
# Multilingual-Metaphor-Detection

This page provides a fine-tuned multilingual language model [XLM-RoBERTa](https://arxiv.org/pdf/1911.02116.pdf) for metaphor detection on a token-level using the [Huggingface token-classification approach](https://huggingface.co/tasks/token-classification). Label 1 corresponds to metaphoric usage.

# Dataset
The dataset the model is trained on is the [VU Amsterdam Metaphor Corpus](http://www.vismet.org/metcor/documentation/home.html) that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages. 

# Results
Following the evaluation criteria from the [2020 Second Shared Task on Metaphor detection](https://competitions.codalab.org/competitions/22188#results) our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 

We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation. 

# Code for Training and Reference
The training and evaluation code is available on [Github](https://github.com/lwachowiak/Multilingual-Metaphor-Detection/). 
Our [paper](https://aclanthology.org/2022.flp-1.7/) describing training and model application is available online:
>@inproceedings{wachowiak2022drum,
>  title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors},
>  author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao},
>  booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)},
>  pages={44--53},
>  year={2022}
>}

{"license": "cc-by-nc-sa-3.0", "metrics": ["f1", "accuracy"], "widget": [{"text": "We are at a relationship crossroad", "example_title": "Metaphoric1"}, {"text": "The car waits at a crossroad", "example_title": "Literal1"}, {"text": "I win the argument", "example_title": "Metaphoric2"}, {"text": "I win the game", "example_title": "Literal2"}]}
transformers pytorch xlm-roberta token-classification arxiv:1911.02116 license:cc-by-nc-sa-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-1911.02116 #license-cc-by-nc-sa-3.0 #autotrain_compatible #endpoints_compatible #region-us 


# Multilingual-Metaphor-Detection

This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using the Huggingface token-classification approach. Label 1 corresponds to metaphoric usage.

# Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Corpus that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages. 

# Results
Following the evaluation criteria from the 2020 Second Shared Task on Metaphor detection our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 

We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation. 

# Code for Training and Reference
The training and evaluation code is available on Github. 
Our paper describing training and model application is available online:
>@inproceedings{wachowiak2022drum,
>  title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors},
>  author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao},
>  booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)},
>  pages={44--53},
>  year={2022}
>}

# Multilingual-Metaphor-Detection

This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using the Huggingface token-classification approach. Label 1 corresponds to metaphoric usage. # Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Corpus that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages. # Results
Following the evaluation criteria from the 2020 Second Shared Task on Metaphor detection our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 

We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation. # Code for Training and Reference
The training and evaluation code is available on Github. 
Our paper describing training and model application is available online:
>@inproceedings{wachowiak2022drum,
>  title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors},
>  author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao},
>  booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)},
>  pages={44--53},
>  year={2022}
>}
TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-1911.02116 #license-cc-by-nc-sa-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Multilingual-Metaphor-Detection

This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using the Huggingface token-classification approach. Label 1 corresponds to metaphoric usage. # Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Corpus that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages. # Results
Following the evaluation criteria from the 2020 Second Shared Task on Metaphor detection our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 

We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation. # Code for Training and Reference
The training and evaluation code is available on Github. 
Our paper describing training and model application is available online:
>@inproceedings{wachowiak2022drum,
>  title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors},
>  author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao},
>  booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)},
>  pages={44--53},
>  year={2022}
>}
passage: TAGS
#transformers #pytorch #xlm-roberta #token-classification #arxiv-1911.02116 #license-cc-by-nc-sa-3.0 #autotrain_compatible #endpoints_compatible #region-us 
# Multilingual-Metaphor-Detection

This page provides a fine-tuned multilingual language model XLM-RoBERTa for metaphor detection on a token-level using the Huggingface token-classification approach. Label 1 corresponds to metaphoric usage.# Dataset
The dataset the model is trained on is the VU Amsterdam Metaphor Corpus that was annotated on a word-level following the metaphor identification protocol. The training corpus is restricted to English, however, XLM-R shows decent zero-shot performances when tested on other languages.# Results
Following the evaluation criteria from the 2020 Second Shared Task on Metaphor detection our model achieves a F1-Score of 0.76 for the metaphor-class when training XLM-R<sub>Base</sub> and 0.77 when training XLM-R<sub>Large.</sub>. 

We train for 8 epochs loading the model with the best evaluation performance at the end and using a learning rate of 2e-5. From the allocated training data 10% are utilized for validation while the final test set is being kept seperate and only used for the final evaluation.# Code for Training and Reference
The training and evaluation code is available on Github. 
Our paper describing training and model application is available online:
>@inproceedings{wachowiak2022drum,
>  title={Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors},
>  author={Wachowiak, Lennart and Gromann, Dagmar and Xu, Chao},
>  booktitle={Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)},
>  pages={44--53},
>  year={2022}
>}