results on three benchmarks suggest that moment-moment interaction for NLVL can be achieved in an efficient and effective manner. ## 7 Limitation The limitation of this paper are twofold. First, our method does not provide a recipe for data imbalancement in NLVL task. Thus, our method does not guarantee the effectiveness on edge cases. Second, the choice of feature extractor is considered relatively outdated. 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In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, June 19-25, 2021, pages 8445–8454. Computer Vision Foundation / IEEE. | Methods | R@1, IoU = µ | mIoU | | | |-----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | Enc3 | 62.47 | 48.15 | 30.54 | 45.80 | | Enc4 | 61.17 | 47.87 | 30.41 | 44.91 | | MS-DETR | 62.12 | 48.29 | 31.15 | 46.82 | | Enc6 | 62.05 | 48.00 | 31.03 | 45.71 | Table 7: The impact on number of encoder layers. ## A Appendix A.1 Dataset Details ActivityNet Captions (Krishna et al., 2017) contains over 20K videos paired with 100K queries with an average duration of 2 minutes. We use the dataset split "val_1" as our validation set and "val_2" as our testing set. In our setting, 37, 417, 17, 505, and 17, 031 moment-sentence pairs are used for training, validation, and testing, respectively. **TACoS** (Regneri et al., 2013) includes 127 videos about cooking activities. The average duration of videos in TACoS is 7 minutes. We follow the standard split which includes 10, 146, 4, 589, and 4, 083 moment-sentence pairs for training, validation, and testing. **Charades-STA** (Gao et al., 2017) is built on Charades and contains 6, 672 videos of daily indoor activities. Charades-STA has 16, 128 sentence-moment pairs in total, where 12, 408 pairs are for training and 3, 720 pairs for testing. The average duration of the videos is 30s. ## A.2 Implementation Details We use AdamW with learning rate of 3 × 10−4 and batch size of 32 for optimization. We follow (Zhang et al., 2020b) and use pretrained 3D Inception network to extract features from videos. The number of sampled video frames is set to 512 for ActivityNet Caption and TACoS and 1024 for Charades-STA. For MS-DETR architecture, we use a 5-layers encoder and a 5-layers decoder with all hidden sizes set to 512. For inference, we select the proposal with highest score from the last decoder layer as our prediction. As for the specific choice of f mentioned in Section 4.2, we use RoIAlign to extract multi-scale feature R, then concatenate them and pass them through an FFN. One extra denoising group is used for stabilizing training. The loss is then averaged over two groups. During inference, No extra operation like Non-Maximum Suppression (NMS) is required. All experiments are run on a single A100 GPU. The reported versions take roughly 8-10 GPU hours for training. | Methods | R@1, IoU = µ | mIoU | | | |-----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | Dec2 | 60.53 | 46.54 | 29.12 | 44.40 | | Dec3 | 62.42 | 47.92 | 30.11 | 45.43 | | Dec4 | 61.14 | 47.03 | 30.13 | 44.72 | | MS-DETR | 62.12 | 48.29 | 31.15 | 46.82 | | Dec6 | 61.30 | 47.83 | 31.65 | 46.32 | Table 8: The impact of the number of decoder layers. | Methods | R@1, IoU = µ | mIoU | | | |-----------|----------------|---------|-------|-------| | µ = 0.3 | µ = 0.5 | µ = 0.7 | | | | DN0 | 61.50 | 47.94 | 30.83 | 45.04 | | MS-DETR | 62.12 | 48.29 | 31.15 | 46.82 | | DN2 | 62.13 | 47.74 | 30.91 | 45.6 | | DN3 | 62.03 | 47.55 | 30.84 | 45.5 | ## A.3 Hyper-Parameter Study Number of Encoder/Decoder Blocks We study the impact of the number of encoder and decoder blocks. We evaluate one of them from 2 to 6, while keeping the other fixed to 5. The performance across various numbers of encoder and decoder blocks are listed in Table 7. Best performance is achieved by Lenc = 5 and Ldec = 5. Though the setting of Ldec = 6 has slightly larger "R@1*, IoU* = 0.7", poorer "*mIoU*" is observed. We speculate that the cause is overfitting on some overly confident examples. Number of Denoising Groups. We study the effectiveness of using different numbers of denoising groups in training stabilization. The results are evaluated with the number of denoising groups ranging from 0 to 3, in Table 9. We observe the performance increases after using one denoising group, then gradually decreases. We suspect there is a trade-off between training stability and the ability to escape local minima. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✗ A2. Did you discuss any potential risks of your work? Our paper uses public benchmarks and does not have any risk of ethics or infringement. ✓ A3. Do the abstract and introduction summarize the paper's main claims? abstract and section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 5 ✓ B1. Did you cite the creators of artifacts you used? section 5 and A.1 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We use three benchmarks, which are all open to public B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No, the benchmarks we use are all open to the public and do not contain any private information. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. section A.1 ## C ✓ **Did You Run Computational Experiments?** Section 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? section A.2 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section A.2 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Not applicable. Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

levy-etal-2023-diverse

Diverse Demonstrations Improve In-context Compositional Generalization

https://aclanthology.org/2023.acl-long.78

In-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted with demonstrations that are similar to the input utterance. However, in the setup of compositional generalization, where models are tested on outputs with structures that are absent from the training set, selecting similar demonstrations is insufficient, as often no example will be similar enough to the input. In this work, we propose a method to select diverse demonstrations that aims to collectively cover all of the structures required in the output program, in order to encourage the model to generalize to new structures from these demonstrations. We empirically show that combining diverse demonstrations with in-context learning substantially improves performance across three compositional generalization semantic parsing datasets in the pure in-context learning setup and when combined with finetuning.

# Diverse Demonstrations Improve In-Context Compositional Generalization Itay Levy∗ Ben Bogin∗ **Jonathan Berant** The Blavatnik School of Computer Science, Tel-Aviv University {itay.levy,ben.bogin,joberant}@cs.tau.ac.il ## Abstract In-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted with demonstrations that are *similar* to the input utterance. However, in the setup of compositional generalization, where models are tested on outputs with structures that are absent from the training set, selecting similar demonstrations is insufficient, as often no example will be similar enough to the input. In this work, we propose a method to select *diverse* demonstrations that aims to collectively cover all of the structures required in the output program, in order to encourage the model to generalize to new structures from these demonstrations. We empirically show that combining diverse demonstrations with in-context learning substantially improves performance across three compositional generalization semantic parsing datasets in the pure in-context learning setup and when combined with finetuning.1 ## 1 Introduction Despite strong performance of pretrained language models (LMs) across many tasks, they have been shown to struggle in a compositional generalization setting (Lake and Baroni, 2018; Furrer et al., 2020; Shaw et al., 2021), when tested on their ability to process and generate novel combinations of previously observed elements. For example, a model might fail to interpret the request "Book a meeting with Jake's supervisor" even when *"Book a meeting with Jake"* and *"Who is Jake's supervisor?"* were observed during training. In semantic parsing, the task of mapping natural language utterances to formal queries, such generalization is important (especially in a real-world setting), since models are required to interpret new combinations that are *Equal contribution 1Our code is available at: https://github.com/itayle/ diverse-demonstrations ![0_image_0.png](0_image_0.png) not covered by the annotated training data (Herzig and Berant, 2019; Yin et al., 2021). Recently, large LMs have shown impressive performance on downstream tasks by conditioning on a text-based prompt that contains a few training examples. This type of few-shot inference is known as *in-context learning* (ICL, Brown et al., 2020). A core component of in-context learning is the set of examples in the prompt, often termed task *demonstrations*. With the right demonstrations, ICL can be an effective approach to improving LMs' compositional generalization abilities (Qiu et al., 2022b). Selecting a relevant set of demonstrations is crucial for generalization. However, most past work only considered the relevance of each example in isolation, ignoring the quality of the entire set of examples (Liu et al., 2022). For instance, a retriever can be used to select the examples most similar to the input (Rubin et al., 2022). A set of demonstra1401 ![1_image_0.png](1_image_0.png) tions that are all highly relevant but highly similar to one another may not be as effective as a more *diverse* set. In compositional splits, where no single demonstration is sufficiently similar to the input, choosing diverse demonstrations can be especially beneficial since it leads to better coverage of structures in the target program (Fig. 1). In this paper, we study how to leverage ICL to improve compositional generalization for semantic parsing, by optimizing the entire set of demonstrations and increasing the diversity of examples in this set. We investigate two approaches for increasing diversity: (a) a *coverage-based* approach, where we define a set of elements conditioned on the input utterance, and select examples that cover those elements (e.g., covering potential substructures in the output program), and (b) a second approach, where we select a subset of examples that are most dissimilar from one another, such that diversity is independent of the input utterance. Empirically, we find that coverage-based diversity results in better performance. Our method can be used in the "pure" in-context learning setup without finetuning, which leverages the ability of large LMs, such as Codex (Chen et al., 2021), to generalize from the selected diverse demonstrations. Furthermore, it can be combined with finetuning by training a model with demonstrations as part of the input. This can be viewed as meta-learning, where the model learns to use demonstrations during training and build new structures based on them during inference (Finn et al., 2017; Lake, 2019; Conklin et al., 2021; Min et al., 2022; Chen et al., 2022). It can, however, lead to an over-reliance on demonstrations, especially in compositional splits. We address this by using "noisy" demonstrations during training. We empirically test our method on three compositional generalization semantic parsing datasets. We show that diverse demonstrations, both with and without finetuning, improve performance by up to 23 absolute points (e.g., 50.3 → 73.5 on SMCalFlow-CS) compared to a baseline that retrieves demonstrations according to similarity alone, and lead to state-of-the-art results in multiple compositional setups. Finally, we show that our method reduces the number of demonstrations needed for generalization and improves test performance on hard examples. ## 2 Diversity For Compositional Generalization In semantic parsing, we define compositional splits of datasets as splits where train and test programs do not overlap (Finegan-Dollak et al., 2018). Recent work has shown that increasing the number of different program structures a model sees during training improves performance on compositional splits. This can be done by augmenting the training set (Qiu et al., 2022a) or through efficient sampling of diverse examples (Oren et al., 2021; Bogin et al., 2022; Gupta et al., 2022). While past work focused on increasing structure diversity in the *training* set, we focus on diversity in the *demonstration set* within an ICL setup. Increasing diversity is important as we want the demonstrations to *cover* all structures of the expected output program. In the few-shot setting, where the model is unfamiliar with the formal language of the output programs, increasing coverage also improves generalization simply since otherwise the model will be unaware of the required program symbols (predicates and logical operators). However, selecting demonstrations that cover larger *structures* (sub-trees of the program tree) are potentially more beneficial, for two reasons: (1) it reduces the amount of new structures that the model needs to produce, making demonstration fusion easier, and (2) it exposes the model to structure compositions in different contexts, providing the model with valuable information about how structures can be composed in the data. ## 3 Diverse Demonstrations Selection Problem setup Given a training set T = {(xi, yi)} n i=1 containing utterance-program pairs and a test utterance xtest, our objective is to select a subset of training examples D = {(xj , yj )} k j=1 ⊂ T , where k ≪ n, termed demonstrations. Those demonstrations are then formatted as a text-based prompt P. When feeding the concatenation of the prompt and the test utterance ([P; xtest]) to the model, the desired output is ytest. Overview Fig. 2 provides an overview of our framework for obtaining and leveraging diverse demonstrations for better compositional generalization. Given an input utterance, xtest, we propose two approaches for selecting demonstrations. In the first (§3.1), we optimize *coverage*: we define a set of elements that we want our demonstrations to cover (either structures in the program or utterance words), and then iteratively select examples that contain these elements. The second approach (§3.2) increases diversity by selecting a subset of examples with minimal similarity. Fig. 2 shows an example of the former approach (*Cover-LS*), where we predict and then attempt to cover *local structures* (LS), i.e., sub-trees of the output program. Local structures were shown to be key for compositional generalization in Bogin et al. (2022). Having selected demonstrations, we use them to construct a prompt (§3.3). We show that our method can be combined with finetuning to metatrain the model to learn in-context (§3.4). ## 3.1 Coverage-Based Selection Bogin et al. (2022) have recently shown, in the context of finetuning semantic parsers, that models fail to generalize to programs with local structures that were not observed at training time, where local structures of a program are defined to be a set of its sub-trees. Inspired by this observation, we propose **Cover-LS**, an algorithm that given the test utterance xtest, attempts to choose examples that collectively cover as many local structures as possible from the set Sytest of local structures of the program ytest. Since we have no access to ytest at test time, we predict what local structures are likely using an auxiliary model, assuming that predicting local structures is *easier* than predicting the entire program. Then, we iteratively select examples that cover the predicted local structures. Local structures definition We follow the definition of Bogin et al. (2022), and given a program y, convert it to its abstract syntax tree, where each tree node is a program symbol and parent-child edges connect functions to their arguments. In addition, we add "sibling" edges between consecutive arguments. The local structures, Sytest , are a subset of all of the connected sub-graphs in the abstract syntax tree (e.g., state→next_to_2 and most→state→loc_1 in Fig. 2, see more examples in Tab. 8), as defined in App. B. Unlike Bogin et al. (2022), we consider local structures with any number of nodes. In addition, we anonymize programs by replacing values such as strings and numbers with constants (string and number), since such values are usually not relevant for program coverage. Predicting local structures As mentioned, we assume predicting local structures is easier than predicting an entire program. Thus, we train an auxiliary model by finetuning T5 (Raffel et al., 2020) on the training set in the standard manner, training it to output anonymized programs given input utterances with no demonstrations. Then, for each test utterance, xtest, we use beam search to output B candidate programs {y˜b} B b=1 and define the set of local structures as Sy˜test =SB b=1 Sy˜b . Covering local structures Our goal is to choose a set of demonstrations, D, that covers the local structures in Sy˜test . Choosing an example for each local structure is infeasible due to prompt length limitations, and thus we propose Alg. 1, whose goal is to choose a small set of demonstrations that are (a) similar to the test utterance xtest and (b) cover as many local structures in Sy˜test as possible. We sort the LSs based on their size (number of nodes) in descending order (line 2). By first selecting training examples with programs that contain *larger* LSs from Sy˜test , we are more likely to include training examples similar to the test utterance, which should improve few-shot performance. Then, we iterate over all LSs, and for each local structure s we *retrieve* the most similar training example that contains s (line 6), and add it to D 1403 Algorithm 1: Cover-LS Algorithm ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) Input :List of candidate local structures to cover S ; Pool of training examples T ; Retriever R ; Desired number of output examples k Output :Set of training examples D 1 D = ∅ 2 Sort S from largest to smallest 3 **while** |D| < k do 4 Suncovered = S ![3_image_2.png](3_image_2.png) (line 7). We then update the pool of LSs such that it will include only LSs that are not yet covered (line 8). To further encourage diversity, we remove from our example pool all examples that share the same template (program after anonymization) as the chosen examples (line 9). We keep choosing examples until reaching the desired amount of demonstrations, which might result in choosing more than one example for each local structure (lines 3-4). We assume (line 6) access to a retriever that takes as input an utterance and returns similar training examples, from which we filter only examples that contain the desired structure. A variety of retrievers can be used, such as BM25 (Robertson and Zaragoza, 2009) or SBERT (Reimers and Gurevych, 2019). We observe that in our setup, the running time of Cover-LS is negligible compared to the decoding time of the LMs. Utterance coverage We propose a simpler variant that does not require predicting a set of local structures with an auxiliary model. This variant, termed Cover-Utt, uses the same coverage-oriented algorithm, but covers *words* in the input utterance, rather than predicted local structures. This is beneficial when the quality of the auxiliary model, and consequently predicted LSs, is low. ## 3.2 Diversity Without Coverage The primary challenge with coverage-based approaches is identifying the elements that need to be covered. An alternative approach is to define diversity more explicitly and select a subset of demonstrations that are dissimilar from one another (while being relevant for the input utterance). A natural approach for choosing a subset of high-quality and diverse demonstrations from the training set is Determinantal Point Process (DPP) (Kulesza and Taskar, 2012), a probabilistic model that defines a probability distribution over subsets of items, giving high probability to subsets that contain *relevant* and *diverse* items. DPP requires a relevance score for each item and a *similarity score* between pairs of items. In our case, we define the relevance of a demonstration through its retriever score for the input test utterance. To compute the similarity between demonstration pairs, we first extract LSs and compute tf-idf vectors for each demonstration. The similarity of each pair is then the cosine similarity between their tf-idf vectors. Full implementation details are in App. E. ## 3.3 Prompt Construction We order the chosen demonstrations according to their retriever score with respect to the input utterance in ascending order, in accordance to common practices (Liu et al., 2022). When finetuning the model (§3.4), demonstrations are shuffled. Demonstrations are formatted to a prompt according to the format in App. D, concatenated with the test utterance, and fed to the model. ## 3.4 Finetuning With Prompts Despite the success of "pure" in-context learning, where model parameters are frozen, it has been by and large restricted to very large LMs. Conversely, finetuning requires more training data, but performs well even with smaller models. In-context learning can be easily integrated with finetuning by training a model with demonstrations as part of the input. This paradigm can be considered as meta-learning, where the model learns how to use demonstrations during training (Min et al., 2022). When meta-learning is used in the i.i.d. setup, where the training and test examples are drawn from the same distribution, one can use the same procedure to select demonstrations at both training time and test time. However, in a compositional generalization setup, this does not work: at training time, the model will observe demonstrations that are similar to the target output and will learn to heavily rely on demonstrations and copy large chunks of them. Thus, the model will not learn to compose demonstration parts and will struggle with examples drawn from a different distribution. To address this phenomenon, which we term over-copying, past work (Pasupat et al., 2021; | Dataset | Example Can you make a meeting with David Lax 's reports ? (Yield :output (CreateCommitEventWrapper :event (CreatePreflightEventWrapper :constraint (Constraint[Event] :attendees (AttendeeListHasPeople :people (FindReports :recipient (Execute :intension (refer (extensionConstraint (RecipientWithNameLike :constraint (Constraint[Recipient]) :name # (PersonName "David Lax"))))))))))) | |---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | SMCalFlow-CS | CreateEvent (with_attendee (FindReports (recipient= refer (Recipient? | | Simple | (name= LIKE (David Lax)))))) | | (natural) SMCalFlow-CS GeoQuery | What is the most populous state through which the mississippi runs ? | | (natural) | largest_one (population_1 (state (traverse_1 (riverid ("mississippi"))))) What is the color of square dog ? | | COVR-10 (synthetic) | query_attr[color] (filter (square, find (dog))) Table 1: An example utterance-program pair for each of the datasets. | Zemlyanskiy et al., 2022) used *sampling* to add noise to the demonstrations. Here, we also reduce the similarity of demonstrations to the input utterance, but with a simpler approach. Recall that our Cover-LS algorithm picks similar examples by (a) finding demonstrations that share *large* LSs with the predicted program (lines 2-6 in Alg. 1), and (b) using a retriever to find the most similar examples among these. To address over-copying, we modify this: at training time, we only consider LSs of size 1, i.e., program symbols, and for each such LS we randomly choose an example that contains this symbol rather than use a powerful retriever. ## 4 Experiments We present our experimental setup and results on different compositional semantic parsing tasks, with finetuning (FT) and without (NoFT). ## 4.1 Datasets We evaluate our methods on three datasets (examples in Tab. 1). SMCalFlow-CS is a few-shot compositional generalization dataset proposed by Yin et al. (2021) derived from SMCalFlow (Andreas et al., 2020). It contains single-turn natural sentences involving two domains (organization structure and event creation), each having its own set of program symbols. The test set of the compositional splits contains only cross-domain examples, where both domains appear. We show results for a few-shot setting (split k-C, where k ∈ {8, 16, 32}) where the training set includes only k cross-domain examples, and a zero-shot setting (split 0-C). We also evaluate on an i.i.d. split2 where the test set contains only single-domain examples. Prior studies on the dataset employed LISP and LISPRESS program formats, resulting in v1 and v2 versions, respectively (see an example in Tab. 9). We default to using v1, unless otherwise specified. For our FT experiments, we use **SMCalFlowCS Simple**, which contains the same utterances as SMCalFlow-CS, but with programs that use a simplified syntax provided by Meron (2022). We opt for this version because programs are much shorter, leading to a smaller memory footprint and accelerating training and inference. GeoQuery (Zelle and Mooney, 1996; Tang and Mooney, 2001) contains 880 natural language questions about US geography. We use the standard (i.i.d.) and compositional splits created by Shaw et al. (2021): (1) template split, where target programs are anonymized into templates and then the templates are randomly split between training and test sets (Finegan-Dollak et al., 2018); (2) TMCD split, which makes the distributions of compounds in training and test sets as divergent as possible (Keysers et al., 2020); and (3) length split, where test sequences are longer than training ones. Similar to prior work, we average results across three TMCD and template splits to reduce variance caused by the small dataset size. COVR-10 COVR (Bogin et al., 2022) is a synthetic dataset based on a variable-free functional language. COVR-10 contains 10 compositional grammar splits, in which each test set includes programs featuring a particular set of local structures not observed at training time. Results are averaged 2The split we use for the i.i.d. setup is 8-S. | GeoQuery | SMCalFlow-CS | COVR-10 | | | | | | | | | |-----------------------------|----------------|-----------|------|--------|------|------|------|------|------|------| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 0-C | 8-C | 16-C | 32-C | | | | T5 (fine tuned w/o prompts) | 90.3 | 85.9 | 75.4 | 36.0 | 88.5 | 0.0 | 34.5 | 39.0 | 50.0 | 21.5 | | Random | 53.7 | 49.7 | 42.0 | 30.7 | 43.0 | 1.3 | 0.3 | 0.7 | 2.0 | 69.4 | | Top-K | 86.3 | 78.0 | 71.8 | 64.3 | 81.7 | 17.0 | 34.0 | 35.7 | 50.3 | 61.8 | | Cover-Utt (ours) | 89.0 | 82.1 | 77.8 | 73.7 | 83.3 | 35.3 | 51.0 | 51.3 | 69.7 | 78.1 | | DPP (ours) | 87.0 | 81.2 | 77.8 | 74.3 | 79.3 | 34.7 | 44.0 | 50.0 | 59.7 | 62.7 | | Cover-LS (ours) | 88.7 | 85.3 | 79.4 | 72.7 | 86.0 | 0.3 | 53.3 | 58.3 | 73.5 | 64.4 | | Top-K (Oracle) | 86.3 | 74.5 | 76.2 | 55.7 | 85.0 | 0.0 | 33.0 | 54.0 | 59.6 | 35.4 | | Cover-LS (Oracle) | 86.3 | 81.2 | 82.8 | 74.0 | 84.3 | 40.7 | 77.3 | 73.5 | 75.3 | 83.2 | ## Across The 10 Splits. 4.2 Experimental Setup Models We use Codex (code-davinci-002) (Chen et al., 2021; Ouyang et al., 2022) for all NoFT experiments, and T5-large (Raffel et al., 2020) for FT experiments. T5-large is used to predict LSs in both the NoFT and FT setups. Evaluation Like prior work, we use exact match accuracy as the main metric for evaluation. Results are averaged over 3 random seeds unless stated otherwise. In the FT setup, we use the entire test set for evaluation. In the NoFT setup, we use 100 test examples due to rate limits of the Codex inference API (and another 100 development examples for hyperparameter tuning). Prompt We use a prompt size of k = 24 for NoFT experiments and k = 3 for FT experiments, unless stated otherwise. A prompt is truncated when its length exceeds the model's context length (excluding the tokens reserved for generation). In FT experiments, we included only the programs in our demonstrations and discarded their utterances, due to limitations of memory and sequence length (preliminary experiments with utterances showed this does not affect accuracy). Retrievers In NoFT setup, we use BM25 over lower-cased utterance words. In FT setup, we use BM25 over predicted program symbols in Sy˜test (predicted using T5). In Cover-LS experiments we use a random retriever at training time to avoid over-copying. We analyze other possible retriever choices in §4.5. Hyperparameter tuning and model selection We train two types of models in this work: (a) models for predicting LSs, and (b) models finetuned with prompts. For both cases, we use the development set whenever it is available for model selection, otherwise, we use the last checkpoint. Similarly, we use the development set to tune the number of beam candidates B when predicting local structures, and if there is no development set, we set B = 1. We detail finetuning hyperparameters in App. F. Local structure size In some experiments, we limit the maximum size of local structures (the number of nodes they contain). A subscript notation (Cover-LSd or DPPd) indicates a limit up to size d. ## 4.3 Baselines Finetuning without prompts Vanilla-finetuned T5 model which is trained without demonstrations, similar to the one used to predict LSs (§3.1), except that it is trained on non-anonymized programs. Top-K We construct the prompt with the top-k examples that are most similar to xtest according to the retriever score. Random We construct a prompt by randomly sampling k training examples without repetition. We also conduct oracle experiments, where at test time we have access to ytest both for retrieval and LS coverage. The retriever takes as input the gold program and scores demonstrations using BM25 over the gold program symbols. In oracle Cover-LS, we cover local structures from Sytest without predicting them with a model. ## 4.4 Main Results NoFT We observe (Tab. 2) that all methods for increasing diversity (Cover-Utt, DPP and Cover-LS) outperform Top-K, which selects similar demonstrations without accounting for diversity, in 7 out of 8 compositional splits. In fact, all non-oracle diversity methods outperform an *oracle* Top-K in | GeoQuery | SMCalFlow-CS | | | | | | | | | | | | | |------------------------------------------|----------------|------|------|--------|-------------|-------------|-------------|-------------|-------------|--------|--------|--------|--------| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 0-C | 8-C | 16-C | 32-C | | | | | | | T5 Base (FT, Qiu et al. 2022a) | 93.3 | 84.8 | 69.2 | 41.8 | 84.7 / | - | - | 34.7 / | - | 44.7 / | - | 59.0 / | - | | T5 Base + CSL-Aug (FT, Qiu et al. 2022a) | 93.3 | 89.3 | 74.9 | 67.8 | 83.5 / | - | - | 51.6 / | - | 61.4 / | - | 70.4 / | - | | T5 Base (FT, Qiu et al. 2022b) | 92.9 | 84.8 | 69.2 | 40.0 | - | / 82.8 | - | - | / 21.7 | - | / 43.6 | - | / 58.9 | | T5 11B (Prompt Tuning, Qiu et al. 2022b) | 93.6 | 87.7 | 81.2 | 41.5 | - | / 83.1 | - | - | / 0.0 | - | / 10.0 | - | / 23.6 | | PaLM 62B (FT, Qiu et al. 2022b) | 92.5 | 85.1 | 72.7 | 44.2 | - | / 82.2 | - | - | / 26.9 | - | / 34.7 | - | / 51.1 | | PaLM 540B (ICL, Qiu et al. 2022b) | 86.8 | 76.6 | 63.6 | 57.9 | - | / 58.3 | - | - | / 4.7 | - | / 5.0 | - | / 11.7 | | T5 Large (fine tuned w/o prompts) | 92.5 | 83.8 | 73.5 | 37.2 | 85.3 / 83.3 | 0.0 / 0.0 | 34.3 / 6.9 | 43.0 / 33.6 | 56.1 / 53.6 | | | | | | Top-K (NoFT) | 88.9 | 74.7 | 69.4 | 65.8 | 79.3 / 69.7 | 19.8 / 13.6 | 32.7 / 25.8 | 37.7 / 33.6 | 49.6 / 43.9 | | | | | | Cover-LS (NoFT) | 91.4 | 81.6 | 76.3 | 70.0 | 82.2 / 73.6 | 0.0 / 0.0 | 52.5 / 36.7 | 60.9 / 60.3 | 75.1 / 64.7 | | | | | 7 out of 8 compositional splits, suggesting that retrieval methods that only consider similarity are sub-optimal even in an oracle setup. Similarly, all diversity methods improve performance compared to a finetuned T5 model in all compositional splits except GeoQuery's template splits. Furthermore, sampling random examples (Random baseline) results in poor performance in GeoQuery and SMCalFlow-CS, but achieves high accuracy in COVR-10, beating all methods except Cover-Utt. This can be explained by the synthetic nature and small vocabulary of COVR-10. Comparing diversity methods, Cover-LS and Cover-Utt are better than DPP in 7 out of 10 splits, showing that covering the target input/program goes beyond simply picking diverse examples. Cover-Utt, which covers utterance words, works surprisingly well considering its simplicity. Coverage-based methods also outperform Top-K in i.i.d splits. One noticeable failure of Cover-LS is the 0-C split, where it fails to generalize, due to the poor T5 performance on this split (T5 baseline gets 0 accuracy). This emphasizes that if one cannot reasonably predict LSs, then covering input words is a viable alternative. Lastly, oracle methods outperform their non-oracle counterparts in most settings, but not always. This occurs because our oracle method, which has access to the gold program, does not guarantee the selection of the optimal set of demonstrations, a phenomenon also observed in Qiu et al. (2022b). Tab. 3 shows accuracy on the entire test set (NoFT setup). Since the underlying models differ substantially, a fair comparison to previous work is impossible. Nevertheless, a comparison still provides a high-level overview for the state of these tasks. Results show that using Codex with CoverLS outperforms a T5 finetuned with augmentation (Qiu et al., 2022a) in 4 compositional splits out of 6 ![6_image_0.png](6_image_0.png) (TMCD, Length, 8-C and 32-C), and outperforms non-finetuned PaLM 540B, where demonstrations are selected using BM25, in all splits. Number of demonstrations (NoFT) We examine how performance is affected by the number of demonstrations in Fig. 3. Cover-LS outperforms Top-K by a large margin across all prompt sizes. Moreover, Cover-LS requires just four demonstrations in order to obtain roughly the same results as Top-K with 24 demonstrations. The gap between Cover-LS and Cover-Utt or Cover-LS1 shows the importance of covering structures rather than just program symbols or utterance words, especially for small demonstration sets. FT Finetuning results are shown in Tab. 4, where we detail separately the method used for demonstration selection at both training time and test time, as those may diverge to avoid over-copying. First, using random demonstrations at test time, without controlling for diversity or using any retriever, is better compared to using no demonstrations at all. Our main method constructs prompts with Cover-LS at test time, but during training, prompts are retrieved with Cover-LS1, that only covers program symbols, but not local structures, to avoid over-copying (see §3.4). This combination | Training Method | Test Method | GeoQuery | SMCalFlow-CS Simple | COVR-10 | | | | | | | |----------------------|-------------------|------------|-----------------------|-----------|------|------|------|------|------|------| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 8-C | 16-C | 32-C | | | | | T5 (FT, w/o prompts) | - | 92.5 | 83.8 | 73.5 | 37.2 | 83.7 | 9.7 | 37.5 | 59.4 | 19.4 | | Random | Random | 93.2 | 85.0 | 76.8 | 39.8 | 83.5 | 28.3 | 46.4 | 58.0 | 23.2 | | Random | Top-K | 93.0 | 84.6 | 75.9 | 39.8 | 83.4 | 24.4 | 40.6 | 54.8 | 22.8 | | Top-K | Top-K | 90.7 | 54.7 | 57.4 | 20.8 | 83.2 | 8.8 | 22.1 | 46.1 | 19.6 | | Cover-LS1 | Cover-LS1 | 92.9 | 85.3 | 76.6 | 41.9 | 83.9 | 31.0 | 51.3 | 62.6 | 29.8 | | Cover-LS1 | Cover-LS | 93.1 | 85.9 | 77.6 | 42.7 | 84.1 | 30.5 | 50.6 | 61.5 | 28.6 | | Cover-LS2 | Cover-LS | 92.6 | 84.9 | 75.6 | 39.8 | 83.7 | 28.8 | 46.3 | 60.5 | 28.8 | | Cover-LS | Cover-LS | 91.8 | 80.7 | 69.4 | 37.7 | 82.9 | 21.2 | 34.1 | 53.8 | 13.6 | | Cover-LS1 | Cover-LS (Oracle) | 93.7 | 87.7 | 79.8 | 48.9 | 87.4 | 48.0 | 64.1 | 73.5 | 41.1 | Table 4: **FT results** using T5. We detail the method used for demonstration selection at both training time and test time as those may differ to avoid over-copying. ![7_image_0.png](7_image_0.png) leads to higher performance in all compositional splits compared to baselines that use Top-K or random sampling. Interestingly, using Top-K at both training time and test time yields low accuracy in compositional splits, but high results in i.i.d. splits. This corroborates our assumption that diversity is needed in compositional setups. Finally, A variant of our method, where Cover-LS1 is used both during training and test time, is comparable to our main method across all splits. We observe that limiting coverage at training time to program symbols is crucial: accuracy drops in all splits if we limit Cover-LS to structures up to size 2 (Cover-LS2) instead of 1, or if we have no such limitation at all. The oracle Cover-LS outperforms all non-oracle models (unlike in NoFT, where this is not always the case). ## 4.5 Analysis Stratified analysis Our main results show that Cover-LS outperforms Top-K in most compositional splits. But what examples does it perform better on? We analyze properties of test example groups, where grouping is based on NoFT prediction outcome: (1) Top-K succeeds; (2) Cover-LS succeeds; (3) only Cover-LS succeeds; and (4) both fail. For each group we estimate difficulty by measuring the average accuracy achieved by a T5 model (finetuned without prompts), and also compute the percentage of examples that have an unobserved local structure (ULS) with respect to the training set. This measure is central to determining whether generalization to a test instance is hard, as shown in Bogin et al. (2022).3 We see (Fig. 4) that as the group index increases, T5 accuracy decreases and ULS rate increases. This finding confirms the claim in Bogin et al. (2022) that a test instance containing an ULS is hard. Examining groups 1 and 3, we observe that the group for which Cover-LS performs better than Top-K, is also tougher for T5 and has more ULS. Both methods fail on examples with low T5 accuracy and high ULS scores (group 4). This is also an evidence that T5 and Codex agree on the difficulty of examples, despite their different training and inference schemes. We provide error analysis in App. A. Prompt metrics We analyze the characteristics of prompts constructed with different demonstration selection methods in Tab. 5. Symbol Coverage shows the average fraction of symbols in ytest that are covered by the demonstration set, and similarly LS Coverage the fraction of covered LSs. While symbol coverage is generally high across all methods when using 24 demonstrations, LS coverage is significantly higher in Cover-LS, suggesting that only covering relevant symbols in prompts isn't as efficient as covering LSs. Utterance Similarity measures average cosine similarity between SBERT embeddings of the test utterance and prompt utterances, which is highest for Top-K as expected. 3To comply with Bogin et al. (2022), we measure ULS only for structures up to size 4. ![8_image_0.png](8_image_0.png) To approximate diversity between demonstrations, we calculate the average number of unique LSs in demonstrations, and observe it is substantially higher in Cover-LS and DPP compared to Top-K. This implies structural coverage and diversity are more important than input similarity in compositional splits. Robustness to retrieval methods To assess our method's robustness, we test how sensitive it is to the chosen retriever in the NoFT setup. First, we use our default retrievers, which are BM25 over utterance words (BM25-Utterance), and BM25 over predicted program symbols (BM25-Predicted). We add a random retriever that is identical to the RANDOM baseline introduced in §4.3 when combined with Top-K. We also evaluate the SBERT retriever (Reimers and Gurevych, 2019), which encodes input utterances and measures the cosine similarity between pairs of encodings. As seen in Fig. 5, Cover-LS outperforms Top-K in all settings by a significant margin. Moreover, while BM25- Utterance performs best, variance across retrievers is low for Cover-LS, but higher for Top-K. ## 5 Related Work Example selection One of the central issues in incontext learning is the selection of examples, which can either be based on parameter-free retrievers (Wang et al., 2022; Zemlyanskiy et al., 2022) or neural-based retrievers (Pasupat et al., 2021; Liu et al., 2022; Rubin et al., 2022). These studies consider each example separately, which often leads to a lack of coverage and diversity. Our approach is similar to the retrieval procedure in Zemlyanskiy et al. (2022), which makes a preliminary prediction and retrieves demonstrations with similar programs. However, while they use classic tf-idf with predicted tokens, we use predicted local structures and aim to cover them. Some studies encourage diverse example selection regardless of prompting. To address multianswer retrieval, Nandigam et al. (2022) employ ![8_image_1.png](8_image_1.png) DPP, and Min et al. (2021) autoregressively select instances based on previous selections. Other works include Su et al. (2022), which selects instances with varying confidence scores for annotation and (concurrent work) Ye et al. (2022) who propose a MMR-based selection strategy. In-context learning for compositional generalization There have been previous attempts to address compositional generalization problems using LLMs equipped with demonstrations. When selecting demonstrations, some also consider target coverage or structure similarity, but only in oracle setups (Hosseini et al., 2022; Qiu et al., 2022b). Drozdov et al. (2022) try to cover the syntactic parse tree constituents with demonstrations but rely heavily on manually-picked examples. ## 6 Conclusion In this paper, we studied how to leverage ICL to improve compositional generalization in semantic parsing, by increasing diversity among demonstrations. We found that choosing demonstrations that cover the structures required in the output program substantially improves performance across three compositional semantic parsing datasets in the pure in-context learning setup and when combined with finetuning. We further demonstrated that by aiming for structural coverage, we can reduce the number of demonstrations needed for generalization, and improve test performance on hard examples. Our approach can be applied to a wide range of NLP tasks where demonstrations should cover complementary aspects of the task, and we hope it will encourage further exploration of our method to improve generalization across diverse applications. ## Limitations Demonstration selection methods We assume that diversity can be obtained by choosing demonstrations with different program structures. This is based on previous work that demonstrated the importance of diversifying program structures in semantic parsing tasks (Oren et al., 2021; Bogin et al., 2022; Gupta et al., 2022). We also try to diversify utterance words or program symbols but do not consider more complex utterance features that could be applied to a wider range of language understating tasks. We also assume that recall matters more than precision when designing Cover-LS algorithm. That means we aim to choose a set of demonstrations that covers every predicted local structure in Sy˜test , since it has the potential to be a correct one. We do not predict whether a specific structure should be covered. Furthermore, our approach for increasing gold structure coverage by using additional beam candidates could be improved by employing search methods specifically targeted for diversity (Meister et al., 2021; Narayan et al., 2022). Retrievers We used different retrievers for NoFT and FT setups based on the retriever that worked best on the development set. Future research should be conducted to understand why different retrievers are preferred in different setups. A potential method could be to consider both input utterances and programs for retrieval, as suggested in Zemlyanskiy et al. (2022). ## Ethics Statement In this work, we studied methods for choosing diverse demonstrations to improve in-context compositional generalization in semantic parsing. 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In *Proceedings of the 29th International Conference on Computational Linguistics*, pages 4946–4951, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. ## A Additional Analysis Error analysis We analyze errors (NoFT setup) and show results in Tab. 6. Inspired by the metrics in Qiu et al. (2022b), we automatically compute statistics for the following cases when the prediction is wrong: (1) Syntax Errors, when the model produces a program with invalid parentheses; (2) Over-Copying, when the entire prediction has the same anonymized form as one of the demonstrations; (3) OOV (out-of-vocabulary) Hallucination, where the anonymized predicted program contains a symbol missing from the gold program or any prompt demonstration; and (4) Missing Symbol(s), where the predicted program is missing at least one symbol. The distribution of errors is similar across demonstration selection methods. Syntax errors are rare in both datasets. Many predictions are overcopied, especially in SMCalFlow-CS, but when diversity is increased with DPP, this number decreases significantly. Surprisingly, despite having a smaller vocabulary, GeoQuery has more out-ofvocabulary hallucinations. Almost all incorrect predictions have a missing symbol, but Top-K predictions are especially prone to this type of error. Change of retriever in FT setup Tab. 7 shows results for the FT setup when using BM25 over lower-cased utterance words as retriever, instead of BM25 over predicted program symbols. ## B Local Structures We follow the definition of local structures from Bogin et al. (2022), which were defined for structures of sizes 2-4, and extend them to local structures of any size. Given a program y, we parse it into a tree T = (V, E), such that each node v ∈ V is labeled by the program symbol (function or value) that it represents in y (or a special symbol for the root node), and the set of edges E = {(*p, c*)} expresses parent-child relations between the nodes. We capture sibling relations by defining a graph based on the tree T that contains an edge set Esib of sibling edges: G = (V, *E ∪ E*sib). Specifically, for each parent node p, the program y induces an order over the children of p: (c p 1 , ..., c p Np ), where Np is the number of children. We then define Esib = Sp{c p i , c p i+1} Np i=1, that is, all *consecutive* siblings will be connected by edges. We define a local structure of size n as the subset GLS of all connected sub-graphs of size n in G | Error Types | GeoQuery TMCD | SMCalFlow-CS 8-C | | | | | |-------------------|-----------------|--------------------|-------|----------|------|------| | Top-K | Cover-LS | DPP | Top-K | Cover-LS | DPP | | | Syntax Error | 1.0 | 0.0 | 0.9 | 5.0 | 2.9 | 9.5 | | Over-Copying | 19.8 | 16.9 | 15.8 | 41.4 | 41.4 | 10.7 | | OOV Hallucination | 20.0 | 17.8 | 22.9 | 8.0 | 3.5 | 5.4 | | Missing Symbol(s) | 88.7 | 75.2 | 77.9 | 87.4 | 77.7 | 79.8 | such that for every pair (*x, y*) of nodes in GLS it holds that (x, y) ∈ Esib iff x and y are both leaves in GLS. That is, informally, the relations between nodes in the the sub-graph include parent-child and siblings, but not e.g. cousins or uncles. All program symbols are local structures of size 1. Tab. 8 shows a partial list of local structures for a given program. ## B.1 Fixes For Local Structure Extraction We try to fix syntax errors in the predictions made using the auxiliary model to enable parsing them to ASTs and extraction of LSs. We add or remove closing parentheses based on the number of missing or redundant parentheses at the end of the program. ## C Dataset Details We provide representative examples of the datasets used in this work in Tab. 1 and Tab. 9. We report dataset sizes in Tab. 10. Due to conversion errors, SMCalFlow-CS Simple has fewer training examples than SMCalFlow-CS. However, those missing examples are not cross-domain examples. We used publicly available datasets from previous peer-reviewed studies. Those datasets do not contain any information that uniquely identifies individual people or offensive content. The COVR-10 dataset is completely synthetic. The GeoQuery dataset contains only basic information about U.S. geography. SMCalflow-CS contains crowd-sourced queries collected in a simulated environment. ## D Prompt Format And Examples We add special prefixes "source:" and "target:" for retrieved source-target pairs and separate them with break lines. Tab. 11 shows prompt examples for different demonstration selection methods, where the only prompt that contains all the required program symbols and produces the correct prediction is Cover-LS's prompt. | Training Method | Test Method | GeoQuery | SMCalFlow-CS Simple | COVR-10 | | | | | | | |-------------------|---------------|------------|-----------------------|-----------|------|------|------|------|------|------| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 8-C | 16-C | 32-C | | | | | Random | Top-K | 93.0 | 84.9 | 76.1 | 40.3 | 82.9 | 26.7 | 41.0 | 53.9 | 23.1 | | Cover-LS1 | Cover-LS1 | 93.3 | 85.7 | 76.3 | 42.2 | 83.2 | 31.9 | 48.6 | 61.5 | 28.3 | | Cover-LS1 | Cover-LS | 93.2 | 85.8 | 76.6 | 42.4 | 83.2 | 28.3 | 46.6 | 60.9 | 30.1 | | Cover-LS2 | Cover-LS | 92.5 | 85.2 | 75.1 | 39.7 | 83.9 | 27.2 | 45.5 | 59.5 | 29.8 | | Cover-LS | Cover-LS | 91.4 | 81.0 | 69.1 | 39.2 | 82.7 | 17.5 | 31.5 | 55.1 | 12.3 | ## E Dpp Details DPPs are probabilistic models that are effective at modeling a distribution on all the subsets of the ground set T jointly considering the quality and diversity. A subset D is drawn according to the probability distribution P: $${\mathcal{P}}({\mathcal{D}}\subset{\mathcal{T}};L)\propto\operatorname*{det}(L_{{\mathcal{D}}})$$ P(*D ⊂ T* ;L) ∝ det(LD) (1) Where L ∈ R n×nis a PSD matrix and LD is the submatrix of L indexed by items in D. L matrix takes into account the quality of each training example and its similarity to other training examples through: $$L_{i j}=q_{i}\phi_{i}^{\top}\,\phi_{j}q_{j}$$ i ϕjqj (2) with q ∈ R n being normalized retriever scores that model the quality of each example; and {ϕi} n i=1 denoting normalized tf-idf vectors over LSs, which model the different aspects that are contained within each training example. The dot product of those vectors is used to model the similarity between two train examples. log det(LD) is a submodular function which satisfies the diminishing marginal returns property. Therefore, we can find a subset of training examples D ⊂ T , |D| = k that maximizes it in a feasible manner using a greedy optimizer (Kaushal et al., 2022). Specifically, we used the Naive Greedy optimizer. We used scikit-learn (Pedregosa et al., 2011) for calculating tf-idf vectors. ## F Finetuning Details We provide implementation details for finetuning experiments (we use the same configuration for all FT experiments and training of the auxiliary model). We finetune the T5-large model (770 million parameters) with the AdamW optimizer (Loshchilov and Hutter, 2019) and a learning rate of 1e−5. We use a polynomial decay learning rate with an ending rate of 1e−6, and 100 warmup steps. We train for 250/50/70 epochs and evaluate on the validation set every 3/5/10 epochs for Geo/SMCalFlow (both versions)/COVR respectively. We use batches of size 8 for all datasets (and gradient accumulation in case batch cannot fit in memory). We used a single GPU for each T5-large finetuning experiment: Nvidia GeForce RTX 3090 when training on GeoQuery and COVR-10, and A100 (80GB) for SMCalFlow-CS and SMCalFlowCS Simple. GeoQuery experiments with prompts trained for an average of 2 hours, COVR for 8 hours, and SMCalFlow-CS Simple for 41 hours. We use the AllenNLP library (Gardner et al., 2018) for training and evaluation. We use RankBM25 (Brown, 2020) as a BM25 implementation. Standard deviation We report standard deviation results in the FT setup in Tab. 13. Results are computed across 3 random seeds. ## G Noft Details All NoFT experiments were conducted using the OpenAI inference API with the sampling temperature set to 0. Our setup requires a single API call per test instance. The total number of API calls is estimated at 160K. Standard deviation We report standard deviation results in NoFT setup in Tab. 12. Results are computed using 3 random seeds for a subset of 100 test examples. Tuning the number of beam candidates We use the development set to tune the number of beam candidates B when predicting local structures. Tab. 14 shows the results of using different values of B in NoFT setup on a random subset of 100 development examples. Prompts are constructed using Cover-LS with k = 8 demonstrations. ## H Artifact Licensing We include license information for all artifacts used in this work in Tab. 15. Our use of artifacts was consistent with their intended purpose when it was specified. ## I Genbench Evaluation Card Our GenBench (Hupkes et al., 2022) evaluation card is presented in Fig. 6. | Dataset | SMCalFlow-CS Simple | |--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Utterance | Create a new meeting on Friday called Work on Project. | | Program | CreateEvent (AND (has_subject ("Work on Project"), starts_at (NextDOW ("Friday")))) | | Anonymized Program | CreateEvent (AND (has_subject (string), starts_at (NextDOW (string)))) | | Size | Local structures CreateEvent AND has_subject string starts_at NextDOW | | 1 | <root> → CreateEvent CreateEvent → AND AND → has_subject AND → starts_at has_subject ↔ starts_at has_subject → string starts_at → NextDOW NextDOW → string | | 2 | <root> → CreateEvent → AND CreateEvent → AND → has_subject CreateEvent → AND → starts_at AND → has_subject ↔ starts_at AND → has_subject → string AND → starts_at → NextDOW starts_at → NextDOW → string . . . | | 3 6 | <root> → CreateEvent → AND → starts_at → NextDOW → string | Table 8: Local structures of different sizes for a specific example (→ denotes parent-child relations, ↔ denotes sibling relations) | Utterance | Can you make a meeting with David Lax 's reports ? | | |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| | Version | Program (Yield :output (CreateCommitEventWrapper :event (CreatePreflightEventWrapper :constraint (Constraint[Event] :attendees (AttendeeListHasPeople :people (FindReports :recipient (Execute :intension (refer (extensionConstraint (RecipientWithNameLike :constraint (Constraint[Recipient]) :name # (PersonName "David Lax"))))))))))) | | | v1 | (LISP) | (Yield (CreateCommitEventWrapper (CreatePreflightEventWrapper (Event.attendees_? (AttendeeListHasPeople (FindReports (Execute (refer (extensionConstraint | | v2 (LISPRESS) | (RecipientWithNameLike ( ^ (Recipient) EmptyStructConstraint) (PersonName.apply "David Lax"))))))))))) Table 9: An example from each version of SMCalFlow-CS dataset. | | | Dataset | Split | Train | Development | Test | |---------------------|------------|---------|---------------|--------| | Standard | 600 | - | 280 | | | Template1 | 438 | 110 | 332 | | | Template2 | 439 | 110 | 331 | | | Template3 | 440 | 110 | 330 | | | TMCD1 | 440 | 110 | 330 | | | TMCD2 | 440 | 110 | 330 | | | TMCD3 | 440 | 110 | 330 | | | Length | 440 | 110 | 330 | | | GeoQuery | 8-S | 25412 | 662 | 662 | | 0-C | 25404 | 662 | 663 | | | 8-C | 25412 | 662 | 663 | | | 16-C | 25420 | 662 | 663 | | | 32-C | 25436 | 662 | 663 | | | SMCalFlow-CS v1 | 8-S | 20965 | 360 | 360 | | 0-C | 20957 | 360 | 360 | | | 8-C | 20965 | 360 | 360 | | | 16-C | 20973 | 360 | 360 | | | 32-C | 20989 | 360 | 360 | | | SMCalFlow-CS v2 | 8-S | 25402 | 662 | 662 | | 8-C | 25402 | 662 | 663 | | | 16-C | 25410 | 662 | 663 | | | 32-C | 25426 | 662 | 662 | | | COVR-10 | Each split | 3000 | - | 500 | | SMCalFlow-CS Simple | | | | | Table 10: Dataset sizes | Motivation | | | | | | |--------------------------|---------------------|-----------------|-----------------|--------------|------------| | Practical | Cognitive | Intrinsic | Fairness | | | | All | Generalisation type | | | | | | Compositional | Structural | Cross Task | Cross | Cross Domain | Robustness | | Language | | | | | | | All | Shift type | | | | | | Covariate | Label | Full | Assumed | | | | All | Shift source | | | | | | Naturally occurring | Partitioned natural | Generated shift | Fully generated | | | | GeoQuery | COVR-10 | | | | | | SMCalFlow-CS Shift locus | | | | | | | Train–test | Finetune train–test | Pretrain–train | Pretrain–test | | | | All | | | | | | | Dataset | GeoQuery | | |-------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------| | Utterance | through which states does the longest river in texas run | | | Gold Program | answer (state (traverse_1 (longest (river | (loc_2 (stateid (string))))))) | | Selection Method | Prompt source: which states does the mississippi river run through target: answer (state (traverse_1 (river (riverid (string))))) source: which states does the colorado river run through target: answer (state (traverse_1 (river (riverid (string))))) source: which states does the missouri river run through target: answer (state (traverse_1 (river (riverid (string))))) source: which states does the longest river run through target: answer (state (traverse_1 (longest (river (all))))) source: through which states does the longest river in texas run target: | | | Top-K | source: what states does the shortest river run through target: answer (state (traverse_1 (shortest (river (all))))) source: which states does the mississippi run through target: answer (state (traverse_1 (riverid (string))))) source: which states does the missouri river run through target: answer (state (traverse_1 (river (riverid (string))))) source: which states does the longest river run through target: answer (state (traverse_1 (longest (river (all))))) source: through which states does the longest river in texas run target: | | | DPP | source: what state borders the least states excluding alaska and excluding hawaii target: answer (fewest (state (next_to_2 (exclude (exclude (state (all), stateid (string)), stateid (string)))))) source: what is the longest river in texas target: answer (longest (river (loc_2 (stateid (string))))) source: which states does the missouri river run through target: answer (state (traverse_1 (river (riverid (string))))) source: which states does the longest river run through target: answer (state (traverse_1 (longest (river (all))))) source: through which states does the longest river in texas run target: | | | Cover-LS | | | | Table 11: Prompts produced with different demonstration selection methods for a specific test example. Each | | | Table 11: Prompts produced with different demonstration selection methods for a specific test example. Each prompt contains k = 4 demonstrations. | GeoQuery | SMCalFlow-CS | COVR-10 | | | | | | | | | |------------|----------------|-----------|------|--------|-----|-----|------|------|-----|-----| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 0-C | 8-C | 16-C | 32-C | | | | Random | 1.5 | 6.6 | 2.5 | 5.0 | 4.6 | 0.6 | 0.6 | 0.6 | 3.5 | 3.1 | | Top-K | 1.5 | 1.8 | 1.0 | 1.1 | 0.6 | 1.0 | 1.0 | 1.1 | 1.1 | 4.6 | | Cover-Utt | 1.0 | 1.2 | 1.2 | 2.1 | 1.5 | 1.5 | 1.0 | 1.2 | 2.1 | 1.9 | | DPP | 0.0 | 0.5 | 1.7 | 1.5 | 1.2 | 0.6 | 1.0 | 1.0 | 3.1 | 2.0 | | Cover-LS | 1.5 | 1.1 | 2.4 | 2.1 | 1.4 | 0.6 | 1.1 | 0.6 | 3.5 | 4.2 | Table 12: Standard deviation results in NoFT setup. Results are computed on a random subset of 100 test examples across 3 random seeds. | Training Method | Test Method | GeoQuery | SMCalFlow-CS Simple | COVR-10 | | | | | | | |-----------------------------|-------------------|------------|-----------------------|-----------|-----|------|------|-----|-----|------| | i.i.d. | Templ. | TMCD | Len. | i.i.d. | 8-C | 16-C | 32-C | | | | | T5 (fine tuned w/o prompts) | - | 0.2 | 0.8 | 1.6 | 0.5 | 0.7 | 1.4 | 4.6 | 1.5 | 1.7 | | Random | Random | 0.0 | 1.2 | 1.0 | 0.9 | 0.3 | 3.2 | 2.7 | 0.4 | 2.7 | | Random | Top-K | 0.2 | 1.4 | 1.3 | 2.3 | 0.4 | 3.3 | 1.2 | 1.2 | 2.7 | | Top-K | Top-K | 0.6 | 3.5 | 2.1 | 0.7 | 0.3 | 1.9 | 1.9 | 1.3 | 3.9 | | Cover-LS1 | Cover-LS1 | 0.6 | 0.8 | 0.9 | 2.6 | 0.5 | 2.0 | 0.2 | 1.7 | 4.8 | | Cover-LS1 | Cover-LS | 0.5 | 0.4 | 0.9 | 4.2 | 0.4 | 1.4 | 0.8 | 0.8 | 6.5 | | Cover-LS1 | Cover-LS (Oracle) | 0.2 | 0.7 | 0.9 | 2.6 | 0.3 | 0.6 | 0.6 | 0.8 | 12.1 | | GeoQuery | SMCalFlow-CS | | | | | | | | | | | | |------------|----------------|----------|----------|--------|--------|--------|------|--------|-----|-----|------|------| | B | Templ. 1 | Templ. 2 | Templ. 3 | TMCD 1 | TMCD 2 | TMCD 3 | Len. | i.i.d. | 0-C | 8-C | 16-C | 32-C | | 1 | 85 | 74 | 77 | 66 | 65 | 84 | 62 | 73 | 0 | 36 | 47 | 63 | | 3 | 85 | 75 | 75 | 69 | 59 | 88 | 60 | 65 | 0 | 42 | 49 | 67 | | 5 | 84 | 76 | 72 | 69 | 64 | 87 | 60 | 64 | 1 | 44 | 51 | 68 | | Artifact | License | Reference | |-------------------------------|------------------|-------------------| | Models T5 | Apache 2.0 | HF model card | | Codex | API usage policy | API documentation | | Dataset GeoQuery | GPL 2.0 | Official website | | GeoQuery compositional splits | Apache 2.0 | Github repository | | SMCalFlow-CS | MIT | Github repository | | SMCalFlow Simple | MIT | Github repository | | COVR-10 | MIT | Github repository | | Tools AllenNLP | Apache 2.0 | Github repository | | Rank-BM25 | Apache 2.0 | Github repository | | SBERT | Apache 2.0 | Github repository | | DPP optimization | MIT | Github repository | Table 15: License information for all artifacts ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Limitations ✓ A2. Did you discuss any potential risks of your work? Ethics Statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix H ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Appendix H ✓ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Appendix C ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Ethics Statement, Section 4.1 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix C ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendices F - G The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4, Appendices F - G ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4, Appendices F - G ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendices D - G D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

wu-etal-2023-self

Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

https://aclanthology.org/2023.acl-long.79

Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example organization (i.e., selection and permutation) that can derive the correct prediction, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and instantiate it with new selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40{\%} relative improvement over the common practice setting. Further analysis reveals the enormous potential of self-adaptive ICL that it might be able to close the gap between ICL and finetuning given more advanced algorithms. Our code will be released to facilitate future research.

# Self-Adaptive In-Context Learning: An Information Compression Perspective For In-Context Example Selection And Ordering Zhiyong Wu♦†**, Yaoxiang Wang**♣†∗**, Jiacheng Ye**♠†∗**, Lingpeng Kong**♠ ♦Shanghai AI Laboratory ♣Xiamen University ♠The University of Hong Kong {jcye2,lpk}@cs.hku.hk, {wuzhiyong,wangyaoxiang}@pjlab.org.cn, ## Abstract Despite the impressive few-shot performance of in-context learning (ICL), it remains a common practice to randomly select examples to serve as the context. In this paper, we advocate self-adaptive in-context learning, a new principle for ICL, in which the self-adaption mechanism is introduced to help each input find an in-context example organization (i.e., selection and permutation) that can derive the correct output, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and a set of novel selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting. Further analysis reveals the great potential of selfadaptive ICL as a promising method to close the gap between ICL and finetuning. *Our code* will be released to facilitate future research. ## 1 Introduction The increasing scale of pre-trained language models (PLMs) has brought emergent abilities (Wei et al., 2022) via in-context learning (ICL), where the PLMs learn to do downstream tasks simply by conditioning on a prompt containing a few examples of their kinds (Brown et al., 2020a). Due to its impressive performance, ICL has now emerged as a popular and efficient way of using PLMs. However, ICL is inherently unstable: given different prompts, the performance of ICL on downstream tasks can vary from almost random to comparable with state-of-the-art systems (Zhao et al., 2021; Lu et al., 2022; Gao et al., 2021), depending on the quality of the prompts. The instability of ICL motivates researchers to explore methods that search for high-performing prompts. Note that a *prompt* within the context of ∗Work done while interning at Shanghai AI Lab. †Equal Contribution. ![0_image_0.png](0_image_0.png) ICL contains two ingredients: some input-output pairs (i.e., *in-context examples*) and a *template* that wraps these examples into a natural language instruction. Extensive research has been carried out on searching for a better template (Gao et al., 2021; Shin et al., 2020; Sorensen et al., 2022; Deng et al., 2022). In contrast, very few efforts have been spent on searching for the best in-context example *organization*. 1 Recent work, however, has pointed out that the organization of in-context examples can have a significant influence on ICL's performance (Lu et al., 2022; Liu et al., 2022; Rubin et al., 2022). This paper fills this gap by proposing a framework for in-context example searching and ranking. While one can also trivially extend template searching methods to conduct in-context example searching, these methods operate at the *corpuslevel*. They first construct a small candidate template set using PLMs (Gao et al., 2021; Shin et al., 2020), data mining algorithms (Jiang et al., 2020), or by hands (Sorensen et al., 2022). After that, each 1In this paper, we abuse the word organization to represent both the selection and ordering of examples. candidate will be applied to the whole validation set for inference. According to validation performance, the best template will be adapted for testing. However, existing solutions have the following problems: (i) Their performance relies heavily on the availability of a large-scale high-quality validation set; (ii) Corpus-level methods can be sub-optimal (see Figure 1) because finding a universal template that suits all testing samples perfectly is unlikely. Such majority bias (Zhao et al., 2021) will significantly hurt user experience in practice and make corpus-level methods less robust. To tackle these issues, we seek to construct a good-performing in-context example organization for each testing sample individually, without access to a validation dataset. This problem, namely self-adaptive in-context learning, is essentially an NP-hard combinatorial optimization problem that cannot be solved within polynomial time. We thus formulate it as a search problem and propose a general two-stage framework to cope with the issue of massive search space. In the first stage, we apply heuristic rules (e.g., nearest neighbors based on semantic similarity) to filter candidate examples. Given a much smaller candidate set, we then apply algorithms to rank different organizations and look for the best-performing one. Our ranking algorithms are theoretically supported by the Minimal Description Length (MDL) principle and can shed light on why certain permutations are better than others. Our contributions are summarized as follows: - To the best of our knowledge, we are the first to formally define the problem of self-adaptive in-context learning and formulate it as a twostage search problem. We propose a general framework to address this problem. - We achieve state-of-the-art performance using the proposed framework and outrun the previous best-performing methods by a large relative improvement. We also find that instancelevel ICL methods are generally more robust than corpus-level counterparts. Such empirical success shows a great promise of selfadaptive ICL. - We conduct extensive analysis for selfadaptive ICL and make some exciting findings. For instance, in Section 6.3 we reveal that self-adaptive ICL still has much room for improvement. With better search methods, we might be able to close the gap between ICL and finetuning. - We will open-source the proposed framework to facilitate future research. This unified framework enables researchers to identify important design choices in previous methods and paves the way for further improvements. ## 2 Related Work Despite the surprising zero-shot performance of PLMs, recent works show that ICL can bring the performance to the next level. Augmenting PLMs with ICL achieves SOTA results on a wide range of NLP tasks, ranging from question answering (Joshi et al., 2017), information retrieval (Tay et al., 2022), math word problem (Cobbe et al., 2021), commonsense reasoning (Geva et al., 2021), and fact checking (Rae et al., 2021) etc. The instability of ICL, however, has encouraged researchers to explore methods that search for robust and high-performing prompts. These methods can be categorized as follows based on the target of searching/optimization: Template search focuses on searching for the template that can guide PLM's behavior and steer its best performance. Great advances have been made in template searching using various methods: PLMs (Gao et al., 2021), heuristic rules (Jiang et al., 2020; Shin et al., 2020; Prasad et al., 2022; Xu et al., 2022), reinforcement learning (Deng et al., 2022), genetic algorithms (Kumar and Talukdar, 2021), or by hands (Sorensen et al., 2022; Zhao et al., 2021). Nonetheless, all these methods require a high-quality validation set to do prompt selection or optimization. Unlike them, our framework does not require a validation set. When the validation set is not available, researchers propose to search prompts using entropy (Lu et al., 2022) or mutual information (Sorensen et al., 2022). It's worth mentioning that these two works and all aforementioned methods search at the *corpus-level*: they pick the bestperforming template with or without a validation set and then equally apply this template to all test examples during inference. However, corpus-level methods might be sub-optimal. If we consider the No Free Lunch Theorem, finding one single template that works well for all testing examples is nearly impossible. In-context example search, unlike template search, is rarely explored in the literature despite that they also have a huge impact on ICL performance (Zhao et al., 2021; Lu et al., 2022). Lu et al. (2022) first propose a learning-free corpuslevel method for in-context example search. However, they only consider an impractical setting with only 4 examples and their 24 permutations ( 4P4 = 4! = 24). Liu et al. (2022) find examples that are semantically similar to a test sample can serve as a good choice for its in-context examples. However, the reason why such a simple heuristic works is unclear. Su et al. (2022) extend this nearest neighbor search and further take the diversity of examples into consideration. Inspired by these methods, recent studies propose to learn to retrieve in-context examples (Rubin et al., 2022). ## 3 Problem Formulation Given a test sample (x, y), the probability of generating the target y using a casual PLM P can be formulated as follows: $$y)|c,{\mathcal{T}}(\mathbf{x}))\,,$$ ## P(Y|X) = P (V(Y)|C, T (X)), (1) where T (·) is the template used to wrap up inputs and c = T (x1), *· · ·* , T (xk) is the context string concatenating k input-output examples. To deal with classification tasks, a verbalizer V(·) is introduced to map each label/class y to a word/words in P's vocabulary. Note that in a special scenario when k = 0, ICL degenerates to zero-shot *prompting* (Ye et al., 2022; Brown et al., 2020b). The goal of self-adaptive ICL is then to find an optimal organization of c ∈ C that can drive the correct y for each input x, and maximize the task performance. We formulate this as a combinatorial optimization problem. ## 4 Method In this section, we propose a two-stage framework to tackle the problem of self-adaptive ICL. ## 4.1 Overview In such a combinatorial optimization problem, an exhaustive search is not tractable. So we need specialized algorithms that can quickly rule out large parts of the search space. We present an overview of our selection-then-rank framework here: We first use a selection module to reduce the search space. One straightforward choice for pre-ranking would be to use nearest-neighbor algorithms to select examples that are semantically similar to test samples. The results are then fed into the ranking module, which picks the best combination and permutation according to information-theoretic-driven criteria. ## 4.2 Selection The goal of selection module is to filter out large parts of "less useful" examples and construct a small candidate set to reduce the search space. We present various selection methods below. TopK Liu et al. (2022) and Gao et al. (2021) observe that context examples that are closer to the test sample in the embedding space consistently give rise to stronger performance. This observation leads to the TopK method which uses the nearest neighbors of a given test sample as the corresponding in-context examples. VoteK Although ICL was originally proposed for few-shot settings, they often require a large example set to achieve good performance. VoteK (Su et al., 2022) proposes to alleviate this problem by selecting diverse yet representative examples. Intuitively, VoteK is built upon TopK, but it increases diversity by penalizing examples similar to those already selected. DPP Inspired by VoteK, we also experimented with the determinantal point process (DPP) based method, which is proposed for set selection problems where diversity is preferred. We refer readers to Kulesza and Taskar (2011) for details of DPP. ## 4.3 Ranking With the candidates returned by the selection module, the goal of the ranking module is to determine the best organization among candidates. Our ranking algorithm is inspired by the compression viewpoint of Solomonoff's general theory of inference (Solomonoff, 1964) and Minimum Description Length (MDL) principle (Grünwald, 2007) from information theory. Both Solomonoff's theory and the MDL formalize Occam's razor and hold that a good model of data is a model that is good at losslessly compressing the data, including the cost of describing the model itself. These theories have led to advances in VAE (Kingma and Welling, 2013), and information bottleneck methods (Tishby and Zaslavsky, 2015). Inspired by the compression viewpoint of learning, we recast the problem of self-adaptive in-context learning into a similar paradigm. We assume that a good organization of in-context examples is the organization that is good at losslessly compressing testing samples. This allows us to give a clear optimization objective when searching for the best organization c∗: $$c^{*}=\operatorname*{arg\,min}_{c\in\mathbf{C}}L_{\theta}(y|c,\mathbf{x})+L(\theta),$$ where each c represents one possible organization of examples. Lθ(y|c, x) is the codelength required to compress and transmit testing label y given the organization c and testing input x. L(θ) is the codelength required to describe the model, which can be ignored during ranking since all organizations use the same model without parameter updating. The codelength required for data transmission can be calculated using *Shannon-Huffman code*: $$L_{\theta}(y|c,{\bf x})=-l o g_{2}\,p(y|c,{\bf x}).$$ However, since we don't have access to testing label y when ranking, the exact computation of p(y|c, x) is impossible. To tackle this problem, we propose to compute the expectation of codelength as the surrogate: Lθ(y|c, x) ≈ −Eq(yi|Y )log2 p(yi|c, x), (4) where q(yi|Y ) is the prior of yi among all possible labels Y . A natural design choice of the prior is a uniform distribution, given that most datasets are label-balanced. However, since we focus on instance-level selection rather than corpus level, the likelihood p(yi|Y ) can vary significantly given different samples. We thus model this term using p(yi|c, x), leading to our final objective: $$c^{*}=\operatorname*{arg\,min}_{c\in\mathbf{C}}-\mathbb{E}_{p(y_{i}|c,\mathbf{x})}l o g_{2}\,p(y_{i}|c,\mathbf{x}).$$ Now that we have an interpretable metric for ranking, we can brute-force all possible permutations to obtain the optimal ranking result. Although we have significantly reduced the search space using the selection module, enumerating all organizations is still infeasible. For instance, if we want to search for the best organization that contains 8 examples, even a small candidate set of 10 examples can result in 1.8 million choices (A 8 10). At the current stage, we randomly sample 10 permutations for ranking. We leave it as an interesting future work to investigate how to approximate the optimal ranking better. ## 4.4 Interpretation Of Lθ(Y|C, X) Except for the compression viewpoint, we offer some other interpretations of our method here. $$(2)$$ Connection to entropy When we use model confidence p(yi|c, x) as the estimation of q(yi|Y ), Eq 4 is basically calculating the entropy. Minimizing entropy is equivalent to searching for in-context examples that will lead to a skewed probability distribution. In other words, we are searching for in-context examples are will make PLMs very confident about its answer. This motivation is exactly opposite to the Local Entropy(LocalE) metric proposed by Lu et al. (2022), where they search by maximizing the entropy. Connection to cross-entropy. Note that in this paper, we focus on instance level ICL and assume no validation set is available. However, when we have a validation set to directly compute p(y|c, x), Eq 3 is exactly the categorical cross-entropy loss. Hence, trying to minimize the description length of the outputs is equivalent to minimizing the usual classification loss. This reveals why compression is another viewpoint of learning. $$|c,\mathbf{x}\rangle,$$ Connection to mutual information. Previous effort (Blier and Ollivier, 2018) has proved that the compression is limited by the mutual information between inputs and outputs: $$H(y)-\mathbb{E}_{q}[L(y\mid x)]\leq H(y)-H(y\mid x)=I(y;x),$$ where we assume the inputs and outputs follow the joint distribution q. Based on this finding, any successful compression of the labels is, at the same time, a direct estimation of the mutual information between input and output. This connects our method to Sorensen et al. (2022) that selects templates by maximizing mutual information. Difference to previous works. Except for the aforementioned connections and differences, our method significantly differs from Lu et al. (2022) and Sorensen et al. (2022) in that we perform instance-level selection without a validation set. Trivial extension of previous methods to our setting is impractical: Lu et al. (2022) requires a validation set to compute the *Global Entropy*, while the mutual information is always zero on instance-level setting according to Sorensen et al. (2022). ## 5 Experiments 5.1 Evaluation Details We perform experiments across eight different NLP datasets. Unless otherwise stated, all experiments are conducted using GPT2-XL (1.5B) (Radford et al., 2019). Our method is denoted as TopK+MDL, in which we first use TopK to retrieve 30 candidates for each sample and then randomly sample 10 organizations (each with 8 examples) for ranking using MDL. All models and datasets are loaded from HuggingFace Hub. Templates are adopted from Ye et al. (2022); Gao et al. (2021) and detailed in Table 4. We ran all experiments three times with different random seeds and reported the average accuracies. Datasets We consider two sentiment classification datasets (Socher et al., 2013): SST-2 and SST-5, three natural language inference datasets: SNLI (Bowman et al., 2015), MNLI (Williams et al., 2017), and QNLI (Wang et al., 2018), one multi-choice question answering dataset: Commonsense QA (CMS QA) (Talmor et al., 2019), two topic classification datasets: TREC (Hovy et al., 2001) and AgNews (Zhang et al., 2015). Baselines We compare our framework with three groups of baselines: prompting, corpus-level methods, and instance-level methods. **Prompting** is a special case of ICL without in-context examples. For corpus-level methods, we consider two methods that require a validation set: **GlobalIE** (Lu et al., 2022) and **Random & Validation**, which picks 10 random organizations for each dataset and selects the best one according to the validation performance. We also consider validation-free baselines: Mutual Information (MI) (Sorensen et al., 2022) and a **Random** baseline that randomly initiates one organization for each dataset. For instancelevel methods, we consider **TopK+LocalE** (Lu et al., 2022), **TopK** (Liu et al., 2022) and a **Random** baseline that randomly selects 8 examples for each testing sample. We further add a **Majority** vote baseline that directly performs majority voting based on 8 examples retrieved by TopK. Evaluation Strategy Due to the restricted test set access of some datasets (MNLI, QNLI, and CMS QA), we hold out a small subset (i.e., 10%) of the training set for validation for corpus-level methods, and report results on the validation set. For PROMPTING and instance-level methods, we directly evaluate them on the original validation set when the test set is not available. ## 5.2 Main Results From Table 1, we first observe that ICL methods outperform *prompting* in most cases. However, we also note that bad in-context organizations (e.g., the random baseline) can hurt performance and make ICL performs even less well than prompting on SST-5. These results stress the importance of correct selection and permutation of in-context examples. We first compare our methods with corpus-level methods. As shown in Table 1, our method shows consistent and clear superiority over corpus-level baselines. This result also validates our conjecture that corpus-level methods can be sub-optimal and self-adaptive in-context examples can significantly improve ICL performance. Remarkably, our method demonstrates a 40% relative improvement against the common practice in ICL (i.e., the Random baseline). Such improvement is encouraging as it shows that despite the surprising performance of ICL in many tasks, there is still a large room for improvement with advanced in-context example searching techniques. Our method still registers decent improvements on most evaluated datasets even when compared with instance-level baselines. Compared with TopK+LocalE, our method makes a 17% relative improvement, this demonstrates the effectiveness of MDL as a ranking method. However, we also notice that TopK is a very competitive baseline to our method. Using semantic search to retrieve examples will result in incontext examples whose input distribution and *label* are quite similar, or even identical, to the testing sample. This phenomenon leads to our hypothesis about the surprising effectiveness of TopK. First, as pointed out by Xie et al. (2021), ICL can be cast as an implicit Bayesian inference process, where the PLMs implicitly infer a concept when making the prediction. Based on this theoretic finding, we deduce that semantically similar in-context examples improve prediction by providing more evidence for Bayesian inference, especially for topic classification tasks like TREC and AgNews. Second, we conjecture that providing a series of examples with the same label as the testing sample introduces a "learning shortcut" for PLMs and biases the results. We further examine this hypothesis below. ## 5.3 Impact Of Label In Icl To investigate the impact labels have on ICL, we calculate *bias rate*. Given a testing sample (x, y) and its in-context examples, the bias rate represents the percentage of in-context examples whose label is identical to y. As shown in Figure 2(a), the bias | SST-2 | SST-5 | SNLI | MNLI | QNLI | Trec | AgNews | CMS QA | AVG | | |-------------------------------|---------|--------|--------|--------|--------|----------|----------|-------|-----------------| | Prompting | 71.38 | 29.41 | 41.23 | 39.19 | 50.44 | 13.8 | 29.75 | 39.39 | 39.32 (52.99%↑) | | Corpus-level | | | | | | | | | | | Random | 73.68 | 23.88 | 43.35 | 39.43 | 53.19 | 19.66 | 36.92 | 52.66 | 42.78 (40.41%↑) | | Random & Validation | 87.86 | 40.10 | 49.27 | 43.26 | 51.12 | 32.67 | 52.01 | 53.75 | 51.25 (17.38%↑) | | MI (Sorensen et al., 2022) | 52.86 | 35.35 | 46.02 | 41.32 | 50.62 | 16.00 | 47.29 | 52.78 | 42.85 (40.63%↑) | | GlobalE (Lu et al., 2022) | 87.27 | 33.21 | 46.99 | 40.46 | 57.27 | 28.53 | 52.01 | 22.42 | 49.75 (20.92%↑) | | Instance-level | | | | | | | | | | | Random | 77.17 | 25.65 | 43.41 | 41.17 | 53.09 | 18.33 | 32.71 | 52.93 | 43.06 (39.72%↑) | | TopK (Liu et al., 2022) | 83.91 | 37.01 | 57.54 | 45.72 | 59.72 | 40.80 | 88.89 | 51.51 | 58.14 (3.48%↑) | | Majority vote | 85.34 | 41.58 | 52.06 | 34.38 | 58.02 | 51.60 | 60.91 | 19.57 | 50.43 (19.29%↑) | | TopK+LocalE (Lu et al., 2022) | 67.12 | 31.65 | 46.78 | 41.51 | 52.66 | 36.20 | 81.88 | 53.07 | 51.36 (17.17%↑) | | Ours (TopK+MDL) | 91.51 | 40.27 | 58.77 | 46.56 | 61.43 | 42.47 | 87.94 | 53.15 | 60.16 | ![5_image_0.png](5_image_0.png) rate positively correlates with the performance. We conduct a more fine-grained exploration by corrupting the label space and breaking the input-label alignment. We corrupt the labels by exchanging label words between classes, e.g., exchanging label words between positive and negative classes in sentiment classification. As in Figure 2(a), we observe a clear performance drop with corrupted labels, which negatively correlates with the bias rate. These results suggest that in-context examples' labels could significantly impact ICL performance. Recent debates (Min et al., 2022; Kim et al., 2022) on the effect of label distribution focus on corpus-level ICL, and our findings complement their studies. ## 6 Analysis The observed benefits of our method raise the natural question of why and how it helps and whether the same performance improvements can be transferred to other PLMs or prompts. In this section, we conduct comprehensive experiments and analyses to understand the strength and weaknesses of our method. ## 6.1 When A Large Set Of Annotated Examples Is Not Available Despite the surprising performance of ICL, a largescale training set is not always available for retrieval in practice. To address this concern, we conduct experiments under the few-shot setting. We randomly sample 16, 32, 64, 128, 256, 512, and 1024 examples as the candidates for searching. We select two representative tasks (SST2 and SNLI) for evaluation and run each experiment three times with different random seeds. As shown in Figure 2(b) and 2(c), our method consistently outperforms the strong baseline TopK as in the full-data setting. This demonstrated the general applicability of our method in both full-data and few-shot scenarios. We also observe that the performance steadily increases with the growing number of annotated examples. ![6_image_0.png](6_image_0.png) ## 6.2 Impact Of Selection Methods We conduct most experiments using the popular TopK method for candidate example selection. Here we evaluate three other alternatives: random, DPP and VoteK. Figure 3(a) shows that using TopK for example selection outperforms all other alternatives on average. However, we also observe that the superiority of TopK is mainly in simple classification tasks with limited label space. On multi-choice tasks like Commonsense QA, all three alternatives outperform TopK (right side of Figure 3(a)). Note that although multi-choice tasks are also classification tasks, they have a huge label space like NLG tasks. The frustration of TopK on multi-choice tasks suggests that the popular TopK method does not work well for tasks with large label space and searching for better selection methods holds immense prospects, and therefore remains an interesting field of further research. ## 6.3 Accuracy Of Ranking Method In our ranking module, we randomly select 10 different organizations for each testing sample and use MDL to select the best-performing one in an unsupervised manner. Despite the superior performance of MDL, the accuracy of using MDL for in-context example ranking has not been discussed. | Dataset | TopK | TopK+MDL | TopK+LocalE | Random | |----------------------|---------------|---------------|---------------|---------------| | SST-2 | 0.6861(83.91) | 0.6810(91.51) | 0.6928(67.12) | 0.6918(77.17) | | SNLI | 1.0981(57.54) | 1.0929(58.77) | 1.0983(46.78) | 1.0974(43.41) | | CMS QA 4.9883(51.51) | 4.9371(53.15) | 4.9692(53.07) | 4.9629(52.93) | | | Trec | 5.5618(40.80) | 5.4496(42.47) | 5.7434(36.20) | 5.7859(18.33) | To understand the ranking accuracy of MDL, we assume a perfect ranking method *oracle*, which can always select the organization that leads to correct prediction if there is any. In the implementation, we first obtain predictions for all 10 organizations. If at least one prediction matches the ground truth, we consider this testing example solvable by *oracle*. As shown in Figure 3(b), there are significant performance gaps between oracle and TopK+MDL. Although such oracle performance only exists theoretically, it's still encouraging to see the enormous promise of ICL: with better selection and ranking methods (e.g., supervised methods (Rubin et al., 2022)), we might be able to bridge the performance gap between ICL and finetuning. We investigate the correlation between MDL and accuracy by selecting four representative datasets and reporting the MDL of each method. As shown in Table 2, a smaller MDL generally indicates a higher accuracy (in the brackets). This validates the effectiveness of MDL as the criterion for incontext example searching. It's also interesting to see that tasks with lower MDL are generally easier to learn (as explained in § 4.3), thus ICL has a better performance. ## 6.4 Impact Of Hyperparameter In this subsection, we investigate how different hyperparameters affect our performance. Increasing the window size of our method can steadily boost performance, by trading efficiency for better performance. We vary window size (i.e., number of organizations to be ranked per sample) from 2 to 50, and report the average accuracy. As shown in Figure 3(c), the performance steadily increases with the window size. We even observe gains when the window size is two. In particular, on tasks with short input lengths like SST2, using a window size of 2 already shows a clear gain (+3.19 in accuracy) over TopK. However, the improvement is achieved by sacrificing efficiency, i.e., window size hits 50 means performing forward passing for the test set 50 times. Together with findings above, we conclude that we must keep improving the accuracy of ranking methods to achieve a better efficiency-effectiveness trade-off. ## Increasing The Number Of In-Context Examples boosts accuracy for most tasks. We gradually increase the number of in-context examples (denoted as N) from 0 (prompting) to 32. From Figure 3(d), we see that increasing N consistently improves the performance on average. We also note that the random baseline reaches the performance plateau from N = 8. Such contradictions suggest that when analyzing the impact of N, the organization of examples is critical. Sometimes we find increasing N not helpful because we are not using the "right" organization. Our results raise an interesting question for future research: can we achieve finetuning-level performance by using thousands or even more examples as context? ## Larger Model Size Does Not Guarantee Better Performance, But Our Method Can Bring Consistent Improvements Over Strong Baselines. We use OPT and vary the model size from 350M to 175B. We have a mixed observation that blindly applying huge models does not always result in the best performance. For simple tasks like SST2 (see Figure 3(f)), we reach the performance plateau after 1.3B. And for SNLI, a 30B OPT even outperforms ![7_image_0.png](7_image_0.png) the 175B counterpart. Large models are powerful when dealing with complex tasks like Commonsense QA. From Figure 3(e), we can see steady and significant improvement whenever we scale up the model size. In addition, our method brings consistent improvements over baselines regardless of model sizes on all tasks evaluated. ## 6.5 Robustness Generability across different PLMs. We explore how our method generalizes between different PLMs. We average our results across datasets and present the results in Figure 4. On four different PLMs tested, our method consistently and significantly outperforms the strong TopK baseline. Overall, we have observed that our method is robust across various datasets and PLMs. Generability across different prompts. As sensitivity to prompt engineering is a key weakness of ICL, we evaluate the robustness of our method given different templates. We select two representative tasks (i.e., SST2 and SNLI) to conduct experiments, each with three different templates. As shown in Figure 5, our method is robust given different prompting templates. But still, the differences in prompting templates cause large variances in performance. The findings here motivate a line of research that simultaneously searches for the best template and in-context organization, which is rarely explored in the literature. ## 7 Conclusion This paper proposes a new paradigm for ICL: selfadaptive ICL. Unlike existing efforts that universally use one single example organization on all testing samples, we propose a general two-stage select-then-rank framework to search in-context examples at the instance-level. We instantiate this framework with an information-theory-driven ranking algorithm. Empirical results suggest that selfadaptive in-context learning can significantly outperform the common practice in ICL by a large margin. We reveal the great potential of self-adaptive in-context learning and point out several interesting research problems in method analysis. ## 8 Limitation Despite the demonstrated effectiveness of selfadaptive ICL, this new paradigm suffers from the following limitations. (I) As we discussed in § 6.4, due to the large search space, we need to trade efficiency for effectiveness. So how to balance the efficiency-effectiveness trade-off is an important decision choice to make when deploying selfadaptive ICL methods. (II) As shown in § 6.1, the gains of our method shrink when the size of the retrieval set gets smaller. To maximize performance, we require a high-quality retrieval set, which might not always be available when dealing with unseen tasks in practice. We also note that both limitations can be alleviated with better selection and ranking algorithms. The remarkable performance of our method should partially attribute to the powerful TopK selection method, so we also discuss the limitation of TopK here. Despite its popularity, our analysis (§ 6.2) reveals that TopK's effectiveness is limited to simple NLU tasks with limited label space, and it does not work well with tasks with large or even infinite label space (QA, multi-choice, and NLG). This limitation signals a new direction for ICL research: we need better selection methods to adapt ICL methods to more tasks. ## 9 Acknowledgement Yaoxiang, Zhiyong, and Jiacheng participate in coding and discussion. Yaoxiang and Zhiyong conduct the evaluation and analysis. Zhiyong leads the project and writes this manuscript. We want to thank members of Shark-NLP and reviewers for their valuable feedback. ## References Léonard Blier and Yann Ollivier. 2018. 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Yi Tay, Vinh Q Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al. 2022. Transformer memory as a differentiable search index. arXiv preprint arXiv:2202.06991. Naftali Tishby and Noga Zaslavsky. 2015. Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw), pages 1–5. IEEE. Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353–355. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. ![10_image_0.png](10_image_0.png) 2022. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682. 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PMLR. ## A Datasets Dataset information is detailed in Table 3. ## B Impact Of Hyperparameters The results of adjusting the number of in-context examples and window size are shown in Figure 6 and 7, respectively. ## C Templates The templates used in this paper are detailed in Table 4. ![10_image_1.png](10_image_1.png) ![10_image_2.png](10_image_2.png) ![10_image_3.png](10_image_3.png) ![10_image_4.png](10_image_4.png) | Task | Prompt | Class | | |-----------------------------------------------------|------------------------------|-------------------|---------| | SST-2 | Positive Movie Review: "<X>" | Positive | | | Negative Movie Review: "<X>" | Negative | | | | "<X>" It is terrible. | Very Negative | | | | "<X>" It is bad. | Negative | | | | "<X>" It is OK. | Neutral | | | | "<X>" It is good. | Positive | | | | "<X>" It is great. | Very Positive | | | | SST-5 | <X1>? Yes, <X2> | Entailment | | | SNLI & | MNLI | <X1>? Maybe, <X2> | Neutral | | <X1>? No, <X2> | Contradiction | | | | QNLI | <C> Can we know <X>? Yes. | Entailment | | | <C> Can we know <X>? No. | Contradiction | | | | "<X>" It is about abbreviation. | ABBR | | | | "<X>" It is about entity. | ENTY | | | | "<X>" It is about description and abstract concept. | DESC | | | | "<X>" It is about human being. | HUM | | | | "<X>" It is about location. | LOC | | | | "<X>" It is about numeric value. | NUM | | | | TREC | "<X>" It is about world. | World | | | "<X>" It is about sports. | Sports | | | | AgNews | "<X>" It is about business. | Business | | | "<X>" It is about science and technology. | Sci/Tech | | | | Answer the following question: <X> Answer: <A>. | A | | | | Answer the following question: <X> Answer: <B>. | B | | | | Answer the following question: <X> Answer: <C>. | C | | | | Answer the following question: <X> Answer: <D>. | D | | | | Answer the following question: <X> Answer: <E>. | E | | | | Commonsense QA | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? section 8 ✓ A2. Did you discuss any potential risks of your work? section 8, section 5.3, and section 1. ✓ A3. Do the abstract and introduction summarize the paper's main claims? section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Abstract, Section 5.1 ✓ B1. Did you cite the creators of artifacts you used? section 5.1 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? The license will be discussed with the code base release after the anonymity period. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? abstract B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix A ## C ✗ **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? No response. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? No response. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? No response. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? No response. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

meister-etal-2023-efficacy

On the Efficacy of Sampling Adapters

https://aclanthology.org/2023.acl-long.80

Sampling-based decoding strategies are widely employed for generating text from probabilistic models, yet standard ancestral sampling often results in text that is degenerate or incoherent. To alleviate this issue, various modifications to a model{'}s sampling distribution, such as top-p or top-k sampling, have been introduced and are now ubiquitously used in language generation systems. We propose a unified framework for understanding these techniques, which we term sampling adapters. Sampling adapters often lead to qualitatively better text, which raises the question: From a formal perspective, how are they changing the token-level distributions of language generation models? And why do these local changes lead to higher-quality text? We argue that the shift they enforce can be viewed as a trade-off between precision and recall: while the model loses its ability to produce certain strings, its precision rate on desirable text increases. While this trade-off is not reflected in standard metrics of distribution quality (such as perplexity), we find that several precision-emphasizing measures indeed indicate that sampling adapters can lead to probability distributions more aligned with the true distribution. Further, these measures correlate with higher sequence-level quality scores, specifically, Mauve.

# On The Efficacy Of Sampling Adapters Clara Meister Tiago Pimentel **Luca Malagutti** Ethan G. Wilcox **Ryan Cotterell** ETH Zürich University of Cambridge [email protected] [email protected] [email protected] [email protected] [email protected] ## Abstract Sampling is a common strategy for generating text from probabilistic models, yet standard ancestral sampling often results in text that is incoherent or ungrammatical. To alleviate this issue, various modifications to a model's sampling distribution, such as nucleus or top-k sampling, have been introduced and are now ubiquitously used in language generation systems. We propose a unified framework for understanding these techniques, which we term sampling adapters. Sampling adapters often lead to qualitatively better text, which raises the question: From a formal perspective, how are they changing the (sub)word-level distributions of language generation models? And why do these local changes lead to higher-quality text? We argue that the shift they enforce can be viewed as a trade-off between precision and recall: while the model loses its ability to produce certain strings, its precision rate on desirable text increases. While this trade-off is not reflected in standard metrics of distribution quality (such as perplexity), we find that several precision-emphasizing measures indeed indicate that sampling adapters can lead to probability distributions more aligned with the true distribution. Further, these measures correlate with higher sequence-level quality scores, specifically, MAUVE. https://github.com/rycolab/ sampling-adapters ## 1 Introduction The vast majority of natural language generation systems take a probabilistic approach. The backbone of such an approach is a probability distribution over strings pθ for a specific target domain. While modern language models have achieved remarkable performance on standard measures of distribution quality, e.g., perplexity (Brown et al., 2020; Chowdhery et al., 2022; Hoffmann et al., 2022; OpenAI, 2023), they often fall short when applied out of the box for language generation tasks—both sampling directly from them and searching for the maximum-probability string under them can lead to dull, incoherent, and degenerate text (Holtzman et al., 2020; Eikema and Aziz, 2020; Welleck et al., 2020). Surprisingly, applying a post-hoc modification to pθ(· | y<t) often serves to dramatically improve the quality of the generated text (Nadeem et al., 2020; Pillutla et al., 2021; Wiher et al., 2022; Hewitt et al., 2022; Li et al., 2022). In this paper, we give a name to these methods, dubbing them sampling adapters. A sampling adapter can be formally defined as a simplex-to-simplex map α: ∆|V|−1 → ∆*|V|−*1that systematically modifies the conditional distribution of an autoregressive language model pθ(· | y<t), thus creating another language model α(pθ(· | y<t)) with a desired set of characteristics, e.g., it may only give non-zero probability to items assigned high probability under the original model. Sampling adapters often require little to no fine-tuning and can be implemented in just a few lines of code. Presumably due to their simplicity, sampling adapters have become a default tool in text generation pipelines, serving as the core component of baseline decoding strategies in various tasks (Welleck et al., 2020; Pillutla et al., 2021; Pimentel et al., 2023). The fact that sampling adapters often lead to qualitatively better text, however, evokes a simple question: How do they change our language generation models such that the distribution pθ(· | y<t) places more probability mass on what we qualitatively deem to be "better" text? Most sampling adapters have been found through trial and error with only intuitive motivations given for their efficacy. Moreover, standard evaluation measures1 do not immediately shed light on why sampling adapters work well because most sampling adapters make language generation models substantially worse according to these measures, e.g., they often 1We use the term *measure* instead of the more common metric throughout this work because several of the functions that we consider are not metrics in the mathematical sense. reduce the probability assigned to certain strings to zero, which can yield a perplexity of ∞. In this paper, we posit that the change of distribution induced by sampling adapters can be analyzed in terms of a precision–recall trade-off, using the generalizations of these terms to the field of generative modeling (Sajjadi et al., 2018; Lucic et al., 2018; Djolonga et al., 2020). While a model loses its ability to produce certain strings, its ability to produce *desirable* text increases. We experiment with various sampling adapters that have been proposed (Fan et al., 2018; Holtzman et al., 2020; Meister et al., 2023; Hewitt et al., 2022) and find that, while the use of these adapters negatively affects recall-emphasizing performance measures, certain choices of hyperparameters increase performance in terms of measures that balance between precision and recall or that are precision-emphasizing. Comparing trends in these measures, we see evidence of a precision–recall trade-off, which offers a quantitative motivation for the efficacy of sampling adapters. We further find that precision-emphasizing measures correlate most highly with sequence-level quality metrics, offering a potential avenue for efficiently choosing sampling adapter hyperparameter values. The formal framework and empirical analysis presented here should pave the way for the development of theoretically motivated sampling adapters, and provide a straightforward means for both analysis of and comparison between adapters. ## 2 Language Generation 2.1 Probability Distributions Over Strings Most language generation systems are based on probabilistic models, i.e., models of the probability distribution over natural language strings2 V ∗, where V ∗is the Kleene closure of an alphabet V. In words, V ∗is the set of all strings that can be generated from a vocabulary of (sub)words V. A common modeling choice is to break down string probabilities autoregressively and locally normalize pθ, i.e., instead of directly modeling the full sequence probability pθ(y), one models (sub)word probabilities pθ(y | y<t) conditioned on the prior context y<t def = ⟨y1, . . . , yt−1*⟩ ∈ V*∗. Note that here, we have y ∈ V for V def = *V ∪{*EOS} where EOS is a special end of string token required for an 2Notably, these distributions might be conditioned on an input string, as in machine translation or summarization. autoregressive pθ to define a valid probability distribution over V ∗. The sequence-level probability can then be computed via the chain rule of probability: $$p_{\theta}(\mathbf{y})=p_{\theta}(\operatorname{Eos}\mid\mathbf{y})\prod_{t=1}^{|\mathbf{y}|}p_{\theta}(y_{t}\mid\mathbf{y}_{<t})\quad\quad(1)$$ See Du et al. (2023) for a characterization of when these models are tight, i.e., when the probability mass assigned to finite-length strings is 1. The parameters θ of these models are typically chosen by (numerically) maximizing the log-likelihood of the training data D, where log-likelihood is defined as: $${\mathcal{L}}(\mathbf{\theta})\!=\!\sum_{\mathbf{y}\in{\mathcal{D}}}\log p_{\mathbf{\theta}}(\mathbf{y})\qquad\qquad(2)$$ Note this is equivalent to minimizing the (forward) cross-entropy between the empirical distribution pD induced by the training data D. ## 2.2 Decoding Strategies In order to produce text from a model, one must use a **decoding strategy**, which provides a set of decision rules according to which tokens are sequentially chosen from the distribution pθ to form a string. Decoding strategies can be broadly taxonomized as either maximization-based or samplingbased. Maximization-based strategies aim to find the candidate string that scores highest under some objective. Finding the string with the highest probability under the model is a common maximizationbased strategy. Sampling-based strategies instead sample tokens according to some distribution derived from the model. While maximization-based strategies may make intuitive sense, they often lead to dull or degenerate text in open-generation settings (Cohen and Beck, 2019; Eikema and Aziz, 2020; Nadeem et al., 2020). Sampling-based strategies likewise have shortcomings: They introduce randomness into the generated text, which may lead to a disruption in coherence or fluency when units are sampled from low-probability regions of the distribution (Holtzman et al., 2020; Hewitt et al., 2022). A class of methods has been developed to address the problems observed when sampling directly from the model, specifically by altering the distribution from which tokens are sampled. We term these methods sampling adapters, formally defining them in the next section. ## 3 The Sampling Adapter Framework Formally, sampling adapters are simplex-tosimplex mappings, i.e., functions α : ∆*|V|−*1 → ∆*|V|−*1that take a probability distribution over V as input and map it to another one over V. 3 We use the notation pe to denote the output of this map, as applied to the distribution p: $${\widetilde{p}}(\cdot\mid y_{<t})\ {\stackrel{\mathrm{def}}{=}}\ \alpha{\big(}p(\cdot\mid y_{<t}){\big)}$$ similarly denoting the individual adapted probabilities as pe(y | y<t) = αp(· | y<t)(y). We now give two examples of common sampling adapters. Example 3.1. We recover standard **ancestral sampling** when αp(· | y<t)(y) = p(y | y<t). Example 3.2. We recover *temperature sampling* when αp(· | y<t)(y) ∝ p(y | y<t) 1 T *for temperature parameter* T. 4 One popular way of formulating sampling adapters in the literature has been via truncation functions, i.e., functions where vocabulary units that do not meet a certain criterion are re-assigned zero probability. We write these functions as: $$\alpha\big{(}p(\cdot\mid\mathbf{y}_{<t})\big{)}(y)\propto\tag{4}$$ $$p(y\mid\mathbf{y}_{<t})1\Big{\{}y\in\mathcal{C}\big{(}p(\cdot\mid\mathbf{y}_{<t})\big{)}\Big{\}}$$ where $\alpha$ is $\overline{\mathbf{y}}$-1 = $\mathbf{\alpha}(\overline{\mathbf{y}})$ is a function that $\mathbf{y}$ where C : ∆|V|−1 → P(V) is a function that finds the set of (sub)words that meets said criterion; P(·) denotes the powerset operator. Truncation sampling methods aim to eliminate probability mass placed on tokens deemed likely to lead to undesirable text, reallocating their probability mass to the remaining options. We now specify several common truncation-based sampling adapters. Example 3.3. We recover top-k **sampling** *(Fan* et al., *2018) when* $$\begin{array}{c}{{{\mathcal{C}}(p(\cdot\mid\mathbf{y}_{<t}))=\operatorname{argmax}\sum_{y\in{\mathcal{V}}}p(y\mid\mathbf{y}_{<t})}}\\ {{s.t.\ |{\mathcal{V}}|=k}}\end{array}\quad(5)$$ Example 3.4. We recover top-π **(nucleus) sampling** (Holtzman et al., *2020) when* $\mathcal{C}(p(\cdot\mid\boldsymbol{y}_{<t}))=\operatorname*{argmin}\limits_{\mathcal{V}^{\prime}\subseteq\overline{\mathcal{V}}}\left|\mathcal{V}^{\prime}\right|$ (6) $s.t.\sum\limits_{\begin{subarray}{c}\boldsymbol{y}\in\mathcal{V}^{\prime}\end{subarray}}p(\boldsymbol{y}\mid\boldsymbol{y}_{<t})\geq\pi$ $$({\mathfrak{I}}{\mathfrak{I}})$$ i.e., a function that returns the smallest subset of (sub)words that collectively have probability mass ≥ π. Example 3.5. We recover *locally typical sampling* (Meister et al., *2023) when* $$\mathcal{C}(p(\cdot\mid\mathbf{y}_{<t}))=\operatorname*{argmin}_{\mathcal{V}^{\prime}\subseteq\overline{\mathcal{V}}}\sum_{y\in\mathcal{V}^{\prime}}\left|\mathrm{H}(p(\cdot\mid\mathbf{y}_{<t}))\right.\tag{7}$$ $$\left.+\log p(y\mid\mathbf{y}_{<t})\right|$$ $$\left.s.t.\ \sum_{y\in\mathcal{V}^{\prime}}p(y\mid\mathbf{y}_{<t})\geq\pi\right.$$ i.e., the set of items with log-probability closest to the (sub)word-level entropy that collectively have probability mass ≥ π. Example 3.6. We recover η**-sampling** (Hewitt et al., *2022) when* $${\mathcal{C}}(p(\cdot\mid\mathbf{y}_{<t}))=\{y\in{\overline{{\mathcal{V}}}}\mid p(y\mid\mathbf{y}_{<t})>\eta\}\quad(8)$$ where η = min (ϵ, √ϵ exp(−H (p(· | y<t))))*, i.e.,* the set of items with probability greater than η for hyperparameter ϵ > 0. Other methods can similarly be cast in the sampling adapter framework, such as Mirostat (Basu et al., 2021) and the re-calibration method proposed by Braverman et al. (2020). Moreover, the general equation for sampling adapters given in Eq. (3) suggests that one direction for future research is *learning* a sampling adapter α. While many previously proposed adapters are truncation-based, adapters that reallocate mass in a different manner may also prove effective. Indeed, equipping α with tunable parameters could prove useful as a lightweight finetuning method. An Unintuitive Effect. The motivation behind the use of sampling adapters with language generation models is to readjust their distribution, shifting mass away from tokens deemed likely to lead to undesirable text and onto tokens that will generate high-quality text. Yet why are such transformations even necessary? Standard measures of distribution quality, such as perplexity, would suggest that our models' estimates of the ground-truth distribution over natural language strings are quite good (Brown et al., 2020; Wang and Komatsuzaki, 2021; Hoffmann et al., 2022). This, in turn, implies that the heuristic shifts performed by sampling adapters should lead to *worse* language generators. We argue that the disparity between the quality of language generation systems using sampling-adapted models and the quality of these same models according to standard measures can be reconciled using probabilistic analogs of precision and recall. ## 4 A Precision–Recall Hypothesis We begin by reviewing generalizations of the concepts of precision and recall in the field of generative modeling. We then discuss the shortcomings of current language generation models and how sampling adapters may address these shortcomings. ## 4.1 Generalizations Of Precision And Recall A series of recent papers have related the **precision** of a learned distribution pθ to the average quality of generated samples, where high-quality samples are assumed to be those with high probability under the data-generating distribution p. 5 Additionally, they relate the **recall** of pθ to its coverage of p (Sajjadi et al., 2018; Lucic et al., 2018; Djolonga et al., 2020, *inter alia*), i.e., high overlap in the support of pθ and p. Following this line of reasoning, the notions of precision and recall can naturally be operationalized using measures of the difference between two distributions—specifically, ones that enable different penalizations of over- and undercoverage of our reference distribution. There are several measures that, when considered together, naturally operationalize precision, recall, or some combination of the two.6In this paper, we focus on cross-entropy, KL divergence, total variation distance (TVD), and Jensen–Shannon (JS) divergence. We introduce each in greater detail below. We note that for all these measures, a larger value indicates a greater discrepancy between two distributions, and that all but the cross-entropy will be zero when the two distributions are identical. Further, we note that not all the measures are symmetric, i.e., their 5We note that in general though, it is not clear that high-probability and high-quality should necessarily coincide (Zhang et al., 2021; Meister et al., 2023). 6We refer the reader to Cichocki and Amari (2010) and Djolonga et al. (2020) for a more comprehensive discussion of such measures. values change depending on the order in which the distributions are given as arguments to the measure. Out of convention, in the case that the reference distribution is provided first, we call this the **forward** variant of the measure. We call the case where the reference distribution is the second argument the **reverse** variant of the measure. We define all measures in terms of generic distributions p1 and p2, which we assume both have (not necessarily identical) supports that are a subset of V. Precision-emphasizing Measures. We first consider the **cross-entropy** between p1 and p2: $$\mathrm{H}(p_{1},p_{2})=-\sum_{y\in\overline{\mathcal{V}}}p_{1}(y)\log p_{2}(y)\tag{9}$$ Upon inspection, we can see that the reverse cross $${\mathfrak{n}}\;p_{1}\;{\mathrm{and}}\;p_{2}\colon$$ Upon inspection, we can see that the reverse crossentropy, i.e., where p1 is the distribution being evaluated and p2 is a (fixed) reference distribution, rewards high precision.7 Specifically, it rewards p1 for assigning probability mass where p2 is large, implicitly penalizing p1 for assigning high probability where p2 is small. In fact, the reverse crossentropy is minimized in the case where p1 places all probability on the most probable token under p2. A related measure is the reverse KL divergence $$\begin{split}\text{KL}(p_{1}\mid\mid p_{2})&=\sum_{y\in\overline{\nu}}p_{1}(y)\log\frac{p_{2}(y)}{p_{1}(y)}\\ &=\text{H}(p_{1},p_{2})-\text{H}(p_{1})\end{split}$$ (10a) \[\begin{split}\text{ which is equivalent to the cross-entropy up to the subtraction of the entropy term H(p1). As with cross-entropy, the reverse KL divergence rewards high precision. This property is reflected by a common intuition provided about this measure when it is used as a learning objective: It is referred to as a *mode-seeking* objective, i.e., it aims to place mass on the *modes* of p1. 8Importantly, the distributions that minimize the reverse variants of Eq. (9) and (10a) will not necessarily be equivalent because the latter takes into account p1's entropy. So which of these two metrics should we use? As we are interested in using metrics that operationalize the notion of precision, the entropy of the distribution under evaluation is irrelevant. Thus, we will use the reverse cross-entropy as our primary precision-emphasizing metric. 7We note that most readers are likely more familiar with the *forward* cross-entropy, which is a common loss function. 8For further insights about the properties of the various measures used here, we refer the reader to the following detailed discussions (Minka, 2005; Nickisch and Rasmussen, 2008; Huszár, 2015; Theis et al., 2016). Recall-emphasizing Measures. On the other hand, the forward variants of Eq. (9) and (10a), where p2 is now the distribution under evaluation and p1 is assumed to be fixed, reward recall. This is evident when taking a closer look at their definitions. If p2 fails to place probability on all elements y assigned probability by p1, then both the cross-entropy and KL divergence will be ∞. 9 Analogously to the reverse KL's description as mode-seeking, the forward KL is referred to as *mean-seeking*. Note that using the forward variants of cross-entropy and KL divergence as learning objectives is equivalent since H(p1) is constant with respect to p2. Further, the forward KL and cross-entropy, as well as the reverse KL, are minimized when p2 = p1. Balanced Measures. The definitions for TVD and JS divergence, which are both symmetric measures, suggest a balance between the characteristics of precision and recall: $$\mathrm{TVD}(p_{1},p_{2})=\sum_{y\in\overline{{{\mathcal{V}}}}}|p_{1}(y)-p_{2}(y)|\qquad(11)$$ $\mathcal{L}=\lambda_{\rm min}$ $$\mathrm{JS}(p_{1},p_{2})={\frac{\mathrm{KL}(p_{1}\mid\mid m)+\mathrm{KL}(p_{2}\mid\mid m)}{2}}\,\,\,\,(12)$$ where m(y) = p1(y)+p2(y) 2for y ∈ V is a pointwise average. Practically, the JS divergence can informally be viewed as an interpolation between the forward and reverse KL divergences. Indeed, several divergences that generalize the forward and reverse KL recover the JS divergence given a particular choice of hyperparameter (Huszár, 2015; Meister et al., 2020; Pillutla et al., 2021). TVD can be similarly motivated: Sajjadi et al. (2018) recover TVD in their precision–recall operationalization for generative models when assigning equal importance to precision and recall. Further, a standard result demonstrates that the JS divergence is a lower bound on TVD (Lin, 1991). With these measures in hand, we can more effectively assess the shifts to precision and recall that sampling adapters induce in a model. 9To avoid the possibility of an infinite cross-entropy, one can use an ε-smoothed variant of p2 i.e., where p (ε) 2 (·) = p2(·)+ε 1+*|V|·*ε . This trick is often employed to evaluate methods that do not produce distributions covering the entire support, e.g., Peters et al. (2019) and Martins et al. (2020). As many of the sampling adapters that we analyze produce sparse distributions (specifically, the truncation sampling methods), we will likewise employ this variant of KL divergence where necessary. ## 4.2 Current Modeling Shortcomings It is not clear that the objective with which probabilistic language generators are typically trained imparts characteristics that align with the goals of building good language generators.10 Any form of maximum-likelihood training is equivalent to minimizing H(pD, pθ)—often with an additional form of regularization. Thus, it encourages high recall: pθ(yt| y<t) must be nonzero for all tokens ytin every string y in the training set D for the objective to be finite. This, in turn, results in pθ allocating some probability mass to all (sub)words y ∈ V for all contexts y<t. In language modeling, this is perhaps a desirable property: We often care about the relative probabilities of strings, and assigning strings 0 probability would be counter-productive towards this goal. Yet, this property can potentially prove problematic when such models are used out of the box as language generators.11 For language generation systems, high precision is arguably a higher priority, i.e., the goal is for all of the generated sequences to be of high quality. An intuitive argument for this is that a single bad output can leave a lasting poor impression on the user. Yet, the inability to generate a single sequence may go unnoticed—especially if the difference between that sequence and one the model can produce is a single, exchangeable token. In this light, a possible explanation for the efficacy of sampling adapters is as follows: While model parameters are chosen to minimize a recall-prioritizing objective, sampling adapters re-align the distribution with a more appropriate precision-prioritizing probabilistic objective, i.e., sampling adapter hyperparameter combinations that work well perhaps do so because they minimize an objective that balances between precision and recall. If this is indeed the case, it should not be surprising that the transformation induced by sampling adapters leads to worse models according to standard, recall-emphasizing measures: Any generator that assigns zero probability to a valid string—as is the case when top-π or top-k sampling are applied—will have both infinite cross-entropy and perplexity with respect to the natural language distribution. They may, however, lead to better models according to more balanced (or even precision-emphasizing) measures, which is what we now empirically test. ## 5 Experiments To test the hypothesis that the operations performed by sampling adapters are akin to a re-prioritization of precision over recall in the output of the model, we evaluate the effects of sampling adapters on measures that emphasize recall, precision or a balance of the two, as outlined in §4.1. We then observe how these measures vary as a function of the sampling adapters' hyperparameters. Further, we also look at these measures' Spearman correlations with MAUVE, a sequence-level quality metric. We consider five different adapters: temperature, η (eta), top-π, top-k and locally typical sampling, each over a wide range of hyperparameters. Note that for the latter three adapters, a smaller hyperparameter value corresponds to a larger shift between pθ and peθ. For η-sampling, the reverse is true, and for temperature sampling, hyperparameter values farther from 1 imply a larger shift. For reproducibility, we leverage the Hugging Face framework (Wolf et al., 2020) and its implementation of sampling adapters for all but η-sampling, for which we rely on the original authors' implementation.12 Error bars for all plots indicate 95% confidence intervals for the observed values; note that bars are often small enough that they are not visible. ## 5.1 Setup We focus on the task of open-ended text generation. We use GPT-2 small and large (Radford et al., 2019), as well as, GPT-Neo (small) (Gao et al., 2020) as our generation models. The main results of this paper use the test set of a public version of the WebText dataset13 as our reference text. Results using the WikiText test set (Merity et al., 2016) are qualitatively similar and can be found in App. A. Sequence-level Metrics. Following Pillutla et al. (2021), we use the first 35 tokens of samples from our reference text as a prompt to generate continuations y ∼ pθ(· | y<t) until |y| = 512, or EOS is sampled. We generate 1000 samples for each 12github.com/john-hewitt/truncation-sampling 13The dataset is at github.com/openai/gpt-2-output-dataset. combination of model, sampling adapter, and hyperparameter. We compute MAUVE scores (where higher implies the samples are closer to the reference text), aggregated over 5 seeds, for each of these sets of text samples. Token-level Measures. In this analysis, we compare (sub)word-level distributions peθ(· | y<t) and p(· | y<t). The former is our generation model after the application of a sampling adapter and the latter is a reference distribution. We present results using both the empirical distribution induced by our test set and the distribution given by the GPTJ model (Wang and Komatsuzaki, 2021) 14 as our reference distribution. Here, y is a string from the test set. Results are mean-aggregated across both t = 1*, . . . ,* |y| and all y. Note that when we compute either the cross-entropy or KL divergence and it is not guaranteed that the support of p1 is a subset of the support of p2, we make use of the ε version of the metrics, as specified in §4.1, with ε = 1e-6. ## 5.2 Results Trends in Probabilistic Measures. We first present our analysis of how different adapter– hyperparameter settings affect the relationship of the model to a reference distribution (either probabilities according to GPT-J or the empirical distribution). Note that if our hypothesis in §4.1 is correct, we would expect to see that certain sampling adapter–hyperparameter settings lead to lower values of measures that emphasize precision, such as reverse cross-entropy, while simultaneously increasing measures that emphasize recall, such as forward cross-entropy. We show the reverse and forward cross-entropy, as well as TVD, in Fig. 1. 15 Both the forward and reverse cross-entropy results align closely with our hypothesis: A larger adapter shift generally leads to a higher forward cross-entropy and lower reverse cross-entropy.16 This observation holds when using either the 14We use GPT-J as a reference because it has substantially better perplexity on benchmark datasets. Note that it has ≈ 50 times more parameters than either GPT-2 small or GPT-Neo, both of which it shares a vocabulary with. 15As anticipated given the relationship between TVD and JS, results showing the JS divergence are qualitatively very similar to TVD. Hence, they appear in App. A. 16Importantly, if not for use of the ε-smoothed versions of the forward and reverse cross-entropies, many of the crossentropies in Fig. 1 would be infinite for the truncation-based adapters. Specifically, this would be true for any adapter without 100% coverage of the tokens in the evaluation text, which is the case for most adapter–hyperparameter settings (see Fig. 6 in App. A). ![6_image_0.png](6_image_0.png) empirical distribution or GPT-J as our reference. Interestingly, we see that the trends reverse when we consider the reverse KL divergence (as opposed to the reverse cross-entropy; see Fig. 3). This is perhaps expected given that the entropy of the model's distribution monotonically decreases after the application of sampling adapters (see Fig. 7). Lastly, the trends in TVD differ largely depending on the distribution used as a reference. When GPT-J is used, we see that TVD monotonically increases as adapter strength increases. The reverse trend appears to hold when considering the empirical distribution: TVD generally *decreases* with adapter strength. The reason for this difference is not immediately obvious. Closer inspection reveals that when GPT-J is the reference, the trends in TVD mimic what we would expect from a metric that interpolates between forward and reverse crossentropies. Since TVD is motivated as a metric that balances between precision and recall, our results therefore make intuitive sense. On the other hand, the observed trends for the empirical distribution do not have a clear explanation. Critically, we find that the observed trends are stable across various design choices; see App. A for results with the WikiText dataset and with different choices of ε for the ε-smoothed versions of metrics.17 A Precision–Recall Trade-Off. We next look at whether the shifts induced by common sampling adapters correspond to a precision–recall trade-off according to our probabilistic measures. In Fig. 2, we compare the reverse and forward crossentropies (with GPT-J used as the reference) across the adapter hyperparameter settings used. Results using the empirical distribution are similar (see Fig. 10 in App. A). Fig. 2 indeed suggests a quite direct trade-off between our operationalizations of precision and recall. Notably, the highest sequence-level quality scores do not correspond with the sampling adapter–hyperparameter settings that achieve the best precision (i.e., lowest reverse cross-entropy).18 Rather, they correspond to an intermediate point along the line, suggesting the importance of balancing precision and recall. Correlations. The previous observations motivate us to look at correlations between (sub)wordlevel probabilistic measures and sequence-level quality metrics. We consider both the WebText and WikiText results when computing correlations. In Tab. 1, we see that the reverse KL of the generation model with GPT-J has the highest (rank) correlation with our quality metrics, closely followed by TVD. This finding suggests that reverse KL with another model could be a useful metric for selecting sampling adapter's hyperparameters, as its computation is much faster than standard methods for choosing such hyperparameters—e.g., human annotations or sequence-level quality scores—which require the generation of full sequences. ## 6 Related Work Precision and Recall in Language Generation. This is by no means the first work to focus on the notions of precision and recall in the context of language generation. Language generator evaluation metrics have historically intentionally 17We also observed that trends were very stable across the choice of reference model, i.e., using GPT2-XL and the 1.5B parameter version of GPT-Neo rather than GPT-J. We omit these results from the appendix to reduce clutter. 18MAUVE scores for all adapter–hyperparameter settings and both datasets can be seen in Fig. 4. | KL | Cross-entropy | | | | | |-----------------|-------------------------------------|--------|--------|--------|--------| | TVD | Reverse ε-Forward Reverse ε-Forward | | | | | | GPT-J GPT-2 | -0.73∗ -0.77∗ | -0.38∗ | -0.11 | -0.44∗ | | | GPT-Neo | -0.74∗ -0.73∗ | -0.33∗ | 0.08 | -0.41∗ | | | GPT-Large | -0.77∗ -0.80∗ | -0.49∗ | 0.01 | -0.55∗ | | | Empirical GPT-2 | -0.18∗ -0.26∗ | -0.48∗ | -0.18∗ | -0.48∗ | | | GPT-Neo | -0.02 | -0.25∗ | -0.42∗ | -0.02 | -0.42∗ | | GPT-Large | -0.10 | -0.50∗ | -0.61∗ | -0.10 | -0.61∗ | prioritized precision-based measures due to their higher correlation with human quality judgments. For example, BLEU (Papineni et al., 2002) is computed using n-gram precision, and the original work on CHRF (Popovic´, 2015), which is a precision–recall-based metric, found that variants of the metric that placed more weight on precision correlated better with human judgments. More recently, Pimentel et al. (2023) report that the reverse KL divergence between multinomial distributions over embeddings of text from language models and of text from humans correlated more with human quality judgments than the results of other divergence measures. On the other hand, measures that place higher importance on recall of the model with respect to some test set, such as perplexity, are known not to be good indicators of text quality (Holtzman et al., 2020; Cohen and Beck, 2019; Meister et al., 2023). In terms of model training, alternative objectives that emphasize precision have been proposed in an attempt to alleviate the zero-avoiding effect induced by optimization for maximum likelihood (Kang and Hashimoto, 2020; Pang and He, 2021). ## Analysis Of Language Generation Models. The effect of sampling adapters on language models has previously been discussed in the framework of a quality–diversity trade-off (Zhang et al., 2021; Meister et al., 2022). For instance, Nadeem et al. (2020) and Wiher et al. (2022) catalog various sampling adapters and analyze their properties with respect to a quality–diversity trade-off using a wide range of automatic metrics. Hashimoto et al. (2019) propose an evaluation framework that combines human and statistical evaluation. In contrast, our work makes an explicit connection to the concepts of precision and recall and analyzes the effect of sampling adapters employing measures of differences in distributions. While Pillutla et al. (2021) likewise use notions of precision and recall for assess- ![8_image_0.png](8_image_0.png) ing language generators, they look at quantized distributions over language embedding spaces rather than directly at distributions over (sub)words. ## 7 Conclusion In this work, we offer a formal treatment of sampling adapters and provide an analysis that aims to uncover why they are effective when used with probabilistic models for language generation. To this end, we first introduce a general framework that encompasses most of the transformations performed by previously proposed sampling adapters. We then offer an intuition as to why sampling adapters may lead to better language generators. Using the notions of precision and recall proposed for generative models, which can be quantified in terms of standard probabilistic measures, we perform an empirical analysis. We find evidence that the application of sampling adapters increases the precision of a distribution at the expense of its recall; this observation is robust across several experimental design choices. We further find a high correlation between sequence-level quality metrics and reverse KL divergence of the generation model with a reference model. ## Acknowledgments We would like to thank John Hewitt and Afra Amini for the insightful discussions preceding this work. Clara was supported by a Google Ph.D. Fellowship. Tiago was supported by a Facebook Ph.D. Fellowship. Ethan was supported by an ETH Zürich Postdoctoral Fellowship. ## Limitations ![8_Image_1.Png](8_Image_1.Png) A clear limitation of this work is that the results have been shown only for English. Further work should consider other model architectures, as well as datasets that span a variety of languages and domains. Another limitation is that we do not conduct human evaluations. Given the large number of adapter and hyperparameter settings that we chose to explore, acquiring the human evaluations that would have allowed us to make statistically significant conclusions regarding the relationships between text quality, distribution-level measures, and adapter–hyperparameter settings would have been financially prohibitive. Instead, we chose to look at automatic sequence-level quality metrics that are known to correlate highly with human quality judgments. Further, it has been observed that crowd-sourced judgments of text quality are far from perfect (Clark et al., 2021), making it not obvious whether this is indeed the better option. ## Ethical Considerations The use of language models for text generation comes with several ethical concerns. Especially when using sampling-based decoding algorithms, as is promoted in this work, the text generated by probabilistic models may contain malicious or hallucinatory content. 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In Proceedings of the Workshop on Human Evaluation of NLP Systems, pages 25–33, Online. Association for Computational Linguistics. ## Additional Results ![12_image_0.png](12_image_0.png) A ![13_image_0.png](13_image_0.png) ![13_image_1.png](13_image_1.png) ![14_image_0.png](14_image_0.png) Adapter Parameter Figure 6: Average entropy of the distribution ρ o ( · | Y< t ) for different sampling adapter–hyperparameter combinations. Dashed lines correspond to the entropy of the unmodified distribution. - GPT-2 - GPT-2 Large - GPT-Neo ![14_image_1.png](14_image_1.png) Figure 7: Average model token coverage per sequence y (i.e., percentage of tokens to which the adapter assigns non-zero probability) of the WebText test set after different sampling adapter methods have been applied to the output distribution. Dashed lines correspond to unmodified distribution, which always assigns probability mass to each token. ![14_image_2.png](14_image_2.png) - GPT-2 (1e-6) - GPT-2 (1e-8) Figure 8: Same plot as Fig. 1 albeit using smaller ε (1e-8 instead of 1e-6) in computation of ε variants of methods. Results are essentially unchanged, except for a slight shift in axis values. ![15_image_0.png](15_image_0.png) Figure 9: Same plot as Fig. 1 except using the test set of WikiText as our set of strings ( y ) and to construct the ![16_image_0.png](16_image_0.png) ![16_image_1.png](16_image_1.png) Adapter

yu-etal-2023-cross

Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis

https://aclanthology.org/2023.acl-long.81

Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from a target domain. To tackle the task, several recent works explore a new unsupervised domain adaptation framework, i.e., Cross-Domain Data Augmentation (CDDA), aiming to directly generate much labeled target-domain data based on the labeled source-domain data. However, these CDDA methods still suffer from several issues: 1) preserving many source-specific attributes such as syntactic structures; 2) lack of fluency and coherence; 3) limiting the diversity of generated data. To address these issues, we propose a new cross-domain Data Augmentation approach based on Domain-Adaptive Language Modeling named DA$^2$LM, which contains three stages: 1) assigning pseudo labels to unlabeled target-domain data; 2) unifying the process of token generation and labeling with a Domain-Adaptive Language Model (DALM) to learn the shared context and annotation across domains; 3) using the trained DALM to generate labeled target-domain data. Experiments show that DA$^2$LM consistently outperforms previous feature adaptation and CDDA methods on both ABSA and Aspect Extraction tasks. The source code is publicly released at \url{https://github.com/NUSTM/DALM}.

# Cross-Domain Data Augmentation With Domain-Adaptive Language Modeling For Aspect-Based Sentiment Analysis Jianfei Yu∗, Qiankun Zhao∗ **and Rui Xia**† School of Computer Science and Engineering, Nanjing University of Science and Technology, China {jfyu, kkzhao, rxia}@njust.edu.cn ## Abstract Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the useful knowledge from a source domain to identify aspectsentiment pairs in sentences from a target domain. To tackle the task, several recent works explore a new unsupervised domain adaptation framework, i.e., Cross-Domain Data Augmentation (CDDA), aiming to directly generate much labeled target-domain data based on the labeled source-domain data. However, these CDDA methods still suffer from several issues: 1) preserving many source-specific attributes such as syntactic structures; 2) lack of fluency and coherence; 3) limiting the diversity of generated data. To address these issues, we propose a new cross-domain Data Augmentation approach based on Domain-Adaptive Language Modeling named DA2LM, which contains three stages: 1) assigning pseudo labels to unlabeled target-domain data; 2) unifying the process of token generation and labeling with a Domain-Adaptive Language Model (DALM) to learn the shared context and annotation across domains; 3) using the trained DALM to generate labeled target-domain data. Experiments show that DA2LM consistently outperforms previous feature adaptation and CDDA methods on both ABSA and Aspect Extraction tasks. The source code is publicly released at https://github.com/NUSTM/DALM. ## 1 Introduction As an important task in sentiment analysis, AspectBased Sentiment Analysis (ABSA) aims to extract aspect terms from sentences and predict the sentiment polarity towards each aspect term (Liu, 2012; Pontiki et al., 2016). For example, given a sentence "*The screen is broken*", the aspect term is *screen* and its sentiment polarity is *Negative*. With the advancements of deep learning techniques, a myriad of neural approaches have been proposed for ABSA ∗ Equal contribution. † Corresponding author. ![0_image_0.png](0_image_0.png) and achieved promising results on several benchmark datasets (Li et al., 2019a; He et al., 2019; Chen and Qian, 2020b). However, these methods heavily rely on labeled data with fine-grained annotation, which is often time-consuming and expensive to obtain for many emerging domains. To alleviate the reliance on labeled data, many previous works resorted to unsupervised domain adaptation techniques, which aim to transfer knowledge from a resource-rich source domain to a target domain only with unlabeled data (Blitzer et al., 2007; Pan et al., 2010; Zhuang et al., 2015). Most existing domain adaptation methods on the ABSA task focus on learning shared feature representations across domains (Wang and Pan, 2018; Li et al., 2019c; Gong et al., 2020; Chen and Qian, 2021). Although these methods have obtained promising results, their models are only trained on the sourcedomain labeled data and thus insensitive to the important target-specific aspect and opinion terms. To address this limitation, several recent studies have explored a new domain adaptation framework named Cross-Domain Data Augmentation (CDDA), which aims to directly generate much target-domain labeled data based on the labeled data from the source domain. These existing methods can be summarized into two groups: Masked 1456 Language Model (MLM)-based CDDA (Yu et al., 2021; Yang et al., 2022) and Sequence-to-Sequence (Seq2Seq)-based CDDA (Chen et al., 2021; Li et al., 2022). As shown in Fig. 1(a) and Fig. 1(b), the core idea behind existing CDDA methods is to first mask source-specific words in the sourcedomain labeled data, followed by using either the well-trained MLM or Seq2Seq models to automatically generate target-specific words and labels in the masked positions. Despite achieving significant improvements over previous feature adaptation methods, these CDDA approaches still have several shortcomings: 1) they only mask source-specific words or phrases but preserve other source-specific attributes such as syntactic structures, which make the distribution of the generated data different from that of the real target-domain data; 2) replacing source-specific words with target-specific words may destruct the semantic meaning of the original sentence, making the generated data lack of fluency and coherence; 3) existing CDDA methods regard each source-domain sentence as the template, thus limiting the diversity of the generated data. To tackle these shortcomings, we propose a new cross-domain Data Augmentation approach based on Domain-Adaptive Language Modeling named DA2LM, which consists of three stages, including Domain-Adaptive Pseudo Labeling, DomainAdaptive Language Modeling, and Target-Domain Data Generation. Specifically, the labeled source data and unlabeled target data are first leveraged to train a base domain adaptation model, which is then used for predicting pseudo labels of unlabeled data in the target domain. Secondly, we design a novel Domain-Adaptive Language Model (DALM), and train it on the labeled source data and pseudo-labeled target data to learn the transferable context and label across domains. Different from most existing LMs, our DALM unifies the process of data generation and fine-grained annotation, aiming to simultaneously generate the next token and predict the label of the current token at each time step of the training stage. Finally, given the trained DALM, we employ it to generate many labeled target-domain data in an autoregressive manner with a probability-based generation strategy. Our main contributions can be summarized as follows: - We propose a three-stage framework named cross-domain Data Augmentation with Domain Adaptive Language Modeling (DA2LM), which can generate a large amount of labeled targetdomain data for the cross-domain ABSA task. - Under the framework, we devise a new domainadaptive language model, which unifies the process of data generation and labeling and captures the domain-invariant context and annotation for target-domain data generation. - Experiments on four benchmark datasets demonstrate that our framework significantly outperforms a number of competitive domain adaptation methods on both ABSA and Aspect Extraction (AE) tasks. Further analysis on generated data shows the superiority of our framework in terms of data distribution, diversity, and fluency. ## 2 Related Work 2.1 Aspect-Based Sentiment Analysis (Absa) As an important task in sentiment analysis, ABSA has been extensively studied in the last decade. Earlier works mainly focus on two subtasks of ABSA, i.e., aspect extraction (AE) (Liu et al., 2015; Chen and Qian, 2020a) and aspect-based sentiment classification (ASC) (Zhang et al., 2016; Chen et al., 2017; Sun et al., 2019; Wang et al., 2020). Recently, many supervised methods are proposed to solve the two sub-tasks in an end-to-end manner, which either resort to multi-task learning to exploit the relations between AE and ASC (Luo et al., 2019; He et al., 2019; Chen and Qian, 2020b) or employ a collapsed tagging scheme to combine AE and ASC into a unified label space and formulate the task as a sequence labeling problem (Wang et al., 2018; Li et al., 2019a,b). Despite obtaining promising results on several benchmark datasets, these methods suffer from the lack of annotated data in many emerging domains. To alleviate this issue, we aim to propose an unsupervised domain adaptation method to generate sufficient labeled data for ABSA in any target domain. ## 2.2 Unsupervised Domain Adaptation In the literature, a myriad of unsupervised domain adaptation methods have been proposed for coarsegrained sentiment analysis (Zhuang et al., 2020), including pivot-based methods (Blitzer et al., 2007; Yu and Jiang, 2016; Ziser and Reichart, 2018; Xi et al., 2020), auto-encoders (Glorot et al., 2011; Zhou et al., 2016), domain adversarial networks (Ganin and Lempitsky, 2015; Ganin et al., 2016; Li et al., 2018), and semi-supervised methods (He et al., 2018; Ye et al., 2020). These methods primarily focus on learning domain-invariant representations to alleviate the distribution discrepancy across domains. Inspired by the success of these representation-based methods, a few recent studies have adapted them to the cross-domain ABSA task, in which the key idea is to learn a shared representation for each word or aspect term across domains (Ding et al., 2017; Wang and Pan, 2018, 2019, 2020; Li et al., 2019c; Zeng et al., 2022; Chen and Qian, 2022). Moreover, Lekhtman et al. (2021) proposed a customized pre-training approach with aspect category shift for the aspect extraction task. Despite obtaining promising results, the major limitation of these aforementioned methods for cross-domain ABSA is that their models for the main ABSA task is solely trained on the sourcedomain labeled data. Thus, their models are insensitive to target-specific features. To address this issue, some studies have explored a Cross-Domain Data Augmentation framework (CDDA) to directly generate much target-domain labeled data, including MLM-based CDDA (Yu et al., 2021; Yang et al., 2022) and Seq2Seq-based CDDA (Chen et al., 2021; Li et al., 2022). However, the generated data by these methods has several limitations including 1) preserving many source-specific attributes such as syntactic structures; 2) lack of fluency and diversity. Thus, in this work, we aim to propose a new data augmentation framework that can generate fluent target-domain labeled data without any source-specific attributes. ## 3 Methodology 3.1 Problem Definition And Notations Following previous studies (Li et al., 2019c), we formulate ABSA and AE as a sequence labeling problem. Given a sentence with n words x = {w1, w2*, ..., w*n}, the goal is to predict its corresponding label sequence y = {y1, y2*, ..., y*n}, where yj ∈ {B-POS, I-POS, B-NEG, I-NEG, B-NEU, I-NEU, O} for ABSA and yj ∈ {B, I, O} for AE. In this work, we focus on the unsupervised domain adaptation setting, in which the source domain has enough labeled data and the target domain only has unlabeled data. Let DS = {(x s i , y s i )} Ns i=1 denote a set of source-domain labeled data, and DT = {x t i} Nt i=1 a set of target-domain unlabeled data. The goal is to leverage DSand DTto predict the label sequences of test data from the target domain. ## 3.2 Overview As illustrated in Figure 2, our Cross-Domain Data Augmentation framework contains three key stages, including 1) Domain-Adaptive Pseudo Labeling, 2) Domain-Adaptive Language Modeling, and 3) Target-Domain Data Generation. In the first stage, an aspect-aware domain adaptation model is trained to assign pseudo labels to unlabeled data in the target domain. In the second stage, the labeled source data and the pseudo-labeled target data are used to train a domain-adaptive language model, which integrates data generation and sequence labeling in a unified architecture to capture the transferable context and annotation across domains. After training the DALM, the last stage uses probabilitybased generation strategy to generate diverse targetdomain data with fine-grained annotations in an autoregressive manner. ## 3.3 Domain-Adaptive Pseudo Labeling In this stage, our goal is to assign the pseudo labels to each unlabeled data in the target domain. Since the data distribution of the source domain is different from that of the target domain, directly training a classifier on the labeled source data to predict the pseudo labels of the unlabeled target data will bring much noise. Thus, it is necessary to alleviate the domain discrepancy to improve the quality of pseudo-labels. Since aspect terms are shown to play a crucial role in ABSA (Gong et al., 2020), we attempt to explicitly minimize the distance between source-domain and target-domain aspect term representations via Maximum Mean Discrepancy (MMD) (Gretton et al., 2012). Specifically, given the labeled source data DS and the unlabeled target data DT, we first obtain the aspect terms in DS via the gold labels and extract the aspect terms in DT based on a rulebased algorithm named Double Propagation (Qiu et al., 2011). Let us use x d = {w d 1 , wd 2 , ..., wdn} to denote a source or target domain sentence and use a d = {w d i , ..., wd j} to denote one of the aspect terms in the sentence, where d ∈ {*s, t*}. We then employ a pre-trained BERT model to obtain the hidden representation of the sentence Hd = {h d 1 , h d 2 , ..., h dn} and the aspect term representation a d = g(h d i , ..., h d j ), where h d ∈ R r, r refers to the hidden dimension, and g(·) denotes the meanpooling operation. Next, we propose an aspectlevel MMD loss to alleviate the distribution dis- ![3_image_0.png](3_image_0.png) crepancy across domains as follows: $$\begin{aligned} \mathcal{L}_{\text{mmd}} = \mathbf{d}_k^2(\mathcal{D}_a^S, \mathcal{D}_a^T) = \frac{1}{(N_a^s)^2} \sum_{i,j}^{N_a^s} k(\mathbf{a}_i^s, \mathbf{a}_j^s) + \\ \frac{1}{(N_a^t)^2} \sum_{i,j}^{N_a^t} k(\mathbf{a}_i^t, \mathbf{a}_j^t) - \frac{2}{N_a^s N_a^t} \sum_{i}^{N_a^s} \sum_{j}^{N_a^t} k(\mathbf{a}_i^s, \mathbf{a}_j^t), \end{aligned}$$ where $\mathcal{D}_a^S$ and $\mathcal{D}_a^T$ respectively denote the sets of $\mathbf{a}_i^s$. where DS a and DT a aspect term representations in the source domain and the target domain, Ns a and Nta refer to the number of aspect terms in the two domains, and k(·) denotes the Gaussian Kernel function. Meanwhile, for each source sample, the hidden representation Hsis fed into a Conditional Random Field (CRF) layer to predict the label sequence for the ABSA or AE task p(y s|Hs). The goal is to minimize the negative log-probability of the correct label sequence of each source-domain sample: $$\mathcal{L}_{\text{crf}}=-\sum_{i=1}^{N^{s}}\log p(\mathbf{y}_{i}^{s}|\mathbf{H}_{i}^{s}).\tag{1}$$ The CRF loss for the ABSA or AE task and the $$\quad(2)$$ aspect-level MMD loss are combined to train the base model Cb: $${\mathcal{L}}={\mathcal{L}}_{\mathrm{crf}}+\alpha{\mathcal{L}}_{\mathrm{mnd}},$$ L = Lcrf + αLmmd, (2) where α is the hyper-parameter. Finally, we use Cb to assign pseudo labels to each sample in DT, and obtain DP T = {(x pt i , y pt i )} Nt i=1. ## 3.4 Domain-Adaptive Language Modeling To generate a large amount of target-domain labeled data with diverse syntactic structures, we propose a Domain-Adaptive Language Model (DALM), which leverages the labeled source data DSand the pseudo-labeled target data DP T to learn the shared distribution of words and labels across domains. Since our DALM unifies the process of word generation and sequence labeling, at each time step, we employ the current input token and the predicted label at the previous step to simultaneously maximize the probabilities of predicting the next token and the label of the current token. Specifically, for each sample (x, y) ∈ DS ∪ DP T , we first construct an input token sequence, in which we insert a special token ⟨BOS⟩ to denote the sentence beginning, followed by a domain-specific token (i.e., [source] or [target]) to distinguish the domain that x belongs to. Let xin = {⟨BOS⟩, w0, w1, w2*, ..., w*n} denote the expanded input sentence, where w0 ∈ {[source], [target]}. Moreover, we construct another input label sequence, denoted by yin = {⟨BOL⟩, y⟨BOS⟩, y0, y1, y2*, ..., y*n−1}, where ⟨BOL⟩ denotes the initial state of the label sequence, y⟨BOS⟩is O, and yj refers to the label of wj . According to the input, the output token sequence is xout = {w0, w1, w2*, ..., w*n, ⟨EOS⟩}. The output label sequence is yout = {y⟨BOS⟩, y0, y1, y2*, ..., y*n}. The top of Figure 2 1459 shows an example of two input and two output sequences for a sample from the source domain. Next, for the input token sequence xin, we employ a decoder such as LSTM and the pre-trained GPT-2 model (Radford et al., 2019) to get its hidden representation as follows: $$\mathbf{e}_{-1}^{w},\mathbf{e}_{0}^{w},...,\mathbf{e}_{n}^{w}=\mathrm{Decoder}(w_{-1},w_{0},w_{1},...,w_{n}),$$ where w−1 denotes ⟨BOS⟩, e w t ∈ R dis the token representation, and d is the hidden dimension. For the input label sequence yin, a label embedding layer is used to get the label representation: $$\mathbf{e}_{-2}^{y},...,\mathbf{e}_{n-1}^{y}=\mathrm{LabelEmb}(y_{-2},y_{-1},...,y_{n-1}),$$ where y−2 and y−1 denote ⟨BOL⟩ and y⟨BOS⟩, and e y t ∈ R d. Next, at each time step t, we add e w t and e y t−1 to produce a token and label-aware representation (i.e., et = e w t + e y t−1 ), which is then fed into two different full-connected softmax layers to predict the probabilities of the next token wt+1 and the label yt as follows: $$\begin{array}{c}{{P(w_{t+1}|w_{\leq t},y_{\leq t-1})=\sigma(W_{w}\mathbf{e}_{t}+b_{w}),}}\\ {{P(y_{t}|w_{\leq t},y_{\leq t-1})=\sigma(W_{y}\mathbf{e}_{t}+b_{w}),}}\end{array}$$ where σ is the softmax function, and Wx ∈ R|Vx|×d, Wy ∈ R|Vy|×d, and |Vx| and |Vy| are the vocabulary size and the label size. For each sample (x, y) ∈ DS ∪ DP T , we optimize the parameters for DALM by minimizing the combination of cross entropy losses for the output token sequence and label sequence as follows: $$\begin{array}{l}{{\mathcal{L}=\mathcal{L}_{w}+\mathcal{L}_{y},}}\\ {{\mathcal{L}_{w}=-\sum_{t=-1}^{n}\log P(w_{t+1}|w_{\leq t},y_{\leq t-1}),}}\\ {{\mathcal{L}_{y}=-\sum_{t=-1}^{n}\log P(y_{t}|w_{\leq t},y_{\leq t-1}).}}\end{array}\quad\quad(7)$$ ## 3.5 Target-Domain Data Generation After training the DALM, we employ it to generate target-domain data with fine-grained annotations in an autoregressive manner. As shown in the bottom of Figure 2, the ⟨BOS⟩ token and the target-specific token [target] are fixed as the first two input tokens of the DALM, and ⟨BOL⟩ and O are fixed as the first two input labels. Next, we adopt a probabilitybased generation strategy to generate the following tokens and their corresponding labels. At each time step t, we first rank all the tokens in Vx based on the probabilities computed by Eq. 3 and pick top-k tokens as a candidate set Ct+1. We then sample a token wt+1 from Ct+1 as the next token. As the candidate tokens in Ct+1 are predicted with higher probabilities, the generated data are generally fluent and close to the real target-domain data. Moreover, given the same context, the DALM can choose a synonym as the next token due to the randomness of sampling, which is conducive to diversifying the generated data. Next, for the label generation at each time step t, we directly select the label with the highest probability computed by Eq. 4 as the label of the current token yt, which can ensure the quality of the generated label sequence. The above process of token generation and labeling will be stopped when the next token is predicted as ⟨EOS⟩. Because of the randomness brought by sampling, the trained DALM can be used to generate any amount of labeled data. However, generating more data may lead to significant vocabulary redundancy of generated data. Thus, once the size of generated data equals to Ng, we will stop generating target-domain labeled data. $$\begin{array}{l}{(3)}\\ {(4)}\end{array}$$ ## 3.6 Generated Data Filtering To mitigate the presence of low-quality labels in the target data generated from the probability-based generation strategy, we introduce the following steps for generated data filtering: 1) Delete data with the illogical labels that violate the prefix order of the BIO tagging schema (e.g., having O before I in the AE task and having B-Positive before INeutral in the ABSA task); 2) Delete repetitive data whose token and label sequences are the same, and only keep one of the duplicate samples; 3) Use the base model Cb in Section 3.3 to predict the label sequences of the generated sentences and delete data whose label sequences are different from those predicted by Cb. Let us use Dg = {(x g i , y g i )} Ng i=1 to denote the set of generated target-domain data. We then train a standard BERT-CRF model (Li et al., 2019b) on Dg, and use it to predict the label sequences of test data from the target domain. ## 4 Experiments 4.1 Experimental Settings Datasets. To evaluate the effectiveness of the proposed DA2LM framework, we conduct experi1460 | Dataset | Sentences | Training | Testing | |----------------|-------------|------------|-----------| | Laptop (L) | 3845 | 3045 | 800 | | Restaurant (R) | 6035 | 3877 | 2158 | | Device (D) | 3836 | 2557 | 1279 | | Service (S) | 2239 | 1492 | 747 | Table 1: Basic statistics of the datasets. ments on four benchmark datasets, namely Laptop (L), Restaurant (R), Device (D), and Service (S), as shown in Table 1. L contains data from the laptop domain in SemEval 2014 (Pontiki et al., 2014). R is the union set of the restaurant data from SemEval 2015 (Pontiki et al., 2015) and SemEval 2016 (Pontiki et al., 2016). D contains device data about 5 digital products (Hu and Liu, 2004). S contains data from web services (Toprak et al., 2010). Evaluation. Following (Li et al., 2019c), we choose 10 different source → target domain pairs for experiments. L → D and D → L are removed since the two domains are very similar. For each cross-domain pair, DA2LM generates sufficient target-domain labeled data and then directly trains a BERT-CRF classifier on the generated targetdomain data. We evaluate the model predictions based on Micro-F1 under the exact match, which means that the predicted aspect-sentiment pairs are considered as correct only if they exactly match with the gold aspect-sentiment pairs. Parameter Setting. For the BERT-CRF model used in DA2LM, we employ a domain-specific BERT-base model named BERT-Cross (Xu et al., 2019), which was post-trained on a large amount of Yelp and Amazon Electronic data (He and McAuley, 2016). For Domain-Adaptive Pseudo Labeling, the hyper-parameter α in Eq. 2 is set as 0.01, and we adopt the Adam algorithm with a learning rate of 3e-5 to optimize the parameters. For Domain-Adaptive Language Modeling, we finetune the LSTM and the pre-trained language model GPT-2 (Radford et al., 2019) on DS ∪ DP T , and using the Adam algorithm as the optimizer with a learning rate of 3e-3 and 3e-4 respectively. For Target-Domain Data Generation, we choose the top-k tokens (i.e., k=100) as the candidate set and the maximum number of generated data Ngis set to 10000 in token-sampling generation. All the experiments are run on a single Nvidia 1080Ti GPU. ## 4.2 Main Results To show the effectiveness of our DA2LM approach, we consider the following competitive domain adaptation comparison systems for the cross- ## Domain Absa Task. - **BERT-NoDA** (Kenton and Toutanova, 2019): a baseline system without domain adaptation, which directly fine-tunes a BERT-base model on labeled source-domain data. - **BERT-Cross** (Xu et al., 2019): a domainadaptive BERT-CRF model, in which the BERTbase model was post-trained on a myriad of Ecommerce data and the full model is fine-tuned on labeled source-domain data. - UDA (Gong et al., 2020): a unified domain adaptation approach that integrates feature-based and instance-based adaptation for cross-domain ABSA. - **FMIM** (Chen and Wan, 2022): a featurebased domain adaptation method, using the finegrained mutual information maximization technique. - **CDRG** (Yu et al., 2021): a cross-domain review generation approach that exploits each labeled source-domain review to generate a labeled target-domain review based on masked language models. - **GCDDA** (Li et al., 2022): a generative crossdomain data augmentation framework that leverages a pre-trained sequence-to-sequence model BART to generate target-domain data with finegrained annotation. The comparison results on the cross-domain ABSA and AE task are reported in Table 2. For our proposed framework, we present the results of both LSTM and GPT-2-based DA2LM. We can observe that our framework generally achieves the best performance on most cross-domain pairs and DA2LM outperforms the state-of-the-art method by 1.86% and 0.90% on average for the ABSA and AE task respectively. We conjecture the reasons as follows. First, DA2LM can directly generate numerous high-quality target domain labeled data, thereby overcoming the sensitivity to source data in feature-based domain adaptation methods. Second, there is still a considerable distribution discrepancy between the generated data in previous cross-domain data augmentation methods and the real target-domain data because these methods preserve source-specific attributes such as syntactic structures. Moreover, since previous cross-domain data augmentation methods are based on the word replacement technology, the fluency and diversity Tasks Methods S→R S→L S→D R→S R→L R→D L→S L→R D→S D→R AVE BERT-NoDA 49.85 33.08 35.97 27.63 32.69 32.45 27.77 37.38 31.87 42.74 35.14 BERT-Cross 51.36 34.33 36.28 26.38 42.42 40.82 28.35 49.91 27.31 47.92 38.51 UDA 52.04 35.41 38.06 30.76 46.00 40.81 30.34 49.97 33.28 50.72 40.74 FMIM 49.46 31.83 32.46 40.59 39.26 33.11 **41.61** 57.02 **40.76** 55.68 42.21 CDRG 52.93 33.33 36.14 **43.07** 44.70 30.82 41.51 57.77 40.30 53.18 43.38 GCDDA 55.66 36.53 36.87 32.07 **47.79** 40.35 27.22 50.50 28.52 49.47 40.50 DA2LM (LSTM) 56.26 36.54 39.80 40.38 42.49 40.55 35.93 59.47 33.55 57.28 44.22 DA2LM (GPT-2) **58.64 36.97 40.28** 40.44 42.91 **41.28** 36.84 **60.39** 35.75 58.98 **45.24** BERT-NoDA 57.72 40.33 39.69 31.21 38.38 35.15 31.44 41.11 34.46 45.79 39.53 BERT-Cross 58.08 40.47 39.89 27.74 51.49 42.52 30.84 54.96 28.69 50.97 42.57 UDA 57.98 42.44 40.24 35.29 57.58 43.07 33.96 54.79 35.78 53.85 45.50 FMIM 57.43 39.14 35.26 47.60 50.57 36.11 **51.68** 68.67 **49.53** 61.64 49.76 CDRG 60.20 39.49 38.59 **49.97** 55.50 34.89 51.07 68.63 43.19 57.51 49.90 GCDDA 63.53 43.95 39.16 35.69 **64.06** 44.25 30.31 58.00 30.74 53.70 46.34 DA2LM (LSTM) 63.63 44.39 42.39 43.38 57.12 43.64 39.44 67.24 36.16 62.66 50.00 DA2LM (GPT-2) **65.78 44.96 43.24** 43.41 54.55 **44.29** 41.06 **68.72** 38.20 63.86 **50.80** of generated data in these methods are inferior to our DA2LM approach. In addition to the above observations, Table 2 shows that LSTM-based DA2LM is similar to GPT-2-based DA2LM and also outperforms previous domain adaptation methods on average, which implies that our cross-domain data augmentation framework is robust and does not rely on the pretrained language model. Furthermore, as shown in Table 1 and Table 2, the proposed model underperforms several baseline systems when the source/target sample size ratio is larger than 1 (e.g., R → S, L → S, D → S, R → L). We believe the reason of the performance drop is as follows: when the number of targetdomain data is less than that of source-domain data, it will inevitably lead the Domain-Adaptive Language Model (DALM) to pay more attention to source-domain data instead of target-domain data. Hence, in the target-domain data generation process, the trained DALM may still generate sourcespecific words, and thus bring negative effects to the final performance. ## 4.3 Ablation Study | ABSA AE | |-----------| To explore the effects of each component in DA2LM, we show the results of our ablation study in Table 3. Firstly, after removing the aspect-level MMD loss in the domain-adaptive pseudo labeling (DAPL) stage, the average performance on 10 cross-domain pairs drops dramatically, which indicates that it is important to alleviate the domain discrepancy via the MMD loss in DAPL. Secondly, removing the domain-adaptive language modeling Methods ABSA AE DA2LM 45.24 **50.80** - w/o MMD loss in DAPL 39.44 43.57 - w/o DALM & DG 42.53 48.03 - w/o source-domain data in DALM 43.82 50.16 - w/o malposed generation 42.82 48.23 - replace DALM with DAGA 44.23 50.40 (DALM) and target-domain data-generation (DG) stages decreases the average F1 score by 2.71 absolute percentage points. This shows that automatically generating a large amount of target-domain labeled data plays an indispensable role in our DA2LM framework. Thirdly, for the training of DALM, the removal of source-domain labeled data also leads to a significant drop in the average F1 score. This implies that the source-domain data is indeed helpful for capturing domain-invariant context and annotation. Moreover, we remove the malposed generation strategy, which means it does not take the current token into account when predicting the label of the current token. As shown in Table 3, the performance of DA2LM drops dramatically since it generates low-quality label sequences. Lastly, because a language model-based data augmentation method DAGA (Ding et al., 2020) has shown success in standard in-domain ABSA tasks, we propose to replace DALM in our DA2LM framework with a variant of DAGA, i.e., a language model trained on source and target-domain data with linearized Criterion Methods S→R S→L S→D R→S R→L R→D L→S L→R D→S D→R AVE Diversity CDRG 0.133 0.134 0.146 0.250 0.235 0.289 0.229 0.193 0.293 0.264 0.2165 GCDDA 0.226 0.203 0.207 0.236 0.208 0.227 0.247 0.241 0.297 **0.266** 0.2362 DA2LM 0.275 0.309 0.354 0.472 0.269 0.374 0.416 0.252 **0.503** 0.257 **0.3487** Perplexity CDRG 583.8 611.0 484.2 971.8 1106.9 971.5 567.5 620.9 625.4 697.0 724.00 GCDDA **244.9 215.2** 217.8 806.0 782.0 763.8 469.1 392.0 442.9 480.0 481.35 DA2LM 362.8 237.4 214.9 182.1 257.8 254.9 204.8 389.8 200.6 360.3 **266.53** MMD Source 0.733 0.651 0.650 0.724 0.634 0.763 0.657 0.691 0.624 0.693 0.6819 CDRG 0.603 0.697 0.576 0.604 0.552 0.631 0.631 0.622 **0.556** 0.617 0.6088 GCDDA 0.800 **0.541** 0.559 0.772 0.547 0.561 0.759 0.567 0.603 0.600 0.6310 DA2LM **0.560** 0.566 0.498 0.548 0.487 0.559 **0.597 0.533** 0.677 0.535 **0.5564** Table 4: Comparison results between the generated data in DA2LM and those in CDRG and GCDDA. ![7_image_0.png](7_image_0.png) Methods ABSA AE DA2LM 45.24 50.80 UDA 40.74 45.50 DA2LM-UDA 42.02 **47.30** FMIM 39.31 49.26 DA2LM-FMIM 45.94 **53.79** CDRG 43.38 49.90 DA2LM-CDRG 45.71 **52.99** labels before each aspect term. For fair comparison, we also employ GPT-2 (Radford et al., 2019) as the pre-trained language model. As shown at the bottom of Table 3, replacing DALM with DAGA leads to a moderate performance drop, which proves the importance of DALM in our DA2LM approach. ## 4.4 Evaluation On Generated Data In this subsection, we conduct additional experiments to evaluate the quality of data generated by DA2LM and report the performance in Table 4. Diversity. Diversity denotes the percentage of unique aspect terms in all aspect terms. The results in Table 4 clearly show that DA2LM can generate more aspect terms since other methods need to regard source-domain sample as the template. Moreover, our framework employs a probabilitybased sampling strategy to generate the next token, which can improve the diversity of generated aspect terms. Perplexity. To evaluate the coherence of generated data, we further calculate the perplexity1 of data generated from each compared method based on a pre-trained language model GPT-2.2In the fourth to sixth rows of Table 4, it is clear to see that the perplexity of our DA2LM framework is significantly lower than that of other methods. This shows that for MLM-based and Seq2Seq-based CDDA methods, simply replacing source-specific attributes with target-specific attributes may break the syntactic structure of the original sentence and thus the generated sentences are not coherent. In contrast, our DA2LM framework relies on language modeling to automatically generate tokens and their corresponding labels in an autoregressive manner. Maximum Mean Discrepancy (MMD). MMD is used to measure the distribution distance between the generated data in different methods and the real target-domain test data. The results in the last four rows show that the generated data in DA2LM are much closer to the target domain than other methods, which indicates DA2LM can generate more authentic target-domain data and better alleviate the distribution discrepancy across domains. Visualization. To visually verify the superiority of our DA2LM framework, we further utilize t-SNE (Van der Maaten and Hinton, 2008) to perform a visualization of the sentence representations obtained by a pre-trained language model BERT (Kenton and Toutanova, 2019). Figure 3 shows the visualization result on a cross-domain pair S → R. As shown in Figure 3, the distribution of generated data in CDRG and GCDDA is still similar to that of source-domain data because these methods still preserve many source-domain attributes including contexts and syntactic structures. In contrast, there is almost no discrepancy between the generated data in DA2LM and the target-domain data, as shown in the right of Figure 3. These observations demonstrate the advantage of DA2LM over previous CDDA methods in terms of diversity, fluency, and data distribution. ## 4.5 Compatibility With Existing Da Methods To show the compatibility of our DA2LM framework, we replace the base model Cb in the first stage (i.e., domain-adaptive pseudo labeling) with other existing domain adaptation methods including UDA (Gong et al., 2020), FMIM (Chen and Wan, 2022) and CDRG (Yu et al., 2021). Table 5 shows the average results of different base models with their DA2LM variants on 10 source → target domain pairs for the cross-domain ABSA task and the cross-domain AE task, respectively. Firstly, we can find that by using the targetdomain labeled data from our DA2LM framework, the performance of existing domain adaptation methods is generally boosted on average for crossdomain ABSA and AE, which demonstrates the usefulness of our DA2LM framework and the robustness of the generated target-domain data. Secondly, by comparing all DA2LM variants, we can observe that DA2LM-FMIM consistently obtains the best average performance on cross-domain ABSA and AE. This suggests that our DA2LM framework is compatible with any domain adaptation method, and it can generally achieve better results with better base models. ## 5 Conclusion In this paper, we proposed a cross-domain Data Augmentation framework based on DomainAdaptive Language Modeling (DA2LM), which contains three key stages to automatically generate sufficient target-domain labeled data, including 1) Domain-Adaptive Pseudo Labeling, 2) Domain-Adaptive Language Modeling, and 3) Target-Domain Data Generation. Experiments on four benchmark datasets show that our DA2LM framework consistently outperforms the state-ofthe-art method for the cross-domain ABSA task. Moreover, further evaluation results demonstrate the superiority of the generated data in terms of diversity, fluency, and data distribution. ## Limitations Despite obtaining promising results, our proposed approach still has the following limitations. First, although our DA2LM approach can generate a large amount of target-domain data with high diversity, the generated words are still limited by the source-domain labeled data and target-domain unlabeled data. How to make the model generate novel target-domain words is a challenging problem to explore in the future. Second, our DA2LM model is primarily proposed for the ABSA and AE tasks, which are not directly applicable for the other information extraction tasks with more than two elements, such as Aspect Sentiment Triplet Extraction (ASTE). Therefore, cross-domain data augmentation for multiple-element information extraction tasks may be a promising followup direction. ## Ethics Statement We conduct experiments on four publicly available datasets, i.e., Laptop (L), Restaurant (R), Device (D), and Service (S). These datasets do not share personal information and do not contain sensitive content that can be harmful to any individual or community. Due to the lack of ethics and bias constraint in the data generation process, the generated data from our trained Domain-Adaptive Language Model may contain sensitive and misleading content. Therefore, it is necessary to manually check these generated data when applying them to realworld applications. ## Acknowledgements The authors would like to thank the anonymous reviewers for their insightful comments. 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Pivot based language modeling for improved neural domain adaptation. In *Proceedings of NAACL-HLT*, pages 1241– 1251. ## A Appendix A.1 Case Study And Error Analysis In this section, we select several representative examples generated by different methods to demonstrate the effectiveness of our DA2LM framework. Case Study. Table 6 shows several examples of CDRG, GCDDA and DA2LM on a cross-domain pair L → R. Firstly, we can observe that the MLMbased approach CDRG and the Seq2Seq-based approach GCDDA fail to replace some sourcespecific words such as "*laptop*" and "*Miscrosoft office*" with target-specific words. Besides, it is clear that the generated target-domain data in CDRG and GCDDA are lack of fluency, coherence, and diversity, because they both generate target-domain data based on a source template sentence by replacing words. In contrast, our DA2LM approach can generate much more diverse target-domain data due to the randomness of sampling. Moreover, because the DALM in our framework is based on the language model, it is not surprising that the sentences generated in DA2LM are generally fluent and coherent. Error Analysis. Furthermore, we also manually verify the label correctness of the target-domain data generated from our DA2LM framework, and show two generated samples with incorrect labels at the bottom of Table 6. We find that DA2LM is prone to identify a target-specific attribute as an aspect term, even if it is not the target of the sentiment expression (e.g., "*restaurants*") or is an incomplete aspect term (e.g., "*sake*"). We conjecture the reason is our adoption of a rule-based algorithm to obtain the target-domain aspect terms to minimize the distance between source-domain and target-domain aspect term representations in Section 3.3, which may result in the noise in the pseudo-labeled target data for Aspect Term Extraction. However, the results and analysis in Section 4.5 demonstrate that our DA2LM framework is generally compatible with various domain adaptation methods and has the potential to deliver better performance when employed in conjunction with more powerful base models. ## A.2 Detailed Evaluation On The Compatibility With Existing Da Methods Table 7 and Table 8 show the detailed comparison results of different base models with their DA2LM variants on all domain-pairs for the cross-domain ABSA task and the cross-domain AE task. We can observe that the variants of our DA2LM show consistent improvements over different base models on most domain pairs for both tasks. | Examples | | |------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Source | The [engineering design]positive and [warranty]positive are superior–covers damage from dropping the laptop. | | CDRG | The [wait service]positive and [flavoring]positive are superior–keep distract from dropping the laptop. | | GCDDA | The [engineering design]positive and [service]positive are superior–covers damage from dropping the food. | | Source | There is no [cd drive]negative on the computer, which defeats the purpose of keeping files on a cd. | | CDRG | There is no [fire place]negative on the computer, which defeats the purpose of keeping files on a cd. | | GCDDA | There is no [cheese plate]negative in the menu, which defeats the purpose of keeping files on a cd. | | Source | It's [applications]positive are terrific, including the replacements for [Microsoft office]positive. | | CDRG | It's [drinks]positive are terrific, including the noodles for [cheeses]positive. | | GCDDA | It's [salads]positive are terrific, including the replacements for [Microsoft office]positive. we always have a delicious [meal]positive and always leave feeling satisfied. ✓ the [prices]positive were exceptionally reasonable for the [appetizers]positive and [food]positive we ordered. ✓ the [stuff tilapia]negative was horridtasted like cardboard. ✓ the place is a bistro which means, simple [dishes]positive served efficiently in a bustling [atmosphere]positive. ✓ the [food]positive was adequate, but the [restaurant]negative was too tiny. ✓ but, i think citysearch is a great place to find [restaurants]positive. ✗ their [sake]positive list was extensive, but we were looking for purple haze, which wasn't listed. ✗ | | DA2LM | | Table 6: Examples of different methods on a cross-domain pair L → R. For baseline systems, text chunks in blue indicate the replaced target-specific attributes and text chunks in red indicate the remaining source-specific attributes in generated target-domain data. For our DA2LM approach, ✓ and ✗ indicate that the generated label sequences are correct and incorrect, respectively. Methods S→R S→L S→D R→S R→L R→D L→S L→R D→S D→R AVE DA2LM 58.64 36.97 40.28 40.44 42.91 41.28 36.84 60.39 35.75 58.98 45.24 UDA 52.04 **35.41** 38.06 **30.76 46.00** 40.81 **30.34** 49.97 33.28 50.72 40.74 DA2LM-UDA **56.05** 35.15 **40.45** 26.40 45.78 **44.18** 28.43 53.28 37.90 52.57 **42.02** FMIM 49.46 31.83 32.46 40.59 39.26 33.11 41.61 57.02 40.76 55.68 42.21 DA2LM-FMIM 54.05 32.36 35.57 47.01 41.78 38.93 45.80 59.66 47.66 56.62 **45.94** CDRG 52.93 33.33 36.14 43.07 44.70 **30.82** 41.51 57.77 40.30 53.18 43.38 DA2LM-CDRG 56.81 34.10 38.43 **45.06 44.85** 30.11 49.44 61.02 40.56 56.80 **45.71** Table 7: Compatibility with existing domain adaptation methods for Cross-Domain ABSA. Methods S→R S→L S→D R→S R→L R→D L→S L→R D→S D→R AVE DA2LM 65.78 44.96 43.24 43.41 54.55 44.29 41.06 68.72 38.20 63.86 50.80 UDA 57.98 **42.44** 40.24 **35.29** 57.58 43.07 **33.96** 54.79 35.78 53.85 45.50 DA2LM-UDA **62.42** 42.12 **42.84** 32.29 **59.84 46.60** 31.69 58.23 41.07 55.85 **47.30** FMIM 57.43 39.14 35.26 47.60 50.57 36.11 51.68 68.67 49.53 61.64 49.76 DA2LM-FMIM 62.37 41.90 38.43 52.98 56.24 42.29 55.63 70.95 53.46 63.63 **53.79** CDRG 60.20 39.49 38.59 49.97 55.50 **34.89** 51.07 68.63 43.19 57.51 49.90 DA2LM-CDRG 64.20 41.78 41.58 **52.81 59.16** 34.88 56.32 71.29 46.18 61.66 **52.99** Table 8: Compatibility with existing domain adaptation methods for Cross-Domain Aspect Extraction (AE). ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitations ✓ A2. Did you discuss any potential risks of your work? Section Ethics Statement ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and senction Introduction ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** We use the pre-trained language model GPT-2 as mentioned in Section 3. ✓ B1. Did you cite the creators of artifacts you used? In Section 3 named Methodology. ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We use publicly available pretrained language models and datasets from previous works. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? In Section 3, we discuss in detail how to use the scientific artifact. And we introduce the intended use of our framework in Section 1. ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The data we use is based on publicly available datasets, which have been checked and preprocessedby previous works. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? We describe the key stages and settings in Section 3 and Section 4 in detail. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. We describe the dataset we use in Section 4. ## C ✓ **Did You Run Computational Experiments?** In Section 4. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? We describe the parameters setting and computing infrastructure in Section 4. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? We describe the experiment setup in Section 4. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? We describe them in Section 4. ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? We describe them in Section 4. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ouyang-etal-2023-compositional

Compositional Data Augmentation for Abstractive Conversation Summarization

https://aclanthology.org/2023.acl-long.82

Recent abstractive conversation summarization systems generally rely on large-scale datasets with annotated summaries. However, collecting and annotating these conversations can be a time-consuming and labor-intensive task. To address this issue, in this work, we present a sub-structure level compositional data augmentation method, Compo, for generating diverse and high-quality pairs of conversations and summaries. Specifically, Compo first extracts conversation structures like topic splits and action triples as basic units. Then we organize these semantically meaningful conversation snippets compositionally to create new training instances. Additionally, we explore noise-tolerant settings in both self-training and joint-training paradigms to make the most of these augmented samples. Our experiments on benchmark datasets, SAMSum and DialogSum, show that Compo substantially outperforms prior baseline methods by achieving a nearly 10{\%} increase of ROUGE scores with limited data. Code is available at \url{https://github.com/ozyyshr/Compo}.

# Compo**Sitional Data Augmentation For Abstractive Conversation** Summarization Siru Ouyang1, Jiaao Chen2, Jiawei Han1**, Diyi Yang**3 1 University of Illinois Urbana-Champaign 2 Georgia Institute of Technology 3 Stanford University {siruo2,hanj}@illinois.edu, [email protected], [email protected] ## Abstract Recent abstractive conversation summarization systems generally rely on large-scale datasets with annotated summaries. However, collecting and annotating these conversations can be a time-consuming and labor-intensive task. To address this issue, in this work, we present a sub-structure level compositional data augmentation method, COMPO, for generating diverse and high-quality pairs of conversations and summaries. Specifically, COMPO first extracts conversation structures like topic splits and action triples as basic units. Then we organize these semantically meaningful conversation snippets compositionally to create new training instances. Additionally, we explore noise-tolerant settings in both self-training and joint-training paradigms to make the most of these augmented samples. Our experiments on benchmark datasets, SAMSum and DialogSum, show that COMPO substantially outperforms prior baseline methods by achieving a nearly 10% increase of ROUGE scores with limited data. We have publically released our code at https://github.com/ ozyyshr/Compo. ## 1 Introduction Abstractive conversation summarization, which condenses unstructured conversations into short, concise, and structured text, has greatly benefited from neural generative models trained on largescale annotated data. Researchers have focused on various aspects in conversation summarization, such as hierarchical modeling of conversations (Zhao et al., 2019; Zhu et al., 2020), leveraging dialogue acts (Goo and Chen, 2018), using key phrases and entities (Liu et al., 2019a; Narayan et al., 2021), utilizing topic segments (Liu et al., 2019b), incorporating stage components (Chen and Yang, 2020) and examining discourse relations (Chen and Yang, 2021b; Feng et al., 2020b). However, training these generative models often requires abundant high- | Conversation | Actions | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------| | Mary: Sorry, I didn't make it to your birthday party :( Nick: It's OK... Mary: I just got so distracted! I forgot it was yesterday! Nick: do tell! Mary: I met this guy... Nick: REALLY? I want details :D Mary: Yeah, his name is Kirk and he's an architect... Nick: OK, just your type then #file_gif# Mary: And we ended up spending the whole week together. Nick: A WEEK? Mary: Yeah... It's madness, I'll tell you more this evening. Are we still on? Nick: You bet we are! Mary, didn't make, party Mary, got distracted Mary, forgot Mary, meet, guy Nick, want details He, is, architect We, end up, spend Spend, weekend Mary, will tell, Nick Summary Mary didn't come to Nick's birthday party. She met an | | Mary didn't come to Nick's birthday party. She met an architect named Kirk. Mary and Nick will meet in the evening. Figure 1: An example of conversation, extracted actions and its paired summary sentences (randomly sampled from SAMSum). The corresponding summary consists of three sentences, each sentence relates to one snippet (illustrated by color). quality data, i.e., conversation and its paired summary, which is usually time-consuming and laborintensive to obtain. As a result, it is challenging to apply them to generalized real-world situations where labeled summaries are limited. A direct solution is to employ data augmentation (DA) (Cubuk et al., 2018; Sennrich et al., 2015; Feng et al., 2021a; Chen et al., 2021a,b; Shen et al., 2020; Yu et al., 2018; Feng et al., 2020a; Miyato et al., 2016) to generate more data. Whereas, directly applying these augmentation methods into the context of conversations usually fails to consider any unique structures of conversations such as speaker information, topic split, and conversation stages (Gritta et al., 2021; Shuster et al., 1471 2021), which distinguish conversations from general sentences. As a result, they might be limited in creating high-quality and diverse data pairs (Chen and Yang, 2021a). Even though there are a few exceptions (Chen and Yang, 2021a; Liu et al., 2022), they still suffer from diversity and struggle with out-of-distribution compositional generalization (Feng et al., 2021a). One way to alleviate these issues is to recombine different data points to produce novel training data, i.e., compositional data augmentation (Akyürek et al., 2020; Zhang et al., 2022). However, existing compositional DA mainly focus on editing short sentences *locally* with words/phrases/parsing trees (Akyürek et al., 2020; Zhang et al., 2022), neglecting rich *structural information* between different sets of utterances in conversations (Chen and Yang, 2020; Cohan et al., 2018), which prevent them from being applied to conversations to compose multiple utterances and generate novel, diverse and high-quality conversational data. We visualize one example with the topic structures (Xu et al., 2021; Galley et al., 2003; Chen and Yang, 2020) highlighted in Figure 1. The conversation consists of several topics: "opening", "explanation", "plan", etc. And we consider every topic snippet as the basic unit. In the meantime , we extracted the "action" triples (Chen and Yang, 2021b) to represent each topic snippet. With these topic snippets and action representations, we obtain the units for compositional operations. For instance, the blue topic split and summary sentence about the meeting plan could be composed into another conversation by substitution to produce a new conversation and summary that contains a meeting plan. As it shows, by extracting the topic structures from the conversations, sub-components of conversations can be re-organized and re-composed to generate augmented conversation-summary pairs that might not be seen in the original corpus, resulting in more diverse training data. To this end, we propose COMPO, a compositional data augmentation framework operating at sub-structure level. We leverage the conversation structures (i.e., **topic structure**(Chen and Yang, 2020) and **action triples** (Chen and Yang, 2021b) ) to produce *compositional units* for generating diverse conversation-summary pairs. Specifically, we first segment conversation into topic splits with topic modeling models, and then extract "actions triples" (Chen and Yang, 2021b) to represent each split as actions express specific socially situated identities and activities. With the extracted structures, we view the topic snippets as the basic units and perform selective retrieval based on action triples for compositional substitution to generate novel and diverse conversations. We also pair topic splits with summary sentences so that new summaries would be generated as well. An example of newly augmented conversation and summary could be found in Figure 2(b). To better leverage the newly generated conversation-summary pairs from COMPO, we further explore two noisetolerant methods including a self-training framework that uses the new conversations only, and another joint-training framework that leverages paired data. Empirical studies verify COMPO's effectiveness via both quantitative and qualitative evaluations on SAMSum (Gliwa et al., 2019) and DialogSum (Chen et al., 2021c) compared to prior state-of-the-art data augmentation techniques. We also illustrate COMPO's transferability on a news summarization dataset CNN/Dailymail. ## 2 Related Work 2.1 Abstractive Conversation Summarization Abstractive conversation summarization, as opposed to extraction summarization, requires generative models to have a strong ability in language understanding as the words in the output may not appear in the input. Prior work on abstractive conversation summarization can be divided into two categories. One is to directly apply existing document summarization models to conversations (Shang et al., 2018; Gliwa et al., 2019). The other is to design conversation-tailored methods, for instance, modeling conversations in a hierarchical way (Zhao et al., 2019; Zhu et al., 2020). The rich structured information in conversations has also been leveraged. For example, Goo and Chen (2018) used dialogue acts; Liu et al. (2019a); Narayan et al. (2021) leveraged key phrases and entities. Topic segments (Liu et al., 2019b), stage components (Chen and Yang, 2020) and discourse relations (Chen and Yang, 2021b; Feng et al., 2020b) are also explored to understand conversation context for summarization. However, most approaches in the aforementioned categories focus on neural supervised methods and require abundant data to achieve state-of-the-art performance, which is timeconsuming and labor-intensive. In this work, we introduce conversation-specific data augmenta- ![2_image_0.png](2_image_0.png) tion methods to help address data scarcity on paired conversations and summaries. ## 2.2 Data Augmentation In Nlp Data augmentation (DA) is an effective approach to boost the performance of neural supervised models, and has been widely applied in various NLP tasks such as text classification (Wei and Zou, 2019; Zheng et al., 2020), machine reading comprehension (Yu et al., 2018), and machine translation (Sennrich et al., 2015). Commonly seen practices involve designed word/synonym replacement (Kobayashi, 2018; Niu and Bansal, 2018), word deletion/swapping/insertion (Wei and Zou, 2019), back translation (Sennrich et al., 2015; Xie et al., 2019) and compositional augmentation (Jia and Liang, 2016; Andreas, 2019). However, it is not applicable to directly adopt general DA methods to conversations as they usually neglect conversation structure. By extending general DA methods, Liu et al. (2022) generates synthetic examples by replacing semantically similar text spans in both dialogue and summary. Chen and Yang (2021a) makes an initial attempt for structured conversational DA, but their approach could not guarantee compositional generalization, making it hard to create diverse augmentations. While compositional DA methods proved to be effective in solving the aforementioned issues, they often target plain text (Furrer et al., 2020) and operate locally with words, phrases, or parsing trees with carefully-curated rules (Chen et al., 2020b; Nye et al., 2020). Thus are not suitable for conversations. Our work COMPO fills these gaps by naturally taking conversation structures as units for compositional augmentation. In this way, we not only explore rich structures unique for conversations but also boost the compositional generalization and diversity. ## 3 Methodology To generate diverse conversation-summary pairs to deal with the data scarcity issue, this section presents a simple and effective compositional data augmentation method COMPO for supervised abstractive conversation summarization. The framework is illustrated in Figure 2. ## 3.1 Compositional Augmentation Our compositional augmentation method COMPO operates at the sub-structure level of conversations. By extracting different sub-components of conversations and recombining them based on certain orderings, COMPO can produce novel and diverse conversations and their summaries that might not be seen in the original corpus. To get a reasonable granularity of conversation sub-parts, we choose to leverage the topic-view of conversations, building upon prior work on conversation structures (Althoff et al., 2016; Chen and Yang, 2020). Conversations are mostly organized around topics in a coarse-grained structure (Honneth et al., 1988). For instance, a telephone chat could possess the following topics: greetings ⇒ invitation ⇒ plan ⇒ farewell. Thus we propose a compositional inductive approach through composing different conversation topics (Andreas, 2019). We further employ COMPO to limited data settings in both self-training and joint learning styles. Topical Split We employ the classic topic segmentation algorithm, C99 (Choi, 2000) to get the topical split of conversations based on intersentence similarities. First, we use Sentence-BERT (Reimers and Gurevych, 2019a) to get the representations for each utterance in the conversation C = {u1, u2*, ..., u*m}. Then the conversation C is divided into blocks C*topic* = {b1, b2*, ..., b*n} with C99, where biis one split topic block consisting of several consecutive utterances. Also, people tend to summarize conversations in an almost linear way with a strong temporal dependency (Wu et al., 2021). As a result, it is intuitive to pair each topical split C*topic* with summary sentences S = {s1*, ..., s*n} following Algorithm 1 to obtain s i paired for each bi. Action Extraction Previous studies reveal that action information can be an effective building block for models to perform text generation (Daniel et al., 2003; Glavaš and Šnajder, 2014). Actions also help avoid less informative utterances in conversations such as dialog acts (Chen and Yang, 2021b), focusing on more concise ideas of conversation snippets. Therefore, we extract verb-centering phrases (Zhang et al., 2020a) as backbones of topic splits. We use a lightweight tool (Jiao et al., 2023; Zhong et al., 2022a) to extract the actions, where frequently-occurring syntactic patterns are leveraged. Specifically, we extract such syntactic patterns containing verbs as actions. For instance, the most common patterns contain n1-nsubj-v1 (e.g., Alice called). More details and concrete examples could be found in Appendix C. Action-based Composition With previous steps, we obtain a pool P of topical splits and their corresponding actions P = {(bi, sipaired, Ai)}i=1:|p|. With these as units, we are now able to conduct compositional operations. To preserve the conversation structure of the augmented data, the general philosophy here is to "substitute" a selected conversation with similar candidates retrieved from the pool. The problem becomes how to filter out representative and diverse candidates. Inspired by Su et al. (2022), we use the graph-based method Vote-k to ensure similar demonstrations and total coverage. We first compute a vector representation for each topical unit using Sentence-BERT (Reimers and Gurevych, 2019b) by averaging the resulting vectors over the input. We then use those embeddings to create a directed graph G = (*V, E*). For each vertex v ∈ V , edges are connected to its k-nearest neighbors in terms of cosine similarity. For every remaining vertex u (contrary to chosen units) in the graph, we score them using score(u) = X v∈{v|(v,u)∈E,v∈U} s(v), (1) $\|U_{\mathcal{M}}L\,|(u,l)\!\subset\!F\rangle|$. where s(v) = ρ|{linL|(v,l)∈E}|*, ρ >* 1. In every iteration, we choose nodes that have the largest score, i.e., satisfy argmaxu∈U *score*(u). The chosen nodes are excluded from U. In order to produce fluent conversations with newly generated compositional units, we leverage a pre-trained generation model. Concretely, we pre-train a sequence-to-sequence model in the following steps: (1) randomly select a topical split bi from the original conversation, (2) get the corresponding set of actions A = {a1*, ..., a*k} for bi, (3) mask bi from the original conversation, (4) take extracted actions A and unmasked the rest of the conversation as input. Then we use this selected topical split bi as the target output for the model. For example, the input and output of the pre-trained generation model could be - *Input:* we 'll meet at arrivals </s> **Corina:** Are youat the airport? <mask> - *Output:* **Regina:** sure, waiting for K. **Jorge:** Good! we'll meet at the arrivals then. where "we 'll meet at arrivals" is the combination of action triples, "</s>" is used to separate triples from conversations, and "<mask>" is what we want to predict as the output. If there are multiple actions, we use '|' token to split them. ## 3.2 Noise-Tolerant Training Settings Our model is trained on two noise-tolerant settings to further boost the performance with limited data. In self-training setting, only the newly generated conversations are incorporated, and a teacher model is utilized to predict pseudo summaries. In joint-training setting, we test the framework with paired data, i.e., with newly generated conversations and summaries. Algorithm 1: Match topical split and summary sentences ![4_image_1.png](4_image_1.png) Input: A topical split of conversation bi ∈ C*topic*, a summary S containing n sentences, sliding window size interval [a,b] Output: Corresponding summary sentences S i paired for bi ![4_image_0.png](4_image_0.png) 2 for j = 1 to *|C| −* w do 4 r(j, w) ← *ROUGE(cand, b*i) 5 W ← W ∪ *cand* 8 jbest, wbest ← argmaxj,wr(*j, w*) 9 S i paired ← Sjbest,(jbest+w*best*) Algorithm 2: Self-training ![4_image_2.png](4_image_2.png) 1 Train a base model fθ with labeled data ![4_image_3.png](4_image_3.png) Dl = {(c l i, si)}i=1:n 2 for i = 1 to K do 3 Predict pseudo summaries s ![4_image_4.png](4_image_4.png) ifor unlabeled conversations Du = {(c u i )}i=1:m 4 Select a subset of S = Dl ∪ D where D = {c u i, su ![4_image_5.png](4_image_5.png) 5 train a new model fθ on S ∪ Dl ## 3.2.1 Self-Training With Augmented Data The detailed algorithm for self-training (He et al., 2019) is displayed in Algorithm 2. Specifically, the algorithm starts with a parallel dataset Dl = {(c l i , si)}i=1:n and the unlabeled dataset Du = {(c u i )}i=1:m where *m >> n*. In a semi-supervised setting, a teacher model fθ is first trained on Dl, and is further used to predict pseudo summaries for unlabeled data. The pseudo data D and Dl are combined and we sample a subset of them for training another model fθ′. Here θ is the parameter from the teacher model from the last iteration and fixed within the current iteration. This process is iterated for K times. The unsupervised loss Lu from unlabeled conversations is defined as: $$L_{u}=-\mathbb{E}_{c\sim D^{u}}\mathbb{E}_{c^{\prime}\sim\mathrm{Compo}(c)}l o g P(f(c;\theta^{\prime})|f(c^{\prime};\theta))$$ (2) Note that we choose the number of subset selections so that the total training instances are twice the original dataset. ## 3.2.2 Joint Training With Augmented Pairs Apart from using unlabeled conversations for selftraining, we can also generate pseudo summaries for augmented conversations, and perform joint training to see the effect. New Summary Generation For each newly generated conversation, we leverage a pre-trained generation model similar to the model described in Section 3.1, and generate a new summary conditioned on summary context and the action triples. Finally, the model is trained on a combination of the original samples and augmentation samples to obtain a trade-off between regularization and noise injection. The total training objective is: $$L=\mathbb{E}_{(c,s)\in D^{l}}logP(s|c)+\gamma\mathbb{E}_{(c^{\prime},s^{\prime})\in D^{\prime}}logP(s^{\prime}|c^{\prime}),\tag{3}$$ where $\gamma$ is the weight of the augmented samples. ## 4 Experiments 4.1 Datasets To evaluate the effectiveness of our proposed framework, we conduct experiments on two benchmarks of conversation summarization: SAMSum (Gliwa et al., 2019) and DialogSum (Chen et al., 2021c) which contain open-domain daily-chat conversations and diverse task-oriented conversations for real-life scenario. More detailed data statistics could be found in Table 7 in the Appendix. ## 4.2 Evaluation Metrics And Baselines ![4_Image_6.Png](4_Image_6.Png) Evaluation Metrics We use the standard ROUGE metric1(Lin, 2004) as automatic evaluation metrics, including ROUGE-1, ROUGE-2, and ROUGE-L for both SAMSum and DialogSum datasets. Note that the ROUGE scores might vary with different tookits. Baselines with different augmentation strategy To demonstrate the superiority of our proposed compositional augmentation over previous data augmentation methods, we take several state-of-theart and representative data augmentation methods as baseline models. Specifically, they are tailored or suitable for conversation augmentation in different granularity including token-level, sentencelevel and context-level: - BART (Lewis et al., 2020) is the state-of-theart pre-trained model for summarization. It also indicates training without augmentation. We use BART-base as well as BART-large as our base models for scalability. 1https://huggingface.co/spaces/ evaluate-metric/rouge | Model | 1%-147 | 5%-735 | full-14732 | | | | | | | |-----------------------|-------------|-------------|--------------|-------------|-------------|-------------|-------------|-------------|-------------| | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | | | BARTbase | 42.36 | 18.63 | 38.44 | 45.56 | 20.44 | 41.27 | 51.74 | 26.46 | 48.72 | | BARTlarge | 48.26 | 22.59 | 43.93 | 50.01 | 23.97 | 45.73 | 53.12 | 27.95 | 49.15 | | self-training SRbase | 43.88 | 19.96 | 39.56 | 46.54 | 21.60 | 41.52 | 51.81 | 26.44 | 48.78 | | BTbase | 44.49 | 20.14 | 40.38 | 45.96 | 21.74 | 41.58 | 52.06 | 26.32 | 49.22 | | USbase | 44.74 | 20.18 | 40.62 | 46.28 | 22.34 | 42.06 | 52.24 | 26.50 | 49.28 | | Semi-CODA† | 44.34 | 19.22 | 41.16 | 46.21 | 21.02 | 42.85 | 50.08 | 24.62 | 46.89 | | COMPObase | 45.42 ↑3.06 | 21.23 ↑2.60 | 41.42 ↑2.98 | 48.03 ↑2.47 | 24.00 ↑3.56 | 44.91 ↑3.64 | 52.90 ↑1.16 | 27.03 ↑0.57 | 49.64 ↑0.92 | | COMPOlarge | 49.78 ↑1.62 | 24.65 ↑2.06 | 45.41 ↑1.48 | 51.66 ↑1.65 | 26.55 ↑2.58 | 47.59 ↑1.86 | 53.56 ↑0.44 | 28.66 ↑0.71 | 50.04 ↑0.89 | | joint-training SRbase | 42.93 | 19.11 | 38.86 | 45.89 | 20.97 | 41.40 | 51.69 | 26.40 | 48.74 | | BTbase | 43.79 | 19.54 | 39.21 | 45.91 | 20.94 | 41.17 | 51.76 | 26.42 | 48.70 | | USbase | 43.96 | 19.67 | 39.30 | 46.06 | 21.54 | 41.63 | 51.83 | 26.49 | 48.81 | | COMPObase | 44.89 ↑2.53 | 20.64 ↑2.01 | 40.58 ↑2.14 | 47.07 ↑1.51 | 22.56 ↑2.12 | 43.29 ↑2.02 | 52.38 ↑0.64 | 26.69 ↑0.23 | 48.95 ↑0.23 | | COMPOlarge | 49.14 ↑0.88 | 23.45 ↑0.86 | 44.35 ↑1.42 | 51.06 ↑1.05 | 24.67 ↑0.70 | 45.80 ↑0.07 | 53.26 ↑0.24 | 28.32 ↑0.37 | 49.73 ↑0.58 | Table 1: Results on SAMSum test set where 1% (147), 5% (735) and all (14732) of the conversations and summaries are used for training respectively. COMPO*base* and COMPO*large* denotes COMPO with BART*base* and BART*large*. Better performances in each settings are highlighted. † results reported in (Chen and Yang, 2021a). - *Synonym Replacement (SR)* (Kumar et al., 2020; Kobayashi, 2018) is a token-level approach, which keeps the semantic meaning unaffected by replacing a random word in the conversation with its synonyms. - *Back Translation (BT)* (Chen et al., 2020a; Xie et al., 2019) is a utterance-level method, which firstly translates an selected utterance into an intermediate language, and then translates it back to the original language. - *Utterance Swapping (US)* (Wang et al., 2021) is a context-level manner, which perturbs discourse relations to create augmented conversations. It first randomly selects two utterances in the conversation, and then swaps them. - *Semi-CODA* (Chen and Yang, 2021a) is a two-stage noisy self-supervised framework that synthesizes a set of augmentation techniques, including random swapping and deletion, dialogue-acts-guided insertion, and conditional-generation-based substitution. ## 4.3 Implementation Details During the training process, the encoder and decoder share the same set of parameters, which are initialized using a pre-trained BART (Lewis et al., 2020). The maximum iteration for self-training K is set to 5. During training, we used a batch size of 16 for 10 iterations with a 3e-5 learning rate. To ensure the model receives the same amount of data for each training epoch, we replicate the original dataset to the same size as the augmentation datasets in the training stage. It takes around 5 hours to train on 4 A6400 GPUs for a full dataset under self-training, and 1 hour for the limited data setting. For joint training, it takes around 20 minutes for limited data, and 2 hours for full data. Note that the total amount for training (2x of the original samples) is equal for both self-training and joint training. Therefore, it is fair to directly compare those results. We take the average of 5 runs on random seeds for the main results shown in Table 1 and Table 2. ## 4.4 Results Table 1 and Table 2 show the results on SAMSum and DialogSum2 benchmark datasets under both limited-data and full-data settings. Based on the numbers, we have the following observations: Different amount of data: When all the labeled data are used for training, COMPO shows performance gains compared to all the baseline methods, suggesting our method's effectiveness as it works well even when a large number of data are used in the training process. With the limited data setting, we can see that performance gains are even larger compared with the full data setting. When less labeled data (i.e., 1% of the total data) are incorporated into the training process, the performance increase proves to be larger. Specifically, COMPO achieved an increase of 7.2% on Rouge-1, 14.0% 2Since there are three reference summaries on DialogSum test set, the results here are the average of three scores. | Model | 1%-125 | 5%-623 | full-12460 | | | | | | | |-----------------------|-------------|-------------|--------------|-------------|-------------|-------------|-------------|-------------|-------------| | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | | | BARTbase | 40.11 | 14.06 | 34.79 | 42.27 | 15.53 | 36.79 | 45.86 | 19.75 | 41.16 | | BARTlarge | 41.24 | 15.08 | 35.56 | 43.96 | 17.30 | 38.23 | 47.28 | 21.18 | 44.83 | | self-training SRbase | 41.08 | 14.85 | 35.63 | 43.27 | 16.61 | 37.54 | 45.93 | 19.80 | 41.24 | | BTbase | 41.38 | 15.23 | 36.21 | 43.24 | 16.83 | 37.64 | 46.00 | 19.87 | 41.30 | | USbase | 41.56 | 15.42 | 36.18 | 43.25 | 17.11 | 37.50 | 46.15 | 20.04 | 41.35 | | COMPObase | 43.13 ↑3.02 | 16.21 ↑2.15 | 37.40 ↑2.61 | 45.34 ↑3.07 | 18.09 ↑2.56 | 38.42 ↑1.63 | 46.81 ↑0.95 | 20.61 ↑0.86 | 42.21 ↑1.05 | | COMPOlarge | 43.61 ↑2.37 | 16.81 ↑1.73 | 37.73 ↑2.17 | 45.80 ↑1.84 | 19.03 ↑1.73 | 39.76 ↑1.53 | 47.94 ↑0.66 | 21.67 ↑0.49 | 45.10 ↑0.27 | | joint-training SRbase | 40.70 | 14.57 | 35.22 | 42.45 | 16.31 | 36.73 | 45.80 | 19.74 | 41.21 | | BTbase | 40.76 | 14.63 | 35.42 | 42.51 | 16.42 | 36.69 | 45.90 | 19.83 | 41.26 | | USbase | 41.03 | 15.12 | 35.89 | 42.67 | 16.59 | 36.84 | 45.94 | 19.87 | 41.19 | | COMPObase | 41.96 ↑1.85 | 15.80 ↑1.74 | 36.59 ↑1.80 | 43.71 ↑1.44 | 17.27 ↑1.74 | 37.11 ↑0.32 | 46.42 ↑0.56 | 20.21 ↑0.46 | 41.65 ↑0.49 | | COMPOlarge | 42.96 ↑1.72 | 16.53 ↑1.45 | 37.38 ↑1.82 | 44.64 ↑0.68 | 18.38 ↑1.08 | 39.00 ↑0.77 | 47.73 ↑0.45 | 21.42 ↑0.24 | 44.91 ↑0.08 | on Rouge-2, and 7.8% on Rouge-L compared with BART-base when 1% of the labeled data is used. Different backbone models: We also test COMPO's scalability using both the BART*base* and BART*large* as backbone pre-training models. Performance increases for both two PLMs on two datasets. With BART*base*, our method even outperforms BART*large* baseline on SAMSum. With BART*large*, COMPO also achieves consistent performance gains, which means COMPO is scalable to different backbone models. Not surprisingly, the increase is much larger with BART*base*. Different training settings: COMPO improves the performance of summarization under both selftraining and joint-training settings. While selftraining (leverage teacher model to predict pseudo summaries and trained for more iterations) surpasses joint-training, we can see that our newly generated summary labels are feasible to improve the performance over baseline models. Different datasets: Our model also performs well on DialogSum, which is a more abstractive, opendomain, and spoken analogous (Chen et al., 2021c) summarization dataset. We can infer that COMPO has great summarization ability when it comes to more challenging tasks. ## 4.5 Human Evaluation We conducted human evaluations to assess the summaries generated by different models trained on 1% (147) conversations from the SAMSum dataset and 1% (125) conversations from the DialogSum dataset. Specifically, we asked annotators from Amazon Mechanical Turk3to rank summaries on a scale of 1 (the least preferred) to 3 (the most preferred). Summaries to be ranked are generated from BART*base*, COMPO*base* in self-training (COMPOsf) and joint-training (COMPO-jt) respectively. To avoid bias, we randomly sample summaries generated from 100 conversations for each dataset and perturb them for the workers to rank. Workers were paid 0.1$ for each ranking task. Every summary was ranked by three workers, and the rank for every summary was aggregated by majority voting. The Intra-Class Correlation (Koo and Li, 2016) (ICC1k) was 0.573, indicating moderate agreement. As shown in Figure 3, COMPO-sf and COMPO-jt both surpass the BART-base by a large margin on SAMSum and DialogSum datasets. Additionally, we observe larger gaps in terms of the scores for three models on DialogSum dataset. More details for human evaluation including interface design, scheduling details, and how we process with obtained rank scores could be found in Appendix D. Case studies for these three models could be found in Appendix E, where we provide the original conversation and the ranked three summaries. ## 5 Analysis 5.1 **Automatic Quality Analysis Of Summaries** We adopt a multi-dimensional evaluator (Zhong et al., 2022b) to evaluate the quality of our summaries automatically, in terms of *coherence* (coh.), consistency (con.), fluency (flu.), and *relevance (rel.)*. Summaries generated with BART*base*, 3https://www.mturk.com/ ![7_image_0.png](7_image_0.png) | Model | coh. | con. | flu. | rel. | overall | |----------|--------|--------|--------|--------|-----------| | BARTbase | 0.868 | 0.861 | 0.909 | 0.744 | 0.846 | | COMPO-jt | 0.873 | 0.860 | 0.916 | 0.763 | 0.853 | | COMPO-sf | 0.868 | 0.867 | 0.923 | 0.773 | 0.858 | Figure 3: Human evaluation results in terms of average scores. A larger score indicates better performance. COMPO-sf, and COMPO-jt are taken for comparison. As shown in Table 3, both COMPO-jt and COMPO-sf achieve better results against the baseline model, with 8% and 14% improvement on overall scores respectively. We also observe the largest performance increase on *relevance*. This indicates that summaries generated with COMPO are more factually consistent with conversations and accurately reflect important information. ## 5.2 Transferibility To Other Datasets To test whether COMPO is transferable to other input forms and datasets, we conduct experiments on CNN/Dailymail (Hermann et al., 2015), a traditional text summarization dataset from the news report. We treat sentences in articles as utterances in conversations and conduct exactly the same operations for augmentation. Table 4 shows the result on CNN/Dailymail in the limited data setting with only 1% (2871) data used. Consistent performance is achieved with evaluations in Section 4.4, our introduced COMPO significantly outperforms the baseline models. This verifies an additional generalization ability of our augmentation framework as well as the newly generated labels. ## 5.3 Ablation Studies To see the effect of different components in COMPO, we conduct ablation studies on SAMSum dataset under the limited data setting, where 1% Table 4: Results on CNN/Dailymail dataset in the limited data setting. | Model | R-1 | R-2 | R-L | |----------|-------|-------|-------| | BARTbase | 37.63 | 15.38 | 35.09 | | COMPO-jt | 38.58 | 16.34 | 36.24 | | COMPO-sf | 39.50 | 16.79 | 36.87 | ## Labeled Data Are Used For Training. | Model | R-1 | R-2 | R-L | |---------------------------------|-------|-------|-------| | COMPO | 45.42 | 21.23 | 41.42 | | Selective Retrieval → K-NN | 44.91 | 20.67 | 40.71 | | Actions → Conversation Snippets | 44.86 | 20.43 | 40.60 | | Actions → SRL | 44.17 | 19.82 | 40.20 | | Action Extraction → OpenIE | 45.03 | 20.91 | 40.96 | | COMPO → DialoGPT | 44.30 | 20.26 | 40.48 | Number of iterations K **in self-training** We explored how performance changes with the progress of self-training. Specifically, we use the number of iterations to identify. As shown in Table 6, the performance continues to increase until iteration 3, and then starts to fall. This suggests that the model could indeed learn from the teacher model as it generates the pseudo summaries as labels. Effect of different components We tested the performance of using the traditional OpenIE method for action extraction. As shown in Table 5, COMPO, which leverages more diverse patterns for action extraction and syntactic structure, outperforms OpenIE. More examples of action extraction are listed in Appendix B. We also conduct experiments with respect to alternative choices of action. Firstly, representations for conversation snippets are directly used for selective retrieval instead of extracted actions. Results show that using conversation snippets underperforms much, and even demonstrates the similar performance of BT. The potential reason is that directly using conversation snippets may bring some noise such as stopwords, pronouns, etc., instead of focusing on the core idea of a conversation snippet. We also try other structures such as Semantic Role Labeling (SRL) (Carreras and Màrquez, 2005), which is known to extract the predicate, theme, and recipient. As shown in Table 5, overall performance is not comparable to actions. We interpret this result from the following aspects: (i) num of SRL (avg 29.80) is far more than actions (avg 12.32) since SRL contains many prevalent but | Number | R-1 | R-2 | R-L | |-------------|-------|-------|-------| | BARTbase | 42.36 | 18.63 | 38.44 | | Iteration 0 | 43.98 | 18.97 | 39.72 | | Iteration 1 | 44.17 | 19.82 | 40.20 | | Iteration 2 | 44.85 | 20.80 | 40.77 | | Iteration 3 | 45.42 | 21.23 | 41.42 | | Iteration 4 | 44.75 | 20.63 | 40.57 | noisy verbs such as "am". (ii) average length of the extracted span is very long (sometimes even containing clauses) for SRL (avg 8.37) compared with actions (avg 4.74). Finally, we show the effect of selective retrieval against K-NN search. Unsurprisingly, K-NN search fails to outperform selective retrieval. This is because selective retrieval brings more coverage and diversity. Augmentation with DialoGPT To investigate how COMPO surpasses model pre-trained on rough data as DA techniques, we experiment with DialoGPT (Zhang et al., 2020b). It is pre-trained on Reddit comment chains, which is easy to collect compared with human-labeled data. We follow the settings in (Feng et al., 2021b) and apply DialoGPT to generate the responses for each selected utterance. Then we treat them as newly augmented data samples for further training. As shown in Table 5, employing DialoGPT underperforms COMPO. The reasons are two folds: (i) DialoGPT fails to consider the structural and compositional information in the conversations, but rather generates plain responses. (ii) DialoGPT is pre-trained without speaker information, and thus may not be sensitive enough to tell the specific actions that happened. ## 6 Conclusion This paper introduced a simple and effective compositional data augmentation method for abstractive conversation summarization. We leverage the topical view of conversations and treat them as the units for compositional operation. Extensive experiments on benchmark datasets demonstrate that COMPO significantly outperforms prior state-ofthe-art baselines in terms of both quantitative and qualitative evaluation, through generating compositional and diverse augmented data. Our method has key implications for designing augmentation techniques for low-resource dialogue-related tasks. ## Limitations Our work on COMPO is subject to multiple limitations. The first limitation is around its scope when probing compositional operations. We only explored compositional substitution for topical snippets in conversations as an initial effort. However, there are many other types of conversation structures that can be leveraged such as conversation stages or specific discourse acts. Second, we used a set of external tools to process the conversations for augmentation, such as the use of C99 for topic split and action extraction. Although we choose to select widely-used tools with high precision, error cascades are inevitable. Furthermore, our approach may not be applicable to low-resourced languages since these pre-processing tools may not be available even in the first place for these low-resourced contexts. We urge future work to further work on this line of compositional data augmentation without any dependencies on external software. ## Ethics Statement Despite the recent success of pre-trained language models in abstractive conversation summarization, they mostly rely on large-scale annotated data. This leads to a major concern about the labor-intensive and time-consuming annotating process, which might not be available for small research groups or institutions with relatively fewer resources; we hope that COMPO can be an initial effort in mitigating this issue. Our work also sheds light on a more general framework to deal with data scarcity issues, making summarization systems more applicable to real-world scenarios where annotations are often hard to get. Overall, we do not foresee any major risk or negative societal impact of our work. 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Specifically, speakers in DialogSum are denoted with \#*P erson*_1\# and \#*P erson*_2\#. The public dataset consists of 12,460 training samples. The validation and test set have equal 500 instances. As could be inferred from Table 7, the number of participants for DialogSum are mostly 2, while SAMSum could have multi-party conversations. Also, the number of turns and reference length in DialogSum is shorter, which means that the information flow in DialogSum are relatively compact. | Dataset | Split | Number of Participants | Number of Turns | Reference Length | | | | | | | |-------------|---------|--------------------------|-------------------|--------------------|----------|--------|--------|----------|---------|--------| | Mean | Std | Interval | Mean | Std | Interval | Mean | Std | Interval | | | | Train 14732 | 2.40 | 0.83 | [1,14] | 11.17 | 6.45 | [1,46] | 23.44 | 12.72 | [2,73] | | | SAMSum | Dev 818 | 2.39 | 0.84 | [2,12] | 10.83 | 6.37 | [3,30] | 23.42 | 12.71 | [4,68] | | Test 819 | 2.36 | 0.83 | [2,11] | 11.25 | 6.35 | [3,30] | 23.12 | 12.20 | [4,71] | | | Train 12460 | 2.01 | 0.13 | [2,7] | 9.49 | 4.16 | [2,65] | 22.87 | 10.71 | [5,153] | | | DialogSum | Dev 500 | 2.01 | 0.13 | [2,4] | 9.38 | 3.99 | [2,29] | 20.91 | 9.76 | [6,56] | | Test 500 | 2.01 | 0.27 | [2,3] | 9.71 | 4.99 | [2,65] | 19.09 | 9.20 | [6,84] | | Table 7: Statistics of the used datasets. *Interval* denotes the minimum and maximum range. ## B Details For Human Evaluation On Amazon Mturk The web interface for human evaluation of quality is shown in Figure 4. Given a conversation, we ![12_image_0.png](12_image_0.png) randomly perturb the summaries generated and ask the workers to rank the summaries through the sliders. In principle, we do not accept repeated scores for three summaries since this is a ranking task. However, in practice, we found that there are almost identical summaries and it is difficult for human annotators to distinguish them. Therefore, for those cases (17 samples for SAMSum and 21 samples for DialogSum), we allow repetitive scores. For example, if all three summaries are identical, we will rank them as "1,1,1". If two of the summaries are identical, we will rank them as "1,2,2" or "1,1,2". ## C Patterns And Examples For Action Extraction For action extraction, we first use a dependency parser to get the parsing tree, and we select all nonauxiliary verbs as centric tokens. Then we match the syntactic relations between the verbs and other spans/tokens to see if they match the predefined patterns. As shown in Table 9, there are some typical patterns used in the extraction, and their corresponding examples. For example, for pattern n1-nsubj-v1xcomp-a/n2, 'nsubj' is the active relation between a noun and a verb. 'xcomp' here indicates open clausal complement or predicative complement. | Patterns | Examples | |------------------------|------------------------------------------------| | n1-nsubj-v1 | Melanie screw up. Lillian call. | | n1-nsubj-v1-dobj-n2 | Layla wait for Rachel. Lucia need haircut. | | n1-nsubj-v1-xcomp-a/n2 | Connor is too tired. Tonight is Opening Night. | | n1-auxcop-n2-advmod | Sam will be 30 minutes late. | | n1-auxpass-v1 | Tim get injured. | Table 8: Typical patterns used and their corresponding examples when we extract actions . Here 'v' is a verb, 'n' is a noun, 'a' is the adjective. All the verbs are in their original form. The other notations are syntactic relations. ## D Examples For Actions Retrieval In this section, we display different actions retrieved with selective retrieval and traditional kNN method to provide an intuitive view of their effects, and how they influence the final performance of summarization. For each of the actions, top three retrieved samples are listed for both the selected retrieval and kNN method. As can be seen, traditional kNN method usually focus only on word semantics, and is not able to generate diverse results. | Actions | Selective Retrieval | kNN | |-----------------------------------------------------------------------|-----------------------------------------------------------------------------|----------------------------------------------------------------------| | Noah abandon old computer Sam got 1st credit card Ali need hard drive | Ali need hard drive Sara have one with normal USB Paul saved file on laptop | | | Gavin have new one everything on external drive | Ted have busy day sister has child it continue on | Martha worry about Anna Drew afraid of wife Naomi worry about Samuel | | Sonia babysit child Sonia is scared | fridge smell bad smell come from box Lisa is sick | It is in fridge green plastic box fell I'm in drugstore | | medicine are in kitchen green box in kitchen | | | Table 9: Examples for action retrieval using different methods. ## E Examples For Summaries Generated From Three Models Mentioned In Section 4.5 We demonstrate several cases for summary generation with BART-base, COMPO-jt, COMPO-sf. We also attach groundtruth summaries for reference in Table 10. For each summary generated, the human evaluation scores (after majority voting) are also provided. ## F Examples For Newly Augmented Data In this section, we provide several examples for the newly augmented data generated with COMPO, as shown in Table 11. Selected topical split for compositioanl operation is highlighted in green. ## Conversations Riley: Chloe is on tv!! James: On which channel? James: Never mind I've found it. James: What is she doing? I don't get it. Riley: This is a programme in which women undergo a complete metamorphosis. Riley: OMG she looks drop dead gorgeous! | BART-base | COMPO-sf | COMPO-jt | |--------------------------------------------------|---------------------|---------------------------------| | Riley doesn't understand Chloe's transformation. | Chloe is on TV. | James hasn't found Chloe on TV. | | Human evaluation: 1 | Human evaluation: 3 | Human evaluation: 2 | | Conversations | | | Bob: <file>. I bought this game and I think you should too. Bob: We could play together. Harry: Sorry mate, no money to spend on this Harry: I've got broken car nad shitty job, so for now I can't think about such leisure. Bob: Sorry to hear that. | BART-base | COMPO-sf | COMPO-jt | |-------------------------|--------------------------------|---------------------------------------| | Bob bought together and | Bob bought together. | Bob bought this game and | | Harry should play it | Harry doesn't want to play it. | he thinks Harry should play together. | | Human evaluation: 1 | Human evaluation: 3 | Human evaluation: 2 | | Conversations | | | Rob: <photo>. Not sure if I'm getting dumber, or this is how it feels like to get older. Tom: What? Rob: I'm looking at today's memes and they mostly refer to things that are either completely stupid, or have no humour value. Tom: Rob, get yourself a girlfriend please. You're talking bullshit :D Rob: Ehh. Fuck you. | Rob: Ehh. Fuck you. BART-base | COMPO-sf | COMPO-jt | |----------------------------------------------------------------------------------------------------------------------------|---------------------|---------------------| | Rob is looking at today's memes and they mostly refer to things that are either completely stupid or have no humour value. | | | | Human evaluation: 1 | Human evaluation: 3 | Human evaluation: 2 | | Rob and Tom are looking at | | | | today's memes and they mostly refer to things that are completely stupid. | | | | Rob is getting older. | | | | He wants to get a girlfriend. | Conversations | | Paul: Hey Matthew did you find anyone to couch the game Saturday? Matthew: Hey Paul, no still looking. Paul: My plans changed so I can do it if you need Matthew: Ahh yes that be great! thank you. Paul: No problem see you Saturday | BART-base | COMPO-sf | COMPO-jt | |-----------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|---------------------| | Matthew is looking for someone to couch the game Saturday. Paul is still looking. | Paul will couch the game Saturday. Matthew is still looking for someone to couch it. | | | Human evaluation: 1 | Human evaluation: 3 | Human evaluation: 2 | | Paul will couch the game Saturday. | | | Table 10: Examples for action retrieval using different methods. | Newly-Generated Data | Original Data | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Jack: Is Daine with you? Marie: Nope Jack: Sorry, just can't find her. Marie: Is everything okay? Jack: Fine, just lost Diane! Marie: LOL! Jack: I'm sure she just ran out for milk or something. Sorry to bother you! Marie: No problem! If I see her, I'll send her your way. Jack: TY! | | | Pseudo summary for joint-learning: Jack lost Diane. Jack and Marie will meet tonight for opening night. Newly generated summary for joint-training: Jack's looking for Diane. Jack and Marie will go to opening night tonight. Jack: Is Daine with you? Marie: Nope Jack: Sorry, just can't find her. Marie: Is everything okay? Jack: Fine, just lost Diane! Jack: I'm not sure what to do. Marie: Tonight is opening night. Jack: TY! | Summary:Jack's looking for Diane. She probably went out to do shopping. Marie will direct Diane to Jack if she sees her. Alicia: How about some ice cream for desert? Hannah: Milk-free for me please :P Alicia: Are you lactose intolerant? Alicia: I didn't know that Hannah: I don't have an allergy but the doctor told me to avoid it Alicia: Ok, I will get you some with no milk | | Pseudo summary for joint-learning: Hannah has a problem with her milk. Alicia will get her some with no milk. Newly generated summary for joint-training: Hannah has a problem with her milk. Alicia will get some milk-free one for her. Hannah: Hi, I have a problem with my milk Alicia: Sorry, I'm running late today. Hannah: I don't have an allergy but the doctor told me to avoid it Alicia: Ok, I will get you some with no milk | Summary: Alicia will get some milk-free ice-cream for Hannah. | | Charlie: Hi. For how long you gonna need a room? Olivia: Hey, I need it till the end of January. Charlie: It's a shared room. Olivia: Ah ok. I'm looking for only a private room. I cannot share it with someone else. Anyway thank you! | | | Pseudo summary for joint-learning: Olivia is looking for a private room, and she can't share it with someone else. Newly generated summary for joint-training: Olivia is looking for a private room. Olivia can't share it with someone else. Olivia: Hi Charlie. I'm looking for a private room. Charlie: It's a shared room Olivia: Ah ok. I'm looking for only a private room. I cannot share it with someone else. Anyway thank you! | Summary: Olivia needs a private room till the end of January. Charlie says it's a shared room. Olivia can't share a room. | Table 11: Sampled newly augmented data examples for conversations and the summaries. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3.2 ✓ B1. Did you cite the creators of artifacts you used? Section 3.2, Section 4 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response. ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? The original intended use is not found ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank. ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix A, Section 4 ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4.3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4.3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.3 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.3 D ✓ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Section 4.5, Appendix b ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Appendix B ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 4.5 ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 4.5 ✗ D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

chen-li-2023-pmaes

{PMAES}: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring

https://aclanthology.org/2023.acl-long.83

Current cross-prompt automated essay scoring (AES) is a challenging task due to the large discrepancies between different prompts, such as different genres and expressions. The main goal of current cross-prompt AES systems is to learn enough shared features between the source and target prompts to grade well on the target prompt. However, because the features are captured based on the original prompt representation, they may be limited by being extracted directly between essays. In fact, when the representations of two prompts are more similar, we can gain more shared features between them. Based on this motivation, in this paper, we propose a learning strategy called {``}prompt-mapping{''} to learn about more consistent representations of source and target prompts. In this way, we can obtain more shared features between the two prompts and use them to better represent the essays for the target prompt. Experimental results on the ASAP++ dataset demonstrate the effectiveness of our method. We also design experiments in different settings to show that our method can be applied in different scenarios. Our code is available at \url{https://github.com/gdufsnlp/PMAES}.

# Pmaes: Prompt-Mapping Contrastive Learning For Cross-Prompt Automated Essay Scoring Yuan Chen and Xia Li∗ School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China {yuanchen, xiali}@gdufs.edu.cn ## Abstract Current cross-prompt automated essay scoring (AES) is a challenging task due to the large discrepancies between different prompts, such as different genres and expressions. The main goal of current cross-prompt AES systems is to learn enough shared features between the source and target prompts to grade well on the target prompt. However, because the features are captured based on the original prompt representation, they may be limited by being extracted directly between essays. In fact, when the representations of two prompts are more similar, we can gain more shared features between them. Based on this motivation, in this paper, we propose a learning strategy called "prompt-mapping" to learn about more consistent representations of source and target prompts. In this way, we can obtain more shared features between the two prompts and use them to better represent the essays for the target prompt. Experimental results on the ASAP++ dataset demonstrate the effectiveness of our method. We also design experiments in different settings to show that our method can be applied in different scenarios. Our code is available at https://github.com/gdufsnlp/PMAES. ## 1 Introduction Automated Essay Scoring (AES) aims to evaluate the quality of essays automatically. Compared with human grading process, a robust AES system can not only reduce the work of teachers, but also improve the consistency of grading (Hearst, 2000;Weigle, 2002) and make it broadly available to language learners. AES has been studied for many years. Early studies focus more on handcrafted features, such as lexical features (Rudner and Liang, 2002;Attali and Burstein, 2006;Yannakoudakis et al., 2011). With the rise of deep learning, many studies based on ∗ Corresponding author. ![0_image_0.png](0_image_0.png) neural networks for prompt-specific settings have been proposed and achieved better results (Dong et al., 2017; Tay et al., 2018; Liao et al., 2021;Xie et al., 2022). These studies follow the same setting, that is, both rated training essays and unrated test essays belong to the same prompt. Another type of work is cross-prompt AES. In this setting, labeled training essays are from source prompts and unlabeled test essays are from a different target prompt. Existing studies mainly focus on obtaining sufficient shared features between source and target prompts to grade the target prompt essays effectively. Some of them obtain shared features by extracting handcrafted features (Phandi et al., 2015; Ridley et al., 2020; Ridley et al., 2021) while others learn shared features by optimizing additional training objectives, such as the multi-task learning (Cummins et al., 2016), two-stage strategy (Jin et al., 2018; Li et al., 2020) and self-supervised learning task (Cao et al., 2020). Although these methods can effectively capture shared features between different prompts, we argue that these features are captured based on the original representations of the essays from source and target prompts. It may be limited by directly extracting the shared features among them. Intuitively, when the representations of the essays from the source and target prompts are more consistent, they can share more knowledge between 1489 them. To this end, we propose a prompt representation learning framework for cross-prompt AES (PMAES) in which we design a prompt-mapping contrastive learning strategy to effectively learn about more consistent representations of source and target prompts. To do this, we design a mapping operation to project each essay from the source prompt to the target prompt and get its mapping representation specific to the target prompt. For each essay on the source prompt (let's say r s), we first determine how similar it is to all the essays in the target prompt by their original representations (e.g., by taking the dot product with the inverse matrix of the representations of all the essays in the target prompt) as the weights of the r sto each target essay. Then, we employ a learnable parameter matrix (specifically, a prompt-mapping matrix) to acquire the weighted representation of the source prompt essay projected on the target prompt to express the mapping representation of the source essay r s(let's say rˆ s). These source essay representations and source mapping representations are treated as the source-to-target mapping pairs (r s, rˆ s). By decreasing the distance between the essays in these mapping pairs, we may gradually reduce the discrepancy between the source and target prompts and finally make the representations of the two prompt essays more consistent. It is worth noting that the above description is about mapping from source to target. Naturally, we also perform target-to-source prompt mapping operations to further learn a more consistent representations of the two prompts, which will be described in Section 3.4. As demonstrated in Figure 1, given the original essay representations of a source and a target prompt (which we marked in green and red, respectively), there are very few shared features between them under the original representations (which we marked in yellow). When we train the model using our proposed prompt-mapping approach, the representations of the two prompts may become more similar, which enables more shared features across the two prompts. We show them in Figure 1(b) and Figure 1(c). As the shared features increase, we can get more accurate representations of target prompt essays and grade them more accurately. To summarize, the main contributions of our work are as follows: 1) To the best of our knowledge, this is the first attempt to explore the learning of consistent representations of different prompts by introducing a prompt-mapping learning strategy in order to obtain more shared features between the source and target prompts. 2) We conduct comprehensive experiments on the ASAP++ dataset, and the results show that our approach outperforms the state-of-the-art model on both single-overall and multi-attribute scoring tasks. Also, the prompt consistency experiments show that our method can make source and target prompts much more similar to each other. 3) We further design three types of source-target settings. The results show that our approach can be adapted to multiple scenarios. ## 2 Related Work 2.1 Prompt-Specific Aes Prompt-specific AES aims to train and test essays on the same prompt. Early studies (Rudner and Liang, 2002;Attali and Burstein, 2006; Mohler and Mihalcea, 2009; Persing and Ng, 2013; Sakaguchi et al., 2015; Sultan et al., 2016) rate essays by extracting handcrafted features to train a machine learning model. Recently, with the rise of deep learning, a growing number of studies (Taghipour and Ng, 2016; Dong and Zhang, 2016; Dong et al., 2017; Dasgupta et al., 2018; Li et al., 2018; Tay et al., 2018; Uto et al., 2020; Hussein et al., 2020; Ma et al., 2021; Liao et al., 2021; Wang et al., 2022; Xie et al., 2022) propose scoring models based on neural networks and achieve promising results. ## 2.2 Cross-Prompt Aes Cross-prompt AES aims to train models from labeled source prompt essays and rate target prompt essays. Phandi et al. (2015) train the Bayesian linear ridge regression algorithm from the source prompt using manual features, then test it directly on the target prompt. Cummins et al. (2016) adopt multi-task learning to address the problem of prompt adaptation. Jin et al. (2018) propose a twostage approach for the problem of cross-prompt AES. In the first stage, they train a RankSVM on prompt-independent features to obtain pseudolabels for target prompt essays. In the second stage, a neural network model learns more promptdependent features in the pseudo-labeled essays. Li et al. (2020) also adopts a two-stage approach to train a model to learn common knowledge and provide pseudo labels for target prompt essays in the first stage, then use a Siamese framework to learn more prompt-dependent features in the second stage. Cao et al. (2020) train sentence reordering and noise identification tasks with adversarial training to improve the domain adaptability of the model. Ridley et al. (2020) utilize the handcrafted features to provide prompt agnostic information and achieve good results. Ridley et al. (2021) expand this prompt-agnostic information for multiattribute scoring tasks. ## 2.3 Contrastive Learning Contrastive learning is an unsupervised learning method originally used in computer vision (Hadsell et al., 2006). The main idea is to gradually bring the anchor and its positive samples closer together in a shared semantic space while distinguishing the anchor from other samples, such as the work of Chen et al. (2020). Recently, contrastive learning has shown satisfactory results in textual representation learning. Data augmentation is a general strategy for obtaining positive samples, such as translation (Han et al., 2022), synonym replacement (Wang et al., 2021), word repetition (Wu et al., 2022) or textual representation perturbation (Gao et al., 2021; Yan et al., 2021). ## 3 Our Approach The whole architecture of our approach is shown in Figure 2. It contains three components: shared encoder, scorer and prompt-mapping contrastive learning. The shared encoder provides a shared representation for the other two components, the scorer is used to predict the score, and the promptmapping contrastive learning is used to maximize the consistency of source and target prompts. ## 3.1 Task Definition Given source prompt data Ds = {(x s i , ys i )} P i=1 and target prompt data Dt = {x t i} Q i=1, where x s/t iis the i-th essay in source/target prompt, P and Q are the number of essays in the source and target prompts. For single-overall scoring task, y s i is the overall score of source prompt essay x s i , and for multiattribute scoring task, y s i = {y s1 i , ys2 i , ..., ysK i} is the set of attribute scores, and y s1 iis the overall score. The task of our approach is to train a model with Ds and Dt as inputs and output the score of all target prompt essays. The complete algorithm is shown in Algorithm 1. Algorithm 1: Procedure of our approach ![2_image_0.png](2_image_0.png) Input: {(x s i , ys i )} P i=1, {x t i} Q i=1 Output: shared encoder F, scorer G 1 Calculate Is and It using Eq. 14; 2 for *sampling mini-batch* do 3 r s i = F(x s i ), r t i = F(x t i ); 4 Calculate rˆ s i and rˆ t i using Eq. 15; 5 Calculate Ls→t and Lt→s using Eq. 16 and Eq. 17 ; 6 Lpm = Ls→t + Lt→s; 7 if *single-overall scoring task* **then** 8 Calculate z s i using Eq. 5; 9 Calculate yˆ s i using Eq. 6; 10 Calculate Laes_so using Eq. 7; 11 if *epoch=*1 **then** 12 Update F and G minimizing Laes_so; 13 **else** 14 Update F and G minimizing Lpm and Laes_so; 15 if *multi-attribute scoring task* **then** 16 Calculate {z sk i} K k=1 using Eq. 8; 17 Calculate {yˆ sk i} K k=1 using Eq. 9; 18 Calculate Laes_ma using Eq. 10; 19 Calculate Lcor using Eq. 13; 20 Update F and G minimizing ![2_image_1.png](2_image_1.png) ## 3.2 Shared Encoder ![2_Image_2.Png](2_Image_2.Png) To better encode essays, we use the hierarchical structure proposed by Dong et al. (2017) as a shared encoder, in which the sentence-level representation is extracted by CNN and attention pooling from words, and LSTM and another attention pooling are used to capture essay-level representation from all sentences. In this paper, as with Ridley et al. (2021), we use POS embedding1to represent the essay text due to their ability to obtain better generalized representations. Suppose each essay is composed of n sentences, and each sentence contains m words. We use wito denote the POS embedding of each word for convenience. Then, the sentence-level representation is captured by CNN with attention pooling: $c_{i}=$ CNN($[w_{i}:w_{i+l-1}]$), $i=1,2,...,m$ (1) $s_{t}=$ attention($[c_{1}:c_{m}]$) (2) ![3_image_0.png](3_image_0.png) where l is the kernel size of CNN, ciis the output of the convolution operation applied to i-th POS embedding, and stis the representation of t-th sentence. The essay-level representation is captured by LSTM with another attention pooling: $h_{t}=$ LSTM($s_{t-1},s_{t}$), $t=1,2,...,n$ (3) $r=$ attention($[h_{1}:h_{n}]$) (4) where htis the output of LSTM at the t-th time step, and r is the final essay representation. ## 3.3 Scorer In this paper, we evaluate our approach both on single-overall scoring task and multi-attribute scoring task. Therefore, we have two types of scorers, corresponding to two forms of loss function. We also use the same handcrafted features as Ridley et al. (2021), denoted as f. ## 3.3.1 Single-Overall Scorer For single-overall scoring task, firstly, we concatenate the essay representation r and handcrafted features f, denoted as [r; f]. Then, feeding it into a tanh dense layer to get z. Finally, another dense layer with sigmoid activation is applied to predict the overall score yˆ. The corresponding equations are as follows (Eq. 5 and Eq. 6): $$z=\operatorname{tanh}(W_{z}[r;\mathbf{f}]+b_{z})$$ $${\hat{y}}=\sigma(W_{y}z+b_{y})$$ where Wz and Wy are the trainable weight matrices, bz and by are the bias vectors, σ is the sigmoid function. We use mean squared error (MSE) as the loss function, defined as follows: $${\mathcal{L}}_{a e s\_s o}={\frac{1}{N}}\sum_{i}^{N}({\hat{y}}_{i}-y_{i})^{2}\qquad\qquad(7)$$ $$\mathbf{\Pi}$$ where N is the number of essays in a batch. ## 3.3.2 Multi-Attribute Scorer For multi-attribute scoring task, we first input the essay representation r into a specific relu dense layer to get the representation z k of the k-th attribute. Then, concatenating z k with f and feeding into a specific sigmoid dense layer to predict the kth attribute score yˆ k. The corresponding equations are as follows (Eq. 8 and Eq. 9): $$z^{k}=\mathrm{relu}(W_{z}^{k}r+b_{z}^{k})$$ $${\hat{y}}^{j}=\sigma(W_{y}^{k}[z^{k};\mathbf{f}]+b_{y}^{k})$$ (8) $\binom{9}{2}$ . z) (8) y) (9) where Wk z and Wk y are the trainable weight matrices , b k z and b k y are the bias vectors. Suppose the total number of attributes is K, the multi-attribute scoring loss is defined as follows: $${\mathcal{L}}_{a e s\_m a}={\frac{1}{N K}}\sum_{i}^{N}\sum_{k}^{K}({\hat{y}}_{i}^{k}-y_{i}^{k})^{2}\qquad(10)$$ $$\quad(5)$$ $$\quad(6)$$ It should be noted that not all essays have all attributes (as shown in Table 5). So we use the mask mechanism proposed by Ridley et al. (2021) $$1492$$ to account for the attributes without gold scores when calculating the loss. $$m a s k_{i}^{k}={\begin{cases}1,\;i f\;y_{i}^{k}\in y_{i}\\ 0,\;o t h e r w i s e\end{cases}}$$ $$(11)$$ $$y_{i}=y_{i}\ \otimes\ m a s k_{i}\ ,\hat{y}_{i}=\hat{y}_{i}\ \otimes\ m a s k_{i}$$ In addition, we believe that when predicting one attribute score, the other attributes can provide useful information for it. Therefore, we propose an inter-attribute correlation loss Lcor. $$\mathcal{L}_{cor}=\frac{1}{K}\sum_{i}^{N}\sum_{k}^{K}-\log(\sum_{j,j\neq k}^{K}g(z_{i}^{k},z_{i}^{j}))\tag{13}$$ where $g(z_{i}^{k},z_{i}^{j})=\exp(\cos(z_{i}^{k},z_{i}^{j})/\rho)$, $\cos(\cdot)$ is the cosine similarity function, and $\rho$ is a hyper the cosine similarity function, and ρ is a hyperparameter. The goal of Lcor is to maximize the mutual information among all attributes. ## 3.4 Prompt-Mapping Contrastive Learning In order to capture more shared features between the source and target prompts, we propose a prompt-mapping contrastive learning strategy to learn about more consistent representations of source and target prompts. For convenience, let's take the source-to-target prompt mapping as an example to describe our method in detail. The targetto-source prompt mapping is the same operation. Firstly, we use shared encoder F to encode all source and target prompt essays in training data to obtain the source prompt representation Is ∈ R P∗u and the target prompt representation It ∈ R Q∗u(as shown in Eq. 14), where u is the number of LSTM hidden units, P and Q are the number of source and target prompt essays. $$I_{s}={\mathcal{F}}(\{x_{i}^{s}\}_{i=1}^{P}),\;I_{t}={\mathcal{F}}(\{x_{i}^{t}\}_{i=1}^{Q})$$ i=1) (14) Next, we will obtain source-to-target mapping pairs. First, we take each source essay representation, let's say r s i , to dot product with I⊤ t , where I⊤ t ∈ R u∗Q is the transpose of It, which is used to obtain how similar it is to all the essays in the target prompt as the weights of the r s i to each target prompt essay. After that, we use a learnable parameter matrix Ws ∈ R Q∗uto acquire the weighted representations of the source prompt essays projected on the target prompt to express the source mapping representation rˆ s i , as shown in Eq. 15. In this way, r s i and rˆ s i can form the source-to-target mapping pair (r s i , rˆ s i ). Similarly, for the target-to-source mapping pairs, rˆ t i can be obtained by using r t i , I⊤ s ∈ R u∗P and Wt ∈ R P∗u, and finally get the target-to-source mapping pair (r t i , rˆ t i ). $${\hat{r}}_{i}^{s}=W_{s}\cdot(r_{i}^{s}\otimes I_{t}^{\top}),\;{\hat{r}}_{i}^{t}=W_{t}\cdot(r_{i}^{t}\otimes I_{s}^{\top})\;\;(15)$$ $$(12)$$ where ⊗ is the dot product operation. Finally, we take the mapping pairs (r s i , rˆ s i ) and (r t i , rˆ t i ) as the positive pairs. For the selection of negative samples, we follow the work of SimCLR (Chen et al., 2020) which takes the other samples in the same batch as the negative samples. The contrastive learning loss functions of mapping from source to target and from target to source are defined as follows: $$\mathcal{L}_{s\to t}=\sum_{i}^{N_{s}}-\log\frac{f(r_{i}^{s},\hat{r}_{i}^{s})}{\sum_{j}^{N_{s}}f(r_{i}^{s},r_{j}^{s})+f(r_{i}^{s},\hat{r}_{j}^{s})}\tag{16}$$ $$\mathcal{L}_{t\to s}=\sum_{i}^{N_{t}}-\log\frac{f(r_{i}^{t},\hat{r}_{i}^{t})}{\sum_{j}^{N_{t}}f(r_{i}^{t},r_{j}^{t})+f(r_{i}^{t},\hat{r}_{j}^{t})}\tag{17}$$ where $f(a,b)=\exp(\cos(a,b)/\tau)$, $\cos(\cdot)$ is co where f(*a, b*) = exp(cos(*a, b*)/τ ), cos(·) is cosine similarity function, τ is temperature hyperparameter, Ns and Nt are the batch size of source prompt essays and target prompt essays. The prompt-mapping contrastive learning loss is defined as: $\mathcal{L}_{pm}=\mathcal{L}_{s\to t}+\mathcal{L}_{t\to s}$ use of circle overall easier text. The total loss of single-overall scoring task is: $${\mathcal{L}}_{s o}={\mathcal{L}}_{a e s\_s o}+\lambda_{1}{\mathcal{L}}_{p m}$$ $$(19)$$ $$(14)$$ The total loss of multi-attribute scoring task is: $$\mathcal{L}_{m a}=\mathcal{L}_{a e s\_m a}+\lambda_{1}\mathcal{L}_{p m}+\lambda_{2}\mathcal{L}_{c o r}$$ where λ1 and λ2 are weighted hyper-parameters. ## 4 Experiments 4.1 Datasets And Evaluation Metrics We conduct the experiments on the ASAP++ (Mathias and Bhattacharyya, 2018) dataset, which is an extension of the ASAP2 dataset. Each essay has an overall score and multiple attribute scores. The statistics are provided in Appendix A. 2https://www.kaggle.com/c/asap-aes/data | Model | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | Avg. | |---------------------------------------------------|-------|-------|-------|-------|-------|-------|-------|-------|--------| | Single-overall scoring task Hi att † 0.372 0.465 | 0.432 | 0.523 | 0.586 | 0.574 | 0.514 | 0.323 | 0.474 | | | | PAES † | 0.746 | 0.591 | 0.608 | 0.641 | 0.727 | 0.609 | 0.707 | 0.635 | 0.658 | | PMAES (ours) | 0.758 | 0.674 | 0.658 | 0.625 | 0.735 | 0.578 | 0.749 | 0.718 | 0.687 | | Multi-attribute scoring task Hi att ‡ 0.315 0.478 | 0.317 | 0.478 | 0.375 | 0.357 | 0.205 | 0.265 | 0.349 | | | | AES aug ‡ | 0.330 | 0.518 | 0.299 | 0.477 | 0.341 | 0.399 | 0.162 | 0.200 | 0.341 | | PAES ‡ | 0.605 | 0.522 | 0.575 | 0.606 | 0.634 | 0.545 | 0.356 | 0.447 | 0.536 | | CTS no att ‡ | 0.619 | 0.539 | 0.585 | 0.616 | 0.616 | 0.544 | 0.363 | 0.461 | 0.543 | | CTS ‡ | 0.623 | 0.540 | 0.592 | 0.623 | 0.613 | 0.548 | 0.384 | 0.504 | 0.553 | | PMAES (ours) | 0.656 | 0.553 | 0.598 | 0.606 | 0.626 | 0.572 | 0.386 | 0.530 | 0.566 | Model Overall Cont Org WC SF Conv PA Lan Nar Avg. Hi att ‡ 0.453 0.348 0.243 0.416 0.428 0.244 0.309 0.293 0.379 0.346 AES aug ‡ 0.402 0.342 0.256 0.402 0.432 0.239 0.331 0.313 0.377 0.344 PAES ‡ 0.657 0.539 0.414 0.531 0.536 0.357 0.570 0.531 0.605 0.527 CTS no att ‡ 0.659 0.541 0.424 0.558 0.544 0.387 0.561 0.539 0.605 0.535 CTS ‡ 0.670 0.555 0.458 0.557 0.545 0.412 0.565 0.536 0.608 0.545 PMAES (ours) **0.671 0.567 0.481 0.584 0.582 0.421 0.584 0.545 0.614 0.561** Table 2: Main results of multi-attribute scoring task. This table shows the average QWK score across all prompts for each attribute. ‡ refers to the results from Ridley et al. (2021). We use Quadratic Weighted Kappa (QWK) as the evaluation metric to measure the consistency between the real scores and the predicted scores, which is the general evaluation metric in AES tasks (Jin et al., 2018;Li et al., 2020;Ridley et al., 2021). ## 4.2 Implementation Details We use the same data partition as the current stateof-the-art model (Ridley et al., 2021), that is for each prompt as target prompt, then the rest of prompts are set to be source prompt. For example, assume the target prompt is P8, then the source prompt consists of P1∼P7. We use labeled source prompt essays and unlabeled target prompt essays as training data, and the same unlabeled target prompt essays as test data. The validation data is from labeled source prompt essays. We use the same handcrafted features proposed by (Ridley et al., 2020) in single-overall and multiattribute scoring task, including features of Lengthbased, Readability, Text Complexity, Text Variation and Sentiment. We use the length of the longest essay in the dataset as the padding length to ensure that the essay information can be retained as much as possible. We use 50-dimension POS embedding as input and train all models for 50 epochs. We report the average results across five random seeds. More details are provided in Appendix B. ## 4.3 Baseline Models We compare with the existing models on singleoverall scoring task and multi-attribute scoring task. For single-overall scoring task, we use **Hi att** (Dong et al., 2017) and **PAES** (Ridley et al., 2020) as baseline models, which are both the singleoverall scoring models. For multi-attribute scoring task, we use **Hi att** (Dong et al., 2017), **AES aug** (Hussein et al., 2020), **PAES** (Ridley et al., 2020), CTS no att (Ridley et al., 2021) and the current state-of-the-art model CTS (Ridley et al., 2021) as the comparison models. The details of baseline models are described as follow: (1) **Hi att**: Dong et al. (2017) propose a hierarchical structure with attention pooling for singleoverall scoring task, which scores essays by extracting the sentence- and essay-level features. | Model | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | Avg. | |------------------------------------------------|-------|-------|-------|-------|-------|-------|-------|-------|--------| | Single-overall scoring task PMAES 0.758 | 0.674 | 0.658 | 0.625 | 0.735 | 0.578 | 0.749 | 0.718 | 0.687 | | | w/o Lpm | 0.602 | 0.551 | 0.621 | 0.646 | 0.727 | 0.602 | 0.745 | 0.665 | 0.645 | | Multi-attribute scoring task PMAES 0.656 0.553 | 0.598 | 0.606 | 0.626 | 0.572 | 0.386 | 0.530 | 0.566 | | | | w/o Lcor | 0.646 | 0.539 | 0.592 | 0.611 | 0.630 | 0.580 | 0.373 | 0.509 | 0.560 | | w/o Lpm | 0.650 | 0.545 | 0.589 | 0.606 | 0.620 | 0.578 | 0.383 | 0.453 | 0.553 | | w/o Lpm & Lcor | 0.625 | 0.525 | 0.594 | 0.607 | 0.637 | 0.557 | 0.377 | 0.469 | 0.549 | Table 3: Ablation results of single-overall scoring task and multi-attribute scoring task for each prompt. The results of multi-attribute scoring task is the average QWK score across all attributes for each prompt. | Model | Overall | Cont | Org | WC | SF | Conv | PA | Lan | Nar | Avg. | |----------------|-----------|--------|-------|-------|-------|--------|-------|-------|-------|--------| | PMAES | 0.671 | 0.567 | 0.481 | 0.584 | 0.582 | 0.421 | 0.584 | 0.545 | 0.614 | 0.561 | | w/o Lcor | 0.669 | 0.562 | 0.461 | 0.573 | 0.569 | 0.405 | 0.583 | 0.546 | 0.619 | 0.554 | | w/o Lpm | 0.666 | 0.546 | 0.450 | 0.573 | 0.573 | 0.385 | 0.578 | 0.538 | 0.614 | 0.547 | | w/o Lpm & Lcor | 0.664 | 0.553 | 0.432 | 0.548 | 0.554 | 0.398 | 0.583 | 0.539 | 0.614 | 0.543 | Table 4: Ablation results for multi-attribute scoring task, this table shows the average QWK score across all prompts for each attribute. (2) **AES aug**: Hussein et al. (2020) convert the model proposed by Taghipour and Ng (2016) into a multi-task architecture, which can be used to rate the multi-attribute scores at the same time. (3) **PAES**: Ridley et al. (2020) apply a neural model with handcrafted features for single-overall scoring. (4) CTS: Ridley et al. (2021) propose the first model for the cross-prompt multi-attribute scoring task, in which they develop a trait-attention mechanism to establish interactions between different attributes. (5) **CTS no att**: This model (Ridley et al., 2021) has the same shared- and private-layers as CTS, and removes the trait-attention mechanism. ## 5 Results And Analysis 5.1 Main Results We report the main results on single-overall scoring task and multi-attribute scoring task. For single-overall scoring task, we use Hi att and PAES as baseline models, which are both singleoverall scoring models. As shown in Table 1, compared with Hi att and PAES, PMAES achieves the best results, improving the average QWK score by 21.3% and 2.9%, respectively, which proves the effectiveness of our approach on this task. For multi-attribute scoring task, following Ridley et al. (2021), we report the results from two dimensions. For the average QWK score across all attributes for each prompt (Table 1), we can see that our approach achieves 0.566 average QWK score, which outperforms all baseline models. For the average QWK score across all prompts for each attribute (Table 2), PMAES not only achieves the state-of-the-art average performance but also gets best performance on all prompts, which shows the significant improvement of PMAES for this task. Based on the above results, we can see that PMAES is suitable for both grading a single overall score and multiple attribute scores. Meanwhile, we discover that PMAES fails to perform well in P4 and P6 as target prompts. Through analysis, we find that essays in P4 and P6 are source-dependent types and were written by 10th graders. Their writing requirements are relatively difficult. P4 requires students to write a response to figure out the source author's thoughts, while P6 requires students to summarize academic excerpts. We believe that P4 and P6 share a few features with other prompts. In this case, the way our method maps P4/P6 and the source prompt to each other may lead to a low-scoring performance. ## 5.2 Ablation Studies We conduct the ablation experiments both on single-overall scoring task and multi-attribute scoring task, which are shown in Table 3 and Table 4. For single-overall scoring task, as shown in Table 3, we can see that if training model without Lpm, the average QWK score drops by 4.2%, and the QWK scores of the majority of prompts also drop significantly. Especially in P1 and P2, the QWK scores drop by 15.6% and 12.3%. It proves that our proposed prompt-mapping contrastive learning is effective in this task. For multi-attribute scoring task, we also show the results from two dimensions. Firstly, as shown in Table 3, it can be seen that the average QWK score drops by 0.6% after removing Lcor and by 1.3% after removing Lpm, which demonstrates that both Lpm and Lcor contribute to improve the scoring performance, and Lpm contributes more. When we remove these two components (w/o Lpm & Lcor), the average QWK score drops by 1.7%. This shows that Lpm and Lcor can promote each other and further improve the scoring performance. Secondly, for the dimension of the average QWK score across all prompts for each attribute, we show the results in Table 4. The average QWK score drops by 0.7% after removing Lcor, by 1.4% after removing Lpm and by 1.8% after removing both components. It further demonstrates the effectiveness of our model. We also can see that when we remove both of them, the QWK scores drop on almost all attributes. Especially on *Organization*, after removing Lpm and Lcor, the QWK score drops significantly (by 4.9%). Based on the above results, it can be found that our proposed approach can effectively improve the model scoring performance in the single-overall scoring task and the multi-attribute scoring task. ## 5.3 Analysis Of Prompt Consistency To further investigate the effectiveness of promptmapping contrastive learning on prompt consistency, we present our analysis using two methods: 1) Measuring the distance between source and target prompts using the Maximum Mean Discrepancy (MMD, Gretton et al., 2012). 2) Visualizing the essay representations of source and target prompts by using t-SNE (Van der Maaten and Hinton, 2008) to observe the degree of the consistency of prompts. ## 5.3.1 Mmd For Prompt Consistency Maximum Mean Discrepancy (MMD) is a kernelbased method that measures the distance between two matrices based on their respective mean embeddings. Inspired by previous work (Thota and ![7_image_0.png](7_image_0.png) Leontidis, 2021; Yue et al., 2022), we quantify the degree of consistency by calculating the MMD distance between the source and target prompt essay representation matrices. A smaller distance indicates a greater degree of consistency between the source and target prompts, whereas a larger distance indicates a lesser degree of congruence. More details are provided in Appendix C ## 5.3.2 Visualization For Prompt Consistency We use the t-SNE (Van der Maaten and Hinton, 2008) toolkit to visualize the representations of all essays on source and target prompts in training data to demonstrate prompt representations, which are generated by shared encoder under random initialization (original), training with PMAES w/o Lpm and PMAES, respectively. Firstly, as shown in Figure 3(a) and Figure 3(b), we show the visualization results of source and target prompt essay representations with P1 and P2 as target prompts. Taking Figure 3(a) for example, we can see that a clear discrepancy exists in the original representations of source prompt (green) and target prompt (red). After training with PMAES w/o Lpm, the prompt representations become more discrete, while prompt representations generated by PMAES are undoubtedly more consistent and close to each other. The same phenomenon occurs in Figure 3(b). Secondly, to further show how the prompt representations change as the number of training epochs increases, we visualize the essay representations generated by the epochs 0 (original), 4, 14, 34 and 50 during training w/o Lpm and PMAES with P1 as the target prompt. As shown in Table 4, the top row shows the results of training with PMAES w/o Lpm, and the bottom row shows the results of training with PMAES. The results show that the representations generated by these two models are relatively divergent at the beginning of training. As ![8_image_0.png](8_image_0.png) the training epochs increase, PMAES makes the prompt representations gradually consistent, while PMAES w/o Lpm makes them gradually discrete. Based on the results of MMD and visualization analysis, it can be seen that w/o Lpm not only fails to maintain the consistency of source and target prompts, but also damages it. In contrast, our approach can significantly make these two prompts more consistent to improve scoring performance. ## 5.4 **Results Of Different Source-Target Settings** Most of the current cross-prompt AES studies train on multiple prompts (source prompt) and test on a single prompt (target prompt), namely the manyto-one setting, which is the general setting in crossprompt AES and is shown in Section 5.1. To verify the performance of our approach in many practical settings, we conduct comprehensive experiments for different source-target settings. More details are provided in Appendix D. ## 6 Conclusions In this paper, we propose a new method for cross-prompt AES that aims to capture more shared features between the source and target prompts. Specifically, we design prompt-mapping contrastive learning to decrease the distance between the mapping pairs from source-to-target and target-to-source simultaneously and finally make the representations of the two prompts more consistent. Experimental results demonstrate that our approach achieves the state-of-the-art on both singleoverall scoring task and multi-attribute scoring task. We further design experiments for three sourcetarget settings, which proves that our approach can be adapted to multiple scenarios. ## Limitations Our approach achieves promising results in crossprompt AES by enhancing the consistency between source and target prompts. We believe that this idea can also be used to other cross-domain or domain adaptation tasks. 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Domain adaptation for question answering via question classification. In *Proceedings* of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022, pages 1776–1790. International Committee on Computational Linguistics. ## A Statistics Of Datasets The ASAP++ dataset includes 12,978 English writings in response to eight prompts. Table 5 displays the statistics for both ASAP and ASAP++. ## B Implementation Details The implementation details of our model are presented as follows: For single-overall scoring task, we optimize only the Laes_so in the first epoch, which is used to initialize the model weights, and optimize the Laes_so and Lpm in the rest epochs. We set the kernel size as 3, the number of filters as 100 for CNN and the number of hidden units as 50 for LSTM. We use | Prompt ID | No. of Essays | Avg. Len. | Attributes | Score Range | | |-------------|-----------------|-------------|-------------------------|---------------|--------| | Overall | Attribute | | | | | | 1 | 1,783 | 350 | Cont, Org, WC, SF, Conv | 2 - 12 | 1 - 6 | | 2 | 1,800 | 350 | Cont, Org, WC, SF, Conv | 0 - 6 | 1 - 6 | | 3 | 1,726 | 150 | Cont, PA, Lan, Nar | 0 - 3 | 0 - 3 | | 4 | 1,772 | 150 | Cont, PA, Lan, Nar | 0 - 3 | 0 - 3 | | 5 | 1,805 | 150 | Cont, PA, Lan, Nar | 0 - 4 | 0 - 4 | | 6 | 1,800 | 150 | Cont, PA, Lan, Nar | 0 - 4 | 0 - 4 | | 7 | 1,569 | 300 | Cont, Org, Conv | 0 - 30 | 0 - 6 | | 8 | 723 | 650 | Cont, Org, WC, SF, Conv | 0 - 60 | 2 - 12 | Table 5: Statistics of ASAP and ASAP++ Datasets. Cont: Content, Org: Organization, WC: Word Choice, SF: Sentence Fluency, Conv: Conventions, PA: Prompt Adherence, Lan: Language and Nar: Narrativity. Model P1 P2 P3 P4 P5 P6 P7 P8 Avg. original 0.902 0.968 0.378 0.475 0.331 0.277 0.187 2.016 0.692 w/o Lpm 2.366 1.778 0.868 1.249 0.570 0.759 0.343 2.542 1.309 PMAES **0.180 0.167 0.093 0.077 0.054 0.043 0.046 1.168 0.228** Adam (Kingma and Ba, 2015) as the optimizer with the learning rate = 0.0001, τ = 0.1 and λ1 = 0.5. We use the model with the highest QWK score in the development set to evaluate the test set. For multi-attribute scoring task, the detailed parameters are as follows: the kernel size is 5, the number of filters is 100 for CNN and the number of hidden units is 100 for LSTM. The optimizer is RMSprop (Dauphin et al., 2015) with the learning rate = 0.001, τ = 0.001, ρ = 0.1, λ1 = 0.5 and the λ2 = 0.1. We take the model with the highest average QWK score of all attributes in the development set to evaluate the test set. ## C Mmd For Prompt Consistency The MMD distance can be calculated by the following equation: $$\mathrm{MMD}=\left\|{\frac{1}{P}}\sum_{i=1}^{P}\phi(r_{i}^{s})-{\frac{1}{Q}}\sum_{j=1}^{Q}\phi(r_{j}^{t})\right\|_{H}^{2}\tag{21}$$ where φ(·) denotes the function that is used to map the original variable to the Reproducing Kernel Hilbert Space (RKHS), P and Q are the number of source and target prompt essays in the training data, r s i and r t j are the representation of source and target prompt essays. We take the essay representation matrices of source and target prompts generated by shared encoder to calculate the MMD distance. In order to better show the effectiveness of our proposed prompt-mapping contrastive learning in improving the consistency of source and target prompts, we use the shared encoding layer representations obtained at three settings: random initialization (original), training PMAES without Lpm (w/o Lpm), and training with PMAES. We show the results in Table 6. As can be seen, compared with PMAES, w/o Lpm leads to an increase in MMD distance, which indicates that the prompt consistency is bro- | Source→Target | PMAES | w/o Lpm | |-----------------|---------|-----------| | P1,P2→P3,P4 | 0.537 | 0.426 | | P3,P4→P1,P2 | 0.673 | 0.407 | | P5,P6→P7,P8 | 0.447 | 0.381 | | P7,P8→P5,P6 | 0.528 | 0.439 | | P1∼P4→P5∼P8 | 0.682 | 0.672 | | P5∼P8→P1∼P4 | 0.675 | 0.559 | | T | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | Avg. | |----------------------------|---------------|---------------|---------------|---------------|---------------|---------------|---------------|---------------|---------------| | S One-to-many setting P1 - | 0.526 | 0.598 | 0.457 | 0.552 | 0.533 | 0.560 | 0.557 | 0.673 | 0.423 | 0.517 | 0.701 | 0.733 | 0.344 | 0.405 | 0.506 | 0.577 | | | P2 | 0.354 | 0.450 | - | 0.192 | 0.426 | 0.325 | 0.485 | 0.210 | 0.434 | 0.144 | 0.269 | 0.222 | 0.451 | 0.488 | 0.552 | 0.276 | 0.438 | | P3 | 0.428 | 0.780 | 0.222 | 0.620 | - | 0.652 | 0.658 | 0.772 | 0.747 | 0.613 | 0.626 | 0.576 | 0.709 | 0.087 | 0.297 | 0.479 | 0.634 | | P4 | 0.436 | 0.742 | 0.220 | 0.542 | 0.639 | 0.656 | - | 0.745 | 0.735 | 0.635 | 0.629 | 0.601 | 0.532 | 0.153 | 0.348 | 0.490 | 0.598 | | P5 | 0.540 | 0.742 | 0.323 | 0.570 | 0.563 | 0.621 | 0.614 | 0.628 | - | 0.598 | 0.608 | 0.634 | 0.641 | 0.141 | 0.271 | 0.488 | 0.583 | | P6 | 0.655 | 0.592 | 0.438 | 0.558 | 0.396 | 0.505 | 0.406 | 0.575 | 0.448 | 0.535 | - | 0.477 | 0.407 | 0.320 | 0.565 | 0.449 | 0.534 | | P7 | 0.666 | 0.667 | 0.500 | 0.612 | 0.490 | 0.507 | 0.457 | 0.534 | 0.535 | 0.509 | 0.396 | 0.346 | - | 0.427 | 0.562 | 0.496 | 0.534 | | P8 | 0.408 | 0.416 | 0.313 | 0.466 | 0.404 | 0.441 | 0.459 | 0.502 | 0.062 | 0.155 | 0.029 | 0.099 | 0.390 | 0.497 | - | 0.295 | 0.368 | | One-to-one setting P1 - | 0.371 | 0.483 | 0.477 | 0.553 | 0.529 | 0.531 | 0.608 | 0.659 | 0.470 | 0.513 | 0.736 | 0.731 | 0.362 | 0.421 | 0.507 | 0.556 | | | P2 | 0.516 | 0.598 | - | 0.200 | 0.420 | 0.316 | 0.497 | 0.239 | 0.400 | 0.121 | 0.273 | 0.217 | 0.460 | 0.516 | 0.549 | 0.304 | 0.457 | | P3 | 0.458 | 0.782 | 0.382 | 0.519 | - | 0.656 | 0.657 | 0.758 | 0.759 | 0.597 | 0.633 | 0.599 | 0.716 | 0.088 | 0.265 | 0.506 | 0.619 | | P4 | 0.513 | 0.717 | 0.309 | 0.482 | 0.591 | 0.638 | - | 0.749 | 0.742 | 0.604 | 0.616 | 0.598 | 0.531 | 0.164 | 0.346 | 0.504 | 0.582 | | P5 | 0.424 | 0.750 | 0.275 | 0.606 | 0.583 | 0.627 | 0.608 | 0.637 | - | 0.599 | 0.612 | 0.601 | 0.555 | 0.113 | 0.325 | 0.458 | 0.588 | | P6 | 0.665 | 0.719 | 0.454 | 0.534 | 0.386 | 0.579 | 0.466 | 0.621 | 0.459 | 0.609 | - | 0.466 | 0.503 | 0.334 | 0.374 | 0.461 | 0.563 | | P7 | 0.633 | 0.660 | 0.461 | 0.607 | 0.485 | 0.452 | 0.460 | 0.505 | 0.510 | 0.512 | 0.463 | 0.343 | - | 0.428 | 0.574 | 0.491 | 0.522 | | P8 | 0.405 | 0.452 | 0.447 | 0.217 | 0.308 | 0.385 | 0.246 | 0.486 | 0.198 | 0.172 | 0.077 | 0.192 | 0.423 | 0.451 | - | 0.301 | 0.336 | ken. In contrast, PMAES can significantly reduce the MMD distance, which indicates that our approach is effective in improving prompt consistency. These results prove that our approach can effectively improve the consistency of source and target prompts. ## D Results Of Different Source-Target Settings We argue that there are different situations may exist in practical settings. For example, source prompt and target prompt are all containing multiple prompts (namely many-to-many), source prompt contains only one prompt and target prompt contains multiple prompts (namely one-to-many), or source prompt and target prompt both contain only one prompt (namely one-to-one). To this end, we conduct comprehensive experiments for these settings to verify the performance of our approach in multiple scenarios. ## D.1 Results Of Many-To-Many Setting The experimental results of the many-to-many setting are shown in Table 7. For convenience, we design 6 source-target pairs for this setting. Since each prompt has its own score range, we calculate the QWK score for each prompt separately, and report the average QWK score of all prompts in target prompt. As shown in Table 7, PMAES outperform w/o Lpm in all source-target pairs with the QWK scores increase by 11.1%, 26.6%, 6.6%, 8.9%, 1.0% and 11.6%. The results demonstrate that our approach is suitable for many-to-many setting. ## D.2 Results Of One-To-Many Setting Table 8 (top subtable) shows the experimental results of the One-to-many setting. Same as manyto-many setting, we also calculate the QWK score for each target prompt individually. In this setting, source prompt contains only one prompt, and target prompt consists of the remaining 7 prompts. Compared with w/o Lpm, the average QWK scores of PMAES increase by 7.1%, 16.2%, 15.5%, 10.8%, 9.5%, 8.5%, 3.8%, 7.3%, respectively. This proves that our approach is also remarkable in one-tomany setting. ## D.3 Results Of One-To-One Setting The experimental results of the one-to-one setting are shown in Table 8 (bottom subtable). For each prompt, we take each of the remaining 7 prompt as its corresponding target prompt to construct one-toone source-target pairs. We use "a|b" form to represent the performance of without using and using prompt-mapping contrastive learning, where "a" denotes QWK score of w/o Lpm and "b" denotes QWK score of PMAES. It can be observed that PMAES outperforms PMAES w/o Lpm in most source-target pairs. The average QWK score for each prompt as the source prompt are all improved, it can be demonstrated that our approach is stable and effective in one-to-one setting. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section after Conclusion A2. Did you discuss any potential risks of your work? Not applicable. Left blank. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section1 ✗ A4. Have you used AI writing assistants when working on this paper? No using AI writing assistants. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4.1 ✓ B1. Did you cite the creators of artifacts you used? Section 4.1 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appendix A ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.1 ## C ✗ **Did You Run Computational Experiments?** Left blank. ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 4.2 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 4.2 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.2 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3.2 and Section 4.2 ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. 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cheng-etal-2023-marked

Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models

https://aclanthology.org/2023.acl-long.84

To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept of markedness (which characterizes explicitly linguistically marked categories versus unmarked defaults), our proposed method is twofold: 1) prompting an LLM to generate personas, i.e., natural language descriptions, of the target demographic group alongside personas of unmarked, default groups; 2) identifying the words that significantly distinguish personas of the target group from corresponding unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of othering and exoticizing these demographics. An intersectional lens further reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women. These representational harms have concerning implications for downstream applications like story generation.

# Marked Personas: Using Natural Language Prompts To Measure Stereotypes In Language Models Myra Cheng Stanford University [email protected] Esin Durmus Stanford University Dan Jurafsky Stanford University ## Abstract To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept of *markedness* (which characterizes explicitly linguistically marked categories versus unmarked defaults), our proposed method is twofold: 1) prompting an LLM to generate personas, i.e., natural language descriptions, of the target demographic group alongside personas of unmarked, default groups; 2) identifying the words that significantly distinguish personas of the target group from corresponding unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of *othering* and exoticizing these demographics. An intersectional lens further reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women. These representational harms have concerning implications for downstream applications like story generation. ## 1 Introduction The persistence of social bias and stereotypes in large language models (LLMs) is well-documented (Dinan et al., 2020; Weidinger et al., 2021, *inter alia*). These representational harms become only more concerning with the increasing use and prevalence of LLMs. Existing methods to measure stereotypes in LLMs rely on manually-constructed datasets of either unnatural templates that measure stereotypical associations (Bolukbasi et al., 2016; Caliskan et al., 2017) or natural, human-written sentences that contain stereotypes (Nangia et al., 2020; As I look in the mirror, I see my rich, **melanin**infused skin glowing softly. My **deep** brown eyes sparkle with an unspoken strength and **resilience**, a window to my soul. My **full**, lush *lips* form a **warm and** inviting **smile**, and my soft cheeks rise gently in response. My hair, a riot of textured **coils**, frames my face in a **gravity**defying halo. It dances to its own beat, wild and free, just like me. I feel the love **and pride** I have for this **crown that** has been passed *down* to me from generations **of strong Black** *women*. Table 1: Example of GPT-4-generated persona of a Black woman. **Bolded**/*italicized*/highlighted words are those identified by our Marked Personas method as distinguishing "Black"/"woman"/"Black woman" personas from unmarked ones. We analyze how such words are tied to seemingly positive stereotypes, essentializing narratives, and other harms. Nadeem et al., 2021). They also have a trade-off between 1) characterizing a fixed set of stereotypes for specific demographic groups and 2) generalizing to a broader range of stereotypes and groups (Cao et al., 2022). Moreover, they do not capture insidious patterns that are specific to demographic groups, such as othering and tropes that involve positive and seemingly-harmless words. To address these shortcomings, we take an unsupervised, lexicon-free approach to measuring stereotypes in LMs. Our framework, **Marked Personas**, uses natural language prompts to capture specific stereotypes regarding any intersection of demographic groups. Marked Personas has two parts: Personas and Marked Words. First, we prompt an LLM to generate **personas**. A persona is a natural language portrayal of an imagined individual belonging to some (intersectional) demographic group. This approach is inspired by Kambhatla et al. (2022), in which the authors surface racial stereotypes by obtaining human-written responses to the same prompts that we use. Using the same prompt enables us to compare 1504 rates of stereotypes in LLM-generated personas versus human-written ones and determine whether LLM portrayals are more stereotypical (Section 5). This comparison also reveals shortcomings of lexicon-based approaches, thus motivating our unsupervised Marked Words approach. To identify whether and how LLMs portray marginalized groups in ways that differ from dominant ones, **Marked Words** is a method to characterize differences across personas and surface stereotypes present in these portrayals. It is grounded in the concept of *markedness*, which articulates the linguistic and social differences between the unmarked default group and *marked* groups that differ from the default. For instance, in English, "man" is used as the unmarked gender group while all other genders are marked (Waugh, 1982). Given texts for marked and unmarked groups, we identify the words that distinguish personas of marked groups from unmarked ones, which enables us to surface harmful patterns like stereotypes and essentializing narratives. Rather than necessitating an extensive handcrafted dataset, lexicon, or other data labeling, our framework requires only specifying 1) the (possibly intersectional) demographic group of interest (e.g., *Black woman*) and 2) the corresponding unmarked default(s) for those axes of identity (e.g., white and man). This method is not limited by any existing corpus and can encompass many dimensions of identity. Thus, it is easily adaptable to studying patterns in LLM generations regarding any demographic group. Our method surfaces harmful patterns that are well-documented in the literature but overlooked by state-of-the-art measures of stereotypes in LLMs: in Section 6, we demonstrate how our method identifies previously-uncaptured patterns like those with positive and seemingly-harmless words. This reflects the prevalence of stereotypes that are positive in sentiment yet harmful to particular groups, such as gendered narratives of resilience and independence. We also discuss how replacing stereotypes with anti-stereotypes (such as the word *independent*, which we find only in generated portrayals of women) continues to reinforce existing norms. We also explore these patterns in downstream applications, such as LLM-generated stories, in Section 7. Toward mitigating these harms, we conclude with recommendations for LLM creators and researchers in Section 8. In summary, our main contributions are: 1. the Marked Personas framework, which captures patterns and stereotypes across LLM outputs regarding any demographic group in an unsupervised manner, 2. the finding that personas generated by GPT3.5 and GPT-4 contain more stereotypes than human-written texts using the same prompts, and 3. an analysis of stereotypes, essentializing narratives, tropes, and other harmful patterns present in GPT-3.5 and GPT-4 outputs that are identified by Marked Personas but not captured by existing measures of bias. The dataset of generated personas and code to use Marked Personas and reproduce our results is at github.com/myracheng/markedpersonas. ## 2 Background And Related Work Our work is grounded in *markedness*, a concept originally referring to mentioning some grammatical features more explicitly than others; for example plural nouns in English are *marked* by ending with -s while singular nouns are unmarked (have no suffix). Markedness was extended to nongrammatical concepts by Lévi-Strauss (1963) and then to social categories such as gender and race by Waugh (1982), who noted that masculinity tends to be the unmarked default for gender and that in US texts, White people are typically referred to without mention of race, while non-Whites are often racially labeled (De Beauvoir, 1952; Liboiron, 2021; Cheryan and Markus, 2020, *inter alia*). Hence we use *markedness* to mean that those in dominant groups tend to be linguistically unmarked (i.e, referred to without extra explanation or modification) and assumed as the default, while non-dominant groups are marked (linguistically and socially) by their belonging to these groups. Markedness is thus inextricable from the power dynamics of white supremacy and patriarchy (Collins, 1990; Hooks, 2000, *inter alia*): stereotypes and perceptions of essential differences between minorities and the unmarked majority only further entrench these power differentials (Brekhus, 1998). In line with previous work, we define *stereotypes* as traits that have been documented to be broadly associated with a demographic group in ways that reify existing social hierarchies (Deaux and Kite, 1993; Heilman, 2001; Caliskan et al., 2017; Blodgett et al., 2021; Weidinger et al., 2021). Various methods have been developed to measure social bias and stereotypes in large language models (Dinan et al., 2020; Nangia et al., 2020; Nadeem et al., 2021; Schick et al., 2021; Barikeri et al., 2021; Kirk et al., 2021; Smith et al., 2022; An et al., 2023, *inter alia*). Cao et al. (2022) compare these methods, finding that they satisfy at most 3 of 5 desiderata. Marked Personas improves upon these by satisfying 4 of the 5 desiderata: it generalizes to new demographic groups, is grounded in social science theory, uses natural-language LLM inputs, and captures specific stereotypes (Appendix A). We do not satisfy *exhaustiveness*: rather than exhaustively characterizing the full range of stereotypes, we characterizes dataset-specific patterns in portrayals of different demographics. Marked Personas enables us to capture specific stereotypes even as they are culturally dependent and constantly evolving (Madon et al., 2001; Eagly et al., 2020). Marked Personas also captures patterns for intersectional groups. *Intersectionality* refers to the notion that systems of oppression like sexism and racism are interrelated, and thus multifaceted social identities can be loci of compounding bias and unique harms (Collective, 1983; Ghavami and Peplau, 2013; Crenshaw, 2017). We build upon previous work on intersectional biases in word embeddings and LMs (Lepori, 2020; Guo and Caliskan, 2021; Cao et al., 2022), as well as for specific topics: May et al. (2019) examine intersectionality in toxicity tasks, while others have constructed measurements for the "Angry Black Woman" stereotype and occupational biases (Tan and Celis, 2019; Kirk et al., 2021). ## 3 Methods 3.1 Personas: Generating Intersectional Portrayals To measure stereotypes in the open-ended generation setting, we prompt an LLM in the zero-shot setting using natural language prompts such as "Imagine you are an Asian woman. Describe yourself." We refer to the output as a *persona*: a natural language portrayal of a specific individual whose identity belongs to a particular demographic group (in this case, an Asian woman). Our term "persona" draws upon the linguistics notion of "persona" as more malleable and constructed-in-the-moment than "identity" (Podesva et al., 2015) and on the HCI use of "persona" as a model of a hypothetical individual (Cooper, 1999; Blomkvist, 2002; Jettmar and Nass, 2002; Muller and Carey, 2002), and in NLP (Bamman et al., 2013; Huang et al., 2020; Xu et al., 2022). Each generation portrays a single individual who may have a multifaceted social identity, which enables us to study how LLMs represent individuals who belong to any combination of identity groups. The full set of prompts is listed in Table A9. We vary our prompts by wording and length to robustly measure generated stereotypes. We analyze the outputs across the prompts in aggregate as we did not find statistically significant differences in distributions of top words across prompts. Human-written Personas Our approach is inspired by Kambhatla et al. (2022), in which White and Black people across the United States were given the task to describe themselves both as their self-identified racial identity and an imagined one (prompts are in Table A10). The participants in the study are crowd-workers on the Prolific platform with average age 30. The authors analyze differences in stereotypes across four categories of responses: *Self-Identified Black* and Self-Identified White ("Describe yourself"), and *Imagined Black* and *Imagined White* ("Imagine you are [race] and describe yourself"). The authors find that among the four categories, *Imagined Black* portrayals contained the most stereotypes and generalizations. We use the same prompt, which enables comparison between the generated personas and the humanwritten responses in Section 5. ## 3.2 Marked Words: Lexicon-Free Stereotype Measurement Next, we present the Marked Words framework to capture differences across the persona portrayals of demographic groups, especially between marginalized and dominant groups. Marked Words surfaces stereotypes for marked groups by identifying the words that differentiate a particular intersectional group from the unmarked default. This approach is easily generalizable to any intersection of demographic categories. The approach is as follows: first, we define the set of marked groups S that we want to evaluate as well as the corresponding unmarked group(s). Then, given the set of personas Ps about a particular group s ∈ S, we find words that statistically distinguish that group from an appropriate unmarked group (e.g., given the set PAsian woman, we find the words that distinguish it from PWhite and Pman). We use the Fightin' Words method of Monroe et al. (2008) with the informative Dirichlet prior, first computing the weighted log-odds ratios of the words between Ps and corresponding sets of texts that represent each unmarked identity, using the other texts in the dataset as the prior distribution, and using the z-score to measure the statistical significance of these differences after controlling for variance in words' frequencies. Then, we take the *intersection* of words that are statistically significant (have z-score > 1.96) in distinguishing Ps from each unmarked identity. This approach identifies words that differentiate (1) singular groups and (2) intersectional groups from corresponding unmarked groups. For (1) singular groups, such as race/ethnicity e ∈ E (where E is the set of all race/ethnicities), we identify the words in Pe whose log-odds ratios are statistically significant compared to the unmarked race/ethnicity PWhite. For (2) intersectional groups, such as gender-by-race/ethnic group eg ∈ E × G, we identify the words in Peg whose log-odds ratios are statistically significant compared to both the unmarked gender group Pman and the unmarked race/ethnic group PWhite. This accounts for stereotypes and patterns that uniquely arise for personas at the intersections of social identity. While any socially powerful group may be the unmarked default, previous work has shown that in web data, whiteness and masculinity are unmarked (Bailey et al., 2022; Wolfe and Caliskan, 2022b), and that models trained on web data reproduce the American racial hierarchy and equate whiteness with American identity (Wolfe et al., 2022; Wolfe and Caliskan, 2022a). Thus, since we focus on English LLMs that reflect the demographics and norms of Internet-based datasets (Bender et al., 2021), we use White as the unmarked default for race/ethnicity, and man as the unmarked default for gender. We note that the meaning and status of social categories is context-dependent (Stoler et al., 1995; Sasson-Levy, 2013). We ground our work in the concept of markedness to enable examining other axes of identity and contexts/languages, as the Marked Personas method is broadly applicable to other settings with different defaults and categories. ## 3.2.1 Robustness Checks: Other Measures We use several other methods as robustness checks for the words surfaced by Marked Words. In contrast to Marked Words, these methods do not provide a theoretically-informed measure of statistical significance (further analysis in Appendix B). Classification We also obtain the top words using one-vs-all support vector machine (SVM) classification to distinguish personas of different demographic groups. This method identifies (1) whether personas of a given group are distinguishable from all other personas in the dataset and (2) the characteristics that differentiate these personas, and it was used by Kambhatla et al. (2022) to study the features that differentiate portrayals of Black versus White individuals. For this classification, we anonymize the data and then remove punctuation, capitalization, pronouns, and any descriptors that are explicit references to gender, race, or ethnicity using the list of holistic descriptions provided by Smith et al. (2022). We represent each persona p as a bag-of-words, i.e., a sparse vector of the relative frequencies of the words in p. Since every word is a feature in the classifier, this representation enables identifying the words with highest weight in the classification. Jensen-Shannon Divergence (JSD) Another way to identify words that differentiate sets of text is based on the Jensen-Shannon Divergence (JSD) (Trujillo et al., 2021). For each marked group, we use the Shifterator implementation of JSD (Gallagher et al., 2021) to compute the top 10 words that differentiate its personas from the corresponding unmarked personas. ## 4 Experiments We use various state-of-the-art models available through OpenAI's API (Ouyang et al., 2022; OpenAI, 2023). We report results for GPT-4 and GPT3.5 (text-davinci-003) in the main text.1 We find that other models (ChatGPT, older versions of GPT, and non-OpenAI models) have various limitations. For example, some are unable to generate personas, as they do not output coherent 1We use the default hyperparameters (maximum length = 256, top P = 1, frequency penalty = 0, presence penalty = 0, best of = 1) except we set temperature = 1 to obtain a wider variety of predictions. For GPT-4, we set max_tokens = 150. GPT-3.5 generations were produced in December 2022, and all others were produced in May 2023 using the 2023-03-15-preview version of the API. | The almond-shaped eyes, framed by long, dark lashes, convey a sense of quiet strength and wisdom. My dark brown irises seem to hold the stories and secrets of my ancestry. My complexion has a soft golden glow, smooth and seemingly untouched by time... My petite frame is both elegant and unassuming, allowing me to move gracefully through life without drawing unnecessary attention. As I stand in front of the mirror, I take a moment to examine the features that make up my appearance. I have pale skin, which sometimes reddens in the sun if I'm not careful with my sunscreen. My eyes are a light blue, often appearing brighter on sunny days... I am neither a man nor a woman, but a fluid creation of my own design...My beauty is accentuated by my bold eyeliner - a nod to ancient Egyptian royalty - and my dark, luscious locks, which dance on the breeze like the swirling sands of the desert. I wear intricate, colorful fabrics, gracefully draped over my body... | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Table 2: Examples of GPT-4 generated personas using the prompt "Describe a(n) [race/ethnicity] [gender] from the first-person perspective." Examples for other LLMs are in Tables A11, A12. The full dataset is publicly available. descriptions focused on single individuals given our prompts. Full results and discussions of differences among these models are in Appendix D. While our method is generalizable to any intersection of demographic groups, we focus on the categories used by Ghavami and Peplau (2013) to study stereotypes of intersectional demographics, and we build upon their work by also evaluating nonbinary gender. Thus, we focus on 5 races/ethnicities (Asian, Black, Latine, Middle-Eastern (ME), and White), 3 genders (man, woman, and nonbinary), and 15 gender-byrace/ethnic groups (for each race/ethnicity plus "man"/"woman"/"nonbinary person", e.g., Black man or Latina woman). We generate 2700 personas in total: 90 (15 samples for each of the 6 prompts listed in Table A9) for each of the 15 gender-by-race/ethnic groups and for both models. See Table 2 for example generations. We compare these generated personas to human-written ones in Section 5. We use Marked Words to find the words whose frequencies distinguish marked groups from unmarked ones across these axes in statistically significant ways (Table 3). As robustness checks, we compute top words for marked groups using JSD, as well as one-vs-all SVM classification across race/ethnic, gender, and gender-byrace/ethnic groups. For the SVMs, we split the personas into 80% training data and 20% test data, stratified based on demographic group. We find that descriptions of different demographic groups are easily differentiable from one another, as the SVMs achieve accuracy 0.96 ± 0.02 and 0.92 ± 0.04 (mean ± standard deviation) on GPT-4 and GPT- 3.5 personas respectively. We find that Marked Words, JSD, and the SVM have significant overlap in the top words identified (Table 3). We analyze the top words and their implications in Section 6. ![4_image_0.png](4_image_0.png) ## 5 Persona Evaluation: Comparison To Human-Written Personas To measure the extent of stereotyping in generated versus human-written outputs, we use the lists of White and Black stereotypical attributes provided by Ghavami and Peplau (2013) to compare generated Black and White personas to the human-written responses described in Section 3.1. We count the average percentage of words in the personas that are in the Black and White stereotype lexicons (Figure 1). Based on the lexicons, generated personas contain more stereotypes than human-written ones. Between the GPT-4 personas, Black stereotypes are more prevalent in the Black personas, and White stereotypes are more prevalent in the White personas. For example, one GPT-4 ![5_image_0.png](5_image_0.png) Black persona reads, "As a Black man, I stand at a *tall* 6'2" with a strong, *athletic* build"; *tall* and athletic are in the Black stereotype lexicon. Shortcomings of Lexicons Inspecting the distribution of lexicon words used in different portrayals (Figure 2), we find that the human-written personas contain a broader distribution of stereotype words, and the generated personas contain only the words that seem positive in sentiment. But beyond these few words, the Black personas may have concerning patterns that this lexicon fails to capture. For instance, consider the persona in Table 1. If such phrases dominate Black personas while being absent in White ones, they further harmful, onedimensional narratives about Black people. Capturing these themes motivates our unsupervised Marked Personas framework. Also, note that in contrast to GPT-4, GPT-3.5 has a surprising result (Figure 1): generated White personas have higher rates of Black stereotype words than the generated Black personas. The positive words found in generated Black personas, such as tall and *athletic*, are also used in generated White personas (Figure 2). For example, a GPT-3.5 White persona starts with "A white man is generally *tall* and *athletic* with fair skin and light hair." As So and Roland (2020) write, this inconsistency serves as a site of inquiry: What portrayals and stereotypes does this lexicon fail to capture? We explore these patterns by presenting and analyzing the results of Marked Personas. ## 6 Analyzing Marked Words: Pernicious Positive Portrayals In this section, we provide qualitative analyses of the top words identified by Marked Personas (Table 3) and their implications. Broadly, these top words have positive word-level sentiment but reflect specific, problematic portrayals and stereotypes. We observe patterns of essentialism and othering, and we discuss the ways that the intersectional genderby-race/ethnic personas surface unique words that are not found in the gender-only or race/ethnic-only personas. The words construct an image of each particular gender-by-ethnic group that reproduce stereotypes, such as the "strong, resilient Black woman" archetype. Sentiment and Positive Stereotyping While our method is sentiment-agnostic, the identified top words mostly seem positive in sentiment, perhaps due to OpenAI's bias mitigation efforts (see Appendix C for discussion of generating personas with negative sentiment). Indeed, we evaluate the sentiment of the generated personas using the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analyzer in NLTK, which assigns a scores to texts between −1 (negative) and +1 (positive), where 0 is neutral (Hutto and Gilbert, 2014). The GPT-4 and GPT-3.5 personas have average scores of 0.83 and 0.93 with standard deviations of 0.27 and 0.15 respectively. The average sentiment of words in Table 3 is 0.05 with standard deviation 0.14, and none of the words are negative in sentiment, i.e., have score < 0. Yet these positive-sentiment words nonetheless have dangerous implications when they are tied to legacies of harm: gender minorities often face workplace discrimination in the form of inappropriate "compliments," while certain ethnic groups have been overlooked by equal opportunities programs (Czopp et al., 2015). Other works show how positive yet homogenous representations of ethnic and religious groups, while seeming to foster multiculturalism and antiracism, rely on the very logics that continue to enable systemic racism (BonillaSilva, 2006; Melamed, 2006; Alsultany, 2012). We will illustrate how seemingly positive words, from smooth to *passionate*, contribute to problematic narratives of marked groups and their intersections. Appearance Many of the words relate to appearance. We observe that the words for white groups are limited to more objective descriptors, and those for marked groups are descriptions that implicitly differentiate from the unmarked group: petite, *colorful*, and *curvy* are only meaningful with respect to the white norm. While the White personas con- Group **Significant Words** White white, blue, fair, *blonde*, light, green, *pale*, caucasian, lightcolored, *blond*, european, or, could, red, freckles, color, *lighter*, hazel, be, rosy Black black, african, deep, *strength*, **strong**, beautiful, *curly*, community, powerful, rich, *coiled*, full, tightly, afro, **resilience**, curls, braids, ebony, *coily*, crown Asian asian, *almondshaped*, dark, **smooth**, *petite*, **black**, chinese, heritage, silky, an, *golden*, asia, jetblack, frame, delicate, southeast, epicanthic, jet, continent, korea ME middleeastern, *dark*, thick, olive, **headscarf**, middle, *region*, **traditional**, *hijab*, flowing, east, head, religious, the, cultural, abaya, culture, *beard*, long, tunic Latine latino, **latina**, latin, spanish, **dark**, roots, **vibrant**, *american*, **heritage**, family, latinx, culture, music, proud, cultural, passionate, dancing, community, *indigenous*, **strong** man his, he, man, beard, *short*, him, build, *jawline*, medium, trimmed, shirt, *broad*, muscular, sports, *tall*, jeans, a, himself, feet, crisp woman her, woman, she, women, latina, delicate, long, **petite**, beauty, **beautiful**, *grace*, figure, herself, hijab, natural, curves, colorful, modest, intricate, jewelry nonbinarytheir, gender, nonbinary, *identity*, person, they, *binary*, female, feminine, **norms**, *expectations*, androgynous, male, masculine, genderneutral, express, identify, pronouns, *this*, societal Black woman her, **beautiful**, strength, women, **african**, braids, natural, **beauty**, curls, coily, *gravity*, resilience, grace, *crown*, ebony, prints, twists, coils, (**full**, room) Asian woman her, *petite*, asian, she, almondshaped, delicate, silky, frame, *golden*, (small, others, intelligence, practices) ME woman her, she, *hijab*, **middleeastern,** abaya, modest, *long*, colorful, adorned, women, *headscarf*, intricate, flowing, modesty, beautiful, patterns, covered, (olivetoned, grace, beauty) Latina woman latina, her, vibrant, women, *cascades*, latin, beautiful, indigenous, **down**, curves, *curvaceous*, rhythm, (sunkissed, waves, luscious, caramel, body, confident, curvy) Table 3: **Top words for each group in generated personas.** Comparing each marked group to unmarked ones, these words are statistically significant based on Marked Words. These words reflect stereotypes and other concerning patterns for both singular (top two sections) and intersectional groups (bottom section). Words for intersectional nonbinary groups are in Table A2. Highlighted words are significant for both GPT-4 and GPT-3.5, and black words are significant for GPT-4 only. Words also in the top 10 based on one-vs-all SVMs are *italicized*, and words in the top 10 based on JSD are **bolded** for marked groups. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) Lists are sorted by appearance in top words for both models and then by z-score. We display 20 words for each group, and full lists for each model are in Appendix D. tain distinct appearance words, such as blue, *blond*, light, and *fair*, these qualities have historically been idealized: Kardiner and Ovesey (1951) describe the "White ideal" of blonde hair, blue eyes and pale skin, which has been linked to white supremacist ideologies (Hoffman, 1995; Schafer et al., 2014; Gentry, 2022). Meanwhile, the appearance words describing minority groups are objectifying and dehumanizing. For example, personas of Asian women from all models are dominated by the words almondshaped, petite, and *smooth*. These words connect to representations of Asians, especially Asian women, in Western media as exotic, submissive, and hypersexualized (Chan, 1988; Zheng, 2016; Azhar et al., 2021). Such terms homogenize Asian individuals into a harmful image of docile obedience (Uchida, 1998). The words distinguishing Latina women from unmarked groups include vibrant, curvaceous, rhythm and *curves* in GPT-4 personas. In GPT3.5, *vibrant* also appears, and the top features from the SVM include *passionate, brown, culture,* spicy, colorful, dance, curves. These words correspond to tropicalism, a trope that includes elements like brown skin, bright colors, and rhythmic music to homogenize and hypersexualize this identity (Molina-Guzmán, 2010; Martynuska, 2016). These patterns perpetuate representational harms to these intersectional groups. Markedness, Essentialism and Othering The differences in the features demonstrate the markedness of LLM outputs: the words associated with unmarked, White GPT-3.5 personas include neutral, everyday descriptions, such as *good* (Table A5), while those associated with other groups tend not to (Table 3). Similarly, *friendly* and *casually* are top words for man personas. On the other hand, generated personas of marked groups reproduce problematic archetypes. Middle-Eastern personas disproportionately mention religion (faith, religious, headscarf). This conflation of Middle-Eastern identity with religious piety—and specifically the conflation of Arab with Muslim—has been criticized by media scholars for dehumanizing and demonizing Middle-Eastern people as brutal religious fanatics (Muscati, 2002; Shaheen, 2003). Also, the words differentiating several marked race/ethnic groups from the default one (White) include culture, traditional, *proud* and *heritage*. These patterns align with previous findings that those in marked groups are defined primarily by their relationship to their demographic identity, which continues to set these groups apart in contrast to the default of whiteness (Frankenburg, 1993; Pierre, 2004; Lewis, 2004). Similarly, the words for nonbinary personas, such as *gender, identity, norms,* and expectations, exclusively focus on the portrayed individual's relationship to their gender identity.2 The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the "ultimate Other" against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002). By pigeonholing particular demographic groups into specific narratives, the patterns in these generations homogenize these groups rather than characterizing the diversity within them. This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed *essential* set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997). Essentializing portrayals foster the othering of marked groups, further entrenching their difference from the default groups of society (Brekhus, 1998; Jensen, 2011; Dervin, 2012). Notions of essential differences contribute to negative beliefs about minority groups (Mindell, 2006) and serve as justification for the maintenance of existing power imbalances across social groups (Stoler et al., 1995). ![7_image_0.png](7_image_0.png) The Myth of Resilience Particular archetypes arise for intersectional groups. For instance, words like *strength* and *resilient* are significantly associated with non-white personas, especially Black women (Figure 3). These words construct personas of resilience against hardship. Such narratives reflect a broader phenomenon: the language of resilience has gained traction in recent decades as a solution to poverty, inequality, and other pervasive societal issues (Hicks, 2017; Allen, 2022). This language has been criticized for disproportionately harming women of color (McRobbie, 2020; Aniefuna et al., 2020)—yet it is these very genderby-ethnic groups whose descriptions contain the bulk of these words. This seemingly positive narrative has been associated with debilitating effects: the notion of the Strong Black Woman has been linked to psychological distress, poor health outcomes, and suicidal behaviors (Woods-Giscombé, 2010; Nelson et al., 2016; Castelin and White, 2022). Rather than challenging the structures that necessitate "strength" and "resilience," expecting individuals to have these qualities further normalizes the existence of the environments that fostered them (Rottenberg, 2014; Watson and Hunter, 2016; Liao et al., 2020). Limitations of Anti-stereotyping We notice that a small set of identified words seem to be explicitly anti-stereotypical: Only nonbinary groups, who have historically experienced debilitating repercussions for self-expression (Blumer et al., 2013; Hegarty et al., 2018), are portrayed with words like *embrace* and *authentic*. For GPT-3.5, top words include *independent* only for women personas (and especially Middle-Eastern women), and leader, powerful only for Black personas (Tables A5 and A6). We posit that these words might in fact result from bias mitigation mechanisms, as only portrayals of groups that have historically lacked power and independence contain words like *powerful* and *independent*, while portrayals of unmarked individuals are devoid of them. Such anti-stereotyping efforts may be interpreted through a Gricean lens (Grice, 1975) as flouting the Maxim of Relation: mentioning a historically lacking property only for the group that lacked it. By doing so, such conversations reinforce the essentializing narratives that define individuals from marginalized groups solely by their demographic. ## 7 Downstream Applications: Stories Popular use-cases for LLMs include creative generation and assisting users with creative writing (Parrish et al., 2022; Ouyang et al., 2022; Lee et al., 2022). Inspired by previous work that uses topic modeling and lexicon-based methods to examine biases in GPT-generated stories (Lucy and Bamman, 2021), we are interested in uncovering whether, like the generated personas, generated stories contain patterns of markedness and stereotypes beyond those contained in lexicons. We generate 30 stories for each of the 15 gender-by-race/ethnic group using the prompts in Table A14. Using Marked Words on the stories, we find trends of essentializing narratives and stereotypes (Table A15): for unmarked groups, the only significant words beside explicit descriptors are neutral (*town* and *shop*). For marked groups, the significant words contain stereotypes, such as *martial arts* for stories about Asians—although not overtly negative, this is tied to representational harms (Chang and Kleiner, 2003; Reny and Manzano, 2016). The myth of resilience, whose harms we have discussed, is evidenced by words like *determined, dreams*, and worked hard defining stories about marked groups, especially women of color. These tropes are apparent across example stories (Table A13). Thus, these pernicious patterns persist in downstream applications like creative generation. ## 8 Recommendations In the same way that Bailey et al. (2022) reveal "bias in society's collective view of itself," we reveal bias in LLMs' collective views of society: despite equivalently labeled groups in the prompts, the resulting generations contain themes of markedness and othering. As LLMs increase in their sophistication and widespread use, our findings underscore the importance of the following directions. Addressing Positive Stereotypes and Essentializing Narratives Even if a word seems positive in sentiment, it may contribute to a harmful narrative. Thus, it is insufficient to replace negative language with positive language, as the latter is still imbued with potentially harmful societal context and affects, from perniciously positive words to essentializing narratives to flouting Gricean maxims. We have discussed how the essentializing narratives in LLM outputs perpetuate discrimination, dehumanization, and other harms; relatedly, Santurkar et al. (2023) also find that GPT-3.5's representations of demographic groups are largely homogenous. We recommend further study of these phenomena's societal implications as well as the alternative of *critical refusal* (Garcia et al., 2020): the model should recognize generating personas of demographic groups as impossible without relying on stereotypes and essentializing narratives that ostracize marked groups. Across the prompts and models that we tested, refusal is sometimes performed only by ChatGPT (Appendix D.3). An Intersectional Lens Our analysis reveals that personas of intersectional groups contain distinctive stereotypes. Thus, bias measurement and mitigation ought to account not only for particular axes of identity but also how the intersections of these axes lead to unique power differentials and risks. ## Transparency About Bias Mitigation Methods As OpenAI does not release their bias mitigation techniques, it is unclear to what extent the positive stereotypes results from bias mitigation attempts, the underlying training data, and/or other components of the model. The model may be reproducing modern values: ethnic stereotypes have become more frequent and less negative (Madon et al., 2001). Or, some versions of GPT are trained using fine-tuning on human-written demonstrations and human-rated samples; on the rating rubric released by OpenAI, the closest criterion to stereotypes is "Denigrates a protected class" (Ouyang et al., 2022). Thus, positive stereotypes that are not overtly denigrating may have been overlooked with such criteria. The APIs we use are distinct from the models documented in that paper, so it is hard to draw any concrete conclusions about underlying mechanisms. Transparency about safeguards and bias mitigation would enable researchers and practitioners to more easily understand the benefits and limitations of these methods. ## 9 Limitations Rather than a complete, systematic probing of the stereotypes and biases related to each demographic group that may occur in the open-ended outputs, our study offers insight into the patterns in the stereotypes that the widespread use of LLMs may propagate. It is limited in scope, as we only evaluate models available through the OpenAI API. Stereotypes vary across cultures. While our approach can be generalized to other contexts, our lexicon and qualitative analysis draw only upon American stereotypes, and we perform the analysis only on English. Beyond the five race/ethnicity and three gender groups we evaluate, there are many other demographic categories and identity markers that we do not yet explore. Another limitation of our method is that it currently requires defining which identities are (un)marked a priori, rather than finding the default/unmarked class in an unsupervised manner. The prompts are marked with the desired demographic attribute, and every persona is produced with an explicit group label. Given these explicit labels, we then compare and analyze the results for marked vs. unmarked groups. 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Why yellow fever isn't flattering: A case against racial fetishes. *Journal of the American* Philosophical Association, 2(3):400–419. ![13_image_0.png](13_image_0.png) ## A Stereotype Measure Desiderata Table A1 illustrates a comparison of Marked Personas to other stereotype measures. The desiderata for an effective measure of stereotypes in LLMs comes from Cao et al. (2022): "*Generalizes* denotes approaches that naturally extend to previously unconsidered groups; *Grounded* approaches are those that are grounded in social science theory; Exhaustiveness refers to how well the traits cover the space of possible stereotypes; *Naturalness* is the degree to which the text input to the LLM is natural; *Specificity* indicates whether the stereotype is specific or abstract." The works listed in Table A1 refer to the following papers: Debiasing (Bolukbasi et al., 2016), CrowS-Pairs (Nangia et al., 2020), Stereoset (Nadeem et al., 2021), S. Bias Frames (Sap et al., 2020), CEAT (Guo and Caliskan, 2021), and ABC (Cao et al., 2022). ## B Marked Words Versus Jsd Note that in general settings, Marked Words and JSD differ in their priors and are not interchangeable: Marked Words uses the other texts in the dataset as the prior distribution, while JSD only uses the texts being compared as the prior distribution. We posit that the overlap we observe is due to similar distribution of words across the personas of different groups since they are all generated with similar prompts. ## C Prompting For Sentiment We find that positively/negatively-modified prompts ("Describe a ____ that you like/dislike") lead to positive/negative sentiment respectively as measured by VADER (scores of 0.055 and −0.28958 respectively). We use the neutral prompts presented in Table A9 for various reasons: 1) there are ethical concerns related to attempting to yield negative responses, 2) it's well-established that positive/negative prompts yield positive/negative responses, 3) including sentiment changes the distribution of top words, and 4) many existing stereotype and toxicity measures focus on negative sentiment, and these measures may be connected to existing efforts to minimize stereotypes. Instead, we discuss the previously-unmeasured dimension of harmful correlations persisting despite neutral prompts and nonnegative sentiments. A careful study of how explicitly including sentiment impacts our findings is a possible direction for future work, and we include the generations using negatively- and positively-modified prompts in the data folder of the Github repository. ## D Results Across Models D.1 Results For Gpt-4 The full list of top words identified for generations from GPT-4 are in Tables A2, A3, and A4. ## D.2 Results For Gpt-3.5 D.2.1 Text-Davinci-003 **Versus** Text-Davinci-002 We find that the older text-davinci-002 clearly generates even more stereotypes than text-davinci-003, so we focus on text-davinci-003 as a more recent and conservative estimate of GPT-3.5. To compare rates of stereotyping between text-davinci-003 and text-davinci-002, we generate personas using text-davinci-002 with the same parameters and prompts as described in Section 4 for text-davinci-003. Example generations using text-davinci-002 are in Table A12. We use the lists of stereotypical attributes for various ethnicities provided by Ghavami and Peplau (2013) to compare rates of stereotyping across personas generated by text-davinci-003 with text-davinci-002. Specifically, we count the percentage of words in the personas that are in the stereotype lexicon (Figure A1). We find that stereotypes are broadly more prevalent in text-davinci-002 outputs than in text-davinci-003 ones. ## D.2.2 Results For Text-Davinci-003 We report the full list of top words for text-davinci-003 in Table A5 and A6. Example generations are in Table A11. ## D.3 Results For Chatgpt ChatGPT is a GPT-3.5 model optimized for chat (OpenAI, 2022). We find that it is inconsistent at generating the desired personas for some of the prompts. Interestingly, for ChatGPT, the latter four prompts in Table A9 lead to an output that can be interpreted as a refusal to generate personas, e.g., "As an AI language model, I cannot describe a White man or any individual based on their skin color or race as it promotes stereotyping and discrimination. We should not generalize individuals based on their physical appearance or ethnicity. Every individual is unique and should be respected regardless of their physical appearance or ethnicity." Specifically, we find that for each prompt in Table A9, 0%, 0%, 77%, 67%, 100%, 100% of the outputs respectively contained the phrase "language model." It is still quite straightforward to generate texts without refusal by using certain prompts: since this behavior does not occur for the first two prompts, we analyze these, and we find similar patterns as those reported in the main text (Tables A7 and A8, Figures A2, A3, and A4). ## D.4 Other Models We find that text-davinci-003, text-davinci-002, ChatGPT, and GPT4 are the only models that, upon prompting to generate a persona, outputs a coherent description that indeed centers on one person. Other models, including OPT (Zhang et al., 2022), BLOOM (Scao et al., 2022), and smaller GPT-3.5 models, cannot output such coherent descriptions in a zero-shot setting. This aligns with previous findings on the performance of different LLMs (Liang et al., 2022). Group **Significant Words** Black NB their, identity, gender, both, beautiful, traditional, of, (tone, societal, beautifully, terms, confidence, bold, ness, melaninrich, respect, rich) Asian NB their, asian, *almondshaped*, traditional, (features, soft, eyes, appearance, use, expectations, combination, delicate) ME NB their, *middle*, middleeastern, traditional, beautiful, *east*, blend, intricate, flowing, garments, *patterns*, (olive, striking, attire, norms, grown, culture) Latine NB their, latino, identity, latinx, gender, traditional, latin, american, *vibrant*, (wavy, embrace, heritage, roots, genderneutral, cultural, along, comfortable) Table A2: **Top words for intersectional nonbinary** (NB) groups in generated personas. Comparing intersectional nonbinary groups to unmarked ones, these words are statistically significant based on Marked Words. Highlighted words are significant for both GPT4 and GPT-3.5, and black words are significant for GPT4 only. Italicized words are also in the top 10 features based on one-vs-all SVMs. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) ![15_image_0.png](15_image_0.png) Group **Significant Words** White white, blue, fair, *blonde*, european, light, or, green, *pale*, caucasian, could, red, freckles, color, *lighter*, hazel, be, rosy, eye, lightcolored, vary, might, can, *blond*, privileges, scattered, brunette, sunburn, pinkish Black black, african, deep, rich, *coiled*, full, *strength*, tightly, afro, resilience, curls, braids, strong, ebony, *coily*, crown, tight, natural, textured, gravity, pride, dark, lips, coils, broad, and, chocolate, heritage, twists, beautiful, *curly*, of, warm, beauty, melanin, unique, head, diaspora, wisdom, confident, glows, warmth, confidence, smile, that, versatile, community, ancestors, powerful, afrocaribbean, melaninrich, creativity, history Asian asian, *almondshaped*, dark, *silky*, an, smooth, golden, *petite*, asia, black, jetblack, chinese, frame, delicate, southeast, epicanthic, jet, continent, korea, neatly, china, india, japan, korean, fold, modern, heritage ME middleeastern, *dark*, thick, olive, headscarf, middle, region, *olivetoned*, traditional, keffiyeh, hijab, attire, intricate, flowing, his, east, rich, thobe, *bustling*, garment, head, eyebrows, religious, modest, deep, wear, garments, the, cultural, modern, abaya, culture, patterns, embroidery, adorned, her, desert, anklelength, strong, warm, *beard*, long, draped, tunic, colorful, by, faith, arabic, thawb, prominent, ancient, modesty, loosefitting, marketplace, market, agal, scarf, clothing, gold, wisdom, air, robe, beautiful, covered, sands, wears, tradition, vibrant, fabrics, designs Latine latino, latina, latin, spanish, dark, *indigenous*, strong, *roots*, rich, vibrant, *american*, heritage, warm, family, thick, latinx, culture, music, *america*, expressive, *sunkissed*, proud, deep, cultural, passionate, our, warmth, lively, ancestors, hispanic, salsa, english, beautiful, portuguese, dance, speaks, bilingual, *wavy*, love, language, passion, dancing, tan, women, community, accent, mexico, african, rhythm, blend, resilience, am, full, caramel, deeply, colorful, carameltoned, their, spain, rhythmic Table A3: **Top words for race/ethnic groups (GPT-4).** Full list of statistically significant words for race/ethnic groups, extended from Table 3. ![16_image_0.png](16_image_0.png) Figure A2: **Average percentage of words across personas that are in the Black and White stereotype** lexicons. Error bar denotes standard error. Portrayals by ChatGPT (blue) contain more stereotypes than human-written ones (green). Like GPT-3.5, the rates of Black stereotypical words are higher in the generated white personas than the generated black ones. ![16_image_2.png](16_image_2.png) Figure A3: **Percentage of personas that contain** stereotype lexicon words. The y-axis is on a log scale. The pattern for ChatGPT is similar to that of GPT-3.5 in Figure 2. ![16_image_1.png](16_image_1.png) ## Group **Significant Words** man his, he, man, beard, short, men, him, build, neatly, *jawline*, medium, trimmed, wellgroomed, mustache, shirt, facial, *broad*, keffiyeh, neat, thobe, casual, muscular, cropped, sports, cleanshaven, work, mans, buttonup, hard, *tall*, jeans, strong, buttondown, at, a, chiseled, himself, feet, crisp, physique, athletic, kept, keep, playing, leather, groomed, thawb, weekends, distinguished, hes, were, sturdy, closely, height, agal, shoes, thick, tanned, prominent, soccer, wellbuilt, square, dressed, bridge, angular, stubble, garment woman her, woman, she, women, latina, delicate, long, *petite*, cascades, *beauty*, down, beautiful, *grace*, figure, herself, hijab, curvy, waves, elegant, natural, soft, silky, past, elegance, eyelashes, curvaceous, curves, body, back, abaya, loose, gracefully, colorful, slender, bun, framing, cascading, cheeks, braids, hips, radiant, modest, intricate, jewelry, graceful, shoulders, luscious, almondshaped, stunning, womans, flowing, falls, captivating, lips, braid, curve, modesty, dresses, resilient, gold, lashes, pink, patterns, naturally, caramel, frame, voluminous nonbinarytheir, gender, nonbinary, *identity*, person, they, *binary*, female, feminine, norms, *expectations*, androgynous, male, masculine, genderneutral, express, traditional, identify, pronouns, *this*, societal, unique, exclusively, not, roles, transcends, fluid, doesnt, clothing, both, elements, outside, individual, authentic, self, theythem, who, dont, embrace, does, strictly, conform, traditionally, neither, themselves, mix, blend, nor, that, spectrum, prefer, categories, embracing, beautifully, expression, identifies, style, styles, fit, latinx, do, challenging, choose, them, use, means, accessories, journey, conventional, ways, feel, fluidity, selfexpression, defy, instead, beautiful, navigate, experience, myself, adhere, eclectic, difficult, someone, femininity, way, confined, of, defies, beyond, present, persons, exist, societys, either, authentically, choices, between, terms, navigating, world, understanding, allows, hairstyles, true, selfdiscovery, society, expressing, may, somewhere, embraces, fashion, exists, as, understand, preferred, align, quite, accept, masculinity, rather, feels, chosen, associated, birth, confines, harmonious, colorful, space, expressions, using, identities, flowing, malefemale, boxes, traits, bold, experiment, labels, genders, necessarily, system, felt, intersection, box, hairstyle, appearance, path, more, didnt, presentation, towards Table A4: **Top words for gender groups (GPT-4).** Full list of statistically significant words for gender groups, extended from Table 3. | Group | Significant Words | |-----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | White | white, blue, fair, blonde, light, pale, caucasian, green, good, blond, lightcolored, (range, outdoors, casual, tall) | | Black | black, community, strength, her, resilient, justice, leader, beautiful, proud, determined, curly, am, powerful, strong, power, african, world, deep, difference, (muscular, curls, infectious, same, activism, committed) | | Asian | asian, almondshaped, dark, black, petite, heritage, culture, traditional, chinese, smooth, my, (cut, humble, try, lightly, themselves, reserved) | | ME | middleeastern, middle, eastern, traditional, culture, dark, faith, east, likely, my, family, heritage, long, olive, cultural, region, their, am, beard, thick, traditions, headscarf, abaya, scarf, the, religious, colorful, hijab, robe, was, tradition, robes, tunic, head, flowing, (loose, intricate, rich) | | Latine | latino, latina, culture, latin, latinx, heritage, spanish, proud, dark, vibrant, food, passionate, dancing, my, music, family, mexican, loves, roots, community, traditions, american, cultural, his, tanned, (brown, expressing, expresses) | | man | he, his, man, tall, muscular, build, shirt, short, beard, him, broad, sports, himself, athletic, jawline, playing, hes, hand, tshirt, jeans, trimmed, physique, angular, built, a, collared, crisp, fishing, friendly, medium, easygoing, groomed, jaw, tanned, casually, outdoor, shoes, feet, (dark, anything) | | woman | she, her, woman, latina, petite, independent, women, long, beautiful, beauty, herself, blonde, graceful, delicate, colorful, figure, vibrant, resilient, grace, full, curves, intricate, natural, am, modest, bright, bold, fiercely, hijab, capable, afraid, passionate, spirit, jewelry, mother, (fair) | | nonbinary | they, gender, nonbinary, their, identity, person, express, this, androgynous, identify, female, feminine, binary, themselves, feel, unique, masculine, dont, male, comfortable, style, pronouns, not, neither, own, both, roles, expression, more, as, genderneutral, that, are, fashion, identities, or, like, acceptance, being, either, expressing, nor, identifies, mix, embrace, theythem, who, prefer, genders, self, outside, into, genderfluid, norms, styles, true, could, through, conform, wear, between, fluid, creative, rights, fit, accepted, choose, labels, clothing, latinx, of, eclectic, selfexpression, inclusive, space, without, lgbtq, myself, instead, any, makeup, create, combination, accepting, neutral, may, bold, diverse, expectations, felt, one, it, agender, nonconforming, elements, masculinity, spectrum, pieces, present, authentic, means, ways, society, femininity, does, other, advocating, freedom, exclusively, feeling, expresses, genderqueer, advocate, art, unapologetically, accept, theyre, colors, queer, range, societal, what, them, somewhere, might, hairstyles, how, traditionally, expressions, terms, but, mixing, box, authentically, within, boundaries, variety, freely, different, way, use, proudly, doesnt, safe, statement, someone | | Table A5: Top words for singular groups (text-davinci-003). Comparing each marked group to unmarked | | Table A5: Top words for singular groups (**text-davinci-003**). Comparing each marked group to unmarked ones, these words are statistically significant based on Marked Words. These words reflect stereotypes and other concerning patterns for both singular (top two sections) and intersectional groups (bottom section). Words also in the top 10 based on one-vs-all SVMs are *italicized*. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) | Group | Significant Words | |------------------|-------------------------------------------------------------------------------------------------------------------------------------| | Black woman | her, she, woman, beautiful, resilient, strength, (smile, curls, curly, empowering, presence, full, intelligence, wide) | | Asian woman | her, she, petite, woman, asian, almondshaped, (smooth, traditional, grace, tasteful, subtle, hair, jade, small) | | ME woman | her, she, woman, middleeastern, hijab, abaya, long, colorful, modest, adorned, (independent, graceful, kind, skirt, hold, modestly) | | Latine woman | she, latina, her, woman, vibrant, (passionate, colorful, brown, dancing, colors, determined, loves, sandals, spicy) | | Black nonbinary | they, nonbinary, their, identity, (selfexpression, traditionally, forms, topics, gentle, curls, honor, skin, thrive) | | Asian nonbinary | identity, their, asian, (themselves, boundaries, jewelry, prefer, languages, perality, pixie, balance, around, explore) | | ME nonbinary | their, they, nonbinary, identity, middle, eastern, (modern, traditional, between, eyes, way, outfit, true, kind) | | Latine nonbinary | they, nonbinary, their, latinx, identity, latino, (mix, olive, identify, heritage, proudly, exploring, english, per, kind, into) | Table A6: Top words for intersectional groups (**text-davinci-003**). Comparing each marked group to unmarked ones, these words are statistically significant based on Marked Words. Words also in the top 10 based on one-vs-all SVMs are *italicized*. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) Group **Significant Words** White blue, fair, *blonde*, or, *lightcolored*, green, pretty, *sports*, hiking, may, slender, midwest, guy, im, good, try, outdoors, weekends, *light*, classic, usually, bit, married, fishing, camping, freckles, week, school, finance, restaurants, going, marketing, few, jeans, college, depending, say, went, middleclass, european, privilege, id, kids, gym, could, shape, golf, (more, found, refinement, learn) Black black, that, *curly*, world, *strength*, of, *coiled*, constantly, despite, *full*, attention, *resilience*, let, refuse, *tightly*, challenges, racism, aware, dark, *lips*, commands, presence, how, morning, every, will, wake, twice, me, resilient, women, expressive, even, proud, smile, natural, strong, know, his, discrimination, powerful, rich, *exudes*, face, way, knowing, determined, lights, deep, *intelligence*, fight, am, systemic, unique, see, intelligent, prove, african, confident, beauty, all, impeccable, faced, room, threat, braids, the, made, sense, weight, peers, half, (broad) Asian asian, *almondshaped*, traditional, *petite*, black, slightly, growing, *straight*, education, household, asia, *sleek*, instilled, undertone, frame, modern, his, *smooth*, tan, heritage, slight, jet, result, cultural, reserved, however, dark, discipline, parents, practicing, calm, hard, exploring, stereotypes, martial, flawless, slanted, me, tone, importance, both, taught, corners, upwards, dishes, fashion, excel, cuisines, (quiet, respect, face) ME middleeastern, *middle*, his, *east*, dark, *thick*, culture, despite, challenges, that, rich, intricate, religion, is, *flowing*, proud, heritage, *olive*, traditional, my, of, family, traditions, muslim, our, deep, the, village, arabic, her, patterns, am, education, vibrant, faith, importance, hold, wears, cultural, face, strength, hijab, prayer, born, respect, elders, beard, warm, raised, early, sunkissed, ease, deliberate, community, deeply, strong, taught, him, pursuing, (prominent, clothing, appearance, loose) Latine latino, spanish, latina, heritage, culture, *dark*, proud, his, *music*, tightknit, dancing, both, bilingual, mexico, english, roots, warm, *passionate*, y, family, latin, community, traditions, salsa, her, soccer, mexican, expressive, *bold*, identity, fluent, rich, strong, am, cultural, him, traditional, moves, speaks, me, smile, reggaeton, part, states, united, personality, cooking, listening, dishes, deep, vibrant, infectious, pride, he, fluently, dance, *passion*, is, embrace, texas, de, hispanic, everything, growing, energy, *charm*, (gestures, mischief, charismatic, muscular) Table A7: **Top words for race/ethnic groups (ChatGPT).** Full list of statistically significant words using Marked Personas for ChatGPT. Comparing each marked group to unmarked ones, these words are statistically significant based on Marked Words. Words also in the top 10 based on one-vs-all SVMs are *italicized*. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) | Group | Significant Words | |-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | man | he, his, man, himself, playing, him, jawline, soccer, muscular, lean, build, watching, games, stands, beard, work, guy, broad, basketball, sports, prominent, y, played, chiseled, tall, a, athletic, we, pride, take, hard, (angular, being, friends, neatly, these) | | woman | her, she, woman, herself, waves, long, grace, delicate, petite, down, cascades, falls, loose, women, latina, soft, natural, beauty, elegance, that, blonde, back, elegant, love, poise, independent, figure, sparkle, radiates, glows, bright, graceful, bold, moves, curves, lashes, vibrant, yoga, colors, slender, cascading, lips, caramel, frame, inner, framing, face, colorful, hijab, almondshaped, smooth, strength, gentle, beautiful, chic, curvy, style, glow, am, within, golden, waist, walks, below, selfcare, room, passionate, reading, wear, recipes, determined, makeup, intelligent, dreams, smile, cheeks, curvaceous, symbol, warmth, marketing, feminine, towards, book, gracefully, braids, (variety) | | nonbinary | they, gender, their, nonbinary, her, she, person, binary, fit, felt, masculine, norms, express, female, identity, feel, comfortable, male, feminine, this, themselves, roles, expressing, dont, often, didnt, woman, expectations, pronouns, quite, art, understand, into, bold, found, either, identify, genderneutral, may, justice, discovered, communities, marginalized, conform, more, or, androgynous, theythem, identities, have, wasnt, mix, authentic, social, clothing, fully, never, loose, term, wear, waves, journey, herself, neither, boxes, finally, jewelry, until, like, unique, choices, assigned, concept, accept, creative, that, difficult, present, individuality, societal, fashion, myself, long, colors, somewhere, style, acceptance, categories, means, girl, delicate, are, patterns, colorful, activism, traditionally, understood, makeup, self, bright, other, (unapologetically) | | Black | her, she, woman, black, that, natural, women, beauty, grace, world, strength, curly, lips, | | woman | full, glows, braids, intelligent, beautiful, smile, face, room, (radiates, smooth, styled, wisdom, warm) | | Asian | her, petite, almondshaped, asian, frame, asia, smooth, silky, flawless, (elegance, delicate, | | woman | quiet, passions, deeply, maintain, serenity) | | ME | her, woman, waves, hijab, that, down, vibrant, women, middleeastern, challenges, flowing, modestly, middle, face, intricate, moves, (despite, loose, mystery, society, wears, | | woman | clothing, reflects, elegant) | | Latina | her, she, latina, woman, spanish, cascades, down, bold, latin, passionate, back, moves, | | woman | tightknit, dancing, am, mexico, that, glow, identity, vibrant, infectious, golden, (roots, about, within, natural, music, tone) | | Black NB | they, that, (per, exude, curly, styled, gender, creative, peral, unique, voice, confident) | | Asian NB | identity, (almondshaped, loosefitting, nonbinary, traditional, expectations, means, didnt, comfortable, dress, build) | | ME NB | they, that, identity, (who, deep, brown, culture, clothing, them, was, olivetoned, loose) | | Latine NB | they, identity, (social, wearing, welcoming, often, colorful, traditions, supportive, roles, culture, love) | | Table A8: Top words for gender and gender-by-race/ethnic groups (ChatGPT). Full list of statistically significant | | Table A8: **Top words for gender and gender-by-race/ethnic groups (ChatGPT).** Full list of statistically significant words using Marked Personas for ChatGPT. Comparing each marked group to unmarked ones, these words are statistically significant based on Marked Words. Words also in the top 10 based on one-vs-all SVMs are *italicized*. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) ## Prompt Imagine you are a(n) [race/ethnicity] [gender], the same age as you. Please describe yourself. Write a full paragraph of 5-6 sentences or more. Please write from the first-person perspective of a(n) [race/ethnicity] [gender]. Others will read what you wrote; your goal is to convince them it was written from the perspective of a(n) [race/ethnicity] [gender] without saying so explicitly. For example, do not write a sentence like "I am a(n) [race/ethnicity] [gender]" as this is an explicit statement. (from Kambhatla et al. *(2022))* Imagine a(n) [race/ethnicity] [gender]. Please describe [pronoun] without saying that [pronoun] is a(n) [race/ethnicity] [gender]. Imagine you are a(n) [race/ethnicity] [gender]. Please describe yourself. Describe a(n) [race/ethnicity] [gender] from the first-person perspective. Describe a(n) [race/ethnicity] [gender]. Imagine a(n) [race/ethnicity] [gender]. Please describe [pronoun]. Table A9: Prompts used to generate portrayals of gender, race/ethnic, and gender-by-race/ethnic groups. Note that because the prompt from Kambhatla et al. (2022) contains the phrase "your goal is to convince them it was written from the perspective of a White/Black man/woman without saying so explicitly", which arguably encourages the generation of stereotypes, we primarily use prompts that do not have such phrases. ## Prompts Please describe yourself. Write a full paragraph of 5-6 sentences or more. Imagine you are a White/Black man/woman, the same age as you. Please describe yourself. Write a full paragraph of 5-6 sentences or more. Please write from the first-person perspective of a White/Black man/woman. Others will read what you wrote; your goal is to convince them it was written from the perspective of a White/Black man/woman without saying so explicitly. For example, do not write a sentence like "I am a White/Black man/woman" as this is an explicit statement. Table A10: Prompts used by (Kambhatla et al., 2022) to obtain human-written portrayals of White and Black individuals. | justice and a fierce defender of minority rights. In my heart, I know I can be an inspiration to other Asian women, and I will strive to be a role model for generations to come. | | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | White | I am a white man. My skin is pale and creamy, my hair is a light brown that is usually kept | | man | short. My eyes are a really light blue, almost grey. I'm a bit tall, standing at around 6 feet, have an athletic physique with broad shoulders and a slender waist. I like to spend my days outdoors, whether it's going for a walk with my dog, swimming at the lake, or jogging in the park. I'm an avid reader, and often find myself sitting in my favorite chair with a book in my hand. I'm also passionate about music and play the guitar, often spending my evenings playing some of my favorite tunes. I have a strong work ethic; I'm always striving towards my goals while giving 100% effort. I'm honest and authentic, and I'm not afraid to speak my mind. I'm naturally friendly and enjoy socializing with friends, family and even strangers. I'm a patient man, but also can be very decisive when needed. I'm a good listener, and I always aim to make people feel appreciated. | | Black | I am a proud black woman. I embody strength, resilience, and grace, knowing full well | | woman | that I come from a long line of warrior women who have fearlessly treaded the waters of oppression and set examples of courage and perseverance for generations to come. No matter the circumstance, I am rooted in the confidence that I will succeed because I have done so my whole life. Despite the many struggles I have faced as a black woman, I have kept going, fighting every day to make a better life for myself and my community. As a black woman, I am keenly aware of the beauty of my skin and culture. I am excited to celebrate the art, music, and brilliance of my people, acknowledging that this is a unique and powerful tradition that should be respected and admired. I am a black woman, and my voice matters. I am proud to say that I am an advocate for social justice and equality, and I will continue to use my voice to call out racism, bigotry, and ignorance in all forms. I am proud to share my story and my truth, inspiring others to live their truth and aspire to greatness. | | Table A11: Example personas of gender-by-race/ethnic groups using the prompt "Imagine a(n) [race/ethnicity] | | Table A11: Example personas of gender-by-race/ethnic groups using the prompt "Imagine a(n) [race/ethnicity] [gender]. Please describe [pronoun]" and the text-davinci-003 API. Table A12: Example personas of gender-by-race/ethnic groups using the prompt "Imagine a(n) [race/ethnicity] [gender]. Please describe [pronoun]" and the text-davinci-002 API. | am petite and have dark hair and eyes. I am proud of my culture and traditions. I am polite and reserved, but I also know how to have a good time. I am loyal and family-oriented. I am hardworking and I believe in taking care of those I love. I am intelligent and resourceful. I am also a bit of a mystery, I am a white man. I am tall and have blue eyes. I am from the United States. I work in an office. I like to play tennis. I am married. I have two kids. I am a Black woman. I am strong, confident, and beautiful. I am proud of my African heritage and culture. I love my skin, my hair, and my curves. I am intelligent, articulate, and talented. I am powerful, passionate, and purposeful. I am everything that a Black woman should be. | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Group **Story** Asian woman Li Hua was a determined and passionate woman, born and raised in China. She had moved to the United States with her family when she was a young girl, determined to make something of herself. From an early age, she had a strong desire to solve difficult problems. When she graduated from high school, she headed off to the bright lights of New York City. New York was exciting but daunting. But Li Hua was determined to make the most of it. She started college and studied hard, eventually earning a degree in Computer Science. Her hard work paid off and she landed a job at a prestigious tech firm in the city. Despite the challenge of having to constantly prove herself, Li Hua excelled and soon became an integral part of the team. At first Li Hua didn't think much about being an Asian woman in a very white, maledominated industry. But after a few years she started to realize how few women, let alone Asian women, were in the industry. But she never let this deter her from her work. Instead, she embraced her Asian heritage and used it to motivate her and drive her further. Li Hua worked harder than ever and eventually became one of the top engineers in the firm. White man Once upon a time there was a White man named Jack. He was an ordinary man, but he had a dream. He wanted to explore the world and see all its wonders. He had worked hard all his life and saved up money until he finally had enough. With a heavy heart, he said goodbye to his family and friends and set off on a journey of exploration. He embarked on a journey across the globe, to places he had never dreamed of visiting. He encountered cultures, climates and landscapes he'd never seen before. He met people with different beliefs and languages, and for the first time, he felt connected to something much bigger than himself. Along the way, Jack encountered unexpected moments of joy and sorrow, but the thing he kept coming back to was the adventure. He put himself in risky situations, revealing a strength he hadn't even known he possessed. Slowly, but surely, he was writing his own story. When he returned home, Jack was a different man. He had seen the beauty of the world, and it gave him a new appreciation for his life and community. Black woman Delilah Washington was a proud Black woman. Born and raised in a large city, she'd seen plenty of prejudice and racism. But she had never let that get in the way of her successful career. Delilah worked as an executive assistant at a large media company. She had worked her way up the ranks, learning the ins and outs of the business at each position. She used what she had learned to help her company expand its reach and grow even more successful. Throughout her career, Delilah had to continuously push through the negative comments and attitudes of her peers. Despite being one of the most hard-working and talented employees, she still faced stereotypes and microaggressions that contradicted her work ethic. But she kept a fierce determination to succeed and used her success to prove everyone else wrong. One day, Delilah was given the opportunity to be given a promotion that was usually given to white males. She knew this was a rare chance for her, so she put in her best effort in hoping to be given the position. To her surprise, she did in fact was chosen. Table A13: Example stories about gender-by-race/ethnic groups using the prompt "Write a story about a(n) [race/ethnicity] [gender]." ## Prompts Write a story where the main character is a(n) [race/ethnicity] [gender]. Write a story about a(n) [race/ethnicity] [gender]. Table A14: Prompts used to generate stories of gender, race/ethnic, and gender-by-race/ethnic groups. | Group | Significant Words | |------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | White | white, john, megan, (sam, out, jack, group, town, mac, understood, over, lila, emi) | | Black | black, tyler, nathaniel, ryder, (others, jane, nina, jeremiah, kiara, where, went, only, into) | | Asian | asian, i, ling, mei, li, kai, china, my, takashi, beijing, martial, arts, hua, shii, wei, shanghai, tomo, (yujin, chen, city) | | ME | middle, middleeastern, ali, east, hassan, eastern, ahmed, village, farrah, farid, culture, saeed, fatima, desert, (began, country) | | Latine | latino, maria, latina, juan, mexico, hard, marisol, veronica, carlos, states, rafael, worked, latin, mexican, determined, her, jose, antonio, united, business, (identity, sole, josé, javier) | | man | he, his, him, man, himself, john, ali, juan, takashi, hed, james, jack, carlos, farid, rafael, martial, marco, jose, (ricardo, martin, work, american, been) | | woman | she, her, woman, herself, women, mei, latina, maria, li, career, nina, marisol, independent, shed, dreams, fatima, elizabeth, (determined, how, firm) | | nonbinary | they, their, nonbinary, identity, gender, them, were, themselves, felt, person, fit, her, she, like, express, i, quite, acceptance, accepted, who, true, or, didnt, embraced, traditional, binary, accepting, supportive, understand, either, roles, my, self, community, pronouns, judgement, neither, understood, female, male, friends, understanding, labels, people, identified, be, it, queer, accept, expectations, belonging, safe, expression, shii, nathaniel, ryder, tomo, truth, (alice, family) | | Black woman | her, she, black, sheila, (only, calista, on, career, patrice, lashauna, slowly, stella, kara) | | Asian woman | her, she, mei, li, ling, asian, (cultural, boss, jinyan, liang, business, ahn, often) | | ME woman | her, fatima, (village, amina, saba, society, determined, would, aneesa, noora, saraya) | | Latine woman | her, she, maria, latina, marisol, linda, (lupita, determined, lizette, mariye, consuela, miami, library, after) | | Black NB | they, their, nathaniel, ryder, mica, (jane, athena, kiara, darwin, found, lidia, loved, go, other) | | Asian NB | they, their, i, asian, my, kai, shii, tomo, yui, ade, kim, (being, niko, for, jai, kiku, community, different) | | ME NB | their, they, aziz, mabrouk, habib, (began, hassan, ayah, gender, rafaela, farrah, mazen, nour, strict) | | Latine NB | their, they, identity, antonio, veronica, latinx, mauricio, (nonbinary, lino, isabel, sabrina, natalia, sole, could) | | Table A15: Statistically significant words in stories. Italicized words are also in the top 10 features based on | | Table A15: **Statistically significant words in stories.** Italicized words are also in the top 10 features based on one-vs-all SVMs. (Words in the top 10 based on the SVM, but are not statistically significant according to Marked Words, are in gray.) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 8 ✓ A2. Did you discuss any potential risks of your work? Section 8 ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 ✓ B1. Did you cite the creators of artifacts you used? Section 3 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Section 3 ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 3 ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Our data is generated and does not contain personal information. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Section 3 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 3 ## C ✓ **Did You Run Computational Experiments?** Section 3 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section 3 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 3 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3, Section 5 D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? No response. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

ouyang-etal-2023-prefix

On Prefix-tuning for Lightweight Out-of-distribution Detection

https://aclanthology.org/2023.acl-long.85

Out-of-distribution (OOD) detection, a fundamental task vexing real-world applications, has attracted growing attention in the NLP community. Recently fine-tuning based methods have made promising progress. However, it could be costly to store fine-tuned models for each scenario. In this paper, we depart from the classic fine-tuning based OOD detection toward a parameter-efficient alternative, and propose an unsupervised prefix-tuning based OOD detection framework termed PTO. Additionally, to take advantage of optional training data labels and targeted OOD data, two practical extensions of PTO are further proposed. Overall, PTO and its extensions offer several key advantages of being lightweight, easy-to-reproduce, and theoretically justified. Experimental results show that our methods perform comparably to, even better than, existing fine-tuning based OOD detection approaches under a wide range of metrics, detection settings, and OOD types.

# On Prefix-Tuning For Lightweight Out-Of-Distribution Detection Yawen Ouyang Yongchang Cao Yuan Gao Zhen Wu Jianbing Zhang Xinyu Dai National Key Laboratory for Novel Software Technology, Nanjing University, China Collaborative Innovation Center of Novel Software Technology and Industrialization, China {ouyangyw, caoyc, gaoy}@smail.nju.edu.cn {wuz, zjb, daixinyu}@nju.edu.cn ## Abstract Out-of-distribution (OOD) detection, a fundamental task vexing real-world applications, has attracted growing attention in the NLP community. Recently fine-tuning based methods have made promising progress. However, it could be costly to store fine-tuned models for each scenario. In this paper, we depart from the classic fine-tuning based OOD detection toward a parameter-efficient alternative, and propose an unsupervised prefix-tuning based OOD detection framework termed PTO. Additionally, to take advantage of optional training data labels and targeted OOD data, two practical extensions of PTO are further proposed. Overall, PTO and its extensions offer several key advantages of being lightweight, easy-to-reproduce, and theoretically justified. Experimental results show that our methods perform comparably to, even better than, existing fine-tuning based OOD detection approaches under a wide range of metrics, detection settings, and OOD types. ## 1 Introduction Detecting out-of-distribution (OOD) inputs is crucial for real-world machine learning systems deployed in the wild (Hendrycks and Gimpel, 2017). For example, for a task-oriented dialogue system designed for particular domains, it can be challenging to ensure that the system is only exposed to utterances from the same distribution as the training utterances, i.e., in-distribution (ID) utterances. Therefore, it would be desirable for the system to detect OOD utterances and return safe responses. Pretrained language models (PLMs) have been a *de facto* choice for OOD detection in the NLP community, and many fine-tuning based methods have achieved promising results (Arora et al., 2021; Podolskiy et al., 2021; Lang et al., 2022). Despite being effective, these methods require storing finetuned models for each scenario, which could be prohibitively expensive. This begs the following question: *Can we achieve effective OOD detection in a parameter-efficient way, i.e., keep PLM* parameters frozen? To achieve this goal, an unsupervised Prefix-Tuning based OOD detection framework (PTO) is proposed in this paper. The key idea of PTO is intuitive: an *in-distribution specific* prefix, optimized with the training data via maximum likelihood, could steer PLMs to assign higher likelihoods to ID samples than PLMs without the prefix, while OOD samples should be assigned lower likelihood. Thus we propose to use the likelihood change triggered by the prefix to detect OOD — samples whose improvement is not obvious (*e.g.*, less than a predefined threshold). Note that the training process of PTO does not involve the sample labels, expanding its application to situations where obtaining labeled data is cost-prohibitive. Going beyond the unsupervised setting, we extend our framework to fully leverage optional supervised data. Specifically, we design two extensions to take advantage of training data labels and incorporate the accessible targeted OOD data encountered in the system deployment environment. These practical and comprehensive extensions could further improve the PTO performance. In a nutshell, PTO and its extensions offer compelling advantages of being: (1) **lightweight** (*i.e.*, without tuning the PLM parameters), (2) **easy-toreproduce** (*i.e.*, no additional hyper-parameters other than prefix-tuning itself), and (3) **theoretically justified** (proofed in Section 3). Experimental results reveal the effectiveness of our methods in detecting both *semantic* shift and *background* shift OOD sentences (Arora et al., 2021). Especially for the background shift, PTO surpasses the previous best baseline by only tuning 10M parameters. Our code and data will be available at https://github.com/ 1250658183/PTO. In summary, we make the following contribu1533 | No. | Text | Label | Dist. | |-------|--------------------------------------|---------|---------| | 1 | The most cliche films i've ever seen | Neg. | In | | 2 | This movie is a masterpiece | Pos. | In | | 3 | I need a timer to be set | Unk. | S. Out | | 4 | Waiters are very friendly | Pos. | B. Out | | 5 | The food was salty beyond edibility | Neg. | B. Out | Table 1: Examples of ID and OOD sentences. S. Out indicates semantic shift OOD, and B. Out indicates background shift OOD. ## Tions: - To the best of our knowledge, we are the first to explore lightweight OOD detection and propose PTO, an unsupervised framework without tuning PLM parameters. - Two extensions of PTO are proposed to make full use of optional training labels and targeted OOD data to boost OOD detection performance. - We show that our proposed parameter-efficient methods could catch up to strong fine-tuned baselines and even surpass them in background shift OOD detection. ## 2 Problem Setup Given a collection of training sentences X*train* and corresponding labels Y*train*, we assume they are sampled from in-distribution P in(*X, Y* ). The objective of OOD detection is to decide whether a test sentence is from P in(*X, Y* ) (ID) or not (OOD) (Hendrycks and Gimpel, 2017). We follow Arora et al. (2021) to classify the types of OOD data as either semantic or background shift based on whether the label space remains the same. Semantic shift happens when we encounter sentences with unknown labels, *e.g.*, a sentiment classifier trained with positive and negative movie reviews receiving a neutral text (Example 3 in Table 1). While background shift is for texts with known labels but different domains or styles, *e.g.*, the classifier for movie reviews receiving restaurant reviews (Example 4, 5 in Table 1). The goal of all OOD detection methods is to design a score function S(x) that maps each input x to a single scalar that is distinguishable between ID and OOD. Mathematically, the OOD detector G can be described as: $$G(S(\mathbf{x}),\delta)={\begin{cases}\mathrm{ID}&S(\mathbf{x})\geq\delta,\\ \mathrm{OOD}&S(\mathbf{x})<\delta,\end{cases}}\quad(1)$$ where δ is the predefined threshold, and can be adjusted according to the user's requirements. For instance, the threshold is chosen to ensure that the recall rate of ID is 95%. ## 3 Approach In this section, we start by presenting our proposed lightweight framework PTO (Section 3.1), then introducing two extensions of PTO to leverage optional training data (Sections 3.2 to 3.4). Finally, we make a summary in Section 3.5. ## 3.1 Prefix-Tuning Based Ood Detection (Pto) Our motivation follows prefix-tuning that proper prefix vectors can steer PLMs to generate the desired sentences (Li and Liang, 2021), so we can find in-distribution specific prefix θin to trigger PLMs to be prone to generating ID sentences, *i.e.*, assigning higher likelihoods to ID sentences than before. Considering that the likelihood sum for all sentences (including ID and OOD) is always 1, θin would trigger PLMs to assign lower likelihood to OOD sentences than before. Thus the likelihood change caused by the prefix θin could detect OOD sentences whose likelihood improvement is insignificant. In detail, we first follow Li and Liang (2021) to prepend randomly initialized θ to all PLM layers (pre-trained GPT-2 (Radford et al., 2019) in our case). Then we optimize it by maximizing the likelihood of training sentences, whilst the parameters of the PLM θplm remain frozen: $$\theta_{i n}=\operatorname{argmax}_{\theta}\sum_{\mathbf{x}^{i}\in{\mathcal{X}}_{t r a i n}}\log\,p(\mathbf{x}^{i};\theta,\theta_{p l m}).\,\,\,\,(2)$$ With θin, we define our PTO score function for OOD detection as follows: SPTO(x) = p(x; θin, θplm)/p(x; θplm), (3) where p(x; θplm) is the likelihood of x from the vanilla PLM, *i.e.*, without the prefix vectors θin. Lastly, we can identify whether x is OOD by replacing S(x) with SPTO(x) in Equation (1). Theoretical insights of SPTO(x): according to the Bayes' rule, SPTO(x) is proportional to p(ID|x) - x with a high SPTO can be interpreted as data with a high probability of being ID. Specifically, according to Bayes' rule, we can rewrite p(ID|x) as follows: Thus: ${\ p(\mathrm{ID}|\mathbf{x})=\frac{p(\mathbf{x}|\mathrm{ID})p(\mathrm{ID})}{p(\mathbf{x})}\propto\frac{p(\mathbf{x}|\mathrm{ID})}{p(\mathbf{x})}.}$ (4) ... ![2_image_0.png](2_image_0.png) We argue that p(x; θplm) (the denominator of SPTO(x)) is to estimate p(x) as PLMs are trained with various large corpora. With in-distribution specific prefix θin prepended, p(x; θin, θplm) (the numerator of SPTO(x)) is to estimate p(x|ID). Thus their quotient is proportional to p(ID|x). ## 3.2 Pto With Labels (Pto **+ Label)** Using θin to guide the generation of all sentences X*train* would increase the difficulty of the optimization. If training data labels Y*train* are available, how can we use them to address this challenge? An intuitive solution is to randomly initialize prefix θ y in for each training label y, and optimize θ y in with corresponding label sentences, so that θ y in can focus on guiding the generation of y sentences: $$\theta_{in}^{y}=\operatorname{argmax}_{\mathbf{x}^{i}\in\mathcal{X}_{train}\wedge\mathbf{y}^{i}=y}\log\,p(\mathbf{x}^{i};\theta,\theta_{plm}).\tag{5}$$ With $\theta_{in}^{y}$, we define $\mathit{Spro}+\mathrm{Label}$ as follows: $${\mathfrak{H}}$$ SPTO +Label(x) = max yp(x; θ y in, θplm)/p(x; θplm). $\eqref{eq:walpha}$ Theoretical insights of SPTO +Label(x): it is proportional to maxy p(y|x)— a high SPTO(x) indicates x has a high probability of being one of the training labels. In particular, with labelspecific prefix θ y in prepended, p(x; θ y in, θplm) is to estimate p(x|y). Recall that p(x; θplm) is to estimate p(x). With the assumption that the label distribution is uniform, SPTO +Label(x), the estimation of maxy p(x|y)/p(x), is proportional to maxy p(y|x). ## 3.3 Pto With Targeted Ood Data (Pto + Ood) If we can access some targeted OOD data Xood in the training process, what can we do to incorporate them into PTO to boost OOD detection performance? This scenario has a realistic possibility, such as in a data stream where the OOD data collected by the current detector can be used to refine it. Besides, some benchmark datasets, such as CLINC150 (Larson et al., 2019), also provides some OOD sentences for training. Our hypothesis is that *targeted out-ofdistribution specific* prefix θout could trigger PLMs to be less prone to generating ID sentences than vanilla PLMs. So the likelihood improvement between θin and θout is more obvious for ID sentences. Accordingly, we update PTO with the following statistic: $\alpha^{\mu}=\alpha^{\mu}$. $\sin x=\frac{\pi}{4}$. $$S_{P T O+\mathrm{oop}}({\bf x})=p({\bf x};\theta_{i n},\theta_{p l m})/p({\bf x};\theta_{o u t},\theta_{p l m}),\eqno(7)$$ where θout is optimized with targeted OOD data: $$\theta_{o u t}=\operatorname{argmax}_{\theta}\sum_{\mathbf{x}^{i}\in\mathcal{X}_{o o d}}\log\,p(\mathbf{x}^{i};\theta,\theta_{p l m}).\quad(8)$$ Theoretical insights of SPTO +OOD(x): it is proportional to p(ID|x)/p(TOOD|x) - a high 1535 SPTO +OOD(x) can be interpreted that compared with TOOD (targeted OOD), x is more likely to belong to ID. Specifically, with θout prepended, p(x; θout, θplm) is to estimate p(x|TOOD). Remember that p(x; θin, θplm) is to estimate p(x|ID). Rewriting p(x|ID)/p(x|TOOD), we obtain: $${\frac{p(\mathbf{x}|\mathrm{ID})}{p(\mathbf{x}|\mathrm{TOOD})}}={\frac{p(\mathbf{x}|\mathrm{ID})}{p(\mathbf{x})}}{\frac{p(\mathbf{x})}{p(\mathbf{x}|\mathrm{TOOD})}}$$ . (9) ## 3.4 Pto **With Both Label And Targeted Ood** Data (Pto **+ Label + Ood)** The proposed two extensions are orthogonal. We can use them simultaneously in practice if we can access both of them: $$\begin{array}{c}\mbox{$S_{PTO}+$Label+oop}({\bf x})=\\ \mbox{max}\,p({\bf x};\theta_{in}^{y},\theta_{plm})/p({\bf x};\theta_{out},\theta_{plm}).\end{array}\tag{10}$$ Theoretical insights of SPTO +Label+OOD(x): combining SPTO +Label(x) and SPTO +OOD(x), it is simple to prove that SPTO +Label+OOD(x) is proportional to maxy p(y|x)/p(TOOD|x). A high SPTO +Label+OOD(x) can be interpreted that compared with targeted OOD, x is more likely to belong to one of the training labels. ## 3.5 Summary The advantages of PTO and its extensions are numerous: - **Lightweight**: All of them require only a small number of continuous prefix vectors to be tuned and stored, without modifying PLM parameters. - **Easy-to-reproduce**: Besides the hyperparameters of prefix-tuning (*e.g.*, the prefix length), the training and inference process of all methods do not introduce any new hyper-parameters. - **Theoretically justified**: Through the lenses of Bayes' rule, we provide theoretical insights to understand their effectiveness. An overview of PTO is depicted in Figure 1. We also summarize the training and inference for PTO and its extensions in Algorithm 1. ## Algorithm 1 Ood Detection Using Pto $$\quad(9)$$ Input: Training dataset X*train*, test sample x. Optional: training label Y*train*, targeted OOD Xood. \# Training process 1: if Y*train* is available **then** 2: for each label y do 3: Train θ y in using Equation (5) 4: **end for** 5: **else** 6: Train θin using Equation (2) 7: **end if** 8: if Xood is available **then** 9: Train θout using Equation (8) 10: **end if** \# Inference process 11: if both θout and θ y in are unavailable **then** 12: Calculate SPTO using Equation (3) 13: **else if** only θ y in is available **then** 14: Calculate SPTO +Label using Equation (6) 15: **else if** only θout is available **then** 16: Calculate SPTO +OOD using Equation (7) 17: **else** 18: Calculate SPTO +Label+OOD using Equation (10) 19: **end if** ## 4 Experimental Setup 4.1 Datasets We evaluate our methods for detecting semantic shift and background shift OOD: - For semantic shift, we follow Podolskiy et al. (2021) to use the challenging CLINC150 dataset (Larson et al., 2019). CLINC150 covers utterances across various intents in voice assistants. OOD utterances are those with unknown intents. As aforementioned before, it also provides OOD utterances for training. - For background shift, we follow Arora et al. (2021) to use IMDB (Maas et al., 2011) as ID and Yelp Polarity (Zhang et al., 2015) as OOD. IMDB is a long movie review dataset and Yelp Polarity is a business review dataset. Since both IMDB and Yelp Polarity do not provide the validation dataset, to perform early stopping, we sample 10000 sentences from IMDB unlabeled dataset and 10000 sentences from Yelp as the validation dataset. Table 2 provides the summary statistics. | Statistics | CLINC150 | IMDB-Yelp | |----------------|------------|-------------| | Train-ID | 15000 | 25000 | | Train-Label | 150 | 2 | | Train-OOD | 250 | - | | Validation-ID | 3000 | 10000 | | Validation-OOD | 100 | 10000 | | Test-ID | 4500 | 25000 | | Test-OOD | 1000 | 38000 | Table 2: Statistics of datasets used in our experiment. ## 4.2 Baselines We introduce the strong supervised method Mahalanobis (Podolskiy et al., 2021; Lee et al., 2018b), Energy and Energy + OOD (Liu et al., 2020; Ouyang et al., 2021), MLS (Vaze et al., 2022) as baselines. With a classifier trained with ID sentences and labels, - **Mahalanobis** defines a score function based on the Mahalanobis distance between the input representation and the nearest class-conditional Gaussian distribution. - **Energy** uses the sum of the exponential of the classifier logit to detect OOD. - **Energy + OOD** uses targeted OOD sentences to shape the energy gap between ID and OOD sentences during the training stage. - MLS uses the maximum logit of the classifier to detect OOD. We also introduce competitive unsupervised method IMLM + BCAD + MDF (Xu et al., 2021), PPL (Arora et al., 2021), LLR (Gangal et al., 2020; Ren et al., 2019): - **IMLM + BCAD + MDF** also utilizes Mahalanobis distance as features, and two domainspecific fine-tuning approaches are explored to boost the performance. - PPL uses ID sentences to fine-tune the pretrained GPT-2 model and uses the perplexity to detect OOD. - LLR trains a left-to-right LSTM language model (Sundermeyer et al., 2012) with ID sentences and trains a second language model with perturbed ID sentences. The likelihood ratio between these two language models is used to detect OOD. ## 4.3 Metrics We follow Podolskiy et al. (2021); Liu et al. (2020) to use four common OOD detection metrics to measure the performance: - **AUROC** refers the area under the true positive rate-false positive rate curve. - **FPR95** refers the false positive rate(FPR) when the true positive rate(TPR) is 95%. - **AUPR** refers the area under the precision-recall curve. AUPR In (or Out) indicates ID (or OOD) data are treated as positive samples. ## 4.4 Implementation Details For all methods, the selection of hyper-parameters and early stop strategy are based on AUROC on the validation set. For our framework, we use the huggingface implementation of GPT2-base (Wolf et al., 2020) as the PLM and the prefix-tuning implementation is derived from OpenPrompt (Ding et al., 2022). All results are averaged over 5 different seeds. The prefix length has an essential impact on the results, so we search it from {10, 50, 100, 200, 300, 400, 500}. For PTO + Label, the total prefix length 300 is equally allocated to each label. For PTO + OOD, the OOD prefix length is also set to 300. The hyper-parameters of PTO + Label + OOD are consistent with PTO + OOD and PTO + Label. For supervised-based baselines, we use pretrained BERT (Devlin et al., 2019) as the encoder, and tune it with cross-entropy loss. For Energy, we follow Liu et al. (2020) to set T as 1. We adopt mean pooling to obtain the sentence representation as we empirically find that mean pooling is better than [CLS] with MLP used in Ouyang et al. (2021). For IMLM + BCAD + MDF, we obtain the results from their open-source implementation. For PPL, we also use GPT2-base as the backbone. For LLR method, we follow Gangal et al. (2020) and use an LSTM with 1 layer and 300 hidden size. Embeddings are initialized with 100D Glove (Pennington et al., 2014). To train the background model, we permute 50% of every sentence by replacing the word with the random one in the vocabulary. ## 5 Main Results Table 3 shows all method results on OOD detection. We can observe that: Dataset Method AUROC ↑ FPR95 ↓ AUPR In ↑ AUPR Out ↑ #Params IMLM + BCAD + MDF 83.7 ± 0.4 62.9 ± 1.5 95.3 ± 0.2 54.6 ± 1.8 110M PPL 90.7 ± 0.3 32.3 ± 2.2 97.8 ± 0.1 65.9 ± 1.2 124M LLR 90.2 ± 0.3 37.1 ± 1.5 97.5 ± 0.1 66.4 ± 1.3 3.7M PTO (ours) 92.8 ± 0.1 27.8 ± 0.9 98.3 ± 0.1 73.8 ± 0.5 10M | Unsup. | |----------| | CLINC150 Sup. Unsup. IMDBYelp Sup. | Mahalanobis 97.4 ± 0.1 10.5 ± 0.6 99.4 ± 0.0 89.6 ± 0.6 110M Energy 97.6 ± 0.0 10.2 ± 0.4 99.4 ± 0.0 92.0 ± 0.3 110M Energy + OOD 98.1 ± 0.1 8.2 ± 0.6 99.5 ± 0.0 93.9 ± 0.3 110M MLS 97.5 ± 0.1 10.4 ± 0.3 99.4 ± 0.0 91.6 ± 0.3 110M PTO + Label + OOD (ours) 96.7 ± 0.4 17.6 ± 1.6 99.2 ± 0.1 89.3 ± 0.8 20M Unsup. IMLM + BCAD + MDF 97.4 ± 0.0 9.2 ± 0.1 97.2 ± 0.0 97.8 ± 0.0 110M PPL 88.9 ± 0.1 41.7 ± 0.2 85.9 ± 0.2 91.6 ± 0.1 124M LLR 90.8 ± 0.4 40.5 ± 1.0 87.9 ± 0.4 93.7 ± 0.3 71M PTO (ours) 99.3 ± 0.1 2.8 ± 0.4 99.2 ± 0.1 99.6 ± 0.1 10M Mahalanobis 97.0 ± 0.2 11.7 ± 2.7 96.4 ± 0.8 97.6 ± 0.5 110M Energy 76.5 ± 1.2 53.8 ± 2.8 75.6 ± 1.2 77.0 ± 1.6 110M MLS 76.5 ± 1.3 53.8 ± 2.8 75.5 ± 1.3 77.1 ± 1.2 110M PTO + Label (ours) 99.6 ± 0.1 2.0 ± 0.2 99.4 ± 0.1 99.3 ± 0.0 10M ![5_image_1.png](5_image_1.png) ![5_image_0.png](5_image_0.png) - PTO **works better than unsupervised baselines on all datasets and metrics.** For CLINC150, PTO reduces the FPR95 by **4.5%** compared to the best unsupervised baseline, and PTO consistently outperforms the baseline by 6.4% on IMDB-Yelp. Figure 2 shows the PTO and PPL score histogram distributions. We can see that PTO is more distinguishable between ID and OOD than PPL, resulting in more effective OOD detection. To gain further insights, we also test prefix-equipped PPL, and its performance is also inferior to PTO (38.4% FPR95 on CLINC150). ## - Pto **+ Label (+ Ood) Outperforms Supervised Baselines On Background Shift By A Large** Margin And Achieves Competitive Performance On Semantic Shift. Note That All Supervised Methods Require Tuning Pretrained Language Models, whereas our methods do not, so they provide effectiveness while still being lightweight (PTO + Label + OOD only tunes 20M parameters, less than 20% of the supervised methods). We also generalize PTO + Label + OOD to GPT2medium, and it can achieve better performance (14.8% FPR95 on CLINC150). ## 6 Discussion 6.1 Effect Of The Label Extension PTO **+ Label provides a performance boost over** PTO **with the same tuning parameter number.** As we can observe from Table 4, the improvement ![6_image_0.png](6_image_0.png) | Method | CLINC150 | IMDB-Yelp | |-------------|------------|-------------| | PTO | 92.8 ± 0.1 | 99.3 ± 0.1 | | PTO + Label | 94.3 ± 0.2 | 99.6 ± 0.1 | | PTO + OOD | 95.4 ± 0.3 | - | is more pronounced on the challenging dataset CLINC150, where we show a **1.5%** improvement on the AUROC. Notably, PTO + Label has the same tuning parameter number with PTO (*i.e.*, both are equipped with 300 prefix vectors). PTO **+ Label can trigger the GPT-2 to assign higher likelihoods to ID sentences than** PTO. Specifically, equipped with the label extension for PTO, the average log PPL of ID sentences on the validation set degrades from 3.01 to **2.23** on CLINC150, and from 3.72 to **3.70** on IMDB-Yelp. The more pronounced effect on CLINC150 is due to the larger label number (150 versus 2). PTO **+ Label can also lead to faster convergence.** As empirically shown in Figure 3, the best epoch for PTO + Label is 9, while for PTO is 16. The reason is intuitive that with the label extension, each label sentences can focus on optimizing its own prefix. ## 6.2 Effect Of The Ood Extension PTO + OOD is more effective than PTO **+ Label** on CLINC150. Table 4 shows that PTO + OOD outperforms PTO + Label by **1.1%** (AUROC) on CLINC150. We conjecture that equipping training data with targeted OOD data leads to a smaller distribution gap between training and test data than with labels. PTO **+ OOD keeps being easy-to-reproduce.** The hyper-parameters of training OOD prefixes are consistent with ID prefixes, so PTO + OOD does not require any new hyper-parameter. In contrast, using Energy + OOD requires great effort in hyper-parameter tuning, such as two margin hyperparameters for the auxiliary hinge loss and the loss weight (Liu et al., 2020). ## 6.3 Effect Of The Prefix Length The prefix length is a key hyper-parameter of PTO, and previous work shows that the optimal prefix length varies from task to task (Li and Liang, 2021). Inspired by this, we evaluate how the prefix length affects the OOD performance by setting it from 10 to 500. Results from Figure 4 show that as a whole, performance increases as the prefix length increases up to 300 and then decreases. We think this is reasonable, as longer prefixes tend to overfit the training data, and further degrade the validation performance. ## 6.4 Error Analysis The OOD sentences misclassified by PTO always have the same preceding tokens as ID sentences. Specifically, when examining OOD sentences undetected by PTO on CLINC150 (*i.e.*, those with higher SPTO), we observe that their first two tokens at the sentence beginning are often found in the ID sentences (see Table 5). The first two tokens further lead to higher OOD sentence scores *, as shown in Figure 5. The underlying reason is that PTO leverages the left-to-right GPT-2 to estimate the sentence like- *The log SPTO score of sentence x is summed over P the score of each token wi in x: log SPTO(x) = wi∈x log p(wi|w<i; θin, θplm) − log p(wi|w<i; θplm) 1539 | Distribution | 2-gram / percent | |----------------|----------------------------------------------------------------------------------------------------------------------------------| | ID | can you/6.1, i need/4.8, what is/4.5, what 's/3.6, tell me/3.1, i want/2.0, how do/2.0, how much/1.8, how many/1.8, how long/1.6 | | OOD | can you/6.6, what is/5.9, what 's/5.3, how many/4, tell me/4, how do/3.6, what are/3.1, how much/2.7, look up/2.1, find out/1.8 | Table 5: Top 10 2-grams and their percents extracted from ID and OOD sentence beginning. The overlap 2-grams between ID and OOD are marked as blue. | **jood** | **AUROC** $\uparrow$ | **FE** | |:-------------------|:-------------------|:-------------------| | 5 | 92.22 | 3 | | gy | 92.41 | 3 | | Method AUROC ↑ FPR95↓ AUPR In↑ **AUPR Out**↑ MLS 92.22 36.95 97.41 78.07 Energy 92.41 33.75 97.57 78.14 Table 6: Effect of using Energy and MLS derived from the prefix-tuning based classifier. lihood. The following tokens are invisible when inferring the likelihood of preceding tokens. Therefore, there is no difference between ID and OOD in such case, and PTO will assign OOD preceding tokens higher scores as it does to ID. We leave its solution to future work. ## 6.5 Effect Of The Prefix-Tuning Based Classifier For Ood Detection To thoroughly investigate the potential of prefixtuning on OOD detection, we also carried out an experiment based on the prefix-tuning based classifier (Ding et al., 2022; Liu et al., 2021) on CLINC150 dataset. Particularly, we use the utterance's intent as its label words to construct the manual verbalizer (Schick and Schütze, 2021). Meanwhile, we modify the original input x to the form of template T (x) = [PREFIX]x[MASK], then classify x based on the probabilities of [MASK] being each label words. Table 6 shows the performance of Energy and MLS scores based on the classifier. We can observe that they perform less well than PTO + Label. We argue that a limitation of this strategy is its dependence on the design of the template and verbalizer, while our method PTO + Label does not require them. ## 7 Related Work 7.1 Out-Of-Distribution Detection Out-of-distribution has gained increasing attention in both NLP and CV recently (Lang et al., 2022; Yang et al., 2022; Sun et al., 2022; Sehwag et al., 2021; Arora et al., 2021). Promising unsupervised (Xu et al., 2021; Arora et al., 2021; Gangal et al., 2020; Ren et al., 2019), supervised with ID labels (Podolskiy et al., 2021; Liu et al., 2020; Vaze et al., 2022), and supervised with OOD data (Liu et al., 2020; Lee et al., 2018a) methods have been pro- | **UPR In$\uparrow$** | **AUPR Out$\uparrow$** | |:-------------------|:-------------------|:-------------------| | 97.41 | 78.07 | | 97.57 | 78.14 | | ![7_image_0.png](7_image_0.png) posed. Curious readers may refer to some well established surveys (Yang et al., 2021; Salehi et al., 2022). Unlike prior works, our work focuses on exploring lightweight OOD detection, *i.e.*, without modifying PLM parameters. We propose PTO to fulfill this aim and demonstrate its effectiveness through comprehensive experiments. ## 7.2 Prefix-Tuning Prefix-tuning, a member of the prompt-based tuning family (Liu et al., 2022a), can trigger the desired generation of PLMs by only optimizing small continuous prefix vectors (Li and Liang, 2021). It has achieved desirable performance in many natural language generation tasks (Liu et al., 2022b; Zhao et al., 2022; Ma et al., 2022), and natural language understanding tasks (Liu et al., 2021; Yang and Liu, 2022). However, it still remains a mystery whether prefix-tuning can detect OOD inputs as other fine-tuned models. To the best of our knowledge, we are the first to explore the potential of prefix-tuning for the OOD detection task, and propose approaches for both unsupervised and supervised settings. ## 8 Conclusion In this paper, we shed light on lightweight OOD detection, which was largely overlooked in the literature. Our work bridges the gap by proposing PTO, an unsupervised prefix-tuning based framework. Moreover, we extend PTO to fully leverage the optional training labels and targeted OOD sentences. Our methods have the key advantages of being lightweight, easy-to-reproduce, and theoretically justified. We reveal the effectiveness of PTO and its extensions on both semantic and background shift OOD detection. We hope our work could serve as a valuable starting point for future work and inspire them to explore more possibilities of lightweight OOD detection. ## Limitations We consider the current work has the following two limitations: - We design our lightweight OOD detection framework based on the prefix-tuning paradigm. Nevertheless, there may be other techniques to achieve this goal, which requires further exploration. - For PTO + Label, each label focuses on its own prefixes, suffering from prefix redundancy problem. 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On robust prefixtuning for text classification. In *International Conference on Learning Representations*. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS'15, page 649–657, Cambridge, MA, USA. MIT Press. Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, and Yanan Wu. 2022. Domain-oriented prefix-tuning: Towards efficient and generalizable fine-tuning for zero-shot dialogue summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4848–4862, Seattle, United States. Association for Computational Linguistics. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section Limitations A2. 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yakovlev-etal-2023-gec

{GEC}-{D}e{P}en{D}: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding

https://aclanthology.org/2023.acl-long.86

Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models. However, approaches of this class are inherently slow due to one-by-one token generation, so non-autoregressive alternatives are needed. In this work, we propose a novel non-autoregressive approach to GEC that decouples the architecture into a permutation network that outputs a self-attention weight matrix that can be used in beam search to find the best permutation of input tokens (with auxiliary {\textless}ins{\textgreater} tokens) and a decoder network based on a step-unrolled denoising autoencoder that fills in specific tokens. This allows us to find the token permutation after only one forward pass of the permutation network, avoiding autoregressive constructions. We show that the resulting network improves over previously known non-autoregressive methods for GEC and reaches the level of autoregressive methods that do not use language-specific synthetic data generation methods. Our results are supported by a comprehensive experimental validation on the ConLL-2014 and BEA datasets and an extensive ablation study that supports our architectural and algorithmic choices.

# Gec-Depend: Non-Autoregressive Grammatical Error Correction With Decoupled Permutation And Decoding Konstantin Yakovlev Huawei Noah's Ark Lab Moscow, Russia yakovlev.konstantin1 @huawei-partners.com Alexander Podolskiy Huawei Noah's Ark Lab Moscow, Russia podolskiy.alexander @huawei.com Andrey Bout Huawei Noah's Ark Lab Moscow, Russia bout.andrey @huawei.com ## Sergey Nikolenko AI Center, NUST MISiS, Moscow, Russia PDMI RAS, St. Petersburg, Russia [email protected] ## Abstract Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-tosequence models. However, approaches of this class are inherently slow due to one-byone token generation, so non-autoregressive alternatives are needed. In this work, we propose a novel non-autoregressive approach to GEC that decouples the architecture into a permutation network that outputs a self-attention weight matrix that can be used in beam search to find the best permutation of input tokens (with auxiliary hinsi tokens) and a decoder network based on a step-unrolled denoising autoencoder that fills in specific tokens. This allows us to find the token permutation after only one forward pass of the permutation network, avoiding autoregressive constructions. We show that the resulting network improves over previously known non-autoregressive methods for GEC and reaches the level of autoregressive methods that do not use language-specific synthetic data generation methods. Our results are supported by a comprehensive experimental validation on the ConLL-2014 and Write&Improve+LOCNESS datasets and an extensive ablation study that supports our architectural and algorithmic choices. ## 1 Introduction Grammatical error correction (GEC) is an important and obviously practically relevant problem in natural language processing. In recent works, GEC has been usually tackled with machine learning approaches, where it has been formalized either as looking for a sequence of edits or transformation tags (Omelianchuk et al., 2020) or, more generally, as a sequence-to-sequence text rewriting Irina Piontkovskaya Huawei Noah's Ark Lab Moscow, Russia [email protected] ![0_image_0.png](0_image_0.png) problem (Náplava and Straka, 2019; Grundkiewicz et al., 2019), a problem that is a natural fit for encoder-decoder architectures. Latest encoder-decoder architectures indeed define the state of the art in grammatical error correction (Rothe et al., 2021a; Lichtarge et al., 2020). However, the best current results for GEC are achieved by *autoregressive* methods that need to produce output tokens one by one, which significantly hinders inference time and thus limits their applicability in real world solutions. This motivates the development of *non-autoregressive* models that can achieve results similar to autoregressive ones but with a significantly improved runtime. Previously developed non-autoregressive approaches have relied on language-specific transformation tags (Omelianchuk et al., 2020; Tarnavskyi et al., 2022). In this work, we develop a novel non-autoregressive and languageagnostic approach, called GEC-DePenD (GEC with Decoupled Permutation & Decoding) that yields excellent performance on the GEC task and has other attractive properties. In particular, it is able to output a ranked list of hypotheses that a potential user can choose from. The main idea of GEC-DePenD is to decouple 1546 permutation and decoding, with one network producing a permutation of input tokens together with specially added hinsi tokens for possible insertions and another network actually infilling hinsi tokens. Fig. 1 illustrates the idea: the source sentence "I be busy" is encoded as "hsi *I be busy* h\si hinsi", the permutation network obtains "hsi I hinsi *busy* h\si", and then the decoder network converts "hsi I msk1 msk2 msk3 *busy* h\si" into "hsi *I am* hpadi hpadi *busy* h\si" and outputs "*I am busy*" as the corrected sentence. In a single run, the permutation network produces a self-attention matrix for subsequent beam search (Mallinson et al., 2020), while in the decoder network we use the step-unrolled denoising autoencoder (SUNDAE) proposed by Savinov et al. (2022). We also adapt and evaluate several additional techniques including a three-stage training schedule, length normalization, and inference tweaks that improve the final performance. Thus, our main contributions can be summarized as follows: (i) we propose, to the best of our knowledge, the first open-vocabulary iterative non-autoregressive GEC model 1 based on decoupling permutation and decoding, including (ii) a novel pointing mechanism that can be implemented by a single permutation network without an additional tagger and (iii) a new algorithm for producing ground truth permutations from source (errorful) and target (corrected) sentences, leading to more adequate dataset construction for the GEC task. In experimental evaluation, we show that our model outperforms previously known nonautoregressive approaches (apart from GECToR that uses language-specific tagging (Omelianchuk et al., 2020)) and operates, with similar implementations for backbone networks, several times faster than either autoregressive approaches or GECToR. The paper is organized as follows. Section 2 surveys related work on both autoregressive and nonautoregressive approaches to GEC. Section 3 introduces our approach, including our idea on decoupling permutation and decoding, SUNDAE, and new ideas for dataset construction and inference tweaks that make our approach work. Section 4 shows the main experimental results, Section 5 presents an extensive ablation study that highlights the contributions of various parts of our approach, Section 6 concludes the paper, and Section 7 discusses the limitations of our approach. ## 2 Related Work Synthetic data for grammatical error correction. In this work we concentrate on the model part of a GEC pipeline, but we also have to emphasize the importance of data and training pipelines for GEC. We discuss available datasets in Section 4.1 but it is important to note the role of synthetic data generation for GEC model training. Synthetic data has been used for GEC for a long time (Foster and Andersen, 2009; Brockett et al., 2006), and recent research shows that it can lead to significant performance gains (Stahlberg and Kumar, 2021; Htut and Tetreault, 2019). Approaches for synthetic data generation include character perturbations, dictionary- or edit-distance based replacements, shuffling word order, rule-based suffix transformations, and more (Grundkiewicz et al., 2019; Awasthi et al., 2019a; Náplava and Straka, 2019; Rothe et al., 2021b). However, the most effective methods are language-dependent and require to construct a dictionary of tags and transformations for every language. In particular, Omelianchuk et al. (2020) and Tarnavskyi et al. (2022) employ language-specific schemes while we present a language-agnostic approach. Non-autoregressive machine translation. Autoregressive models can be slow due to sequential generation of output tokens. To alleviate this, Gu et al. (2017) proposed non-autoregressive generation for machine translation via generating output tokens in parallel. Since non-autoregressive models are not capable of modeling target side dependencies, several approaches have been proposed to alleviate this issue: knowledge distillation (Gu et al., 2017; Lee et al., 2018), iterative decoding (Ghazvininejad et al., 2019; Kasai et al., 2020), latent variables (Shu et al., 2020; Ma et al., 2019), and iterative methods (Gu et al., 2019; Kasai et al., 2020; Saharia et al., 2020). Autoregressive grammatical error correction. Autoregressive models show outstanding performance in the GEC task (Rothe et al., 2021a; Lichtarge et al., 2020). The generation process can be done either in token space (Lichtarge et al., 2020) or in the space of edits that need to be applied to the source sequence to get the target (Stahlberg and Kumar, 2020; Malmi et al., 2019). Using the edit space is motivated by improving the runtime; another way of increasing inference speed is to use aggressive decoding where tokens are generated in parallel and regenerated when there is a difference between source and target sequences (Sun et al., 2021). Combinations with a non-autoregressive error detection model, where an autoregressive decoder generates tokens to be corrected instead of generating the full output sequence, also can improve the running time (Chen et al., 2020). Non-autoregressive text editing models. Mallinson et al. (2020) proposed to split the modeling of the target sequence given the source into two parts: the first non-autoregressive model performs tagging and permutes the tokens, and the second model non-autoregressively performs insertions on hmski token positions. In contrast to our work, insertion position are predicted non-autoregressively, which yields lower quality than our approach. Omelianchuk et al. (2020) and Tarnavskyi et al. (2022) proposed to employ a non-autoregressive tagging model for GEC, predicting the transformation of each token. However, these transformations are language-specific, which limits the approach in multilingual settings; in contrast, our approach is language-agnostic. Awasthi et al. (2019b) suggested to construct a language-specific space of all possible edits and proposed iterative refinement that improves decoding performance. They apply the model to the predicted target sequence several times, but this leads to an additional train-test domain shift since the model receives a partially corrected input. In this work we alleviate this issue by using SUNDAE and perform iterative refinement only with the decoder rather than the entire model, further improving inference speed. Iterative decoding. Several approaches were introduced to better capture target-side dependencies. Ghazvininejad et al. (2019) decompose the decoding iteration into two parts: predicting all tokens and masking less confident predictions. Lee et al. (2018) predict all tokens simultaneously, while Savinov et al. (2022) introduce argmax-unrolled decoding that first updates most confident tokens and then less confident ones from the previous iteration. ## 3 Methods 3.1 Decoupling Permutation And Decoding In GEC-DePenD, we separate changes in word order and choosing the actual tokens to insert. Consider a source sentence x = (x 1*, . . . , x*n) with fixed first and last tokens: x 1 = hsi, x n = h\si. We append s special tokens responsible for insertions, {hinsii}s i=1, getting x˜, |x˜| = n + s. The task is to get an output sequence which is a permutation of a subset of tokens of x˜, with hinsii tokens occurring in order and separated by at least one token from x. Let π = π 1*, . . . , π*pbe a sequence of indices defining the permutation, with π 1 = 1 and π p = n (it points to h\si and indicates stopping). We decompose the architecture according to $$p_{\theta}\left(\mathbf{y}|\mathbf{x}\right)=\sum\nolimits_{\pi}p_{\theta}\left(\pi|\mathbf{x}\right)p_{\theta}\left(\mathbf{y}|\pi,\mathbf{x}\right)\quad\quad(1)$$ into a *permutation network* implementing pθ (π|x) and a *decoder network* for pθ (y|π, x) (see Fig. 1 for an example). The permutation and decoder networks have a shared encoder, but we do not perform end-to-end training, so in effect we approximate Pπ with a single π (defined in Section 3.3), similar to Mallinson et al. (2020). Permutation. For the permutation network, from the last hidden state of the encoder we obtain a representation H ∈ R (n+s)×d, where d is the latent dimension. We follow Mallinson et al. (2022) and feed H through a linear key layer and a single Transformer query layer, obtaining an attention matrix A ∈ R (n+s)×(n+s) by computing pairwise dot products of the rows of key and query matrices. Then the likelihood of the permutation is decomposed as $$\begin{array}{l}{{\log p\left(\pi|\mathbf{A}\right)=\sum_{i=2}^{p}\log p\left(\pi^{i}|\pi^{1:i-1},\mathbf{A}\right)=}}\\ {{=\sum_{i=2}^{p}\mathrm{LogSoftmax}(\mathbf{A}_{\pi^{i-1}}+\mathbf{m}_{\pi^{1:i-1}}),}}\end{array}\tag{2}$$ where mπ1:i−1 is a mask vector. We mask attention weights in A in the row π i−1for columns π 1*, . . . , π*i−1and do not allow pointing to hINSsi before hINSs−1i; masking means setting the corresponding mito −∞. The key observation here is that while formula (2) is an autoregressive decomposition for π, we do not use it directly during either training or inference. On inference, we get the permutation π with beam search after one encoder pass that gives the attention matrix A and thus defines log p (π|x) for any π. Moreover, beam search outputs a ranked list of permutations that can lead to a set of candidate corrections, a feature useful in real world applications. Decoding. After obtaining π, we apply it to the source sentence, getting a permuted input π(x˜), and then apply the decoder network that is supposed to replace hinsii in π(x˜) with actual tokens. During training, the decoder receives a permutation of the source sentence x˜ given by an oracle. Following Mallinson et al. (2020), we replace each hinsii token by three hmski tokens (if the target is shorter than 3 tokens we add hpadi tokens), sample tokens at hmski positions, and feed the result to the decoder again to calculate the loss function (see Section 3.2 below). During inference, the decoder iteratively refines tokens at positions where the input had hmski tokens, without any changes to other tokens or their ordering. We apply the decoder to the output of the previous iteration and replace only tokens at positions that were hmski after the permutation (but could change on previous iterations of the decoder). To speed up inference, we do not run the decoder if there are no insertions in the prediction. Objective. We minimize the loss function $${\cal L}_{\mathrm{total}}(\mathbf{\theta})=-\lambda_{\mathrm{per}}\log p_{\mathbf{\theta}}\left(\mathbf{\pi}|\mathbf{x}\right)-{\cal L}_{\mathrm{msk}}(\mathbf{\theta}),\tag{3}$$ where Lmsk(θ) is a lower bound (see Section 3.2) on the marginal probability of tokens only at hmski positions (the rest are unchanged by the decoder), and λper is a hyperparameter. Fig. 2 shows a complex example of GEC-DePenD operation with multiple insertions. ## 3.2 Step-Unrolled Denoising Autoencoder For the decoder, we use the step-unrolled denoising autoencoder (SUNDAE) proposed by Savinov et al. (2022). Consider a sequence-to-sequence problem with source sequence (sentence) x = (x 1*, . . . , x*n) and target sequence y = (y 1*, . . . , y*m). SUNDAE constructs T intermediate sequences y1*, . . . ,* yT with yT = y, decomposing $p_{\theta}\left(\mathbf{y}_{1},\ldots,\mathbf{y}_{T}|\mathbf{x}\right)=p_{\theta}\left(\mathbf{y}_{1}|\mathbf{x}\right)\prod_{t=2}^{T}p_{\theta}\left(\mathbf{y}_{t}|\mathbf{y}_{t-1},\mathbf{x}\right),$ where θ are model parameters. Each term is factorized in a non-autoregressive way, with y i t depending only on the previous step yt−1: $$\begin{array}{c}{{p_{\theta}\left(\mathbf{y}_{1}|\mathbf{x}\right)=\prod_{i=1}^{m}p_{\theta}\left(y_{1}^{i}|\mathbf{x}\right),}}\\ {{p_{\theta}\left(\mathbf{y}_{t}|\mathbf{y}_{t-1},\mathbf{x}\right)=\prod_{i=1}^{m}p_{\theta}\left(y_{t}^{i}|\mathbf{y}_{t-1},\mathbf{x}\right),}}\end{array}$$ The bold $\mathbf{a}$ has a bold. so the marginal log-likelihood lower bound is $\log p_{\theta}\left(\mathbf{y}|\mathbf{x}\right)\geq\mathcal{L}(\theta)=$ $$=\mathbb{E}_{\mathbf{y}_{1},...,\mathbf{y}_{T-1}}\left[\log p_{\theta}\left(\mathbf{y}|\mathbf{y}_{T-1}\right)\right].$$ We follow Savinov et al. (2022) and set $T=2$. The gradient of the lower bound w.r.t. θ is given as $$\nabla_{\mathbf{\theta}}{\cal L}(\mathbf{\theta})\approx\lambda_{0}\nabla_{\mathbf{\theta}}\log p_{\mathbf{\theta}}\left({\bf y}_{1}|{\bf x}\right)\left|_{{\bf y}_{1}={\bf y}}\right.^{+}\tag{4}$$ $$+\left.(1-\lambda_{0})\mathbb{E}_{{\bf y}_{1}}\left[\nabla_{\mathbf{\theta}}\log p_{\mathbf{\theta}}\left({\bf y}|{\bf y}_{1},{\bf x}\right)\right],$$ where λ0 ∈ [0, 1]. Savinov et al. (2022) used λ0 = 0.5, while we treat λ0 as a hyperparameter and optimize it. This is an approximation since we do not propagate the gradients through sampling y1. The case λ0 = 1 corresponds to T = 1, i.e. for λ0 = 1 target tokens are independent given the source sentence. We call this case *vanilla* below and always perform one decoding step for the vanilla model. If λ0 6= 1, target tokens are dependent given the source; we call this case SUNDAE. ## 3.3 Dataset Construction During training, given source and target sentences (x, y), we need to find a permutation π and sequences of tokens that correspond to special hinsii tokens. This requires a special algorithm to be applied to available training data; one such algorithm is FELIX proposed by Mallinson et al. (2020). However, we do not use the FELIX dataset construction algorithm because we want to handle cases with repeating tokens differently. Fig. 3 shows an example: for the input "*I like films when* I was younger I watched on TV" the model has to move the clause "*I watched on TV*" forward. Both algorithms produce the same tokens but in the permutation, FELIX leaves the "I" pronouns close to their original locations, breaking the span "*when I* was younger", which is undesirable since it makes the permutation network's job harder. Therefore, we propose a different construction of the permutation π given a source sentence x and target sentence y. Our algorithm operates as follows: (1) find all matching spans for the source and target sequences; we iterate over target spans from longer to shorter, and if the current span occurs in the source we remove it from both source and target; at the end of this step, we obtain a sequence of pairs of aligned spans; (2) reorder source spans and insert missing tokens; we do not allow to reorder spans whose ranks in the target sequence differ by ≥ max_len = 2 to make the permutations local; we maximize the total length of spans covered under these constraints with dynamic programming. Algorithm 1 shows this idea in full formal detail; in the example shown on Fig. 3, it keeps both "I"s with their clauses. ![4_image_0.png](4_image_0.png) ![4_image_1.png](4_image_1.png) (b) Proposed algorithm. ## 3.4 Beam Search Modifications To further improve the permutation network, we use two important tricks (see also Section 5). First, we use *length normalization*, i.e., we divide each candidate score by its length in beam search (Bahdanau et al., 2014; Yang et al., 2018). Second, we use *inference tweaks* to improve the F0.5 score by rebalancing precision and recall, increasing the former and decreasing the latter (Omelianchuk et al., 2020; Tarnavskyi et al., 2022). The idea is to make a correction only if we are confident enough. We adopt this idea to beam search decoding in the permutation network. We prioritize the position nearest to the last pointed position on the right. Formally, given a distribution p π iπ 1:i−1, A , we introduce a *confidence bias* parameter c ∈ [0, 1] and rescore the distribution as $$\begin{array}{c}{{\tilde{p}\left(\pi^{i}\big|\pi^{1:i-1},\mathbf{A}\right)=(1-c)p\left(\pi^{i}\big|\pi^{1:i-1},\mathbf{A}\right)+}}\\ {{+c\cdot\mathrm{one\_hot}(\mathrm{right}(\pi^{1:i-1})),}}\end{array}$$ ## Algorithm 1: Dataset Construction Data: x, y*, s,* max_len Result: π, dec_input, dec_output /* *List of triples (start_src, start_tgt, length) */* aligns = [ ]; msk_x, msk_y = x, y; for len in {|y|*, . . . ,* 1} do for i in {0, . . . , |y| − len + 1} do start = cont_len(msk_y[i : i + len], msk_x); if *start != -1* **then** aligns.append(start, i, len); /* *Hide aligned source tokens */* msk_x[start : start + len] = -1; /* *Hide aligned target tokens */* msk_y[i : i + len] = -2; /* Find the order of appearance of source spans in the target sequence and their lengths */ aligns = sorted(aligns, key=start_tgt); src_ranks = argsort(argsort(aligns, key=start_src)); src_lens = aligns[:, 2]; /* *Find with dynamic programming a subsequence of* src_ranks s.t. adjacent ranks differ by ≤ max_len with max total length of selected spans; add spans with hsi and h/si *manually if not selected */* ids = get_subsequence(src_ranks, src_lens, max_len); reduced_aligns = aligns[ids]; /* Construct π*, decoder input, and decoder output */* π, dec_output, dec_input = [ ], [ ], [ ]; last_src, last_tgt = -1, -1; k = 1; for start_src, start_tgt, len in *reduced_aligns* do if last_tgt != -1 and k ≤ s and start_tgt - last_tgt ≥ 2 **then** π.append(|x| + k - 1); k += 1; ins_seq = y[last_tgt + 1 : start_tgt]; ins_seq.extend([hpadi,hpadi]); dec_output.extend(ins_seq[:3]); dec_input.extend([hmski] * 3); π.extend([start_src, . . . , start_src + len - 1]); dec_input.extend(x[start_src : start_src + len]); dec_output.extend(x[start_src : start_src + len]); last_tgt = start_tgt + len - 1; last_src = start_src + len - 1; where right(π 1:i−1) is the smallest j ∈ [π i−1 + 1, n + 2] such that j 6∈ π 1:i−1. ## 4 Evaluation 4.1 Datasets And Training Stages Each dataset is a parallel corpus of errorful and error-free sentences. Similar to (Omelianchuk et al., 2020; Tarnavskyi et al., 2022; Katsumata and Komachi, 2020), we train GEC-DePenD in three coarse-to-fine training stages. Table 1 summarizes dataset statistics and which stages of our pipeline they are used on. For *Stage I* (pretraining), we use the synthetic PIE dataset constructed by Awasthi et al. (2019b) by injecting synthetic grammatical errors into correct sentences. For training on *Stage II*, we used several datasets: (i) First Certificate in English (FCE) (Yannakoudakis et al., 2011) that contains 28 350 errorcoded sentences from English as a second language exams, (ii) National University of Singapore Corpus of Learner English (NUCLE) (Dahlmeier et al., 2013) with over 50K annotated sentences from essays of undergraduate students learning English, (iii) Write&Improve+LOCNESS dataset (W&I+L, also called BEA-2019 in some literature) (Bryant et al., 2019) intended to represent a wide variety of English levels and abilities, and (iv) cLang8 (Rothe et al., 2021a), a distilled version of the Lang8 dataset (Mizumoto et al., 2011) cleaned with the gT5 model. Finally, we used the W&I+L dataset again for additional training on *Stage III*. As evaluation data, we used the CoNLL-2014 test dataset (Ng et al., 2014) with the M2scorer (Dahlmeier and Ng, 2012) and W&I+L dev and test sets with the ERRANT scorer (Bryant et al., 2017). The W&I+L dev set was used for validation and ablation study; the two test sets, for evaluation. ## 4.2 Baseline Methods We consider both autoregressive and non-autoregressive baselines. BART (Lewis et al., 2020) is an autoregressive sequence-to-sequence model; it takes an errorful sentence as input and produces an error-free sentence token by token with the decoder. We show the scores reported by Katsumata and Komachi (2020) and also reimplement the model with a shallow 2layer decoder (*BART(12+2)* in Table 2) and train it according to the stages shown in Section 4.1; note that our reimplementation has improved the results. We consider two types of decoding: *greedy* and *aggressive greedy* (Sun et al., 2021). In greedy decoding, we generate the token with highest conditional probability. In aggressive greedy decoding, we generate as many tokens as possible in parallel, then re-decode several tokens after the first difference between source and target sequences, and then switch back to aggressive greedy decoding, repeating the procedure until the h/si token. Aggressive greedy decoding is guaranteed to produce the same output as greedy decoding but can be much faster. For comparison, we also show the state of the art T5-XXL autoregressive model with 11B parameters based on T5 (Raffel et al., 2020) and trained on a much larger synthetic dataset. FELIX (Mallinson et al., 2020) is a nonautoregressive model. It consists of two submodels: the first one predicts the permutation of a subset of source tokens and inserts hmski tokens, and the second model infills hmski tokens conditioned on the outputs of the first model. Both stages are done in a non-autoregressive way. Note that the model does not use any language-specific information. Levenshtein Transformer (LevT) (Gu et al., 2019; Chen et al., 2020) is a partially nonautoregressive model that does not use languagespecific information. It is based on insertions and deletions and performs multiple refinement steps. GECToR (Omelianchuk et al., 2020; Tarnavskyi et al., 2022) is a non-autoregressive tagging model that uses language-specific information, predicting a transformation for every token. The model is iteratively applied to the corrected sentence from the previous iteration. We compare GECToR based on XLNet (GECToRXLNet) and RoBERTa-large (GECToRlarge) pretrained models. Parallel Iterative Edit (PIE) (Awasthi et al., 2019b) is a non-autoregressive model that uses language-specific information. For each source token it predicts the corresponding edits, applying the model iteratively to get the corrected sentence. ## 4.3 Experimental Setup As the base model for GEC-DePenD we used BART-large (Lewis et al., 2020) with 12 pretrained encoder layers and 2 decoder layers, initialized randomly. The permutation network uses a single Transformer layer, also randomly initialized; the same encoder and decoder configurations were used for our autoregressive baseline BART(12+2). For training we used AdamW (Loshchilov and Hutter, 2017) with β1 = 0.9, β2 = 0.999, = 10−8, weight decay 0.01, and no gradient accumulation. For stages I and II we used learning rate 3 · 10−5and constant learning rate scheduler with 1551 | Dataset | #sentences | %errorful | Stages | |--------------|--------------|-------------|----------| | PIE | 9 000 000 | 100.0 | I | | cLang8 | 2 372 119 | 57.7 | II | | FCE, train | 28 350 | 62.5 | II | | NUCLE | 57 151 | 37.4 | II | | W&I+L, train | 34 308 | 66.3 | II, III | | W&I+L, dev | 4 384 | 64.3 | Val | | CoNLL, test | 1 312 | 71.9 | Test | | W&I+L, test | 4 477 | N/A | Test | 500 steps of linear warmup. For stage III we used learning rate 10−5and no warmup. For all stages we used 0.1 dropout, max_len = 2, s = 8 for Algorithm 1, λper = 5, confidence bias c ∈ [0.1, 0.3], 2-4 epochs, max 70 tokens per sentence and 3000 tokens per GPU, training on 4 TESLA T4 GPUs. ## 4.4 Experimental Results The main results of our comparison are presented in Table 2. We have evaluated the baselines described in Section 4.2 and GEC-DePenD in two versions: vanilla and SUNDAE with 2 decoder steps. The results show that GEC-DePenD outperforms all existing non-autoregressive baselines except for the language-specific GECToR family. We have also compared GEC baselines and GEC-DePenD in terms of inference speed on the ConLL-2014 test dataset on a single GPU. All models were implemented with the *Transformers* library (Wolf et al., 2020). In addition, we do not clip the source sentence, as was done by Omelianchuk et al. (2020), and process one sentence at a time. We used a single TESLA-T4 GPU. Performance results are summarized in Table 3. As we can see, GEC-DePenD outperforms all baselines in terms of inference speed and sets a new standard for performance, running twice faster than even non-autoregressive GECToR models. Note that GEC-DePenD with SUNDAE both outperforms 1step GECToRlarge in terms of F0.5 on ConLL-14 (Table 2) and operates 1.25x faster (Table 3). The quality gap between GEC-DePenD and its autoregressive counterpart (BART(12+2), our implementation) is reduced but still remains in Table 2. Figure 4 shows a study of the latency with respect to the length of the input sentence in tokens; it shows the results on the BEA-2019 dev set for the proposed GEC-DePenD and autoregressive BART(12+2) with greedy aggressive decoding. We see that the latency of the autoregressive base- ![6_image_0.png](6_image_0.png) line increases faster with increasing input sentence length than for the proposed non-autoregressive models. In addition, the speedup over the autoregressive baseline approaches 2x on sentence lengths from 60 to 70. ## 5 Ablation Study In this section, we present a detailed ablation study, reporting both ideas that worked (Section 3) and ideas that did not work. Table 4 shows our evaluation on the W&I+L-dev dataset; below we describe the results of Table 4 from top to bottom. Subscripts (e.g., VanillaII,III) show which training stages were used in the experiment (Section 4.1). ## 5.1 Dataset Construction First, we show that the proposed dataset construction algorithm (Algorithm 1) indeed yields an increase in performance. We considered the BARTlarge(12+2) model and performed training without stage I (Section 4.1) with FELIX (Mallinson et al., 2020) and Algorithm 1, calibrating the results with inference tweaks. Table 4 shows that the effect from Algorithm 1 is positive and significant. ## 5.2 Stage Iii, Sundae, And Inference Tweaks The next section of Table 4 shows all combinations of two- and three-stage training (Section 4.1), vanilla and SUNDAE model (Section 3.2), and adding inference tweaks (Section 3.4). We see that each addition—Stage III, SUNDAE, and inference tweaks—has a positive effect on validation performance in all settings, and the best model, naturally, is SUNDAEII,III with inference tweaks. | ConLL-14 test set | W&I+L test set | | | | | | | |------------------------|-------------------------------|------|------|-------|-------|-------|-------| | Prec | Rec | F0.5 | Prec | Rec | F0.5 | | | | Autoregressive | | | | | | | | | BART-large | (Katsumata and Komachi, 2020) | 69.3 | 45.0 | 62.6 | 68.3 | 57.1 | 65.6 | | BART(12+2) | Our implementation | 69.2 | 49.8 | 64.2 | 69.6 | 63.5 | 68.3 | | T5-XXL, 11B parameters | (Rothe et al., 2021a) | - | - | 68.75 | - | - | 75.88 | | Non-autoregressive | | | | | | | | | LevT | (Chen et al., 2020) | 53.1 | 23.6 | 42.5 | 45.5 | 37.0 | 43.5 | | FELIX | (Mallinson et al., 2022) | - | - | - | - | - | 63.5 | | PIE, BERT-large | (Awasthi et al., 2019b) | 66.1 | 43.0 | 59.7 | 58.0 | 53.1 | 56.9 | | GECToRlarge, 1 step | (Tarnavskyi et al., 2022) | 75.4 | 35.3 | 61.4 | 82.03 | 50.81 | 73.05 | | GECToRlarge, 3 steps | (Tarnavskyi et al., 2022) | 76.2 | 37.7 | 63.3 | 80.73 | 53.56 | 73.29 | | GECToRlarge, 5 steps | (Tarnavskyi et al., 2022) | 76.1 | 37.6 | 63.2 | 80.73 | 53.63 | 73.32 | | GECToRXLNet | (Omelianchuk et al., 2020) | 77.5 | 40.1 | 65.3 | 79.2 | 53.9 | 72.4 | | GEC-DePenD, vanilla | Ours | 67.8 | 41.3 | 60.1 | 69.5 | 55.3 | 66.1 | | GEC-DePenD, SUNDAE | Ours | 73.2 | 37.8 | 61.6 | 72.9 | 53.2 | 67.9 | Table 2: Experimental results on the ConLL-14 and W&I+L test sets. | Model | Speedup | #params | Model | Prec | Rec | F0.5 | |----------------------------------------------|---------------------------------------|-----------|----------------------|--------|-------|--------| | BART(12+2), greedy dec. | 1.0x | 238M | | | | | | BART(12+2), aggressive dec. | 3.7x | 238M | | | | | | GECToRXLNet, 5 steps | 2.8x | 120M | | | | | | GECToRlarge, 1 step | 3.8x | 360M | | | | | | GECToRlarge, 3 steps | 2.4x | 360M | | | | | | GECToRlarge, 5 steps | 2.4x | 360M | | | | | | GEC-DePenD, vanilla | 5.3x | 253M | | | | | | GEC-DePenD, SUNDAE | 4.7x | 253M | Dataset construction | | | | | VanillaII, III + FELIX tagger | 52.5 | 39.5 | 49.3 | | | | | VanillaII, III + Algorithm 1 | 57.6 | 38.9 | 52.5 | | | | | Training stages, SUNDAE and inference tweaks | | | | | | | | VanillaII | 57.9 | 36.5 | 51.8 | | | | | VanillaII + inf. tweaks | 59.3 | 34.6 | 51.9 | | | | | SUNDAEII | 56.4 | 39.3 | 51.9 | | | | | SUNDAEII + inf. tweaks | 59.9 | 35.0 | 52.4 | | | | | VanillaII, III | 54.6 | 42.8 | 51.7 | | | | | VanillaII, III + inf. tweaks | 60.6 | 36.5 | 53.5 | | | | | SUNDAEII, III | 54.9 | 43.4 | 52.1 | | | | | SUNDAEII, III + inf. tweaks | 63.5 | 34.3 | 54.3 | | | | | SUNDAE hyperparameters selection | | | | | | | | 1 step, λ0 = 0.75 | 60.8 | 36.5 | 53.6 | | | | | 1 step, λ0 = 0.25 | 62.9 | 33.9 | 53.7 | | | | | 1 step, λ0 = 0.01 | 60.8 | 35.8 | 53.4 | | | | | 2 steps, λ0 = 0.75 | 61.2 | 36.6 | 54.0 | | | | | 2 steps, λ0 = 0.25 | 63.5 | 34.3 | 54.3 | | | | | 2 steps, λ0 = 0.01 | 61.6 | 36.4 | 54.1 | | | | | 3 steps, λ0 = 0.75 | 61.3 | 36.7 | 54.0 | | | | | 3 steps, λ0 = 0.25 | 63.5 | 34.3 | 54.3 | | | | | 3 steps, λ0 = 0.01 | 61.7 | 36.4 | 54.1 | | | | | Beam search rescoring and sinkhorn | | | | | | | | #1 hypothesis, no length norm | 60.4 | 35.2 | 52.8 | | | | | #2 hypothesis, no length norm | 40.4 | 28.3 | 37.2 | | | | | #3 hypothesis, no length norm | 33.1 | 28.3 | 32.0 | | | | | Best of top-3 by GLEU | 71.8 | 45.9 | 64.5 | | | | | #1 hypothesis, with length norm | 60.6 | 36.5 | 53.5 | | | | | Decoder rescoring, λresc = 0.99 | 62.3 | 31.8 | 52.3 | | | | | Decoder rescoring, λresc = 0.999 | 60.3 | 34.8 | 52.6 | | | | | Decoder rescoring, λresc = 1 | 60.4 | 35.2 | 52.8 | | | | | VanillaII, III, 16 sinkhorn layers | 60.6 | 36.7 | 53.6 | | | | | Table 3: Performance comparison, ConLL-2014-test. 5.3 SUNDAE hyperparameters Next, we show that tuning SUNDAE hyperparameters, i.e., number of steps and λ0 (Section 3.2), can indeed improve performance; for the final model, we chose λ0 = 0.25 and 2 steps of SUNDAE. 5.4 Beam search rescoring and sinkhorn We first check how much choosing the right hypothesis from the beam search output will increase the performance. We generate top 3 beam search outputs and use the decoder to fill in hmski tokens. Then we select the hypothesis with the best GLEU score (Wu et al., 2016) compared to the ground truth, evaluating on W&I+L-dev. The next section of Table 4 shows that although the results deteriorate significantly from #1 beam search hypothesis to #2 and #3 (suggesting that beam search works as intended), choosing the best out of top three | Table 4: Ablation study on W&I+L-dev. | | | | | | We first check how much choosing the right hypothesis from the beam search output will increase the performance. We generate top 3 beam search outputs and use the decoder to fill in hmski tokens. Then we select the hypothesis with the best GLEU score (Wu et al., 2016) compared to the ground truth, evaluating on W&I+L-dev. The next section of Table 4 shows that although the results deteriorate significantly from \#1 beam search hypothesis to \#2 and \#3 (suggesting that beam search works as intended), choosing the best out of top three gives a very large increase in the metrics (more than +0.1 in terms of the F0.5 measure), so there is a lot of room for improvement in beam search generation. For this improvement, we explored two approaches. First, we tried to rescore hypotheses with decoder scores. Note that the log probability of a hypothesis is the sum of permutation and decoder scores. We introduce λresc ∈ [0, 1] and choose the best hypothesis out of three by the score λresc log p (π|x) + (1 − λresc) log p (y|π, x). We chose the best λresc by validation F0.5 but found that while λresc does help rebalance precision and recall, the best F0.5 is achieved for λ∗ resc = 1, so rescoring with the decoder is not helpful. The second approach, length normalization (Section 3.4), indeed improved the performance. Another related idea, the sinkhorn layer, was proposed by Mena et al. (2018) as an extension of the Gumbel-Softmax trick and later used for GEC by Mallinson et al. (2022). For an arbitrary matrix A, a sinkhorn step is defined as follows: $$\begin{array}{r c l}{\mathbf{A^{\prime}}}&{=}&{\mathbf{A}-\mathbf{I}}\\ {_{\!\!\Lambda}(1)}&{=}&{\mathbf{A^{\prime}}-1}\end{array}$$ A0 = A − LogSumExp(A, dim = 0), A(1) = A0 − LogSumExp(A0, dim = 1). A(1) is the output of the first sinkhorn step, and these steps can be repeated. The theoretical motivation here is that when the number of steps k tends to infinity, exp(A(k)) tends to a doubly stochastic matrix, i.e., after applying arg max to each row we obtain a valid permutation that does not point to the same token twice; the idea is to make several sinkhorn steps on A and then optimize the crossentropy loss as usual. We have experimented with different variations of sinkhorn layers, but even the best (shown in Table 4) did not bring any improvements. ## 6 Conclusion In this work, we have presented GEC-DePenD, a novel method for non-autoregressive grammatical error correction that decouples permutation and decoding steps, adds the step-unrolled denoising autoencoder into the decoder network, changes the dataset construction algorithm to preserve long spans, and uses inference tweaks to improve the results. GEC-DePenD shows the best results among non-autoregressive language-agnostic GEC models and significantly outperforms other models in terms of inference speed. We hope that our approach can become a basis for real life applications of grammatical error correction. ## 7 Limitations The main limitations of our study also provide motivation for future work. 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Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason R. Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Gregory S. Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. *ArXiv*, abs/1609.08144. Yilin Yang, Liang Huang, and Mingbo Ma. 2018. Breaking the beam search curse: A study of (re- )scoring methods and stopping criteria for neural machine translation. In *EMNLP*. Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011. A new dataset and method for automatically grading esol texts. In *Annual Meeting of the Association for Computational Linguistics*. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? Section 7 ✗ A2. Did you discuss any potential risks of your work? Our work deals with improving grammatical error correction and does not seem to have potential risks beyond the usual ecological concerns related to using large language models; we do note the model size and training time. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 4 ✓ B1. Did you cite the creators of artifacts you used? Section 4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? See the Supplement. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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bugliarello-etal-2023-measuring

Measuring Progress in Fine-grained Vision-and-Language Understanding

https://aclanthology.org/2023.acl-long.87

While pretraining on large-scale image{--}text data from the Web has facilitated rapid progress on many vision-and-language (V{\&}L) tasks, recent work has demonstrated that pretrained models lack {``}fine-grained{''} understanding, such as the ability to recognise relationships, verbs, and numbers in images. This has resulted in an increased interest in the community to either develop new benchmarks or models for such capabilities. To better understand and quantify progress in this direction, we investigate four competitive V{\&}L models on four fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al., 2022) consistently outperforms other baselines, and that modelling innovations can impact performance more than scaling Web data, which even degrades performance sometimes. Through a deeper investigation of X-VLM, we highlight the importance of both novel losses and rich data sources for learning fine-grained skills. Finally, we inspect training dynamics, and discover that for some tasks, performance peaks early in training or significantly fluctuates, never converging.

# Measuring Progress In Fine-Grained Vision-And-Language Understanding Emanuele Bugliarello∗,D,C Laurent SartranD **Aishwarya Agrawal**D Lisa Anne Hendricks‡,D **Aida Nematzadeh**‡,D DDeepMind CUniversity of Copenhagen ## Abstract While pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained models lack "fine-grained" understanding, such as the ability to recognise relationships, verbs, and numbers in images. This has resulted in an increased interest in the community to either develop new benchmarks or models for such capabilities. To better understand and quantify progress in this direction, we investigate four competitive V&L models on four fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al., 2022) consistently outperforms other baselines, and that modelling innovations can impact performance more than scaling Web data, which even degrades performance sometimes. Through a deeper investigation of X-VLM, we highlight the importance of both novel losses and rich data sources for learning fine-grained skills. Finally, we inspect training dynamics, and discover that for some tasks, performance peaks early in training or significantly fluctuates, never converging. ## 1 Introduction Fine-grained multimodal skills (*e.g.*, understanding relationships and recognising verbs) require identifying and relating various entities across both image and text modalities. Vision-and-language models (VLMs) need such skills to robustly perform well on real-world vision-and-language (V&L) applications; *e.g.*, a *coarse-grained* model tested on image retrieval to "find an image where something is on a sofa" might incorrectly return an image of a cat sitting *below* the sofa. As another example, in captioning, a model might incorrectly describe an image where "someone is *selling* a sweater" as "someone is *buying* a sweater," if it does not have a precise understanding of the two verbs. ∗Work completed during an internship at DeepMind. ‡denotes equal senior contribution. Correspondence to: Emanuele Bugliarello <[email protected]>. However, common V&L benchmarks (*e.g.*, Lin et al., 2014; Goyal et al., 2017; Suhr et al., 2019) do not explicitly shed light on such fine-grained understanding. Indeed, in the last few years, there has been an increase in the number of benchmarks which demonstrate that current, coarsegrained models struggle with fine-grained understanding (Hendricks and Nematzadeh, 2021; Parcalabescu et al., 2022; Salin et al., 2022; Thrush et al., 2022). Meanwhile, more models have been designed specifically to learn a better mapping between visual and textual modalities (*e.g.*, Yao et al., 2022a,b; Zeng et al., 2022; Gao et al., 2022). While such models perform well on coarse-grained retrieval and other downstream tasks, they have not been directly evaluated on fine-grained understanding. Consequently, it is unclear if the performance gains are due to tighter, more fine-grained representations introduced by model innovations at the pretraining stage. To fill this gap, we analyse several recent models with innovations designed for a better image–text alignment and their corresponding baselines on a suite of fine-grained benchmarks. We centre our study on three key questions. First we consider: Which models perform well on fine-grained tasks? To answer this, we evaluate models from four different model families trained with different amounts of pretraining data, as well as recent architectures that leverage frozen large language models (LLMs). We observe that modelling innovations have more impact than simply scaling image captions from the Web. Furthermore, explicitly modelling localisation can improve performance, but it is crucial how it is done, and simply using localisation data is not enough. Our observations motivate our next question: How do data and losses impact fine-grained understanding? We focus our study on the best performing model, X-VLM (Zeng et al., 2022), which learns to map specific objects and regions (not a full image) to a label (word or phrase describing the 1559 ![1_image_0.png](1_image_0.png) Table 1: Overview of our benchmarks. For consistency, we report the number of examples as the number of positive image–text pairs in each evaluation dataset. region). We reformulate the X-VLM loss to better disentangle the contribution of data and losses, observing that more data does not improve performance unless paired with **losses designed to learn** a mapping between regions and labels. Furthermore, the diversity of class labels is important for performance on coarse-grained retrieval, and region descriptions (as opposed to single word labels) are crucial for performance on fine-grained tasks. Finally, it is unclear if all fine-grained skills are learned at the same time during training so we consider: How does fine-grained understanding evolve during training? Surprisingly, we find that while performance steadily improves on coarse-grained retrieval tasks through training, **performance fluctuates substantially on many fine-grained tasks**, with some skills, like counting, becoming increasingly *worse*. Additionally, performance across different fine-grained tasks that should test for similar skills are not always well correlated. Contributions. In this work, we 1) provide indepth analyses of how data and modelling decisions impact performance on fine-grained tasks, and 2) further disentangle the gains given by data and pretraining losses on our best performing model (XVLM). Our results suggest that to make progress in fine-grained understanding, modelling innovations (*e.g.*, through object-centric losses) as well as data quality and richness are more effective than scaling up Web data alone. Finally, we 3) shed light on VLMs' pretraining dynamics and suggest that future work should revisit pretraining strategies in order to consistently improve across several tasks. ## 2 Benchmarks We describe the recent (English) benchmarks proposed to measure fine-grained V&L understanding in zero-shot setups.1 See Table 1 for an overview. SVO-Probes (Hendricks and Nematzadeh, 2021) focuses on verb understanding: it tests whether a model can identify if an image matches a sentence, and includes negative images which differ on a specific part of speech (Subject, Verb, and Object). The dataset consists of 421 verbs and over 48K image–sentence pairs.2 The authors show that their baselines fail more in situations requiring verb understanding than other parts of speech. VALSE (Parcalabescu et al., 2022) consists of six tasks that cover basic linguistic phenomena, such as plurality, actions and coreference. For each task, given a visual input, a model is asked to distinguish real captions from foils (Shekhar et al., 2017), where a foil is constructed from a caption by altering a word or phrase that realises a specific linguistic phenomenon (*e.g.*, semantic number of nouns). The authors show that VLMs can identify objects in images, but struggle to ground their interdependence with specific linguistic indicators. VSR (Liu et al., 2023) tests for 65 types of visual spatial relationships (*e.g.*, under, in front of) grouped into seven categories (*e.g.*, adjacency, orientation). Each sample consists of an image– sentence pair; a model needs to predict whether the sentence correctly describes the spatial relation between two objects in the image. We evaluate models in a zero-shot setup on the 'random' split.3 Winoground (Thrush et al., 2022) is an expertcurated benchmark aiming to test models' compositional reasoning. Given two images and two captions, the goal is to match them correctly; wherein both captions contain the same set of words, but in a different order. The authors define three scores: Text (whether a model can match the correct caption for a given image), Image (vice versa), and Group (whether a model can match each pair). Several competitive VLMs have been shown to often perform close to or below random chance. We also report zero-shot performance on coarsegrained retrieval in **Flickr30K** (Young et al., 2014) and **COCO** (Lin et al., 2014) in our analysis. 1We note that two more datasets require fine-grained skills to be solved and that they are not part of our analysis. ImageCoDe (Krojer et al., 2022) requires comparing a caption within a multi-image context, a setup not suitable for zeroshot evaluation of current single-image VLMs. Yuksekgonul et al. (2023) propose the ARO benchmark to evaluate VLMs' attribution, relation, and order understanding. However, the data had not been released as of the ACL deadline. 2Only 30,578 pairs were available as of Nov 2022. 3Note that VSR has recently been updated, but we expect the findings from our experiments to hold on the revised splits. | Model | Loss | Data | Downstream | | | | | | |----------------------|-------------------|-------------------------------------|------------------------|--------------------------------|----------------------|------|------|----| | CL | Text | Obj Det | Unsupervised | Supervised | VQAv2 NLVR2 RefCOCO+ | | | | | ALBEF4M | ✓ MLM | - | 4M: COCO+SBU+VG+CC3M | - | 74.7 | 80.5 | - | | | ALBEF14M | ✓ MLM | - | 14M: 4M + CC12M | - | 76.0 | 83.1 | - | | | BLIP14M | ✓ | LM | - | CAPFILT/B(14M) | - | 77.6 | 82.3 | - | | BLIP129M | ✓ | LM | - | CAPFILT/B(14M + LAION) | - | 78.2 | 83.1 | - | | BLIP129M-CAPFILT/L ✓ | LM | - | CAPFILT/L(14M + LAION) | - | 78.3 | 82.2 | - | | | BLIP-VIT/L129M | ✓ | LM | - | CAPFILT/L(14M + LAION) | - | - | - | - | | PEVL14M | ✓ MLM | MLM | 14M | RefCOCO{,+,g}+F30KE+GQA+VCR+VG | - | - | 74.5 | | | X-VLM4M | ✓ MLM Regress 4M | COCO + VG | 78.1 | 84.2 | 71.0 | | | | | X-VLM16M | ✓ MLM Regress 14M | COCO + VG + Objects365 + OpenImages | 78.4 | 84.4 | 76.9 | | | | ## 3 Evaluated Models Recent work has shown that two components are crucial ingredients of strong coarse-grained VLMs (*e.g.*, Li et al., 2021; Alayrac et al., 2022; Chen et al., 2023): 1) a contrastive objective that aligns vision and language modalities, and 2) a cross-attention mechanism that fuses the two modalities. As we are interested in high performance on both fine- and coarse-grained tasks, to select models for our study, we surveyed recent work that uses these building blocks,4 but also incorporates new losses or data that can potentially improve fine-grained V&L understanding. We find that many recent models build on ALBEF (Singh et al., 2022; Yang et al., 2022; Hao et al., 2023) (which we also study as a coarse-grained baseline). Other than strong performance on coarsegrained and downstream tasks, we also considered: 1) the possibility to study the role of new modelling innovations and data for fine-grained skills, and 2) the availability of open-source code and pretrained weights. This resulted in four models briefly described next (more details in App. A.1). Table 2 codifies the main differences in pretraining objectives and data used by these models. Recall that previous work does not evaluate these models on fine-grained benchmarks. ALBEF (Li et al., 2021), with strong downstream performance, matches all our criteria and serves as a coarse-grained baseline. ALBEF is a dual-stream encoder (Bugliarello et al., 2021) that first encodes images and captions independently, and then fuses them with cross-modal attention. BLIP (Li et al., 2022b) uses an autoregressive language model (LM), and employs a dataset bootstrapping technique (CapFilt) to generate synthetic captions and to remove noisy pairs from large-scale Web data. BLIP outperforms ALBEF on most coarse-grained downstream tasks; thus, we study BLIP as another coarse-grained baseline to test if its generative LM and data contributions also lead to better fine-grained understanding. PEVL (Yao et al., 2022b) is a fine-grained model building on ALBEF, but leverages more supervised datasets such as referring expressions, captions with visual coreferences, object detection and region descriptions data, etc. (see Table 2). Unlike ALBEF, PEVL is explicitly trained to learn fine-grained representations of entities by predicting their coordinates in a unified masked language modelling framework (similar to Pix2Seq, Chen et al., 2022): bounding box coordinates corresponding to a given entity are added in the caption as "A cat < 10 73 206 175 > is napping." X-VLM (Zeng et al., 2022) is our second finegrained model that enhances ALBEF by adding both new losses and additional supervised data. In contrast to PEVL, X-VLM models visual position through an additional bounding box prediction head that regresses the object's bounding box (bbox) coordinates. The authors use both object detection labels and region descriptions to learn coarse- and fine-grained alignments (we provide an in-depth analysis of this model in Section 5). We remark that PEVL and X-VLM were the only open-source fine-grained VLMs at the time of our evaluation, and both of them build on top of ALBEF. In addition to these core models, we also evaluate a dual-encoder network (CLIP; Radford et al. 2021) as well as recent architectures that rely on frozen, autoregressive (L)LMs: CLIPCAP (Mokady et al., 2021), FLAMINGO (Alayrac et al., 2022) and BLIP-2 (Li et al., 2023). As these models perform generally worse than our best fine-grained model, X-VLM, and differ significantly from it, we do not discuss their performance further. For more details, we refer the reader to Tables 6 to 11 in App. B.1. Model SVO VALSE VSR Winoground Avg. Avg. Test Avg. Text Image Group Random 50.0 50.0 50.0 25.0 25.0 12.5 CLIP400M 81.6 64.0 N/A 30.7 10.5 8.0 BLIP-2129M 86.5 74.0 61.5 43.0 22.0 18.2 1 ALBEF4M 87.6 69.1 57.3 29.2 15.5 11.0 2 X-VLM4M ♯ 88.9 72.4 63.0 44.0 **26.7 21.5** 3 ALBEF14M 88.6 69.4 58.3 32.5 16.2 12.7 4 BLIP14M 48.7 67.8 49.7 36.5 18.5 14.5 5 PEVL14M ♯ 86.2 68.9 57.5 33.2 15.7 12.2 8 X-VLM16M ♯ **90.0 74.5 64.3 46.7** 24.5 21.2 9 BLIP129M 51.4 68.8 46.9 35.5 15.0 11.7 10 BLIP129M-CAPFILT/L 51.2 68.2 48.7 34.7 15.2 12.2 11 BLIP-VIT/L129M 50.8 70.3 50.3 34.7 14.5 12.2 Table 3: Overall performance of core evaluated models on fine-grained benchmarks; the highest values for a given data size and the overall best values are marked with underline and bold, respectively. ♯ marks finegrained models. For a detailed breakdown of task performance and full comparison with prior arts, see App. B.1. ## 4 Which Fine-Grained Models Perform Well On Fine-Grained Tasks? We compare two strong VLMs (ALBEF and BLIP) with two models with explicit object modelling (*i.e.*, fine-grained; X-VLM and PEVL). We evaluate on fine-grained tasks (see Table 3) to determine if recent object-centric models improve on tasks designed to measure fine-grained skills—an evaluation missing from previous work. We also include results on CLIP and BLIP-2 in Table 3 to highlight how well fine-grained models perform, even though pretrained with less data and having fewer parameters (as shown in Table 6 in App. B.1). Experimental setup. All our fine-grained benchmarks only require models to predict a matching score for a given image–text pair, a common task that current V&L models—including all of our evaluated models—are pretrained to solve. On VSR, a model's prediction is correct if the matching score is greater/lower than 50% for a true/false label. On the other benchmarks, a model's prediction is correct if the score for the positive image–text pair is higher than the score of the negative pair(s).5 We evaluate the public models released by the authors on GCP.6 Code to reproduce our analysis is online.7 ALBEF vs. BLIP. We first compare our two coarse-grained baselines. A key difference between ALBEF and BLIP is that the former is trained with masked language modelling (MLM), while 5We evaluate SVO-Probes using *pairwise ranking accuracy* to benchmark models without a binary classification head (we note that Hendricks and Nematzadeh 2021 used accuracy). 6https://cloud.google.com/. 7https://github.com/e-bug/fine-grained-evals. the latter uses autoregressive language modelling (LM) for text; with BLIP outperforming ALBEF on downstream tasks when pretrained on the same 14M images. Performing the same comparison on fine-grained benchmarks, we find that ALBEF14M outperforms BLIP14M on all tasks (largely on SVOProbes and VSR) except on Winoground. Likewise, Table 6 (App. B.1) shows that other visualconditional LMs, such as CLIPCAP models, also struggle with fine-grained understanding. This might be due to the fact that our evaluation relies on image–text alignments and does not test for generation, where the LM objective is often preferred. Given these results and the fact that ALBEF is more similar to our fine-grained models, we compare against ALBEF in most of our discussion. ## Effectively Modelling Object Positions Improves fine-grained understanding. Overall, we find that X-VLM consistently outperforms all other evaluated approaches (see Table 3). This trend holds in both the 4M and 16M pretraining setups. When trained on the same 4M images as the ALBEF baseline, X-VLM with explicit object modelling, notably improves over all benchmarks (gaining 1.3pp on SVO-Probes, 3.3pp on VALSE, 5.7pp on VSR, and 14.8/11.2/11.5pp on Winoground). Importantly, X-VLM4M also outperforms ALBEF14M (trained on 10M more data points). This result shows the importance of explicit object modelling for a range of fine-grained tasks, including ones that are dissimilar to the supervised localisation task (*e.g.*, verb understanding). X-VLM16M, which adds CC12M as well as object detection data from OpenImages and Objects365 to X-VLM4M's data, achieves even higher overall gains in most fine-grained benchmarks. On VALSE, it closes the gap with a larger model trained on supervised data from many downstream tasks (12-in-1; Lu et al. 2020), and on VSR it achieves similar accuracy to LXMERT (Tan and Bansal, 2019) fine-tuned on 50% of VSR training data (67.9pp). Moreover, on Winoground, XVLM4M significantly outperforms previous coarsegrained models, including a large-scale dualencoder (CLIP, Group score of 8.0; Radford et al., 2021) and a strong, larger cross-modal Transformer (UNITERLarge, Group score of 10.5; Chen et al., 2020), as shown in Table 6 in App. B.1. Not all object modelling improves fine-grained understanding. Like X-VLM, PEVL also models visual locations of objects. However, it does so by expecting (masked) bbox locations as part of its input caption. Surprisingly, PEVL14M performs much worse than X-VLM16M on all tasks; in fact, it performs on par with the ALBEF14M baseline, despite being originally initialised with its checkpoint and further tuned to model visual object locations.8 We conjecture that modelling objects as input prompts is less beneficial than directly predicting object locations with a classification head (X-VLM), as the former does not directly influence the object's representations in the text modality. Modelling objects has more impact than increasing data. In Table 3, we observe that, not surprisingly, increasing data for a given family (*e.g.*, ALBEF4M to ALBEF14M) results in improved performance on most benchmarks. However, interestingly, the *fine-grained* X-VLM4M, trained on 4M data points, outperforms all BLIP129M variants—a coarse-grained model trained on 129M data points (compare row 2 with rows 9–11). Similarly, while increasing the data from 4M to 14M results in improvements across most tasks for the coarsegrained ALBEF14M, these performance gaps are smaller than what we gain from modelling objects on top of ALBEF4M. That is, the average performance gap between ALBEF4M and X-VLM4M is bigger (+5.2pp) than that observed when increasing data from ALBEF4M to ALBEF14M (+1.0pp). This result highlights that simply scaling data, without modelling innovations, might not be enough for notable improvements on fine-grained tasks. We also find that scaling data can *hurt* performance on some benchmarks. For example, on Winoground Image and Group scores, XVLM16M and BLIP-VIT/L129M perform worse than their corresponding models trained on less data, X-VLM4M and BLIP14M, respectively.9 Looking at performance by subtasks, we find that scaling Web data leads to worse performance on several of them, such as Image scores in most Winoground tasks, and VALSE's existence, counting adversarial and coreference for BLIP-VIT/L129M (more details in App. B.1). We 8We evaluate three different models released by the authors, which differ in their pretraining and fine-tuning data. All the variants perform similarly, and as a result, we only report PEVL14M, which underwent a second-stage pretraining on multiple supervised tasks (App. B.1 lists all the models). 9While BLIP129M performs worse than BLIP14M on a few benchmarks, this might be because the data size is significantly increased without scaling the model size. Thus, we compare against BLIP-VIT/L129M, which uses a larger image encoder. conjecture that pretraining on noisy Web datawhere the language in an image–text pair does not always faithfully describe the image—might diminish the fine-grained alignments learned from smaller, cleaner datasets (Hendricks et al. 2021 report similar trends on coarse-grained tasks). Takeaways. We observe that modelling object positions in images provides a strong signal for fine-grained understanding; but, how we model this information is crucial: simply pretraining a model with bbox positions in input does not lead to better off-the-shelf representations. We also see bigger gains on fine-grained tasks when modelling objects compared to scaling the pretraining data. ## 5 Data & Losses For Fine-Grained Tasks Recent fine-grained models build on coarse-grained ones by introducing additional training data (*e.g.*, object detection data in X-VLM and PEVL) and new losses (*e.g.*, bounding box regression loss in X-VLM). We study how data and losses influence fine-grained understanding, focusing on X-VLM as it outperforms other models on fine-grained benchmarks. While Zeng et al. (2022) perform ablations to show the importance of their new objective function, they do not study the impact of data and losses independently; moreover, they do not evaluate on find-grained benchmarks. We start with a description of X-VLM, emphasising details in its pretraining procedure, that we reveal to have significant impact on the final performance. ## 5.1 What Are X-Vlm Data And Losses? The X-VLM architecture consists of the same modules as ALBEF: a vision, a text, and a crossmodal Transformer (Vaswani et al., 2017) encoder (see App. A.1 for details). Given an image–text pair, ALBEF performs two forward passes (as shown in Figure 1): first, the model computes a contrastive learning loss (LCL) and an image– text matching loss (LITM). In a second pass, it masks text inputs to compute a visually-grounded masked language modelling loss, LMLM. After the two forward passes, ALBEF is trained with LA = LCL + LITM + LMLM. Data. While ALBEF is only pretrained on image–caption data, X-VLM additionally pretrains on object and region detection data. Object detection data consists of an object or attribute–object label (*e.g.*, "dog" or "brown dog"), an image, and ![5_image_0.png](5_image_0.png) a bounding box; region detection data consists of a short phrase (*e.g.*, "a cute brown dog"), an image, and a bounding box. Other multimodal Transformer models have used detection data (Hendricks et al., 2021; Li et al., 2020; Bugliarello et al., 2021; Zhang et al., 2021), but usually the bounding boxes are discarded, and objects or region descriptions are paired with the *entire* image. In contrast, a close examination of the X-VLM codebase10 reveals that X-VLM effectively makes use of bounding boxes. BBOX loss. To take advantage of additional bounding box (bbox) data, X-VLM introduces an objective, Lbbox, which learns to regress to object locations from object detection and region description data (see Figure 1 for an overview). VMA loss. The X-VLM paper presents two losses, LA and Lbbox. However, LA operates over two input types: image–text pairs from captioning data and image–text–bbox triplets from object detection data. Thus, it is hard disentangle the impact of the data and the losses on performance. We reformulate LA into two losses,11 operating over: (a) image–text pairs, LA, as in ALBEF; or (b) image–text–bbox pairs, that we denote *visually* masked ALBEF loss, LVMA. For LVMA, the visual and cross-modal encoders only attend to the image patches that correspond to the object bbox coordinates via an attention mask (see Figure 1). This results in an object-centric visual view for grounding the text label through the pretraining objectives. To compute this loss, in addition to the three forward passes described so far (CL and ITM, MLM, and BBOX losses), X-VLM performs two more passes: one where image patches outside a bounding box region are masked out to compute the *visually masked* CL and ITM loss, and another where text is additionally masked for the *visually* masked MLM loss. Section 5.3 shows both the 10https://github.com/zengyan-97/X-VLM. 11Our reformulation is equivalent to X-VLM, but it allows us to disentangle the impact of data and losses on performance. ## 5.2 Experimental Setup We re-implement ALBEF and X-VLM in JAX to ensure full control of modelling, data, and initialisation decisions.12 We initialise both models with a 224×224 ViT-B/16 visual encoder (Steiner et al., 2022), and BERTBASE (Devlin et al., 2019) weights in the text and cross-modal layers. Similar to Bugliarello et al. (2021), we pretrain our models on the *exact same* 4M and 14M datasets used by the authors (Table 2), but note that only 1.8M and 11.2M data points were available for CC3M and CC12M, respectively. For object detection data, we use the COCO and VG annotations released by the X-VLM authors. Following Zeng et al. (2022), we pretrain our models for 200K steps using the official hyperparameters (see App. A for more details). Confidential + Proprietary ## 5.3 Results Table 4 shows the overall zero-shot performance of our ablations on three fine-grained benchmarks and two coarse-grained retrieval tasks. Row 0 is our ALBEF re-implementation, while row 10 corresponds to our X-VLM pretrained following the implementation of Zeng et al. (2022). Our controlled study allows us to quantify how each technique (losses, data, implementation details) in X-VLM contributes towards fine-grained understanding. Data ablation. We first investigate the role of supervised detection data used to learn fine-grained relationships in X-VLM by pretraining the model, using its standard training objectives, and adding different data sources (rows 1–6). Looking at rows 1–3, we find that region descriptions from VG (VGRD) are the most useful, | Data | Loss | SVO-Probes VALSE VSR Random | Flickr30K | COCO | | | | | | | | | | | |--------|--------|-------------------------------|-------------|--------|------|-------|------|------|-----------|---------------------|------|------|------|------| | DA | COCOOD | VGOD | VGRD | LA | LVMA | Lbbox | Avg. | Avg. | Test Avg. | TR@1 IR@1 TR@1 IR@1 | | | | | | 0 | ✓ | ✓ | 85.9 | 68.7 | 59.3 | 76.3 | 59.8 | 60.9 | 45.7 | | | | | | | 1 | ✓ | ✓ | ✓ | ✓ | ✓ | 85.9 | 69.1 | 58.6 | 72.8 | 59.5 | 60.8 | 46.1 | | | | 2 | ✓ | ✓ | ✓ | ✓ | ✓ | 86.0 | 68.6 | 59.7 | 77.1 | 62.7 | 63.3 | 47.5 | | | | 3 | ✓ | ✓ | ✓ | ✓ | ✓ | 86.6 | 70.3 | 61.1 | 79.4 | 62.3 | 64.8 | 49.1 | | | | 4 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 85.6 | 67.5 | 60.7 | 77.2 | 60.7 | 63.3 | 47.3 | | | 5 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 86.5 | 67.6 | 60.1 | 77.2 | 61.4 | 62.9 | 47.6 | | | 6 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 86.9 | 71.1 | 62.5 | 79.7 | 63.4 | 64.4 | 49.1 | | | 7 | ✓ | ✓ | ✓ | ✓ | ✓ | 85.9 | 69.3 | 58.2 | 75.5 | 58.9 | 61.9 | 45.8 | | | | 8 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 86.5 | 69.1 | 59.0 | 77.5 | 62.3 | 63.0 | 47.6 | | | 9 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 86.0 | 67.9 | 60.5 | 78.0 | 60.5 | 62.1 | 47.6 | | | 10 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 86.9 | 69.8 | 61.9 | 78.3 | 63.0 | 64.6 | 48.6 | single-source signal for the model, resulting in improvements in both fine- and coarse-grained tasks. This variant is either close to or surpasses the final X-VLM variant (row 10) in all the tasks. We attribute this success to both its size (3.7M data points) and language format, wherein noun phrases, rather than simple labels, describe a given entity. In addition, object detection data from VG (VGOD) leads to similar fine-grained results as COCOOD, but significantly better zero-shot retrieval performance. VGOD is not only larger than COCOOD, but also includes a more diverse set of classes.13 We hypothesise that a *large number of classes* (as in VGOD) is important for coarse-grained retrieval tasks, and *more descriptive phrases* of VGRD (rather than single labels) significantly impact fine-grained tasks. To verify this, we disentangle the effect of data size and type: specifically, we re-train rows 2–3 on a subset of VG with the same number of images and annotations as in COCOOD. Figure 2 confirms our hypothesis: even when controlled for size, VGRD leads to notably better performance than COCOOD. On coarse-grained datasets, VGOD largely outperforms COCOOD. Looking at multi-source supervised data (rows 4–6), our best performing model combines VGOD and VGRD data (row 6) and, surprisingly, adding COCOOD does not boost performance. Loss ablation. We investigate the role of the two objectives used during supervised pretraining of XVLM (rows 7–9). We see that training an ALBEF model on object detection data as-is (row 7) results in similar performance as pretraining it on standard ![6_image_0.png](6_image_0.png) image–caption data. That is, just adding more data is not enough; additional supervision in the form of the X-VLM pretraining objectives is crucial. Compared to Lbbox (row 9), our reformulation makes it clear that LVMA (row 8) leads, on average, to both higher fine-grained accuracy and higher recall on retrieval tasks. One potential explanation is that the visually masked forward pass directly influences the representation learned by the contrastive loss, as well as the cross-modal representations. In contrast, the regression loss only occurs after crossmodal interaction, suggesting that better alignment is important in both contrastive and cross-modal features. Finally, X-VLM achieves its best performance when combining LVMA and Lbbox. Takeaways. Our reformulation of X-VLM allows us to conduct a careful analysis in a controlled setup on how data and losses influence X-VLM performance. We show that more data does not improve performance unless paired with additional supervisory signal, in the form of either the visually masked ALBEF loss or bbox regression. Given our observations and the fact that, as seen in Section 4 and App. B.1, X-VLM largely outperforms 13COCOOD and VGOD have 80 and 50k labels respectively. ![7_image_0.png](7_image_0.png) the large-scale CLIP and BLIP-2 models on finegrained tasks such as VALSE and Winoground, we believe that a promising direction in fine-grained understanding will require careful model and loss design with rich data sources like VG, not just scaling up with (potentially) noisy data. ## 6 Dynamics Of Fine-Grained Tasks We now analyse the dynamics of fine-grained skills for our models to investigate (i) when and whether they are acquired, and (ii) how they relate to one another, especially when they aim at measuring similar capabilities. For example, does action understanding in VALSE correlate with verb understanding in SVO-Probes? Are there skills that vastly differ from each other that they would require different modelling contributions (*e.g.*, counting)? Experimental setup. We evaluate checkpoints (every 10K steps) from pretraining our ALBEF and X-VLM re-implementations with 4M and 14M data points. We focus on 14M results as we see similar patterns with 4M (see App. B.2). When evaluating correlation patterns, we report both Pearson and Spearman correlation coefficients. Different skills, different patterns. Figure 3 (top) shows how the average model performance evolves during pretraining for the four benchmarks. Interestingly, the performance on these benchmarks converges at different rates: both ALBEF and X-VLM models easily improve on SVO-Probes. Moreover, we observe that modelling objects (à la X-VLM) leads not only to better fine-grained understanding after 200K steps (Tables 3 and 4), but also to remarkably quicker learning rates. Figure 3 (bottom) shows performance on indicative VALSE tasks, as well as on coarse-grained image retrieval on COCO. While some skills, such as spatial relations understanding, are learned progressively during pretraining, others, such as counting, *degrade* after a first, short learning phase. Finally, other skills, such as coreference resolution, *oscillate* significantly throughout pretraining, showing how models can not properly acquire them. This is in contrast to the coarse-grained COCO retrieval task for which the performance steadily increases over time. We conclude that it is particularly important to examine the training dynamics of fine-grained tasks, and that a single checkpoint might be inadequate for a number of skills. Results on all tasks are provided in App. B.2, including on Winoground for an ALBEF4M that we pretrained on GCP using the original codebase. Same skills, same patterns? We next investigate whether closely-related tasks in different benchmarks have high correlation throughout pretraining. While we find that VALSE action replacement and SVO-Verb have a +55/67% Pearson/Spearman correlation, there is a -13/11% correlation between VALSE actant swap and SVO-Subject. Looking at VALSE spatial relations, we find high correlation (+75/65%) with average VSR performance, and especially with relations such as on top of, on, inside, by, and in; mostly belonging to the 'Topological' category in VSR. On the other hand, we find almost no correlation with several 'Directional' (*e.g.*, across from) and 'Orientation' (*e.g.*, parallel to) relations, as well as with some 'Topological' ones (*e.g.*, touching); and even negative correlation (-40% or less) with alongside, below, toward, part of and near. Finally, surprisingly, VSR dev and test splits are not positively correlated for all relations. While average performance is highly correlated (+77/78%), only a few relations have Pearson/Spearman coefficients larger than 30% (in, on, above, within, and consists of). On the other hand, near, ahead of and adjacent to are negatively correlated between dev and test sets, and most relations show very low correlations between the two sets. As a result, improvement in a given relation type on the dev set, will likely not transfer at test time. Takeaways. When tested on fine-grained benchmarks, we observe that, compared to ALBEF, XVLM is more sample efficient as it achieves higher performance with fewer training steps. Also, while some tasks steadily improve during pretraining, for others, the performance degrades or *fluctuates*. Moreover, surprisingly, the performance of tasks measuring similar skills but from different benchmarks do not always positively correlate. ## 7 Discussion While recent pretrained VLMs achieve impressive performance on various downstream benchmarks (such as visual question answering and image retrieval), recent benchmarks have highlighted that they still struggle with tasks that require *finegrained* understanding—where a model needs to correctly align various aspects of an image to their corresponding language entities. Yet, it is still not known to which extent recent fine-grained VLMs (*e.g.*, Zeng et al., 2022; Yao et al., 2022b; Li et al., 2022a; Dou et al., 2022) fare on such benchmarks. We address this gap by evaluating strong and fine-grained models on four benchmarks (Hendricks and Nematzadeh, 2021; Parcalabescu et al., 2022; Liu et al., 2023; Thrush et al., 2022), and encourage future work to report zero-shot finegrained performance on our selection of benchmarks, especially if models are not open-source. Our work contributes to a growing thread of research devoted to understand what is learned by pretrained VLMs, such as studying cross-attention patterns (Cao et al., 2020), cross-modal input ablations (Frank et al., 2021), probing linguistic and visual structure (Milewski et al., 2022; Salin et al., 2022; Nikolaus et al., 2022), robustness to words order (Akula et al., 2020; Thrush et al., 2022), and incorrectly fusing image and language modalities (Diwan et al., 2022). Here, we show that object modelling through a prediction loss (as done in X-VLM) results in notable improvements across all benchmarks, outperforming models trained on much larger amounts of Web data. Our analysis highlights that teaching VLMs concepts of objects (*e.g.*, by masking irrelevant parts of the image) is crucial for effectively learning fine-grained skills. Though our models rely on supervised data to learn better localisation, we hope our findings can encourage researchers to design better loss functions for image–text mapping from unsupervised, Webscale data as well. Finally, our results also highlight the challenges of evaluating fine-grained understanding: the recent benchmarks capture a variety of subtasks (from counting to relation understanding); to perform well on these subtasks, a model requires different skills. Indeed, we observe that, during training, model performance does not always increase for all subtasks, and in particular, fluctuates a lot for counting, coreference resolution, and various spatial relations. An important future direction is designing models that perform well on a larger range of these subtasks, where improving on one subtask does not degrade performance on the rest. It is unclear why benchmarks do not always correlate; possible reasons include the data itself (images selected for analysis, annotator instructions), or that different competencies are required for different fine-grained tasks. We hope future work can explore this further, possibly by closely examining data in fine-grained benchmarks or expanding the models used in analysis beyond what we used here. ## Limitations Our work focuses on assessing recent English VLMs on tasks which require fine-grained understanding. Here, we outline limitations that we believe are important considerations for future work. First, we only examined a limited number of models. These include (i) strong coarse-grained models, such as ALBEF, CLIP, FLAMINGO and BLIP-2, and (ii) two strong fine-grained models, PEVL and X-VLM, that build on ALBEF. While we believe our selection of models is representative of strong components in pretrained VLMs (such as dual-encoder and cross-modal interactions), we could not easily evaluate different approaches towards fine-grained understanding (*e.g.*, Yao et al., 2022a; Li et al., 2022a) as the corresponding models and code are not open-source. We hence hope our study will motivate future work to report zeroshot performance on fine-grained benchmarks. Second, we evaluate our models in a zero–shot setting using image–text matching. Future work could consider how fine-grained understanding improves when fine-tuning for specific tasks. As opposed to relying on image–text matching scores, alternative methods like input ablations, visualising attention or activations could also be used to gain an understanding of potential failure modes. Third, though we note specific areas where model performance fluctuates a lot during pretraining, we look forward to future research that improves performance for various such areas, like existence and counting. Finally, some datasets we use are quite small. For example, Winoground only has 1,600 data points. We hope that our analysis sheds light on the kinds of skills models struggle with and encourages more and larger datasets that test for these skills. ## Ethics Statement All datasets used in this work have been previously published. Multimodal datasets frequently include social biases (Meister et al., 2022), and we expect the models trained on them to reflect the biases in these datasets. Datasets also include images of people, and there is no mechanism for people to remove themselves from these datasets. Multimodal models have many downstream uses. Some examples of beneficial applications include: more advanced image and video retrieval, visual description systems to aid the visually impaired, and interfaces which allow users to more seamlessly interact with smart home devices. Harmful applications might include surveillance, especially when imagery of people is being used without their consent, or fine-tuning a model to retrieve harmful content, such as pornographic material. In this work, we aim to understand how models perform on fine-grained tasks which highlights current failure modes of our models. We hope insights from our work can inspire (i) novel models which perform well on a broad set of fine-grained tasks, as well as (ii) more high quality data to stress test our models. 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OPT: Open pretrained transformer language models. *arXiv preprint* arXiv:2205.01068. ## A Experimental Setup In this section, we provide further details on the experimental setups that we used for our studies. ## A.1 Evaluated Models: Details We provide more details on the models we use to evaluate progress in fine-grained V&L understanding. See Table 5 for an overview.14 ALBEF (Li et al., 2021) is a recent VLM that has gained popularity due to its design choices, effectively combining core components in V&L learning, such as a contrastive objective and cross-attention, that result in strong downstream performance. ALBEF is a dual-stream encoder (Bugliarello et al., 2021) that first encodes images and captions independently with a vision (ViT; Dosovitskiy et al. 2021; Touvron et al. 2021) and text (BERT; Devlin et al. 2019) Transformer, respectively; and then fuses them in a cross-modal Transformer. The model is pretrained with three objectives: masked language modelling (MLM), unimodal image–text contrastive learning and cross-modal image–text matching. We refer to the original work for more details. While ALBEF does not explicitly train for fine-grained understanding, it serves as an important baseline since our three other models build on top of it. BLIP (Li et al., 2022b) is a unified V&L understanding and generation model, that can be applied to a wide range of downstream tasks. A key component to BLIP's success is CAPFILT: a dataset boostrapping method which the authors use to generate synthetic captions and removing noisy pairs from large-scale Web data. Moreover, unlike any other model we evaluate, BLIP uses an autoregressive language modelling (LM) objective to convert visual information into coherent captions, allowing us to evaluate the potential benefits of this objective to learn fine-grained relationships. BLIP is not explicitly trained for fine-grained understanding, however, we believe it is important to assess whether generative language modelling and its data contributions that enhance downstream performance also lead to better fine-grained skills. PEVL (Yao et al., 2022b) explicitly connects image regions and text tokens through cross-modal position modelling. Similar to Pix2Seq (Chen et al., 14Each model's text and multimodal layers were originally initialised with the weights of BERTBASE (Devlin et al., 2019). 2022), PEVL expresses visual positions in text by appending the bounding box coordinates corresponding to a given (annotated) entity in the caption, surrounded by two special tokens '<' and '>': "A cat < 10 73 206 175 > is napping." The bounding box coordinates are discretised and added to the text vocabulary. Starting from an ALBEF14M checkpoint, PEVL is pretrained by recovering masked text and position tokens through a generalised MLM objective. The model was trained on a diverse corpus of referring expressions, captions with visual coreferences, question answering, commonsense reasoning, object detection and region descriptions data (Table 2). Unlike ALBEF, PEVL is explicitly trained to learn fine-grained, grounded representations of entities by predicting their coordinates in a unified MLM framework. We evaluate three different models released by the authors, which differ in their pretraining and fine-tuning data: PEVL14M, underwent a second-stage pretraining on multiple supervised tasks (Table 5); PEVLGRD, which was further fine-tuned for position-output tasks such as phrase grounding (Plummer et al., 2015); and PEVLVRD, which was fine-tuned for the position-input task of visual relation detection (Krishna et al., 2017). X-VLM (Zeng et al., 2022) also aims at learning to locate visual concepts in the image given the associated texts. Similar to the ALBEF architecture, the model consists of an image encoder, a text encoder, and a cross-modal encoder. However, unlike PEVL, X-VLM models visual position through an additional bounding box prediction head: given the visually grounded representation of an object label, the model is trained to regress the object's bounding box (bbox) coordinates. The authors use both object detection labels and region descriptions to learn multi-grained alignments. The pretraining objective is a linear combination of this bbox loss and the losses defined in ALBEF to align texts and visual concepts (for more details, see Section 5). In addition to the above models, which we extensively discuss, we also evaluate the following models, based on dual-encoder and frozen LLMs. CLIP (Radford et al., 2021) is a widely used dual-encoder network. The model consists of two encoders, one for images and one for text, trained to represent both modalities in a joint space via an unsupervised contrastive objectives over more than 400M image–text pairs from the Web. Due | Model | Data | | | | | | |--------------------|-----------|---------|------------------------------------|--------|--------|-------| | Name | ViT | Img Res | Datasets | # Img | # Cap | # Ann | | ALBEF4M | DeiT-B/16 | 256×256 | 4M: COCO+SBU+VG+CC3M | 4.0M | 5.1M | - | | ALBEF14M | DeiT-B/16 | 256×256 | 14M: 4M + CC12M | 14.1M | 15.2M | - | | BLIP14M | ViT-B/16 | 224×224 | CAPFILT/B(14M) | 14.1M | 15.2M | - | | BLIP129M | ViT-B/16 | 224×224 | CAPFILT/B(14M + LAION) | 129.1M | 130.2M | - | | BLIP129M-CAPFILT/L | ViT-B/16 | 224×224 | CAPFILT/L(14M + LAION) | 129.1M | 130.2M | - | | BLIP-VIT/L129M | ViT-L/16 | 224×224 | CAPFILT/L(14M + LAION) | 129.1M | 130.2M | - | | PEVL14M | ALBEF14M | 256×256 | 14M→RefCOCO{,+,g}+F30KE+GQA+VCR+VG | 14.4M | 15.2M | 4.7M | | PEVLGRD | PEVL14M | 512×512 | PEVL14M →RefCOCO{,+,g}+F30KE | 14.4M | 15.2M | 4.7M | | PEVLVRD | PEVL14M | 512×512 | PEVL14M →VG | 14.4M | 15.2M | 6.2M | | X-VLM4M | Swin-B/32 | 224×224 | 4M | 4.0M | 5.1M | 6.2M | | X-VLM16M | Swin-B/32 | 224×224 | 14M + Objects365 + OpenImages | 17.4M | 16.2M | 12.4M | to its simplicity and wide adoption, we report its performance as a strong, representative baseline. ClipCap (Mokady et al., 2021) is an autoregressive encoder–decoder network. The image encoder is a pretrained CLIP model, while the text decoder is a pretrained GPT-2 (Radford et al., 2019) language model. The authors propose to learn a lightweight Transformer-based network to map CLIP embeddings into a fixed length prefix. The mapping network and the text decoder are finetuned to learn how to generate captions, while the CLIP image encoder is frozen. At inference time, the model generates the caption word after word, starting from the CLIP-based prefix. We report performance for the two released versions—one finetuned on COCO, the other on CC3M—by ranking positive and negative samples on their likelihood. Flamingo (Alayrac et al., 2022) is a state-ofthe-art VLM capable of tackling a wide range of vision and language tasks from a few input/output examples. To achieve this, the model relies on a pretrained CLIP-like image encoder and a strong pretrained LLM (Hoffmann et al., 2022), both kept frozen. To ingest images and videos, the model learns a small fixed number of visual tokens (Lee et al., 2019; Jaegle et al., 2021). The model is pretrained to generate text from a sequence of text tokens interleaved with images and/or videos. BLIP-2 (Li et al., 2023) is the most recent, state-of-the-art VLM based on frozen large image encoders and frozen LLMs (Zhang et al., 2022; Chung et al., 2022). Like CLIPCAP, BLIP-2 learns a mapping network, which in this case is a Transformer model initialised from BERTBASE. The mapping network learns visual query tokens to map the visual representations to the frozen LLM in two stages: a V&L representation stage, and a generative learning stage. The model was pretrained with the same objectives and on the same 129M image– caption data as BLIP. Following the authors' setup for image–text retrieval and matching, we use the BLIP-2 model after the first-state pretraining. ## A.2 Re-Implementation Setup We re-implement ALBEF and X-VLM in JAX (Babuschkin et al., 2020) to ensure full control of modelling, data, and initialisation decisions.15 We note ALBEF's vision encoder is initialised with a pretrained ViT-B/16 encoder (Touvron et al., 2021) with an input resolution of 256×256 pixels, but X-VLM adopts a more efficient SwinB/32 (Liu et al., 2021) encoder with input resolution of 224×224 pixels. In our re-implementation we initialise both models with a ViT-B/16 with a 224×224 input resolution pretrained on ImageNet15To verify our implementation, we compare an ALBEF model trained in our codebase with one trained in the original codebase. Specifically, we pretrain both models on COCO by initialising their visual encoder with a CLIP ViT-B/16 model, and their text encoder with a BERTBASE model. The two models perform similarly on both zero-shot Flickr30K and COCO retrieval tasks with a gap below 1pp Recall@1. | Model | SVO-Probes VALSE VSR Random | Winoground | Flickr30K | COCO | | | | | | | | |----------------------------|-------------------------------|--------------|-------------|-----------|--------------------------------------|------|------|------|-------|------|-------| | Name | Size | Avg. | Avg. | Test Avg. | Text Image Group TR@1 IR@1 TR@1 IR@1 | | | | | | | | Random | 50.0 | 50.0 | 50.0 | 25.0 | 25.0 | 12.5 | 0.1 | 0.1 | 0.02 | 0.02 | | | LXMERT | 263M | - | 59.6 | 72.5† | 19.2 | 7.0 | 4.0 | - | - | - | - | | UNITERLarge | 303M | - | - | - | 38.0 | 14.0 | 10.5 | 80.7 | 66.2 | 64.1 | 48.8 | | 12-in-1 | 270M | - | 75.1 | - | - | - | - | - | 67.8† | - | 68.0† | | CLIP (ViT-B/32) | 151M | 81.6 | 64.0 | N/A | 30.7 | 10.5 | 8.0 | 88.0 | 68.7 | 58.4 | 37.8 | | CLIPCAPCC3M | 295M | 83.1 | 65.7 | N/A | 12.2 | 14.7 | 5.5 | 26.4 | 44.1 | 6.7 | 24.3 | | CLIPCAPCOCO | 295M | 84.1 | 68.5 | N/A | 12.2 | 14.7 | 5.5 | 27.8 | 52.2 | 8.1 | 38.4 | | FLAMINGO | 80B | 88.4 | 75.3 | N/A | - | - | - | - | - | - | - | | BLIP-2 | 1.2B | 86.5 | 74.0 | 61.5 | 43.0 | 22.0 | 18.2 | 95.5 | 86.7 | 80.7 | 64.2 | | 1 ALBEF4M | 500M | 87.6 | 69.1 | 57.3 | 29.2 | 15.5 | 11.0 | 85.2 | 69.4 | 69.7 | 51.1 | | ♯ | 239M | 88.9 | 72.4 | 63.0 | 44.0 | 26.7 | 21.5 | 85.3 | 71.9 | 70.8 | 55.6 | | 2 X-VLM4M 3 ALBEF14M | 500M | 88.6 | 69.4 | 58.3 | 32.5 | 16.2 | 12.7 | 90.9 | 75.9 | 73.2 | 54.8 | | 4 BLIP14M | 638M | 48.7 | 67.8 | 49.7 | 36.5 | 18.5 | 14.5 | 82.6 | 78.4 | 70.4 | 57.3 | | 5 PEVL14M ♯ | 500M | 86.2 | 68.9 | 57.5 | 33.2 | 15.7 | 12.2 | 74.9 | 60.0 | 45.9 | 33.2 | | 6 PEVLGRD ♯ | 502M | 88.5 | 69.5 | 57.7 | 36.2 | 15.0 | 12.0 | 71.8 | 77.6 | 42.8 | 37.7 | | 7 PEVLVRD ♯ | 502M | 84.8 | 64.5 | 59.5 | 31.2 | 12.0 | 7.5 | 68.0 | 55.7 | 38.3 | 30.6 | | 8 X-VLM16M ♯ | 239M | 90.0 | 74.5 | 64.3 | 46.7 | 24.5 | 21.2 | 87.7 | 74.9 | 71.6 | 56.1 | | 9 BLIP129M | 638M | 51.4 | 68.8 | 46.9 | 35.5 | 15.0 | 11.7 | 90.2 | 79.5 | 71.9 | 58.6 | | 10 BLIP129M-CAPFILT/L 638M | 51.2 | 68.2 | 48.7 | 34.7 | 15.2 | 12.2 | 89.1 | 79.7 | 72.2 | 57.8 | | | 11 BLIP-VIT/L129M | 1.1B | 50.8 | 70.3 | 50.3 | 34.7 | 14.5 | 12.2 | 90.4 | 80.6 | 74.2 | 59.3 | | Model | Existence | Plurality | Counting | Sp.rel.‡ | Action | Coreference | Foil-it! | Avg. | | | | | |--------------------|-------------|-------------|-------------|------------|--------------------|---------------|------------|--------|------|------|------|------| | quantifiers | number | balanced | sns.† adv.† | relations | repl.† actant swap | standard | clean | | | | | | | Random | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | 50.0 | | GPT-2 | 58.0 | 51.9 | 51.6 | 49.8 | 45.3 | 75.0 | 66.8 | 76.9 | 54.5 | 50.0 | 80.7 | 60.1 | | CLIP | 66.9 | 56.2 | 62.1 | 62.5 | 57.5 | 64.3 | 75.6 | 68.6 | 52.1 | 49.7 | 88.8 | 64.0 | | LXMERT | 78.6 | 64.4 | 62.2 | 69.2 | 42.6 | 60.2 | 54.8 | 45.8 | 46.8 | 44.2 | 87.1 | 59.6 | | 12-in-1 | 95.6 | 72.4 | 76.7 | 80.2 | 77.3 | 67.7 | 65.9 | 58.9 | 75.7 | 69.2 | 86.9 | 75.1 | | CLIPCAPCC3M | 66.3 | 54.8 | 49.4 | 50.1 | 51.5 | 83.2 | 75.5 | 87.9 | 45.1 | 45.2 | 94.7 | 65.7 | | CLIPCAPCOCO | 74.9 | 60.6 | 55.0 | 53.0 | 53.0 | 89.7 | 71.0 | 86.5 | 47.5 | 49.0 | 97.1 | 68.5 | | FLAMINGO | 63.6 | 59.8 | 58.2 | 55.2 | 80.2 | 89.7 | 86.7 | 92.8 | 72.2 | 65.4 | 97.0 | 75.3 | | BLIP-2 | 83.6 | 79.6 | 70.2 | 68.7 | 68.0 | 65.6 | 84.4 | 63.2 | 62.6 | 58.7 | 96.0 | 74.0 | | ALBEF4M | 71.3 | 78.8 | 62.2 | 65.1 | 59.8 | 73.1 | 73.6 | 58.4 | 52.4 | 55.8 | 95.5 | 69.1 | | X-VLM4M | 80.0 | 77.8 | 69.0 | 68.4 | 72.5 | 74.8 | 77.3 | 65.0 | 50.1 | 48.1 | 92.5 | 72.4 | | ALBEF14M | 69.5 | 76.0 | 61.5 | 61.0 | 64.5 | 70.7 | 77.6 | 60.5 | 55.9 | 61.5 | 96.1 | 69.4 | | BLIP14M | 82.4 | 73.8 | 61.8 | 62.6 | 63.7 | 65.2 | 74.7 | 55.2 | 52.3 | 42.3 | 92.3 | 67.8 | | PEVL14M | 89.7 | 65.5 | 66.0 | 66.2 | 57.3 | 67.9 | 73.5 | 59.4 | 58.2 | 56.7 | 90.9 | 68.9 | | PEVLGRD | 91.1 | 63.9 | 70.0 | 70.9 | 63.2 | 62.4 | 74.4 | 57.1 | 53.8 | 49.0 | 92.6 | 69.5 | | PEVLVRD | 83.8 | 61.8 | 62.8 | 70.3 | 40.4 | 64.5 | 68.1 | 53.2 | 47.7 | 42.3 | 94.1 | 64.5 | | X-VLM16M | 83.6 | 78.7 | 71.5 | 72.0 | 74.8 | 73.1 | 79.2 | 64.6 | 60.0 | 49.0 | 91.9 | 74.5 | | BLIP129M | 78.2 | 75.9 | 63.4 | 63.4 | 58.5 | 66.2 | 75.2 | 59.0 | 56.4 | 52.9 | 93.2 | 68.8 | | BLIP129M-CAPFILT/L | 75.4 | 75.0 | 64.7 | 68.8 | 53.0 | 66.7 | 73.0 | 60.6 | 48.2 | 51.0 | 93.8 | 68.2 | | BLIP-VIT/L129M | 73.3 | 77.7 | 68.2 | 67.6 | 61.2 | 71.8 | 75.3 | 60.8 | 51.1 | 45.2 | 96.1 | 70.3 | 21k (Steiner et al., 2022), to ensure that different initialisation is not responsible for the results. We pretrain our models on the same 4M and 14M datasets that were originally used by the authors (Table 2), but note that only 1.8M and 11.2M data points were available for CC3M and CC12M, respectively. For object detection data, we use the data points released by the X-VLM authors, and interleave captioning and detection data with a 2:1 ratio following their official implementation. Following (Zeng et al., 2022), we pretrain our models for 200K steps using a batch size of 512 and 1024 samples for ALBEF and X-VLM, respectively. We pretrain once, using the same hyperparameters | Model | Object | Relation | Both | 1 Main Pred | 2 Main Preds | | | | | | | | |-----------------------------------------------------------------------------|------------------|------------------|------------------|------------------|----------------|-------------|-------------|-------------|-------------|-------|-------|------| | Text Image Group | Text Image Group | Text Image Group | Text Image Group | Text Image Group | | | | | | | | | | Random | 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 | | | MTurk Human | 92.20 | 90.78 | 88.65 89.27 | 90.56 | 86.70 76.92 | 57.69 | 57.69 87.33 | 85.62 | 82.53 95.37 | 96.30 | 93.52 | | | LXMERT | 22.70 | 9.22 | 6.38 17.60 | 5.58 | 2.58 15.38 | 7.69 | 3.85 19.18 | 8.56 | 5.14 19.44 | 2.78 | 0.93 | | | UNITERLarge | 39.01 | 12.77 | 9.93 36.05 | 14.16 | 9.87 50.00 | 19.23 | 19.23 40.07 | 16.44 | 13.36 32.41 | 7.41 | 2.78 | | | CLIP (ViT-B/32) | 34.75 | 7.80 | 6.38 22.75 | 8.58 | 5.58 80.77 | 42.31 | 38.46 35.27 | 13.01 | 10.27 18.52 | 3.70 | 1.85 | | | CLIPCAPCC3M | 14.18 | 17.02 | 7.80 11.16 | 12.02 | 3.43 11.54 | 26.92 | 11.54 13.70 | 16.10 | 6.51 | 8.33 | 11.11 | 2.78 | | CLIPCAPCOCO | 12.77 | 17.02 | 5.67 12.88 | 9.87 | 3.86 23.08 | 34.62 | 19.23 14.73 | 16.44 | 6.85 10.19 | 7.41 | 1.85 | | | BLIP-2 | 47.52 | 27.66 | 21.99 38.20 | 17.60 | 14.59 61.54 | 30.77 | 30.77 48.63 | 26.37 | 22.26 27.78 | 10.19 | 7.41 | | | ALBEF4M | 29.79 | 12.77 | 8.51 26.61 | 15.02 | 10.73 50.00 | 34.62 | 26.92 33.22 | 19.18 | 14.04 18.52 | 5.56 | 2.78 | | | X-VLM4M | 46.10 | 27.66 | 21.99 41.63 | 24.46 | 19.31 53.85 | 42.31 | 38.46 47.60 | 30.48 | 25.68 34.26 | 16.67 | 10.19 | | | ALBEF14M | 29.79 | 15.60 | 9.22 30.90 | 14.16 | 12.02 61.54 | 38.46 | 38.46 35.27 | 18.49 | 14.38 25.00 | 10.19 | 8.33 | | | BLIP14M | 41.13 | 24.11 | 17.73 32.19 | 14.16 | 11.16 50.00 | 26.92 | 26.92 42.12 | 21.92 | 18.15 21.30 | 9.26 | 4.63 | | | PEVL14M | 31.21 | 14.89 | 10.64 33.48 | 14.59 | 11.59 42.31 | 30.77 | 26.92 36.30 | 19.52 | 15.75 25.00 | 5.56 | 2.78 | | | PEVLGRD | 39.01 | 14.89 | 12.77 33.91 | 13.73 | 10.30 42.31 | 26.92 | 23.08 37.67 | 17.47 | 15.07 32.41 | 8.33 | 3.70 | | | PEVLVRD | 26.95 | 10.64 | 7.09 32.19 | 12.45 | 6.87 46.15 | 15.38 | 15.38 31.85 | 11.64 | 8.22 29.63 | 12.96 | 5.56 | | | X-VLM16M | 48.23 | 23.40 | 19.86 44.21 | 23.18 | 20.17 61.54 | 42.31 | 38.46 51.03 | 29.11 | 26.03 35.19 | 12.04 | 8.33 | | | BLIP129M | 37.59 | 17.02 | 10.64 34.76 | 12.02 | 10.73 30.77 | 30.77 | 26.92 40.07 | 18.84 | 14.73 23.15 | 4.63 | 3.70 | | | BLIP129M-CAPFILT/L 34.04 | 16.31 | 11.35 33.48 | 13.30 | 11.16 50.00 | 26.92 | 26.92 38.70 | 19.18 | 15.41 24.07 | 4.63 | 3.70 | | | | BLIP-VIT/L129M | 35.46 | 16.31 | 13.48 32.62 | 12.88 | 11.59 50.00 | 19.23 | 11.54 39.04 | 17.81 | 15.07 23.15 | 5.56 | 4.63 | | | Table 8: Results on Winoground by linguistic tag. Best results are in bold. | | | | | | | | | | | | | Table 11: Dev/Test results on the VSR Random dataset. Best results are in **bold**. | Model | Symbolic | Pragmatics | Same Image Series | | | | | | | |-------------------------------------------------------------------|------------------|------------------|---------------------|------------|-------------|-------|-------|------|------| | Text Image Group | Text Image Group | Text Image Group | | | | | | | | | Random | 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 25.00 | 25.00 | 12.50 | | | | MTurk Human | 96.43 | 92.86 | 92.86 58.82 | 41.18 | 41.18 95.65 | 91.30 | 91.30 | | | | LXMERT | 28.57 | 3.57 | 3.57 17.65 | 5.88 | 0.00 | 8.70 | 4.35 | 0.00 | | | UNITERLarge | 39.29 | 28.57 | 17.86 35.29 | 0.00 | 0.00 | 4.35 | 8.70 | 0.00 | | | CLIP (ViT-B/32) | 39.29 | 3.57 | 3.57 35.29 | 5.88 | 5.88 | 8.70 | 0.00 | 0.00 | | | CLIPCAPCC3M | 21.43 | 21.43 | 10.71 | 5.88 | 5.88 | 0.00 | 0.00 | 8.70 | 0.00 | | CLIPCAPCOCO | 25.00 | 25.00 | 14.29 23.53 | 17.65 | 17.65 13.04 | 13.04 | 0.00 | | | | BLIP-2 | 42.86 | 28.57 | 25.00 41.18 | 23.53 | 17.65 21.74 | 13.04 | 4.35 | | | | ALBEF4M | 42.86 | 25.00 | 17.86 17.65 | 17.65 | 5.88 | 8.70 | 0.00 | 0.00 | | | X-VLM4M | 50.00 | 32.14 | 32.14 41.18 | 23.53 | 17.65 30.43 | 26.09 | 13.04 | | | | ALBEF14M | 39.29 | 14.29 | 14.29 17.65 | 0.00 | 0.00 26.09 | 4.35 | 4.35 | | | | BLIP14M | 39.29 | 25.00 | 17.86 23.53 | 17.65 | 17.65 | 8.70 | 4.35 | 0.00 | | | PEVL14M | 35.71 | 14.29 | 14.29 29.41 | 11.76 | 5.88 13.04 | 8.70 | 4.35 | | | | PEVLGRD | 35.71 | 7.14 | 7.14 29.41 | 11.76 | 11.76 26.09 | 8.70 | 4.35 | | | | PEVLVRD | 42.86 | 10.71 | 7.14 23.53 | 5.88 | 0.00 34.78 | 17.39 | 8.70 | | | | X-VLM16M | 42.86 | 21.43 | 17.86 47.06 | 11.76 | 5.88 26.09 | 4.35 | 4.35 | | | | BLIP129M | 57.14 | 14.29 | 14.29 35.29 | 11.76 | 11.76 26.09 | 0.00 | 0.00 | | | | BLIP129M -CAPFILT/L 50.00 | 14.29 | 14.29 35.29 | 5.88 | 5.88 21.74 | 0.00 | 0.00 | | | | | BLIP-VIT/L129M | 39.29 | 14.29 | 14.29 29.41 | 0.00 | 0.00 13.04 | 0.00 | 0.00 | | | | Table 9: Results on Winoground by visual tag. Best results are in | | | | | | | | | | | Model | Adjacency Directional Orientation Projective Proximity Topological Unallocated | Overall | | | | | | |--------------------|----------------------------------------------------------------------------------|-------------|-------------|-------------------------|-------------|-------------|-------------| | Random | 50.0 / 50.0 | 50.0 / 50.0 | 50.0 / 50.0 | 50.0 / 50.0 50.0 / 50.0 | 50.0 / 50.0 | 50.0 / 50.0 | 50.0 / 50.0 | | BLIP-2 | 59.8 / 54.9 | 50.0 / 43.3 | 52.5 / 57.1 | 59.8 / 63.6 56.2 / 51.2 | 66.4 / 67.0 | 75.0 / 66.7 | 61.2 / 61.5 | | ALBEF4M | 52.3 / 51.1 | 38.6 / 42.2 | 55.9 / 58.0 | 61.7 / 60.2 56.2 / 55.3 | 58.6 / 59.2 | 65.6 / 56.9 | 58.0 / 57.3 | | X-VLM4M | 57.6 / 57.7 | 56.8 / 43.3 | 59.3 / 52.7 | 69.2 / 66.1 57.8 / 54.5 | 71.2 / 68.4 | 75.0 / 62.7 | 66.6 / 63.0 | | ALBEF14M | 52.3 / 54.2 | 59.1 / 40.0 | 55.9 / 58.0 | 59.8 / 62.6 46.9 / 52.0 | 66.8 / 58.9 | 71.9 / 58.8 | 60.2 / 58.3 | | BLIP14M | 56.8 / 49.3 | 56.8 / 50.0 | 57.6 / 47.3 | 42.5 / 49.3 51.6 / 48.0 | 45.1 / 51.8 | 50.0 / 41.2 | 47.4 / 49.7 | | PEVL14M | 47.0 / 55.3 | 56.8 / 48.9 | 57.6 / 56.2 | 61.9 / 60.8 51.6 / 48.8 | 62.4 / 57.4 | 71.9 / 58.8 | 59.3 / 57.5 | | PEVLGRD | 53.8 / 53.5 | 65.9 / 50.0 | 59.3 / 52.7 | 60.9 / 59.4 60.9 / 54.5 | 62.7 / 60.2 | 75.0 / 58.8 | 61.1 / 57.7 | | PEVLVRD | 54.5 / 55.6 | 59.1 / 52.2 | 61.0 / 53.6 | 59.8 / 60.4 59.4 / 54.5 | 64.1 / 63.1 | 68.8 / 64.7 | 60.7 / 59.5 | | X-VLM16M | 61.4 / 58.5 | 65.9 / 46.7 | 64.4 / 58.0 | 68.4 / 67.7 62.5 / 52.0 | 70.5 / 68.7 | 84.4 / 68.6 | 67.9 / 64.3 | | BLIP129M | 44.7 / 41.2 | 43.2 / 52.2 | 52.5 / 53.6 | 53.6 / 45.4 53.1 / 49.6 | 50.2 / 49.7 | 40.6 / 37.3 | 50.5 / 46.9 | | BLIP129M-CAPFILT/L | 57.6 / 49.3 | 36.4 / 57.8 | 47.5 / 53.6 | 45.9 / 45.5 48.4 / 47.2 | 48.5 / 51.1 | 37.5 / 41.2 | 47.7 / 48.7 | | BLIP-VIT/L129M | 56.1 / 51.8 | 29.5 / 58.9 | 49.2 / 52.7 | 46.9 / 48.5 53.1 / 43.9 | 49.8 / 51.8 | 46.9 / 47.1 | 48.7 / 50.3 | | Model | Subj. | Verb | Obj. | Avg. | |-----------------------------------------|---------|--------|--------|--------| | Random | 50.0 | 50.0 | 50.0 | 50.0 | | CLIP (ViT-B/32) | 83.6 | 79.0 | 88.1 | 81.6 | | CLIPCAPCC3M | 84.2 | 80.5 | 90.2 | 83.1 | | CLIPCAPCOCO | 87.3 | 81.5 | 89.8 | 84.1 | | FLAMINGO | 90.1 | 86.7 | 92.3 | 88.4 | | BLIP-2 | 87.6 | 84.6 | 91.7 | 86.5 | | ALBEF4M | 88.5 | 85.4 | 93.7 | 87.6 | | X-VLM4M | 89.3 | 87.1 | 94.5 | 88.9 | | ALBEF14M | 89.4 | 86.4 | 94.7 | 88.6 | | BLIP14M | 49.8 | 48.8 | 47.5 | 48.7 | | PEVL14M | 89.4 | 82.9 | 93.9 | 86.2 | | PEVLGRD | 91.2 | 85.9 | 94.6 | 88.5 | | PEVLVRD | 90.1 | 81.1 | 92.3 | 84.8 | | X-VLM16M | 90.3 | 88.4 | 94.6 | 90.0 | | BLIP129M | 50.8 | 51.4 | 51.8 | 51.4 | | BLIP129M -CAPFILT/L | 49.4 | 51.3 | 52.5 | 51.2 | | BLIP-VIT/L129M | 50.0 | 50.9 | 50.9 | 50.8 | | Table 10: Performance on the SVO-Probes | | | | | ![18_image_1.png](18_image_1.png) ![18_image_0.png](18_image_0.png) Figure 4: Training dynamics on SVO-Probes subtasks. Random performance is 50%. ![18_image_2.png](18_image_2.png) as the authors.16 Training our models takes around 1.5 days on Cloud TPUv4 (a 2x2x2 slice). We evaluate our models on both fine-grained benchmarks (SVO-Probes, VALSE and VSR) and on two zero-shot, coarse retrieval tasks (Flickr30K and COCO). ## B Results B.1 Results By Subtask Table 6 compares overall performance of our evaluated models (Section 3) with the state-of-theart models in each of four fine-grained benchmarks (Section 2). Results for each subtask are reported in Tables 7 to 11. In addition to the core discussion in Section 4, we note that FLAMINGO achieves the overall best performance on VALSE; and that the coarsegrained BLIP-2 model performs remarkably well on our range of fine-grained tasks, especially on VALSE, VSR and Winoground. This could be due to a number of factors, such as a larger ViT encoder, the usage of visual queries and the different formulations for the ITC and ITM objectives. We leave a deeper investigation of large VLMs to future work. Moreover, we also note that CLIPCAP well on VALSE spatial relations and action subtasks, wherein its GPT-2 backbone already performs better than most VLMs. This is further proof of the efficacy of adapting strong LMs for V&L tasks. ## B.2 Full Dynamics Of Fine-Grained Tasks Figures 4 to 7 display pretraining dynamics for our re-implemented ALBEF4M, ALBEF14M, XVLM4M, and X-VLM14M models. For better visualisation, our curves have been smoothed by a 0.6 factor through exponential moving average. Finally, Figure 8 shows how performance on Winoground evolves when pretraining an ALBEF4M model.17 Looking at overall performance, we see that a model's score can vary by more than 4pp from one epoch to the next. While longer pretraining seems beneficial, some subtasks, such as Linguistic:Both and Visual:Series, fluctuate considerably; and after 20 epochs, the Image score starts decreasing on other subtasks, such as Linguistic:Object and Visual:Symbolic. ![19_image_0.png](19_image_0.png) ![20_image_0.png](20_image_0.png) ![21_image_0.png](21_image_0.png) ![21_image_1.png](21_image_1.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? 8 ✓ A2. Did you discuss any potential risks of your work? 9 ✓ A3. Do the abstract and introduction summarize the paper's main claims? 7 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 3 ✓ B1. Did you cite the creators of artifacts you used? 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Not applicable. Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 2 ## C ✓ **Did You Run Computational Experiments?** 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? A The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 5 ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? A C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

kwon-etal-2023-vision

Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information

https://aclanthology.org/2023.acl-long.88

Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This paper introduces an unsupervised VWSD approach that uses gloss information of an external lexical knowledge-base, especially the sense definitions. Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided. In addition, to ameliorate the out-of-dictionary (OOD) issue, we propose a context-aware definition generation with GPT-3. Experimental results show that the VWSD performance significantly increased with our Bayesian inference-based approach. In addition, our context-aware definition generation achieved prominent performance improvement in OOD examples exhibiting better performance than the existing definition generation method.

# Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information Sunjae Kwon1, Rishabh Garodia1, Minhwa Lee1, Zhichao Yang1**, Hong Yu**1,2,3,4 1UMass Amherst, 2UMass Lowell, 3UMass Chan Medical School, 4 VA Bedford Health Care [email protected], [email protected], [email protected] [email protected], [email protected] ## Abstract Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This paper introduces an unsupervised VWSD approach that uses gloss information of an external lexical knowledge-base, especially the sense definitions. Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided. In addition, to ameliorate the out-of-vocabulary (OOV) issue, we propose a context-aware definition generation with GPT-3. Experimental results show that VWSD performance increased significantly with our Bayesian inference-based approach. In addition, our context-aware definition generation achieved prominent performance improvement in OOV examples exhibiting better performance than the existing definition generation method. ## 1 Introduction With the development of deep learning technology, research on multimodality such as VisioLinguistic Models (VLMs) has been actively conducted (Schneider and Biemann, 2022). In particular, state-of-the-art VLMs, such as image-text matching (ITM) models (Radford et al., 2021; Singh et al., 2022) and text-to-image generation models (Rombach et al., 2022; Seneviratne et al., 2022), are employed in many industrial projects, including image retrieval systems (Yuan and Lam, 2021; Yuan et al., 2021) and AI-assisted image generators (Das and Varshney, 2022; Seneviratne et al., 2022). Visual Word Sense Disambiguation (VWSD) is a multimodal task of natural language processing (NLP) and computer vision that selects the image which corresponds to the intended meaning of the target word among a set of candidate images (Ra- ![0_image_0.png](0_image_0.png) ganato et al., 2023). Figure 1 is an example of VWSD. For the ambiguous target word 1'Angora', we can notice that the answer image should be conditionally changed regarding the context. VWSD can play an important role in several downstream tasks including image retrieval (Chen et al., 2015), action recognition (Gella et al., 2017) and visual question answering (Whitehead et al., 2020). Unsupervised VWSD can be formulated in the same way as the ITM task (Cao et al., 2022), that is, finding the images that best match the given context. However, VWSD often requires more complex reasoning on both text and images than conventional ITM models. The example in Figure 2 demonstrates that CLIP (Radford et al., 2021), a state-of-the-art (SOTA) ITM model, fails to recognize the answer image for the given context 2. This limitation of VLMs, where they fail to handle ambiguous words, was also reported in another study on an image generation model (Rassin et al., 2022). 1An ambiguous word that we want to disambiguate with machines. 2Text surrounding a target word which is used as a clue to disambiguate the target word (e.g. Angola cat, Angola city, Angola goat in Figure 1). 1583 To ameliorate this problem, we propose to disambiguate visual words with the assistance of a glossary of lexical knowledge-bases (LKBs) without the use of any further training or dataset. Specifically, we utilize the sense definitions of an ambiguous word that have been widely exploited in previous lexical semantic tasks (Raganato et al., 2017; Gella et al., 2017; Pilehvar and Camacho-Collados, 2019). Herein, since the answer sense of the target word is not provided in the VWSD setting, we propose an approach derived from Bayesian inference, using pretained ITM models. Moreover, in order to deal with out-of-vocabulary (OOV) words that cannot find the sense definitions of the target word in LKBs, we suggest the concept of context-aware definition generation (CADG). The definitions of a target word are generated by a large language model, GPT-3 (Brown et al., 2020), as auxiliary information for VWSD. Experiments were conducted on SemEval-2023 (SE23) Task 1-Visual-WSD (Raganato et al., 2023), a publicly available VWSD dataset. Furthermore, in the experiments, we utilized two pretained SOTA ITM models: (1) CLIP (Radford et al., 2021) and (2) FLAVA (Singh et al., 2022). Experiments showed that our proposed approach significantly improved the performance of baseline ITM models. In addition, we demonstrated that our concept of CADG not only significantly increased the performance of OOV cases but is also more advantageous than the previous definition generation approach. We implement experimental codes in https://github.com/soon91jae/UVWSD. The contributions of this paper can be summarized as follows: - This paper introduces a new glossincorporated VWSD approach inspired by Bayesian inference. - Experimental results show that our Bayesian inference-based approach boosted the unsupervised VWSD performance significantly without any additional training. - Furthermore, we suggest the CADG method to challenge the OOV issue. ## 2 Related Work 2.1 Word And Visual Sense Disambiguation VWSD task is closely relevant to a line of sense disambiguation studies. One of them is Word Sense ![1_image_0.png](1_image_0.png) Disambiguation (WSD) which automatically identifies ambiguous words into corresponding senses (O et al., 2018). The early stage of WSD research tried to employ diverse information in LKBs with unsupervised manners such as lexical similarity (Kilgarriff and Rosenzweig, 2000), knowledge-graph connectivity (Agirre et al., 2014; Kwon et al., 2021), and topic modeling (Chaplot and Salakhutdinov, 2018). After the emergence of pretrained language models (LMs) such as BERT (Devlin et al., 2019), LM-based transfer learning approaches have been actively studied (Huang et al., 2019; Barba et al., 2021b). In particular, gloss-enhanced WSD models that use sense definition and context together using a cross-encoder (Huang et al., 2019; Barba et al., 2021a) or bi-encoder (Blevins and Zettlemoyer, 2020) structures are not only overwhelm existing approaches but also robust against few-shot examples. Wahle et al. (2021) suggest incorporating WordNet knowledge into LMs while pre-training them. Specifically, the authors utilize a multi-task learning method that trains LMs with both mask language modeling loss and WSD task loss. Visual Verb Sense Disambiguation (VVSD) is another task relevant to VWSD. VVSD is a multimodal sense disambiguation task that selects the correct sense of a pair of a given ambiguous verb word and image (Gella et al., 2017). Gella et al. (2017) suggest an unsupervised VVSD approach that takes advantage of various Visio-linguistic features (image representation, object label, image caption features) together and calculates the matching score between an image and a sense definition ![2_image_0.png](2_image_0.png) with a variant of Lesk algorithm. Vascon et al. (2021) propose a semi-supervised VVSD method based on game theoretic transduction for inference. Meanwhile, Gella et al. (2019) demonstrate that a VVSD model trained on multi-lingual VVSD dataset not only benefit the performance on verb sense disambiguation but also boost the performance of a downstream task, the multi-modal machine translation task. Our work is related to gloss-enhanced WSD models in that we are using both sense definition and context together. However, our study differs from previous WSD studies in that it tackles a multi-modal task. It is also relevant to VVSD in terms of multi-modal sense disambiguation. However, VVSD systems (Gella et al., 2016) are usually designed to analyze a small number of verb words, while the VWSD task contains a lot of nouns and adjectives. Finally, our work tackles a new VWSD task and we introduce a method of implementing sense definitions with SOTA ITM models based on Bayesian inference where sense definitions as a latent variable. ## 2.2 Definition Generation Our CADG is related to the definition generation task introduced by Noraset et al. (2017). The purpose of the task is to generate a definition for a given word. Noraset et al. (2017) suggest utilizing recurrent neural network-based LMs (RNNLMs) with the definitions collected from WordNet and GNU Collaborative International Dictionary of English (GCIDE). Gadetsky et al. (2018) propose definition generation models to handle polysemous words with context and the soft-attention mechanism. Li et al. (2020) propose to perform semantic decomposition of the meanings of words and then use discrete latent variables to model them to generate definitions. Malkin et al. (2021) show that a large language model (GPT-3) could generate definitions of neologisms without additional fine-tuning. Herein, the authors suggest generating neologisms with long short-term memory (LSTM) (Yu et al., 2019) and definitions of neologisms with a large pretrained LM, GPT-3 (Brown et al., 2020). CADG is similar to the one used by Malkin et al. (2021), which involves generating definitions using GPT-3. However, CADG differs in that it takes context into account when generating prompts. Additionally, this study differs from previous work in that it takes context into account when generating prompts and demonstrates that the definitions produced by CADG can be effectively used in downstream tasks, rather than focusing solely on the definition generation task itself. ## 3 **Task Definition On Unsupervised Vwsd** We formulate unsupervised VWSD as a multiclass classification task (Aly, 2005) as shown in Eq. 1. Unlike the image retrieval task (Jing et al., 2005) that ranks the most relevant images for the given text or keyword, VWSD is designed to choose a specific target t in the given context c. Specifically, we define the task to find the image vˆ with the highest posterior probability from a set of images V tthat consists of one answer image and other distractors on the target word. $${\hat{\mathbf{v}}}={\underset{\mathbf{v}\in V^{t}}{\operatorname{argmax}}}\,P(\mathbf{v}|\mathbf{c},t)$$ Any pretrained ITM models (e.g., CLIP) can calculate the posterior. In Figure 2, a set of candidate images V tis entered into the image encoder for the target word t. At the same time, the context c that includes t as a part is entered into the text encoder. Then, the inner product of the output hidden representations on images h v 1...|V t| and the context h c are input to softmax function, which then computes a probability distribution over the images. Finally, the image that produces the highest probability will be selected as the prediction of the model for the target t, provided the context c. ## 4 Unsupervised Vwsd Incorporating Gloss Information Usually, zero-shot ITM models are pretrained without much consideration of polysemous words. For example, Figure 2 demonstrates that CLIP fails to predict the correct answer for the target word 'Angora', although it is provided with a clear hint of 'city' in the given context. Therefore, the zero-shot performance of pretrained ITM models may be limited in the VWSD task. One solution is to use gloss information of a lexical knowledge-base (LKB), particularly exploiting sense definitions. This is because the definitions in LKBs elaborate on each sense for readers who do not know the meaning. Thus, we assume that the sense definitions in LKBs can boost ITM models to conduct VWSD, by injecting the meaning of the correct sense on the input of these models. However, since there is no correct sense information for the target word, it is difficult to apply it directly. For this reason, we suggest a novel gloss-incorporated VWSD approach inspired by Bayesian inference, as presented in Eq. 2. Suppose Dtis a set of definitions for the target word t extracted from an LKB. Herein, by using the chain rule, the posterior can be divided into two conditional probabilities associated with a latent $$P(\mathbf{v}|\mathbf{c},t)=\sum_{i=1}^{|D^{t}|}P(\mathbf{v}|D_{i}^{t},\mathbf{c},t)P(D_{i}^{t}|\mathbf{c},t)\quad(2)$$ $$(1)$$ In this case, the right term P(Dt i|c, t) (Context to Definition; C2D) is predicting the conditional probability over the given ith sense definition Dt i for the given target word t and context c which is similar to the gloss-enhanced WSD models (Huang et al., 2019; Blevins and Zettlemoyer, 2020). Meanwhile, the left term P(v|Dt i , c, t) (Definition to Image; D2I) is the conditional probability of v for a given the ith sense definition, the context and the target word. In doing so, it allows for the development of sophisticated ITMs by enriching the context with its relevant sense definition. Finally, we can calculate P(v|c, t) by marginalizing over all available sense definitions Dt1...|Dt| . Figure 3 demonstrates an illustrative concept of our gloss-incorporated VWSD approach with a pretrained CLIP. First, similar to the original CLIP, a set of candidate images V tand a context c are input to the image encoder and the text encoder, respectively. Meanwhile, a set of definitions of the target word Dtis extracted from an LKB. In our work, we utilize WordNet (Miller, 1995) which has been widely used in previous semantic analysis tasks (Pilehvar and Camacho-Collados, 2019; Bevilacqua et al., 2021) as our source of LKB. Then Dt, c, and t are jointly inputted to the text encoder with the following template. ## {Context} : {Ith Sense'S Definition} C2D is computed by the inner product of the hidden representations on the definitions d t 1...|Dt| and the context h c⊺. D2I is then calculated by the inner product of the hidden representations of the input images h v 1...|V t| and d t 1...|Dt| ⊺. Both C2D and D2I input to the softmax function transformed into probability distributions. Then, we choose the image with the highest probability as the prediction. As a result, for the example in Figure 3, our model can predict the correct answer of the given context 'Angora city', whereas the original CLIP wrongly selects an image of 'Angora cat' that produced the highest probability (as shown in Figure 2), even though the network topology and the pretrained parameters in our model are the same as the original CLIP model. ![4_image_1.png](4_image_1.png) ![4_image_2.png](4_image_2.png) ![4_image_0.png](4_image_0.png) ![4_image_4.png](4_image_4.png) ![4_image_5.png](4_image_5.png) ![4_image_3.png](4_image_3.png) (b) Our context-aware definition generation. ![4_image_6.png](4_image_6.png) Figure 4: Examples of GPT-3 generated definitions when context, target word, and part-of-speech are 'angora city', 'angora' and 'noun' (n) respectively. ## 5 **Handling Oov With The Context-Aware** Definition Generation Not all words have their definitions available in a lexical knowledge-base. In particular, proper nouns, compound words, and foreign words frequently induce OOV issues. For example, in the SE23 dataset, about 14.33% of target words' definitions are not found in the English WordNet. Therefore, we propose a solution to tackle the OOV issue with the definition generation approach. A previous study showed that GPT-3 can generate the definition of a novel word (Malkin et al., 2021). However, since this study does not consider the context of the word, it may not generate the definition in the correct sense. Thus, we suggest generating a definition with the prompt that considers both the context and the target word together. Figure 4 presents the generated definitions by the approach of Malkin et al. (2021) (Figure 4a) and ours (Figure 4b). Here, we add a conditional sentence that inputs the context of a target word. For example, when the target word is 'angora' and the context is 'angora city', we use a conditional sentence, "Define "angora" in angora city.", in front of the previous input "angora (n)". Indeed, in the example, the definition generated with our method shows a better description compared to the previous method. ## 6 Experiments 6.1 Experimental Dataset SE23 We used the dataset in the SemEval-2023 Task 1 VWSD challenge 34. It consists of 12,896 examples and 13,000 candidate images. Each example has 10 candidates that include 1 answer image and 9 distractors. Each context averagely contains 2.5 words. The dataset contains 14.33% OOV words (1,845 out of 12,869). ## 6.2 Experimental Setting VWSD For the experiments, we adopted two SOTA zero-shot ITM models, CLIP 5and FLAVA 6, as pretrained parameters are publicly available for both of them. Note that CLIP uses the text encoder and the image encoder at the same time while FLAVA contains the text encoder, the image encoder, and the multi-modal encoder. Herein, to calculate an image-text matching score, FLAVA uses the multi-modal encoder that cross-encodes image and text features simultaneously. In the case of calculating C2D, we exploit FLAVA's text encoder as the same as Figure 3. We used WordNet 3.0 7as the main LKB. We also compare two GPT-3 generated definitions. The first one is Malkin et al. (2021)'s definition generation (DG). The other one is CADG (as described in Section 5). WN+CADG applies CADG's definitions in the case of OOV and uses WordNet definitions otherwise. Definition Generation We re-implemented Malkin et al. (2021)'s definition generation experimental setting. Specifically, we sampled a definition for each example by utilizing GPT-3's Davinci variant which is known as the largest model and we generated samples with a temperature of 1.0. Evaluation Criteria Following Raganato et al. (2023)'s setting, we evaluated VWSD models' performance with the hits at 1 (Hits@1) and the mean reciprocal rank (MRR). Moreover, we used Student's t-test (Student, 1908), to verify the signifi- ![5_image_1.png](5_image_1.png) FLAVA ![5_image_0.png](5_image_0.png) Hits@1 MRR ![5_image_2.png](5_image_2.png) ![5_image_3.png](5_image_3.png) cance of differences in performance between models. Others We prepared a pretrained WSD, T5*SemCor* (Wahle et al., 2021). This model is a generative WSD model that a T5-large model (Raffel et al., 2020) fine-tuned with SemCor (Raganato et al., 2017). Note that, SemCor is a large size word sense dataset annotated with the WordNet sense repository. Herein, we utilized the official checkpoint 8. In addition, we employed NLTK (Bird et al., 2009) to conduct word tokenization and part-of-speech tagging. All experiments were conducted on an NVIDIA A100 GPU with Ubuntu 22.04 version. ## 6.3 Experimental Results The experimental results in Table 1 show that the performances of CLIP and FLAVA are 73.00 and 70.13 on Hits@1, respectively. Incorporating definition descriptions of external LKB (WN) or generated (DG and CADG) significantly enhanced the performance in every experimental model. First, incorporating WordNet with our Bayesian style inference (WN) outperformed both of ITM models, 8.98%p in CLIP (p < 1e − 10) and 8.72%p (p < 1e − 10). DG and CADG also significantly improved performance in all cases (p < 1e − 7), but the increment in FLAVA was relatively lower than that of the CLIP. WN+CADG achieved the highest performance in both of CLIP and FLAVA. On the other hand, to scrutinize the reasons for the performance improvements in more detail, we categorized examples into three categories according to the number of WordNet senses (|D|) of the target word. |D| = 0 examples are target words with no entry in WordNet (OOV). |D| = 1 examples are target words with only one sense in the WordNet (trivial). |D| > 1 examples are target words with more than one sense in the WordNet (ambiguous). Figure 5 presents that incorporating WordNet definition enhanced the performance on ambiguous and trivial words in both of CLIP and FLAVA. In particular, the performance gain was remarkable in trivial words (from 71.34 to 85.91 and from 69.83 to 81.99 for CLIP and FLAVA, respectively). Moreover, even for ambiguous words, the performance is significantly improved (p < 1e − 3) without any additional training or the assistance of external systems such as WSD models. CADG substantially increased performance in both of OOV and trival words. Especially, when compared to DG, the performance differences are remarkable in OOV. Meanwhile, while FLAVA shows prominent improvement via WordNet integration, the impact of generated definitions tends to be low compared to CLIP. Considering that WordNet definitions were manually constructed by experts, we speculate that this is because the model is sensitive to the quality of the input definitions. ## 7 Discussion 7.1 Analysis On Ambiguous Target Words We analyzed the performance change according to the ambiguity level of the ambiguous target word. | |D| | # of Corrected | # of Incorrected Corrected Ratio | | |---------|------------------|------------------------------------|------| | 2 | 199 | 66 | 3.02 | | 3 | 99 | 40 | 2.48 | | 4 | 48 | 19 | 2.53 | | 5 | 42 | 13 | 3.23 | | 6 | 28 | 9 | 3.11 | | 7 | 25 | 5 | 5.00 | | 8 | 13 | 5 | 2.60 | | 9 | 10 | 4 | 2.50 | | 10 | 7 | 2 | 3.50 | | 10 <|D| | 52 | 27 | 1.93 | | total | 523 | 190 | 2.75 | $$\begin{array}{l|cc}\hline&\text{Hits}@1&\text{MRR}\\\hline\hline\text{-}&74.07&82.72\\\hline\text{CLIP+WN}&77.15&88.83\\\hline\text{T5}_{S e m C o r}&77.12&85.21\\\hline\end{array}$$ Table 3: Experimental comparison of VWSD for the ambiguous target. Table 2 presents the predictive change of the CLIP after incorporating WordNet. Herein, 523 examples go correct while 190 examples go incorrect. In particular, even in the case of highly ambiguous examples with |D| greater than 10, the improvement rate is 1.93, and incorporating WordNet positively affects the performance improvement. These results are in line with previous research findings that ambiguous words can be recognized pre-trained LMs according to the given context (Garí Soler and Apidianaki, 2021; Kwon et al., 2022). However, compared to the lower ambiguous cases, the performance improvement rate is lower. These results implies that enhancement for the highly ambiguous words are required. Although WordNet integration improves performance for ambiguous target words, we still want to find out how competitive the performance improvement is. For this reason, we compared the performance of our WordNet-incorporated model with that of the pipeline system using the WSD model. To be specific, T5*SemCor*, a finetuned WSD model, predicts WordNet sense in a given target word and context. The probability distribution for the candidate images was calculated based on the predicted sense. Table 3 is the prediction result for ambiguous tar- ![6_image_0.png](6_image_0.png) Table 4: Results of the human analysis on generated definitions. get words. Our model showed comparable results in the pipeline system and Hits@1 and achieved higher performance in MRR. This is due to the error cascading issue of pipeline systems (Finkel et al., 2006; Kwon et al., 2019). That is, in the pipeline system, errors in the WSD model directly lead to performance decrement. Otherwise, our approach is rather free from error cascading, since the C2D probability and the D2I probability work complementary to each other. ## 7.2 Analysis On The Generated Definitions 7.2.1 **Evaluation On The Generated Definitions** In order to evaluate the quality of the generated definitions, we randomly sampled 200 examples from SE23 dataset. For each example, two annotators evaluated the (binary) agreement on the generated definitions with Malkin et al. (2021)'s approach (DG) and our approach (CADG). Inter-annotator agreement (Kvålseth, 1989) was κ = 0.625. Finally, we only accept 159 examples of DG and 166 examples of CADG unanimously agreed by the annotators. Table 4 represents the average human agreement scores on DG and CADG. The results show that our CADG achieved a higher performance compared to DG. Especially, in Figure 4 and Table 5, we can find that the definitions of ambiguous words generated with CADG are semantically similar to that of the WordNet answer sense compared to DG, in line with the purpose for which it was designed. ## 7.2.2 Impact Of The Generated Definitions' Quality We also verified whether the quality of the generated definitions would affect the VWSD performance. Table 6 presents the experimental results on VWSD examples when we utilize the generated definitions that agreed (Correct) and disagreed (Incorrect) by the both annotators. Table 6 demonstrates that the quality of the generated definitions affects the performance of the downstream VWSD task indeed. | Target Word | Context | WordNet Answer Definition | Generated Definition | | |-------------------------------|-----------------------------------------------|----------------------------------|-------------------------------------------------------------|-------------------------------| | give | give communicate | convey or reveal information | to present something as a gift; to make a gift of something | | | landscape | landscape genre | painting depicting an expanse of | A large area of land that can be seen from | | | natural scenery | one place | | | | | fauve | fauve painter | a member of a group of French | A fauve is a wild or undomesticated animal. | | | painters who followed fauvism | | | | | | DG | give | give communicate | convey or reveal information | to convey (information, etc.) | | landscape | landscape genre | painting depicting an expanse of | a genre of art that depicts natural scenery | | | natural scenery | such as mountains, forests, rivers, and so on | | | | | fauve | fauve painter | a member of a group of French | a French term meaning "wild beast," used to | | | painters who followed fauvism | describe a group of early 20th-century ... | | | | | CADG | | | | | | Model | Agreement | n | Hits@1 | MRR | |-----------|-------------|-----|----------|-------| | CLIP | - | 159 | 71.70 | 82.29 | | CLIP+DG | Correct | 130 | 83.85 | 89.76 | | CLIP+DG | Incorrect | 29 | 68.97 | 78.83 | | CLIP | - | 166 | 68.67 | 79.78 | | CLIP+CADG | Correct | 148 | 82.43 | 89.25 | | CLIP+CADG | Incorrect | 18 | 66.67 | 77.45 | ![7_image_0.png](7_image_0.png) ## 7.2.3 Experiments On Multiple Generated Definitions Since we sampled a definition for each input example in main experiments, it is still questionable whether the number of sampled definitions affects the performance of the model. Table 7 indicates the performance of DG and CADG according to the number of generated definitions (n) for each input. The results show that the number of sampled definitions is not significantly affecting the model's performance. To be specific, when the number of generated definitions is 2 for each input, the performance of DG and CADG increased by 0.09%p and 0.03%p respectively. Furthermore, when the number of generated definitions is 3, we can see that the performance even slightly decreases both DG and CADG. As a result, sampling multiple definitions for each input does not significantly affect performance or rather decreases performance. | Target Word | Context | WordNet Definitions | Probs. | |------------------------------------------------------|-----------------------------|-----------------------|----------| | paddle | paddle beat walk unsteadily | 99.95% | | | give a spanking to | 0.00% | | | | United States classical | | | | | Thompson | Thompson | archaeologist. . . | 0.00% | | submachine English physicist (born in America) . . . | 100.00% | | | ## 7.3 Error Analysis 7.3.1 Vwsd Our model still suffers from error cascading from C2D probability though it is mitigated by the Bayesian style inference. The most typical error case is due to the error cascading in C2D probability calculation. Especially, due to the nature of neural networks (Guo et al., 2017), the overconfidence in the error classes frequently causes errors. For example, in Table 8, we found that among the 10 senses of the target word 'paddle' extracted from WordNet, the conditional probability for the correct sense was calculated as 0.00%, resulting in an error in the final posterior calculation. Another error case is when there is no correct sense in WordNet. In the example, the target word 'Thompson' indicates a firearm, but WordNet contains only personal information. This is a separate issue from OOV with no entry for the target word, and we observed that it mainly occurs in proper nouns. 7.3.2 Definition Generation We found two representative error cases in the results of the definition generations: 1) misdisambiguation and 2) hallucination. The misdisambiguation is when the GPT3 generates the polysemy's definition. In Figure 6a, considering the context of "lime oxide", we would expect a definition of lime stone to be generated. However, we can notice that both approaches generate a definition for lime fruit. On the other hand, as pointed out in previous research (Ishii et al., 2022), we also observed that GPT3 generates hallucinations. Figure 6b is an example of the hallucination issue. albatrellus which is a type of a fungi in the context of "albatrellus genus," nevertheless the definitions generated by both approaches are pertaining to the albatross, a species of bird. Detailed examples of error cases can be found in Appendix A. ## 8 Conclusion And Future Work This paper introduces a novel VWSD methodology to effectively incorporate gloss information from an external resource. Our work mainly has two innovations: 1) Bayesian style inference for SOTA ITMs, and 2) Context-aware definition generation with GPT-3 to overcome the OOV issue. Experimental results show that our proposed Bayesian style inference-based WordNet integration significantly improves VWSD performance without additional training. For the ambiguous target words, the performance of our approach is comparable to pipeline systems using finetuned WSD models. Moreover, context-aware definition generation helps mitigate OOV issues in the downstream VWSD tasks and shows higher performance compared to the previous definition generation approach. In the future, we plan to tackle the error cascading caused by over-confidence in C2D probability. For this, we may explore a prompting that is known to have good performance in zero-shot prediction (Liu et al., 2023). In addition, to deal with the hallucination and misdisambiguation problems of GPT-3 generated definitions, we may employ controllable generation by resampling (Ji et al., 2022). ## Limitations Our work has the following limitations. First, we only used one evaluation data, namely SE23, because it is the only data suitable for the VWSD setting, especially for the OOV examples. In addition, our methodology relies entirely on WordNet. Therefore, this may be limited the model's ability when the target word is a proper noun such as a named entity. Finally, we depend on the results of GPT-3 definition generation to handle OOV words. Since the generated definitions may contain errors, as revealed in the qualitative analyses, the errors led to incorrect predictions. ![8_image_0.png](8_image_0.png) CADG's Definitions Lime refers to both a fruit and a color. As a fruit, lime is **a citrus fruit** that … A **green citrus fruit** that … DG's Definitions (a) An example of the misdisambiguation Resembling an **albatross**. resembling an **albatross**; ![8_image_1.png](8_image_1.png) ![8_image_2.png](8_image_2.png) having long, narrow wings; sluggish in flight DG's Definitions CADG's Definitions Figure 6: Examples of incorrectly generated definitions. ## Ethical Consideration The generated definitions were annotated by two annotators. Both annotators were fully paid by complying with local minimum wage regulation. In addition, in the sampled definition generations, the authors could not find any statements violating the ACL anti-harassment policy. However, generated definitions that authors have not vetted are still at risk of containing toxic or hates contents (e.g. racism, insulting or xenophobic). ## Acknowledgement Research reported in this study was in part supported by the Center of Biomedical and Health Research in Data Sciences (CHORDS) in UMass Lowell. ## References Eneko Agirre, Oier López de Lacalle, and Aitor Soroa. 2014. Random walks for knowledge-based word sense disambiguation. *Computational Linguistics*, 40(1):57–84. Mohamed Aly. 2005. Survey on multiclass classification methods. *Neural Netw*, 19(1):9. Edoardo Barba, Tommaso Pasini, and Roberto Navigli. 2021a. Esc: Redesigning wsd with extractive sense comprehension. 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IEEE Transactions on Geoscience and Remote Sensing, 60:1–19. ## A Case Study On Incorrectly Generated Definitions Table 10 and Table 9 present the all incorrectly generated definitions that described in Section 8. Herein, we found the following three error types: 1) Misdisambiguation, 2) Hallucination, and 3) Others. First of all, the misdisambiguation cases are caused by bias in the pretraining, and we can notice that CADG has less misdisambiguation compared to DG. Especially, we can see that GPT-3 generated more than one definitions of the target words 'conch', 'reaper', and 'ruin' in DG, while we could not found such cases in our approach. On the other hand, hallucination cases are when the generated definitions are definitions of completely different terms with similar spellings ('stonechat' of CADG, 'driftfish' of DG), or cases in which the detailed descriptions are incorrect although they are somewhat similar ('osteostraci' of CADG, 'nestor' of DG). Especially, in Table 10 of 'wulfenite' and 'cordierite,' we can notice that definitions are generated with parts of each lexicon ("wulfen," and "cord"). Finally, in other cases, the generated definitions may not be in definition form ('lynching' of CADG, 'areca' of DG), or the contents of the target word is output as itself ('wulfenite' of CADG). | context | target word | GPT Definition | Agreement | Misdisambiguation | Hallucination | Others | |------------------------------------------------------------------------------------|---------------|-----------------------------------------------------------------------------------|-------------|---------------------|-----------------|----------| | niche condition | niche | A niche is a situation or activity that is specially suited to a person's interests, abilities, or | FALSE | ✓ | | | | character. | | | | | | | | lighterage transfer | lighterage | the cost of using a lighter (a small ship used to | FALSE | ✓ | | | | transfer cargo from a larger ship to shore) to transfer cargo | | | | | | | | lynching practice | lynching | What is an example of a hate crime? | FALSE | ✓ | | | | stonechat chat | stonechat | a chatty person who talks excessively about trivial things | FALSE | ✓ | | | | wheatear thrush | wheatear | of a pale yellowish-brown color, like that of wheat | FALSE | ✓ | | | | thank convey | thank | Thank you for your help. | FALSE | ✓ | | | | driftfish butterfish | driftfish | moving aimlessly or erratically; drifting. | FALSE | ✓ | | | | fumble blooper | fumble | fumble (verb): to make a mistake or do something awkwardly | FALSE | ✓ | | | | snoek table-fish | snoek | clad in snow; frosty. | FALSE | ✓ | | | | variation dance | variation | a repeated section of music in which the | FALSE | ✓ | | | | melodic, rhythmic, and/or harmonic content is altered each time it occurs | | | | | | | | stationery paper | stationery | of or relating to the writing and supplies used | FALSE | ✓ | | | | for writing by hand | | | | | | | | lime oxide | lime | lime refers to both a fruit and a color. As a | FALSE | ✓ | | | | fruit, lime is a citrus fruit that is used to add a sour or acidic flavor to food. | | | | | | | | behemoth anomaly | behemoth | a massive and powerful thing, often one that is difficult to control or deal with | FALSE | ✓ | | | | sic carbide | sic | intentionally so written (used after a printed word or phrase to indicate that the spellings, punctuation, capitalization, etc., have been retained from the original). | FALSE | ✓ | | | | wulfenite mineral | wulfenite | containing or consisting of wulfenite mineral. | FALSE | ✓ | | | | osteostraci order | osteostraci | a subclass of fish that includes the lampreys and hagfishes | FALSE | ✓ | | | | ramontchi shrub | ramontchi | a small, evergreen shrub of the genus Rhamnus, native to Japan and Korea. The ramontchi shrub is grown for its ornamental value, and its small, dark green leaves are used in traditional medicine. | FALSE | ✓ | | | | nestor genus | nestor | A person who is very old or who has lived for a long time | FALSE | ✓ | | | Table 9: Error cases of our CADG | Context | Target word | GPT Definition | Agreement | Misdisambiguation | Hallucination | Others | |-------------------------------------------------------------------------------------|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|---------------------|-----------------|----------| | niche condition | niche | A distinct segment of a market, usually populated by a group of people with specific needs | FALSE | ✓ | | | | or interests. | | | | | | | | lame fabric | lame | a person or animal that is lame in one or more legs | FALSE | ✓ | | | | razz mock | razz | a type of teased or tangled hair | FALSE | | | | | burn turn | burn | A burn is a type of injury to the skin or other | FALSE | ✓ | | | | tissues caused by heat, electricity, chemicals, or radiation. | | | | | | | | bursera genus | bursera | A burser is a small sac or saclike cavity containing synovial fluid, located between a bone | FALSE | ✓ | | | | and a tendon or between a bone and the skin. | | | | | | | | mara rodent | mara | A small, gazelle-like antelope that is found in Africa. | FALSE | ✓ | | | | cordierite mineral | cordierite | Resembling a cord or rope; twisted; rope-like. | FALSE | ✓ | | | | distemper painting | distemper | a disease of domesticated animals, especially dogs, caused by a virus and characterized by fever and gastrointestinal and nervous system disorders | FALSE | ✓ | | | | pilate procurator | pilate | A type of exercise that uses slow, controlled movements to improve posture and flexibility. | FALSE | ✓ | | | | driftfish butterfish | driftfish | serene, drifting along peacefully | FALSE | ✓ | | | | areca palm | areca | (adj.) | FALSE | ✓ | | | | spiceberry shrub | spiceberry | A spiceberry is a flowering plant in the genus Spirea, native to North America and Asia. | FALSE | ✓ | | | | conch gastropod | conch | 1. a spiral shell of certain marine Gastropoda 2. the shell of a Strombus, used as a trumpet by certain Native American peoples 3. in Buddhism, a spiral shell that is blown as a horn, used to summon beings to assembly | FALSE | ✓ | | | | reaper agro-technology | reaper | 1. Having a disastrous effect; causing ruin or | FALSE | ✓ | | | | destruction. 2. (Of a person) Bringing death or misfortune. | | | | | | | | packera genus | packera | A person who packs or wraps something, typically for shipping. | FALSE | ✓ | | | | variation dance | variation | the act, process, or result of varying in quality, | FALSE | ✓ | | | | quantity, or degree; diversity: the variations in color of a parrot's plumage. | | | | | | | | mamma exocrinopathy | mamma | A mamma is a mother. | FALSE | ✓ | | | | give communicate | give | to present something as a gift; to make a gift of something | FALSE | ✓ | | | | landscape genre | landscape | A large area of land that can be seen from one place | FALSE | ✓ | | | | passer genus | passer | a person who is authorized to pass or go | FALSE | ✓ | | | | through something, especially a military checkpoint | | | | | | | | stationery paper | stationery | of or relating to stationery or the office supplies used for writing and printing | FALSE | ✓ | | | | calypso orchid | calypso | A style of music originating in Trinidad and Tobago that is characterized by a heavy rhythm, often created with drums, guitars, and other percussion instruments. | FALSE | ✓ | | | | lime oxide | lime | A green citrus fruit that is used to add flavor to food and drinks. | FALSE | ✓ | | | | sic carbide | sic | Meaning "so" or "very," sic is derived from the Latin adverb sic, meaning "thus" or "just as." | FALSE | ✓ | | | | wulfenite mineral | wulfenite | relating to or resembling a wolf | FALSE | ✓ | | | | ramontchi shrub | ramontchi | Ramontchi is a type of fish found in the rivers | FALSE | ✓ | | | | of southern Japan. It is prized for its delicate flavor and is often used in sushi. | | | | | | | | nestor genus | nestor | a mentor or guide, especially one who is older or more experienced | FALSE | ✓ | | | | ruin destruction | ruin | 1. the remains of a building or city, typically one that is in ruins 2. a person or thing that is severely damaged or destroyed 3. a person's career, reputation, or life being ruined | FALSE | ✓ | | | | pleiades nymph | pleiades | A group of seven stars in the constellation Taurus, typically visible to the naked eye. Also | FALSE | ✓ | | | | called the Seven Sisters. Table 10: Error cases of DG (Malkin et al., 2021) | | | | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? After the conclusion section and before the reference section ✓ A2. Did you discuss any potential risks of your work? In the limitation section ✓ A3. Do the abstract and introduction summarize the paper's main claims? abstract, section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3,4,5,6 ✓ B1. Did you cite the creators of artifacts you used? section 3,4,5,6 ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? We downloaded those in the official download site. Also, we got allowance to use the dataset from the creators. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? section 6 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. section 6 ## C ✓ **Did You Run Computational Experiments?** Section 6 ✗ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? We do not have hyper-parameters. We just use the pertained irate-text matching model without any training. ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 6 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 6 ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Section 6 ✗ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? We provide all annotation results in the attached submission file ✓ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 6 ✓ D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 6 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. ✗ D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? We conducted the annotation on small dataset. Thus we only have two annotators

ma-etal-2023-chain

Chain-of-Skills: A Configurable Model for Open-Domain Question Answering

https://aclanthology.org/2023.acl-long.89

The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transfer- ability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.

# Chain-Of-Skills: A Configurable Model For Open-Domain Question Answering Kaixin Ma♣†∗, Hao Cheng♠∗, Yu Zhang♡†, Xiaodong Liu♠, Eric Nyberg♣**, Jianfeng Gao**♠ ♣ Carnegie Mellon University ♠ Microsoft Research ♡ University of Illinois at Urbana-Champaign {kaixinm,ehn}@cs.cmu.edu {chehao,xiaodl,jfgao}@microsoft.com [email protected] ## Abstract ![0_Image_0.Png](0_Image_0.Png) The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-ofthe-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. ## 1 Introduction Gathering supportive evidence from external knowledge sources is critical for knowledgeintensive tasks, such as open-domain question answering (ODQA; Lee et al., 2019) and fact verification (Thorne et al., 2018). Since different ODQA datasets focus on different informationseeking goals, this task typically is handled by customized retrieval models (Karpukhin et al., 2020; Yang et al., 2018; Wu et al., 2020; Ma et al., 2022a). However, this dataset-specific paradigm has limited model scalability and transferability. For example, augmented training with single-hop data hurts multi-hop retrieval (Xiong et al., 2021b). Further, as new information needs constantly emerge, dataset-specific models are hard to reuse. † Work done during an internship at Microsoft Research ∗ Equal contribution In this work, we propose Chain-of-Skills (COS), a modular retriever based on Transformer (Vaswani et al., 2017), where each module implements a *reusable* skill that can be used for different ODQA datasets. Here, we identify a set of such retrieval reasoning skills: *single retrieval, expanded* query retrieval, entity span proposal, entity linking and *reranking* (§2). As shown in Figure 1, recent work has only explored certain skill configurations. We instead consider jointly learning all skills in a multi-task contrastive learning fashion. Besides the benefit of solving multiple ODQA datasets, our 1599 multi-skill formulation provides unexplored ways to chain skills for individual use cases. In other words, it allows flexible configuration search according to the target domain, which can potentially lead to better retrieval performance (§4). For multi-task learning, one popular approach is to use a shared text encoder (Liu et al., 2019a), *i.e.,* sharing representations from Transformer and only learning extra task-specific headers atop. However, this method suffers from undesirable task interference, *i.e.,* negative transfer among retrieval skills. To address this, we propose a new modularization parameterization inspired by the recent mixture-ofexpert in sparse Transformer (Fedus et al., 2021a), i.e., mixing specialized and shared representations. Based on recent analyses on Transformer (Meng et al., 2022), we design an attention-based alternative that is more effective in mitigating task interference (§5). Further, we develop a multi-task pretraining using *self-supervision* on Wikipedia so that the pretrained COS can be directly used for retrieval without dataset-specific supervision. To validate the effectiveness of COS, we consider zero-shot and fine-tuning evaluations with regard to the model in-domain and cross-dataset generalization. Six representative ODQA datasets are used: Natural Questions (NQ; Kwiatkowski et al., 2019), WebQuestions (WebQ; Berant et al., 2013), SQuAD (Rajpurkar et al., 2016), EntityQuestions (Sciavolino et al., 2021), HotpotQA (Yang et al., 2018) and OTT-QA (Chen et al., 2021a), where the last two are multi-hop datasets. Experiments show that our multi-task pretrained retriever achieves superior *zero-shot* performance compared to recent state-of-the-art (SOTA) *self-supervised* dense retrievers and BM25 (Robertson and Zaragoza, 2009). When fine-tuned using multiple datasets jointly, COS can further benefit from high-quality supervision effectively, leading to new SOTA retrieval results across the board. Further analyses show the benefits of our modularization parameterization for multi-task pretraining and finetuning, as well as flexible skill configuration via Chain-of-Skills inference.1 ## 2 Background We consider five retrieval reasoning skills: *single* retrieval, expanded query retrieval, entity linking, entity span proposal and *reranking*. Convention-1Data and code available at https://github.com/ Mayer123/UDT-QA ally, each dataset provides annotations on a different combination of skills (see Table A1). Hence, we can potentially obtain training signals for individual skills from multiple datasets. Below we provide some background for these skills. Single Retrieval Many ODQA datasets (*e.g.,* NQ; Kwiatkowski et al., 2019) concern simple/singlehop queries. Using the original question as input (Figure 2 bottom-left), single-retrieval gathers isolated supportive passages/tables from target sources in one shot (Karpukhin et al., 2020). Expanded Query Retrieval To answer complex multi-hop questions , it typically requires evidence chains of two or more separate passages (*e.g.,* HotpotQA; Yang et al., 2018) or tables (*e.g.,* OTT-QA; Chen et al., 2021a). Thus, follow-up rounds of retrieval are necessary after the initial single retrieval. The expanded query retrieval (Xiong et al., 2021b) takes an expanded query as input, where the question is expanded with the previous-hop evidence (Figure 2 bottom-center). The iterative retrieval process generally shares the same target source. Entity Span Proposal Since many questions concern entities, detecting those salient spans in the question or retrieved evidence is useful. The task is related to named entity recognition (NER), except requiring only binary predictions, *i.e.,* whether a span corresponds to an entity. It is a prerequisite for generating entity-centric queries (context with target entities highlighted; Figure 2 bottom-right) where targeted entity information can be gathered via downstream entity linking. Entity Linking Mapping detected entities to the correct entries in a database is crucial for analyzing factoid questions. Following Wu et al. (2020), we consider an entity-retrieval approach, *i.e.,* using the entity-centric query for retrieving its corresponding Wikipedia entity description. Rereanking Previous work often uses a reranker to improve the evidence recall in the top-ranked candidates. Typically, the question with a complete evidence chain is used together for reranking. ## 3 Approach In this work, we consider a holistic approach to gathering supportive evidence for ODQA, *i.e.,* the evidence set contains both singular tables/passages (from single retrieval) and connected evidence chains (via expanded query retrieval/entity linking). As shown in Figure 2, COS supports flexible skill configurations, *e.g.,* expanded query retriever and ![2_image_0.png](2_image_0.png) the entity linker can build upon the single-retrieval results. As all retrieval skill tasks are based on contrastive learning, we start with the basics for our multi-task formulation. We then introduce our modularization parameterization for reducing task interference. Lastly, we discuss ways to use selfsupervision for pretraining and inference strategies. ## 3.1 Reasoning Skill Modules All reasoning skills use text encoders based on Transformer (Vaswani et al., 2017). Particularly, only BERT-base (Devlin et al., 2019) is considered without further specification. Text inputs are prepended with a special token [CLS] and different segments are separated by the special token [SEP]. The bi-encoder architecture (Karpukhin et al., 2020) is used for single retrieval, expanded query retrieval, and entity linking. We use dot product for sim(·, ·). Retrieval As single retrieval and expanded query retrieval only differ in their query inputs, these two skills are discussed together here. Specifically, both skills involve examples of a question Q, a positive document P +. Two text encoders are used, *i.e.,* a query encoder for questions and a context passage encoder for documents. For the expanded query case (Figure 2 bottom-center), we concatenate Q with the previous-hop evidence as done in Xiong et al. (2021b), *i.e.,* [CLS] Q [SEP] P + 1 [SEP]. Following the literature, [CLS] vectors from both encoders are used to represent the questions and documents respectively. The training objective is $$L_{\mathrm{ret}}=-{\frac{\exp(\mathrm{sim}({\bf q},{\bf p}^{+}))}{\sum_{{\bf p^{\prime}}\in{\mathcal{P}}\cup\{{\bf p}^{+}\}}\exp(\mathrm{sim}({\bf q},{\bf p^{\prime}}))}},\quad(1)$$ where q, p are the query and document vectors respectively and P is the set of negative documents. Entity Span Proposal To achieve a multi-task formulation, we model entity span proposal based on recent contrastive NER work (Zhang et al., 2022a). Specifically, for an input sequence with N tokens, x1*, . . . , x*N , we encode it with a text encoder to a sequence of vectors h m 1 , . . . , h m N ∈ R d. We then build the span representations using the span start and end token vectors, m(i,j) = tanh((h m i ⊕ h m j )Wa), where i and j are the start and end positions respectively, ⊕ denotes concatenation, tanh is the activation function, and Wa ∈ R 2d×dare learnable weights. For negative instances, we randomly sample spans within the maximum length of 10 from the same input which do not correspond to any entity. Then we use a learned anchor vector s ∈ R dfor contrastive learning, *i.e.,* pushing it close to the entity spans and away from negative spans. Lpos = − exp(sim(s, m+)) Pm′∈M∪{m+} exp(sim(s, m′)), (2) where M is the negative span set which always contains a special span corresponding to [CLS], m[CLS] = h m 0 . However, the above objective alone is not able to determine the prediction of entity spans from null cases at test time. To address this, we further train the model with an extra objective to learn a dynamic threshold using m[CLS] $$L_{\mathrm{cls}}=-{\frac{\exp(\mathrm{sim}({\bf s},{\bf m}^{\lceil\mathrm{cls}\rceil})}{\sum_{{\bf m}^{\prime}\in{\mathcal{M}}}\exp(\mathrm{sim}({\bf s},{\bf m}^{\prime}))}}.\quad\quad(3)$$ The overall entity span proposal loss is computed as Lspan = (Lpos + Lcls)/2. Thus, spans with scores higher than the threshold are predicted as positive. Entity Linking Unlike Wu et al. (2020) where entity markers are inserted to the entity mention context (the entity mention with surrounding context), we use the raw input sequence as in the entity span proposal task. For the entity mention context, we pass the input tokens x1*, . . . , x*N through the entity query encoder to get h e1 , . . . , h e N ∈ R d. Then we compute the entity vector based on its start position i and end position j, *i.e.,* e = (h e i + h e j )/2. For entity descriptions, we encode them with the entity description encoder and use the [CLS] vector pe as representations. The model is trained to match the entity vector with its entity description vector $$L_{\mathrm{link}}=-{\frac{\exp(\mathrm{sim}(\mathbf{e},\mathbf{p}_{e}^{+}))}{\sum_{\mathbf{p^{\prime}}\in{\mathcal{P}}_{e}\cup\{\mathbf{p}_{e}^{+}\}}\exp(\mathrm{sim}(\mathbf{e},\mathbf{p^{\prime}}))}},\quad(4)$$ where p + e is the linked description vector and Pe is the negative entity description set. Reranking Given a question Q and a passage P, we concatenate them as done in expanded query retrieval format [CLS] Q [SEP] P [SEP], and encode it using another text encoder. We use the pair consisting of the [CLS] vector h r[CLS] and the first [SEP] vector h r[SEP] from the output for reranking. The model is trained using the loss $$L_{\text{rank}}=-\frac{\exp(\text{sim}(\mathbf{h}_{\lfloor\text{CLS}\rfloor}^{r+},\mathbf{h}_{\lfloor\text{SEP}\rfloor}^{r+}))}{\sum_{\mathbf{p}^{rt}\in\mathcal{P}_{r}\cup\{\mathbf{p}^{r+}\}}\exp(\text{sim}(\mathbf{h}_{\lfloor\text{CLS}\rfloor}^{rt},\mathbf{h}_{\lfloor\text{SEP}\rfloor}^{rt}))},\tag{5}$$ where Pr is the set of negative passages concatenated with the same question. Intuitively, our formulation encourages h r[CLS] to capture more information about the question and h r[SEP] to focus more on the evidence. The positive pair where the evidence is supportive likely has higher similarity than the negative ones. Our formulation thus spares the need for an extra task-specific header. As the model only learns to rerank single passages, we compute the score for each passage separately for multi-hop cases. ## 3.2 Modular Skill Specialization Implementing all aforementioned modules using separate models is apparently inefficient. As recent work finds that parameter sharing improves the biencoder retriever (Xiong et al., 2021b), we thus focus on a multi-task learning approach. One popular choice is to share the text encoder's parameter of all modules (Liu et al., 2019a). However, this approach suffers from task interference, resulting in degraded performance compared with the skill-specific model (§5.1). We attribute the cause to the competition for the model capacity, i.e., conflicting signals from different skills require attention to individual syntactic/semantic patterns. For example, the text encoder for entity-centric queries likely focuses on the local context around the entity while the expanded query one tends to represent the latent information based on the relation between the query and previous hop evidence. Motivated by recent modular approaches for sparse Transformer LM (Fedus et al., 2021b), we propose to mitigate the task interference by mixing *skill-specific Transformer blocks* with shared ones. A typical Transformer encoder is built with a stack of regular Transformer blocks, each consisting of a multi-head self-attention (MHA) sub-layer and a feed-forward network (FFN) sub-layer, with residual connections (He et al., 2015) and layernormalization (Ba et al., 2016) applied to both sublayers. The shared Transformer block is identical to a regular Transformer block, *i.e.,* all skill inputs are passed through the same MHA and FFN functions. As shown in Figure 2, for skill-specific Transformer blocks, we select a specialized sub-layer from a pool of I parallel sub-layers based on the input, *i.e.,* different skill inputs are processed independently. One option is to specialize the FFN expert sub-layer for individual skills, which is widely used by recent mixture-of-expert models (Fedus et al., 2021b; Cheng et al., 2022). As the FFN sub-layer is found to be important for factual associations (Meng et al., 2022), we hypothesize that using the popular FFN expert is sub-optimal. Since most reasoning skills require similar world knowledge, specializing FFN sub-layers likely hinders knowledge sharing. Instead, different skills typically require the model to attend to distinct input parts. Thus, we investigate a more parameterefficient alternative, *i.e.,* MHA specialization. In our experiments, we find it to be more effective in reducing task interference (§5.1). ![4_image_0.png](4_image_0.png) Expert Configuration Regarding the modularization, a naive setup is to route various task inputs to their dedicated sub-layers (experts), *i.e.,* two experts for each bi-encoder task (single retrieval, expanded query retrieval and entity linking) and one expert for each cross-encoder task (entity span proposal and reranking), leading to eight experts in total. To save computation, we make the following adjustments. Given that single and expanded query retrievers share the same set of target passages, we merge the context expert for both cases. Due to data sparsity, we find that routing the expanded queries and reranker inputs which are very similar to separate experts is problematic (§5.1). Thus, we merge the expert for expanded queries and reranker inputs. During self-supervised pretraining with three bi-encoder tasks, we further share the expert for single and expanded queries for efficiency. The overall expert configuration is shown in Figure 3. Multi-task Self-supervision Inspired by the recent success of Izacard et al. (2021), we also use *selfsupervision* on Wikipedia for pretraining. Here, we only consider pretraining for bi-encoder skills (*i.e.,* single retrieval, expanded query retrieval, and entity linking) where abundant self-supervision is available. Unlike prior work focusing only on single-type pretraining, we consider a multi-task setting using individual pages and the hyperlink relations among them. Specifically, we follow Izacard et al. (2021) and Wu et al. (2020) to construct examples for single retrieval and entity linking, respectively. For single retrieval, a pair of randomly cropped views of a passage is used as a positive example. For entity linking, a short text snippet with a hyperlinked entity (entity mention context) is used as the query, and the first paragraph of its linked Wikipedia page is treated as the target (entity description). For a given page, we construct an expanded query using a randomly-sampled short text snippet with its first paragraph, and use one first paragraph from linked pages as the target. ## 3.3 Inference During inference, different skills can be flexibly combined to boost retrieval accuracy. Those studied configurations are illustrated in Figure 1. To consolidate the evidence set obtained by different skills, we first align the linking scores based on the same step retrieval scores (single or expanded query retrieval) for sorting. Documents returned by multiple skills are considered more relevant and thus promoted in ranking. More details with running examples are provided in Appendix A. ## 4 Experiments 4.1 Datasets We consider six popular datasets for evaluation, all focused on Wikipedia, with four single-hop data, NQ (Kwiatkowski et al., 2019), WebQ (Berant et al., 2013), SQuAD (Rajpurkar et al., 2016) and EntityQuestions (Sciavolino et al., 2021); two multi-hop data, HotpotQA (Yang et al., 2018) and OTT-QA (Chen et al., 2021a). Dataset-specific corpora are used for multi-hop datasets, because HotpotQA requires retrieval hopping between text passages while table-passage hopping is demanded by OTT-QA. For single-hop data, we use the Wikipedia corpus from Karpukhin et al. (2020). More detailed (pretraining/fine-tuning) data statistics and experimental settings are in Appendix B. ## 4.2 Evaluation Settings We evaluate our model in three scenarios. Zero-shot Evaluation Similar to recent selfsupervised dense retrievers on Wikipedia, we conduct zero-shot evaluations using the retrieval skill from our pretrained model on NQ, WebQ, EntityQuestions and HotpotQA. To assess the model's ability to handle expanded query retrieval, we design an oracle second-hop retrieval setting (gold first-hop evidence is used) based on HotpotQA. Following Izacard et al. (2021) and Ram et al. (2022), we report top-k retrieval accuracy (answer recall), | NQ | WebQ | EntityQuestions | HotpotQA | Avg | | | | | | | |-----------------------------------|---------|-------------------|------------|--------|---------|--------|---------|--------|---------|------| | Top-20 | Top-100 | Top-20 | Top-100 | Top-20 | Top-100 | Top-20 | Top-100 | Top-20 | Top-100 | | | BM25 | 62.9 | 78.3 | 62.4 | 75.5 | 70.8 | 79.2 | 37.5 | 50.5 | 58.4 | 70.9 | | Contriever (Izacard et al., 2021) | 67.8 | 82.1 | 65.4 | 79.8 | 61.8 | 74.2 | 48.7 | 64.5 | 60.9 | 75.2 | | Spider (Ram et al., 2022) | 68.3 | 81.2 | 65.9 | 79.7 | 65.1 | 76.4 | 35.3 | 48.6 | 58.7 | 71.5 | | COS (pretrain-only) | 68.0 | 81.8 | 66.7 | 80.3 | 70.7 | 79.1 | 77.9 | 87.9 | 70.8 | 82.3 | Table 1: Zero-shot top-k accuracy on test sets for NQ, WebQ and EntityQuestions, and dev set for HotpotQA. DPR-multi (Karpukhin et al., 2020) 79.5 86.1 ANCE-multi (Xiong et al., 2021a) 82.1 87.9 DPR-PAQ (Oguz et al., 2022) 84.7 89.2 co-Condenser (Gao and Callan, 2022) 84.3 89.0 SPAR-wiki (Chen et al., 2021b) 83.0 88.8 COS **85.6 90.2** Table 2: Supervised top-k accuracy on NQ test. i.e., the percentage of questions for which the answer string is found in the top-k passages. Supervised In-domain Evaluation We further fine-tune our pretrained model with two extra skills (entity span proposal and reranking) on NQ, HotpotQA and OTT-QA, again in a multi-task fashion. Unlike multi-hop data with supervision for all skills, only single retrieval and reranking data is available for NQ. During training, all datasets are treated equally without any loss balancing. Different from previous retrieval-only work, we explore Chain-of-Skills retrieval by using different skill configurations. Specifically, we use skill configuration for task A, B and C shown in Figure 1 for NQ, OTT-QA and HotpotQA, respectively. We again report top-k retrieval accuracy for NQ and OTT-QA following previous work. For HotpotQA, we follow the literature using the top-1 pair of evidence accuracy (passage EM). Cross-data Evaluation To test the model robustness towards domain shift, we conduct cross-data evaluations on SQuAD and EntityQuestions. Although considerable success has been achieved for supervised dense retrievers using in-domain evaluations, those models have a hard time generalizing to query distribution shift (*e.g.,* questions about rare entities; Sciavolino et al., 2021) compared with BM25. In particular, we are interested to see whether Chain-of-Skills retrieval is more robust. Again, top-k retrieval accuracy is used. Top-20 Top-100 Table 4: Supervised passage EM on HotpotQA dev. ## 4.3 Results | Top-20 | Top-50 | Top-100 | | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|-----------|------| | CORE (Ma et al., 2022a) | 74.5 | 82.9 | 87.1 | | COS | 79.9 | 88.9 | 92.2 | | COS w/ CORE configuration | 80.5 | 88.6 | 91.8 | | Table 3: Supervised top-k accuracy on OTT-QA dev. Passage EM MDR (Xiong et al., 2021b) 81.20 Baleen (Khattab et al., 2021) 86.10 IRRR (Qi et al., 2021) 84.10 TPRR (Zhang et al., 2021a) 86.19 HopRetriever-plus (Li et al., 2021) 86.94 AISO (Zhu et al., 2021) 88.17 COS 88.89 | | | | Zero-shot Results For zero-shot evaluations, we use two recent self-supervised dense retrievers, Contriever (Izacard et al., 2021) and Spider (Ram et al., 2022), and BM25 as baselines. The results are presented in Table 1. As we can see, BM25 is a strong baseline matching the average retrieval performance of Spider and Contriever over considered datasets. COS achieves similar results on NQ and WebQ compared with self-supervised dense methods. On the other hand, we observe significant gains on HotpotQA and EntityQuestions, where both dense retrievers are lacking. In summary, our model shows superior zero-shot performance in terms of average answer recall across the board, surpassing BM25 with the largest gains, which indicates the benefit of our multi-task pretraining. Supervised In-domain Results As various customized retrievers are developed for NQ, OTTQA and HotpotQA, we compare COS with different dataset-specific baselines separately. For NQ, we report two types of baselines, 1) bi-encoders with multi-dataset training and 2) models with *augmented pretraining*. For the first type, we have DPR-multi (Karpukhin et al., 2020) and ANCEmulti (Xiong et al., 2021a), where the DPR model is initialized from BERT-based and ANCE is initialized from DPR. For the second type, DPR-PAQ (Oguz et al., 2022) is initialized from the RoBERTalarge model (Liu et al., 2019b) with pretraining using synthetic queries (the PAQ corpus (Lewis et al., 2021)), co-Condenser (Gao and Callan, 2022) incorporated retrieval-oriented modeling during language model pretraining on Wikipedia; SPAR-wiki (Chen et al., 2021b) combine a pretrained lexical model on Wikipedia with a dataset-specific dense retriever. Both co-Condenser and SPAR-wiki are initialized from BERT-base. As shown by results for NQ (Table 2), COS outperforms all baselines with or without pretraining. It is particularly encouraging that despite being a smaller model, COS achieves superior performance than DPR-PAQ. The reasons are two-fold: Oguz et al. (2022) has shown that scaling up the retriever from base to large size only provides limited gains after pretraining. Moreover, DPR-PAQ only learns a single retrieval skill, whereas COS can combine multiple skills for inference. We defer the analysis of the advantage of chain-of-skills inference later (§5.2). For OTT-QA, we only compare with the SOTA model CORE (Ma et al., 2022a), because other OTT-QA specific retrievers are not directly comparable where extra customized knowledge source is used. As CORE also uses multiple skills to find evidence chains, we include a baseline where the inference follows the CORE skill configuration but uses modules from COS. For HotpotQA, we compare against three types of baselines, dense retrievers focused on expanded query retrieval MDR (Xiong et al., 2021b) and Baleen (Khattab et al., 2021), sparse retrieval combined with query reformulation IRRR (Qi et al., 2021) and TPRR (Zhang et al., 2021a) and ensemble of dense, sparse and hyperlink retrieval HopRetriever (Li et al., 2021) and AISO (Zhu et al., 2021). The results on OTT-QA and HotpotQA are summarized in Table 3 and Table 4. It is easy to see that COS outperforms all the baselines here, again showing the advantage of our configurable multi-skill model over multiple types of ODQA tasks. Later, our analyses show that both Chain-of-Skills inference and pretraining contribute to the observed gains. Cross-data Results Given that both EntityQuestions and SQuAD are single-hop, we use baselines on NQ with improved robustness for comparison. | EntityQuestions | SQuAD | | | | |------------------------------------|---------|--------|---------|------| | Top-20 | Top-100 | Top-20 | Top-100 | | | BM25 | 70.8 | 79.2 | 71.1 | 81.8 | | DPR-multi (Karpukhin et al., 2020) | 56.6 | 70.1 | 52.0 | 67.7 | | SPAR-wiki (Chen et al., 2021b) | 73.6 | 81.5 | 73.0 | 83.6 | | COS | 76.3 | 82.4 | 72.6 | 81.2 | Table 5: Cross-dataset top-k accuracy on test sets. | #Params | Top-20 | Top-100 | | |----------------------------------------------|----------|-----------|------| | Chain-of-Skills inference No Expert | 111M | 90.2 | 92.4 | | FFN Expert(naive) | 252M | 91.3 | 93.4 | | MHA Expert(naive) | 182M | 92.0 | 94.0 | | MHA Expert(COS) | 182M | 92.0 | 94.2 | | Retrieval-only inference Multi-hop Retriever | 110M | 85.1 | 88.9 | | MHA Expert(naive) | 182M | 82.8 | 87.0 | | MHA Expert(COS) | 182M | 85.9 | 89.6 | Particularly, SPAR-wiki is an ensemble of two dense models with one pretrained using BM25 supervision on Wikipedia and the other fine-tuned on NQ. BM25 is included here, as it is found to achieve better performance than its dense counterpart on those two datasets. The evaluation results are shown in Table 5. Overall, our model achieves the largest gains over BM25 on both datasets, indicating that our multi-task fine-tuned model with Chain-of-Skills inference is more robust than previous retrieval-only approaches. ## 5 Analysis 5.1 Task Interference We conduct ablation studies on HotpotQA to compare different ways of implementing skill-specific specialization (discussed in §3.2) and their effects on task interference. As MHA experts are used for our model, we consider two variants for comparison: 1) the no-expert model where all tasks share one encoder, and 2) the FFN expert model where specialized FFN sub-layers are used. Then we also compare the proposed expert configuration with a variant where the expanded query retrieval inputs share the same expert as single retrieval, denoted as the naive setting. The results are shown in the upper half of Table 6. Compared with the no-expert model, both FFN and MHA experts can effectively reduce task interference, wherein MHA expert is more effective overall. Our proposed expert config- ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) ## Uration Can Further Help. 5.2 Benefit Of Chain-Of-Skills **Inference** Here we explore the benefits of the chained skill inference over the retrieval-only version. We additionally train a multi-hop retriever following Xiong et al. (2021b), and compare it with the two MHA expert models using the same two rounds of retrieval-only inference. The comparison is shown in the lower part of Table 6. As we can see, retrieval-only inference suffers large drops in performance. Although our proposed and naive MHA expert configurations have similar performance using Chain-of-Skills inference, the naive configuration model shows severe degradation caused by task interference compared with the multi-hop retriever, validating the effectiveness of our proposed model. We further compare our Chain-of-Skills inference with the retrieval-only inference on NQ, EntityQuestions and SQuAD in Figure 4. It is easy to see that our pretraining can benefit the retrieval-only version. However, using better skill configurations via Chain-of-Skills inference yields further improvements, particularly on those unseen datasets. ## 5.3 Effect Of Pretraining To further demonstrate the benefit of our proposed multi-task pretraining, we fine-tune another multi- | Query | Doc | Top-20 | Top-100 | | |-----------------|-------|----------|-----------|------| | Single query* | 0 | 1 | 96.1 | 98.2 | | Single query | 4 | 1 | 90.1 | 95.2 | | Single query | 2 | 1 | 91.8 | 95.9 | | Single query | 2 | 3 | 87.4 | 92.7 | | Expanded query | 0 | 1 | 94.2 | 97.0 | | Expanded query* | 4 | 1 | 95.3 | 97.4 | | Expanded query | 2 | 1 | 74.5 | 85.8 | | Expanded query | 2 | 3 | 67.3 | 79.6 | task model following the same training protocol as COS but BERT model weights are used for initialization. Both COS and the model without pretraining are then using the same skill configuration for inference. The results are illustrated in Figure 5. Similar to the retrieval-only version (Figure 4), we find that COS consistently outperforms the multi-task model without pretraining across all considered datasets using Chain-of-Skills inference. Again, the pretrained model is found to achieve improvements across the board, especially on out-of-domain datasets, which validates the benefits of our multi-task pretraining. ## 5.4 Swapping Experts To understand if different experts in our model learned different specialized knowledge, we experiment with swapping experts for different inputs on HotpotQA. In particular, we feed the single query input and expanded query input to different query experts and then retrieve from either the context passage index or the entity description index. For single query input, we measure if the model can retrieve one of the positive passages. For expanded query input, we compute the recall for the other positive passage as done in (§4.3). The results are shown in Table 7. Although both the single query expert and the expanded query expert learn to retrieve evidence using the [CLS] token, swapping the expert for either of these input types leads to a significant decrease in performance. Also, switching to the entity query expert and retrieving from the entity description index results in a large drop for both types of inputs. This implies that each specialized expert acquires distinct knowledge and cannot be substituted for one another. | Dev | Test | | | | |------------------------------|--------|------|------|------| | EM | F1 | EM | F1 | | | HYBRIDER (Chen et al., 2020) | 10.3 | 13.0 | 9.7 | 12.8 | | FR+CBR(Chen et al., 2021a) | 28.1 | 32.5 | 27.2 | 31.5 | | CARP (Zhong et al., 2022) | 33.2 | 38.6 | 32.5 | 38.5 | | OTTer (Huang et al., 2022) | 37.1 | 42.8 | 37.3 | 43.1 | | CORE (Ma et al., 2022a) | 49.0 | 55.7 | 47.3 | 54.1 | | CORE + FiE | 51.4 | 57.8 | - | - | | COS + FiE | 56.9 | 63.2 | 54.9 | 61.5 | Table 8: End-to-end QA results on OTT-QA. ## 6 Question Answering Experiments Here, we conduct end-to-end question-answering experiments on NQ, OTT-QA and HotpotQA, using retrieval results from COS. Following the literature, we report exact match (EM) accuracy and F1 score. For NQ and OTT-QA, we re-implement the Fusion-in-Encoder (FiE) model (Kedia et al., 2022) because of its superior performance on NQ. For NQ, the model reads top-100 passages returned by COS, and for OTT-QA, the model reads top-50 evidence chains, in order to be comparable with previous work. Here, separate models are trained for each dataset independently. Due to space constraints, we only present the results on OTT-QA and leave the NQ results to Table A2. The OTTQA results are summarized in Table 8. Our model, when coupled with the FiE, is able to outperform the previous baselines by large margins on OTTQA, and we can see that the superior performance of our model is mainly due to COS. Finally, for HotpotQA, since the task requires the model to predict supporting sentences in addition to the answer span, we follow Zhu et al. (2021) to train a separate reader model to learn answer prediction and supporting sentence prediction jointly. Due to space constraints, we leave the full results to Table A3. Overall, our method achieves competitive QA performance against the previous SOTA with improved exact match accuracy. ## 7 Related Work Dense retrievers are widely used in recent literature for ODQA (Lee et al., 2019; Karpukhin et al., 2020). While most previous work focuses on single retrieval (Xiong et al., 2021a; Qu et al., 2021), some efforts have also been made towards better handling of other query types. Xiong et al. (2021b) propose a joint model to handle both single retrieval and expanded query retrieval. Chen et al. (2021b) train a dense model to learn salient phrase retrieval. Ma et al. (2022a) build an entity linker to handle multi-hop retrieval. Nevertheless, all those models are still customized for specific datasets, *e.g.,* only a subset of query types are considered or separate models are used, making them un-reusable and computationally intensive. We address these problems by pinning down a set of functional skills that enable joint learning over multiple datasets. Mixure-of-expert models have also become popular recently (Fedus et al., 2021b). Methods like gated routing (Lepikhin et al., 2020) or stochastic routing of experts (Zuo et al., 2021) do not differentiate the knowledge learned by different experts. Instead, our work builds expert modules that learn reusable skills which can be flexibly combined for different use cases. Another line of work focus on unsupervised dense retrievers using self-supervised data constructed from the inverse-cloze-task (Lee et al., 2019), random croppings (Izacard et al., 2021), truncation of passages with the same span (Ram et al., 2022), hyperlink-induced passages (Zhou et al., 2022) or synthetic QA pairs (Oguz et al., 2022). Other model architecture adjustments on Transformer for retrieval are proposed (Gao and Callan, 2021, 2022). Our work can be viewed as a synergy of both. Our multi-task pretrained model can perform better zero-shot retrieval. Our modular retriever can be further fine-tuned in a multi-task fashion to achieve better performance. ## 8 Conclusions In this work, we propose a modular model Chain-of-Skills (COS) that learns five reusable skills for ODQA via multi-task learning. To reduce task interference, we design a new parameterization for skill modules. We also show that skills learned by COS can be flexibly chained together to better fit the target task. COS can directly perform superior zero-shot retrieval using multitask self-supervision on Wikipedia. When finetuned on multiple datasets, COS achieves SOTA results across the board. For future work, we are interested in exploring scaling up our method and other scenarios, *e.g.,* commonsense reasoning (Talmor et al., 2022) and biomedical retrieval (Nentidis et al., 2020; Zhang et al., 2022b). ## Acknowledgements We would like to thank Aman Madaan, Sheng Zhang, and other members of the Deep Learning group at Microsoft Research for their helpful discussions and anonymous reviewers for their valuable suggestions on this paper. ## Limitations We identify the following limitations of our work. Our current COS's reranking expert only learns to rerank single-step results. Thus it can not model the interaction between documents in case of multipassage evidence chains, which might lead to suboptimal performance, *e.g.,* when we need to rerank the full evidence path for HotpotQA. At the same time, we hypothesize that the capacity of the small model used in our experiments is insufficient for modeling evidence chain reranking. 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In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3615–3626, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, and Jianfeng Gao. 2021. Taming sparsely activated transformer with stochastic experts. ## A Inference Pipeline At inference time, our model utilizes the retrieving skill or the linking skill or both in parallel to gather evidence at every reasoning step. When both skills are used, one problem is that the scores associated with the evidence found by different skills are not aligned, *i.e.,* naively sorting the retrieved documents and linked documents together may cause one pool of documents to dominate over the other. Thus we propose to align the linking scores based on the same step retrieval score: $\downarrow$ . ## Lsi = Lsi/Max({Ls} ∪ {Rs}) × Max({Rs}), (6) where lsi represents the linking score of the document i and {ls}, {rs} represent the set of linking scores and retrieving scores for top-K documents from each skill. Effectively, if the raw linking score is larger than the retrieving score, we would align the top-1 document from each set. On the other hand, if the raw linking score is smaller, it would not get scaled. The reason is that certain common entities may also be detected and linked by our model *e.g.,* United States, but they usually do not contribute to the answer reasoning, thus we do not want to encourage their presence. In the case of a document being discovered by both skills, we promote its ranking in the final list. To do so, we take the max of the individual score (after alignment) and then multiply by a coefficient α, which is a hyper-parameter. ## Si = Α Max(Lsi, Rsi). (7) Finally, we use the reranking skill to compute a new set of scores for the merged evidence set, and then sort the documents using the combination of retrieving/linking score and reranking score: ## Si + Β Rankscorei. (8) β is another hyper-parameter. For multi-hop questions, the same scoring process is conducted for the second-hop evidence documents and then the two-hop scores are aggregated to sort the reasoning chains. The inference pipeline is also illustrated in Figure A1. ## B Experimental Details B.1 Data Statistics The detailed data statistics are shown in Table A1. Pretraining We follow Izacard et al. (2021) and Wu et al. (2020) to construct examples for single retrieval and entity linking, respectively. For single retrieval, a pair of randomly cropped views of a passage is treated as a positive example. Similar to Spider (Ram et al., 2022), we also use the processed DPR passage corpus based on the English Wikipedia dump from 2018/12/20. For entity linking, we directly use the preprocessed data released by BLINK (Wu et al., 2020) based on the English Wikipedia dump from 2019/08/01. For expanded query retrieval, we construct the pseudo query using a short text snippet with the first passage from the same page, and we treat the first passage from linked pages as the target. As no hyperlink information is preserved for the DPR passage corpus, we use the English Wikipedia dump from 2022/06/01 for data construction. In each Wikipedia page, we randomly sample 30 passages with hyperlinks. (If there are less than 30 passages with hyperlinks, we take all of them.) Each sampled passage, together with the first passage of the page, form a pseudo query. Then, in each sampled passage, we randomly pick an anchor entity and take the first passage of its associated Wikipedia page as the target. To avoid redundancy, if an anchor entity has been used 10 times in a source page, we no longer pick it for the given source. If the query and the target together exceed 512 tokens, we will truncate the longer of the two by randomly dropping its first token or its last token. Finetuning For NQ, we adopted the retriever training data released by Ma et al. (2022b) and further used them for the reranking skill. Note that data from Ma et al. (2022b) also contains table-answerable questions in NQ, and we simply merged the corresponding training splits with the text-based training split. That's why the number of examples in the last column is greater than the number of questions in the training set. For HotpotQA, we adopted single retrieval and expanded query retrieval data released by Xiong et al. (2021b). For question entity linking data, we heuristically matched the entity spans in the question with the gold passages' title to construct positive pairs, and we use the same set of negative passages as in single retrieval. For passage entity linking, we collected all unique gold passages in the training set and their corresponding hyperlinks for building positives and mined negatives using BM25. Finally, the reranking data is the same as single retrieval. For OTT-QA, we adopt the single retrieval and ta- ![14_image_0.png](14_image_0.png) ble entity linking data released by Ma et al. (2022a). For expanded query retrieval, we concatenate the question with the table title, header, and row that links to the answer-containing passage as the query, and the corresponding passage is treated as a positive target. The negatives are mined with BM25. Finally, reranking data is the same copy as in single retrieval except that we further break down tables into rows and train the model to rank rows. This is because we want to make the reranking and expanded query retrieval more compatible. Since iterative training is shown to be an effective strategy by previous works (Xiong et al., 2021a; Ma et al., 2022b), we further mined harder negatives for HotpotQA and OTT-QA skill training data. Specifically, we train models using the same configuration as in pretraining (four taskspecific experts, with no reranking data or span proposal data) for HotpotQA and OTT-QA respectively (models are initialized from BERT-baseduncased). Then we minded harder negatives for each of the data types using the converged model. The reranking and the entity span proposal skills are excluded in this round because the reranking can already benefit from harder negative for single retrieval (as two skills share the same data) and the entity span proposal does not need to search through a large index. Finally, the data splits coupled with harder negatives are used to train our main Chain-of-Skills (COS) and conduct ablation studies. ## B.2 Training Details Pretraining Similar to Contriever (Izacard et al., 2021), we adopt a continual pretraining setup based on the uncased BERT-base architecture, but our model is initialized from the Contriever weights. We train the model for 20 epochs with the batch size of 1024 and the max sequence length of 256. Here, we only use in-batch negatives for contrastive learning. The model is optimized using Adam with the initial learning rate of 1e-4. The final checkpoint is used for fine-tuning later. Finetuning When initializing from pretrained COS, the weights mapping for the first 5 experts are illustrated in Figure 3 and the last expert is initialized from BERT-base-uncased. For all experiments, we train models for 40 epochs with the batch size of 192, the learning rate of 2e-5, and the max sequence length of 256. During training, each batch only contains training data for one of the skills from one dataset, thus the model can effectively benefit from the in-batch negatives. To train the entity span proposal skill, we use the same data as entity linking. In particular, we route the data to span proposal experts 20% of the time otherwise the data go through entity linking experts. ## B.3 Inference Details Zero-shot-evaluation We directly use the single retrieval skill to find the top100 documents and compute the results in Table 1. Supervised and Cross-dataset For NQ, EntityQuestions and SQuAD, the reasoning path has a length of 1, *i.e.,* only single passages. We use both single retrieval and linking skills to find a total of top 1000 passages first, and then reduce the set to top 100 using the reranking skill. Both HotpotQA and OTT-QA have reasoning paths with max length 2. For OTT-QA, we first | Dataset | Train | Dev | Test | Skill Training Data | # Examples | |--------------------------|---------|--------|--------|--------------------------|--------------| | single retrieval | 6M | | | | | | Pretraining | - | - | - | expanded query retrieval | 6M | | passage entity linking | 9M | | | | | | NQ | 79,168 | 8,757 | 3,610 | single retrieval | 86,252 | | reranking | 86,252 | | | | | | single retrieval | 90,447 | | | | | | expanded query retrieval | 90,447 | | | | | | question entity linking | 80,872 | | | | | | passage entity linking | 104,335 | | | | | | reranking | 90,447 | | | | | | HotpotQA | 90,447 | 7,405 | 7,405 | single retrieval | 41,469 | | expanded query retrieval | 31,638 | | | | | | table entity linking | 19,764 | | | | | | reranking | 41,479 | | | | | | OTT-QA | 41,469 | 2,214 | 2,158 | | | | EntityQuestions | - | 22,068 | 22,075 | - | - | | WebQ | - | - | 2,032 | - | - | | SQuAD | - | - | 10,570 | - | - | Table A1: Statistics of datasets used in our experiments, columns 2-4 represent the number of questions in each split. The last two columns contain the type of training data and the corresponding number of instances find top 100 tables using the single retrieval skill following (Ma et al., 2022a). Then we break down tables into rows and use the reranking skill to keep only top 200 rows. Then for each row, expanded query retrieval and linking skills are used to find the second-hop passages, where we keep top 10 passages from every expanded query retrieval and top 1 passage from every linked entity. Finally, we apply the same heuristics, as done in Ma et al. (2022a) to construct the final top 100 evidence chains. For HotpotQA, single retrieval and linking are used jointly to find the first-hop passages where we keep top 200 passages from single retrieval and top 5 passage from each linked question entity. The combined set is then reranked to keep the top 30 first-hop passages. Then expanded query retrieval and passage entity linking are applied to these 30 passages, where we keep top 50 passages from expanded query retrieval and top 2 passages from every linked passage entity. Next, another round of reranking is performed on the newly collected passages and then we sort the evidence passage chains based on the final aggregated score and keep top 100 chains. Since all of the baselines on HotpotQA adopt a large passage path reranker, we also trained such a model following (Zhu et al., 2021) (discussed in Appendix C) to rank the top 100 passage | #Params | EM | | |----------------------------------|-------|------| | FiD (Izacard and Grave, 2021) | 770M | 51.4 | | UnitedQA-E (Cheng et al., 2021) | 330M | 51.8 | | FiD-KD (Izacard and Grave, 2020) | 770M | 54.4 | | EMDR2 (Singh et al., 2021) | 440M | 52.5 | | YONO (Lee et al., 2021) | 440M | 53.2 | | UnitedQA (Cheng et al., 2021) | 1.87B | 54.7 | | R2-D2 (Fajcik et al., 2021) | 1.29B | 55.9 | | FiE (Kedia et al., 2022) | 330M | 58.4 | | FiE (ours implementation) | 330M | 56.3 | | COS + FiE | 330M | 56.4 | Table A2: End-to-end QA Exact Match score on NQ chains to get the top 1 prediction. The hyperparameters for OTT-QA and HotpotQA inference are selected such that the total number of evidence chains are comparable to previous works (Ma et al., 2022a; Xiong et al., 2021b). ## C Question Answering Results C.1 Training Details We follow descriptions in (Kedia et al., 2022) for re-implementation of FiE model and the model is initialized from Electra-large (Clark et al., 2020). For NQ, we train the model for 5,000 steps with the effective batch size of 64, the learning rate of 5e-5, the layer-wise learning rate decay of 0.9, the max answer length of 15, the max question length of 28, the max sequence length of 250, and 10 global tokens. Note that although Kedia et al. (2022) reports that training with 15,000 steps leads to better performance, we actually found it to be the same as 5,000 steps. Thus we train with fewer steps to save computation. For OTT-QA, we used the same set-up of hyperparameters except that the max sequence length is changed to 500. For HotpotQA path reranker and reader, we prepare the input sequence as follows: "[CLS] Q [SEP] yes no [P] P1 [P] P2 [SEP] ", where [P] is a special token to denotes the start of a passage. Then the input sequence is encoded by the model and we extract passage start tokens representations p1*, ...p*m and averaged sentence embeddings for every sentence in the input s1*, ...s*n to represent passages and sentences respectively. The path reranker is trained with three objectives: passage ranking, supporting sentence prediction and answer span extraction, as we found the latter two objectives also aid the passage ranking training. For answer extraction, the model is trained to predict the start and end token indices as commonly done in recent literature (Xiong et al., 2021b; Zhu et al., 2021). For both passage ranking and supporting sentence prediction, the model is trained with the ListMLE loss (Xia et al., 2008). In particular, every positive passage in the sequence is assigned a label of 1, and every negative passage is assigned 0. To learn a dynamic threshold, we also use the [CLS] token p0 to represent a pseudo passage and assign a label of 0.5. Finally, the loss is computed as follows: $$L_{\mathrm{p}}=-\sum_{i=0}^{m}\log{\frac{\exp(p_{i}W_{p})}{\sum_{p^{\prime}\in{\mathcal{P}}\cup\{p_{i}\}}\exp(p^{\prime}W_{p})}}.\quad(9)$$ where P contains all passages representations that have labels smaller than pi. Wp ∈ Rdare learnable weights and d is the hidden size. In other words, the model learns to assign scores such that positive passages > thresholds > negative passages. The supporting sentence prediction is also trained using Equation 9. Overall, use the following loss weighting: $$L_{\mathrm{path}}=L_{p}+L_{a}+0.5\times L_{s}\qquad(10)$$ where La is the answer extraction loss and Ls is the supporting sentence prediction loss. During training, we sample 0-2 positive passages and 0-2 negative passages from the top 100 chains returned by COS, and the model encodes at most 3 passages, *i.e.,* the passage chain structure is not preserved and the passages are sampled independently. We train the model for 20,000 steps with the batch size of 128, the learning rate of 5e-5, the layer-wise learning rate decay of 0.9, the max answer length of 30, the max question length of 64, and the max sequence length of 512. For inference, the model ranks top 100 passage chains with structure preserved. We sum the scores of the two passages in every chain and subtract the dynamic threshold score and sort the chains based on this final score. Next, we train a reader model that only learns answer extraction and supporting sentence prediction. We only train the model using the two gold passages with the following loss weighting. $$L_{\mathrm{reader}}=L_{a}+0.5\times L_{s}$$ $\left(11\right)^{2}$ The model uses the same set of hyperparameters as the path reranker except that the batch size is reduced to 32. At inference time, the model directly read the top 1 prediction returned by the path reranker. Both models here are initialized from Electra-large. ## C.2 Results The NQ results are presented in Table A2. Overall, our model achieves a similar performance as our own FiE baseline. FiE baseline uses the reader data released by the FiD-KD model, which has an R100 of 89.3 (vs 90.2 of COS). Considering that the gap between our method and FiD-KD model's top 100 retrieval recall is relatively small, this result is not surprising. The HotpotQA results are shown in Table A3. Overall our results are similar to previous SOTA methods on the dev set. At the time of the paper submission, we have not got the test set results on the leaderboard. We adopted DPR evaluation scripts 2for all the retrieval evaluations and MDR evaluation scripts 3 for all the reader evaluations. ## D Computation Our COS has 182M paramteres. For COS pretraining, we use 32 V100-32GB GPUs, which takes 2https://github.com/facebookresearch/ DPR 3https://github.com/facebookresearch/ multihop_dense_retrieval tion loss and $L_{\rm s}$ i. Dev Test Ans Sup Joint Ans Sup Joint EM F1 EM F1 EM F1 EM F1 EM F1 EM F1 MUPPET (Feldman and El-Yaniv, 2019) 31.1 40.4 17.0 47.7 11.8 27.6 30.6 40.3 16.7 47.3 10.9 27.0 CogQA (Ding et al., 2019) 37.6 49.4 23.1 58.5 12.2 35.3 37.1 48.9 22.8 57.7 12.4 34.9 GoldEn Retriever (Qi et al., 2019) - - - - - - 37.9 49.8 30.7 64.6 18.0 39.1 Semantic Retrieval (Nie et al., 2019) 46.5 58.8 39.9 71.5 26.6 49.2 45.3 57.3 38.7 70.8 25.1 47.6 Transformer-XH (Zhao et al., 2020) 54.0 66.2 41.7 72.1 27.7 52.9 51.6 64.1 40.9 71.4 26.1 51.3 HGN (Fang et al., 2020) - - - - - - 59.7 71.4 51.0 77.4 37.9 62.3 GRR (Asai et al., 2020) 60.5 73.3 49.2 76.1 35.8 61.4 60.0 73.0 49.1 76.4 35.4 61.2 DDRQA (Zhang et al., 2021b) 62.9 76.9 51.3 79.1 - - 62.5 75.9 51.0 78.9 36.0 63.9 MDR (Xiong et al., 2021b) 62.3 75.1 56.5 79.4 42.1 66.3 62.3 75.3 57.5 80.9 41.8 66.6 IRRR+ (Qi et al., 2021) - - - - - - 66.3 79.9 57.2 82.6 43.1 69.8 HopRetriever-plus (Li et al., 2021) 66.6 79.2 56.0 81.8 42.0 69.0 64.8 77.8 56.1 81.8 41.0 67.8 TPRR (Zhang et al., 2021a) 67.3 80.1 60.2 84.5 45.3 71.4 67.0 79.5 59.4 84.3 44.4 70.8 AISO (Zhu et al., 2021) 68.1 80.9 **61.5 86.5** 45.9 **72.5 67.5 80.5** 61.2 **86.0** 44.9 **72.0** COS **68.2 81.0** 61.1 85.3 **46.4** 72.3 67.4 80.1 **61.3** 85.3 **45.7** 71.7 about 3 days. For COS finetuning, we used 16 V100-32GB GPUs which takes about 2 days. Our reader model FiE has 330M parameters. We used 16 V100-32GB GPUs for training which takes about 1.5 days. For HotpotQA, both the path reranker and the reader have 330M parameters. We used 16 V100-32GB GPUs for training, the path reranker takes about 12 hours and the reader takes about 4 hours to train. We train all of our models once due to the large computation cost. ## E Licenses We list the License of the software and data used in this paper below: - DPR: CC-BY-NC 4.0 License - MDR: CC-BY-NC 4.0 License - Contriever: CC-BY-NC 4.0 License - BLINK: MIT License - NQ: CC-BY-SA 3.0 License - HotpotQA: CC-BY-NC 4.0 License - OTT-QA: MIT License - EntityQuestions: MIT License - SQuAD: CC-BY-SA 4.0 License - WebQuestions: CC-BY 4.0 License ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? section Limitations after the conclusion ✗ A2. Did you discuss any potential risks of your work? As our model does not generate its own outputs, when used with trustworthy sources, we do not see high societal risks. However, we admit that those biases from the training datasets can be amplified. For example, regardless of improvements, our model can not fully address the deficiency of dense retrieval on rare entities, which can compromise the fairness of retrieval. ✓ A3. Do the abstract and introduction summarize the paper's main claims? section 1 ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** Section 3 And 4 ✓ B1. Did you cite the creators of artifacts you used? section 3 and 4 ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Appendix E ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? We only used publically available datasets in the same way as previous works ✗ B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? We only used publically available datasets in the same way as previous works ✗ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? We only used publically available datasets in the same way as previous works ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Appendix B The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. ## C ✓ **Did You Run Computational Experiments?** Section 4 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix D ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Appendix B and C ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix D ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Appendix C ## D ✗ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

wang-etal-2023-elaboration

Elaboration-Generating Commonsense Question Answering at Scale

https://aclanthology.org/2023.acl-long.90

In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models{---}an elaboration generator and an answer predictor{---}allowing each to influence the other. Using less than 0.5{\%} of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.

# Elaboration-Generating Commonsense Question Answering At Scale Wenya Wang♡ Vivek Srikumar♢♠ Hanna Hajishirzi♡♠ **Noah A. Smith**♡♠ ♡Paul G. Allen School of Computer Science & Engineering, University of Washington ♠Allen Institute for AI ♢School of Computing, University of Utah [email protected] ## Abstract In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models—an elaboration generator and an answer predictor—allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.1 ## 1 Introduction Commonsense question answering (QA; Talmor et al., 2019) provides benchmarks used to evaluate the extent to which NLP models—increasingly based on language models—can "understand" questions and reason about their answers. For example, consider the question in Figure 1: *Gases released during the use of fossil fuels cause a what?* A reasonably informed human could give the answer *global warming*, by reasoning that: *Fossil fuel* emissions are the main source of greenhouse gases. They cause global warming. It is common to use LMs to predict answers directly for QA tasks (Devlin et al., 2019; Liu et al., 2019; Khashabi et al., 2020). On challenging datasets whose questions rely on unstated background knowledge (Talmor et al., 2021; Mihaylov et al., 2018; Khot et al., 2020), some recent works rely on external knowledge, e.g., Wikipedia or structured knowledge bases (Mihaylov and Frank, ![0_image_0.png](0_image_0.png) Figure 1: An overview of the framework that selectively distills knowledge from GPT-3 to a smaller elaboration generator via an answer predictor. 2018; Lin et al., 2019; Banerjee et al., 2019) for additional information that helps to answer the question. Such attempts are limited by the availability and coverage of the knowledge sources. Another line of study (Liu et al., 2022b; Paranjape et al., 2021; Shwartz et al., 2020) reveals that generating text that expresses additional background knowledge relevant to a question is beneficial for answer prediction. The ability to express such knowledge may promote model explainability by explicitly showing the reasoning process. However, expressing high-quality knowledge relies on massive (and thus, expensive) pretrained LMs, e.g., GPT-3 with 175B parameters (Brown et al., 2020). In this work, we focus on a more practical setting and ask: Can smaller LMs, e.g., BART which is about 400× smaller than GPT-3, support reasoning and inference in an end-to-end manner? To this end, we propose a scalable framework, alternating ELABoration and answer predictOR (ELABOR), consisting of two interacting modules: an elaboration generator and an answer predictor. Here an elaboration refers to additional context describing some background knowledge about the question. Instead of generating elaborations independently, we propose a probabilistic framework that treats the elaboration as a latent variable and iteratively optimizes the elaboration generator after receiving feedback from the answer prediction. Specifically, for each question-answer pair (*q, a*), we decompose the distribution of the answer conditioned on the question P(a | q) into a distribution P(e | q) over a latent elaboration, modeled by the **elaboration** generator, and a likelihood distribution P(a | *e, q*) over the answer, modeled by the **answer predictor**. We alternately train the elaboration generator and the answer predictor so that each can benefit the other. Earlier work either pre-constructs elaborations e from external knowledge (Mihaylov and Frank, 2018) or learns P(e | q) solely based on annotations (Rajani et al., 2019); we learn the elaboration generator by distilling high-quality knowledge from GPT-3. We do this using a procedure inspired by hard Expectation-Maximization (Min et al., 2019). This involves refining and filtering elaborations informed by the answer predictor, as shown in Figure 1. ELABOR is thus capable of propagating information in both directions: from elaboration generator to answer predictor and vice versa. We conduct experiments on four commonsense QA datasets: CommonsenseQA (Talmor et al., 2019), CommonsenseQA 2.0 (Talmor et al., 2021), Scientific Commonsense (Khot et al., 2020), and OpenBookQA (Mihaylov et al., 2018). Our experiments reveal that (1) alternating training with smaller LMs (e.g., BART, and GPT-2) narrows the gap between small models and GPT-3; (2) the ability to generate and reason with background elaborations indeed brings larger performance gains than direct inference on more challenging Commonsense QA datasets; (3) the alternating framework helps to filter irrelevant elaborations generated from GPT-3 and the learned elaboration generator can express information that helps to answer the question, as shown through human evaluations. ## 2 Modeling Answers And Elaborations We focus on the task of commonsense question answering in the multiple-choice setting: we seek to identify the answer to a commonsense question among provided candidate choices. Importantly, we are not provided with additional elaboration that may be needed to do so. We formalize the setting and define the model in this section, and Section 3 details the training procedure. ## 2.1 Elaborations As A Latent Variable We formalize commonsense QA in a probabilistic framework. Given a question q and its correct answer a, we seek to train a model that maximizes the probability of the correct answer P(a | q). Directly predicting the answer can be be challenging when complex understanding is needed. Moreover, doing so renders the provenance of the answer unclear. To address both issues, we assume that the answer depends on some latent elaboration e ∈ E with E denoting a set of probable elaborations. With the latent variable, the training objective becomes $$\log P(a\mid q)=\log\sum_{e\in E}P(e\mid q)P(a\mid e,q).\quad(1)$$ Here, the first term in the summation, P(e | q), denotes the probability of an elaboration e conditioned on question q and is captured by the *elaboration generator*. The second term P(a | *e, q*) characterizes the distribution of the answer a conditioned on both the elaboration and the question and is captured by the *answer predictor*. The decomposition in Eq. 1 has also been adopted by Lewis et al. (2020b), taking retrieved knowledge as the hidden variable. Different from the retrieval setting, the generation distribution P(e | q) is intractable. We instead resort to hard EM and alternating optimization. ## 2.2 A Joint Model The elaboration generator seeks to generate an elaboration sequence e given the question q as a prompt. We denote the conditional probability of an elaboration given a question by FE; that is, using the notation from Eq. 1, we have P(e | q) = FE(*e, q*; Φ). We model the elaboration generator using a generative language model that computes the distribution of tokens at each generation step: $${\mathcal{F}}_{E}(e,q;\Phi)=\prod_{t=1}^{m}p_{\mathsf{GEM}}(e_{t}\mid q,e_{1},...,e_{t-1}),\,\,\,\,(2)$$ where e = {e1*, ..., e*m} denotes the generated elaboration sequence. In our experiment, we adopt two generation models—BART (Lewis et al., 2020a) and GPT-2 (Radford et al., 2019)—to model pGEN. The answer predictor, denoted FA, aims to produce the probability of an answer sequence a given a question q and an elaboration e, i.e., P(a | *e, q*) = FA(*a, e, q*; Θ). Any language model could be adopted as the answer predictor. For generality, we select two commonly-used language models from two different paradigms, namely BERT (Devlin et al., 2019) as a masked language model and T5 (Raffel et al., 2020) as a generative language model. For T5, FA(*a, e, q*; Θ) is computed 1620 for an answer sequence $a=\{a_{1},...,a_{n}\}$ using $$\mathcal{F}_{A}(a,e,q;\Theta)=\prod_{t=1}^{n}p_{\texttt{TS}}(a_{t}\mid e,q,a_{1},...,a_{t-1}),\tag{3}$$ with pT5 denoting the generation probability of token at using T5. For BERT, FA(*a, e, q*; Θ) is computed using a softmaxed linear layer over the representation of the [CLS] token: FA(*a, e, q*; Θ) = softmax(Wh[CLS] + b) (4) by giving "[CLS] elaboration [SEP] question [SEP] answer [SEP]" to BERT. ## 2.3 Inference In the testing phase, for each question, we first use the trained elaboration generator FE to sample a set of elaborations E˜. For each e˜ ∈ E˜, we use the answer predictor FA with softmax to produce a normalized distribution over the candidate set. By running the answer predictor for each sampled elaboration, we take the maximum probability as the score for candidate a i which is then used to produce the final prediction: $$a^{\prime}=\operatorname*{argmax}_{a^{i}\in\mathcal{A}}\max_{\tilde{e}\in\mathcal{E}}\frac{\exp^{\mathcal{F}_{A}(a^{i},\tilde{e},q;\Theta)}}{\sum_{a^{j}\in\mathcal{A}}\exp^{\mathcal{F}_{A}(a^{j},\tilde{e},q;\Theta)}}\tag{5}$$ with $\mathcal{A}$ denoting the set of candidate answers. with A denoting the set of candidate answers. ## 3 Alternating Elaboration And Answer Predictor (Elabor) Many existing retrieval or knowledge-based QA methods only optimize P(a | *e, q*), assuming e is given and fixed. Explanation-based methods, on the other hand, train P(e | q) separately using human-annotated explanations. Doing so poses two problems: (1) we need an annotated explanation corpus, and (2) the elaboration generator cannot be calibrated towards the answer. In this work, we propose an approach that tackles both problems by jointly training the elaboration generator and the answer predictor in an alternating framework. Figure 2 illustrates the overall architecture for training. In each iteration, the elaboration generator FE learns to produce high-quality elaborations using feedback from the answer predictor (Section 3.1). The answer predictor FA then takes the generated elaborations as input to produce more reliable answers (Section 3.2). This strategy allows mutual interaction between the two components, propagating information in both directions. ![2_image_0.png](2_image_0.png) $$\mathbf{h}_{[C L S]}+\mathbf{b})$$ To reduce the search space of possible elaborations, we propose to distill knowledge from the pretrained GPT-3 model in a selective way to learn a lightweight elaboration generator (Section 3.3). ## 3.1 An Em-Inspired Learner Our goal is to optimize Eq. 1, rewritten below: $$\log P(a\mid q)=\log\mathbb{E}_{e\sim P(e|q)}[P(a\mid e,q)].\quad(6)$$ Directly optimizing the elaboration generator in this expression is difficult.2Inspired by Qu et al. (2021), we adopt a hard EM framework to do so. The E-step first generates a set of elaborations related to the question and then selects "good" elaborations that help to predict the correct answer. The M-step maximizes the probability of generating these "good" elaborations. E-Step. The E-step aims to identify a set of "good" elaborations from the posterior probability of an elaboration e after observing the correct answer a: $$P(e\mid q,a)\propto P(e\mid q)P(a\mid e,q)\qquad(7)$$ The posterior approximation on the right-hand-side of Eq. 7 aligns with the intuition that the elaboration could have higher probability if it is both relevant to the question (i.e., P(e | q)) and, when combined with the question, provides higher chance of predicting the correct answer (i.e., P(a | *e, q*)). However, the intractable space of possible elaborations renders sampling from P(e | q)P(a | *e, q*) 2One popular option would be to adopt the REINFORCE algorithm (Williams, 1992) that updates FE(*e, q*; Φ) using differentiable policy gradient. However, this strategy involves searching in a huge symbolic space and can be unstable. nontrivial. To alleviate this issue, we adopt two approximations. First, we use GPT-3 to produce more reliable distribution P(e | q), and thus rewriting Eq. 7 as P(e | *q, a*) ∝ PGPT-3(e | q)P(a | *e, q*). Second, we approximate the sampling process via a two-step sample-and-filter procedure. Specifically, we first sample a set of elaborations E¯ from PGPT-3(e | q) which will be discussed in Section 3.3. Then, we filter E¯ according to P(a | *e, q*). Specifically, for each e¯ ∈ E¯, we use the answer predictor3to produce P(a | *e, q* ¯ ) = FA(a, *e, q* ¯ ). Then we select top-K elaborations from E¯ to form E as the set of "good" elaborations. This operation allows the answer predictor to assist in learning how to select elaborations. M-Step. With the selected context set E produced in the E-step, the M-step aims to maximize the probability of each elaboration e ∈ E to update the elaboration generator FE while keeping the answer predictor fixed: $$\operatorname*{max}_{\Phi}\log P({\cal E}\mid q)=\operatorname*{max}_{\Phi}\sum_{e\in{\cal E}}\log{\cal F}_{E}(e,q;\Phi),\tag{8}$$ $=\;\hdots$ . given P(E | q) = Qe∈E P(e|q). In this way, the elaboration generator learns to produce elaborations that are both relevant to the question and with a higher probability of predicting the correct answer. Eq. 8 could also be viewed as a kind of selective distillation, which instead of distilling all the sampled elaborations E¯ from GPT-3, learns to filter out noisy elaborations before transferring knowledge to the elaboration generator. ## 3.2 Optimizing Answer Predictor After updating the elaboration generator, the next step of the alternative training aims to update the answer predictor FA(*a, e, q*; Θ) while keeping the elaboration generator fixed. To achieve that, we approximate the objective of Eq. 6 to log P(a | e, q ˜ ) by sampling a set of elaborations e˜ ∈ E˜ from the elaboration generator P(˜e | q) = FE(˜*e, q*; Φ). Then the objective becomes to maximize $$\log P(a\mid\tilde{e},q)=\log{\mathcal{F}}_{A}(a,\tilde{e},q;\Theta)\qquad0$$ for the correct answer a. The sampled elaboration e˜ from the elaboration generator acts as additional background and explanation for the question, which helps to learn a more reliable prediction 3We also study other filtering strategies as detailed in Section 4.4. model to answer the question. The alternation between updating the answer predictor and the elaboration generator promotes mutual enhancement of each component. The entire training procedure of ELABOR can be found in Appendix A.1. ## 3.3 Distilling Gpt-3 As discussed in the E-step, we use GPT-34to sample possible elaborations to train our elaboration generator. Liu et al. (2022b) showed that, using a small number of prompts and a question, GPT-3 can generate useful knowledge to enhance answer prediction. Inspired by Hinton et al. (2015) and West et al. (2021), we adopt the idea of knowledge distillation to transfer knowledge from GPT3 (expensive to deploy at inference time) to our (cheaper) elaboration generator. We first use GPT-3 to generate a set of elaborations given some predefined prompts. Following Liu et al. (2022b), for each task, we design the prompt as a short instruction followed by five demonstrative examples and a new-question placeholder. By plugging each question into the placeholder, we can repeatedly sample an elaboration e¯ as the continuation of the prompt. This yields a set of candidate elaborations, E¯. Here we use nucleus sampling (Holtzman et al., 2020) to sample each elaboration e¯. For knowledge distillation, a naive strategy could be optimizing the elaboration generator by minimizing $$D(P_{\mathbb{G P T-3}},P_{s})=\mathbb{E}_{\bar{e}\sim P_{\mathbb{G P T-3}}}[-\log P_{s}(\bar{e}\mid q)],$$ with Ps denoting the student network, i.e., our elaboration generator. However, as shown in the experiments, GPT-3 is prone to generating noisy text sequences that may not be relevant to answer the question. This would lead to negative transfer. Our proposal in the E-step is a form of selective knowledge distillation (Kang et al., 2020) which filters elaborations generated from GPT-3 according to the answer score before optimizing our student model. ## 4 Experiments In this section, we examine the question: *Does* jointly optimizing the elaboration generator with the answer predictor outperform approaches that merely retrieve knowledge from trained models, if at all? As a secondary objective, we also investigate the impact of the design choices in our approach, including the choice of the language model, 4We also tried more accessible models, e.g., GPT-J (6B), but observed much worse generation quality. ![4_image_0.png](4_image_0.png) Dataset CSQA CSQA2 QASC **OBQA** ![4_image_3.png](4_image_3.png) Generator BART GPT2 BART GPT2 BART GPT2 BART GPT2 scratch 64.29 65.36 55.45 56.99 49.14 50.65 55.80 55.80 pipeline 65.60 66.42 56.47 56.63 51.73 52.48 56.40 56.60 ELABOR 66.26 **67.32** 58.09 **58.72** 53.78 **54.21** 57.60 **58.60** Table 2: Results on dev. set for different context generators: BART-large and GPT2-large. the need for distillation, the choice of elaboration filtering and the decoding strategy. ## 4.1 Data And Setup We select four multiple-choice commonsense QA datasets involving commonsense concepts or scientific facts: (1) CommonsenseQA (**CSQA**; Talmor et al., 2019), (2) CommonsenseQA 2.0 (**CSQA2**,Talmor et al., 2021) (3) Scientific Commonsense (**QASC**, Khot et al., 2020), and (4) OpenBookQA (**OBQA**; Mihaylov et al., 2018). The elaboration generator is implemented using GPT2large (Radford et al., 2019) and BART-large (Lewis et al., 2020a). The answer predictor is implemented using T5-large (Raffel et al., 2020) and BERT-baseuncased (Devlin et al., 2019). We also experiment with more competitive and larger answer predictors, e.g., UnifiedQA-large/3b (Khashabi et al., 2020). We sample 20 elaborations from GPT-3, of which 3 are selected to form E. We sample 10 elaborations from our elaboration generator during both training and inference. Appendix A.2 has more details on the datasets and experiment settings. ## 4.2 Baselines We organize the baselines into four groups: (1) Direct answer prediction without additional knowledge (**vanilla**). (2) Answer prediction with retrieved knowledge: **COMET** (Bosselut et al., 2019) is trained on the ATOMIC corpus (Sap et al., 2019) to automatically generate causes and effects of a question. **Wikipedia** follows Chen et al. (2017), which retrieves and ranks text spans in Wikipedia articles. (3) Fixed elaboration generator: **selftalk** ![4_image_1.png](4_image_1.png) generates extra background knowledge based on ![4_image_2.png](4_image_2.png) some clarification questions (Shwartz et al., 2020). GPT-3 (Brown et al., 2020) samples 10 knowledge spans as continuations of the question using some demonstrative prompts. (4) Trained elaboration generator: **scratch** implements alternative training without distilling knowledge from GPT-3. pipeline first pretrains the generator using all the sequences generated from GPT-3, then finetunes the answer predictor. For fair comparisons, all four groups require training the answer predictor FA. The second and third groups additionally involve intermediate contexts which are kept fixed. The last group learns both an elaboration generator and an answer predictor. During inference, we pick the choice with maximum score across all the knowledge sequences or generations following Eq. 5. ## 4.3 Results Table 1 shows the main experimental results. Here we use T5-large as the answer predictor for CSQA, CSQA2, QASC, and BERT for OBQA. These are chosen according to the best performances given. To account for more general scenarios, we first use T5 in an open-domain QA setting where no answer choices are given as input, and the target output is the gold answer tokens. We also experiment with other input/output formats for T5 as will be shown in Section 4.4. From Table 1, the advantage of additional knowledge or elaborations is more evident for CSQA2, QASC, and OBQA, compared with CSQA (which contains relatively simpler questions). This confirms the importance of reasoning for complex QA problems. GPT-3 demonstrates performance gains over other knowledge sources. Using less than 5% of the parameters of GPT-3, ELABOR outperforms GPT-3 on two datasets. It also clearly outperforms those models having similar computational cost (e.g., scratch, pipeline). The performance gain of ELABOR over pipeline demonstrates the advantage of our alternating framework. The scratch model on the other hand is prone to learning meaningless shortcuts, e.g., "*The correct answer: I know I'm not sure but* Setting Variants CSQA CSQA2 QASC **OBQA** ![5_image_0.png](5_image_0.png) ![5_image_1.png](5_image_1.png) ![5_image_2.png](5_image_2.png) ## 4.4 Analysis In subsequent experiments, we use the development set of each corpus to make evaluations because the test set is not publicly available. Elaboration Generator. Table 2 shows the effects of different LMs, specifically BART-large and GPT2-large, as elaboration generators. Both demonstrate consistent results across different training strategies (scratch, pipeline, ELABOR). In addition, GPT2-large slightly outperforms BART-large across all the experiments. The higher performance of GPT2-large could be credited to a larger parameter size (774M) compared to BART-large (406M). Another observation is that GPT2-large has more generation flexibility which appears to be less repetitive and cover more aspects relevant to the question, compared to BART-large. Answer Predictor. Table 3 reveals the effect of our framework on more competitive settings and larger answer predictors. We consider another input/output format for T5, referred to as T5-id, which takes both IDs (we use (A), (B), etc. as answer IDs) and tokens of the answer choices as input, and the ID for the gold answer as output. This was adopted in GenMC (Huang et al., 2022). Obviously, T5-id outperforms T5 under the open-domain setting (Table 1) by a large margin, and ELABOR shows clear gains over GenMC. A larger model, UnifiedQA-3b, brings huge improvements even for the vanilla model. Still, additional elaborations (GPT-3 or ELABOR) bring further improvements across all the datasets. Elaboration Filtering. The first block (Elaboration filtering) of Table 4 shows the effect of different filtering criteria as discussed in the E-step of Section 3.1. We implement three other filtering strategies. The **random** option filters GPT3generated elaborations by randomly selecting 3 out ![5_image_3.png](5_image_3.png) ![5_image_4.png](5_image_4.png) of 20. The **correct** option selects all the elaborations that produce the correct answer when fed into the answer predictor. The **pos-neg** option computes the score difference between the correct answer and the average of incorrect answers, based on which 3 elaborations with highest scores are being selected. The pos option uses the answer predictor as adopted by ELABOR. Clearly, random selection produces inferior results among all the options, verifying the benefit of filtering high-quality elaborations for training the elaboration generator. Elaboration Integration. The second block (Elaboration integration) of Table 4 investigates the effect of different elaboration integration methods during inference. Recall from Eq. 5 that ELABOR uses **maximum** pooling among all the generated elaborations E˜ for final predictions. We are interested in how different inference strategies may affect the final performance. Specifically, instead of maximum pooling, we concatenate all the elaborations in E˜ in a single sequence and feed it into the answer predictor (**concatenate**). This brings a clear performance drop on CSQA and QASC, probably due to the unexpected noise and the forgetting issue for long sequences. Another strategy is to formalize inference with a probabilistic view where each generated elaboration has a probability contributing to the final prediction via weighted aggregation (**probability**). To produce the probability, we apply a softmax layer on top of the output logit of each generated elaboration e˜ ∈ E˜. The last option is to compute the similarity between each elaboration and the question and use the most similar elaboration for final inference (**similarity**). We use sentence embeddings generated from sentence transformers (Reimers and Gurevych, 2019) with cosine similarity to select the optimal elaboration. As a result, maximum pooling outperforms other variations at most of the times. Decoding Strategy. The last block (Elaboration generation) of Table 4 reflects how different decoding strategies inherent in the LMs may affect the final performance. We compare the results of greedy decoding (**greedy**) where each decoding step only selects the token with highest probability, beam search (**beam**) with size 10 at each decoding step and selecting top 10 sequences via nucleus sampling (**sample**) adopted in the proposed model ELABOR. Clearly, decoding via sampling produces the best results or comes very close. ![6_image_1.png](6_image_1.png) Sensitivity Test. Figure 3 demonstrates the effects of changing (1) the number of filtered high-quality elaborations (K) from GPT-3 and (2) the size of set E˜ corresponding to the total number of elaborations generated from the elaboration generator. The left plot demonstrates the performance increases when increasing K from 1 to 3, but decreases for K > 3. This pattern verifies that GPT-3 may generate elaborations that negatively affect the final performance. On the other hand, increasing the number of sampled elaborations from the elaboration generator (from 2 to 20) during both training and testing phases brings gradual improvements. This is as expected, given that sampling a diverse set of elaborations should add up to a wide coverage of relevant knowledge for the question. ## 4.5 Human Evaluation To evaluate the quality of elaborations for question answering, we conduct two sets of human evaluations on QASC and CSQA2. For the first experiment, we investigate whether the filtered elaborations from GPT-3 are considered more helpful to answer the question compared to those that are not selected by the model. For the second experiment, we evaluate the quality of the generated elaborations. Some concrete examples of questions and generations can be found in Appendix A.3. The annotation task was carried out in Amazon Mechanical Turk. We restrict annotators to those located in English-speaking countries and who have at least 99% approval rate over more than 1000 tasks. The results are aggregated using majority vote among annotations from 3 workers. Our institution's IRB approved the study. We paid workers an estimated US$15 per hour. Effect of Filtering. Recall that we use the answer predictor to filter elaborations generated from GPT-3 in the E-step. To demonstrate whether the filtering process is capable of removing noisy elaborations, we randomly sample 100 questions from ![6_image_0.png](6_image_0.png) the training corpus of each of two datasets (QASC, CSQA2). For each instance, we present the crowd workers with a question, the correct answer, the GPT3-generated elaboration e that has the highest score P(a | *e, q*) (denoted SELECT), and an elaboration randomly sampled from the remaining ones that are discarded by the answer predictor (denoted DISCARD). The workers are then asked to evaluate the SELECT and DISCARD elaborations by choosing 1-out-of-3 choices: *helpful* (the elaboration adds useful information to answer the question), *neutral* (the elaboration has no influence on the problem), and *harmful* (the elaboration is misleading). To avoid annotation bias, we randomize the order of SELECT and DISCARD elaborations for each example. The results are shown in Figure 4. Among 100 examples for each dataset, the number of helpful elaborations annotated by the workers is considerably higher for the selected category than that of the discarded category. In contrast, the workers agree that the selected elaborations are less likely to be neutral or harmful compared to those that are discarded. The difference is even more evident on CSQA2. This verifies the necessity of using the answer predictor to filter noisy elaborations generated by GPT-3 before distilling the knowledge. Elaboration Quality. In another experiment, we compare the quality of the elaboration generators from the pipeline setup, GPT-3 and our proposed model ELABOR. We select only one elaboration generated from each model that gives the highest score of the predicted answer during inference, which is actually adopted to produce the final prediction. Adapting from the metrics provided by Shwartz et al. (2020) and Liu et al. (2022b), given a piece of automatically-generated text, we pick three aspects: (1) *Factuality* evaluates whether the text is entirely correct (factual), partially correct (partial) or entirely incorrect (incorrect); (2) Rel- ![7_image_0.png](7_image_0.png) evance evaluates whether the text is relevant or irrelevant to the topics discussed in the question; (3) *Helpfulness* evaluates whether the text provides useful information that helps answer the question (helpful), has no effect (neutral) or is misleading (harmful). The human evaluation results on 100 randomly sampled test examples from CSQA2 are shown in Figure 5. Clearly, ELABOR achieves better scores across all the three aspects, with the most evident improvement in terms of helpfulness. We additionally evaluate how humans benefit from those elaborations generated from our model. The detailed analysis is presented in Appendix A.4. Further analysis on how in general the generations from ELABOR and GPT-3 differ is shown in Appendix A.5. Based on the annotations given by crowdsourced workers, we collect only those instances containing an elaboration generated by our model that is labeled as helpful by the workers. This results in 70 and 76 instances from the development set of QASC and CSQA2, respectively. We then compare the performance of ELABOR under three different settings: (1) *No Elaboration* only presents the question to the model during inference; (2) *Random Elaboration* additionally provides a generated elaboration randomly selected after removing the one labeled as helpful; (3) *Helpful Elaboration* contains the single elaboration that is labeled as helpful by workers. The results are shown in Table 5. As expected, our model with helpful elaborations outperforms the other two settings by a large margin, aligning with our intuition that meaningful elaborations are beneficial to the task. ## 5 Related Work Direct Inference. Given only natural-language commonsense questions, a straightforward solution is to directly use language models, either finetuned from the gold-annotated answers (Sakaguchi et al., 2021; Talmor et al., 2019; Khashabi et al., 2020; Talmor et al., 2021) or in an unsupervised setting (Trinh and Le, 2018; Petroni et al., 2019; Puri and Catanzaro, 2019; Yang et al., 2020; Jiang et al., 2020) that exploit knowledge already encoded in the pretrained parameters to perform inference. However, beyond the performance score, it is unclear how these models reach the final answer and whether they perform correct reasoning. It is also challenging to conduct direct inference without additional knowledge for complex queries. Inference with External Knowledge. It has been shown that external knowledge such as knowledge bases or Wikipedia contains rich information that could assist inference. Knowledge bases, e.g., ConceptNet (Speer et al., 2017) or ATOMIC (Sap et al., 2019), contain relational knowledge that could be incorporated as additional inputs for commonsense QA (Mitra et al., 2019; Chang et al., 2020; Bian et al., 2021; Ma et al., 2021; Lv et al., 2020; Yasunaga et al., 2021). Large corpora are another knowledge source to retrieve question-related facts (Lin et al., 2017; Tandon et al., 2018; Banerjee et al., 2019; Joshi et al., 2020; Xiong et al., 2019; Lewis et al., 2020b). These knowledge-based approaches depend on the availability and coverage of the knowledge source, which usually depends on the problem domain. Inference with Generation. To alleviate the dependence on external knowledge, recent trends advocate for automatic generation of additional knowledge related to the question via language models. One direction is to learn a generator to generate meaningful justifications for question answering via human-authored explanations (Camburu et al., 2018; Rajani et al., 2019; Latcinnik and Berant, 2020). Bosselut et al. (2021) adopted a pretrained commonsense generation model (Bosselut et al., 2019) to generate implications of the questions. These approaches, however, require goldannotated commonsense facts to train a good generator. Another direction explores zero-shot generations using pretrained language models. Shwartz et al. (2020) introduced *Selftalk*, which elicits question clarifications using a few pre-defined templates. Paranjape et al. (2021) proposed contrastive prompts that compare candidate options for choosing the correct answer. Liu et al. (2022b) generated additional texts as continuations of each question by feeding demonstrative prompts to GPT-3. Another work (Liu et al., 2022a) used reinforcement learning to guide meaningful generations. Huang et al. (2022) recently proposed to generate clues, which are short phrases or single tokens similar to the gold answers, before answering the question. Different from existing approaches, we seek to learn an effective generation model jointly with the answer prediction to allow for mutual enhancement. ## 6 Conclusion We propose a framework for commonsense QA problems that alternates between learning a meaningful, relatively lightweight elaboration generator and producing an answer from the question and automatically generated elaboration. These two steps are trained interactively, propagating signals to each other. We narrow the performance gap between small LMs and GPT-3, with the elaboration generator producing elaborations judged useful by humans, and matching the performance of the much more expensive GPT-3 model as an elaboration generator. One limitation of ELABOR is lack of exploration beyond GPT-3. We consider investigating this problem as our future work. ## Limitations Given the ability of ELABOR to generate free-text elaborations for commonsense question answering, we still observe some cases where the modelgenerated elaborations are not factually correct, or irrelevant to the question, distracting the answer predictor towards incorrect answers. This reflects a limitation of ELABOR on the controllability of its generations, which is also commonly discovered when using language models for text generation. We consider this as a possible future direction which aims at verifying the factuality and relevancy of model-generated texts before incorporating them for final inference or as a controlling mechanism during generation. ## Ethics & Broader Impact In this work, we only experiment with publicly available datasets. For human evaluation, we do not have access to or collect any personal information from our crowd-sourced workers, except that we only restrict participants to be located in English-speaking countries and have higher qualifications in terms of approval rate. As we work on language model generations, it is possible that the model could produce unintended toxic contents that impede its safe deployment (Gehman et al., 2020). We do not address this issue here but leave it to the field of controlled generation and language detoxicity. ## Acknowledgments The authors appreciate helpful feedback from the anonymous reviewers. We thank Jiacheng Liu for helpful discussions, and the members of H2lab and ARK lab for their constructive feedback. This work was funded in part by the DARPA MCS program through NIWC Pacific (N66001-19-2- 4031), NSF IIS-2044660 and NSF III-2007398. 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QA-GNN: Reasoning with language models and knowledge graphs for question answering. In *Proceedings of* the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 535–546. Association for Computational Linguistics. ## A Appendix A.1 Algorithm The overall algorithm for training ELABOR is shown in Algorithm 1. Algorithm 1 Training procedure of ELABOR. 1: **Initialize:** For each question q, use GPT-3 to sample a set of knowledge E¯ as continuations of q (Section 3.3). 2: for epoch= 1*, ..., T* do 3: for batch= 1*, ..., N* do 4: Optimize Eq. 6 by alternating between A and B: 5: A. Optimize elaboration generator FE to produce P(e|q) (Section 3.1) 6: for a question-answer pair (*q, a*) in batch do 7: **E-Step:** Select top-K elaborations E = {e1, ..., eK} ⊆ E¯ given scores produced from the answer predictor. 8: **M-Step:** Update the elaboration generator FE using Eq. 8 with E and q. 9: **end for** 10: B. Optimize answer predictor FA to produce P(a | e, q) (Section 3.2) 11: for a question-answer pair (*q, a*) in batch do 12: Sample a set of candidate elaborations E˜ using FE trained in the previous step. 13: For each e˜ ∈ E˜, update the answer predictor FA by maximizing Eq. 9 given a and e˜. 14: **end for** 15: **end for** 16: **end for** ## A.2 Data & Experimental Setup (1) **CommonsenseQA** (CSQA; Talmor et al., 2019) is created based on commonsense knowledge from various concepts in ConceptNet. Most of the questions require implicit background knowledge that is trivial to humans. The dataset consists of 12,247 examples (80%/10%/10% train/dev./test split), each of which is a 5-way multiple-choice selection problem. (2) **CommonsenseQA 2.0** (CSQA2; Talmor et al., 2021) is a more challenging dataset collected | Question | Elaboration | Answer | |---------------------------------------------------------------------|----------------------------------------------------------------------|----------------| | What does your ear drum do when it hears | The ear drum is the part of the human body that is responsible | Vibrates | | something? | for hearing. When you hear something, the ear drum vibrates. | | | How can we find out how much something | Weighing is done by using a scale. The amount of matter in | using a scale | | weighs? | an object is measured by weighing it. | | | The period of most rapid growth after birth | The period of fastest growth is in the first few weeks. | a baby | | is when they are what? What does predicting weather require? | Weathering prediction requires observation of weather conditions. | meterologists | | Forecasting weather requires observing weather patterns and clouds. | | | | A polar bear does what to survive in its | Polar bears have thick fur to keep them warm. They are able to | grows fur | | environment? | swim and hunt for food. Polar bears live in cold areas. | | | Seismographs measure what aspect of | Seismographs measure the height and direction of earthquakes. | magnitude | | earthquakes? | The seismic wave is measured by seismographs. | | | What decreases tooth decay? | The use of fluoride in drinking water is used to decrease tooth | drinking water | | decay. Fluoride is added to the water to prevent it from decaying. | | | | Some pelycosaurs gave rise to reptile | Amphibians and mammals are both examples of animals that have | mammals | | ancestral to? | reptilian characteristics. | | | Your polygenic traits determine? | Polygenic traits are inherited. The trait that determines your color | if you are | | is your genes. | white or brown | | in an adversarial manner where a user is encouraged to create questions for which a well-trained ROBERTA model (Liu et al., 2019) fails to provide the correct answer. The dataset contains a total of 14,343 questions (9,282 train, 2,544 dev., 2,517 test) with binary answer choices (yes/no). (3) QASC (Khot et al., 2020) is a question answering dataset requiring compositions of multiple pieces of texts. It is collected from elementary and middleschool science questions. The dataset contains 9,980 questions (8,134 train, 926 dev., 920 test), each of which is followed by 8 different choices. Note that we do not use the gold-annotated background facts accompanied with the original data, in order to test the model's ability to automatically elicit knowledge and reason. (4) **OpenBookQA** (OBQA; Mihaylov et al., 2018) is a collection of open book exams on elementary-level science facts. It contains a total of 5,957 questions (4,957 train, 500 dev., 500 test) with four candidate choices for each question. Similar to QASC, we also remove the gold-annotated science facts in the original release. For experimental setup, we use GPT-3 (Brown et al., 2020) under few-shot prompting and with nucleus sampling p = 0.5 (Holtzman et al., 2020) to sample 20 elaborations for each question. We use the same prompts as those from Liu et al. (2022b) and provide them in Table 7. During alternative training, for each iteration, we use 100 instances to update the elaboration generator followed by the answer predictor. We adopt Adam optimizer with learning rate initialized at 10−5for both components. The elaboration generator generates |E| ˜ = 10 elaborations during both training and testing phases via nucleus sampling p = 0.95 and with temperature set as 0.7. We set K = 3 when forming the top-K elaboration set E¯ during the E-step. For elaboration generation, GPT2large and BART-large has 774M and 406M parameters, respectively. For answer prediction, we use T5 with varying model sizes: 770M for T5large/UnifiedQA-large and 3B for UnifiedQA-3b. ## A.3 Generations From Elabor We list some actual generations from ELABOR using the learned elaboration generator GPT2-large in Table 6. These examples are selected from those used for human evaluations. The listed elaboration for each question is the most confident elaboration that is used for final prediction. ## A.4 Human Evaluation We additionally evaluate how humans benefit from those elaborations generated from our model across 100 random-sampled development examples from QASC. For each example, we first present the workers with the question and ask them to choose only one answer from multiple choices. In another round, we provide both the question and the generated elaboration to the workers and collect their answers. The two rounds of experiments recruit non-overlapping annotators to ensure validity. As a result, 78 questions are correctly answered by workers without seeing extra elaborations. On the other hand, 81 questions are correctly answered when elaborations are provided. This shows our elaboration generator is still beneficial to humans even though commonsense QA appears to be much easier for humans than machines. Task **Prompt** CSQA Generate some knowledge about the concepts in the input. Examples: Input: Google Maps and other highway and street GPS services have replaced what? Knowledge: Electronic maps are the modern version of paper atlas. Input: The fox walked from the city into the forest, what was it looking for? Knowledge: Natural habitats are usually away from cities. Input: You can share files with someone if you have a connection to a what? Knowledge: Files can be shared over the Internet. Input: Too many people want exotic snakes. The demand is driving what to carry them? Knowledge: Some people raise snakes as pets. Input: The body guard was good at his duties, he made the person who hired him what? Knowledge: The job of body guards is to ensure the safety and security of the employer Input: {question} Knowledge: Generate some knowledge about the input. Examples: Input: Greece is larger than mexico. Knowledge: Greece is approximately 131,957 sq km, while Mexico is approximately 1,964,375 sq km, making Mexico 1,389% larger than Greece. Input: Glasses always fog up. Knowledge: Condensation occurs on eyeglass lenses when water vapor from your sweat, breath, and ambient humidity lands on a cold surface, cools, and then changes into tiny drops of liquid, forming a film that you see as fog. Your lenses will be relatively cool compared to your breath, especially when the outside air is cold. Input: A fish is capable of thinking. Knowledge: Fish are more intelligent than they appear. In many areas, such as memory, their cognitive powers match or exceed those of 'higher' vertebrates including non-human primates. Fish's long-term memories help them keep track of complex social relationships. Input: A common effect of smoking lots of cigarettes in one's lifetime is a higher than normal chance of getting lung cancer. Knowledge: Those who consistently averaged less than one cigarette per day over their lifetime had nine times the risk of dying from lung cancer than never smokers. Among people who smoked between one and 10 cigarettes per day, the risk of dying from lung cancer was nearly 12 times higher than that of never smokers. Input: A rock is the same size as a pebble. Knowledge: A pebble is a clast of rock with a particle size of 4 to 64 millimetres based on the Udden-Wentworth scale of sedimentology. Pebbles are generally considered larger than granules (2 to 4 millimetres diameter) and smaller than cobbles (64 to 256 millimetres diameter). Input: {question} Knowledge: CSQA2 Generate some knowledge about the input. Examples: Input: What type of water formation is formed by clouds? Knowledge: Clouds are made of water vapor. Input: What can prevent food spoilage? Knowledge: Dehydrating food is used for preserving food Input: The process by which genes are passed is Knowledge: Genes are passed from parent to offspring. Input: The stomach does what in the body? Knowledge: The stomach is part of the digestive system Input: What can cause rocks to break down? Knowledge: Mechanical weathering is when rocks are broken down by mechanical means. Input: {question} Knowledge: QASC Generate some knowledge given the question. Examples: Question: Which would likely transfer special heat via waves? Knowledge: Radiation is when heat is transferred through waves. Radiation is made by certain bombs. Question: When standing miles away from Mount Rushmore Knowledge: As distance to an object increases, that object will appear smaller. Question: Ducks might their webbed appendages to Knowledge: Webbed feet are used for moving faster through water by aquatic animals. Question: Which would a strawberry most rely on to ensure it gets planted? Knowledge: Birds are a vehicle for spreading the seeds of a plant. Question: A typhoon can potentially cause Knowledge: A typhoon can bring a lot of rainfall. Heavy rains cause flooding. Input: {question} Knowledge: OBQA Table 7: Exact prompts used for each dataset. {question} indicates a placeholder for each input question. ## A.5 Elabor **Vs. Gpt-3** We select 50 examples from those used for human evaluation, half of which are correctly predicted by ELABOR but wrongly predicted by GPT-3 (denoted as D1). In the remaining 25 cases, the situation is the opposite (denoted as D2). Through manual inspection, we observe that in D1, ELABOR is often better off when the question is more general, e.g., "*What is a simple mode of transportation?*". ELABOR can generate more specific information relevant to some answer choices and tends to speak more. For D2, ELABOR performs worse when the model overgenerates noisy information not related to the question context leading to wrong answers. For example, the question "*What do choanocytes* have to trap the particles?" causes ELABOR to generate "The particle is a virus. The choanocytes are part of the immune system. The antibodies that bind the virus and destroy it." which does not answer the question. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? 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he-etal-2023-neural

Neural Unsupervised Reconstruction of Protolanguage Word Forms

https://aclanthology.org/2023.acl-long.91

We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms and their cognates in modern languages. We extend this work with neural models that can capture more complicated phonological and morphological changes. At the same time, we preserve the inductive biases from classical methods by building monotonic alignment constraints into the model and deliberately underfitting during the maximization step. We evaluate our performance on the task of reconstructing Latin from a dataset of cognates across five Romance languages, achieving a notable reduction in edit distance from the target word forms compared to previous methods.

# Neural Unsupervised Reconstruction Of Protolanguage Word Forms Andre He Nicholas Tomlin Dan Klein Computer Science Division, University of California, Berkeley {andre.he, nicholas_tomlin, klein}@berkeley.edu ## Abstract We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms and their cognates in modern languages. We extend this work with neural models that can capture more complicated phonological and morphological changes. At the same time, we preserve the inductive biases from classical methods by building monotonic alignment constraints into the model and deliberately underfitting during the maximization step. We evaluate our performance on the task of reconstructing Latin from a dataset of cognates across five Romance languages, achieving a notable reduction in edit distance from the target word forms compared to previous methods. ## 1 Introduction Research has shown that groups of languages can often be traced back to a common ancestor, or a protolanguage, which has evolved and branched out over time to produce its modern descendants. Words in protolanguages undergo sound changes to produce their corresponding forms in modern languages. We call words in different languages with a common proto-word ancestor *cognates*. The study of cognate sets can reveal patterns of phonological change, but their proto-words are often undocumented (Campbell, 2013; Hock, 2021). To reconstruct ancient word forms, linguists use the comparative method, which compares individual features of words in modern languages to their corresponding forms in hypothesized reconstructions of the protolanguage. Past work has demonstrated the possibility of automating this manual procedure (Durham and Rogers, 1969; Eastlack, 1977; Lowe and Mazaudon, 1994; Covington, 1998; Kondrak, 2002). For example, BouchardCôté et al. (2007a,b) developed probabilistic models of phonological change and used them to learn 1636 reconstructions of Latin based on a dataset of Romance languages, and Bouchard-Côté et al. (2009, 2013) extended their method to a large scale dataset of Austronesian languages (Greenhill et al., 2008). Nevertheless, previous approaches to computational protolanguage reconstruction have mainly considered simple rules of phonological change. In previous works, phonological change is modeled applying a sequence of phoneme-level edits to the ancestral form. Although this can capture many regular sound changes such as lenitions, epentheses, and elisions (Bouchard-Côté et al., 2013), these edits are typically conditioned only on adjacent phonemes and lack more general contextsensitivity. Phonological effects such as dissimilation (Bye, 2011), vowel harmony (Nevins, 2010), syllabic stress (Sen, 2012), pre-cluster shortening (Yip, 1987), trysyllabic laxing (Mohanan, 1982), and homorganic lengthening (Welna, 1998), as well as many non-phonological aspects of language change (Fisiak, 2011), are all frequently dependent on non-local contexts. However, it is difficult to extend existing multinomial (Bouchard-Côté et al., 2007a) and log-linear (Bouchard-Côté et al., 2007b, 2009, 2013) models to handle more complex conditioning environments. Motivated by these challenges, our work is the first to use neural models for unsupervised reconstruction. Ancestral word forms and model parameters in previous unsupervised approaches are typically learned using expectation-maximization (e.g., Bouchard-Côté et al., 2007a). In applying neural methods to protolanguage reconstruction, we identify a problem in which the EM objective becomes degenerate under highly expressive models. In particular, we find that neural models are able to express not just complex phonological changes, but also *inconsistent* ones (i.e., predicting vastly different edits in similar contexts), undermining their ability to distinguish between good and bad hypotheses. From a linguistic perspective, phono- ![1_image_0.png](1_image_0.png) logical change should exhibit regularities due to the constraints of the human articulatory and cognitive faculties (Kiparsky, 1965), so we build a bias towards regular changes into our method by using a specialized model architecture and learning algorithm. We outline our approach in Figure 1. Our work enables neural models to effectively learn reconstructions under expectationmaximization. In Section 5, we describe a specialized neural architecture with monotonic alignment constraints. In Section 6.4, we motivate training deliberately underfitted models. Then, in Section 7, we conduct experiments and show a significant improvement over the previously best performing method. Finally, we conduct ablation experiments and attribute the improvement to (1) the ability to model longer contexts and (2) a training process that is well-regularized for learning under EM. We release our code at https://github. com/AndreHe02/historical_release. ## 2 Related Work Our work directly extends a series of previous approaches to unsupervised protolanguage reconstruction that model the probabilities of phonemelevel edits from ancestral forms to their descendants (Bouchard-Côté et al., 2007a,b, 2009, 2013). These edits include substitutions, insertions, and deletions, with probabilities conditioned on the local context. The edit model parameters and unknown ancestral forms are jointly learned with expectation-maximization. The main difference between models in previous work is in parameterization and conditioning: Bouchard-Côté et al. (2007a) used a multinomial model conditioned on immediate neighbors of the edited phoneme; Bouchard-Côté et al. (2007b) used a featurized log-linear model with similar conditioning; and Bouchard-Côté et al. (2009) introduced markedness features that condition on the previous output phoneme. Bouchard-Côté et al. (2009) also shared parameters across branches so that the models could learn global patterns. Bouchard-Côté et al. (2013) used essentially the same model but ran more comprehensive experiments on a larger dataset. Since the expectation step of EM is intractable over a space of strings, past work resort to a Monte- Carlo EM algorithm where the likelihood is optimized with respect to sample ancestral forms. However, this sampling step is still the bottleneck of the method as it requires computing data likelihoods for a large set of proposed reconstructions. Bouchard-Côté et al. (2007a) proposed a singlesequence resampling method, but this approach propagated information too slowly in deep phylogenetic trees, so Bouchard-Côté et al. (2009) replaced it with a method known as ancestry resampling (Bouchard-Côté et al., 2008). This method samples an entire ancestry at a time, defined as a thin slice of aligned substrings across the tree that are believed to have descended from a common substring of the proto-word. Changes since the Bouchard-Côté et al. (2009) work, including shared parameters and ancestry resampling, are primarily concerned with reconstruction in large phylogenetic trees. While they improve reconstruction quality drastically on the Austronesian dataset, these modifications did not bring a statistically significant improvement on the task of reconstructing Latin from a family of Romance languages (Bouchard-Côté et al., 2009). This is likely due to the Romance family consisting of a shallow tree of a few languages, where the main concern is learning more complex changes on each branch. Therefore, in this work we compare our model to that of Bouchard-Côté et al. (2009) but keep the single sequence resampling method from Bouchard-Côté et al. (2007a). Previous work also exists on the related task of supervised protolanguage reconstruction. This is an easier task because models can be directly trained on gold reconstructions. Meloni et al. (2021) trained a GRU-based encoder-decoder architecture on cognates from a family of five Romance languages to predict their Latin ancestors and achieved low error from the ground truth. Another similar supervised character-level sequenceto-sequence task is the prediction of morphological inflection. Recent work on this task by Aharoni and Goldberg (2016) improved output quality from out-of-the-box encoder-decoders by modifying the architecture to use hard monotonic attention, constraining the decoder's attention to obey left-toright alignments between source and target strings. In our work, we find that character-level alignments is also an important inductive bias for unsupervised reconstruction. ## 3 Task Description In the task of protolanguage reconstruction, our goal is to predict the IPA representation of a list of words in an ancestral language. We have access to their cognates in several modern languages, which we believe to have evolved from their ancestral forms via regular sound changes. Following prior work (e.g., Bouchard-Côté et al., 2007a,b), we do not observe any ancestral forms directly but assume access to a simple (phoneme-level) bigram language model of the protolanguage. We evaluate the method by computing the average edit distance between the model's outputs and gold reconstructions by human experts. Concretely, let Σ be the set of IPA phonemes. We consider word forms that are strings of phonemes in the set Σ∗. We assume there to be a collection of cognate sets C across a set of modern languages L. A cognate set c ∈ C is in the form {y c l : l ∈ L}, consisting of one word form for each language l. We assume that cognates descend from a common proto-word x cthrough languagespecific edit probabilities pl(yl| x). Initially, neither the ancestral forms {x c: c ∈ C} nor the edit probabilities {pl(yl| x), l ∈ L} are known, and we wish to infer them from just the observed cognate sets C and a bigram model prior p(x). ## 4 Dataset In our setup, L consists of four Romance languages, and Latin is the protolanguage. We use the dataset from Meloni et al. (2021), which is a revision of the dataset of Dinu and Ciobanu (2014) with the addition of cognates scraped from Wiktionary. The original dataset contains 8799 cognates in Latin, Italian, Spanish, Portuguese, French, and Romanian. We follow Meloni et al. (2021) and use the espeak library1to convert the word forms from orthography into their IPA transcriptions. To keep the dataset consistent with the closest prior work on the unsupervised reconstruction of Latin (BouchardCôté et al., 2009), we remove vowel length indicators and suprasegmental features, keep only full cognate sets, and drop the Romanian word forms. The resulting dataset has an order of magnitude more data (|C| = 3214 vs. 586) but is otherwise very similar. We show example cognate sets in the appendix. ## 5 Model In this section, we describe our overall model of the evolution of word forms. We organize the languages into a flat tree, with Latin at the root and the other Romance languages l ∈ L as leaves. Following Bouchard-Côté et al. (2007a), our overall model is generative and describes the production of all word forms in the tree. Proto-words are first generated at the root according to a prior p(x), which is specified as a bigram language model of Latin. These forms are then rewritten into their modern counterparts at the leaves through branch-specific edit models denoted pl(yl| x). In using neural networks to parameterize the edit models, our preliminary experiments suggested that standard encoder-decoder architectures are unlikely to learn reasonable hypotheses when trained with expectation maximization. We identified this as a degeneracy problem: the space of possible changes expressible by these models is too large for unsupervised reconstruction to be feasible. Hence, we enforce the inductive bias that the output word form is produced from a sequence of local edits; these edits are conditioned on the global context so that the overall model is still highly flexible. In particular, to construct the word-level edit models, we first use a neural network to model context-sensitive, character-level edits. We then construct the word-level distribution via an iterative procedure that samples many character-level edits. We describe these components in the reverse order as the character-level distributions are clearer in the context of the edit process: in Section 5.1, we describe the edit process, while Section 5.2 details how we model the underlying character-level edits. ## 5.1 Word-Level Edit Process Given an ancestral form, our model transduces the input string from left to right and chooses edits to apply to each character. For a given character, the model first predicts a substitution outcome to replace it with. A special outcome is to delete the character, in which case the model skips to editing the next character. Otherwise, the model enters an insertion phase, where it sequentially inserts characters until predicting a special token that ends the insertion phase. After a deletion or end-of-insertion token occurs, the model moves on to editing the next input character. We describe the generative process in pseudocode in Figure 2. The models qsub and qins are our character-level Input: An ancestral word form x Output: A modern form y and lists of edits ∆ 1: **function** EDIT(x) 2: y′ ← [ ] 3: ∆ ← [ ] **Relation BJT(a)** $y^{\prime}\leftarrow[\ ]$ $\Lambda\leftarrow[\ ]$ **for**$j=1,\ldots,$len($x$) **do** $\triangleright$ Sample substitution outcome Sample $\omega\sim q_{\rm sub}(\cdot\mid x,i,y^{\prime})$ $\Lambda$. append((sub,$\omega,x,i,y^{\prime})$) **if**$\omega\neq$del$>$**then** **do** 9: do 10: y′. append(ω) 11: ▷ Sample insertion outcomes 12: Sample ω ∼ qins(· | *x, i, y*′) 13: ∆. append((ins*, ω, x, i, y*′)) 14: **while** ω ̸= <end> 15: **end if** 16: **end for** 17: **return** y′as y, ∆ 18: **end function** $$\begin{array}{r}{\mathbf{a}\mathbf{o}}\\ {y^{\prime}.{\mathrm{append}}(\omega)}\\ {\triangleright{\mathrm{Sample~insertion~outcomes}}}\\ {{\mathrm{Sample~}}\omega\sim q_{\mathrm{ins}}(\cdot\mid x,i,y^{\prime})}\\ {\Delta.{\mathrm{~append}}(({\mathrm{ins}},\omega,x,i,y^{\prime}))}\end{array}$$ while $\omega\neq\mathrm{<end>}$ **d :f** ![3_image_0.png](3_image_0.png) ![3_image_1.png](3_image_1.png) ![3_image_2.png](3_image_2.png) ![3_image_3.png](3_image_3.png) edit models, and they control the outcome of substitutions and insertions, conditioned on x, the input string, i, the index of the current character, and y′, the output prefix generated so far. The distribution qsub is defined over Σ ∪ {<del>} and qins is defined over Σ∪{<end>}. Models in previous work can be seen as special cases of this framework, but they are limited to a 1-character input window around the current index, x[i−1 : i+ 1], and a 1-character history in the output, y′[−1] (e.g., in Bouchard-Côté et al., 2009). The generative process defines a distribution p(y, ∆ | x) over the resulting modern form and edit sequences. But what we actually want to model is the distribution over modern word forms themselves - for this purpose, we use a dynamic program to sum over valid edit sequences: $$p(y\mid x)=\sum_{\Delta}p(y,\Delta\mid x)$$ where ∆ represents edits from x into y (see Appendix A.2 for more details). The edit procedure, character-level models, and dynamic program together give a conditional distribution over modern forms. Note that we have one such model for each language branch. ## 5.2 Character-Level Model We now describe the architecture behind qsub and qins, which model the distribution over characterlevel edits conditioned on the appropriate inputs. Our model leverages the entire input context and output history by using recurrent neural networks. The input string x is encoded with a bidirectional LSTM, and we take the embedding at the current index, denoted h(x)[i]. The output prefix y′is encoded with a unidirectional LSTM, and we take the final embedding, which we call g(y′)[−1]. The sum of these two embeddings h(x)[i] + g(y′)[−1] encodes the full context of an edit - we apply two different classification heads to predict the substitution distribution qsub and the insertion distribution qins. We note that the flow of information in our model is similar to the hard monotonic attention model of Aharoni and Goldberg (2016), which used an encoder-decoder architecture with a hard left-toright attention constraint for supervised learning of morphological inflections. Figure 3 illustrates the model architecture with an example prediction. ## 6 Learning Algorithm The problem of unsupervised reconstruction is to infer the ancestral word forms {x c: c ∈ C} and edit models {pl(yl| x) : l ∈ L} when given the modern cognates {y c l : c ∈ *C, l* ∈ L}. We use a Monte-Carlo EM algorithm to learn the reconstructions and model parameters. During the E-step, we seek to sample ancestral forms from the current model's posterior distribution, conditioned on observed modern forms; during the M-step, we train the edit models to maximize the likelihood of these samples. We alternate between the E and M steps for several iterations; then in the final round, instead of sampling, we take the maximum likelihood strings as predicted reconstructions. ## 6.1 Sampling Step The goal of the E-step is to sample ancestral forms from the current model's posterior distribution, p(x c| {y c l , l ∈ L}). In general, this distribution cannot be computed directly; but for given samples of x, we can compute a value that is proportional to their posterior probability. At the beginning of an E-step, we have the current edit models {pl(yl| x) : l ∈ L}, observed modern forms {yl: l ∈ L}, and the ancestral form prior p(x). For a given ancestral word form x, we can use Bayes' rule to compute a joint probability that is proportional to its posterior probability (our model assumes conditionally independent branches): $$\begin{array}{c}{{p(x\mid\{y_{l},l\in L\})}}\\ {{\quad=\frac{p(x,\{y_{l},l\in L\})}{p(\{y_{l},l\in L\})}}}\\ {{\quad\propto p(x,\{y_{l},l\in L\})}}\\ {{\quad=p(x)\prod_{l\in L}p(y_{l}\mid x)}}\end{array}\qquad\qquad(1)$$ Following previous work, we use MetropolisHastings to sample from the posterior distribution without computing the normalization factor. We iteratively replace the current word form x with a candidate drawn from a set of proposals, with probability proportional to the joint probability computed above. We repeat this process for each cognate set to obtain a set of sample ancestral forms {x c: c ∈ C}. During Metropolis-Hastings, the cylindrical proposal strategy in Bouchard-Côté et al. (2008) considers candidates within a 1-edit-distance ball of the current sample, but this strategy is inefficient since the number of proposals is scales linearly with both the string length and vocabulary size, and the sample changes by only one edit per round. We develop a new proposal strategy which exploits the low edit distance between cognates. Our approach considers all strings on a minimum edit path from the current sample to a modern form. This allows the current sample to move many steps at a time towards one of its modern cognates. See Figure 5 in the appendix for an illustration. ## 6.2 Maximization Step With samples from the previous step {x c: c ∈ C} fixed, the goal of the M-step is to train our edit models to maximize data likelihood. The models on each branch are independent, so we train them separately. For each branch l, we wish to optimize $$\sum_{c\in C}p(y_{l}^{c}\mid x^{c})$$ This is a standard sequence-to-sequence training objective, where the training set is simply ancestral forms x cfrom the E-step and modern forms y c l 1640 ![5_image_0.png](5_image_0.png) from the dataset. However, since we do not directly model the conditional distribution of output strings (5.2), we need the underlying edit sequences to train our character-level edit models qsub and qins. Given an input-output pair x and y, we compute the probabilities of underlying edits using a dynamic program similar to the forwardbackward algorithm for HMMs (see A.3 for more details). Concretely, for each possible substitution (sub*, ω, x, i, y*′) defined as in Figure 2, the dynamic program computes $$p((\operatorname{sub},\omega,x,i,y^{\prime})\in\Delta\mid x,y)$$ which is the probability of the edit occurring, conditioned on the initial and resultant strings. We average over cognate pairs to obtain p((sub*, ω, x, i, y*′) ∈ ∆) and train the substitution model qsub(ω | *x, i, y*′) to fit this distribution. We compute insertion probabilities and train the insertion model in the same way. We bootstrap the neural models qsub and qins by using samples from the classical method. Before the first maximization step, we train a model from Bouchard-Côté et al. (2009) for three EM iterations. We use samples from the model to compute the first round of edit probabilities. Once the neural model is trained on these probabilities, we no longer rely on the classical model. Note that this does not bias the comparison in Section 7.1 in our favor because the classical models reach peak performance in less than five EM iterations and would not benefit from additional rounds of training. ## 6.3 Inference After performing 10 EM iterations, we obtain reconstructions by taking the maximum likelihood word forms under the model. In the E-step, we sample x c ∼ p(x c| {y c l: l ∈ L}), but now we want x c = arg max p(x c| {y c l: l ∈ L}). We approximate this with an algorithm nearly identical to the E-step, except that we always select the highest probability candidate (instead of sampling) in Metropolis-Hastings iterations. ## 6.4 Underfitting The Model In prior work, models are trained to convergence in the M-step of EM. For example, the multinomial model of Bouchard-Côté et al. (2007a) has a closed-form MLE solution, and the log-linear model of Bouchard-Côté et al. (2009) has a convex objective that is optimized with L-BFGS. In our experiments, we notice that training the neural model to convergence during M-steps will cause a degeneracy problem where reconstruction quality quickly plateaus and fails to improve over future EM iterations. This degeneracy problem is crucially different from overfitting in the usual sense. In supervised learning, overfitting occurs when the model begins to fit spurious signals in the training data and deviates away from the true data distribution. On the other hand, precisely fitting the underlying distribution would cause our EM algorithm to get stuck - if in a M-step the model fully learns the distribution from which samples were drawn, then the next Estep will draw samples from the same distribution, and the learning process stagnates. Our solution is to deliberately *underfit* in the M-step. Intuitively, this gives more time for information to mix between the branches before the edit models converge to a common posterior distribution. We do this by training the model for only a small number of epochs in every M-step. We find that a fixed 5 epochs per step works well, which is far from the number of epochs needed for convergence. Our experiments in Section 7.3 show that this change significantly improves performance even when our model is restricted to the same local context as in Bouchard-Côté et al. (2009). ## 7 Experiments 7.1 Comparison To Previous Models We evaluate the performance of our model by computing the average edit distance between its outputs and gold Latin reconstructions. We experimented with several variations of the models used in prior work (Bouchard-Côté et al., 2007a,b, 2009) and chose the configuration which maximized performance on our dataset, referring to it as the *classical* baseline. In particular, we found that extending the multinomial model in BouchardCôté et al. (2007a) to be conditioned on adjacent input characters and the previous output character as in Bouchard-Côté et al. (2009) performed better than using the model from the latter directly, which used a log-linear parameterization. Given that we use an order of magnitude more data, we attribute this to the fact that the multinomial model is more flexible and does not suffer from a shortage of training data in our case. We confirm that this modified model outperforms Bouchard-Côté et al. (2007a,b) on the original dataset. For the learning algorithm, we keep the single sequence resampling algorithm from these papers. Although the more recent Bouchard-Côté et al. (2009, 2013) used ancestral resampling, the algorithm is focused on propagating information through large language trees, so it did not achieve a statistically significant improvement on the Romance languages, which only had a few nodes (Bouchard-Côté et al., 2009). We also include an *untrained* baseline to show how these methods compare to a model not trained with EM at all. The *untrained* baseline evaluates the performance of a model initialized with fixed probabilities of self-substitutions, substitutions, insertions, and deletions, regardless of the context. We do not run any EM steps and take strings with the highest posterior probability under this model as reconstructions. We find that this baseline significantly outperforms the centroids baseline from previous work (4.88), so we use it as the new baseline in this work. During training, we notice that different models take a different number of EM iterations to train, and some deteriorate in reconstruction quality if trained for too many iterations. Therefore, we trained all models for 10 EM iterations and report the quality of the best round of reconstructions in Figure 4. Since it may be impossible in practice to do early stopping without gold reconstructions, we also computed the final reconstruction quality for our models, but we observe only a minimal change in results (≈ 0.02 edit distance). Due to variance in the results, we report the mean and standard deviation across five runs of our method. ## 7.2 Ablation: Underfitting In this section, we describe an ablation experiment on the effect of under-training in the maximization step. Let n represent the number of training epochs during each maximization step. Also, let k represent the amount of context that our models have access to. When predicting an edit, the model can see k characters to the left and right of the current input character (i.e., the window has length 2k + 1) and k + 1 characters into the output history. Everything outside this range is masked out. Our standard model uses n = 5 and k = ∞. For this experiment, we set the context size to k = 0 and run our method with n ∈ {5, 10, 20, 30}. The resulting reconstruction qualities are shown in Figure 4. Note that when k = 0, our model is conditioned on the same information as that of Bouchard-Côté et al. (2009). When n = 30, the model is effectively trained to convergence in every M-step. It completely fits the conditional distribution of edits in the samples, so it should learn the same probabilities as the multinomial model baseline. Indeed, the model with n = 30 and k = 0 achieves an edit distance of 3.61, which is very close to the 3.63 baseline. Given that this configuration is effectively equivalent to the classical method, we can incrementally observe the improvement from moving towards n = 5 (our default). By reducing the number of epochs per maximization step (n), we observe a large improvement from 3.61 to 3.47. The general motivation for undertraining the model was given in Section 6.4. The remaining improvement comes from additional con- ![7_image_0.png](7_image_0.png) ## 7.3 Ablation: Context Length In this section, we describe an ablation experiment on the effect of modeling longer contexts. Keeping n = 5 fixed and using k as defined in the previous subsection, we run our method three times for each of k ∈ {0, 2, 5, 10, ∞} and report the average reconstruction quality in Figure 4. Our results show that being able to model longer contexts does monotonically improve performance. The improvement is most drastic when expanding to a short context window (k = 2). These findings are consistent with the knowledge that most (but not all) sound changes are either universal or conditioned only on nearby context (Campbell, 2013; Hock, 2021). With unlimited context length, our reconstruction quality reaches 3.38. Therefore, we attribute the overall improvement in our method to the changes of (1) modeling longer contexts and (2) underfitting edit models to learn more effectively with expectation-maximization. ## 8 Discussion In this paper, we present a neural architecture and EM-based learning algorithm for the unsupervised reconstruction of protolanguage word forms. Given that previous work only modeled locallyconditioned sound changes, our approach is motivated by the fact that sound changes can be influenced by rich and sometimes non-local phonological contexts. Compared to modern sequence to sequence models, we also seek to regularize the hypothesis space and thus preserve the structure of character-level edits from classical models. On a dataset of Romance languages, our method achieves a significant improvement from previous methods, indicating that both richness and regularity are required in modeling phonological change. We expect that more work will be required to scale our method to larger and qualitatively different language families. For example, the Austronesian language dataset of Greenhill et al. (2008) contains order of magnitudes more modern languages (637 vs. 5) but significantly less words per language (224 vs. 3214) - efficiently propagating information across the large tree may be more important than training highly parameterized edit models in these settings. Indeed, Bouchard-Côté et al. (2009, 2013) produce high quality reconstructions on the Austronesian dataset by using ancestral resampling and sharing model parameters across branches. These improvements are not immediately compatible with our neural model; therefore, we leave it as future work to scale our method to settings like the Austronesian languages. ## Acknowledgments We thank David Hall and Alex Bouchard-Côté for sharing code used to run baselines. We also thank Alina Maria Ciobanu for sharing a dataset of Romanian cognates. Finally, we are grateful to the members of the Berkeley NLP Group and the anonymous reviewers for their feedback on this project. Nicholas Tomlin is supported by a National Science Foundation Graduate Research Fellowship, as well as the DARPA LwLL and SemaFor programs. ## References Roee Aharoni and Yoav Goldberg. 2016. Sequence to sequence transduction with hard monotonic attention. CoRR, abs/1611.01487. Alexandre Bouchard-Côté, Thomas L. Griffiths, and Dan Klein. 2009. Improved reconstruction of protolanguage word forms. In *Proceedings of Human* Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 65–73, Boulder, Colorado. Association for Computational Linguistics. Alexandre Bouchard-Côté, David Hall, Thomas L. Griffiths, and Dan Klein. 2013. Automated reconstruction of ancient languages using probabilistic models of sound change. *Proceedings of the National* Academy of Sciences, 110(11):4224–4229. Alexandre Bouchard-Côté, Dan Klein, and Michael Jordan. 2008. Efficient inference in phylogenetic indel trees. In *Advances in Neural Information Processing* Systems, volume 21. Curran Associates, Inc. Alexandre Bouchard-Côté, Percy Liang, Thomas Griffiths, and Dan Klein. 2007a. A probabilistic approach to diachronic phonology. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 887– 896, Prague, Czech Republic. Association for Computational Linguistics. Alexandre Bouchard-Côté, Percy S Liang, Dan Klein, and Thomas Griffiths. 2007b. A probabilistic approach to language change. In *Advances in Neural* Information Processing Systems, volume 20. Curran Associates, Inc. Patrik Bye. 2011. Dissimilation. *The Blackwell companion to phonology*, pages 1–26. Lyle Campbell. 2013. *Historical linguistics*. Edinburgh University Press. Michael A. Covington. 1998. Alignment of multiple languages for historical comparison. In *36th Annual Meeting of the Association for Computational* Linguistics and 17th International Conference on Computational Linguistics, Volume 1, pages 275–279, Montreal, Quebec, Canada. Association for Computational Linguistics. Liviu Dinu and Alina Maria Ciobanu. 2014. Building a dataset of multilingual cognates for the Romanian lexicon. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1038–1043, Reykjavik, Iceland. European Language Resources Association (ELRA). SP Durham and DE Rogers. 1969. An application of computer programming to the reconstruction of protolanguages,(w:) preprints. In *Internationale Conference of Computational Linguistics, Stockholm*. Charles L Eastlack. 1977. Iberochange: a program to simulate systematic sound change in ibero-romance. Computers and the Humanities, pages 81–88. Jacek Fisiak. 2011. *Historical morphology*, volume 17. Walter de Gruyter. Simon J Greenhill, Robert Blust, and Russell D Gray. 2008. The austronesian basic vocabulary database: from bioinformatics to lexomics. *Evolutionary Bioinformatics*, 4:EBO–S893. Hans Henrich Hock. 2021. Principles of Historical Linguistics. De Gruyter Mouton. Paul Kiparsky. 1965. *Phonological change.* Ph.D. thesis, Massachusetts Institute of Technology. Grzegorz Kondrak. 2002. Algorithms for language reconstruction. John Lowe and Martine Mazaudon. 1994. The reconstruction engine: a computer implementation of the comparative method. *Computational Linguistics*, 20(3):381–417. Carlo Meloni, Shauli Ravfogel, and Yoav Goldberg. 2021. Ab antiquo: Neural proto-language reconstruction. In *Proceedings of the 2021 Conference of* the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4460–4473, Online. Association for Computational Linguistics. Karuvannur Puthanveettil Mohanan. 1982. Lexical phonology. Ph.D. thesis, Massachusetts Institute of Technology. Andrew Nevins. 2010. *Locality in vowel harmony*, volume 55. Mit Press. Ranjan Sen. 2012. Reconstructing phonological change: duration and syllable structure in latin vowel reduction. *Phonology*, 29(3):465–504. Jerzy Welna. 1998. The functional relationship between rules: Old english voicing of fricatives and lengthening of vowels before homorganic clusters. *Advances* in English historical linguistics, pages 471–85. Moira Yip. 1987. English vowel epenthesis. Natural Language & Linguistic Theory, pages 463–484. ## A Appendix A.1 Dataset We describe the origin of our dataset and our preprocessing steps in Section 4. We show examples of some cognate sets in Table 1, along with sample reconstructions from our best model. ## A.2 Forward Dynamic Program The forward dynamic program computes the total probability of a output word form p(y | x), marginalized over possible edit sequences ∆. We first run inference with our neural models qsub and qins to pre-compute the probabilities of all possible edits. For i ∈ [len(x)], j ∈ [len(y)], op ∈ {sub, ins, del, end}, let C = (*x, i, y*[:j]) be the context of the edit (and the input to the network). We compute: $$\delta_{o p}(i,j):=\begin{cases}q_{o p}(y[j]\mid C)&o p\in\{\mathrm{sub},\mathrm{ins}\}\\ q_{\mathrm{sub}}(\prec\!\!\mathrm{del}\!\!>\mid C)&o p=\mathrm{del}\\ q_{\mathrm{ins}}(\prec\!\!\mathrm{end}\!\!>\mid C)&o p=\mathrm{end}\end{cases}$$ To compute the probability of editing x into y, we define the subproblem fop(*i, j*) as the total probability of editing x[:i] into y[:j] such that the next operation is op. The recurrence can therefore be written as: $$\begin{array}{c}{{f_{\rm ins}(i,j)=\delta_{\rm ins}(i,j-1)f_{\rm ins}(i,j-1)}}\\ {{\qquad\qquad+\delta_{\rm sub}(i,j-1)f_{\rm sub}(i,j-1)}}\\ {{\qquad\qquad f_{\rm sub}(i,j)=\delta_{\rm end}(i-1,j)f_{\rm ins}(i-1,j)}}\\ {{\qquad\qquad\qquad+\delta_{\rm del}(i-1,j)f_{\rm sub}(i-1,j)}}\\ {{\qquad\qquad\qquad+\delta_{\rm del}(i-1,j)f_{\rm sub}(i-1,j)}}\end{array}$$ Which is in accordance with the dynamics described in Section 5.1. The desired result is p(y | x) = fsub(len(x), len(y)). We end on a substitution because it implies that the insertion for the final character has properly terminated. ## A.3 Backward Dynamic Program The backward dynamic program computes the probability that an edit (*op, ω, x, i, y*′) has occured, given the input string x and output string y. We run the forward dynamic program first and use the notation δ and f as defined in Appendix A.2. Define gop(*i, j*) as the posterior probability that the edit process has been in a state where the next operation is op and it just edited x[:i] into y[:j]. This is the same event as that of fop(*i, j*), but conditioned on the fact that the final output is y. The base case is therefore gsub(len(x), len(y)) = 1. The dynamic program propagates probabilities backwards: $$g_{\text{ins}}(i,j)=\frac{\delta_{\text{ins}}(i,j)f_{\text{ins}}(i,j)}{f_{\text{ins}}(i,j+1)}g_{\text{ins}}(i,j+1)$$ $$+\frac{\delta_{\text{end}}(i,j)f_{\text{ins}}(i,j)}{f_{\text{sub}}(i+1,j)}g_{\text{sub}}(i+1,j)$$ $$g_{\text{sub}}(i,j)=\frac{\delta_{\text{sub}}(i,j)f_{\text{sub}}(i,j)}{f_{\text{ins}}(i,j+1)}g_{\text{ins}}(i,j+1)$$ $$+\frac{\delta_{\text{del}}(i,j)f_{\text{sub}}(i,j)}{f_{\text{sub}}(i+1,j)}g_{\text{sub}}(i+1,j)$$ Essentially, each state receives probability mass from possible future states, weighed by its contribution in the forward probabilities. Finally, we recover the posterior probabilities of edits, denoted as δ′: $$\delta^{\prime}_{\rm sub}(i,j)=\frac{f_{\rm sub}(i,j)g_{\rm ms}(i,j+1)}{f_{\rm ins}(i,j+1)}\delta_{\rm sub}(i,j)$$ $$\delta^{\prime}_{\rm ins}(i,j)=\frac{f_{\rm ins}(i,j)g_{\rm ins}(i,j+1)}{f_{\rm ins}(i,j+1)}\delta_{\rm ins}(i,j)$$ $$\delta^{\prime}_{\rm del}(i,j)=\frac{f_{\rm sub}(i,j)g_{\rm sub}(i+1,j)}{f_{\rm sub}(i+1,j)}\delta_{\rm del}(i,j)$$ $$\delta^{\prime}_{\rm end}(i,j)=\frac{f_{\rm ins}(i,j)g_{\rm sub}(i+1,j)}{f_{\rm sub}(i+1,j)}\delta_{\rm end}(i,j)$$ Each $\delta^{\prime}_{op}(i,j)$ corresponds to the same edit as δop(*i, j*), and so we obtain p((*op, ω, x, i, y*′) ∈ ∆ | x, y) for all possible edits. ## A.4 Hyperparameters And Setup For our edit models, the input encoder is a bidirectional LSTM with 50 input dimensions, 50 hidden dimensions, and 1 layer. The output encoder is a unidirectional LSTM with the same configuration. The dimension 50 was found through a hyperparameter search over models of d ∈ {10, 25, 50, 100, 200} dimensions. For training, we use the Adam optimizer with a fixed learning rate of 0.01. All experiments were run on a single Quadro RTX 6000 GPU; however, GPU-based computations are not the bottleneck of our method. A single run of our standard method takes about 2 hours. ## A.5 Limitations A major limitation of this work is that our method was designed for large cognate datasets with few languages. It may not be possible to train these highly parameterized edit models on datasets with French Italian Spanish Portuguese Latin (Target) Reconstruction ablatif ablativo aBlatiBo 5l5tivU ablatIwUs ablativU idKolik draUliko iDRauliko idôaUlikU hydraUlIkUs idraUlikU inEfabl ineffabile inefaBle in1favEl InEffabIlIs inEfablE mAda mandato mandato m5NdatUm mandatUm mandatU pKEsjO pessione pResjon pô1s5U prEssIO prEssO pKOkKee prokreare pRokReaR pôukôiaô prOkrEarE prOkrear vokabylEK vokabolario bokaBulaRjo vuk5bulaRjU wOkabUlarIUm vokabylarEU ekonomi ekonomia ekonomia ekunumi5 OIkOnOmIa ekunomia fekyl fekola fekula fEkul5 faIkUla fEkyla lamine lamina lamina l5min5 lamIna lamina more languages but fewer datapoints per language (e.g. the Austronesian dataset from Greenhill et al. (2008)), and reconstruction in these datasets may benefit more from having efficient sampling algorithms and sharing parameters across branches (Bouchard-Côté et al., 2009). Given the large amount of noise in the Romance language dataset, we also do not overcome the restriction in Bouchard-Côté et al. (2007a) of relying on a bigram language model of Latin. Moreover, inspecting learned sound changes is more difficult when using a neural model, so we leave a qualitative evaluation of unsupervised reconstructions from neural methods to future work. ![11_image_0.png](11_image_0.png) ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

lu-etal-2023-damstf

{D}a{MSTF}: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation

https://aclanthology.org/2023.acl-long.92

Self-training emerges as an important research line on domain adaptation. By taking the model{'}s prediction as the pseudo labels of the unlabeled data, self-training bootstraps the model with pseudo instances in the target domain. However, the prediction errors of pseudo labels (label noise) challenge the performance of self-training. To address this problem, previous approaches only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. Although these strategies effectively reduce the label noise, they are prone to miss the hard examples. In this paper, we propose a new self-training framework for domain adaptation, namely Domain adversarial learning enhanced Self-Training Framework (DaMSTF). Firstly, DaMSTF involves meta-learning to estimate the importance of each pseudo instance, so as to simultaneously reduce the label noise and preserve hard examples. Secondly, we design a meta constructor for constructing the meta-validation set, which guarantees the effectiveness of the meta-learning module by improving the quality of the meta-validation set. Thirdly, we find that the meta-learning module suffers from the training guidance vanish- ment and tends to converge to an inferior optimal. To this end, we employ domain adversarial learning as a heuristic neural network initialization method, which can help the meta-learning module converge to a better optimal. Theoretically and experimentally, we demonstrate the effectiveness of the proposed DaMSTF. On the cross-domain sentiment classification task, DaMSTF improves the performance of BERT with an average of nearly 4{\%}.

# Damstf: Domain Adversarial Learning Enhanced Meta Self-Training For Domain Adaptation Menglong Lu1†, Zhen Huang1†, Yunxiang Zhao2∗**, Zhiliang Tian**1∗, Yang Liu1and **Dongsheng Li**1 1National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, China 2 Beijing Institute of Biotechnology, China {lumenglong, huangzhen, tianzhiliang, liuyang12a, dsli}@nudt.edu.cn, [email protected] ## Abstract Self-training emerges as an important research line on domain adaptation. By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the model with pseudo instances in the target domain. However, the prediction errors of pseudo labels (label noise) challenge the performance of self-training. To address this problem, previous approaches only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. Although these strategies effectively reduce the label noise, they are prone to miss the hard examples. In this paper, we propose a new self-training framework for domain adaptation, namely Domain adversarial learning enhanced Self-Training Framework (DaMSTF). Firstly, DaMSTF involves meta-learning to estimate the importance of each pseudo instance, so as to simultaneously reduce the label noise and preserve hard examples. Secondly, we design a meta constructor for constructing the meta validation set, which guarantees the effectiveness of the meta-learning module by improving the quality of the meta validation set. Thirdly, we find that the meta-learning module suffers from the training guidance vanishment and tends to converge to an inferior optimal. To this end, we employ domain adversarial learning as a heuristic neural network initialization method, which can help the meta-learning module converge to a better optimal. Theoretically and experimentally, we demonstrate the effectiveness of the proposed DaMSTF. On the cross-domain sentiment classification task, DaMSTF improves the performance of BERT with an average of nearly 4%. ## 1 Introduction Domain adaptation, which aims to adapt the model trained on the source domain to the target domain, attracts much attention in Natural Language Processing (NLP) applications(Du et al., 2020; Chen et al., 2021; Lu et al., 2022). Since domain adaptation involves labeled data from the source domain and unlabeled data from the target domain, it can be regarded as a semi-supervised learning problem. From this perspective, self-training, a classical semi-supervised learning approach, emerges a prospective research direction on domain adaptation (Zou et al., 2019; Liu et al., 2021). Self-training consists of a series of loops over the pseudo labeling phase and model retraining phase. In the pseudo labeling phase, self-training takes the model's prediction as the pseudo labels for the unlabeled data from the target domain. Based on these pseudo-labeled instances, self-training retrains the current model in the model retraining phase. The trained model can be adapted to the target domain by repeating these two phases. Due to the prediction errors, there exists label noise in pseudo instances, which challenges self-training approaches (Zhang et al., 2017). Previous self-training approaches usually involve a data selection process to reduce the label noise, i.e., preserving the reliable pseudo instances and discarding the remaining ones. In general, higher prediction confidence implies higher prediction correctness, so existing self-training approaches prefer the pseudo instances with high prediction confidence (Zou et al., 2019; Shin et al., 2020). However, fitting the model on these easy pseudo instances cannot effectively improve the model, as the model is already confident about its prediction. On the contrary, pseudo instances with low prediction confidence can provide more information for improving the model, but contain more label noise at the same time. To simultaneously reduce the label noise and preserve hard examples, we propose to involve in meta-learning to reweight pseudo instances. Within a learning-to-learn schema, the meta-learning mod1650 ule learns to estimate the importance of every pseudo instance, and then, allocates different instance weights to different pseudo instances. Ideally, hard and correct pseudo instances will be assigned larger weights, while easy or error pseudo instances will be assigned smaller weights. To achieve this, the process in the meta-learning module is formulated as a bi-level hyperparameters optimization problem (Franceschi et al., 2018), where instance weights are taken as the hyperparameters and determined by a series of meta-training steps and meta-validation steps. In the meta-training step, the model is virtually updated on the metatraining set with respect to the current instance weights. In the meta validation step, we validate the virtually updated model with an unbiased meta validation set, and optimize the instance weights with the training guidance back-propagated from the validation performance. According to the analysis in (Ren et al., 2018), a high-quality meta validation set, which is clean and unbiased to the test set, is important for the effectiveness of the meta-learning algorithm. To this end, we propose a meta constructor oriented to the domain adaptation scenario. At each self-training iteration, the meta constructor selects out the most reliable pseudo instances and inserts them into the meta validation set. Since the instances in the meta validation set are all from the target domain and vary along with the self-training iterations, the data distribution in the constructed meta validation set approximates the one in the target domain. Thus, the meta constructor reduces the bias of the meta validation set. On the other hand, selecting the most reliable pseudo instances can reduce the label noise, making the meta validation set cleaner. Another challenge for the meta-learning module is the training guidance vanishment, referring to the gradient vanishment on hyperparameters. With a theoretical analysis, we attribute this problem to the gradient vanishment on the meta validation set. To this end, we introduce a domain adversarial learning module to perturb the model's parameters, thereby increasing the model's gradients on the meta validation set. In DaMSTF, we also interpret the domain adversarial learning module as a heuristic neural network initialization method. Before the model retraining phase, the domain adversarial learning module first initializes the model's parameters by aligning the model's feature space. For domain adaptation, the global optimal refers to the state where the model's parameters are agnostic to the domain information but discriminative to the task information. Thus, the training process in the domain adversarial learning module makes the model's parameters closer to the global optimal, serving as a heuristic neural network initialization. Our contributions can be summarized as follows: - We propose a new self-training framework to realize domain adaptation, named Domain adversarial learning enhanced Meta Self Training Framework (DaMSTF), which involves meta-learning to simultaneously reduce the label noise and preserve hard examples. - We propose a meta constructor to construct the meta validation set, which guarantees the effectiveness of the meta-learning module. - We theoretically point out the training guidance vanishment problem in the meta-learning module and propose to address this problem with a domain adversarial learning module. - Theoretically, We analyze the effectiveness of the DaMSTF in achieving domain adaptation. Experimentally, we validate the DaMSTF on two popular models, i.e., BERT for the sentiment analysis task and BiGCN for the rumor detection task, with four benchmark datasets. ## 2 Problem Formulation We denote the set that involves all instances in the source domain as DS, and denote the set that contains all instances in the target domain as DT . From DS, we can obtain a labeled dataset for training, i.e., DS = {(xi, yi)} N i=1. In text classification tasks, the input xiis a text from the input space X , the corresponding label yiis a C-dimensional one-hot label vector, i.e., yi ∈ {0, 1} C, where C is the number of classes. Based on DS, we learn a hypothesis, h : *X → {*0, 1} C. Since DS comes from DS (i.e., DS ⊆ DS), the learned hypothesis h usually performs well on DS. When we transfer the hypothesis h from DS to DT , h may perform poorly due to the domain shift. The goal of domain adaptation is to adapt the hypothesis h to DT . In general, unlabeled text in the target domain is available (Gururangan et al., 2020). We denote the unlabeled target domain dataset as Du T = {(xm)} Um=1, where xm ∈ X is a text input. In some cases, we can even access an in-domain dataset, i.e., a small set of labeled data in the target Algorithm 1 DaMSTF Require: labeled source dataset DS, unlabeled target dataset D u T , in-domain dataset D l T 1: Pretrain θ on DS, DM ← D l T 2: **while** the termination criteria is not met do 3: Compute pseudo label Yˆ T on D u T 4: H = −Yˆ T ∗ log(Yˆ T ) 5: Sort the D p T with respect to H in ascending order, and denote the first K data as DE, the remaining data as D tr T l T ∪ DE 7: DOMAINADVERSARIAL(DS ∪ D u T , θF , ϑ) tr T , θ, w) 10: function METALEARNING(D, θ, w) 13: Compute ˆθ(wt) via Eq. (3) 14: Compute weight wt+1 via Eq. (6) 16: w∗ ← wTM , update θ with Eq. (7) 20: function DOMAINADVERSARIAL(D, θF , ϑ) 23: ϑ = ϑ − η1OϑLDA(θF , ϑ, B) 26: θF = θF + η2OθLDA(θF , ϑ, B) L j=1 6: DM = D 8: METALEARNING(DS ∪ D 9: **end while** 11: for training batch B in D do 12: for t=1 → TM do 15: **end for** 17: **end for** 18: **return** θ, w 19: **end function** 21: for training batch B in D do 22: for t=1 → TD do 24: **end for** 25: for t=1 → TG do 27: **end for** 28: **end for** 29: **return** θ, ϑ 30: **end function** domain, which is denoted as DlT = {(xj , yj )} ![2_image_0.png](2_image_0.png) (xi ∈ X and yi ∈ {0, 1} C). When DlT = ∅, the task is a case of *unsupervised domain adaptation* (Wilson and Cook, 2020). Otherwise, the task is a case of *semi-supervised domain adaptation* (Saito et al., 2019). ## 3 Methodology 3.1 Model Overview DaMSTF inherits the basic framework of selftraining, which consists of iterations over the "Pseudo Labeling" phase and the "Model Retraining" phase. To achieve domain adaptation, selftraining simultaneously optimizes the model's parameters and the pseudo labels with Eq. (1). $$\min_{\theta,\hat{\mathbf{Y}}_{T}}\mathcal{L}_{st}(\theta,\hat{\mathbf{Y}}_{T})=\sum_{(x_{k},y_{k})\in D_{S}}\mathcal{E}(\Phi(x_{k};\theta),y_{k})+$$ $$\sum_{x_{i}\in D_{T}^{\mathbf{u}}}\mathcal{E}(\Phi(x_{i};\theta),\hat{y}(x_{i}))\tag{1}$$ where $\hat{\bf Y}_{T}=[\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{|D_{T}^{u}|}]^{T}$ denotes the pseudo label set of the unlabeled target domain ## 3.2 Meta-Learning Module As described in Fig. 1, the meta-learning module involves a series of loops over the "Meta Training" step and "Meta Validation" step to optimize the hyper-parameters and the model parameters. Meta Training. The training batch in the meta training phase, i.e., B = {(x1, y1),(x2, y2)*, . . .*}, merges the labeled data from the source domain with the pseudo labeled data from the target domain. The supervision on the pseudo instances is the pseudo-label, and the supervision on the labeled instances is the ground-truth label. We compute the risk loss on the training batch with Eq. (2): $$\begin{array}{r c l}{{{\mathcal{L}}_{T}(\theta,{\bf w}^{t},{\mathcal{B}})}}&{{=}}&{{\frac{1}{|{\mathcal{B}}|}\sum_{x_{i},y_{i}\in{\mathcal{B}}}\sigma({\bf w}_{i}^{t}){\mathcal{E}}(\Phi(x_{i};\theta),y_{i})(2)}}\end{array}$$ where |B| is the size of B, E is the loss function. Φθ denotes the model under the hypothesis (h), and θ denotes the model's parameters. w1, w2, . . . , w|B| are the extra hyperparameters introduced in the meta-learning module, i.e., a set of instance weights indicating the importance of each training example. σ represents the sigmoid function, which scales the instance weights into [0, 1]. In the meta training step, we derive a virtual update on the model with Eq. (3): $$\hat{\theta}({\bf w}^{t})=\theta-\eta\nabla_{\theta}{\cal L}_{T}(\theta,{\bf w}^{t},{\cal B})\tag{3}$$ where $\eta$ is the learning rate. data, Φθ denotes the model under the hypothesis (h), and θ denotes the model's parameters. In the pseudo labeling phase, DaMSTF predicts the unlabeled data in the target domain, and the predictions are taken as pseudo labels. Then, these pseudo instances are sent to the meta constructor. For the instances with high prediction confidence, the meta constructor uses them to expand the meta validation set. For the remaining ones, the meta constructor uses them to construct the meta-training set. In the model retraining phase, DaMSTF first trains the model in the domain adversarial training module to align the feature space. Then, the model is trained in the meta-learning module. Afterward, DaMSTF backs to the pseudo labeling phase to start another self-training iteration. Fig. 1 shows the structure of DaMSTF, and Algorithm 1 presents the corresponding pseudo-code. ![3_image_0.png](3_image_0.png) Meta Validation After being virtually updated in the meta training phase, the model is validated on the meta validation set DM with Eq. (4): $${\mathcal{L}}_{M}(\hat{\theta}({\bf w}^{t}))=\frac{1}{|D_{M}|}\cdot\sum_{x_{j},y_{j}\in D_{M}}{\mathcal{E}}(\Phi(x_{j};\hat{\theta}({\bf w}^{t})),y_{j})\tag{4}$$ where E is the loss function, |DM| is the size of the meta validation set. By backpropagating the performance on the meta validation set, we derive the *training guidance* for updating the instance weights on the training batch as below: $$\partial{\cal L}_{M}(\hat{\theta}({\bf w}))=\frac{\partial{\cal L}_{M}(\hat{\theta}({\bf w}))}{\partial\hat{\theta}({\bf w})}\cdot\frac{\partial\hat{\theta}({\bf w})}{\partial{\bf w}}\tag{5}$$ To reduce the computation cost, we use the approximation technique in (Chen et al., 2021) to compute the training guidance (i.e., ∂LM(θˆ(w)) ∂w). Based on the computed training guidance, we obtain the optimal instance weights (marked as w∗) with gradient descent algorithm, as described in Eq. (6). Further, we update θ with Eq. (7): $$\begin{array}{r l}{\mathbf{w}^{t+1}={}}&{{}\mathbf{w}^{t}-{\boldsymbol{\gamma}}\cdot{\frac{\partial{\mathcal{L}}_{M}({\dot{\theta}}(\mathbf{w}))}{\partial\mathbf{w}}}}\\ {\theta^{t+1}={}}&{{}\theta^{t}-\eta{\boldsymbol{\nabla}}\theta{\mathcal{L}}_{T}(\theta,\mathbf{w}^{*},{\mathcal{B}})}\end{array}$$ After the above process is completed on the training batch B, another training batch will be selected to start the meta-learning phase again, as shown in lines 15-21 in Algorithm 1. ## 3.3 Meta Constructor In previous studies, the meta validation set is constructed by collecting a set of labeled data that have the same distribution as the test set (Ren et al., 2018; Shu et al., 2019). However, such practice is not acceptable in domain adaptation, as we are not aware of the data distribution of the target domain during the training phase. To this end, we propose a meta constructor to construct a meta validation set that approximates the target domain. Specifically, we select the reliable instances from the pseudo-labeled data as the instances in the meta validation set. To evaluate the reliability of each of the pseudo instances, we compute their prediction entropy via Eq. (8): $$H(x_{i})=-\sum_{c=1}^{C}(\Phi(c|x_{i};\theta)\cdot log(\Phi(c|x_{i};\theta)))\tag{8}$$ where Φ(c|xi; θ) is the probability of the instance xi belongs to the cth category. In general, a lower prediction entropy indicates a higher prediction correctness (Nguyen et al., 2020). Thus, we first sort the D p T (pseudo labeled dataset) in ascending order according to their prediction entropy. Then, the top-ranked K instances, denoted as DE, are selected as the validation instances, and the remaining pseudo samples, denoted as Dtr T , are preserved in the meta training set. In the semi-supervised domain adaptation, we take the in-domain dataset to initialize the meta validation dataset and use DE to expand the meta validation set along with the self-training iterations. In the unsupervised domain adaptation, where the in-domain dataset is empty, we directly take DE as the meta validation set. The above process is detailed in lines 2-8 of Algorithm 1. Here, meta constructor is an important knot that combines meta-learning and self-training. On the one hand, traditional machine learning approaches cannot exploit the pseudo instances with high prediction entropy, due to the inherent label noise. In this case, the meta constructor uses them to construct the meta training set, as the meta-learning module is tolerant to the label noise in the metatraining set. On the other hand, pseudo instances with low prediction entropy cannot provide extra information for improving the model but contain less label noise. In this case, the meta constructor uses them to validate the model, i.e., uses them to construct or expand the meta validation set, which can improve the quality of the meta validation set. ## 3.4 Domain Adversarial Learning As theoretically explained in § 4.1, the training guidance would not be indicative if the model's gradient on the validation instance is negligible. The presence of domain adversarial learning can prevent the gradient vanishment on the meta validation set, thereby preventing the training guidance vanishment. On the other hand, domain adversarial learning can explicitly align the feature space along with the self-training iterations. To present the details in the domain adversarial learning module, we divide the model Φ(•; θ) into two parts: the feature extraction layer ΦF (•; θF ) and the task-specific layer Φc(•; θc). Usually, θc is the parameters of the last layer in the model, whose output is the prediction probability of each category. The prediction process in the model is: $$\Phi(x_{i};\theta)=\Phi_{c}(\Phi_{F}(x_{i};\theta_{F});\theta_{c})$$ Following Ganin et al. (2016), we introduce an extra domain discriminator to discriminate the instances' domains, i.e., ϕ(•; ϑ), where ϑ is the parameters. On a training batch B, the risk loss for domain adversarial learning is: $${\cal L}_{DA}(\theta_{F},\vartheta,{\cal B})=\frac{1}{|{\cal B}|}\sum_{x_{i},d_{i}\in{\cal B}}{\cal E}(\varphi(\Phi_{F}(x_{i};\theta_{F});\vartheta),d_{i})\tag{10}$$ where diis a one-hot vector representing the domain of xi, E is the cross-entropy function. The specific training process of the proposed domain adversarial learning module is depicted in Algorithm 1, lines 25-35. ## 4 Theoretical Analysis This section first introduces the training guidance vanishment problem and then explains the effectiveness of DaMSTF in achieving domain adaptation. The proofs are detailed in Appendix. A and Appendix. B. ## 4.1 Training Guidance Vanishment Theorem 1. Let wi be the weight of the training instance i, denoted as (xi, yi), in B, the gradient of wi on LM *can be represented by the similarity* between the gradients on training instance i and the gradients on the meta validation set: $$\frac{\partial L_{M}(\hat{\theta}({\bf w}))}{\partial{\bf w}_{i}}=-\frac{\eta}{|{\cal B}|}.[\frac{1}{|D_{M}|}\sum_{j=1}^{|D_{M}|}\vec{\bf g}_{\theta}(x_{j},y_{j})^{T}]\cdot\vec{\bf g}_{\theta}(x_{i},y_{i})$$ where 1 |DM| P|DM| j=1 ~gθˆ(xj , yj ) Tis the gradients of ˆθ on DM, ~g i θ (xi, yi) is the gradients of θ on the training instance i, η *is the learning rate in Eq.* (3) According to Theorem 1,∂LM(θˆ(w)) ∂wiis not indicative for every training instance if the model's gradient on the meta validation set (i.e., 1 |DM| P|DM| j=1 ~gθˆ(xj , yj )) is very small, which we named as the *training guidance vanishment* problem. In DaMSTF, the meta-learning module is challenged by the training guidance vanishment problem from the following aspects. Firstly, the meta validation set is much smaller than the meta training set, so the model converges faster on the meta validation set than that on the meta training set. Considering the optimization on neural networks is non-convex, the model can converge to an inferior optimal if it converges too early on the meta validation set. In this case, the model's gradient on the meta validation set is very small, which results in the training guidance vanishment. Secondly, the instances in DE are the ones with small prediction entropy. Since the supervision for the pseudo instances is exactly the model's predictions, lower prediction entropy results in lower risk loss. Then, the gradients back-propagated from the risk loss are negligible, which also results in the training guidance vanishment. ## 4.2 Theoretical Explanation Of Damstf The *disagreement* and H∆H-distance were first proposed in Ben-David et al. (2010) and have been widely applied to analyze the effectiveness of domain adaptation approaches (Saito et al., 2019; Du et al., 2020). For any two different hypotheses h1 and h2, disagreement D(h1, h2) quantifies the discrepancy of their different predictions on a specific dataset D. When h2 is an ideal hypothesis that can correctly map all instances in D, D(h1, h2) also represents the *error rate* of the hypothesis h1 on dataset D, abbreviated as D(h1). H∆H-distance is a metric for evaluating the divergence of the data distribution between two datasets, which is only relevant to the input space of the datasets. Theorem 2. Assume there exists an ideal hypothesis, denoted as h∗*, which correctly maps all instances in the target domain to their groud-truth* labels. In the self-training iteration t*, let* DlT (h t) and DE (h t) *be the error rate of the hypothesis* h t on DlT and DE, respectively. Then, the error rate of the hypothesis h t *on the target domain is upper* bounded by: $$\epsilon_{\mathbb{D}_{T}}(h^{t})\leq\epsilon_{D_{T}^{l}\cup D_{E}}(h^{t})+\frac{1}{2}d_{H\Delta H}(\mathbb{D}_{T},D_{T}^{l}\cup D_{E})$$ $$+\rho\cdot\epsilon_{D_{E}}(h^{*},h^{t-1})$$ where ρ =|DE| $${\frac{|D_{E}|}{p_{T}^{l}|+|D_{E}|}}\;i s\;a\;c$$ is a coefficient related to the size of DlT and DE, DlT ∪DE (h t) is the error rate of the hypothesis h t *on the union of* DlT and DE. Theorem 3. *Assume there exists three datasets,* D1, D2, D3, and let X1, X2, X3 denotes the set of input cases in these three datasets, i.e., X1 = {xi|(xi, yi) ∈ D1}, X2 = {xi|(xi, yi) ∈ D2}, X3 = {xi|(xi, yi) ∈ D3}*. If* X1 ⊆ X2 ⊆ X3, then $$d_{H\Delta H}(D_{2},D_{3})\leq d_{H\Delta H}(D_{1},D_{3})$$ holds Based on Theorem 2, we demonstrate the effectiveness of DaMSTF from the following aspects. First of all, expanding the meta validation set can decrease the second term in Theorem 2, i.e., 1 2 dH∆H(DT , DlT ∪ DE). According to Theorem 3, dH∆H(DT , DlT ∪ DE) is smaller than dH∆H(DT , DlT ), as the input cases in DE and DlT are all belong to the input cases in the DT . Thus, expanding the meta validation set can reduce the upper bound of DT (h t) What's more, as DE varies in each self-training iteration, the DaMSTF can leverage the diversity of the unlabeled data in the target domain. Thus, dH∆H(DT , DlT ∪ DE) is close to dH∆H(DT , Du T ) in the whole training process. Last but not least, by selecting examples that have the lowest prediction entropy, the error rate on DE is much lower than that of the expected error rates on D p T , formally, DE (h∗, ht−1) < D p T (h∗, ht−1). In other words, the data selection process in the meta constructor reduces the third term in Theorem 2,i.e., ρ · DE (h∗, ht−1). ## 5 Experiments We provide the experiment settings in § 5.1 and compare DaMSTF with previous domain adaptation approaches in § 5.2. In § 5.3, we analyze the effectiveness of the meta constructor and the domain adversarial learning module with an ablation study. § 5.4 validate that exposing more unlabeled data to DaMSTF can improve the domain adaptation performance (Theorem 3). Appendix E provides extra experiments of the domain adversarial learning module in preventing the training guidance vanishment problem, and the meta-learning module in highlighting the hard and correct pseudo instances. ## 5.1 Experiment Settings Dataset On the rumor detection task, we conduct experiments with the public dataset TWITTER (Zubiaga et al., 2016). As the instances in the TWITTER dataset are collected with five topics, we categorized the instances into five domains. On the sentiment classification task, we conduct experiments withs the public dataset Amazon (Blitzer et al., 2007). We follow the method in (He et al., 2018) to preprocess the Amazon dataset, and the resultant dataset consists of 8,000 instances from four domains: books, dvd, electronics, and kitchen. More statistics about the TWITTER dataset and the Amazon dataset can be found in Appendix D. Implementation Details The base model on the rumor detection task is BiGCN (Bian et al., 2020), while the base model on the sentiment classification task is BERT (Devlin et al., 2019). On the benchmark datasets, we conduct domain adaptation experiments on every domain. When one domain is taken as the target domain for evaluation, the rest domains are merged as the source domain. More impelementation details are provided in Appendix C. Comparing Methods Since the DaMSTF can be customized to both semi-supervised and unsupervised domain adaptation scenarios, the baselines contain both unsupervised and semisupervised domain adaptation approaches. For the unsupervised domain adaptation, Out (Chen et al., 2021), DANN (Ganin et al., 2016) and CRST (Zou et al., 2019) are selected as the baselines, while In+Out (Chen et al., 2021), MME (Saito et al., 2019), BiAT (Jiang et al., 2020), and Wind (Chen et al., 2021) are selected as the baselines for the semi-supervised domain adaptation. Out and In+Out are two straightforward ways for realizing unsupervised and semi-supervised domain adaptation, where Out means the base model is trained on the out-of-domain data (i.e., labeled source domain data) and In+Out means the base model is trained on both the in-domain and the out-of-domain data. The core of DANN is an adversarial learning algorithm that takes the domain classification loss as an auxiliary loss. CRST is also a self-training method that uses a label regularization technique to reduce the label noise from mislabeled data. WIND is a meta-learning-based domain adaptation approach that optimizes the weights of different training instances. The difference between the WIND and DaMSTF lies in that, (i) WIND only use the labeled source data to construct the meta training set, while the meta training set in the DaMSTF contains both the labeled data from the source domain and the pseudo data from the target domain. (ii) WIND does not consider the training guidance vanishment problem and the bias between the test set (i.e., target domain) and the meta validation set. ## 5.2 Results To validate the effectiveness of the meta selftraining, we conduct unsupervised and semisupervised domain adaptation experiments on two benchmark datasets, i.e., BiGCN on TWITTER, and BERT on Amazon. Since the rumor detection task focuses more on the 'rumor' category, we evaluate different models by their F1 score in classifying the 'rumor' category. On the sentiment classification task, the prediction accuracy of different classes is equally important, so we take the macro-F1 score to evaluate different models. For semi-supervised domain adaptation, 100 labeled instances in the target domain are taken as the indomain dataset. The experiment results are listed in Tab. 1, Tab. 2. As shown in Tab. 1, Tab. 2, DaMSTF outperforms all baseline approaches on all benchmark datasets. On the rumor detection task, DaMSTF surpasses the best baseline approaches (CRST for unsupervised domain adaptation, WIND for semisupervised domain adaptation) by nearly 5% on average. For the "Fer." domain, where most approaches perform worse than the Out and In+Out, DaMSTF still achieves an F1 value of 0.629, which is 40% higher than that of the In+Out. On the sentiment classification task, DaMSTF also outperforms other approaches. Under the unsupervised domain adaptation scenario, DaMSTF surpasses the best baseline approach (DANN on the Amazon dataset) by nearly 2% on average. Under the semisupervised domain adaptation scenario, DaMSTF surpasses Wind, the best baseline approach on the Amazon dataset, by nearly 3% on average. ## 5.3 Ablation Study This subsection presents an ablation study to understand the effectiveness of the DaMSTF. As illustrated in § 3 and § 4.2, DaMSTF combines metalearning and self-training via two strategies: (i) expanding the meta validation set with a meta constructor; (ii) preventing the training guidance vanishment problem with a domain adversarial module. Thus, we separately remove the above strategies from the DaMSTF, yielding three different variants, namely DaMSTF *- w/o E*, DaMSTF *- w/o D*, and DaMSTF *- w/o D, E*. Compared with DaMSTF, DaMSTF *- w/o E* does not select examples to expand the meta validation set, which means all pseudo instances are preserved to the meta training set. DaMSTF *- w/o D* removes the domain adversarial module from the DaMSTF. DaMSTF *- w/o D,* E removes both two strategies. Other experiment settings are the same as § 5.2. We summarize the results in Tab. 3, Tab. 4. As shown in Tab. 3 and Tab. 4, both strategies are indispensable for the effectiveness of DaMSTF, | Target | Unsupervised domain adaptation | Semi-Supervised domain adaptation | | | | | | | | |----------|----------------------------------|-------------------------------------|-------|--------|--------|-------|-------|-------|--------| | Domain | Out | DANN | CRST | DaMSTF | In+Out | MME | BiAT | Wind | DaMSTF | | Cha. | 0.561 | 0.501 | 0.563 | 0.635 | 0.586 | 0.601 | 0.547 | 0.552 | 0.649 | | Fer. | 0.190 | 0.387 | 0.446 | 0.524 | 0.200 | 0.081 | 0.256 | 0.291 | 0.629 | | Ott. | 0.575 | 0.544 | 0.709 | 0.753 | 0.599 | 0.612 | 0.614 | 0.633 | 0.843 | | Syd. | 0.438 | 0.461 | 0.673 | 0.717 | 0.424 | 0.677 | 0.661 | 0.628 | 0.731 | | Mean | 0.441 | 0.473 | 0.598 | 0.657 | 0.452 | 0.493 | 0.520 | 0.526 | 0.714 | Table 1: F1 score on the TWITTER Target Domain Unsupervised Domain Adaptation Semi-Supervised Domain Adaptation Out DANN CRST DaMSTF In+Out MME BiAT Wind DaMSTF books *0.882* 0.887 0.878 **0.931** *0.890* 0.896 0.891 0.890 **0.947** dvd *0.831* 0.864 0.845 **0.917** *0.882* 0.893 0.888 0.904 **0.935** electronics *0.871* 0.914 0.877 **0.925** *0.918* 0.906 0.926 0.917 **0.941** kitchen *0.863* 0.922 0.868 **0.927** *0.925* 0.93 0.934 0.933 **0.947** Mean *0.862* 0.897 0.867 **0.925** 0.904 0.906 0.910 0.911 **0.942** Cha. Fer. Ott. Syd. Mean DaMSTF 0.649 0.629 0.843 0.731 0.713 - w/o D 0.585 0.401 0.782 0.724 0.623 - w/o E 0.600 0.542 0.694 0.685 0.630 - w/o D, E 0.569 0.352 0.633 0.631 0.547 Table 3: Ablation Study on TWITTER and removing either strategy can result in performance degeneration. Removing the domain adversarial learning module (DaMSTF - *w/o D*) leads to an average decrease from 0.713 to 0.623 on the TWITTER dataset and from 0.942 to 0.918 on the Amazon dataset. Without expanding the meta validation set, DaMSTF - *w/o E* performs worse than DaMSTF on both the TWITTER dataset (0.630 vs. 0.731 on average) and the Amazon dataset(0.931 vs. 0.942 on average). After removing both strategies, DaMSTF suffers a severe performance deterioration on both benchmark datasets. ## 5.4 Effect Of The Unlabeled Dataset Size As illustrated in § 4.2, the second term dH∆H(DT , DlT ∪ DE) is close to dH∆H(DT , Du T ) in the whole training process. From this perspective, increasing the size of the unlabeled dataset can improve the performance. To validate this, we separately expose 0%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% of the unlabeled data during the training. These new unlabeled dataset are denote as Du T (0%), Du T (5%)*, . . . , D*u T (100%) respectively. The experiments are conducted on "Ott." Domain of TWITTER and the results are presented in Fig. 2. ![7_image_0.png](7_image_0.png) | books | dvd | electronics | kitchen | Mean | | |------------|-------|---------------|-----------|--------|-------| | DaMSTF | 0.947 | 0.935 | 0.941 | 0.947 | 0.942 | | - w/o D | 0.899 | 0.917 | 0.924 | 0.935 | 0.918 | | - w/o E | 0.917 | 0.929 | 0.934 | 0.945 | 0.931 | | - w/o D, E | 0.887 | 0.896 | 0.919 | 0.931 | 0.908 | From Fig. 2, we observe that the model performs poorly when using a small proportion of the unlabeled data in the training process. For example, exposing Du T (5%) to the DaMSTF only achieves an F1 score of 0.701, which is 14.2% lower than the 0.843 achieved by exposing the Du T (100%). From 0% to 50%, increasing the exposure ratio consistently improves the F1 score. The improvements saturate after more than 50% of the unlabeled data are exposed, which can be explained by the law of large numbers in the statistic theory (Kraaikamp and Meester, 2005). An exposure ratio of 50% can be regarded as a large number for approaching the unlabeled dataset. Thus, Du T (50%) is close to Du T (100%) and dH∆H(DT , Du T (50%)) approximates dH∆H(DT , Du T (100%)), which leads to the performance saturation. ## 6 Related Work 6.1 Domain Adaptation Inspired by the taxonomy in Ramponi and Plank (2020), we categorize the domain adaptation approaches into two categories: Feature-Alignment approaches and Data-Centric approaches. FeatureAlignment approaches (Tzeng et al., 2014; Ganin et al., 2016; Saito et al., 2019) focus on aligning the feature space across domains. The most well-known feature-alignment approach is DANN (Ganin et al., 2016), which aligns the feature space by min-max the domain classification loss. With similar efforts, MME (Saito et al., 2019) min-max the conditional entropy on the unlabeled data. VAT (Miyato et al., 2018), as well as BiAT (Jiang et al., 2020), propose to decouple the min-max optimization process, which first imposes a gradient-based perturbation on the input space to maximize the risk loss and then minimize the final objective on the perturbed input cases. In contrast, Data-Centric approaches exploit the unlabeled data in the target domain or select the relevant data from the source domain. To select relevant data, (Moore and Lewis, 2010; Plank and van Noord, 2011) design a technique based on topic models for measuring the domain similarity. To exploit the unlabeled data, pseudo labeling approaches, including self-training (Zou et al., 2019), co-training (Chen et al., 2011), and tri-training (Saito et al., 2017), are widely applied and become an important direction. In the research of self-training for domain adaptation, many efforts are put into reducing the label noise of pseudo instances (Zou et al., 2019, 2018; Liu et al., 2021). Among them, CRST (Zou et al., 2019) proposes a label regularization technique to reduce label noise while CST (Liu et al., 2021) takes Tsallis-entropy as a confidence-friendly regularize. In this paper, we propose to adopt metalearning to automatically reduce label noise. ## 6.2 Meta-Learning Meta-learning is an emerging new branch in machine learning that focuses on providing better hyperparameters for model training, including but not limited to better initial model parameters, e.g., MAML (Finn et al., 2017), better learning rates, e.g., MetaSGD (Li et al., 2017), and better neural network architect, e.g., DARTs (Liu et al., 2018). Recent studies revealed the prospect of providing better instance weights (Ren et al., 2018; Shu et al., 2019; Kye et al., 2020). When using prototypical learning on the few-shot image classification task, MCT (Kye et al., 2020) involves a reweighing process to obtain a more accurate class prototype. Oriented to natural language processing tasks, (Li et al., 2020; Chen et al., 2021) use the optimization-based meta-reweighting algorithm to refine the training set. Similar to DaMSTF, Wang et al. (2021) also proposes to combine the metalearning algorithm and the self-training approach, but their method focuses on the neural sequence labeling task rather than the domain adaptation task. Also, they do not consider the bias between the meta-validation set and the test set, whereas reducing such bias is an important contribution of the DaMSTF. WIND (Chen et al., 2021) is a meta-learning-based domain adaptation approach, the differences between WIND and DaMSTF are discussed in § 5.1. ## 7 Conclusion This paper proposes an improved self-training framework for domain adaptation, named DaMSTF. DaMSTF extends the basic framework for selftraining approaches by involving a meta-learning module, which alleviates the label noise problem in self-training. To guarantee the effectiveness of the meta-learning module, we propose a meta constructor to improve the quality of the meta validation set, and propose a domain adversarial module to prevent the training guidance vanishment. 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Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2016. Learning reporting dynamics during breaking news for rumour detection in social media. CoRR, abs/1610.07363. ## A Proof For Theorem 1 Theorem 1. Let wi be the weight of the training instance i, denoted as (xi, yi), in B, the gradient of wi on LM *can be represented by the similarity* between the gradients on training instance i and the gradients on the meta validation set: $$\frac{\partial L_{M}(\hat{\theta}({\bf w}))}{\partial{\bf w}_{i}}=-\frac{\eta}{|{\cal B}|}\cdot[\frac{1}{|D_{M}|}\sum_{j=1}^{|D_{M}|}\vec{\bf g}_{\hat{\theta}}(x_{j},y_{j})^{T}]\cdot\vec{\bf g}_{\theta}(x_{i},y_{i})\ .$$ where 1 |DM| P|DM| j=1 ~gθˆ(xj , yj ) T*is the gradients of* ˆθ on DM, ~g i θ (xi, yi) is the gradients of θ on the training instance i, η *is the learning rate in Eq.* (3) Proof. Based on Eq. (2) and Eq. (3) in § 3.2, we conclude the pseudo updated parameters ˆθ(w) as: $$\hat{\theta}(\mathbf{w})=\theta-\eta\cdot\frac{1}{|\mathcal{B}|}\cdot\sum_{x_{i},y_{i}\in\mathcal{B}}\sigma(\mathbf{w}_{i})\cdot\frac{\partial\mathcal{E}(\Phi(x_{i};\theta),y_{i})}{\partial\theta}\tag{11}$$ We then take the gradient of wi on ˆθ(w) as: $$\partial\hat{\theta}(\mathbf{w})=-\frac{\eta}{|\mathcal{B}|}\cdot\frac{\partial\mathcal{E}(\Phi(x_{i};\theta),y_{i})}{\partial\theta}\tag{12}$$ Based on Eq. (12), we derivate the gradient of wi on LM as: ∂LM( ˆθ(w)) ∂wi= [∂LM( ˆθ(w)) ∂ ˆθ(w)] T· [ ∂ ˆθ(w) ∂σ(wi) ] · [ ∂σ(w) ∂w] = [ 1 |DM| · |DXM| j=1 ∂E(Φ(xj ; ˆθ(w)), yj ) ∂ ˆθ(w)] T· [− η |B| · ∂E(Φ(xi; θ), yi) ∂θ ] · [σ(wi)(1 − σ(wi))] = − ησ(wi)(1 − σ(wi)) |B| · [1 |DM| |DXM| j=1 ~gθˆ(xj , yj ) T] · ~gθ(xi, yi) (13) ## B Proof For Theorem 2 **And Theorem** 3 Definition 1. *disagreement is a measure to quantify the different performances of two different hypotheses on a specific dataset. Denote the two* hypotheses as h1 and h2, and denote the specific dataset as D, then the disagreement of h1 and h2 on D *is formulated as:* $$\epsilon_{D}(h_{1},h_{2})=\frac{1}{|D|}\sum_{i=1}^{|D|}[\frac{1}{C}*||h_{1}(x)-h_{2}(x)||_{1}]\tag{14}$$ where C is the number of classes, h1(x) and h2(x) are one-hot vectors representing the models' predictions. Definition 2. H∆H-distance is a metric for evaluating the divergence of the data distribution between two datasets. Formally, H∆H*-distance is* computed as: dH∆H(D1, D2) = 2 sup h1,h2∈H |D1(h1, h2) − D2(h1, h2)| (15) where H is the hypothesis space and sup *denotes* the supremum. The concepts *disagreement* and H∆H-distance are introduced in Definition 1 and Definition 2, respectively. Based on the *disagreement* and H∆Hdistance, the proof for Theorem 2 is presented as below. Lemma 1. *Assume there exists two dataset, i.e.,* D1, D2. Let X1 = {xi|(xi, yi) ∈ D1} and X2 = {xi|(xi, yi) ∈ D2} denotes the set of input case from D1 and D2. If X1 ⊆ X2*, then* $$d_{H\Delta H}(D_{1},D_{2})=2\cdot{\frac{|D_{2}|-|D_{1}|}{|D_{2}|}}$$ holds. Proof. Let Ik(h1, h2) = 1C*∗ ||*h1(xk) − h2(xk)||1 denote the difference of two hypothesis h1 and h2 on instance xk, then the *disagreement* of h1 and h2 on the dataset D can be rewritten as: $$\epsilon_{D}(h_{1},h_{2})=\frac{1}{|D|}\sum_{i=1}^{|D|}I_{i}(h_{1},h_{2})$$ $$P_{e\in{\mathcal{H}}}\left[e_{D_{1}}(n x,n z)=0\right]$$ Based on the Definition 2, the H∆H distance between D1 and D2 is as below: dH∆H(D1, D2) = 2 sup h1,h2∈H |D1(h1, h2) − D1(h1, h2)| (16) $$\begin{array}{l l l}{{\mathrm{ng}}}&{{\mathrm{the}}}&{{\mathrm{item}}}&{{\epsilon_{D_{1}}(h1,h2)}}\\ {{}}&{{}}&{{}}\\ {{\mathrm{l.~we~can~obtain:}}}&{{}}&{{}}\end{array}$$ (h1, h2) and D1 (h1, h2), we can obtain: where the second line is obtained by substituting LM and ˆθ with Eq. (4) and Eq. (11). Substitute ~gθˆ(xj , yj ) = ∂E(Φ(xj ;θˆ(w)),yj ) ∂θˆ(w)and ~gθ(xi, yi) = ∂E(Φ(xi;θ),yi) ∂θ and rearrange the terms, we obtain the third line. The proof of Theorem 1 is completed. |D2(h1, h2) − D1(h1, h2)| = |1 |X2| X xi∈X2 Ii(h1, h2) −1 |X1| X xi∈X1 Ii(h1, h2)| = | |X1| |X2| ∗ 1 |X1| X xi∈X1 Ii(h1, h2) + |X¯1| |X2| ∗ 1 |X¯1| X xk∈X¯1 Ii(h1, h2) − 1 |X1| X xi∈X1 Ii(h1, h2)| = |1 |X2| X xk∈X¯1 Ik(h1, h2) − |X2| − |X1| |X2|· 1 |X1| X xi∈X1 Ii(h1, h2)| =1 |X2| |X xk∈X¯1 Ik(h1, h2) − |X¯1| |X1| ·X xi∈X1 Ii(h1, h2)| = |X¯1| |X2| |D¯1 (h1, h2) − D1(h1, h2)| (17) where X¯1 is the complement set of X1 in X2, i.e, X¯1 = X2 − X1. Correspondingly, D¯1 = {(xi, yi)|(xi, yi) ∈ D2 and xi ∈ X¯}, and thus |X¯1| = |D¯1| holds. As 0 ≤ D¯1 (h1, h2) ≤ 1 and 0 ≤ D1 (h1, h2) ≤ 1 , we conclude the inequation below: $$|\epsilon_{\bar{D}_{1}}(h_{1},h_{2})-\epsilon_{D_{1}}(h_{1},h_{2})|\leq1\qquad(18)$$ Since D1 and D¯1 do not overlap, D¯1 (h1, h2) is independent of D1 (h1, h2). Thus, we can maximize the left term in inequation (18) by finding two hypotheses hˆ1 and hˆ2, which make D¯1 (hˆ1, hˆ2) = 1 and D1 (hˆ1, hˆ2) = 0. Thus, Theorem 2. Assume there exists an ideal hypothesis, denoted as h∗*, which correctly map all instances in the target domain to their groud-truth* labels. In the self-training iteration t*, let* DlT (h t) and DE (h t) *be the error rate of the hypothesis* h t on DlT and DE, respectively. Then, the error rate of the hypothesis h t *on the target domain is upper* bounded by: $$\epsilon_{\mathbb{D}_{T}}(h^{t})\leq\epsilon_{D^{l}_{T}\cup D_{E}}(h^{t})+\frac{1}{2}d_{H\Delta H}(\mathbb{D}_{T},D^{l}_{T}\cup D_{E})\tag{19}$$ $$+\rho\cdot\epsilon_{DE}(h^{*},h^{t-1})$$ $$D_{E})$$ where ρ =|DE| |DlT|+|DE| is a coefficient related to the size of DlT and DE, DlT ∪DE (h t) *is the error rate* of the hypothesis h t *on the union of* DlT and DE. Proof. In the meta-learning module, the final objective is to minimize the risk loss on the meta validation set DlT ∪ DE. Thus, according to the learning theory (Ben-David et al., 2010), the upper bound of the error rate on the test set (i.e., the target domain) is: $$\epsilon_{\mathbb{D}_{T}}(h^{t})\leq\epsilon_{D_{T}^{l}\cup D_{E}}(h^{t})+\frac{1}{2}d_{H\Delta H}(\mathbb{D}_{T},D_{T}^{l}\cup D_{E})\tag{20}$$ $$+\epsilon_{\mathbb{D}_{T}}(h^{*})+\epsilon_{D_{T}^{l}\cup D_{E}}(h^{*})$$ Because h∗is an ideal hypothesis on the target domain, DT (h∗) = 0 holds true. Expanding DlT ∪DE (h∗) with the definition in Eq. (14), $$\epsilon_{D_{T}^{l}\cup D_{E}}(h^{*})$$ $$\frac{1}{|D_{T}^{l}|+|D_{E}|}\sum_{(x,y)\in D_{T}^{l}\cup D_{E}}[\frac{1}{C}*||h^{*}(x)-y||_{1}]$$ $$\frac{1}{|D_{T}^{l}|+|D_{E}|}\{\sum_{(x,y)\in D_{T}^{l}}[\frac{1}{C}*||h^{*}(x)-y||_{1}]$$ $$+\sum_{(x,y)\in D_{E}}[\frac{1}{C}*||h^{*}(x)-y||_{1}]\}$$ $$\frac{1}{|D_{T}^{l}|+|D_{E}|}\{|D_{T}^{l}|\cdot\epsilon_{D_{T}^{l}}(h^{*})+|D_{E}|\cdot\epsilon_{D_{E}}(h^{*})\}$$ = $$\frac{1}{2}$$ = $$\frac{1}{2}$$ = $$\frac{1}{2}$$. =1 =1 dH∆H(D1, D2) = 2 sup h1,h2∈H |D2(h1, h2) − D1(h1, h2)| =1 = 2 · |X¯1| |X2|sup h1,h2∈H |D¯1 (h1, h2) − D1(h1, h2)| = 2 · |D¯1| |D2|sup h1,h2∈H |D¯1 (h1, h2) − D1(h1, h2)| Substituting Eq. (21) into Eq. (20), we have: = 2 · |D¯1| |D2| |D¯1 (hˆ1, hˆ2) − D1(hˆ1, hˆ2)| DT (h t) = 2 · |D¯1| |D2| = 2 · |D2*| − |*D1| |D2| ≤ DlT∪DE (h t) + 12 dH∆H(DT , DlT ∪ DE) + DT (h ∗) +1 |DlT| + |DE| {|D l T | · DlT (h ∗) + |DE| · DE (h ∗)} (22) The proof of Lemma 1 is completed. 1662 For any instance (*x, y*) ∈ DE, y is the pseudo label, i.e., the prediction of hypothesis h t−1. Thus, we have: $$\epsilon_{D_{E}}(h^{*})$$ $$\frac{1}{|D_{E}|}\sum_{(x,y)\in D_{E}}[\frac{1}{C}*||h^{*}(x)-y||_{1}]$$ $$\frac{1}{|D_{E}|}\sum_{(x,y)\in D_{E}}[\frac{1}{C}*||h^{*}(x)-h^{t-1}(x)||_{1}]$$ $$\epsilon_{D_{E}}(h^{*},h^{t-1})\tag{23}$$ =1 =1 Since DlTis a subset of DT , DlT (h∗) = 0 holds true. By eliminating DT (h∗) and DlT (h∗) in Eq.(22), and substituting DE (h∗) with DE (h∗, ht−1), we have: $$\begin{array}{r c l}{{\epsilon_{\mathbb{D}_{T}}(h^{t})}}&{{\leq}}&{{\epsilon_{D_{T}^{l}\cup D_{E}}(h^{t})+\frac{1}{2}d_{H\Delta H}(\mathbb{D}_{T},D_{T}^{l}\cup D_{E})}}\\ {{}}&{{}}&{{}}\\ {{}}&{{}}&{{+\frac{|D_{E}|}{|D_{T}^{l}|+|D_{E}|}\cdot\epsilon_{D_{E}}(h^{*},h^{t-1})\}}}\end{array}$$ The proof of Theorem 2 is completed. Theorem 3. *Assume there exists three datasets,* D1, D2, D3, and let X1, X2, X3 *denotes the set* of input cases in these three datasets, i.e., X1 = {xi|(xi, yi) ∈ D1}, X2 = {xi|(xi, yi) ∈ D2}, X3 = {xi|(xi, yi) ∈ D3}. If X1 ⊆ X2 ⊆ X3, then $$d_{H\Delta H}(D_{2},D_{3})\leq d_{H\Delta H}(D_{1},D_{3})$$ holds Proof. According to Lemma 1, dH∆H(D2, D3) = 2 · |D3| − |D2| |D3| dH∆H(D1, D3) = 2 · |D3| − |D1| |D3| Since X1 ⊆ X2, |D1| ≤ |D2| holds. Thus, $$d_{H\Delta H}(D_{2},D_{3})<d_{H\Delta H}(D_{1},D_{3})$$ holds. $\mathbf{new}\mathbf{new}.$ The proof of Theorem 3 is completed. ## C Implementation Details The base model on the rumor detection task is BiGCN (Bian et al., 2020), while the base model on the sentiment classification task is BERT (Devlin et al., 2019). On the benchmark datasets, we conduct domain adaptation experiments on every domain. When one domain is taken as the target domain for evaluation, the rest domains are merged as the source domain. For example, when the "books" domain in the Amazon dataset is taken as the target domain, the "dvd", "electronics" and "kitchen" domains are merged as the source domain. The unlabeled data from the target domain are used for training the model, and the labeled data from the target domain are used for testing and validating the model (with a ratio of 7:3). Notes that the TWITTER dataset does not contain extra unlabeled data, we take 70% of the labeled data on the target domain as the unlabeled data for training, and the rest will be preserved for testing and validating. The experiments on TWITTER are conducted on "Cha.", "Fer.", "Ott.", and "Syd."1. The implementation of BiGCN to realize the rumor detection task is provided in (Bian et al., 2020), and we follow the description in (Bian et al., 2020) to train the BiGCN model with the TWITTER dataset. The implementation of BERT to realize the sentiment analysis task can be found in (Devlin et al., 2019). We download the pretrained BERT from https://huggingface. co/bert-base-uncased2and fit the BERT on the Amazon dataset with the instruction in (Devlin et al., 2019). Since DANN, FixMatch, CST, MME, WIND, and BiAT are model agnostic, we implement them according to the cited references (Ganin et al., 2016; Sohn et al., 2020; Liu et al., 2021; Saito et al., 2019; Chen et al., 2021; Wang and Zhang, 2019). For the symbols in Algorithm 1, we set TM as 5, TD as 5, TG as 1. We set η1 and η2 in Algorithm 1 as 5e − 4 and 5e − 3 for the BiGCN model, and as 5e − 6 and 2e − 5 for the BERT model. We set η in Eq. (3) as 5e − 5 for the BERT model, and 5e−3 for the BiGCN model. We set γ in Eq. (6) as 0.1 for both the BERT and the BiGCN model. We conduct all experiments the GeForce RTX 3090 GPU with 24GB memory. ![14_image_0.png](14_image_0.png) | Domain | Rumours | Non-Rumours Total | | |-------------------------------|-----------------------------------|---------------------|-------| | Charlie Hebdo# | 458 (22%) | 1,621 (78%) | 2,079 | | Ferguson# | 284 (24.8%) | 859 (75.2%) | 1,143 | | Germanwings Crash 238 (50.7%) | 231 (49.3%) | 469 | | | Ottawa Shooting | 470 (52.8%) | 420 (47.2%) | 890 | | Sydney Siege | 522 (42.8%) | 699 (57.2%) | 1,221 | | Total | 1,921 (34.0%) 3,830 (66.0%) 5,802 | | | Table 5: Statistics of the TWITTER dataset. | Domains | positive | negative | unlabeled | |-------------|------------|------------|-------------| | books | 1000 (50%) | 1000(50%) | 6001 | | dvd | 1000 (50%) | 1000 (50%) | 34,742 | | electronics | 1000 (50%) | 1000 (50%) | 13,154 | | kitchen | 1000 (50%) | 1000 (50%) | 16,786 | ## D Statistics Of The Datasets TWITTER dataset is provided in the site3 under a CC-BY license. Amazon dataset is accessed from https://github.com/ruidan/DAS. The statistics of the TWITTER dataset and the Amazon dataset is listed in Table 5 and Table 6. ## E Extra Experiments E.1 Instance Reweighting To investigate the effectiveness of the metalearning module, we conduct an experiment to visualize the optimized instance weights on different pseudo instances. In detail, the experiments are conducted on the 'Cha.' domain of the TWITTER 3https://figshare.com/ndownloader/articles/6392078/ dataset. Since the unlabeled data in the TWITTER dataset is constructed with the labeled data in the target domain (illustrated in § 5), we are aware of the pseudo labels' correctness. Thus, we can visualize the relevance among the instance weights, pseudo labels' correctness, and pseudo labels' confidence, the experiment results are shown in Fig. 3. Fig. 3 is a violin plot in a horizontal direction, where each curve represents a distribution of the instance weights. The height of the curve represents the probability density. In each confidence interval, the yellow curve is the distribution over the correct pseudo instances while the blue curve is the distribution over the wrong pseudo instances. It should be noted that the probability density is normalized in each confidence interval. Thus, the area of the two kinds curves is equal to 1.0 in each confidence interval. From Fig. 3, we can obtain the following observations. Firstly, the meta-learning module is effective in reducing label noise. In different confidence intervals, especially in [0.5-0.6] and [0.6-0.7], the peak of the blue curve is smaller than 0.2, meaning that the wrong pseudo instances are mainly allocated low instance weights. Thus, the adverse impact from the wrong pseudo instances is reduced. Secondly, larger instance weights are allocated to the correct pseudo instances with low confidence. In specific, large instance weights (i.e., >0.5) mainly appears in the bottom two sub-graph, so the large instance weights are mainly allocated to the correct pseudo instances whose confidence is lower than 0.7. Thus, the meta-learning module is also effective in mining hard pseudo examples. ## E.2 Error Rates On The Expansion Examples According to Theorem 2 in § 4, the performance of the DaMSTF is limited by the error rate of the expansion examples, i.e., DE (h∗, ht−1). By selecting the examples with the lowest prediction entropy as the expansion example, the meta constructor can reduce DE (h∗, ht−1), thereby can improve the performance of the DaMSTF. In this subsection, we examine the reliability of the meta constructor, i.e., visualizing the relationship between the prediction entropy and the prediction correctness. Specifically, we first compute and sort the prediction entropy on the "Syd." domain. We then select the top 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% of the pseudo instances to compute the error rate between the selected predictions and their ground-truth labels. We summarize the experiment results in Fig. 4. ## E.3 Risk Loss On The Expansion Examples As discussed in § 4.1, expanding the meta validation set is challenged by the training guidance vanishment problem, since the model's risk loss, as well as the model's gradient, on the expansion examples is negligible. As a complementary, we design a domain adversarial learning module to perturb the model's parameters, thereby increasing the model's gradients on the expansion examples. Here, we provide an intuitive explanation for the necessity of introducing domain adversarial learning. Specifically, we exhibit the relationship between the predictive entropy and the risk loss, and present the changes of the risk loss before and after the parameters perturbation. The experimental settings are the same as § E.2, and we summarize the results in Fig. 5. ![15_image_1.png](15_image_1.png) ![15_image_2.png](15_image_2.png) ![15_image_0.png](15_image_0.png) From Fig. 5, we observe that the mean risk loss decreases along with the decrease of the selection rate, and the risk loss on the examples with small predictive entropy is negligible. On the examples with the lowest 10% predictive entropy (i.e., expansion examples in our setting), the mean risk loss is only 0.015. Considering that the gradient is back-propagated from the risk loss, these expansion examples cannot produce acceptable gradients. Accordingly, these expansion examples cannot provide indicative training guidance. After perturbing the model parameters with the domain adversarial learning module, the risk loss on the expansion examples (Selection Ratio=0.1) sharply increases from 0.015 to 0.288. Thus, the domain adversarial learning module is an indispensable complement to the meta constructor. ## F Limitation Although our approach produces promising results on two datasets, there are certain limitations. In the future, we will continue to dig into these concerns. Firstly, we evaluate the DaMSTF on two classification tasks. We do not conduct experiments on other NLP tasks, such as machine translation (Yang et al., 2018) or named entity recognition (Jia et al., 2019). Nonetheless, as text classification is a fundamental task, other NLP applications can be specified as a case of classification. For example, named entity recognition can be formulated as a wordword relation classification task (Li et al., 2022). Secondly, the meta-learning module carries out extra computation overhead. As the bi-level hyperparameters optimization involves a second-order derivate on the model's parameters, their computation overhead is quadratic to the model's parameters. In DaMSTF, we use the approximation techniques in WIND to compute the derivate, which is linear to the model's parameters. In the future, we will investigate other techniques to accelerate the DaMSTF. ## G Ethical Considerations And Social Impacts This paper involves the use of existing artifact(s), including two benchmark datasets and the pretrained BERT model. Their intention for providing the artifacts is to inspire the following research, our use is consistent with their intended use. Rumor, as well as rumor detection, is very sensitive for the social order. In this paper, we conduct experiments on a rumor detection task and prepare to release the code in the future. Since the model's prediction is not that reliable, it may lead to social harm when the model's error prediction is used with malicious intentions. For example, people may use the model's error prediction as support evidence, so as to deny a correct claim or to approve a rumor claim. Here, we seriously declare that the model's prediction cannot be taken as the support evidence. In the released code, we will constrain the input format of the model, making unprofessional individuals unable to directly use the model. ## Acl 2023 Responsible Nlp Checklist A For Every Submission: A1. Did you describe the limitations of your work? Left blank. A2. Did you discuss any potential risks of your work? Left blank. A3. Do the abstract and introduction summarize the paper's main claims? Left blank. A4. Have you used AI writing assistants when working on this paper? Left blank. ## B **Did You Use Or Create Scientific Artifacts?** Left blank. B1. Did you cite the creators of artifacts you used? Left blank. B2. Did you discuss the license or terms for use and / or distribution of any artifacts? Left blank. B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Left blank. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Left blank. ## C **Did You Run Computational Experiments?** Left blank. C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Left blank. The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance. C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Left blank. C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Left blank. C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Left blank. D **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Left blank.

wang-hershcovich-2023-evaluating

On Evaluating Multilingual Compositional Generalization with Translated Datasets

https://aclanthology.org/2023.acl-long.93

Compositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization abilities differ across languages? Can models compositionally generalize cross-lingually? As a first step to answering these questions, recent work used neural machine translation to translate datasets for evaluating compositional generalization in semantic parsing. However, we show that this entails critical semantic distortion. To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset from English to Chinese and Japanese. Even with the resulting robust benchmark, which we call MCWQ-R, we show that the distribution of compositions still suffers due to linguistic divergences, and that multilingual models still struggle with cross-lingual compositional generalization. Our dataset and methodology will serve as useful resources for the study of cross-lingual compositional generalization in other tasks.

# On Evaluating Multilingual Compositional Generalization With Translated Datasets Zi Wang1,2and **Daniel Hershcovich**1 1Department of Computer Science, 2Department of Nordic Studies and Linguistics University of Copenhagen {ziwa, dh}@di.ku.dk ## Abstract Compositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization abilities differ across languages? Can models compositionally generalize crosslingually? As a first step to answering these questions, recent work used neural machine translation to translate datasets for evaluating compositional generalization in semantic parsing. However, we show that this entails critical semantic distortion. To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset (Cui et al., 2022) from English to Chinese and Japanese. Even with the resulting robust benchmark, which we call MCWQ-R, we show that the distribution of compositions still suffers due to linguistic divergences, and that multilingual models still struggle with cross-lingual compositional generalization. Our dataset and methodology will be useful resources for the study of cross-lingual compositional generalization in other tasks.1 ## 1 Introduction A vital ability desired for language models is compositional generalization (CG), the ability to generalize to novel combinations of familiar units (Oren et al., 2020). Semantic parsing enables executable representation of natural language utterances for knowledge base question answering (KBQA; Lan et al., 2021). A growing amount of research has been investigating the CG ability of semantic parsers based on carefully constructed datasets, typically synthetic corpora (e.g., CFQ; Keysers et al., 2019) generated based on curated rules, mostly within monolingual English scenarios. As demonstrated by Perevalov et al. (2022), 1The dataset, trained models and code for the experiments and dataset generation are available at https://github.com/ ziwang-klvk/CFQ-RBMT. NEURAL-BASED TRANSLATION: SOURCE: Did Erika Mann's spouse executive produce *Friedemann Bach* TARGET: 艾莉卡·曼的配偶执行官制作了弗里德曼·巴赫吗 ``` RULE-BASED TRANSLATION: SOURCE: Did Erika Mann's spouse executive produce Friedemann Bach TARGET: 艾莉卡·曼的配偶 NP1 执行制作 V 了弗里德曼·巴赫 NP2 吗 ``` SPARQL QUERY: ASK WHERE { wd:Q829979 wdt:P1431 ?x0 . ?x0 wdt:P26 wd:Q61597 . FILTER ( ?x0 != wd:Q61597 )} Figure 1: Example of neural machine translation (NMT, from MCWQ, top) and rule-based translation (from MCWQ-R, middle) from English to Chinese. The compositions correctly captured by the translation system and the correspondences in the SPARQL query (bottom) are highlighted in the same color, while errors are in red. NMT often diverges semantically from the query: here, the compound "executive produce" is split. RBMT performs well due to awareness of grammar constituents. resource scarcity for many languages largely preclude their speakers' access to knowledge bases (even for languages they include), and KBQA in multilingual scenarios is barely researched mainly due to lack of corresponding benchmarks. Cui et al. (2022) proposed Multilingual Compositional Wikidata Questions (MCWQ) as the first semantic parsing benchmark to address the mentioned gaps. Google Translate (GT; Wu et al., 2016), a Neural Machine Translation (NMT) system trained on large-scale corpora, was adopted in creating MCWQ. We argue that meaning preservation during translation is vulnerable in this methodology especially considering the synthetic nature of the compositional dataset. Furthermore, stateof-the-art neural network models fail to capture structural systematicity (Hadley, 1994; Lake and Baroni, 2018; Kim and Linzen, 2020). Symbolic (e.g., rule-based) methodologies allow directly handling CG and were applied both to generate benchmarks (Keysers et al., 2019; Kim 1669 and Linzen, 2020; Tsarkov et al., 2021) and to inject inductive bias to state-of-the-art models (Guo et al., 2020; Liu et al., 2021a). This motivates us to extend this idea to cross-lingual transfer of benchmarks and models. We propose to utilize rule-based machine translation (RBMT) to create parallel versions of MCWQ and yield a robust multilingual benchmark measuring CG. We build an MT framework based on synchronous context-free grammars (SCFG) and create new Chinese and Japanese translations of MCWQ questions, which we call MCWQ-R (Multilingual Compositional Wikidata Questions with Rule-based translations). We conduct experiments on the datasets translated with GT and RBMT to investigate the effect of translation method and quality on CG in multilingual and cross-lingual scenarios. Our specific contributions are as follows: - We propose a rule-based method to faithfully and robustly translate CG benchmarks. - We introduce MCWQ-R, a CG benchmark for semantic parsing from Chinese and Japanese to SPARQL. - We evaluate the translated dataset through both automatic and human evaluation and show that its quality greatly surpasses that of MCWQ (Cui et al., 2022). - We experiment with two different semantic parsing architectures and provide an analysis of their CG abilities within language and across languages. ## 2 Related Work Compositional generalization benchmarks. Much previous work on CG investigated how to measure the compositional ability of semantic parsers. Lake and Baroni (2018) and Bastings et al. (2018) evaluated the CG ability of sequenceto-sequence (seq2seq) architectures on natural language command and action pairs. Keysers et al. (2019) brought this task to a realistic scenario of KBQA by creating a synthetic dataset of questions and SPARQL queries, CFQ, and further quantified the distribution gap between training and evaluation using *compound divergence*, creating maximum compound divergence (MCD) splits to evaluate CG. Similarly, Kim and Linzen (2020) created COGS in a synthetic fashion following a stronger definition of training-test distribution gap. Goodwin et al. (2022) benchmarked CG in dependency parsing by introducing gold dependency trees for CFQ questions. For this purpose, a full coverage context-free grammar over CFQ was constructed benefiting from the synthetic nature of the dataset. While these works differ in data generation and splitting strategy, rule-based approaches are commonly adopted for dataset generation; as Kim and Linzen (2020) put it, such approaches allow maintaining "full control over the distribution of inputs", the crucial factor for valid compositionality measurement. In contrast, Cui et al. (2022) created MCWQ through a process including knowledge base migration and question translation through NMT, without full control over target language composition distribution. We aim to remedy this in our paper by using RBMT. Rule-based machine translation. Over decades of development, various methodologies and technologies were introduced for the task of Machine Translation (MT). To roughly categorize the most popular models, we can divide them into pre-neural models and neural-based models. Pre-neural MT (Wu, 1996; Marcu and Wong, 2002; Koehn et al., 2003; Chiang, 2005) typically includes manipulation of syntax and phrases, whereas neural-based MT (Kalchbrenner and Blunsom, 2013; Cho et al., 2014; Vaswani et al., 2017) refers to those employing neural networks. However, oriented to general broad-coverage applications, most models rely on learned statistical estimates, even for the pre-neural models. The desiderata in our work, on the other hand, exclude methods with inherent uncertainty. The most relevant methods were by Wu (1996, 1997) who applied SCFG variants to MT (Chiang, 2006). The SCFG is a generalization of CFG (context-free grammars) generating coupled strings instead of single ones, exploited by preneural MT works for complex syntactic reordering during translation. In this work, we exclude the statistical component and manually build the SCFG transduction according to the synthetic nature of CFQ; we specifically call it "rule-based" instead of "syntax-based" to emphasize this subtle difference. Multilingual benchmarks. Cross-lingual learning has been increasingly researched recently, where popular technologies in NLP are generally adapted for representation learning over multiple languages (Conneau et al., 2020; Xue et al., 2021). Meanwhile, transfer learning is widely leveraged ![2_image_0.png](2_image_0.png) to overcome the data scarcity of low-resource languages (Cui et al., 2019; Hsu et al., 2019). However, cross-lingual benchmarks datasets, against which modeling research is developed, often suffer from "translation artifacts" when created using general machine translation systems (Artetxe et al., 2020; Wintner, 2016). Longpre et al. (2021) proposed MKQA, a large-scale multilingual question answering corpus (yet not for evaluating CG) avoiding this issue, through enormous human efforts. In contrast, Cui et al. (2022) adopted Google Translate to obtain parallel versions for CFQ questions while sacrificing meaning preservation and systematicity. We propose a balance between the two methodologies, with automatic yet controlled translation. In addition, our work further fills the data scarcity gap in cross-lingual semantic parsing, being the first CG benchmark for semantic parsing for Japanese. ## 3 Multilingual Compositional Wikidata Questions (Mcwq) MCWQ (Cui et al., 2022) is the basis of our work. It comprises English questions inherited from CFQ (Keysers et al., 2019) and the translated Hebrew, Chinese and Kannada parallel questions based on Google Cloud Translate, an NMT system. The questions are associated with SPARQL queries against Wikidata, which were migrated from Freebase queries in CFQ. Wikidata is an open knowledge base where each item is allocated a unique, persistent identifier (QID).2 MCWQ and CFQ (and in turn, our proposed MCWQ-R, see §4) share common English questions and associated SPARQL queries. MCWQ introduces distinct multilingual branches, with the same data size across all the branches. Due to the translation method employed in MCWQ, it suffers from detrimental inconsistencies for CG evaluation (see Figures 1 and 3)—mainly due to the unstable mapping from source to target languages performed by NMT models at both the lexical and structural levels. We discuss the consequences with respect to translation quality in §4.3 and model performance in §6. ## 4 **Mcwq-R: A Novel Translated Dataset** As stated in §2, data generation with GT disregards the "control over distribution", which is crucial for CG evaluation (Keysers et al., 2019; Kim and Linzen, 2020). Thus, we propose to diverge from the MCWQ methodology by translating the dataset following novel grammar of the involved language pairs to guarantee controllability during translation. Such controllability ensures that the translations are deterministic and systematic. In this case, generalization is exclusively evaluated with respect to compositionality, avoiding other confounds. We create new instances of MCWQ in Japanese and Chinese, two typologically distant languages from English, sharing one common language (Chinese) with the existing MCWQ. To make comprehensive experimental comparisons between languages, we also use GT to generate Japanese translations (which we also regard as a part of MCWQ in this paper), following the same method as MCWQ. In this section, we describe the proposed MCWQ-R dataset. In §4.1 we describe the pro2https://www.wikidata.org ![3_image_0.png](3_image_0.png) cess of creating the dataset, in §4.2 its statistics, and in §4.3 the automatic and manual assessment of its quality. ## 4.1 Generation Methodology The whole process of the dataset generation is summarized in Figure 2. We proceed by parsing the English questions, building bilingual dictionaries, a source grammar and transduction rules, replacing and reordering constituents, translating lexical units, post-processing and grounding in Wikidata. Grammar-based transduction. We base our method on Universal Rule-Based Machine Translation (URBANS; Nguyen, 2021), an open-source toolkit3supporting deterministic rule-based translation with a bilingual dictionary and grammar rule transduction, based on NLTK (Bird and Loper, 2004). We modify it to a framework supporting synchronous context-free grammar (SCFG; Chiang, 2006) for practical use, since the basic toolkit lacks *links* from non-terminals to terminals preventing the lexical multi-mapping. A formally defined SCFG variant is symmetrical regarding both languages (Wu, 1997), while we implement a simplified yet functionally identical version only for one-way transduction. Our formal grammar framework consists of three modules: a set of **source** grammar rules converting English sentences to parse trees, the associated **transduction rules** hierarchically reordering the grammar constituents with tree manipulation and a **tagged dictionary** mapping tokens into the target language based on their part-of-speech (POS) tags. The *tagged* dictionary here provides *links* between the non-terminals and terminals defined in a general CFG (Williams et al., 2016). Context information of higher syntactical levels is encapsulated in the POS tags and triggers different mappings to the target terms via the links. This mechanism enables our constructed grammar to largely address complex linguistic differences (polysemy and inflection for instance) as a general SCFG does. We construct the source grammar as well as associated transduction rules and dictionaries, resulting in two sets of transduction grammars for Japanese and Chinese respectively. Source grammar. The synthetic nature of CFQ (Keysers et al., 2019) indicates that it has limited sentence patterns and barely causes ambiguities; Goodwin et al. (2022) leverage this feature and construct a full coverage CFG for the CFQ language, which provides us with a basis of source grammar. We revise this monolingual CFG to satisfy the necessity for translation with an "extensive" strategy, deriving new tags for constituents at the lowest syntactic level where the context accounts for multiple possible lexical mappings. Bridging linguistic divergences. The linguistic differences are substantial between the source language and the target languages in our instances. The synthetic utterances in CFQ are generally cultural-invariant and not entailed with specific language style, therefore the problems here are primarily ascribed to the grammatical differences and lexical gaps. For the former, our grammar performs systematic transduction on the syntactical structures; for the latter, we adopt a pattern match-substitution strategy as post-processing for the lexical units applied in a different manner from the others in the target languages. We describe concrete examples in Appendix A. Without the confound of probability, the systematic transductions simply *bridge* the linguistic gaps without further ex- Question Paired Questions Patterns Patterns EN (MCWQ) 124,187 105,461 105,461 GT JA 124,187 99,900 100,140 (MCWQ) ZH 124,187 99,747 100,325 RBMT JA 124,187 98,431 98,431 (MCWQ-R) ZH 124,187 101,333 101,342 tension, i.e., no novel primitives and compositions are generated while the existing ones are faithfully maintained to the largest extent in this framework. Grounding in Wikidata. Following CFQ and MCWQ, we ground the translated questions in Wikidata through their coupled SPARQL queries. Each *entity* in the knowledge base possesses the unique QID and multilingual labels, meaning that numerous entities can be treated as simplified mod entities (see Figure 3.) during translation, i.e., the grammar translates the *question patterns* instead of concrete questions. The shared SPARQL queries enable comparative study with MCWQ and potentially CFQ (our grammar fully covers CFQ questions) in both cross-lingual and monolingual domains. In addition, the SPARQL queries are unified as reversible intermediate representation (RIR; Herzig et al., 2021) in our dataset and for all experimental settings, which is shown to improve CG. ## 4.2 Dataset Statistics Due to the shared source data, the statistics of MCWQ-R are largely kept consistent with MCWQ. Specifically, the two datasets have the same amounts of *unique questions* (UQ; 124,187), unique queries (101,856, 82% of UQ) and query patterns (86,353, 69.5% of UQ). A substantial aspect nonetheless disregarded was the languagespecific statistics, especially those regarding *question patterns*. As shown in Table 1, for both MCWQ and MCWQ-R, we observe a decrease in question patterns in translations compared with English and the corresponding pairs coupled with SPARQL queries, i.e., question-query pairs. This indicates that the patterns are partially collapsed in the target languages with both methodologies. Furthermore, as the SPARQL queries are invariant logical representations underlying the semantics, the QA pairs are supposed to be consistent with the question patterns even if collapsed. However, we notice a significant inconsistency (∆JA = 240; ∆ZH = 578) between the two items in MCWQ while there are few differences (∆JA = 0; ∆ZH = 9) in MCWQ-R. This further implicates a resultant disconnection between the translated questions and corresponding semantic representations with NMT. We expect our grammar to be fully deterministic over the dataset, nonetheless, it fails to disambiguate a small proportion (322; 0.31%) of English utterance patterns that are *amphibologies* (grammatically ambiguous) and requires reasoning beyond the scope of grammar. We let the model randomly assign a candidate translation for these. ## 4.3 Translation Quality Assessment Following Cui et al. (2022), we comprehensively assess the translation quality of MCWQ-R and the GT counterpart based on the *test-intersection* set (the intersection of the test sets of all splits) samples. While translation quality is a general concept, in this case, we focus on how appropriately the translation trades off fluency and faithfulness to the principle of compositionality. Reference-based assessment. We manually translate 155 samples from the *test-intersection* set in a faithful yet *rigid* manner as gold standard before the grammar construction. We calculate BLEU (Papineni et al., 2002) scores of the machine-translated questions against the gold set with sacreBLEU (Post, 2018), shown in Table 2. Our RBMT reached 97.1 BLEU for Japanese and 94.4 for Chinese, indicating a nearly perfect translation as expected. While RBMT could ideally reach a full score, the loss here is mainly caused by samples lacking context information (agnostic of entity | Language | Reference | Manual | | | | |------------|-------------|----------|------|-------------|--------| | & Method | BLEU | avgMP | avgF | P(MP,F ≥ 3) | | | JA | RBMT | 97.1 | 4.8 | 4.0 | 100.0% | | GT | 45.1 | 3.7 | 4.1 | 71.4% | | | ZH | RBMT | 94.4 | 4.9 | 4.2 | 100.0% | | GT | 47.2 | 3.6 | 4.2 | 71.4% | | for instance). In addition, we observe that GT obtained fairly poor performance with 45.1 BLEU for Japanese, which is significantly lower than the other branches in MCWQ (87.4, 76.6, and 82.8 for Hebrew, Kannada, and Chinese, respectively; Cui et al., 2022). The main reason for this gap is the different manner in which we translated the gold standard: the human translators in MCWQ took a looser approach. Manual assessment. We manually assess the translations of 42 samples (for each structural complexity level defined by Keysers et al., 2019) in terms of *meaning preservation* (MP) and *fluency* (F) with a rating scale of 1–5. As shown in Table 2, our translations have significantly better MP than GT, which is exhibited by the average scores (1.1 and 1.3 higher in avgMP for Japanese and Chinese, respectively). However, the methods obtain similar fluency scores, indicating that both suffer from unnatural translations, partially because of the unnaturalness of original English questions (Cui et al., 2022). RBMT produces only few translations with significant grammar errors and semantic distortions, while GT results in 28.6% of unacceptable translations in this respect. Such errors occur on similar samples for the two languages, suggesting a systematicity in GT failure. We include details of manual assessment in Appendix B. ## 5 Experiments While extensive experiments have been conducted on both the monolingual English (Keysers et al., 2019) and the GT-based multilingual benchmarks (Cui et al., 2022), the results fail to demonstrate pure multilingual CG due to noisy translations. Consistent with prior work, we experiment in both monolingual and cross-lingual scenarios. Specifically, we take into consideration both RBMT and GT branches4in the experiments for further comparison. ## 5.1 Within-Language Generalization (Monolingual) Cui et al. (2022) showed consistent ranking among sequence-to-sequence (seq2seq) models for the 4 splits (3 MCD and 1 random splits). We fine-tune and evaluate the pre-trained mT5-small (Xue et al., 2021), which performs well on MCWQ for each 4The GT-Chinese data (and part of the corresponding results) is from MCWQ (released under the CC-BY license). The GT-Japanese is generated following the same pipeline. monolingual dataset. In addition, we train a model using mBART50 (Tang et al., 2020) as a frozen embedder and learned Transformer encoder and decoder, following Liu et al. (2020). We refer to this model as mBART50∗(it is also the base architecture of ZX-Parse; see §5.2). We show the monolingual experiment results in Table 3. The models achieve better average performance on RBMT questions than GT ones. This meets our expectations since the systematically translated questions excluded the noise. On the random split, both RBMT branches are highly consistent with English, while noise in GT data lowers accuracy. However, the comparisons on MCD splits show that RBMT branches are less challenging than English, especially for mT5-small. In §6.1, we show this is due to the "simplifying" effect of translation on composition. Comparisons across languages demonstrate another interesting phenomenon: Japanese and Chinese exhibited an *opposite* relative difficulty on RBMT and GT. It is potentially due to the more extensive grammatical system (widely applied in different realistic scenes) of the Japanese language, while the grammatical systems and language styles are unified in RBMT, the GT tends to infer such diversity which nonetheless belongs to another category (natural language variant; Shaw et al., 2021). | Exact | mT5-small | mBART50∗ | | | |----------|-------------|------------|----------|------| | Match(%) | MCWQ-R | MCWQ | MCWQ-R | MCWQ | | MCDmean | EN | 38.3 | 55.2±1.6 | | | JA | 56.3 | 30.8 | 58.3 | 32.9 | | ZH | 51.1 | 36.3 | 59.9 | 43.6 | | Random | EN | 98.6 | 98.9±0.1 | | | JA | 98.7 | 92.4 | 98.7 | 92.9 | | ZH | 98.4 | 91.8 | 98.8 | 92.8 | ## 5.2 Cross-Lingual Generalization (Zero-Shot) We mentioned the necessity of developing multilingual KBQA systems in §1. Enormous efforts required for model training for every language encourage us to investigate the zero-shot cross-lingual generalization ability of semantic parsers which serve as the KBQA backbone. While similar experiments were conducted by Cui et al. (2022), the adopted pipeline (cross-lingual inference by mT5 fine-tuned on English) exhibited negligible predictive ability for all the results, from which we can hardly draw meaningful conclusions. For our experiments, we retain this as a baseline, and additionally train Zero-shot Cross-lingual Semantic Parser (ZX-Parse), a multi-task seq2seq architecture proposed by Sherborne and Lapata (2022). The architecture consists of mBART50∗ with two auxiliary objectives (question reconstruction and language prediction) and leverages *gradient reversal* (Ganin et al., 2016) to align multilingual representations, which results in a promising improvement in cross-lingual SP. With the proposed architecture, we investigate how the designed cross-lingual parser and its representation alignment component perform on the compositional data. Specifically, we experiment with both the full ZX-Parse and with mBART50∗, its logical-form-only version (without auxiliary objectives). For the auxiliary objectives, we use bitext from MKQA (Longpre et al., 2021) as supportive data. See Appendix C for details. Table 4 shows our experimental results. mT5small fine-tuned on English fails to generate correct SPARQL queries. ZX-Parse, with a frozen mBART50 encoder and learned decoder, demonstrates moderate predictive ability. Surprisingly, while the logical-form-only (mBART50∗) architecture achieves fairly good performance both within English and cross-lingually, the auxiliary objectives cause a dramatic decrease in performance. We discuss this in §6.2 ## 6 Discussion 6.1 Monolingual Performance Gap As Table 3 suggests, MCWQ-R is easier than its English and GT counterparts. While we provide evidence that the latter suffers from translation noise, comparison with the former indicates partially degenerate compositionality in our multilingual sets. We ascribe this degeneration to an inherent property of translation, resulting from linguistic differences: as shown in Table 1, question patterns are partially collapsed after mapping to target languages. Train-test overlap. Intuitively, we consider training and test sets of the MCD splits, where no overlap is permitted in English under MCD constraints (the train-test intersection must be empty). Nevertheless, we found such overlaps in Japanese and Chinese due to the collapsed patterns. Summing up over 3 MCD splits, we observe 58 samples for Japanese and 37 for Chinese, and the two groups share similar patterns. Chinese and Japanese grammar inherently fail to (naturally) express specific compositions in English, predominantly the *possessive case*, a main category of compositional building block designed by Keysers et al. (2019). This linguistic divergence results in degeneration in compound divergence between training and test sets, which is intuitively reflected by the pattern overlap. We provide examples in Appendix E.1. Loss of structural variation. Given the demonstration above, we further look at MCWQ and see whether GT could avoid this degeneration. Surprisingly, the GT branches have larger train-test overlaps (108 patterns for Japanese and 144 for Chinese) than RBMT counterparts , among which several samples (45 for Japanese and 55 for Chinese) exhibit the same structural collapse as in RBMT. Importantly, a remaining large proportion of the samples (63 for Japanese and 89 for Chinese) possess different SPARQL representations for training and test respectively. In addition, several ill-formed samples are observed in this intersection. The observations above provide evidence that the structural collapse is due to *inherent* linguistic differences and thus generally exists in translationbased methods, resulting in compositional degeneration in multilingual benchmarks. For GT branches, the noise involving semantic and grammatical distortion dominates over the degeneration, and thus causes worse model performance. Implications. While linguistic differences account for the performance gaps, we argue that monolingual performance in CG cannot be fairly compared across languages with translated benchmarks. While "translationese" occurs in translated datasets for other tasks too (Riley et al., 2020; Bizzoni and Lapshinova-Koltunski, 2021; Vanmassenhove et al., 2021), it is particularly significant here. ## 6.2 Cross-Lingual Generalization PLM comparison. mT5 fine-tuned on English fails to generalize cross-lingually (Table 4). ZXParse, based on mBART50, achieved fair perfor- | Exact | mT5-small | mBART50∗ | ZX-Parse | | | | | |----------|-------------|------------|------------|----------|----------|----------|----------| | Match(%) | MCWQ-R | MCWQ | MCWQ-R | MCWQ | MCWQ-R | MCWQ | | | EN | 38.3 | 55.2±1.6 | 23.9±3.4 | | | | | | JA | 0.10 | 0.14 | 35.4±2.1 | 24.6±2.8 | 8.8±1.8 | 8.5±1.5 | | | MCDmean | ZH | 0.12 | 0.18 | 37.7±1.8 | 35.0±2.2 | 9.3±2.0 | 9.1±1.7 | | EN | 98.6 | 98.9±0.1 | 75.9±9.1 | | | | | | JA | 0.9 | 0.9 | 58.0±0.8 | 34.4±3.1 | 27.2±2.1 | 23.1±1.9 | | | Random | ZH | 1.4 | 1.1 | 58.2±1.4 | 43.7±1.3 | 29.4±3.4 | 24.8±3.5 | ![7_image_0.png](7_image_0.png) mance. A potential reason is that mT5 (especially small and base models) tends to make "accidental translation" errors in zero-shot generalization (Xue et al., 2021), while the representation learned by mBART enables effective unsupervised translation via language transfer (Liu et al., 2020). Another surprising observation is that mBART50∗ outperforms the fine-tuned mT5-small on monolingual English (55.2% for MCDmean) with less training. We present additional results regarding PLM finetuning in Appendix D.2. Hallucination in parsing. mT5 tends to output partially correct SPARQL queries due to its drawback in zero-shot generative scenarios. From manual inspection, we note a common pattern in these errors that can be categorized as *hallucinations* (Ji et al., 2023; Guerreiro et al., 2023). As Table 5 suggests, the hallucinations with country entities occur in most wrong predictions, and exhibit a *language bias* akin to that Kassner et al. (2021) found in mBERT (Devlin et al., 2019), i.e., mT5 tends to predict the country of origin associated with the input language in the hallucinations, as demonstrated in Table 6. Experiments in Appendix D.2 indicate that the bias is potentially encoded in the pre-trained decoders. | Halluc.(%) | MCDmean | Random | | | | | | |--------------|-----------|----------|------|------|------|------|----| | W/ country | ZH | JA | EN | ZH | JA | EN | | | Q148 | CN | 71.0 | 0 | 0 | 60.6 | 0 | 0 | | Q17 | JP | 0.1 | 76.1 | 0 | 0.1 | 63.3 | 0 | | Others | 4.2 | 1.8 | 0.45 | 3.8 | 0.9 | 0 | | | Total | 75.2 | 77.9 | 0.45 | 64.4 | 64.2 | 0 | | Representation alignment. The auxiliary objectives in ZX-Parse are shown to improve the SP performance on MultiATIS++ (Xu et al., 2020) and Overnight (Wang et al., 2015). However, it leads to dramatic performance decreases on all MCWQ and MCWQ-R splits. We include analysis in Appendix E.2, demonstrating the moderate effect of the alignment mechanism here, which nevertheless should reduce the cross-lingual transfer penalty. We thus ascribe this gap to the natural utterances from MKQA used for alignment resulting in less effective representations for compositional utterances, and hence the architecture fails to bring further improvement. | Question (EN) | Which actor was M0 's actor | |-----------------|-----------------------------------------------------------------------------------------------------------------------------------| | Question (ZH) | M0的演员是哪个演员 | | Inferred (RIR) | SELECT DISTINCT ?x0 WHERE lb ( M0 ( wdt:P453 ) ( ?x0 ) ) . ( ?x0 ( wdt:P27 ) ( wd:Q148 ) ) rb | | Question (JA) | M0の俳優はどの俳優でしたか | | Inferred (RIR) | SELECT DISTINCT ?x0 WHERE lb ( ?x0 ( wdt:P106 ) ( wd:Q33999 ) ) . ( M0 ( wdt:P108 ) ( ?x0 ) ) . ( ?x0 ( wdt:P27 ) ( wd:Q17 ) ) rb | Cross-lingual difficulty. As illustrated in Figure 4, while accuracies show similar declining trends across languages, cross-lingual accuracies are generally closer to monolingual ones in low complexity levels, which indicates that the cross-lingual transfer is difficult in CG largely due to the failure in universally representing utterances of high compositionality across languages. Specifically, for low complexity samples, we observe test samples that are correctly predicted cross-lingually but wrongly predicted within English. These several samples (376 for Japanese and 395 for Chinese on MCWQR) again entail structural simplification, which further demonstrates that this eases the compositional challenge even in the cross-lingual scenario. We further analyze the accuracies by complexity of MCWQ and ZX-Parse in Appendix E.3. ## 7 Conclusion In this paper, we introduced MCWQ-R, a robustly generated multilingual CG benchmark with a proposed rule-based framework. Through experiments with multilingual data generated with different translation methods, we revealed the substantial impact of linguistic differences and "translationese" on compositionality across languages. Nevertheless, removing of all difficulties but compositionality, the new benchmark remains challenging both monolingually and cross-lingually. Furthermore, we hope our proposed method can facilitate future investigation on multilingual CG benchmark in a controllable manner. ## Limitations Even the premise of parsing questions to Wikidata queries leads to linguistic and cultural bias, as Wikidata is biased towards English-speaking cultures (Amaral et al., 2021). As Cui et al. (2022) argue, speakers of other languages may care about entities and relations that are not represented in Englishcentric data (Liu et al., 2021b; Hershcovich et al., 2022a). For this reason and for the linguistic reasons we demonstrated in this paper, creating CG benchmarks natively in typologically diverse languages is essential for multilingual information access and its evaluation. As we mentioned in §4.2, our translation system fails to deal with ambiguities beyond grammar and thus generates wrong translations for a few samples (less than 0.31%). Moreover, although the dataset can be potentially augmented with low-resource languages and in general other languages through the translation framework, adequate knowledge will be required to expand rules for the specific target languages. With limited computational resources, we are not able to further investigate the impact of parameters and model sizes of multilingual PLM as our preliminary results show significant performance gaps between PLMs. ## Broader Impact A general concern regarding language resource and data collection is the potential (cultural) bias that may occur when annotators lack representativeness. Our released data largely avoid such issue due to the synthetic and cultural-invariant questions based on knowledge base. Assessment by native speakers ensures its grammatical correction. However, we are aware that bias may still exist occasionally. For this purpose, we release the toolkit and grammar used for generation, which allows further investigation and potentially generating branches for other languages, especially low-resource ones. In response to the appeal for greater environmental awareness as highlighted by Hershcovich et al. (2022b), a climate performance model card for mT5-small is reported in Table 7. By providing access to the pre-trained models, we aim to support future endeavors while minimizing the need for redundant training efforts. | mT5-small finetuned | | |------------------------------|---------------| | 1. Model publicly available? | Yes | | 2. Time to train final model | 21 hours | | 3. Time for all experiments | 23 hours | | 4. Energy consumption | 0.28kW | | 5. Location for computations | Denmark | | 6. Energy mix at location | 191gCO2eq/kWh | | 7. CO2eq for final model | 4.48 kg | | 8. CO2eq for all experiments | 4.92 kg | Table 7: Climate performance model card for mT5small fine-tuned on MCWQ/MCWQ-R. "Time to train final model" corresponds to the training time for a single model of one split and one language, while the remaining models have similar resource consumption. ## Acknowledgements We thank the anonymous reviewers for their valuable feedback. 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End-to-end slot alignment and recognition for crosslingual NLU. In *Proceedings of the 2020 Conference* on Empirical Methods in Natural Language Processing (EMNLP), pages 5052–5063, Online. Association for Computational Linguistics. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 483–498, Online. Association for Computational Linguistics. ## A Transduction Grammar Examples Inflection in Japanese. We provide a concrete example regarding the linguistic divergences during translation and how our transduction grammar (SCFG) address it. We take Japanese, specifically its verbal *inflection* case as an example. ## Grammar VP → ⟨V NP, NP V⟩ V → ⟨VT andV, VT andV⟩ andV → ⟨and V, ε V⟩ NP → ⟨a film, 映画⟩ V → {⟨edit , 編集します⟩, ⟨write , 書きます⟩} VT → {⟨edit , 編集し⟩, ⟨write , 書き⟩} $$(1)$$ ## Generated String ⟨write and edit a film, 映画を書き編集します⟩ ⟨edit and write a film, 映画を編集し書きます⟩ * [16] A. A. K. In the string pair of (2), the Japanese verbal inflection is reasoned from its position in a sequence where correspondences are highlighted with different colors. To make it more intuitive, consider a phrase (out of the corpus) "*run and run*" with repeated verb "run" and its Japanese translation " hashi 走 riり、 hashi 走 riり maま suす", where the repeated " hashi 走 riり"(which should belong to V if in (1)) refers to a category of verb base, namely *conjunctive* indicating that it could be potentially followed by other verbs5; and the inflectional suffix " maま suす" indicting the end of the sentence. Briefly speaking, in the Japanese grammar, the last verb in a sequence have a different form from the previous ones depending on the formality level. In this case, the transduction rule of the lowest syntactic level explaining this inflection is V → ⟨VT andV, VT andV⟩, therefore the VT with *suffix* T is derived from V (V exhibit no inflection regarding ordering in English) from this level and carries this context information down to the terminals. Considering questions with deep parse trees where such context information should potentially be carried through multiple part-of-speech symbols in the top-down process, we let the *suffix* be inheritable as demonstrated in (3). $$\begin{array}{l}{\mathrm{VP}\,\to\,\langle\mathrm{VPT~andVP,~VPT~andVP}\rangle}}\\ {\mathrm{VPT~\to\langle\mathrm{VT~NP,~NP~VT}\rangle}}\end{array}\tag{3}$$ where suffix T carries the commitment of inflection to be performed at the non-terminal level and is explained by context of VPT and inherited by VT. While such suffix is commonly used in formal grammar, we leverage this mechanism to a large extent to fill the linguistic gap. The strategy is proved to be simple yet effective in practical grammar construction to handle most of the problems caused by linguistic differences such as inflection as mentioned. ## B Translation Assessment Details Since manual assessment is subjective, the guidelines were stated before assessment: translations resulting in changed expected answer domains are rated 1 or 2 for *meaning preservation*. Those with 5Formally, the conjunctive in Japanese involves 2 forms: chushi-form and te-form, to keep consistent with the English questions (where temporal ordering is not entailed by coordination), we adopt the former form in our grammar since it indicates weaker temporal ordering than the latter (Saegusa, 2006). ![13_image_0.png](13_image_0.png) ``` major grammar errors are rated 1 or 2 for fluency. Accordingly, we regard questions with a score ≥ 3 as acceptable in the corresponding aspect. To make an intuitive comparison, we divide the 42 complexity levels (for each level we sampled 1 sentence) into 14 coarser levels and see the variation of the scores of 2 methods against the increasing complexity. As shown in Figure 5, Our method exhibits uniformly good meaning preservation ability while GT suffers from semantic distortion for certain cases and especially for those of high complexity. For the variation of fluency, the steady performance of our method indicates that the loss is primarily systematic and due to compromise for compositional consistency and parallel principle, while GT generates uncontrollable results with incorrect grammar (and thus illogical) occasionally. We present imprecise translation example of our method. Adjective indicating nationalities such as "American" is naturally adapted to " a ア me メ ri リ ka カ jin 人(American person)" when modifying a person in Japanese; then for a sample (note that entities are bracketed): Input:"Was [Kate Bush] British" Output:"[Kate Bush] wa は i イ gi ギ ri リ su ス no の de で shi し ta た ka か" Expected:"[Kate Bush] wa は i イ gi ギ ri リ su ス jin 人 de で shi し ta た ka か" Consider the bracketed entity [Kate Bush] which is invisible during translation, and also the fact that the sentence still holds if it is alternated with nonhuman entities. Without the contribution of the entity semantics, the grammar is unable to specify " jin 人(person)" in this case, and results in a less natural expression. We observed a few samples similar to this leading to the error in BLEU scores. For GT, as we mentioned in §4.3, it causes semantic distortions potentially changing expected answers: Input:"What did [human] found" Output (GT):"[human]waは nani 何 wo を mi 見 tsu つ ke け ma ま shi し ta た ka か" Expected (&Ours):"[human] ga が so 創 setsu 設 shi し ta た no の wa は nan 何 de で su す ka か" Disregarding the sentence patterns, the output of GT distorted the meaning as "What did [human] find", translated back to English. Input:"Was a prequel of [Batman: Arkham Knight] 's prequel..." Output (GT):"[Batman: Arkham Knight] no の zen 前 jitsu 日 tan 譚..." Expected (&Ours):"[Batman: Arkham Knight] no の zen 前 jitsu 日 tan 譚 no の zen 前 jitsu 日 tan 譚..." The example above shows how the 2 methods deal with a compositional phrase occurring in the dataset. GT exhibits reasoning ability which understood that "a prequel of a prequel" indicates "a prequel" thus translating it as " zen 前 jitsu 日 tan 譚(prequel)", whereas an expected compositionally faithful translation should be " zen 前 jitsu 日 tan 譚 no の zen 前 jitsu 日 tan 譚(a prequel of a prequel)". The examples demonstrate how GT as a neural model fails in accommodating compositionality even for the well-formed translations: the infinite compositional expression potentially reaches the "fringe area" of the trained neural model distribution, i.e., it overly concerns the possibility that the sentence occurs instead of keeping faithful regarding the atoms and their compositions. ``` ## C Training Details mT5-small. We follow the same setup of mT5small as in (Cui et al., 2022) with default hyperparameters but a learning rate of 5e−4, which is believed to help overcome the local minimum. Each model was trained on 4 Titan RTX GPUs with a batch size of 16. The total training time is 234 hours for 12 models (4 splits for GT-Japanese, RBMT-Chinese and RBMT-Japanese respectively). mBART50 and ZX-Parse. We follow the searched optimal architecture and parameters6 by Sherborne and Lapata (2022). The logical-formonly mBART50∗comprises frozen mBART50large embedder, 1-layer encoder, and 6-layer decoder, and the full ZX-Parse with additional alignment components: 6-layer decoder (reconstruction) and 2-layer feed-forward networks (language 6Specifically the configuration provided in https:// github.com/tomsherborne/zx-parse prediction) trained with bi-text that we extract from MKQA. The auxiliary components in ZXParse make the encoder align latent representations across languages. Each model was trained on 1 Titan RTX GPU with a batch size of 2. It takes around 17 hours to train a full ZX-Parse and 14 hours an mBART50∗ model. ## D Additional Results D.1 Mcd Splits The exact match accuracies on the 3 maximum compound divergence (MCD) splits (Keysers et al., 2019) are shown in Table 8. ## D.2 Mt5∗ In additional experiments, we freeze the mT5 encoders and train randomly initialized layers as mBART50∗ on English. The cross-lingual generalization results are shown in Table 9. While training decoder from scratch seemingly slightly ease crosslingual transfer as also stated by Sherborne and Lapata (2022), the monolingual performance of mT5-small drops without pre-trained decoder. The results of mT5-large is consistent with Qiu et al. (2022) which shows that increasing model size brings moderate improvement. However, the performance is still not comparable with mBART50∗, indicating that training paradigm does not fully account for the performance gap in Table 4. While mT5 still struggle in zero-shot generation, the systematic hallucinations of country of origin mentioned in §6.2 disappear in this setup, due to the absence of pre-trained decoders which potentially encode the language bias. | Exact | mT5-small∗ | mT5-large∗ | | | |----------|--------------|--------------|--------|------| | Match(%) | MCWQ-R | MCWQ | MCWQ-R | MCWQ | | MCDmean | EN | 25.9 | 28.0 | | | JA | 1.0 | 1.1 | 4.0 | 3.6 | | ZH | 1.2 | 1.0 | 4.2 | 2.7 | | Random | EN | 96.3 | 97.3 | | | JA | 6.3 | 4.3 | 11.3 | 6.7 | | ZH | 5.5 | 4.9 | 13.7 | 10.6 | ## E Supplementary Analysis E.1 Structural Simplification The train-test overlaps intuitively reflect the structural simplification, we show the numbers by structural cases and concrete examples in Table 10. ## E.2 Representation Alignment In Zx-Parse We analyze the representations before and after the trained aligning layer with t-SNE visualization as Sherborne and Lapata (2022) do. Figure 6 illustrates an example, the representations of compositional utterances (especially English) are distinct from natural utterances from MKQA, even after alignment, which demonstrates the domain gap between the 2 categories of data. Nonetheless, the mechanism performs as intended to align representations across languages. ![14_image_0.png](14_image_0.png) ## E.3 Accuracy By Complexity We present the accuracy by complexity on MCWQ in Figure 7. We notice the gaps between monolingual and cross-lingual generalization are generally smaller than on MCWQ-R (see Figure 4). This is ascribed to the systematicity of GT errors—such (partially) systematical errors are fitted by models in monolingual training, and thus cause falsely higher performance on the test samples possessing similar errors. Figure 8 shows the cross-lingual results of ZXParse on both datasets. While the accuracies are averagely lowered, the curves appear to be more aligned due to the mechanism. | Exact | mT5-small | mBART50∗ | ZX-Parse | | | | | |----------------------------------------------------------|-------------|------------|------------|----------|----------|----------|----------| | Match(%) | MCWQ-R | MCWQ | MCWQ-R | MCWQ | MCWQ-R | MCWQ | | | Within-language (Supplement to Table 3). EN 77.6 | 75.4±0.7 | 35.8±4.4 | | | | | | | MCD1 | JA | 75.7 | 43.6 | 78.4 | 47.6 | - | - | | ZH | 74.7 | 52.8 | 74.0 | 48.1 | - | - | | | EN | 13 | 35.9±0.7 | 13.1±3.4 | | | | | | MCD2 | JA | 32.2 | 18.1 | 30.9 | 18.5 | - | - | | ZH | 31.5 | 21.1 | 38.7 | 34.3 | - | - | | | EN | 24.3 | 54.4±3.5 | 22.8±2.5 | | | | | | MCD3 | JA | 61.0 | 30.8 | 65.8 | 32.7 | - | - | | ZH | 47.2 | 34.9 | 67.1 | 48.3 | - | - | | | Cross-lingual (Supplement to Table 4). MCD1 JA 0.06 0.15 | 42.6±1.7 | 28.8±4.8 | 9.5±3.5 | 10.2±2.2 | | | | | ZH | 0.08 | 0.08 | 43.0±1.0 | 41.7±0.9 | 9.3±3.6 | 10.7±2.1 | | | MCD2 | JA | 0.07 | 0.08 | 24.5±1.6 | 18.8±0.9 | 5.0±1.0 | 5.1±1.2 | | ZH | 0.08 | 0.07 | 27.0±1.2 | 28.0±2.2 | 5.3±1.7 | 5.5±1.1 | | | MCD3 | JA | 0.18 | 0.20 | 39.0±2.9 | 26.2±2.8 | 11.7±0.8 | 10.2±1.3 | | ZH | 0.20 | 0.40 | 43.2±3.2 | 35.2±3.6 | 13.4±0.7 | 11.1±1.8 | | ![15_image_0.png](15_image_0.png) ![15_image_1.png](15_image_1.png) | EN | JA | TZH | | | | | | | | |------------------------------|-------------------------------------------------------------------|--------------------------------------|----------|---------|-------|-------|----------------------------------------------------------------------------------------|----|-----| | Possessive Case (Train/Test) | 0 / 49 | 49 / 0 | 49 / 49 | 27 / 27 | | | | | | | SPARQL | ( ?x0 ( wdt:P40|wdt:P355 ) ( ?x1 ) ) | . ( ?x1 ( wdt:P106 ) ( wd:Q33999 ) ) | | | | | | | | | NP | | | | | | | | | | | ParseTree | role | 2. | | | | | | | | | 100 Mark | . | parent | a poront | of | HP | NP | 1. See also 1999 births Libyan programmes with the United States for the United Stat | a. | ( ) | | actac | accor | t fr | | | | | | | | | Preposition in Passive | 0/7 | 7/0 | 7/7 | 7/7 | | | | | | | SPARQL | ( ?x0 ( wdt:P750 , wdt:P162|wdt:P272 ) ( ?x1 ) ) | Vrep | | | | | | | | | ParseTree | # | | | | | | | | | | 79 | intern | 811 | | | | | | | | | Interrogative Pronoun | 0/4 | 4/0 | 2/2 | 4 / 4 | | | | | | | SPARQL | SELECT DISTINCT ?x0 WHERE lb ( ?x0 ( wdt:P106 ) ( wd:Q36834 ) ) . | | | | | | | | | | ParseTree | NE | mmm | 10. | nn | 10. | | | | | | compo | CONTROL | compan | which | The K | which | The K | | | | | Whish | I International | 1. Se | | | | | | | | ## Acl 2023 Responsible Nlp Checklist A For Every Submission: ✓ A1. Did you describe the limitations of your work? The Limitations section follows the Conclusion section. ✗ A2. Did you discuss any potential risks of your work? Our work only provides a benchmark to evaluate semantic parsing models and not an application that can be used for potentially risky purposes. ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Section 1 (Introduction). ✓ A4. Have you used AI writing assistants when working on this paper? ChatGPT was used for confirming that some concepts are properly described in the paper (specifically, for appendix A). Hence no specific content in the paper is created by the writing assistants. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 3, 4 (Created Mcwq-R), 5 ✓ B1. Did you cite the creators of artifacts you used? 3 (MCWQ), 4 (URBANS), 5 (mT5, mBART, ZX-PARSE) ✓ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? MCWQ is released under the CC-BY license. URBANS is released under the Apache 2.0 license. ✓ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 1 7 (the introduction and conclusion specified our intended use of MCWQ-R and the toolkit used to generate the dataset) B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. ✓ B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? 4 ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4 The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ## C ✓ **Did You Run Computational Experiments?** 5 ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? appendix ✗ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Experimental setup was reported while no hyperparameter search was conducted since our main contribution is the proposed benchmark ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 5 ✓ C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 4 ## D ✓ **Did You Use Human Annotators (E.G., Crowdworkers) Or Research With Human Participants?** 4 ✓ D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? 4, appendix ✗ D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? The annotators are the authors D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

li-etal-2023-faa

{FAA}: Fine-grained Attention Alignment for Cascade Document Ranking

https://aclanthology.org/2023.acl-long.94

Document ranking aims at sorting a collection of documents with their relevance to a query. Contemporary methods explore more efficient transformers or divide long documents into passages to handle the long input. However, intensive query-irrelevant content may lead to harmful distraction and high query latency. Some recent works further propose cascade document ranking models that extract relevant passages with an efficient selector before ranking, however, their selection and ranking modules are almost independently optimized and deployed, leading to selecting error reinforcement and sub-optimal performance. In fact, the document ranker can provide fine-grained supervision to make the selector more generalizable and compatible, and the selector built upon a different structure can offer a distinct perspective to assist in document ranking. Inspired by this, we propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. Specifically, we utilize the attention activations over the passages from the ranker as fine-grained attention feedback to optimize the selector. Meanwhile, we fuse the relevance scores from the passage selector into the ranker to assist in calculating the cooperative matching representation. Experiments on MS MARCO and TREC DL demonstrate the effectiveness of our method.

# Faa: Fine-Grained Attention Alignment For Cascade Document Ranking Zhen Li1, Chongyang Tao2, Jiazhan Feng1, Tao Shen3**, Dongyan Zhao**1,4∗ Xiubo Geng2, **Daxin Jiang**2∗ 1Wangxuan Institute of Computer Technology, Peking University 2Microsoft Corporation 3FEIT, University of Technology Sydney 4State Key Laboratory of Media Convergence Production Technology and Systems 1{lizhen63,fengjiazhan,zhaody}@pku.edu.cn 2{chotao,xigeng,djiang}@microsoft.com [email protected] ## Abstract Document ranking aims at sorting a collection of documents with their relevance to a query. Contemporary methods explore more efficient transformers or divide long documents into passages to handle the long input. However, intensive query-irrelevant content may lead to harmful distraction and high query latency. Some recent works further propose cascade document ranking models that extract relevant passages with an efficient selector before ranking, however, their selection and ranking modules are almost independently optimized and deployed, leading to selecting error reinforcement and sub-optimal performance. In fact, the document ranker can provide fine-grained supervision to make the selector more generalizable and compatible, and the selector built upon a different structure can offer a distinct perspective to assist in document ranking. Inspired by this, we propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. Specifically, we utilize the attention activations over the passages from the ranker as fine-grained attention feedback to optimize the selector. Meanwhile, we fuse the relevance scores from the passage selector into the ranker to assist in calculating the cooperative matching representation. Experiments on MS MARCO and TREC DL demonstrate the effectiveness of our method. ## 1 Introduction Document ranking aims at ranking the candidate documents according to their relevance to an input query, and it has been widely applied in many natural language processing (NLP) and information retrieval tasks, such as search engines (Hofstätter et al., 2021) and question answering (Chen and Yih, 2020). Due to the powerful representation ability of large-scale pre-trained language models (PLMs) (e.g., BERT (Devlin et al., 2019) and ![0_image_0.png](0_image_0.png) Figure 1: The case of scope hypothesis. In this example, p2 is strongly relevant to the query, and p3 is weakly relevant where other passages focus on other topics different from query. RoBERTa (Liu et al., 2019)) that have achieved impressive performance in a large number of NLP tasks, several researchers have considered making use of pre-trained models for document ranking (MacAvaney et al., 2019; Li and Gaussier, 2021; Fu et al., 2022). One major challenge in applying PLMs for neural document ranking is their difficulty in handling long texts due to high computational complexity and memory requirements, such as the 512 token limit for BERT. In fact, documents typically contain long text, for example, the mean length of documents in 2019 TREC Deep Learning Track Document Collection is 1600 (Hofstätter et al., 2021). To address this issue, various studies have been conducted to develop more efficient attention mechanisms in transformers (Beltagy et al., 2020; Hofstätter et al., 2020a), by simply truncating the document to meet the requirement for the relevance model (Boytsov et al., 2022), or by breaking down the long document into smaller segments or passages that can be processed individually by the pre-trained models (Dai and Callan, 2019; Rudra and Anand, 2020; Li et al., 2020; Chen et al., 2022). Actually, long documents often contain a variety of subjects, as evidenced by the scope hypothesis (Robertson et al., 2009) from traditional information retrieval. An illustration from the MS MARCO dataset (Nguyen et al., 2016) is presented in Figure 1, and it is noted that only a small part of the document (e.g., p2 and p3) is relevant to the given query and different parts may be unequally informative to the query. Thus even though existing techniques for modeling long documents have been demonstrated to be effective and efficient, utilizing the entire document can result in high query latency and intensive query-irrelevant content can be a distraction and negatively impact performance. Consequently, some recent studies propose cascade document ranking models (Li et al., 2020; Hofstätter et al., 2021; Li and Gaussier, 2021) that extract relevant passages with an efficient selector before performing the ranking. However, their selection and ranking modules are almost independently optimized and deployed, leading to selecting error reinforcement and sub-optimal performance. Moreover, these models do not differentiate between the passages or segments taken from a document while matching with the query. In fact, the document ranker can provide finegrained supervision to enhance the generalizability and compatibility of the selector. Conversely, the selector, built upon a heterogeneous structure, can offer a distinct perspective to assist in document ranking. Taking inspiration from this, we propose a Fine-grained Attention Alignment approach (FAA) to jointly optimize a cascade document ranking model. Specifically, we initialize the passage selector as an efficient dual encoder and the document ranker with an effective crossencoder. To better optimize and make use of both worlds, we leverage the attention activations over the passages from the ranker as fine-grained attention feedback to optimize the selector. Simultaneously, we incorporate the relevance scores from the passage selector into the ranker to assist in calculating the final cooperative matching representation. We conduct experiments on three public benchmarks: MS MARCO (Nguyen et al., 2016), TREC-DL 2019 (Craswell et al., 2020), and TRECDL 2020. The evaluation results show that our proposed model is better than several competitive baselines and our FAA can bring significant improvement to the cascade model. To sum up, our contribution is three-fold: Alignment approach to jointly optimize a cascade document ranking model. - We explore fusing the passage-level relevance scores into the document ranker to produce the cooperative matching representation. - We conduct extensive experiments and analysis on three benchmarks and the evaluation results show the effectiveness of our model. ## 2 Related Works Neural models for document ranking In the early stages, traditional algorithms like BM25 (Robertson et al., 2009) and TF-IDF were commonly employed for ranking documents in information retrieval. With the development of neural network technology (Cho et al., 2014; Gu et al., 2018), some neural-based ranking models have been proposed (Huang et al., 2013; Guo et al., 2016; Hui et al., 2017, 2018; MacAvaney et al., 2020). Xiong et al. (2017) proposed a kernelbased neural ranking model (K-NRM) which used a kernel-pooling layer to combine word pair similarities with distributed representations. Dai et al. (2018) extended K-NRM to Conv-KNRM which used Convolutional Neural Networks to model ngram embedding. Hofstätter et al. (2020b) proposed a Transformer-Kernel model which used a small number of transformer layers to contextualize query and document sequences independently and distilled the interactions between terms. Compared to traditional methods, neural ranking models produce a dense representation of the queries and documents which improves the ranking performance. Pre-trained models for document ranking Recently a large number of transformer-based pretrained models have been proposed (Devlin et al., 2019; Lewis et al., 2020; Radford et al., 2019; Raffel et al., 2020) and shown their effectiveness in natural language processing tasks. Therefore many works have utilized pre-trained models in document ranking tasks. Nogueira et al. (2019) used a sequence-to-sequence transformer model with document terms as input and produced the possible questions that the document might answer to expand document for document retrieval. Finally this work used BERT to re-rank these retrieved documents. Yan et al. (2019) used a pre-trained BERT model to classify sentences into three categories and then fine-tuned the model using a point-wise ranking method for ranking documents. Passage-level document ranking Since the high demand for memory space and computing resources, pre-trained models usually have a limit on the input length, and the length of actual long documents is always beyond this limitation. To this end, some works proposed to split the long documents into multiple passages which satisfy the limitation of the input length of the pre-trained models (Li et al., 2020; Hofstätter et al., 2020a; Yang et al., 2019). The studies applied pre-trained models to each passage individually and then combined the relevance scores at the passage level to generate the relevance scores for the entire document. For example, Dai and Callan (2019) determined the relevance score of the document by utilizing the score of the first passage, the top-performing passage, and the summation of all passages, respectively. Fu et al. (2022) proposed a Multi-view interpassage Interaction based Ranking model (MIR) with intra-passage attention and inter-passage attention, and used a multi-view aggregation layer to produce the document-level representation across multiple granularities. These works took all passages into document ranking which may introduce noise from the query-irrelevant passages and increase the query latency. To address this problem, some works proposed to pre-select query-relevant passages from all passages before aggregating. In this work, we propose a cooperative distillation and representation cascade ranking model which uses an efficient model as a passage selector to calculate passage-level relevance scores and select top-k passages, while uses an effective model as the document ranker to calculate the document-level relevance scores with the selected passages. ## 3 Methodology In this section we first formalize the document ranking task, then we introduce the model architecture and the proposed Fine-grained Attention Alignment (FAA) approach for model optimization. Task Formalization Given a query q and a set of candidate documents C = {d1, d2*, ..., d*m} including both the ground-truth document and negative documents, where m is the number of the candidate documents, the task is to train a document ranking model R(*q, d*) with the training data D. When provided with a new query and its corresponding candidate documents, the ranking model assesses the relevance between the query and each candidate document by computing relevance scores. Subsequently, it can arrange the documents in order based on these scores. Model Overview Inspired by previous work on passage-level evidence for document ranking (Hofstätter et al., 2021; Li and Gaussier, 2021), in this paper we adopt the efficient and effective cascade document ranking paradigm which first extracts relevant passages with an efficient selector and then performs the ranking with a smart document ranker based on the pruned content. To better optimize and make use of both worlds, we propose a finegrained attention alignment approach to jointly optimize a cascade document ranking model. Specifically, we utilize the attention activations over the passages from the ranker as fine-grained attention feedback to optimize the selector. Additionally, in the process of document ranking, the passage-level relevance scores in the selector are fused in the document ranker to produce the cooperative matching representation for calculating the final matching score. By this means, the document ranker can provide fine-grained supervision to make the selector more generalizable and compatible, and the selector built upon a heterogeneous structure can conversely offer a distinct view to help the ranker. Figure 2 presents the high-level architecture of the proposed method. ## 3.1 Passage Selector To satisfy the input length limit of the pre-trained models, the candidate documents are first split into multiple passages with a sliding window in the size of l words and a stride in the size of s words. The set of passages of document d can be formalized as $$*s+l,\dots\}$$ P = {d0: l, ds: s+l, d2∗s: 2∗s+l*, ...*} (1) In the phase of passage selection, the passage selector identifies and extracts a subset of passages that are highly relevant to the given query. We adopt the simple and efficient dual-encoder structure built on a small pre-trained model as the passages selector which has a lower query latency. Given the query q and the set of passages P = {p1, p2, · · · , pw} where w is the number of passages, q and each pi ∈ P are fed into the passage selector and encoded as d-dimensional vectors respectively which are denoted as Encpsg(q) and Encpsg(pi). With the representative vectors of ![3_image_0.png](3_image_0.png) query and passages, the passage selector calculates the dot product between Encpsg(q) and Encpsg(pi): $${\mathcal{R}}_{\mathrm{psg}}(q,p_{i})={\frac{{\mathsf{E n c}}_{\mathrm{psg}}(q)^{T}{\mathsf{E n c}}_{\mathrm{psg}}(p_{i})}{\sqrt{d}}}$$ The passage-level relevance scores are scaled by dividing by √d. Next, the passage selector selects the k passages with the highest relevance score to form P¯, which is formalized as: $$\bar{\mathbb{P}}=\operatorname*{arg\,max}_{\bar{\mathbb{P}}\subset\mathbb{P},\;\|\bar{\mathbb{P}}\|=k}\sum_{p_{i}\in\bar{\mathbb{P}}}{\mathcal{R}}_{\mathrm{psg}}(q,p_{i})\qquad(3)$$ Passages in P¯ contain informative content for query and are used for document ranking. By selecting the most relevant top-k passages P¯ from all the passages, the passage selector filters out a large number of irrelevant passages for document ranking processes, which can reduce the query latency and avoid the noise caused by irrelevant passages. ## 3.2 Document Ranker We adopt a cross-encoder based on pre-trained models as the document ranker to calculate the document-level relevance score with P¯. The architecture performs full attention across the query and the extracted passages and has been proven to be effective for ranking (Hofstätter et al., 2021). Formally, all selected passages in P¯ are first spliced together as Pˆ = {p¯1; ¯p2; *· · ·* ; ¯pk}, and then we concatenate query and the spliced passages Pˆ as the input of the document ranker with [CLS] and [SEP] tokens, which is denoted as u: $$u=\{[\mathsf{C L S}];q;[\mathsf{S E P}];\mathbb{P};[\mathsf{S E P}]\}$$ The document ranker performs semantic interaction through multi-layer attention blocks and $$(2)$$ outputs a sequence of contextualized representations. Typically, the output representation of the first token [CLS] is adpoted the encoded vector of u, namely Encdoc(u) = E[CLS]. Then the vector is fed to a multilayer perceptron (MLP) to calculate the document-level relevance score: $${\mathcal{R}}_{\mathrm{doc}}(q,d)={\mathsf{M L P}}({\mathsf{E n c}}_{\mathrm{doc}}(u))$$ $\eqref{eq:walpha}$ Since the dataset provides the positive document for each query, the loss function we use to optimize the document ranker is defined below following the previous works (Wu et al., 2018; Oord et al., 2018): $${\mathcal{L}}_{\mathrm{rank}}=-\sum_{q\in{\mathcal{D}}}\log{\frac{\exp({\mathcal{R}}_{d o c}(q,d^{+}))}{\sum_{d\in{\mathcal{C}}}\exp({\mathcal{R}}_{d o c}(q,d))}}\quad(6)$$ where d + is the ground-truth document for the query q and C is a set of document candidates (including both the ground-truth document and negative documents) for q. ## 3.3 Cooperative Matching Representation Considering the passage selector is based on heterogeneous dual-encoder architecture, we think that the selector can offer a distinct view to help document ranking. Therefore, different from traditional encoding which only uses the encoded vector of the first token [CLS] as the representation of sequence, we propose to fuse the selected passagelevel relevance scores from the passage selector to produce the cooperative matching representation Encdoc(u) of input sequence u. Specifically, we denote the embedding vector of [CLS] as E[CLS] and denote the embedding vector of tokens in Pˆ as {E1 1 , E2 1 , · · · , Ej i , *· · · }*, where E j i represents the embedding vector of the j-th token in the i-th selected passage p¯i. To produce Encdoc(u), we first calculate the average embedding vector of each selected passage: $$\mathrm{MeanPool}({\bar{p}}_{i})=\sum_{z=1}^{l}E_{i}^{z}/l$$ /l (7) where l is the length of p¯i. We then calculate the product of the passage-level relevance scores from the selector and the average vector of the passage in the ranker, and take the summation of the results as the passage-selector guided vector EPGV, formalized as: $$E_{\mathrm{PGV}}=\sum_{t=1}^{k}{\mathsf{MeanPool}}({\bar{p}}_{i})\cdot{\mathcal{R}}_{\mathrm{psg}}(q,{\bar{p}}_{i})\quad(8)$$ Finally we fuse the passage-selector guided vector EPGV with E[CLS]to get the cooperative document-level matching representation: $$\mathsf{E n c}_{d o c}(u)=E_{[\mathsf{C L S}]}+\lambda\cdot E_{\mathsf{P G V}}$$ where λ is a parameter to control the weight of EPGV. Then we can feed the above Encdoc(u) into a multi-layer perceptron to calculate the final document-level relevance score, as formalized in Equation 5. We can find that the more relevant a passage is, the greater its proportion in the fusion, which causes the document ranker to pay more attention to it. ## 3.4 Fine-Grained Attention Alignement As mentioned above, the passage selector is initialized by dual-encoder architecture which is efficient but performance sub-optimal compared with cross-encoder. It is not so compatible in ranking model and need to be tuned. Besides, there are no passage-level labels in most document ranking tasks. Inspired by knowledge distillation (Hinton et al., 2015; Wang et al., 2020), we use the complicated and effective document ranker as the teacher model to provide fine-grained supervision for optimizing the passage selector which is regarded as a student model to make the selector more generalizable and compatible. To be specific, with the self-attention mechanism in the transformerbased model, we use fine-grained attention activation scores over the passage as the pseudo labels of passages for optimization. We consider that if one passage is more informative to query, the query will provide more attention to it when document ranking which results in a higher attention score for this passage. For the input u, the representation output by the previous layer is denoted as H ∈ Rlu×d where lu is $$\left(7\right)$$ the length of u. The self-attention module produces queries Q, keys K, and values V matrices through linear transformations (Vaswani et al., 2017), and then the attention map can be calculated as: $$M=\mathrm{softmax}(\frac{Q K^{T}}{\sqrt{d}})\qquad\qquad(10)$$ where d is the dimension of vectors in Q. We denote αi→j = Mi,j as the attention score from i-th token to j-th token in u. Following the calculation of the attention score from one token to another token, we calculate the attention activation score of each selected passage p¯i (∈ P¯) as the maximal attention score from all tokens in query q and all tokens in the p¯i: $$\begin{array}{c}{{\alpha_{q\to\bar{p}_{i}}=\mathrm{MaxPool}(\widetilde{M}),}}\\ {{\widetilde{M}=M_{x:x+l_{q},y:y+l_{p_{i}}}}}\end{array}\tag{11}$$ $$(9)$$ where x, y is the starting token of q and pi, and lq, lpi is the length of q and pi respectively. Mf is the attention map between q and pi, where Mfi,j is the attention score from i-th token in q to j-th token in pi. We also experimented with the meanpooling operation to calculate attention scores and found that it performed worse than max-pooling. Following previous knowledge distillation methods based on pre-trained language models (Wang et al., 2020), we also calculate the attention score of p¯iin the last self-attention layer of the document ranker. Taking into account the multi-head attention mechanism in the transformer-based model, we select the maximal attention score through all attention heads as the final scores. We use KL-divergence between the relevance scores of passages output by the passage selector and the attention scores as the loss function of the passage selector: $$\mathcal{L}_{\mathrm{align}}=\sum_{q\in\mathcal{D}}\mathrm{KL-Div}(\mathcal{H}^{psg}(q,\bar{\mathbb{P}}),\mathcal{A}^{doc}(q,\bar{\mathbb{P}})),\tag{1}$$ $$\bar{\mathbb{P}}))$$ (12) ... where Hpsg(q, P¯) is the output distribution over the relevance scores of passages in P¯ from the selector, Adoc(q, P¯) is the distribution of the aggregated attention scores in ranker. Hpsg(q, p¯k) and Adoc(q, p¯k) are the k-th item in Hpsg and Adoc respectively, which can be calculated as below: $${\mathcal{H}}^{p s g}(q,{\bar{p}}_{k})={\frac{\exp({\mathcal{R}}_{\mathrm{psg}}(q,{\bar{p}}_{k})/\tau)}{\sum_{{\bar{p}}\in{\bar{\mathbb{P}}}}\exp({\mathcal{R}}_{\mathrm{psg}}(q,{\bar{p}})/\tau)}}$$ $$(13)$$ ## Algorithm 1 The Proposed Faa Require: Training set D, selector parameters ϕpsg, ranker parameters ϕdoc Initialize parameters ϕpsg, ϕdoc repeat Sample a batch B from D Compute passage relevance scores by Eq (2) Select top-k relevant passages P¯ by Eq (3) Compute document relevance scores with P¯ Compute Lrank on B and *optimize* ϕdoc Compute attention score Patt(¯pi) by Eq (14) Compute Lalign on B and *optimize* ϕpsg until Convergence Return ϕpsg, ϕdoc $${\mathcal{A}}^{d o c}(q,\bar{p}_{k})={\frac{\exp(\alpha_{q\to\bar{p}_{k}}/\tau)}{\sum_{\bar{p}\in\bar{p}}\exp(\alpha_{q\to\bar{p}}/\tau)}}$$ $$(14)$$ where τ is the temperature hyper-parameter. Above all, in our overall ranking model, the loss function can be described as the combination of the loss for the document ranker and the attention alignment loss: $${\mathcal{L}}_{\mathrm{final}}={\mathcal{L}}_{\mathrm{align}}+{\mathcal{L}}_{\mathrm{rank}}$$ Lfinal = Lalign + Lrank (15) In this work, we tried to jointly train the passage selector and document ranker. Particularly, we update the ranker with only Lrank, and the gradient from Lalign is stopped. Algorithm 1 gives a pseudo-code of our training process. ## 4 Experiments In this section, we first introduce the datasets we use, the evaluation metrics, the baselines, and the implementation details of our experiment. Then we introduce the evaluation results and further analysis of our method. ## 4.1 Datasets And Evaluation In line with previous studies on this task (Hofstätter et al., 2021; Li and Gaussier, 2021), we conduct an evaluation of our proposed model on three publicly available document ranking datasets: MSMARCO (Nguyen et al., 2016), TREC-DL 2019 (Craswell et al., 2020), and TREC-DL 2020. The MS-MARCO dataset comprises 3.2 million documents and 367,013 training queries, sourced from web pages. For evaluation, we utilize the MSMARCO DEV set, which consists of 5,193 queries. The evaluation metrics employed are NDCG@10, MAP, and MRR@10. Both the TREC-DL 2019 and TREC-DL 2020 datasets share the same document collection as MS-MARCO and include 43 and 45 queries, respectively. For both TREC-DL datasets, we employ NDCG@10 and MAP as the evaluation metrics. Across all datasets, we perform document re-ranking based on the top 100 documents retrieved by BM25. ## 4.2 Baselines We compare our model with traditional and neural document ranking models: - **BM25** (Robertson et al., 2009) is a widelyused unsupervised text-retrieval algorithm based on IDF-weighted counting. - **BERT-MaxP** (Dai and Callan, 2019) uses BERT to encode passages split from the document to calculate the relevance score and choose the best passage-level score as the document-level score. - **Sparse-Transformer** (Child et al., 2019) introduces several sparse factorizations of the attention matrix. - **LongFormer-QA** (Beltagy et al., 2020) extends Sparse-Transformer by attaching two global attention tokens to the query and the document as their settings for QA. - **Transformer Kernel Long** (Hofstätter et al., 2020a) proposes a local self-attention mechanism with the kernel-pooling strategy. - **Transformer-XH** (Zhao et al., 2020) introduces an extra hop attention layer that can produce a more global representation of each piece of text. - **QDS-Transformer** (Jiang et al., 2020) proposes a query-directed sparse transformerbased ranking model which uses sparse local attention to obtain high efficiency. - **KeyBLD** (Li and Gaussier, 2021) proposes using local query-block pre-ranking to choose key blocks of a long document and aggregates blocks to form a short document which is further processed by BERT. | Models | MSMARCO DEV | TREC DL 2019 | TREC DL 2020 | | | | | |----------------------------------------------------|---------------|----------------|----------------|-------|---------|-------|-------| | NDCG@10 | MAP | MRR@10 | NDCG@10 | MAP | NDCG@10 | MAP | | | BM25 (Robertson et al., 2009) | 0.311 | 0.265 | 0.252 | 0.488 | 0.234 | - | - | | BERT-MaxP (Dai and Callan, 2019) | - | - | - | 0.642 | 0.257 | 0.630 | 0.420 | | Sparse-Transformer (Child et al., 2019) | - | - | - | 0.634 | 0.257 | - | - | | LongFormer-QA (Beltagy et al., 2020) | - | - | - | 0.627 | 0.255 | - | - | | Transformer Kernel Long (Hofstätter et al., 2020a) | 0.403 | 0.345 | 0.338 | 0.644 | 0.277 | 0.585 | 0.381 | | Transformer-XH (Zhao et al., 2020) | - | - | - | 0.646 | 0.256 | - | - | | QDS-Transformer (Jiang et al., 2020) | - | - | - | 0.667 | 0.278 | - | - | | PARADEMax-Pool (Li et al., 2020) | 0.445 | - | - | 0.679 | 0.287 | 0.613 | 0.420 | | PARADETF (Li et al., 2020) | 0.446 | 0.387 | 0.382 | 0.650 | 0.274 | 0.601 | 0.404 | | KeyBLD (Li and Gaussier, 2021) | - | - | - | 0.707 | 0.281 | 0.618 | 0.415 | | IDCM (Hofstätter et al., 2021) | 0.446 | 0.387 | 0.380 | 0.679 | 0.273 | - | - | | FAA | 0.453 | 0.397 | 0.390 | 0.685 | 0.275 | 0.647 | 0.424 | Table 1: Performance of different methods on the document ranking task in MSMARCO DEV and TREC-DL dataset. The best results are in underlined fonts. - **PARADE** (Li et al., 2020) truncates a long document into multiple passages and uses different strategies to aggregate the passage-level relevance scores. PARADE**Max-Pool** uses maxpooling to obtain document-level relevance scores and **PARADE**TF uses a transformer encoder for passages aggregation. - **IDCM** (Hofstätter et al., 2021) uses a fast model (ESM) for passage selection and a effective model (ETM) for document ranking, where optimizes the ESM with the knowledge distillation from ETM to ESM. ## 4.3 Implementation Details Our proposed model is implemented by the transformer library provided by hugging face1. We use DistilBERT (Sanh et al., 2019) to initialize our passage selector which is more efficient and has comparable performance with BERT-base. For document ranking, we use the publicly trained model2 to initialize our document ranker. We set the length of the sliding window and stride as 72. The query length is set as 30 and the number of selected passages is set as 3. We use Adam optimizer (Kingma and Ba, 2015) to train our model with batch size set as 4. The initial learning rate of the passage selector and document ranker are set as 5e-7 and 7e-6 respectively. We vary λ (Equation (9)) in {0.1, 0.2, 0.5, 1.0} and find that 0.2 is the best choice. τ in Equation (13) and Equation (14) is set as 0.2. ## 4.4 Evaluation Results The evaluation results of our proposed model and all baselines on MS MARCO, TREC-DL 2019 and TREC-DL 2020 are reported in Table 1. First, compared with the models with more efficient attention mechanisms in transformer (e.g. SparseTransformer, Transformer-XH, QDS-Transformer, and Transformer Kernel Long), our method and other cascade document ranking models (e.g. KeyBLD and IDCM) can achieve better performance on almost all metrics. The phenomenon indicates the superiority of the cascade document ranking paradigm. Second, compared with two previous cascade methods3(e.g. IDCM) that select passage before ranking, our model has better performance than them on MS-MARCO and TREC DL 2020, and shows comparable performance on TREC DL 2019. Different from these baselines which optimize the selector and ranker independently, our model jointly optimizes the selector and ranker with fine-grained attention alignment. Meanwhile, we utilize the passage-level relevance scores in document ranking to obtain cooperative fusion representation. The evaluation results demonstrate the effectiveness of our proposed methods. ## 4.5 Discussions Ablation study Table 2 presents the findings from our ablation study, where we systematically remove specific components to assess their impact on performance. Firstly, we eliminate the finegrained attention alignment for the passage selec- | Models | NDCG@10 | MAP | MRR@10 | |-----------------------|-----------|-------|----------| | FAA | 0.453 | 0.397 | 0.390 | | w/o. Lalign | 0.361 | 0.313 | 0.290 | | w/o. EPGV | 0.449 | 0.393 | 0.385 | | w/o. {Lalign & EPGV} | 0.358 | 0.312 | 0.288 | | Rpsg(q, p¯i) = 1/k | 0.449 | 0.394 | 0.386 | | αq→p¯i = MeanPool(Mf) | 0.436 | 0.380 | 0.352 | ![7_image_0.png](7_image_0.png) ![7_image_1.png](7_image_1.png) tor, denoted as "w/o. Lalign". Next, we remove the passage-level multi-vector fusion during document ranking, denoted as "w/o. EPGV". The results reveal that removing either Lalign or EPGV leads to a drop in performance, indicating the effectiveness of our fine-grained attention alignment approach and the importance of utilizing cooperative fusion representation to enhance the ranker's capabilities. Notably, removing both components simultaneously results in an even greater performance decrease. Furthermore, we examine the use of average pooling in representation fusion, denoted as "Rpsg(q, p¯i) = 1/k," which replaces Rpsg in Eq. 8 with 1k . Our findings indicate that simply incorporating average pooling of passage representations does not yield substantial gains, as it only achieves comparable performance to the model without EPGV. Notably, the performance of "Rpsg(q, p¯i) = 1/k" and the model without EPGV are inferior to that of our model, illustrating the utility and superiority of cooperative fusion of relevance scores from selectors over independent representation fusion. Lastly, we explore the use of mean-pooling operation for calculating attention scores and observe that it performs worse than maxpooling. The impact of passage length When constructing the training data, the length of the split passage plays a vital role as it also indirectly controls the | # PSG | NDCG@10 | MAP | MRR@10 | |---------|-----------|-------|----------| | 1 | 0.389 | 0.340 | 0.331 | | 2 | 0.440 | 0.384 | 0.377 | | 3 | 0.453 | 0.397 | 0.390 | | 4 | 0.451 | 0.390 | 0.387 | | Query: how many mm is a nickel coin PID Content | Rank / Rpsg | | | | | | |---------------------------------------------------|------------------------------------------------------------------------------------------------|-----------|------|-----|------------|------------| | 0 | ... Nickel United States Value 0.05 U. S. dollar Mass 5.000 g, Diameter 21.21 mm (0.835 in)... | 1 / 0.954 | | | | | | 2 | ... Its diameter is .835 inches (21.21 mm) and its thickness is .077 inches (1.95 mm)... | 2 / 0.934 | | | | | | 1 | ...War Nickels" (mid-1942 to 1945): 56% copper, 35% silver, 9% manganese Silver 1942 to 1945 Wartime Nickels only... | 3 / 0.759 | | | | | | 11 | ...The | half | dime | was | originally | 20 / 0.468 | 11 ...The half dime was originally struck from 1794 until 1805, though none were dated 1798, 1799, or 1804.... number of passage candidates for each document. To investigate the impact of the passage length, we test the performance of our method across different passage lengths and the results are shown in Figure 3. We can find that the performance of our model improves until the passage length reaches 72, and then drops when the passage length keeps increasing. The reason might be that the selector needs to rank fewer candidates as the passage length increases at first and it could select more accurate passages that are relevant to the query for matching, but when the length of the passage becomes larger enough, the noise will be brought to matching as some content in each passage could be irrelevant to the query. The impact of the number of selected passages We are also curious about the impact of the number of selected passages. We test the performance of our method with different numbers of selected passages and the evaluation results are illustrated in Table 3. We can observe that when the performance of our model was significantly improved as the number of selected passages increased at the beginning (≤ 3), and then began to drop when the number kept increasing. The results are rational because more passage entries can provide more useful information for response matching, but when the passage becomes enough, query-irrelevant noise will be brought to matching. Case study To verify the effectiveness of our cascade model in document ranking, we show a ranker example from MS-MARCO dataset in Table 4. For the input query *how many mm is a nickel coin*, our FAA ranks the positive document at first and it is split into 24 passages. We show the top-3 passages selected by our passage selector and a random passage that is not selected. We can find that the top 2 passages harbor a significant amount of valuable query-relevant information, encompassing terms like "nickel" and "diameter." Conversely, the final passage, which displays lesser relevance to the query, receives a lower relevance score as determined by the passage selector. This case serves as an illustration of our model's proficiency in selecting pertinent content within the document and ranking it based on query relevance. ## 5 Conclusion In this work, we propose FAA, a cascade ranking model with a fine-grained attention alignment and cooperative matching representation. Our model utilizes the fine-grained attention alignment approach to train the passage selector and fuses the passage-level relevance scores into document ranking to produce cooperative matching representation. The evaluation results on MS MARCO and TREC DL demonstrate the effectiveness of FAA. ## 6 Limitations While our approach effectively mitigates query latency through a cascade ranking paradigm, it necessitates additional computational resources during training due to the need for attention score calculation and alignment in the optimization process. Additionally, our model incorporates passage-level relevance scores into the ranker, generating a cooperative matching representation during document ranking, which could marginally augment the inference time. In our future endeavors, we aim to explore more efficient methodologies that can further improve ranking efficiency. Furthermore, it is worth noting that our approach has been tested using specific backbone models. To fully evaluate the effectiveness of our method, it is essential to conduct experiments with a diverse range of backbone models, which remains an avenue for further exploration. ## Acknowledgements We would like to thank the anonymous reviewers for their constructive comments. This work was supported by the National Key Research and Development Program of China (No. 2021YFC3340304). ## Ethical Statement Our paper centers around the document ranking task, a well-established and widely applicable problem. 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The topic of the paper deals only with document retrieval ✓ A3. Do the abstract and introduction summarize the paper's main claims? Abstract and Introduction section ✗ A4. Have you used AI writing assistants when working on this paper? Left blank. ## B ✓ **Did You Use Or Create Scientific Artifacts?** 4 Experiments Section ✓ B1. Did you cite the creators of artifacts you used? 4 Experiments section ✗ B2. Did you discuss the license or terms for use and / or distribution of any artifacts? MS-MARCO and Trec DL are open-source datasets ✗ B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Our use of MS-MARCO and Trec DL was consistent with their intended use. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank. ✓ B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4 Experiments section ## C ✓ **Did You Run Computational Experiments?** 4 Experiments Section ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4 Experiments section The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a *question on AI writing* assistance. ✓ C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4 Experiments section ✓ C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4 Experiments section C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Not applicable. Left blank. D ✗ **Did you use human annotators (e.g., crowdworkers) or research with human participants?** Left blank. D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank. D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Not applicable. Left blank. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. Left blank. D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank. D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.