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2305.13490
|
2023-05-22T21:15:12Z
|
Detection of healthy and diseased crops in drone captured images using
Deep Learning
|
[
"Jai Vardhan",
"Kothapalli Sai Swetha"
] |
Monitoring plant health is crucial for maintaining agricultural productivity
and food safety. Disruptions in the plant's normal state, caused by diseases,
often interfere with essential plant activities, and timely detection of these
diseases can significantly mitigate crop loss. In this study, we propose a deep
learning-based approach for efficient detection of plant diseases using
drone-captured imagery. A comprehensive database of various plant species,
exhibiting numerous diseases, was compiled from the Internet and utilized as
the training and test dataset. A Convolutional Neural Network (CNN), renowned
for its performance in image classification tasks, was employed as our primary
predictive model. The CNN model, trained on this rich dataset, demonstrated
superior proficiency in crop disease categorization and detection, even under
challenging imaging conditions. For field implementation, we deployed a
prototype drone model equipped with a high-resolution camera for live
monitoring of extensive agricultural fields. The captured images served as the
input for our trained model, enabling real-time identification of healthy and
diseased plants. Our approach promises an efficient and scalable solution for
improving crop health monitoring systems.
|
[
"cs.CV"
] | false |
2305.13500
|
2023-05-22T21:36:55Z
|
Learning Emotion Representations from Verbal and Nonverbal Communication
|
[
"Sitao Zhang",
"Yimu Pan",
"James Z. Wang"
] |
Emotion understanding is an essential but highly challenging component of
artificial general intelligence. The absence of extensively annotated datasets
has significantly impeded advancements in this field. We present EmotionCLIP,
the first pre-training paradigm to extract visual emotion representations from
verbal and nonverbal communication using only uncurated data. Compared to
numerical labels or descriptions used in previous methods, communication
naturally contains emotion information. Furthermore, acquiring emotion
representations from communication is more congruent with the human learning
process. We guide EmotionCLIP to attend to nonverbal emotion cues through
subject-aware context encoding and verbal emotion cues using sentiment-guided
contrastive learning. Extensive experiments validate the effectiveness and
transferability of EmotionCLIP. Using merely linear-probe evaluation protocol,
EmotionCLIP outperforms the state-of-the-art supervised visual emotion
recognition methods and rivals many multimodal approaches across various
benchmarks. We anticipate that the advent of EmotionCLIP will address the
prevailing issue of data scarcity in emotion understanding, thereby fostering
progress in related domains. The code and pre-trained models are available at
https://github.com/Xeaver/EmotionCLIP.
|
[
"cs.CV"
] | false |
2305.14522
|
2023-05-22T05:16:12Z
|
Design a Delicious Lunchbox in Style
|
[
"Yutong Zhou"
] |
We propose a cyclic generative adversarial network with spatial-wise and
channel-wise attention modules for text-to-image synthesis. To accurately
depict and design scenes with multiple occluded objects, we design a
pre-trained ordering recovery model and a generative adversarial network to
predict layout and composite novel box lunch presentations. In the experiments,
we devise the Bento800 dataset to evaluate the performance of the text-to-image
synthesis model and the layout generation & image composition model. This paper
is the continuation of our previous paper works. We also present additional
experiments and qualitative performance comparisons to verify the effectiveness
of our proposed method. Bento800 dataset is available at
https://github.com/Yutong-Zhou-cv/Bento800_Dataset
|
[
"cs.CV"
] | false |
2305.12626
|
2023-05-22T01:32:24Z
|
You Only Look at One: Category-Level Object Representations for Pose
Estimation From a Single Example
|
[
"Walter Goodwin",
"Ioannis Havoutis",
"Ingmar Posner"
] |
In order to meaningfully interact with the world, robot manipulators must be
able to interpret objects they encounter. A critical aspect of this
interpretation is pose estimation: inferring quantities that describe the
position and orientation of an object in 3D space. Most existing approaches to
pose estimation make limiting assumptions, often working only for specific,
known object instances, or at best generalising to an object category using
large pose-labelled datasets. In this work, we present a method for achieving
category-level pose estimation by inspection of just a single object from a
desired category. We show that we can subsequently perform accurate pose
estimation for unseen objects from an inspected category, and considerably
outperform prior work by exploiting multi-view correspondences. We demonstrate
that our method runs in real-time, enabling a robot manipulator equipped with
an RGBD sensor to perform online 6D pose estimation for novel objects. Finally,
we showcase our method in a continual learning setting, with a robot able to
determine whether objects belong to known categories, and if not, use active
perception to produce a one-shot category representation for subsequent pose
estimation.
|
[
"cs.RO",
"cs.CV"
] | false |
2305.12646
|
2023-05-22T02:42:12Z
|
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud
Up-sampling from a Single Image
|
[
"Bowen Hu",
"Baiying Lei",
"Shuqiang Wang"
] |
In minimally-invasive brain surgeries with indirect and narrow operating
environments, 3D brain reconstruction is crucial. However, as requirements of
accuracy for some new minimally-invasive surgeries (such as brain-computer
interface surgery) are higher and higher, the outputs of conventional 3D
reconstruction, such as point cloud (PC), are facing the challenges that sample
points are too sparse and the precision is insufficient. On the other hand,
there is a scarcity of high-density point cloud datasets, which makes it
challenging to train models for direct reconstruction of high-density brain
point clouds. In this work, a novel model named stereoscopic-aware graph
generative adversarial network (SG-GAN) with two stages is proposed to generate
fine high-density PC conditioned on a single image. The Stage-I GAN sketches
the primitive shape and basic structure of the organ based on the given image,
yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I
and generates high-density point clouds with detailed features. The Stage-II
GAN is capable of correcting defects and restoring the detailed features of the
region of interest (ROI) through the up-sampling process. Furthermore, a
parameter-free-attention-based free-transforming module is developed to learn
the efficient features of input, while upholding a promising performance.
Comparing with the existing methods, the SG-GAN model shows superior
performance in terms of visual quality, objective measurements, and performance
in classification, as demonstrated by comprehensive results measured by several
evaluation metrics including PC-to-PC error and Chamfer distance.
|
[
"eess.IV",
"cs.CV"
] | false |
2305.12683
|
2023-05-22T03:43:34Z
|
Mist: Towards Improved Adversarial Examples for Diffusion Models
|
[
"Chumeng Liang",
"Xiaoyu Wu"
] |
Diffusion Models (DMs) have empowered great success in
artificial-intelligence-generated content, especially in artwork creation, yet
raising new concerns in intellectual properties and copyright. For example,
infringers can make profits by imitating non-authorized human-created paintings
with DMs. Recent researches suggest that various adversarial examples for
diffusion models can be effective tools against these copyright infringements.
However, current adversarial examples show weakness in transferability over
different painting-imitating methods and robustness under straightforward
adversarial defense, for example, noise purification. We surprisingly find that
the transferability of adversarial examples can be significantly enhanced by
exploiting a fused and modified adversarial loss term under consistent
parameters. In this work, we comprehensively evaluate the cross-method
transferability of adversarial examples. The experimental observation shows
that our method generates more transferable adversarial examples with even
stronger robustness against the simple adversarial defense.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.12734
|
2023-05-22T05:50:57Z
|
EMEF: Ensemble Multi-Exposure Image Fusion
|
[
"Renshuai Liu",
"Chengyang Li",
"Haitao Cao",
"Yinglin Zheng",
"Ming Zeng",
"Xuan Cheng"
] |
Although remarkable progress has been made in recent years, current
multi-exposure image fusion (MEF) research is still bounded by the lack of real
ground truth, objective evaluation function, and robust fusion strategy. In
this paper, we study the MEF problem from a new perspective. We don't utilize
any synthesized ground truth, design any loss function, or develop any fusion
strategy. Our proposed method EMEF takes advantage of the wisdom of multiple
imperfect MEF contributors including both conventional and deep learning-based
methods. Specifically, EMEF consists of two main stages: pre-train an imitator
network and tune the imitator in the runtime. In the first stage, we make a
unified network imitate different MEF targets in a style modulation way. In the
second stage, we tune the imitator network by optimizing the style code, in
order to find an optimal fusion result for each input pair. In the experiment,
we construct EMEF from four state-of-the-art MEF methods and then make
comparisons with the individuals and several other competitive methods on the
latest released MEF benchmark dataset. The promising experimental results
demonstrate that our ensemble framework can "get the best of all worlds". The
code is available at https://github.com/medalwill/EMEF.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.12775
|
2023-05-22T07:09:35Z
|
Semantic Segmentation of Radar Detections using Convolutions on Point
Clouds
|
[
"Marco Braun",
"Alessandro Cennamo",
"Markus Schoeler",
"Kevin Kollek",
"Anton Kummert"
] |
For autonomous driving, radar sensors provide superior reliability regardless
of weather conditions as well as a significantly high detection range.
State-of-the-art algorithms for environment perception based on radar scans
build up on deep neural network architectures that can be costly in terms of
memory and computation. By processing radar scans as point clouds, however, an
increase in efficiency can be achieved in this respect. While Convolutional
Neural Networks show superior performance on pattern recognition of regular
data formats like images, the concept of convolutions is not yet fully
established in the domain of radar detections represented as point clouds. The
main challenge in convolving point clouds lies in their irregular and unordered
data format and the associated permutation variance. Therefore, we apply a
deep-learning based method introduced by PointCNN that weights and permutes
grouped radar detections allowing the resulting permutation invariant cluster
to be convolved. In addition, we further adapt this algorithm to radar-specific
properties through distance-dependent clustering and pre-processing of input
point clouds. Finally, we show that our network outperforms state-of-the-art
approaches that are based on PointNet++ on the task of semantic segmentation of
radar point clouds.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.12796
|
2023-05-22T07:47:27Z
|
Spatiotemporal Attention-based Semantic Compression for Real-time Video
Recognition
|
[
"Nan Li",
"Mehdi Bennis",
"Alexandros Iosifidis",
"Qi Zhang"
] |
This paper studies the computational offloading of video action recognition
in edge computing. To achieve effective semantic information extraction and
compression, following semantic communication we propose a novel spatiotemporal
attention-based autoencoder (STAE) architecture, including a frame attention
module and a spatial attention module, to evaluate the importance of frames and
pixels in each frame. Additionally, we use entropy encoding to remove
statistical redundancy in the compressed data to further reduce communication
overhead. At the receiver, we develop a lightweight decoder that leverages a
3D-2D CNN combined architecture to reconstruct missing information by
simultaneously learning temporal and spatial information from the received data
to improve accuracy. To fasten convergence, we use a step-by-step approach to
train the resulting STAE-based vision transformer (ViT_STAE) models.
Experimental results show that ViT_STAE can compress the video dataset HMDB51
by 104x with only 5% accuracy loss, outperforming the state-of-the-art baseline
DeepISC. The proposed ViT_STAE achieves faster inference and higher accuracy
than the DeepISC-based ViT model under time-varying wireless channel, which
highlights the effectiveness of STAE in guaranteeing higher accuracy under time
constraints.
|
[
"cs.CV",
"cs.NI"
] | false |
2305.12845
|
2023-05-22T09:10:22Z
|
Bright Channel Prior Attention for Multispectral Pedestrian Detection
|
[
"Chenhang Cui",
"Jinyu Xie",
"Yechenhao Yang"
] |
Multispectral methods have gained considerable attention due to their
promising performance across various fields. However, most existing methods
cannot effectively utilize information from two modalities while optimizing
time efficiency. These methods often prioritize accuracy or time efficiency,
leaving room for improvement in their performance. To this end, we propose a
new method bright channel prior attention for enhancing pedestrian detection in
low-light conditions by integrating image enhancement and detection within a
unified framework. The method uses the V-channel of the HSV image of the
thermal image as an attention map to trigger the unsupervised auto-encoder for
visible light images, which gradually emphasizes pedestrian features across
layers. Moreover, we utilize unsupervised bright channel prior algorithms to
address light compensation in low light images. The proposed method includes a
self-attention enhancement module and a detection module, which work together
to improve object detection. An initial illumination map is estimated using the
BCP, guiding the learning of the self-attention map from the enhancement
network to obtain more informative representation focused on pedestrians. The
extensive experiments show effectiveness of the proposed method is demonstrated
through.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.12880
|
2023-05-22T10:01:15Z
|
Yes, this Way! Learning to Ground Referring Expressions into Actions
with Intra-episodic Feedback from Supportive Teachers
|
[
"Philipp Sadler",
"Sherzod Hakimov",
"David Schlangen"
] |
The ability to pick up on language signals in an ongoing interaction is
crucial for future machine learning models to collaborate and interact with
humans naturally. In this paper, we present an initial study that evaluates
intra-episodic feedback given in a collaborative setting. We use a referential
language game as a controllable example of a task-oriented collaborative joint
activity. A teacher utters a referring expression generated by a well-known
symbolic algorithm (the "Incremental Algorithm") as an initial instruction and
then monitors the follower's actions to possibly intervene with intra-episodic
feedback (which does not explicitly have to be requested). We frame this task
as a reinforcement learning problem with sparse rewards and learn a follower
policy for a heuristic teacher. Our results show that intra-episodic feedback
allows the follower to generalize on aspects of scene complexity and performs
better than providing only the initial statement.
|
[
"cs.CV",
"cs.CL"
] | false |
2305.12881
|
2023-05-22T10:01:28Z
|
Building an Invisible Shield for Your Portrait against Deepfakes
|
[
"Jiazhi Guan",
"Tianshu Hu",
"Hang Zhou",
"Zhizhi Guo",
"Lirui Deng",
"Chengbin Quan",
"Errui Ding",
"Youjian Zhao"
] |
The issue of detecting deepfakes has garnered significant attention in the
research community, with the goal of identifying facial manipulations for abuse
prevention. Although recent studies have focused on developing generalized
models that can detect various types of deepfakes, their performance is not
always be reliable and stable, which poses limitations in real-world
applications. Instead of learning a forgery detector, in this paper, we propose
a novel framework - Integrity Encryptor, aiming to protect portraits in a
proactive strategy. Our methodology involves covertly encoding messages that
are closely associated with key facial attributes into authentic images prior
to their public release. Unlike authentic images, where the hidden messages can
be extracted with precision, manipulating the facial attributes through
deepfake techniques can disrupt the decoding process. Consequently, the
modified facial attributes serve as a mean of detecting manipulated images
through a comparison of the decoded messages. Our encryption approach is
characterized by its brevity and efficiency, and the resulting method exhibits
a good robustness against typical image processing traces, such as image
degradation and noise. When compared to baselines that struggle to detect
deepfakes in a black-box setting, our method utilizing conditional encryption
showcases superior performance when presented with a range of different types
of forgeries. In experiments conducted on our protected data, our approach
outperforms existing state-of-the-art methods by a significant margin.
|
[
"cs.CV",
"cs.MM"
] | false |
2305.13051
|
2023-05-22T14:03:38Z
|
Learning Pedestrian Actions to Ensure Safe Autonomous Driving
|
[
"Jia Huang",
"Alvika Gautam",
"Srikanth Saripalli"
] |
To ensure safe autonomous driving in urban environments with complex
vehicle-pedestrian interactions, it is critical for Autonomous Vehicles (AVs)
to have the ability to predict pedestrians' short-term and immediate actions in
real-time. In recent years, various methods have been developed to study
estimating pedestrian behaviors for autonomous driving scenarios, but there is
a lack of clear definitions for pedestrian behaviors. In this work, the
literature gaps are investigated and a taxonomy is presented for pedestrian
behavior characterization. Further, a novel multi-task sequence to sequence
Transformer encoders-decoders (TF-ed) architecture is proposed for pedestrian
action and trajectory prediction using only ego vehicle camera observations as
inputs. The proposed approach is compared against an existing LSTM encoders
decoders (LSTM-ed) architecture for action and trajectory prediction. The
performance of both models is evaluated on the publicly available Joint
Attention Autonomous Driving (JAAD) dataset, CARLA simulation data as well as
real-time self-driving shuttle data collected on university campus. Evaluation
results illustrate that the proposed method reaches an accuracy of 81% on
action prediction task on JAAD testing data and outperforms the LSTM-ed by
7.4%, while LSTM counterpart performs much better on trajectory prediction task
for a prediction sequence length of 25 frames.
|
[
"cs.RO",
"cs.CV"
] | false |
2305.13095
|
2023-05-22T14:59:50Z
|
Open-world Semi-supervised Novel Class Discovery
|
[
"Jiaming Liu",
"Yangqiming Wang",
"Tongze Zhang",
"Yulu Fan",
"Qinli Yang",
"Junming Shao"
] |
Traditional semi-supervised learning tasks assume that both labeled and
unlabeled data follow the same class distribution, but the realistic open-world
scenarios are of more complexity with unknown novel classes mixed in the
unlabeled set. Therefore, it is of great challenge to not only recognize
samples from known classes but also discover the unknown number of novel
classes within the unlabeled data. In this paper, we introduce a new open-world
semi-supervised novel class discovery approach named OpenNCD, a progressive
bi-level contrastive learning method over multiple prototypes. The proposed
method is composed of two reciprocally enhanced parts. First, a bi-level
contrastive learning method is introduced, which maintains the pair-wise
similarity of the prototypes and the prototype group levels for better
representation learning. Then, a reliable prototype similarity metric is
proposed based on the common representing instances. Prototypes with high
similarities will be grouped progressively for known class recognition and
novel class discovery. Extensive experiments on three image datasets are
conducted and the results show the effectiveness of the proposed method in
open-world scenarios, especially with scarce known classes and labels.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.13284
|
2023-05-22T17:46:26Z
|
Target-Aware Generative Augmentations for Single-Shot Adaptation
|
[
"Kowshik Thopalli",
"Rakshith Subramanyam",
"Pavan Turaga",
"Jayaraman J. Thiagarajan"
] |
In this paper, we address the problem of adapting models from a source domain
to a target domain, a task that has become increasingly important due to the
brittle generalization of deep neural networks. While several test-time
adaptation techniques have emerged, they typically rely on synthetic toolbox
data augmentations in cases of limited target data availability. We consider
the challenging setting of single-shot adaptation and explore the design of
augmentation strategies. We argue that augmentations utilized by existing
methods are insufficient to handle large distribution shifts, and hence propose
a new approach SiSTA, which first fine-tunes a generative model from the source
domain using a single-shot target, and then employs novel sampling strategies
for curating synthetic target data. Using experiments on a variety of
benchmarks, distribution shifts and image corruptions, we find that SiSTA
produces significantly improved generalization over existing baselines in face
attribute detection and multi-class object recognition. Furthermore, SiSTA
performs competitively to models obtained by training on larger target
datasets. Our codes can be accessed at https://github.com/Rakshith-2905/SiSTA.
|
[
"cs.CV",
"cs.AI"
] | false |
2305.13391
|
2023-05-22T18:09:55Z
|
EnSiam: Self-Supervised Learning With Ensemble Representations
|
[
"Kyoungmin Han",
"Minsik Lee"
] |
Recently, contrastive self-supervised learning, where the proximity of
representations is determined based on the identities of samples, has made
remarkable progress in unsupervised representation learning. SimSiam is a
well-known example in this area, known for its simplicity yet powerful
performance. However, it is known to be sensitive to changes in training
configurations, such as hyperparameters and augmentation settings, due to its
structural characteristics. To address this issue, we focus on the similarity
between contrastive learning and the teacher-student framework in knowledge
distillation. Inspired by the ensemble-based knowledge distillation approach,
the proposed method, EnSiam, aims to improve the contrastive learning procedure
using ensemble representations. This can provide stable pseudo labels,
providing better performance. Experiments demonstrate that EnSiam outperforms
previous state-of-the-art methods in most cases, including the experiments on
ImageNet, which shows that EnSiam is capable of learning high-quality
representations.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.13456
|
2023-05-22T19:57:13Z
|
Revisiting pre-trained remote sensing model benchmarks: resizing and
normalization matters
|
[
"Isaac Corley",
"Caleb Robinson",
"Rahul Dodhia",
"Juan M. Lavista Ferres",
"Peyman Najafirad"
] |
Research in self-supervised learning (SSL) with natural images has progressed
rapidly in recent years and is now increasingly being applied to and
benchmarked with datasets containing remotely sensed imagery. A common
benchmark case is to evaluate SSL pre-trained model embeddings on datasets of
remotely sensed imagery with small patch sizes, e.g., 32x32 pixels, whereas
standard SSL pre-training takes place with larger patch sizes, e.g., 224x224.
Furthermore, pre-training methods tend to use different image normalization
preprocessing steps depending on the dataset. In this paper, we show, across
seven satellite and aerial imagery datasets of varying resolution, that by
simply following the preprocessing steps used in pre-training (precisely, image
sizing and normalization methods), one can achieve significant performance
improvements when evaluating the extracted features on downstream tasks -- an
important detail overlooked in previous work in this space. We show that by
following these steps, ImageNet pre-training remains a competitive baseline for
satellite imagery based transfer learning tasks -- for example we find that
these steps give +32.28 to overall accuracy on the So2Sat random split dataset
and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark
results with a variety of simple baseline methods for each of the seven
datasets, forming an initial benchmark suite for remote sensing imagery.
|
[
"cs.CV",
"cs.LG"
] | false |
2305.13548
|
2023-05-22T23:50:43Z
|
Attribute-Guided Encryption with Facial Texture Masking
|
[
"Chun Pong Lau",
"Jiang Liu",
"Rama Chellappa"
] |
The increasingly pervasive facial recognition (FR) systems raise serious
concerns about personal privacy, especially for billions of users who have
publicly shared their photos on social media. Several attempts have been made
to protect individuals from unauthorized FR systems utilizing adversarial
attacks to generate encrypted face images to protect users from being
identified by FR systems. However, existing methods suffer from poor visual
quality or low attack success rates, which limit their usability in practice.
In this paper, we propose Attribute Guided Encryption with Facial Texture
Masking (AGE-FTM) that performs a dual manifold adversarial attack on FR
systems to achieve both good visual quality and high black box attack success
rates. In particular, AGE-FTM utilizes a high fidelity generative adversarial
network (GAN) to generate natural on-manifold adversarial samples by modifying
facial attributes, and performs the facial texture masking attack to generate
imperceptible off-manifold adversarial samples. Extensive experiments on the
CelebA-HQ dataset demonstrate that our proposed method produces more
natural-looking encrypted images than state-of-the-art methods while achieving
competitive attack performance. We further evaluate the effectiveness of
AGE-FTM in the real world using a commercial FR API and validate its usefulness
in practice through an user study.
|
[
"cs.CV",
"cs.CR"
] | false |
2305.12822
|
2023-05-22T08:29:43Z
|
Quantifying the effect of X-ray scattering for data generation in
real-time defect detection
|
[
"Vladyslav Andriiashen",
"Robert van Liere",
"Tristan van Leeuwen",
"K. Joost Batenburg"
] |
X-ray imaging is widely used for non-destructive detection of defects in
industrial products on a conveyor belt. Real-time detection requires highly
accurate, robust, and fast algorithms to analyze X-ray images. Deep
convolutional neural networks (DCNNs) satisfy these requirements if a large
amount of labeled data is available. To overcome the challenge of collecting
these data, different methods of X-ray image generation can be considered.
Depending on the desired level of similarity to real data, various physical
effects either should be simulated or can be ignored. X-ray scattering is known
to be computationally expensive to simulate, and this effect can heavily
influence the accuracy of a generated X-ray image. We propose a methodology for
quantitative evaluation of the effect of scattering on defect detection. This
methodology compares the accuracy of DCNNs trained on different versions of the
same data that include and exclude the scattering signal. We use the
Probability of Detection (POD) curves to find the size of the smallest defect
that can be detected with a DCNN and evaluate how this size is affected by the
choice of training data. We apply the proposed methodology to a model problem
of defect detection in cylinders. Our results show that the exclusion of the
scattering signal from the training data has the largest effect on the smallest
detectable defects. Furthermore, we demonstrate that accurate inspection is
more reliant on high-quality training data for images with a high quantity of
scattering. We discuss how the presented methodology can be used for other
tasks and objects.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.12844
|
2023-05-22T09:08:59Z
|
An efficient deep learning model to categorize brain tumor using
reconstruction and fine-tuning
|
[
"Md. Alamin Talukder",
"Md. Manowarul Islam",
"Md Ashraf Uddin",
"Arnisha Akhter",
"Md. Alamgir Jalil Pramanik",
"Sunil Aryal",
"Muhammad Ali Abdulllah Almoyad",
"Khondokar Fida Hasan",
"Mohammad Ali Moni"
] |
Brain tumors are among the most fatal and devastating diseases, often
resulting in significantly reduced life expectancy. An accurate diagnosis of
brain tumors is crucial to devise treatment plans that can extend the lives of
affected individuals. Manually identifying and analyzing large volumes of MRI
data is both challenging and time-consuming. Consequently, there is a pressing
need for a reliable deep learning (DL) model to accurately diagnose brain
tumors. In this study, we propose a novel DL approach based on transfer
learning to effectively classify brain tumors. Our novel method incorporates
extensive pre-processing, transfer learning architecture reconstruction, and
fine-tuning. We employ several transfer learning algorithms, including
Xception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments used
the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved
accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2,
InceptionResNetV2, and DenseNet201, respectively. Our findings reveal that
ResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRI
brain tumor dataset, outperforming existing models. Therefore, our proposed
model's ability to accurately classify brain tumors in a short timeframe can
aid neurologists and clinicians in making prompt and precise diagnostic
decisions for brain tumor patients.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.12852
|
2023-05-22T09:23:18Z
|
Cycle Consistency-based Uncertainty Quantification of Neural Networks in
Inverse Imaging Problems
|
[
"Luzhe Huang",
"Jianing Li",
"Xiaofu Ding",
"Yijie Zhang",
"Hanlong Chen",
"Aydogan Ozcan"
] |
Uncertainty estimation is critical for numerous applications of deep neural
networks and draws growing attention from researchers. Here, we demonstrate an
uncertainty quantification approach for deep neural networks used in inverse
problems based on cycle consistency. We build forward-backward cycles using the
physical forward model available and a trained deep neural network solving the
inverse problem at hand, and accordingly derive uncertainty estimators through
regression analysis on the consistency of these forward-backward cycles. We
theoretically analyze cycle consistency metrics and derive their relationship
with respect to uncertainty, bias, and robustness of the neural network
inference. To demonstrate the effectiveness of these cycle consistency-based
uncertainty estimators, we classified corrupted and out-of-distribution input
image data using some of the widely used image deblurring and super-resolution
neural networks as testbeds. The blind testing of our method outperformed other
models in identifying unseen input data corruption and distribution shifts.
This work provides a simple-to-implement and rapid uncertainty quantification
method that can be universally applied to various neural networks used for
solving inverse problems.
|
[
"cs.CV",
"cs.LG",
"eess.IV",
"physics.optics"
] | false |
2305.12903
|
2023-05-22T10:37:27Z
|
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment
|
[
"Shentong Mo",
"Jing Shi",
"Yapeng Tian"
] |
Text-to-audio (TTA) generation is a recent popular problem that aims to
synthesize general audio given text descriptions. Previous methods utilized
latent diffusion models to learn audio embedding in a latent space with text
embedding as the condition. However, they ignored the synchronization between
audio and visual content in the video, and tended to generate audio mismatching
from video frames. In this work, we propose a novel and personalized
text-to-sound generation approach with visual alignment based on latent
diffusion models, namely DiffAVA, that can simply fine-tune lightweight
visual-text alignment modules with frozen modality-specific encoders to update
visual-aligned text embeddings as the condition. Specifically, our DiffAVA
leverages a multi-head attention transformer to aggregate temporal information
from video features, and a dual multi-modal residual network to fuse temporal
visual representations with text embeddings. Then, a contrastive learning
objective is applied to match visual-aligned text embeddings with audio
features. Experimental results on the AudioCaps dataset demonstrate that the
proposed DiffAVA can achieve competitive performance on visual-aligned
text-to-audio generation.
|
[
"cs.CV",
"cs.LG",
"cs.MM"
] | false |
2305.12983
|
2023-05-22T12:42:32Z
|
Why current rain denoising models fail on CycleGAN created rain images
in autonomous driving
|
[
"Michael Kranl",
"Hubert Ramsauer",
"Bernhard Knapp"
] |
One of the main tasks of an autonomous agent in a vehicle is to correctly
perceive its environment. Much of the data that needs to be processed is
collected by optical sensors such as cameras. Unfortunately, the data collected
in this way can be affected by a variety of factors, including environmental
influences such as inclement weather conditions (e.g., rain). Such noisy data
can cause autonomous agents to take wrong decisions with potentially fatal
outcomes. This paper addresses the rain image challenge by two steps: First,
rain is artificially added to a set of clear-weather condition images using a
Generative Adversarial Network (GAN). This yields good/bad weather image pairs
for training de-raining models. This artificial generation of rain images is
sufficiently realistic as in 7 out of 10 cases, human test subjects believed
the generated rain images to be real. In a second step, this paired good/bad
weather image data is used to train two rain denoising models, one based
primarily on a Convolutional Neural Network (CNN) and the other using a Vision
Transformer. This rain de-noising step showed limited performance as the
quality gain was only about 15%. This lack of performance on realistic rain
images as used in our study is likely due to current rain de-noising models
being developed for simplistic rain overlay data. Our study shows that there is
ample space for improvement of de-raining models in autonomous driving.
|
[
"cs.CV",
"cs.LG",
"eess.IV",
"I.4"
] | false |
2305.13019
|
2023-05-22T13:21:59Z
|
Robots in the Garden: Artificial Intelligence and Adaptive Landscapes
|
[
"Zihao Zhang",
"Susan L. Epstein",
"Casey Breen",
"Sophia Xia",
"Zhigang Zhu",
"Christian Volkmann"
] |
This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture,
a collaboration among landscape architects, architects and computer scientists
who specialize in artificial intelligence, robotics and computer vision. ELUA
has two gantry robots, one indoors and the other outside on the rooftop of a
6-story campus building. Each robot can seed, water, weed, and prune in its
garden. To support responsive landscape research, ELUA also includes sensor
arrays, an AI-powered camera, and an extensive network infrastructure. This
project demonstrates a way to integrate artificial intelligence into an
evolving urban ecosystem, and encourages landscape architects to develop an
adaptive design framework where design becomes a long-term engagement with the
environment.
|
[
"cs.RO",
"cs.AI",
"cs.CV",
"cs.CY"
] | false |
2305.13046
|
2023-05-22T13:54:14Z
|
POEM: Polarization of Embeddings for Domain-Invariant Representations
|
[
"Sang-Yeong Jo",
"Sung Whan Yoon"
] |
Handling out-of-distribution samples is a long-lasting challenge for deep
visual models. In particular, domain generalization (DG) is one of the most
relevant tasks that aims to train a model with a generalization capability on
novel domains. Most existing DG approaches share the same philosophy to
minimize the discrepancy between domains by finding the domain-invariant
representations. On the contrary, our proposed method called POEM acquires a
strong DG capability by learning domain-invariant and domain-specific
representations and polarizing them. Specifically, POEM cotrains
category-classifying and domain-classifying embeddings while regularizing them
to be orthogonal via minimizing the cosine-similarity between their features,
i.e., the polarization of embeddings. The clear separation of embeddings
suppresses domain-specific features in the domain-invariant embeddings. The
concept of POEM shows a unique direction to enhance the domain robustness of
representations that brings considerable and consistent performance gains when
combined with existing DG methods. Extensive simulation results in popular DG
benchmarks with the PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet
datasets show that POEM indeed facilitates the category-classifying embedding
to be more domain-invariant.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.13050
|
2023-05-22T14:02:44Z
|
AudioToken: Adaptation of Text-Conditioned Diffusion Models for
Audio-to-Image Generation
|
[
"Guy Yariv",
"Itai Gat",
"Lior Wolf",
"Yossi Adi",
"Idan Schwartz"
] |
In recent years, image generation has shown a great leap in performance,
where diffusion models play a central role. Although generating high-quality
images, such models are mainly conditioned on textual descriptions. This begs
the question: "how can we adopt such models to be conditioned on other
modalities?". In this paper, we propose a novel method utilizing latent
diffusion models trained for text-to-image-generation to generate images
conditioned on audio recordings. Using a pre-trained audio encoding model, the
proposed method encodes audio into a new token, which can be considered as an
adaptation layer between the audio and text representations. Such a modeling
paradigm requires a small number of trainable parameters, making the proposed
approach appealing for lightweight optimization. Results suggest the proposed
method is superior to the evaluated baseline methods, considering objective and
subjective metrics. Code and samples are available at:
https://pages.cs.huji.ac.il/adiyoss-lab/AudioToken.
|
[
"cs.SD",
"cs.CV",
"cs.LG",
"eess.AS"
] | true |
2305.13055
|
2023-05-22T14:13:28Z
|
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V
Cluster for Nano-UAV Navigation
|
[
"Jonas Kühne",
"Michele Magno",
"Luca Benini"
] |
Optical flow estimation is crucial for autonomous navigation and localization
of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time
calculation of the optical flow is run on low power and resource-constrained
microcontroller units (MCUs). Thus, lightweight algorithms for optical flow
have been proposed targeting real-time execution on traditional single-core
MCUs. This paper introduces an efficient parallelization strategy for optical
flow computation targeting new-generation multicore low power RISC-V based
microcontroller units. Our approach enables higher frame rates at lower clock
speeds. It has been implemented and evaluated on the eight-core cluster of a
commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of
7.21 allowing for a frame rate of 500 frames per second when running on a 50
MHz clock frequency. The proposed parallel algorithm significantly boosts the
camera frame rate on micro unmanned aerial vehicles, which enables higher
flight speeds: the maximum flight speed can be doubled, while using less than a
third of the clock frequency of previous single-core implementations.
|
[
"cs.CV",
"cs.RO",
"eess.IV"
] | false |
2305.13128
|
2023-05-22T15:27:20Z
|
GSURE-Based Diffusion Model Training with Corrupted Data
|
[
"Bahjat Kawar",
"Noam Elata",
"Tomer Michaeli",
"Michael Elad"
] |
Diffusion models have demonstrated impressive results in both data generation
and downstream tasks such as inverse problems, text-based editing,
classification, and more. However, training such models usually requires large
amounts of clean signals which are often difficult or impossible to obtain. In
this work, we propose a novel training technique for generative diffusion
models based only on corrupted data. We introduce a loss function based on the
Generalized Stein's Unbiased Risk Estimator (GSURE), and prove that under some
conditions, it is equivalent to the training objective used in fully supervised
diffusion models. We demonstrate our technique on face images as well as
Magnetic Resonance Imaging (MRI), where the use of undersampled data
significantly alleviates data collection costs. Our approach achieves
generative performance comparable to its fully supervised counterpart without
training on any clean signals. In addition, we deploy the resulting diffusion
model in various downstream tasks beyond the degradation present in the
training set, showcasing promising results.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2305.13291
|
2023-05-22T17:50:48Z
|
Materialistic: Selecting Similar Materials in Images
|
[
"Prafull Sharma",
"Julien Philip",
"Michaël Gharbi",
"William T. Freeman",
"Fredo Durand",
"Valentin Deschaintre"
] |
Separating an image into meaningful underlying components is a crucial first
step for both editing and understanding images. We present a method capable of
selecting the regions of a photograph exhibiting the same material as an
artist-chosen area. Our proposed approach is robust to shading, specular
highlights, and cast shadows, enabling selection in real images. As we do not
rely on semantic segmentation (different woods or metal should not be selected
together), we formulate the problem as a similarity-based grouping problem
based on a user-provided image location. In particular, we propose to leverage
the unsupervised DINO features coupled with a proposed Cross-Similarity module
and an MLP head to extract material similarities in an image. We train our
model on a new synthetic image dataset, that we release. We show that our
method generalizes well to real-world images. We carefully analyze our model's
behavior on varying material properties and lighting. Additionally, we evaluate
it against a hand-annotated benchmark of 50 real photographs. We further
demonstrate our model on a set of applications, including material editing,
in-video selection, and retrieval of object photographs with similar materials.
|
[
"cs.CV",
"cs.GR",
"cs.LG"
] | false |
2305.13398
|
2023-05-22T18:18:26Z
|
nnDetection for Intracranial Aneurysms Detection and Localization
|
[
"Maysam Orouskhani",
"Negar Firoozeh",
"Shaojun Xia",
"Mahmud Mossa-Basha",
"Chengcheng Zhu"
] |
Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
https://github.com/orouskhani/AneurysmDetection.
|
[
"cs.CV",
"cs.LG",
"q-bio.QM",
"68T07"
] | false |
2305.13509
|
2023-05-22T21:56:35Z
|
ColMix -- A Simple Data Augmentation Framework to Improve Object
Detector Performance and Robustness in Aerial Images
|
[
"Cuong Ly",
"Grayson Jorgenson",
"Dan Rosa de Jesus",
"Henry Kvinge",
"Adam Attarian",
"Yijing Watkins"
] |
In the last decade, Convolutional Neural Network (CNN) and transformer based
object detectors have achieved high performance on a large variety of datasets.
Though the majority of detection literature has developed this capability on
datasets such as MS COCO, these detectors have still proven effective for
remote sensing applications. Challenges in this particular domain, such as
small numbers of annotated objects and low object density, hinder overall
performance. In this work, we present a novel augmentation method, called
collage pasting, for increasing the object density without a need for
segmentation masks, thereby improving the detector performance. We demonstrate
that collage pasting improves precision and recall beyond related methods, such
as mosaic augmentation, and enables greater control of object density. However,
we find that collage pasting is vulnerable to certain out-of-distribution
shifts, such as image corruptions. To address this, we introduce two simple
approaches for combining collage pasting with PixMix augmentation method, and
refer to our combined techniques as ColMix. Through extensive experiments, we
show that employing ColMix results in detectors with superior performance on
aerial imagery datasets and robust to various corruptions.
|
[
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | false |
2305.13520
|
2023-05-22T22:23:40Z
|
Tied-Augment: Controlling Representation Similarity Improves Data
Augmentation
|
[
"Emirhan Kurtulus",
"Zichao Li",
"Yann Dauphin",
"Ekin Dogus Cubuk"
] |
Data augmentation methods have played an important role in the recent advance
of deep learning models, and have become an indispensable component of
state-of-the-art models in semi-supervised, self-supervised, and supervised
training for vision. Despite incurring no additional latency at test time, data
augmentation often requires more epochs of training to be effective. For
example, even the simple flips-and-crops augmentation requires training for
more than 5 epochs to improve performance, whereas RandAugment requires more
than 90 epochs. We propose a general framework called Tied-Augment, which
improves the efficacy of data augmentation in a wide range of applications by
adding a simple term to the loss that can control the similarity of
representations under distortions. Tied-Augment can improve state-of-the-art
methods from data augmentation (e.g. RandAugment, mixup), optimization (e.g.
SAM), and semi-supervised learning (e.g. FixMatch). For example,
Tied-RandAugment can outperform RandAugment by 2.0% on ImageNet. Notably, using
Tied-Augment, data augmentation can be made to improve generalization even when
training for a few epochs and when fine-tuning. We open source our code at
https://github.com/ekurtulus/tied-augment/tree/main.
|
[
"cs.CV",
"cs.AI",
"cs.LG"
] | false |
2305.13541
|
2023-05-22T23:27:24Z
|
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
|
[
"Shuai Shao",
"Yu Guan",
"Bing Zhai",
"Paolo Missier",
"Thomas Ploetz"
] |
Human activity recognition (HAR) is one of the core research themes in
ubiquitous and wearable computing. With the shift to deep learning (DL) based
analysis approaches, it has become possible to extract high-level features and
perform classification in an end-to-end manner. Despite their promising overall
capabilities, DL-based HAR may suffer from overfitting due to the notoriously
small, often inadequate, amounts of labeled sample data that are available for
typical HAR applications. In response to such challenges, we propose ConvBoost
-- a novel, three-layer, structured model architecture and boosting framework
for convolutional network based HAR. Our framework generates additional
training data from three different perspectives for improved HAR, aiming to
alleviate the shortness of labeled training data in the field. Specifically,
with the introduction of three conceptual layers--Sampling Layer, Data
Augmentation Layer, and Resilient Layer -- we develop three "boosters" --
R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by
dense-sampling, synthesizing, and simulating, respectively. These new
conceptual layers and boosters, that are universally applicable for any kind of
convolutional network, have been designed based on the characteristics of the
sensor data and the concept of frame-wise HAR. In our experimental evaluation
on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the
effectiveness of our ConvBoost framework for HAR applications based on variants
of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We
achieved substantial performance gains for all of them, which suggests that the
proposed approach is generic and can serve as a practical solution for boosting
the performance of existing ConvNet-based HAR models. This is an open-source
project, and the code can be found at https://github.com/sshao2013/ConvBoost
|
[
"cs.LG",
"cs.AI",
"cs.CV",
"cs.HC"
] | false |
2305.14384
|
2023-05-22T15:02:40Z
|
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety
of Text-to-Image Models
|
[
"Alicia Parrish",
"Hannah Rose Kirk",
"Jessica Quaye",
"Charvi Rastogi",
"Max Bartolo",
"Oana Inel",
"Juan Ciro",
"Rafael Mosquera",
"Addison Howard",
"Will Cukierski",
"D. Sculley",
"Vijay Janapa Reddi",
"Lora Aroyo"
] |
The generative AI revolution in recent years has been spurred by an expansion
in compute power and data quantity, which together enable extensive
pre-training of powerful text-to-image (T2I) models. With their greater
capabilities to generate realistic and creative content, these T2I models like
DALL-E, MidJourney, Imagen or Stable Diffusion are reaching ever wider
audiences. Any unsafe behaviors inherited from pretraining on uncurated
internet-scraped datasets thus have the potential to cause wide-reaching harm,
for example, through generated images which are violent, sexually explicit, or
contain biased and derogatory stereotypes. Despite this risk of harm, we lack
systematic and structured evaluation datasets to scrutinize model behavior,
especially adversarial attacks that bypass existing safety filters. A typical
bottleneck in safety evaluation is achieving a wide coverage of different types
of challenging examples in the evaluation set, i.e., identifying 'unknown
unknowns' or long-tail problems. To address this need, we introduce the
Adversarial Nibbler challenge. The goal of this challenge is to crowdsource a
diverse set of failure modes and reward challenge participants for successfully
finding safety vulnerabilities in current state-of-the-art T2I models.
Ultimately, we aim to provide greater awareness of these issues and assist
developers in improving the future safety and reliability of generative AI
models. Adversarial Nibbler is a data-centric challenge, part of the DataPerf
challenge suite, organized and supported by Kaggle and MLCommons.
|
[
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV",
"14J68 (Primary)"
] | false |
2306.01752
|
2023-05-22T16:56:07Z
|
Handling Label Uncertainty on the Example of Automatic Detection of
Shepherd's Crook RCA in Coronary CT Angiography
|
[
"Felix Denzinger",
"Michael Wels",
"Oliver Taubmann",
"Florian Kordon",
"Fabian Wagner",
"Stephanie Mehltretter",
"Mehmet A. Gülsün",
"Max Schöbinger",
"Florian André",
"Sebastian Buss",
"Johannes Görich",
"Michael Sühling",
"Andreas Maier"
] |
Coronary artery disease (CAD) is often treated minimally invasively with a
catheter being inserted into the diseased coronary vessel. If a patient
exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical
norm variant of the coronary vasculature - the complexity of this procedure is
increased. Automated reporting of this variant from coronary CT angiography
screening would ease prior risk assessment. We propose a 1D convolutional
neural network which leverages a sequence of residual dilated convolutions to
automatically determine this norm variant from a prior extracted vessel
centerline. As the SC RCA is not clearly defined with respect to concrete
measurements, labeling also includes qualitative aspects. Therefore, 4.23%
samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs,
with 5.97% being labeled as sure SC RCAs. We explore measures to handle this
label uncertainty, namely global/model-wise random assignment, exclusion, and
soft label assignment. Furthermore, we evaluate how this uncertainty can be
leveraged for the determination of a rejection class. With our best
configuration, we reach an area under the receiver operating characteristic
curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of
up to 0.020 AUC when rejecting 10% of the data and leveraging the labeling
uncertainty information in the exclusion process.
|
[
"eess.IV",
"cs.CV",
"cs.LG"
] | false |
2306.01753
|
2023-05-22T16:57:52Z
|
Preconditioned Visual Language Inference with Weak Supervision
|
[
"Ehsan Qasemi",
"Amani R. Maina-Kilaas",
"Devadutta Dash",
"Khalid Alsaggaf",
"Muhao Chen"
] |
Humans can infer the affordance of objects by extracting related contextual
preconditions for each scenario. For example, upon seeing an image of a broken
cup, we can infer that this precondition prevents the cup from being used for
drinking. Reasoning with preconditions of commonsense is studied in NLP where
the model explicitly gets the contextual precondition. However, it is unclear
if SOTA visual language models (VLMs) can extract such preconditions and infer
the affordance of objects with them. In this work, we introduce the task of
preconditioned visual language inference and rationalization (PVLIR). We
propose a learning resource based on three strategies to retrieve weak
supervision signals for the task and develop a human-verified test set for
evaluation. Our results reveal the shortcomings of SOTA VLM models in the task
and draw a road map to address the challenges ahead in improving them.
|
[
"cs.CL",
"cs.AI",
"cs.CV"
] | false |
2305.12620
|
2023-05-22T01:02:45Z
|
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI
Data and Models
|
[
"Ioana Baldini",
"Chhavi Yadav",
"Payel Das",
"Kush R. Varshney"
] |
Auditing unwanted social bias in language models (LMs) is inherently hard due
to the multidisciplinary nature of the work. In addition, the rapid evolution
of LMs can make benchmarks irrelevant in no time. Bias auditing is further
complicated by LM brittleness: when a presumably biased outcome is observed, is
it due to model bias or model brittleness? We propose enlisting the models
themselves to help construct bias auditing datasets that remain challenging,
and introduce bias measures that distinguish between types of model errors.
First, we extend an existing bias benchmark for NLI (BBNLI) using a combination
of LM-generated lexical variations, adversarial filtering, and human
validation. We demonstrate that the newly created dataset (BBNLInext) is more
challenging than BBNLI: on average, BBNLI-next reduces the accuracy of
state-of-the-art NLI models from 95.3%, as observed by BBNLI, to 58.6%. Second,
we employ BBNLI-next to showcase the interplay between robustness and bias, and
the subtlety in differentiating between the two. Third, we point out
shortcomings in current bias scores used in the literature and propose bias
measures that take into account pro-/anti-stereotype bias and model
brittleness. We will publicly release the BBNLI-next dataset to inspire
research on rapidly expanding benchmarks to keep up with model evolution, along
with research on the robustness-bias interplay in bias auditing.
Note: This paper contains offensive text examples.
|
[
"cs.CL"
] | false |
2305.12709
|
2023-05-22T04:37:49Z
|
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis
|
[
"Seraphina Goldfarb-Tarrant",
"Björn Ross",
"Adam Lopez"
] |
Sentiment analysis (SA) systems are widely deployed in many of the world's
languages, and there is well-documented evidence of demographic bias in these
systems. In languages beyond English, scarcer training data is often
supplemented with transfer learning using pre-trained models, including
multilingual models trained on other languages. In some cases, even supervision
data comes from other languages. Does cross-lingual transfer also import new
biases? To answer this question, we use counterfactual evaluation to test
whether gender or racial biases are imported when using cross-lingual transfer,
compared to a monolingual transfer setting. Across five languages, we find that
systems using cross-lingual transfer usually become more biased than their
monolingual counterparts. We also find racial biases to be much more prevalent
than gender biases. To spur further research on this topic, we release the
sentiment models we used for this study, and the intermediate checkpoints
throughout training, yielding 1,525 distinct models; we also release our
evaluation code.
|
[
"cs.CL"
] | false |
2305.12740
|
2023-05-22T06:07:58Z
|
Can We Edit Factual Knowledge by In-Context Learning?
|
[
"Ce Zheng",
"Lei Li",
"Qingxiu Dong",
"Yuxuan Fan",
"Zhiyong Wu",
"Jingjing Xu",
"Baobao Chang"
] |
Previous studies have shown that large language models (LLMs) like GPTs store
massive factual knowledge in their parameters. However, the stored knowledge
could be false or out-dated. Traditional knowledge editing methods refine LLMs
via fine-tuning on texts containing specific knowledge. However, with the
increasing scales of LLMs, these gradient-based approaches bring large
computation costs. The trend of model-as-a-service also makes it impossible to
modify knowledge in black-box LMs. Inspired by in-context learning (ICL), a new
paradigm based on demonstration contexts without parameter updating, we explore
whether ICL can edit factual knowledge. To answer this question, we give a
comprehensive empirical study of ICL strategies. Experiments show that
in-context knowledge editing (IKE), without any gradient and parameter
updating, achieves a competitive success rate compared to gradient-based
methods on GPT-J (6B) but with much fewer side effects, including less
over-editing on similar but unrelated facts and less knowledge forgetting on
previously stored knowledge. We also apply the method to larger LMs with tens
or hundreds of parameters like OPT-175B, which shows the scalability of our
method. The code is available at https://github.com/Zce1112zslx/IKE.
|
[
"cs.CL"
] | false |
2305.12749
|
2023-05-22T06:18:23Z
|
A Benchmark on Extremely Weakly Supervised Text Classification:
Reconcile Seed Matching and Prompting Approaches
|
[
"Zihan Wang",
"Tianle Wang",
"Dheeraj Mekala",
"Jingbo Shang"
] |
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text
classification based on minimal high-level human guidance, such as a few
label-indicative seed words or classification instructions. There are two
mainstream approaches for XWS-TC, however, never being rigorously compared: (1)
training classifiers based on pseudo-labels generated by (softly) matching seed
words (SEED) and (2) prompting (and calibrating) language models using
classification instruction (and raw texts) to decode label words (PROMPT). This
paper presents the first XWS-TC benchmark to compare the two approaches on fair
grounds, where the datasets, supervisions, and hyperparameter choices are
standardized across methods. Our benchmarking results suggest that (1) Both
SEED and PROMPT approaches are competitive and there is no clear winner; (2)
SEED is empirically more tolerant than PROMPT to human guidance (e.g., seed
words, classification instructions, and label words) changes; (3) SEED is
empirically more selective than PROMPT to the pre-trained language models; (4)
Recent SEED and PROMPT methods have close connections and a clustering
post-processing step based on raw in-domain texts is a strong performance
booster to both. We hope this benchmark serves as a guideline in selecting
XWS-TC methods in different scenarios and stimulate interest in developing
guidance- and model-robust XWS-TC methods. We release the repo at
https://github.com/ZihanWangKi/x-TC.
|
[
"cs.CL"
] | false |
2305.12757
|
2023-05-22T06:28:48Z
|
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language
Models
|
[
"Seraphina Goldfarb-Tarrant",
"Eddie Ungless",
"Esma Balkir",
"Su Lin Blodgett"
] |
Bias research in NLP seeks to analyse models for social biases, thus helping
NLP practitioners uncover, measure, and mitigate social harms. We analyse the
body of work that uses prompts and templates to assess bias in language models.
We draw on a measurement modelling framework to create a taxonomy of attributes
that capture what a bias test aims to measure and how that measurement is
carried out. By applying this taxonomy to 90 bias tests, we illustrate
qualitatively and quantitatively that core aspects of bias test
conceptualisations and operationalisations are frequently unstated or
ambiguous, carry implicit assumptions, or be mismatched. Our analysis
illuminates the scope of possible bias types the field is able to measure, and
reveals types that are as yet under-researched. We offer guidance to enable the
community to explore a wider section of the possible bias space, and to better
close the gap between desired outcomes and experimental design, both for bias
and for evaluating language models more broadly.
|
[
"cs.CL"
] | false |
2305.12759
|
2023-05-22T06:30:02Z
|
Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods
by Language Models
|
[
"Hao Wang",
"Hirofumi Shimizu",
"Daisuke Kawahara"
] |
Recent studies in natural language processing (NLP) have focused on modern
languages and achieved state-of-the-art results in many tasks. Meanwhile,
little attention has been paid to ancient texts and related tasks. Classical
Chinese first came to Japan approximately 2,000 years ago. It was gradually
adapted to a Japanese form called Kanbun-Kundoku (Kanbun) in Japanese reading
and translating methods, which has significantly impacted Japanese literature.
However, compared to the rich resources for ancient texts in mainland China,
Kanbun resources remain scarce in Japan. To solve this problem, we construct
the first Classical-Chinese-to-Kanbun dataset in the world. Furthermore, we
introduce two tasks, character reordering and machine translation, both of
which play a significant role in Kanbun comprehension. We also test the current
language models on these tasks and discuss the best evaluation method by
comparing the results with human scores. We release our code and dataset on
GitHub.
|
[
"cs.CL"
] | false |
2305.12777
|
2023-05-22T07:15:33Z
|
Evaluating Pragmatic Abilities of Image Captioners on A3DS
|
[
"Polina Tsvilodub",
"Michael Franke"
] |
Evaluating grounded neural language model performance with respect to
pragmatic qualities like the trade off between truthfulness, contrastivity and
overinformativity of generated utterances remains a challenge in absence of
data collected from humans. To enable such evaluation, we present a novel open
source image-text dataset "Annotated 3D Shapes" (A3DS) comprising over nine
million exhaustive natural language annotations and over 12 million
variable-granularity captions for the 480,000 images provided by Burges & Kim
(2018). We showcase the evaluation of pragmatic abilities developed by a
task-neutral image captioner fine-tuned in a multi-agent communication setting
to produce contrastive captions. The evaluation is enabled by the dataset
because the exhaustive annotations allow to quantify the presence of
contrastive features in the model's generations. We show that the model
develops human-like patterns (informativity, brevity, over-informativity for
specific features (e.g., shape, color biases)).
|
[
"cs.CL"
] | false |
2305.12816
|
2023-05-22T08:18:58Z
|
Farewell to Aimless Large-scale Pretraining: Influential Subset
Selection for Language Model
|
[
"Xiao Wang",
"Weikang Zhou",
"Qi Zhang",
"Jie Zhou",
"Songyang Gao",
"Junzhe Wang",
"Menghan Zhang",
"Xiang Gao",
"Yunwen Chen",
"Tao Gui"
] |
Pretrained language models have achieved remarkable success in various
natural language processing tasks. However, pretraining has recently shifted
toward larger models and larger data, and this has resulted in significant
computational and energy costs. In this paper, we propose Influence Subset
Selection (ISS) for language model, which explicitly utilizes end-task
knowledge to select a tiny subset of the pretraining corpus. Specifically, the
ISS selects the samples that will provide the most positive influence on the
performance of the end-task. Furthermore, we design a gradient matching based
influence estimation method, which can drastically reduce the computation time
of influence. With only 0.45% of the data and a three-orders-of-magnitude lower
computational cost, ISS outperformed pretrained models (e.g., RoBERTa) on eight
datasets covering four domains.
|
[
"cs.CL"
] | false |
2305.12839
|
2023-05-22T09:03:11Z
|
CopyNE: Better Contextual ASR by Copying Named Entities
|
[
"Shilin Zhou",
"Zhenghua Li",
"Yu Hong",
"Min Zhang",
"Zhefeng Wang",
"Baoxing Huai"
] |
Recent years have seen remarkable progress in automatic speech recognition
(ASR). However, traditional token-level ASR models have struggled with
accurately transcribing entities due to the problem of homophonic and
near-homophonic tokens. This paper introduces a novel approach called CopyNE,
which uses a span-level copying mechanism to improve ASR in transcribing
entities. CopyNE can copy all tokens of an entity at once, effectively avoiding
errors caused by homophonic or near-homophonic tokens that occur when
predicting multiple tokens separately. Experiments on Aishell and ST-cmds
datasets demonstrate that CopyNE achieves significant reductions in character
error rate (CER) and named entity CER (NE-CER), especially in entity-rich
scenarios. Furthermore, even when compared to the strong Whisper baseline,
CopyNE still achieves notable reductions in CER and NE-CER. Qualitative
comparisons with previous approaches demonstrate that CopyNE can better handle
entities, effectively improving the accuracy of ASR.
|
[
"cs.CL"
] | false |
2305.12908
|
2023-05-22T10:41:30Z
|
Language Models for German Text Simplification: Overcoming Parallel Data
Scarcity through Style-specific Pre-training
|
[
"Miriam Anschütz",
"Joshua Oehms",
"Thomas Wimmer",
"Bartłomiej Jezierski",
"Georg Groh"
] |
Automatic text simplification systems help to reduce textual information
barriers on the internet. However, for languages other than English, only few
parallel data to train these systems exists. We propose a two-step approach to
overcome this data scarcity issue. First, we fine-tuned language models on a
corpus of German Easy Language, a specific style of German. Then, we used these
models as decoders in a sequence-to-sequence simplification task. We show that
the language models adapt to the style characteristics of Easy Language and
output more accessible texts. Moreover, with the style-specific pre-training,
we reduced the number of trainable parameters in text simplification models.
Hence, less parallel data is sufficient for training. Our results indicate that
pre-training on unaligned data can reduce the required parallel data while
improving the performance on downstream tasks.
|
[
"cs.CL"
] | false |
2305.12940
|
2023-05-22T11:38:27Z
|
GEST: the Graph of Events in Space and Time as a Common Representation
between Vision and Language
|
[
"Mihai Masala",
"Nicolae Cudlenco",
"Traian Rebedea",
"Marius Leordeanu"
] |
One of the essential human skills is the ability to seamlessly build an inner
representation of the world. By exploiting this representation, humans are
capable of easily finding consensus between visual, auditory and linguistic
perspectives. In this work, we set out to understand and emulate this ability
through an explicit representation for both vision and language - Graphs of
Events in Space and Time (GEST). GEST alows us to measure the similarity
between texts and videos in a semantic and fully explainable way, through graph
matching. It also allows us to generate text and videos from a common
representation that provides a well understood content. In this work we show
that the graph matching similarity metrics based on GEST outperform classical
text generation metrics and can also boost the performance of state of art,
heavily trained metrics.
|
[
"cs.CL"
] | false |
2305.13000
|
2023-05-22T13:04:48Z
|
Bidirectional Transformer Reranker for Grammatical Error Correction
|
[
"Ying Zhang",
"Hidetaka Kamigaito",
"Manabu Okumura"
] |
Pre-trained seq2seq models have achieved state-of-the-art results in the
grammatical error correction task. However, these models still suffer from a
prediction bias due to their unidirectional decoding. Thus, we propose a
bidirectional Transformer reranker (BTR), that re-estimates the probability of
each candidate sentence generated by the pre-trained seq2seq model. The BTR
preserves the seq2seq-style Transformer architecture but utilizes a BERT-style
self-attention mechanism in the decoder to compute the probability of each
target token by using masked language modeling to capture bidirectional
representations from the target context. For guiding the reranking, the BTR
adopts negative sampling in the objective function to minimize the
unlikelihood. During inference, the BTR gives final results after comparing the
reranked top-1 results with the original ones by an acceptance threshold.
Experimental results show that, in reranking candidates from a pre-trained
seq2seq model, T5-base, the BTR on top of T5-base could yield 65.47 and 71.27
F0.5 scores on the CoNLL-14 and BEA test sets, respectively, and yield 59.52
GLEU score on the JFLEG corpus, with improvements of 0.36, 0.76 and 0.48 points
compared with the original T5-base. Furthermore, when reranking candidates from
T5-large, the BTR on top of T5-base improved the original T5-large by 0.26
points on the BEA test set.
|
[
"cs.CL"
] | false |
2305.13047
|
2023-05-22T13:56:35Z
|
Automated stance detection in complex topics and small languages: the
challenging case of immigration in polarizing news media
|
[
"Mark Mets",
"Andres Karjus",
"Indrek Ibrus",
"Maximilian Schich"
] |
Automated stance detection and related machine learning methods can provide
useful insights for media monitoring and academic research. Many of these
approaches require annotated training datasets, which limits their
applicability for languages where these may not be readily available. This
paper explores the applicability of large language models for automated stance
detection in a challenging scenario, involving a morphologically complex,
lower-resource language, and a socio-culturally complex topic, immigration. If
the approach works in this case, it can be expected to perform as well or
better in less demanding scenarios. We annotate a large set of pro and
anti-immigration examples, and compare the performance of multiple language
models as supervised learners. We also probe the usability of ChatGPT as an
instructable zero-shot classifier for the same task. Supervised achieves
acceptable performance, and ChatGPT yields similar accuracy. This is promising
as a potentially simpler and cheaper alternative for text classification tasks,
including in lower-resource languages. We further use the best-performing model
to investigate diachronic trends over seven years in two corpora of Estonian
mainstream and right-wing populist news sources, demonstrating the
applicability of the approach for news analytics and media monitoring settings,
and discuss correspondences between stance changes and real-world events.
|
[
"cs.CL"
] | false |
2305.13058
|
2023-05-22T14:16:23Z
|
Retrieval-augmented Multi-label Text Classification
|
[
"Ilias Chalkidis",
"Yova Kementchedjhieva"
] |
Multi-label text classification (MLC) is a challenging task in settings of
large label sets, where label support follows a Zipfian distribution. In this
paper, we address this problem through retrieval augmentation, aiming to
improve the sample efficiency of classification models. Our approach closely
follows the standard MLC architecture of a Transformer-based encoder paired
with a set of classification heads. In our case, however, the input document
representation is augmented through cross-attention to similar documents
retrieved from the training set and represented in a task-specific manner. We
evaluate this approach on four datasets from the legal and biomedical domains,
all of which feature highly skewed label distributions. Our experiments show
that retrieval augmentation substantially improves model performance on the
long tail of infrequent labels especially so for lower-resource training
scenarios and more challenging long-document data scenarios.
|
[
"cs.CL"
] | false |
2305.13076
|
2023-05-22T14:47:59Z
|
An Abstract Specification of VoxML as an Annotation Language
|
[
"Kiyong Lee",
"Nikhil Krishnaswamy",
"James Pustejovsky"
] |
VoxML is a modeling language used to map natural language expressions into
real-time visualizations using commonsense semantic knowledge of objects and
events. Its utility has been demonstrated in embodied simulation environments
and in agent-object interactions in situated multimodal human-agent
collaboration and communication. It introduces the notion of object affordance
(both Gibsonian and Telic) from HRI and robotics, as well as the concept of
habitat (an object's context of use) for interactions between a rational agent
and an object. This paper aims to specify VoxML as an annotation language in
general abstract terms. It then shows how it works on annotating linguistic
data that express visually perceptible human-object interactions. The
annotation structures thus generated will be interpreted against the enriched
minimal model created by VoxML as a modeling language while supporting the
modeling purposes of VoxML linguistically.
|
[
"cs.CL"
] | false |
2305.13083
|
2023-05-22T14:52:32Z
|
InheritSumm: A General, Versatile and Compact Summarizer by Distilling
from GPT
|
[
"Yichong Xu",
"Ruochen Xu",
"Dan Iter",
"Yang Liu",
"Shuohang Wang",
"Chenguang Zhu",
"Michael Zeng"
] |
While large models such as GPT-3 demonstrate exceptional performance in
zeroshot and fewshot summarization tasks, their extensive serving and
fine-tuning costs hinder their utilization in various applications. Conversely,
previous studies have found that although automatic metrics tend to favor
smaller fine-tuned models, the quality of the summaries they generate is
inferior to that of larger models like GPT-3 when assessed by human evaluators.
To address this issue, we propose InheritSumm, a versatile and compact
summarization model derived from GPT-3.5 through distillation. InheritSumm not
only exhibits comparable zeroshot and fewshot summarization capabilities to
GPT-3.5 but is also sufficiently compact for fine-tuning purposes. Experimental
results demonstrate that InheritSumm achieves similar or superior performance
to GPT-3.5 in zeroshot and fewshot settings. Furthermore, it outperforms the
previously established best small models in both prefix-tuning and full-data
fine-tuning scenarios.
|
[
"cs.CL"
] | false |
2305.13086
|
2023-05-22T14:53:45Z
|
LMGQS: A Large-scale Dataset for Query-focused Summarization
|
[
"Ruochen Xu",
"Song Wang",
"Yang Liu",
"Shuohang Wang",
"Yichong Xu",
"Dan Iter",
"Chenguang Zhu",
"Michael Zeng"
] |
Query-focused summarization (QFS) aims to extract or generate a summary of an
input document that directly answers or is relevant to a given query. The lack
of large-scale datasets in the form of documents, queries, and summaries has
hindered model development in this area. In contrast, multiple large-scale
high-quality datasets for generic summarization exist. We hypothesize that
there is a hidden query for each summary sentence in a generic summarization
annotation, and we utilize a large-scale pretrained language model to recover
it. In this way, we convert four generic summarization benchmarks into a new
QFS benchmark dataset, LMGQS, which consists of over 1 million
document-query-summary samples. We thoroughly investigate the properties of our
proposed dataset and establish baselines with state-of-the-art summarization
models. By fine-tuning a language model on LMGQS, we achieve state-of-the-art
zero-shot and supervised performance on multiple existing QFS benchmarks,
demonstrating the high quality and diversity of LMGQS.
|
[
"cs.CL"
] | false |
2305.13092
|
2023-05-22T14:58:54Z
|
Improved Compositional Generalization by Generating Demonstrations for
Meta-Learning
|
[
"Sam Spilsbury",
"Alexander Ilin"
] |
Meta-learning and few-shot prompting are viable methods to induce certain
types of compositional behaviour. However, these methods can be very sensitive
to the choice of support examples used. Choosing good supports from the
training data for a given test query is already a difficult problem, but in
some cases solving this may not even be enough. We consider a grounded language
learning problem (gSCAN) where good support examples for certain test splits
might not even exist in the training data, or would be infeasible to search
for. We design an agent which instead generates possible supports which are
relevant to the test query and current state of the world, then uses these
supports via meta-learning to solve the test query. We show substantially
improved performance on a previously unsolved compositional behaviour split
without a loss of performance on other splits. Further experiments show that in
this case, searching for relevant demonstrations even with an oracle function
is not sufficient to attain good performance when using meta-learning.
|
[
"cs.CL"
] | false |
2305.13140
|
2023-05-22T15:33:21Z
|
Extrapolating Multilingual Understanding Models as Multilingual
Generators
|
[
"Bohong Wu",
"Fei Yuan",
"Hai Zhao",
"Lei Li",
"Jingjing Xu"
] |
Multilingual understanding models (or encoder-based), pre-trained via masked
language modeling, have achieved promising results on many language
understanding tasks (e.g., mBERT). However, these non-autoregressive (NAR)
models still struggle to generate high-quality texts compared with
autoregressive (AR) models. Considering that encoder-based models have the
advantage of efficient generation and self-correction abilities, this paper
explores methods to empower multilingual understanding models the generation
abilities to get a unified model. Specifically, we start from a multilingual
encoder (XLM-R) and propose a \textbf{S}emantic-\textbf{G}uided
\textbf{A}lignment-then-Denoising (SGA) approach to adapt an encoder to a
multilingual generator with a small number of new parameters. Experiments show
that the proposed approach is an effective adaption method, outperforming
widely-used initialization-based methods with gains of 9.4 BLEU on machine
translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story
generation on XLM-R$_{large}$. On the other hand, we observe that XLM-R is
still inferior to mBART in supervised settings despite better results on
zero-shot settings, indicating that more exploration is required to make
understanding models strong generators.
|
[
"cs.CL"
] | false |
2305.13142
|
2023-05-22T15:35:39Z
|
Better Sampling of Negatives for Distantly Supervised Named Entity
Recognition
|
[
"Lu Xu",
"Lidong Bing",
"Wei Lu"
] |
Distantly supervised named entity recognition (DS-NER) has been proposed to
exploit the automatically labeled training data instead of human annotations.
The distantly annotated datasets are often noisy and contain a considerable
number of false negatives. The recent approach uses a weighted sampling
approach to select a subset of negative samples for training. However, it
requires a good classifier to assign weights to the negative samples. In this
paper, we propose a simple and straightforward approach for selecting the top
negative samples that have high similarities with all the positive samples for
training. Our method achieves consistent performance improvements on four
distantly supervised NER datasets. Our analysis also shows that it is critical
to differentiate the true negatives from the false negatives.
|
[
"cs.CL"
] | false |
2305.13198
|
2023-05-22T16:29:04Z
|
Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil
Demographic Biases in Languages at Scale
|
[
"Marta R. Costa-jussà",
"Pierre Andrews",
"Eric Smith",
"Prangthip Hansanti",
"Christophe Ropers",
"Elahe Kalbassi",
"Cynthia Gao",
"Daniel Licht",
"Carleigh Wood"
] |
We introduce a multilingual extension of the HOLISTICBIAS dataset, the
largest English template-based taxonomy of textual people references:
MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50
languages distributed across all 13 demographic axes. Source sentences are
built from combinations of 118 demographic descriptors and three patterns,
excluding nonsensical combinations. Multilingual translations include
alternatives for gendered languages that cover gendered translations when there
is ambiguity in English. Our benchmark is intended to uncover demographic
imbalances and be the tool to quantify mitigations towards them.
Our initial findings show that translation quality for EN-to-XX translations
is an average of 8 spBLEU better when evaluating with the masculine human
reference compared to feminine. In the opposite direction, XX-to-EN, we compare
the robustness of the model when the source input only differs in gender
(masculine or feminine) and masculine translations are an average of almost 4
spBLEU better than feminine. When embedding sentences to a joint multilingual
sentence representations space, we find that for most languages masculine
translations are significantly closer to the English neutral sentences when
embedded.
|
[
"cs.CL",
"I.2.7"
] | false |
2305.13199
|
2023-05-22T16:29:20Z
|
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
|
[
"Yucheng Cai",
"Hong Liu",
"Zhijian Ou",
"Yi Huang",
"Junlan Feng"
] |
Most existing task-oriented dialog (TOD) systems track dialog states in terms
of slots and values and use them to query a database to get relevant knowledge
to generate responses. In real-life applications, user utterances are noisier,
and thus it is more difficult to accurately track dialog states and correctly
secure relevant knowledge. Recently, a progress in question answering and
document-grounded dialog systems is retrieval-augmented methods with a
knowledge retriever. Inspired by such progress, we propose a retrieval-based
method to enhance knowledge selection in TOD systems, which significantly
outperforms the traditional database query method for real-life dialogs.
Further, we develop latent variable model based semi-supervised learning, which
can work with the knowledge retriever to leverage both labeled and unlabeled
dialog data. Joint Stochastic Approximation (JSA) algorithm is employed for
semi-supervised model training, and the whole system is referred to as that
JSA-KRTOD. Experiments are conducted on a real-life dataset from China Mobile
Custom-Service, called MobileCS, and show that JSA-KRTOD achieves superior
performances in both labeled-only and semi-supervised settings.
|
[
"cs.CL"
] | false |
2305.13214
|
2023-05-22T16:45:50Z
|
Logical Reasoning for Natural Language Inference Using Generated Facts
as Atoms
|
[
"Joe Stacey",
"Pasquale Minervini",
"Haim Dubossarsky",
"Oana-Maria Camburu",
"Marek Rei"
] |
State-of-the-art neural models can now reach human performance levels across
various natural language understanding tasks. However, despite this impressive
performance, models are known to learn from annotation artefacts at the expense
of the underlying task. While interpretability methods can identify influential
features for each prediction, there are no guarantees that these features are
responsible for the model decisions. Instead, we introduce a model-agnostic
logical framework to determine the specific information in an input responsible
for each model decision. This method creates interpretable Natural Language
Inference (NLI) models that maintain their predictive power. We achieve this by
generating facts that decompose complex NLI observations into individual
logical atoms. Our model makes predictions for each atom and uses logical rules
to decide the class of the observation based on the predictions for each atom.
We apply our method to the highly challenging ANLI dataset, where our framework
improves the performance of both a DeBERTa-base and BERT baseline. Our method
performs best on the most challenging examples, achieving a new
state-of-the-art for the ANLI round 3 test set. We outperform every baseline in
a reduced-data setting, and despite using no annotations for the generated
facts, our model predictions for individual facts align with human
expectations.
|
[
"cs.CL",
"I.2.7"
] | false |
2305.13242
|
2023-05-22T17:13:29Z
|
Deepfake Text Detection in the Wild
|
[
"Yafu Li",
"Qintong Li",
"Leyang Cui",
"Wei Bi",
"Longyue Wang",
"Linyi Yang",
"Shuming Shi",
"Yue Zhang"
] |
Recent advances in large language models have enabled them to reach a level
of text generation comparable to that of humans. These models show powerful
capabilities across a wide range of content, including news article writing,
story generation, and scientific writing. Such capability further narrows the
gap between human-authored and machine-generated texts, highlighting the
importance of deepfake text detection to avoid potential risks such as fake
news propagation and plagiarism. However, previous work has been limited in
that they testify methods on testbed of specific domains or certain language
models. In practical scenarios, the detector faces texts from various domains
or LLMs without knowing their sources. To this end, we build a wild testbed by
gathering texts from various human writings and deepfake texts generated by
different LLMs. Human annotators are only slightly better than random guessing
at identifying machine-generated texts. Empirical results on automatic
detection methods further showcase the challenges of deepfake text detection in
a wild testbed. In addition, out-of-distribution poses a greater challenge for
a detector to be employed in realistic application scenarios. We release our
resources at https://github.com/yafuly/DeepfakeTextDetect.
|
[
"cs.CL"
] | false |
2305.13281
|
2023-05-22T17:42:14Z
|
LM vs LM: Detecting Factual Errors via Cross Examination
|
[
"Roi Cohen",
"May Hamri",
"Mor Geva",
"Amir Globerson"
] |
A prominent weakness of modern language models (LMs) is their tendency to
generate factually incorrect text, which hinders their usability. A natural
question is whether such factual errors can be detected automatically. Inspired
by truth-seeking mechanisms in law, we propose a factuality evaluation
framework for LMs that is based on cross-examination. Our key idea is that an
incorrect claim is likely to result in inconsistency with other claims that the
model generates. To discover such inconsistencies, we facilitate a multi-turn
interaction between the LM that generated the claim and another LM (acting as
an examiner) which introduces questions to discover inconsistencies. We
empirically evaluate our method on factual claims made by multiple recent LMs
on four benchmarks, finding that it outperforms existing methods and baselines,
often by a large gap. Our results demonstrate the potential of using
interacting LMs for capturing factual errors.
|
[
"cs.CL"
] | false |
2305.13286
|
2023-05-22T17:47:41Z
|
How do languages influence each other? Studying cross-lingual data
sharing during LLM fine-tuning
|
[
"Rochelle Choenni",
"Dan Garrette",
"Ekaterina Shutova"
] |
Multilingual large language models (MLLMs) are jointly trained on data from
many different languages such that representation of individual languages can
benefit from other languages' data. Impressive performance on zero-shot
cross-lingual transfer shows that these models are capable of exploiting data
from other languages. Yet, it remains unclear to what extent, and under which
conditions, languages rely on each other's data. In this study, we use TracIn
(Pruthi et al., 2020), a training data attribution (TDA) method, to retrieve
the most influential training samples seen during multilingual fine-tuning for
a particular test language. This allows us to analyse cross-lingual sharing
mechanisms of MLLMs from a new perspective. While previous work studied
cross-lingual sharing at the level of model parameters, we present the first
approach to study cross-lingual sharing at the data level. We find that MLLMs
rely on data from multiple languages from the early stages of fine-tuning and
that this reliance gradually increases as fine-tuning progresses. We further
study how different fine-tuning languages influence model performance on a
given test language and find that they can both reinforce and complement the
knowledge acquired from data of the test language itself.
|
[
"cs.CL"
] | false |
2305.13298
|
2023-05-22T17:56:12Z
|
DiffusionNER: Boundary Diffusion for Named Entity Recognition
|
[
"Yongliang Shen",
"Kaitao Song",
"Xu Tan",
"Dongsheng Li",
"Weiming Lu",
"Yueting Zhuang"
] |
In this paper, we propose DiffusionNER, which formulates the named entity
recognition task as a boundary-denoising diffusion process and thus generates
named entities from noisy spans. During training, DiffusionNER gradually adds
noises to the golden entity boundaries by a fixed forward diffusion process and
learns a reverse diffusion process to recover the entity boundaries. In
inference, DiffusionNER first randomly samples some noisy spans from a standard
Gaussian distribution and then generates the named entities by denoising them
with the learned reverse diffusion process. The proposed boundary-denoising
diffusion process allows progressive refinement and dynamic sampling of
entities, empowering DiffusionNER with efficient and flexible entity generation
capability. Experiments on multiple flat and nested NER datasets demonstrate
that DiffusionNER achieves comparable or even better performance than previous
state-of-the-art models.
|
[
"cs.CL"
] | false |
2305.13309
|
2023-05-22T17:59:42Z
|
Evaluating Factual Consistency of Texts with Semantic Role Labeling
|
[
"Jing Fan",
"Dennis Aumiller",
"Michael Gertz"
] |
Automated evaluation of text generation systems has recently seen increasing
attention, particularly checking whether generated text stays truthful to input
sources. Existing methods frequently rely on an evaluation using task-specific
language models, which in turn allows for little interpretability of generated
scores. We introduce SRLScore, a reference-free evaluation metric designed with
text summarization in mind. Our approach generates fact tuples constructed from
Semantic Role Labels, applied to both input and summary texts. A final
factuality score is computed by an adjustable scoring mechanism, which allows
for easy adaption of the method across domains. Correlation with human
judgments on English summarization datasets shows that SRLScore is competitive
with state-of-the-art methods and exhibits stable generalization across
datasets without requiring further training or hyperparameter tuning. We
experiment with an optional co-reference resolution step, but find that the
performance boost is mostly outweighed by the additional compute required. Our
metric is available online at https://github.com/heyjing/SRLScore.
|
[
"cs.CL"
] | false |
2305.13401
|
2023-05-22T18:28:02Z
|
A study of conceptual language similarity: comparison and evaluation
|
[
"Haotian Ye",
"Yihong Liu",
"Hinrich Schütze"
] |
An interesting line of research in natural language processing (NLP) aims to
incorporate linguistic typology to bridge linguistic diversity and assist the
research of low-resource languages. While most works construct linguistic
similarity measures based on lexical or typological features, such as word
order and verbal inflection, recent work has introduced a novel approach to
defining language similarity based on how they represent basic concepts, which
is complementary to existing similarity measures. In this work, we study the
conceptual similarity in detail and evaluate it extensively on a binary
classification task.
|
[
"cs.CL"
] | false |
2305.13403
|
2023-05-22T18:34:12Z
|
GATology for Linguistics: What Syntactic Dependencies It Knows
|
[
"Yuqian Dai",
"Serge Sharoff",
"Marc de Kamps"
] |
Graph Attention Network (GAT) is a graph neural network which is one of the
strategies for modeling and representing explicit syntactic knowledge and can
work with pre-trained models, such as BERT, in downstream tasks. Currently,
there is still a lack of investigation into how GAT learns syntactic knowledge
from the perspective of model structure. As one of the strategies for modeling
explicit syntactic knowledge, GAT and BERT have never been applied and
discussed in Machine Translation (MT) scenarios. We design a dependency
relation prediction task to study how GAT learns syntactic knowledge of three
languages as a function of the number of attention heads and layers. We also
use a paired t-test and F1-score to clarify the differences in syntactic
dependency prediction between GAT and BERT fine-tuned by the MT task (MT-B).
The experiments show that better performance can be achieved by appropriately
increasing the number of attention heads with two GAT layers. With more than
two layers, learning suffers. Moreover, GAT is more competitive in training
speed and syntactic dependency prediction than MT-B, which may reveal a better
incorporation of modeling explicit syntactic knowledge and the possibility of
combining GAT and BERT in the MT tasks.
|
[
"cs.CL"
] | false |
2305.13412
|
2023-05-22T18:54:35Z
|
Element-aware Summarization with Large Language Models: Expert-aligned
Evaluation and Chain-of-Thought Method
|
[
"Yiming Wang",
"Zhuosheng Zhang",
"Rui Wang"
] |
Automatic summarization generates concise summaries that contain key ideas of
source documents. As the most mainstream datasets for the news sub-domain,
CNN/DailyMail and BBC XSum have been widely used for performance benchmarking.
However, the reference summaries of those datasets turn out to be noisy, mainly
in terms of factual hallucination and information redundancy. To address this
challenge, we first annotate new expert-writing Element-aware test sets
following the "Lasswell Communication Model" proposed by Lasswell (1948),
allowing reference summaries to focus on more fine-grained news elements
objectively and comprehensively. Utilizing the new test sets, we observe the
surprising zero-shot summary ability of LLMs, which addresses the issue of the
inconsistent results between human preference and automatic evaluation metrics
of LLMs' zero-shot summaries in prior work. Further, we propose a Summary
Chain-of-Thought (SumCoT) technique to elicit LLMs to generate summaries step
by step, which helps them integrate more fine-grained details of source
documents into the final summaries that correlate with the human writing
mindset. Experimental results show our method outperforms state-of-the-art
fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 in ROUGE-L on the two
datasets, respectively. Dataset and code are publicly available at
https://github.com/Alsace08/SumCoT.
|
[
"cs.CL"
] | false |
2305.13413
|
2023-05-22T18:56:14Z
|
Syntactic Knowledge via Graph Attention with BERT in Machine Translation
|
[
"Yuqian Dai",
"Serge Sharoff",
"Marc de Kamps"
] |
Although the Transformer model can effectively acquire context features via a
self-attention mechanism, deeper syntactic knowledge is still not effectively
modeled. To alleviate the above problem, we propose Syntactic knowledge via
Graph attention with BERT (SGB) in Machine Translation (MT) scenarios. Graph
Attention Network (GAT) and BERT jointly represent syntactic dependency feature
as explicit knowledge of the source language to enrich source language
representations and guide target language generation. Our experiments use gold
syntax-annotation sentences and Quality Estimation (QE) model to obtain
interpretability of translation quality improvement regarding syntactic
knowledge without being limited to a BLEU score. Experiments show that the
proposed SGB engines improve translation quality across the three MT tasks
without sacrificing BLEU scores. We investigate what length of source sentences
benefits the most and what dependencies are better identified by the SGB
engines. We also find that learning of specific dependency relations by GAT can
be reflected in the translation quality containing such relations and that
syntax on the graph leads to new modeling of syntactic aspects of source
sentences in the middle and bottom layers of BERT.
|
[
"cs.CL"
] | false |
2305.13523
|
2023-05-22T22:37:24Z
|
A Study of Generative Large Language Model for Medical Research and
Healthcare
|
[
"Cheng Peng",
"Xi Yang",
"Aokun Chen",
"Kaleb E Smith",
"Nima PourNejatian",
"Anthony B Costa",
"Cheryl Martin",
"Mona G Flores",
"Ying Zhang",
"Tanja Magoc",
"Gloria Lipori",
"Duane A Mitchell",
"Naykky S Ospina",
"Mustafa M Ahmed",
"William R Hogan",
"Elizabeth A Shenkman",
"Yi Guo",
"Jiang Bian",
"Yonghui Wu"
] |
There is enormous enthusiasm and concerns in using large language models
(LLMs) in healthcare, yet current assumptions are all based on general-purpose
LLMs such as ChatGPT. This study develops a clinical generative LLM,
GatorTronGPT, using 277 billion words of mixed clinical and English text with a
GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical
natural language processing for medical research. Synthetic NLP models trained
using GatorTronGPT generated text outperform NLP models trained using
real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best)
scale shows that there is no significant difference in linguistic readability
(p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical
relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that
physicians cannot differentiate them (p < 0.001). This study provides insights
on the opportunities and challenges of LLMs for medical research and
healthcare.
|
[
"cs.CL"
] | false |
2305.13534
|
2023-05-22T23:14:28Z
|
How Language Model Hallucinations Can Snowball
|
[
"Muru Zhang",
"Ofir Press",
"William Merrill",
"Alisa Liu",
"Noah A. Smith"
] |
A major risk of using language models in practical applications is their
tendency to hallucinate incorrect statements. Hallucinations are often
attributed to knowledge gaps in LMs, but we hypothesize that in some cases,
when justifying previously generated hallucinations, LMs output false claims
that they can separately recognize as incorrect. We construct three
question-answering datasets where ChatGPT and GPT-4 often state an incorrect
answer and offer an explanation with at least one incorrect claim. Crucially,
we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes,
respectively. We refer to this phenomenon as hallucination snowballing: an LM
over-commits to early mistakes, leading to more mistakes that it otherwise
would not make.
|
[
"cs.CL"
] | true |
2305.12627
|
2023-05-22T01:32:50Z
|
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
|
[
"Zhibin Gou",
"Qingyan Guo",
"Yujiu Yang"
] |
Generative methods greatly promote aspect-based sentiment analysis via
generating a sequence of sentiment elements in a specified format. However,
existing studies usually predict sentiment elements in a fixed order, which
ignores the effect of the interdependence of the elements in a sentiment tuple
and the diversity of language expression on the results. In this work, we
propose Multi-view Prompting (MvP) that aggregates sentiment elements generated
in different orders, leveraging the intuition of human-like problem-solving
processes from different views. Specifically, MvP introduces element order
prompts to guide the language model to generate multiple sentiment tuples, each
with a different element order, and then selects the most reasonable tuples by
voting. MvP can naturally model multi-view and multi-task as permutations and
combinations of elements, respectively, outperforming previous task-specific
designed methods on multiple ABSA tasks with a single model. Extensive
experiments show that MvP significantly advances the state-of-the-art
performance on 10 datasets of 4 benchmark tasks, and performs quite effectively
in low-resource settings. Detailed evaluation verified the effectiveness,
flexibility, and cross-task transferability of MvP.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12692
|
2023-05-22T04:00:38Z
|
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta
Learning
|
[
"Zhenrui Yue",
"Huimin Zeng",
"Yang Zhang",
"Lanyu Shang",
"Dong Wang"
] |
With emerging topics (e.g., COVID-19) on social media as a source for the
spreading misinformation, overcoming the distributional shifts between the
original training domain (i.e., source domain) and such target domains remains
a non-trivial task for misinformation detection. This presents an elusive
challenge for early-stage misinformation detection, where a good amount of data
and annotations from the target domain is not available for training. To
address the data scarcity issue, we propose MetaAdapt, a meta learning based
approach for domain adaptive few-shot misinformation detection. MetaAdapt
leverages limited target examples to provide feedback and guide the knowledge
transfer from the source to the target domain (i.e., learn to adapt). In
particular, we train the initial model with multiple source tasks and compute
their similarity scores to the meta task. Based on the similarity scores, we
rescale the meta gradients to adaptively learn from the source tasks. As such,
MetaAdapt can learn how to adapt the misinformation detection model and exploit
the source data for improved performance in the target domain. To demonstrate
the efficiency and effectiveness of our method, we perform extensive
experiments to compare MetaAdapt with state-of-the-art baselines and large
language models (LLMs) such as LLaMA, where MetaAdapt achieves better
performance in domain adaptive few-shot misinformation detection with
substantially reduced parameters on real-world datasets.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12694
|
2023-05-22T04:03:20Z
|
Automatic Spell Checker and Correction for Under-represented Spoken
Languages: Case Study on Wolof
|
[
"Thierno Ibrahima Cissé",
"Fatiha Sadat"
] |
This paper presents a spell checker and correction tool specifically designed
for Wolof, an under-represented spoken language in Africa. The proposed spell
checker leverages a combination of a trie data structure, dynamic programming,
and the weighted Levenshtein distance to generate suggestions for misspelled
words. We created novel linguistic resources for Wolof, such as a lexicon and a
corpus of misspelled words, using a semi-automatic approach that combines
manual and automatic annotation methods. Despite the limited data available for
the Wolof language, the spell checker's performance showed a predictive
accuracy of 98.31% and a suggestion accuracy of 93.33%. Our primary focus
remains the revitalization and preservation of Wolof as an Indigenous and
spoken language in Africa, providing our efforts to develop novel linguistic
resources. This work represents a valuable contribution to the growth of
computational tools and resources for the Wolof language and provides a strong
foundation for future studies in the automatic spell checking and correction
field.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12717
|
2023-05-22T04:53:59Z
|
TADA: Efficient Task-Agnostic Domain Adaptation for Transformers
|
[
"Chia-Chien Hung",
"Lukas Lange",
"Jannik Strötgen"
] |
Intermediate training of pre-trained transformer-based language models on
domain-specific data leads to substantial gains for downstream tasks. To
increase efficiency and prevent catastrophic forgetting alleviated from full
domain-adaptive pre-training, approaches such as adapters have been developed.
However, these require additional parameters for each layer, and are criticized
for their limited expressiveness. In this work, we introduce TADA, a novel
task-agnostic domain adaptation method which is modular, parameter-efficient,
and thus, data-efficient. Within TADA, we retrain the embeddings to learn
domain-aware input representations and tokenizers for the transformer encoder,
while freezing all other parameters of the model. Then, task-specific
fine-tuning is performed. We further conduct experiments with meta-embeddings
and newly introduced meta-tokenizers, resulting in one model per task in
multi-domain use cases. Our broad evaluation in 4 downstream tasks for 14
domains across single- and multi-domain setups and high- and low-resource
scenarios reveals that TADA is an effective and efficient alternative to full
domain-adaptive pre-training and adapters for domain adaptation, while not
introducing additional parameters or complex training steps.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.12720
|
2023-05-22T04:59:33Z
|
llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large
Language Models and its Methodology
|
[
"Masanori Hirano",
"Masahiro Suzuki",
"Hiroki Sakaji"
] |
This study constructed a Japanese chat dataset for tuning large language
models (LLMs), which consist of about 8.4 million records. Recently, LLMs have
been developed and gaining popularity. However, high-performing LLMs are
usually mainly for English. There are two ways to support languages other than
English by those LLMs: constructing LLMs from scratch or tuning existing
models. However, in both ways, datasets are necessary parts. In this study, we
focused on supporting Japanese in those LLMs and making a dataset for training
or tuning LLMs in Japanese. The dataset we constructed consisted of various
tasks, such as translation and knowledge tasks. In our experiment, we tuned an
existing LLM using our dataset and evaluated the performance qualitatively. The
results suggest that our dataset is possibly beneficial for LLMs. However, we
also revealed some difficulties in constructing LLMs in languages other than
English.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12723
|
2023-05-22T05:14:38Z
|
Enhancing Small Medical Learners with Privacy-preserving Contextual
Prompting
|
[
"Xinlu Zhang",
"Shiyang Li",
"Xianjun Yang",
"Chenxin Tian",
"Yao Qin",
"Linda Ruth Petzold"
] |
Large language models (LLMs) demonstrate remarkable medical expertise, but
data privacy concerns impede their direct use in healthcare environments.
Although offering improved data privacy protection, domain-specific small
language models (SLMs) often underperform LLMs, emphasizing the need for
methods that reduce this performance gap while alleviating privacy concerns. In
this paper, we present a simple yet effective method that harnesses LLMs'
medical proficiency to boost SLM performance in medical tasks under
privacy-restricted scenarios. Specifically, we mitigate patient privacy issues
by extracting keywords from medical data and prompting the LLM to generate a
medical knowledge-intensive context by simulating clinicians' thought
processes. This context serves as additional input for SLMs, augmenting their
decision-making capabilities. Our method significantly enhances performance in
both few-shot and full training settings across three medical
knowledge-intensive tasks, achieving up to a 22.57% increase in absolute
accuracy compared to SLM fine-tuning without context, and sets new
state-of-the-art results in two medical tasks within privacy-restricted
scenarios. Further out-of-domain testing and experiments in two general domain
datasets showcase its generalizability and broad applicability.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12737
|
2023-05-22T05:57:47Z
|
The Best of Both Worlds: Combining Human and Machine Translations for
Multilingual Semantic Parsing with Active Learning
|
[
"Zhuang Li",
"Lizhen Qu",
"Philip R. Cohen",
"Raj V. Tumuluri",
"Gholamreza Haffari"
] |
Multilingual semantic parsing aims to leverage the knowledge from the
high-resource languages to improve low-resource semantic parsing, yet commonly
suffers from the data imbalance problem. Prior works propose to utilize the
translations by either humans or machines to alleviate such issues. However,
human translations are expensive, while machine translations are cheap but
prone to error and bias. In this work, we propose an active learning approach
that exploits the strengths of both human and machine translations by
iteratively adding small batches of human translations into the
machine-translated training set. Besides, we propose novel aggregated
acquisition criteria that help our active learning method select utterances to
be manually translated. Our experiments demonstrate that an ideal utterance
selection can significantly reduce the error and bias in the translated data,
resulting in higher parser accuracies than the parsers merely trained on the
machine-translated data.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12744
|
2023-05-22T06:11:15Z
|
Fact-Checking Complex Claims with Program-Guided Reasoning
|
[
"Liangming Pan",
"Xiaobao Wu",
"Xinyuan Lu",
"Anh Tuan Luu",
"William Yang Wang",
"Min-Yen Kan",
"Preslav Nakov"
] |
Fact-checking real-world claims often requires collecting multiple pieces of
evidence and applying complex multi-step reasoning. In this paper, we present
Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that
decomposes complex claims into simpler sub-tasks that can be solved using a
shared library of specialized functions. We first leverage the in-context
learning ability of large language models to generate reasoning programs to
guide the verification process. Afterward, we execute the program by delegating
each sub-task to the corresponding sub-task handler. This process makes our
model both explanatory and data-efficient, providing clear explanations of its
reasoning process and requiring minimal training data. We evaluate ProgramFC on
two challenging fact-checking datasets and show that it outperforms seven
fact-checking baselines across different settings of evidence availability,
with explicit output programs that benefit human debugging. Our codes and data
are publicly available at https://github.com/mbzuai-nlp/ProgramFC.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12753
|
2023-05-22T06:25:09Z
|
Learning to Rank Utterances for Query-Focused Meeting Summarization
|
[
"Xingxian Liu",
"Yajing Xu"
] |
Query-focused meeting summarization(QFMS) aims to generate a specific summary
for the given query according to the meeting transcripts. Due to the conflict
between long meetings and limited input size, previous works mainly adopt
extract-then-summarize methods, which use extractors to simulate binary labels
or ROUGE scores to extract utterances related to the query and then generate a
summary. However, the previous approach fails to fully use the comparison
between utterances. To the extractor, comparison orders are more important than
specific scores. In this paper, we propose a Ranker-Generator framework. It
learns to rank the utterances by comparing them in pairs and learning from the
global orders, then uses top utterances as the generator's input. We show that
learning to rank utterances helps to select utterances related to the query
effectively, and the summarizer can benefit from it. Experimental results on
QMSum show that the proposed model outperforms all existing multi-stage models
with fewer parameters.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12761
|
2023-05-22T06:31:29Z
|
Enhancing Cross-lingual Natural Language Inference by Soft Prompting
with Multilingual Verbalizer
|
[
"Shuang Li",
"Xuming Hu",
"Aiwei Liu",
"Yawen Yang",
"Fukun Ma",
"Philip S. Yu",
"Lijie Wen"
] |
Cross-lingual natural language inference is a fundamental problem in
cross-lingual language understanding. Many recent works have used prompt
learning to address the lack of annotated parallel corpora in XNLI. However,
these methods adopt discrete prompting by simply translating the templates to
the target language and need external expert knowledge to design the templates.
Besides, discrete prompts of human-designed template words are not trainable
vectors and can not be migrated to target languages in the inference stage
flexibly. In this paper, we propose a novel Soft prompt learning framework with
the Multilingual Verbalizer (SoftMV) for XNLI. SoftMV first constructs
cloze-style question with soft prompts for the input sample. Then we leverage
bilingual dictionaries to generate an augmented multilingual question for the
original question. SoftMV adopts a multilingual verbalizer to align the
representations of original and augmented multilingual questions into the same
semantic space with consistency regularization. Experimental results on XNLI
demonstrate that SoftMV can achieve state-of-the-art performance and
significantly outperform the previous methods under the few-shot and full-shot
cross-lingual transfer settings.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12782
|
2023-05-22T07:24:29Z
|
Towards Robust Personalized Dialogue Generation via Order-Insensitive
Representation Regularization
|
[
"Liang Chen",
"Hongru Wang",
"Yang Deng",
"Wai-Chung Kwan",
"Zezhong Wang",
"Kam-Fai Wong"
] |
Generating persona consistent dialogue response is important for developing
an intelligent conversational agent. Recent works typically fine-tune
large-scale pre-trained models on this task by concatenating persona texts and
dialogue history as a single input sequence to generate the target response.
While simple and effective, our analysis shows that this popular practice is
seriously affected by order sensitivity where different input orders of persona
sentences significantly impact the quality and consistency of generated
response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and
83.2% on BART). To mitigate the order sensitivity problem, we propose a
model-agnostic framework, ORder Insensitive Generation (ORIG), which enables
dialogue models to learn robust representation under different persona orders
and improve the consistency of response generation. Experiments on the
Persona-Chat dataset justify the effectiveness and superiority of our method
with two dominant pre-trained models (GPT2 and BART).
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12792
|
2023-05-22T07:42:35Z
|
Semantic Structure Enhanced Event Causality Identification
|
[
"Zhilei Hu",
"Zixuan Li",
"Xiaolong Jin",
"Long Bai",
"Saiping Guan",
"Jiafeng Guo",
"Xueqi Cheng"
] |
Event Causality Identification (ECI) aims to identify causal relations
between events in unstructured texts. This is a very challenging task, because
causal relations are usually expressed by implicit associations between events.
Existing methods usually capture such associations by directly modeling the
texts with pre-trained language models, which underestimate two kinds of
semantic structures vital to the ECI task, namely, event-centric structure and
event-associated structure. The former includes important semantic elements
related to the events to describe them more precisely, while the latter
contains semantic paths between two events to provide possible supports for
ECI. In this paper, we study the implicit associations between events by
modeling the above explicit semantic structures, and propose a Semantic
Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator
to integrate the event-centric structure information, and employs an LSTM-based
path aggregator to capture the event-associated structure information between
two events. Experimental results on three widely used datasets show that SemSIn
achieves significant improvements over baseline methods.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12802
|
2023-05-22T08:00:56Z
|
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple
Clustering Based Strategy
|
[
"Na Li",
"Zied Bouraoui",
"Steven Schockaert"
] |
Ultra-fine entity typing (UFET) is the task of inferring the semantic types,
from a large set of fine-grained candidates, that apply to a given entity
mention. This task is especially challenging because we only have a small
number of training examples for many of the types, even with distant
supervision strategies. State-of-the-art models, therefore, have to rely on
prior knowledge about the type labels in some way. In this paper, we show that
the performance of existing methods can be improved using a simple technique:
we use pre-trained label embeddings to cluster the labels into semantic domains
and then treat these domains as additional types. We show that this strategy
consistently leads to improved results, as long as high-quality label
embeddings are used. We furthermore use the label clusters as part of a simple
post-processing technique, which results in further performance gains. Both
strategies treat the UFET model as a black box and can thus straightforwardly
be used to improve a wide range of existing models.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12835
|
2023-05-22T08:57:42Z
|
Open-Domain Event Graph Induction for Mitigating Framing Bias
|
[
"Siyi Liu",
"Hongming Zhang",
"Hongwei Wang",
"Kaiqiang Song",
"Dan Roth",
"Dong Yu"
] |
Researchers have proposed various information extraction (IE) techniques to
convert news articles into structured knowledge for news understanding.
However, none of the existing methods have explicitly addressed the issue of
framing bias that is inherent in news articles. We argue that studying and
identifying framing bias is a crucial step towards trustworthy event
understanding. We propose a novel task, neutral event graph induction, to
address this problem. An event graph is a network of events and their temporal
relations. Our task aims to induce such structural knowledge with minimal
framing bias in an open domain. We propose a three-step framework to induce a
neutral event graph from multiple input sources. The process starts by inducing
an event graph from each input source, then merging them into one merged event
graph, and lastly using a Graph Convolutional Network to remove event nodes
with biased connotations. We demonstrate the effectiveness of our framework
through the use of graph prediction metrics and bias-focused metrics.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12918
|
2023-05-22T11:01:38Z
|
Improving Metrics for Speech Translation
|
[
"Claudio Paonessa",
"Dominik Frefel",
"Manfred Vogel"
] |
We introduce Parallel Paraphrasing ($\text{Para}_\text{both}$), an
augmentation method for translation metrics making use of automatic
paraphrasing of both the reference and hypothesis. This method counteracts the
typically misleading results of speech translation metrics such as WER, CER,
and BLEU if only a single reference is available. We introduce two new datasets
explicitly created to measure the quality of metrics intended to be applied to
Swiss German speech-to-text systems. Based on these datasets, we show that we
are able to significantly improve the correlation with human quality perception
if our method is applied to commonly used metrics.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12941
|
2023-05-22T11:41:29Z
|
On the Correspondence between Compositionality and Imitation in Emergent
Neural Communication
|
[
"Emily Cheng",
"Mathieu Rita",
"Thierry Poibeau"
] |
Compositionality is a hallmark of human language that not only enables
linguistic generalization, but also potentially facilitates acquisition. When
simulating language emergence with neural networks, compositionality has been
shown to improve communication performance; however, its impact on imitation
learning has yet to be investigated. Our work explores the link between
compositionality and imitation in a Lewis game played by deep neural agents.
Our contributions are twofold: first, we show that the learning algorithm used
to imitate is crucial: supervised learning tends to produce more average
languages, while reinforcement learning introduces a selection pressure toward
more compositional languages. Second, our study reveals that compositional
languages are easier to imitate, which may induce the pressure toward
compositional languages in RL imitation settings.
|
[
"cs.CL",
"cs.NE"
] | false |
2305.12951
|
2023-05-22T11:54:19Z
|
Cross-functional Analysis of Generalisation in Behavioural Learning
|
[
"Pedro Henrique Luz de Araujo",
"Benjamin Roth"
] |
In behavioural testing, system functionalities underrepresented in the
standard evaluation setting (with a held-out test set) are validated through
controlled input-output pairs. Optimising performance on the behavioural tests
during training (behavioural learning) would improve coverage of phenomena not
sufficiently represented in the i.i.d. data and could lead to seemingly more
robust models. However, there is the risk that the model narrowly captures
spurious correlations from the behavioural test suite, leading to
overestimation and misrepresentation of model performance -- one of the
original pitfalls of traditional evaluation. In this work, we introduce BeLUGA,
an analysis method for evaluating behavioural learning considering
generalisation across dimensions of different granularity levels. We optimise
behaviour-specific loss functions and evaluate models on several partitions of
the behavioural test suite controlled to leave out specific phenomena. An
aggregate score measures generalisation to unseen functionalities (or
overfitting). We use BeLUGA to examine three representative NLP tasks
(sentiment analysis, paraphrase identification and reading comprehension) and
compare the impact of a diverse set of regularisation and domain generalisation
methods on generalisation performance.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.13067
|
2023-05-22T14:37:05Z
|
Improving Robustness in Knowledge Distillation Using Domain-Targeted
Data Augmentation
|
[
"Joe Stacey",
"Marek Rei"
] |
Applying knowledge distillation encourages a student model to behave more
like a teacher model, largely retaining the performance of the teacher model,
even though the student model may have substantially fewer parameters. However,
while distillation helps student models behave more like teacher models
in-distribution, this is not necessarily the case out-of-distribution. To
address this, we use a language model to create task-specific unlabeled data
that mimics the data in targeted out-of-distribution domains. We use this
generated data for knowledge distillation on the task of Natural Language
Inference (NLI), encouraging the student models to behave more like the teacher
models for these examples. Our domain-targeted augmentation is highly
effective, and outperforms previous robustness methods when evaluating
out-of-distribution performance on MNLI. Surprisingly, this method also
improves performance on out-of-distribution domains that the data was not
generated for. We additionally introduce Distilled Minority Upsampling (DMU), a
method for identifying and upsampling minority examples during the
distillation. DMU is complementary to the domain-targeted augmentation, and
substantially improves performance on SNLI-hard. Finally, we show
out-of-distribution improvements on HANS from both of our methods, despite
augmenting the training data with fewer than 5k examples.
|
[
"cs.CL",
"cs.LG",
"I.2.7"
] | false |
2305.13120
|
2023-05-22T15:18:38Z
|
Partial Annotation Learning for Biomedical Entity Recognition
|
[
"Liangping Ding",
"Giovanni Colavizza",
"Zhixiong Zhang"
] |
Motivation: Named Entity Recognition (NER) is a key task to support
biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining
high-quality expert annotated data is laborious and expensive, leading to the
development of automatic approaches such as distant supervision. However,
manually and automatically generated data often suffer from the unlabeled
entity problem, whereby many entity annotations are missing, degrading the
performance of full annotation NER models. Results: To address this problem, we
systematically study the effectiveness of partial annotation learning methods
for biomedical entity recognition over different simulated scenarios of missing
entity annotations. Furthermore, we propose a TS-PubMedBERT-Partial-CRF partial
annotation learning model. We harmonize 15 biomedical NER corpora encompassing
five entity types to serve as a gold standard and compare against two commonly
used partial annotation learning models, BiLSTM-Partial-CRF and EER-PubMedBERT,
and the state-of-the-art full annotation learning BioNER model PubMedBERT
tagger. Results show that partial annotation learning-based methods can
effectively learn from biomedical corpora with missing entity annotations. Our
proposed model outperforms alternatives and, specifically, the PubMedBERT
tagger by 38% in F1-score under high missing entity rates. The recall of entity
mentions in our model is also competitive with the upper bound on the fully
annotated dataset.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.13197
|
2023-05-22T16:27:10Z
|
Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense
Passage Retrieval
|
[
"Zehan Li",
"Yanzhao Zhang",
"Dingkun Long",
"Pengjun Xie"
] |
Recently, various studies have been directed towards exploring dense passage
retrieval techniques employing pre-trained language models, among which the
masked auto-encoder (MAE) pre-training architecture has emerged as the most
promising. The conventional MAE framework relies on leveraging the passage
reconstruction of decoder to bolster the text representation ability of
encoder, thereby enhancing the performance of resulting dense retrieval
systems. Within the context of building the representation ability of the
encoder through passage reconstruction of decoder, it is reasonable to
postulate that a ``more demanding'' decoder will necessitate a corresponding
increase in the encoder's ability. To this end, we propose a novel token
importance aware masking strategy based on pointwise mutual information to
intensify the challenge of the decoder. Importantly, our approach can be
implemented in an unsupervised manner, without adding additional expenses to
the pre-training phase. Our experiments verify that the proposed method is both
effective and robust on large-scale supervised passage retrieval datasets and
out-of-domain zero-shot retrieval benchmarks.
|
[
"cs.IR",
"cs.CL"
] | false |
2305.13246
|
2023-05-22T17:18:29Z
|
Interactive Natural Language Processing
|
[
"Zekun Wang",
"Ge Zhang",
"Kexin Yang",
"Ning Shi",
"Wangchunshu Zhou",
"Shaochun Hao",
"Guangzheng Xiong",
"Yizhi Li",
"Mong Yuan Sim",
"Xiuying Chen",
"Qingqing Zhu",
"Zhenzhu Yang",
"Adam Nik",
"Qi Liu",
"Chenghua Lin",
"Shi Wang",
"Ruibo Liu",
"Wenhu Chen",
"Ke Xu",
"Dayiheng Liu",
"Yike Guo",
"Jie Fu"
] |
Interactive Natural Language Processing (iNLP) has emerged as a novel
paradigm within the field of NLP, aimed at addressing limitations in existing
frameworks while aligning with the ultimate goals of artificial intelligence.
This paradigm considers language models as agents capable of observing, acting,
and receiving feedback iteratively from external entities. Specifically,
language models in this context can: (1) interact with humans for better
understanding and addressing user needs, personalizing responses, aligning with
human values, and improving the overall user experience; (2) interact with
knowledge bases for enriching language representations with factual knowledge,
enhancing the contextual relevance of responses, and dynamically leveraging
external information to generate more accurate and informed responses; (3)
interact with models and tools for effectively decomposing and addressing
complex tasks, leveraging specialized expertise for specific subtasks, and
fostering the simulation of social behaviors; and (4) interact with
environments for learning grounded representations of language, and effectively
tackling embodied tasks such as reasoning, planning, and decision-making in
response to environmental observations. This paper offers a comprehensive
survey of iNLP, starting by proposing a unified definition and framework of the
concept. We then provide a systematic classification of iNLP, dissecting its
various components, including interactive objects, interaction interfaces, and
interaction methods. We proceed to delve into the evaluation methodologies used
in the field, explore its diverse applications, scrutinize its ethical and
safety issues, and discuss prospective research directions. This survey serves
as an entry point for researchers who are interested in this rapidly evolving
area and offers a broad view of the current landscape and future trajectory of
iNLP.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13267
|
2023-05-22T17:33:44Z
|
Enhance Reasoning Ability of Visual-Language Models via Large Language
Models
|
[
"Yueting Yang",
"Xintong Zhang",
"Wenjuan Han"
] |
Pre-trained visual language models (VLM) have shown excellent performance in
image caption tasks. However, it sometimes shows insufficient reasoning
ability. In contrast, large language models (LLMs) emerge with powerful
reasoning capabilities. Therefore, we propose a method called TReE, which
transfers the reasoning ability of a large language model to a visual language
model in zero-shot scenarios. TReE contains three stages: observation,
thinking, and re-thinking. Observation stage indicates that VLM obtains the
overall information of the relative image. Thinking stage combines the image
information and task description as the prompt of the LLM, inference with the
rationals. Re-Thinking stage learns from rationale and then inference the final
result through VLM.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13282
|
2023-05-22T17:42:44Z
|
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for
Out-of-Domain Detection
|
[
"Rheeya Uppaal",
"Junjie Hu",
"Yixuan Li"
] |
Out-of-distribution (OOD) detection is a critical task for reliable
predictions over text. Fine-tuning with pre-trained language models has been a
de facto procedure to derive OOD detectors with respect to in-distribution (ID)
data. Despite its common use, the understanding of the role of fine-tuning and
its necessity for OOD detection is largely unexplored. In this paper, we raise
the question: is fine-tuning necessary for OOD detection? We present a study
investigating the efficacy of directly leveraging pre-trained language models
for OOD detection, without any model fine-tuning on the ID data. We compare the
approach with several competitive fine-tuning objectives, and offer new
insights under various types of distributional shifts. Extensive evaluations on
8 diverse ID-OOD dataset pairs demonstrate near-perfect OOD detection
performance (with 0% FPR95 in many cases), strongly outperforming its
fine-tuned counterparts. We show that using distance-based detection methods,
pre-trained language models are near-perfect OOD detectors when the
distribution shift involves a domain change. Furthermore, we study the effect
of fine-tuning on OOD detection and identify how to balance ID accuracy with
OOD detection performance. Our code is publically available at
https://github.com/Uppaal/lm-ood.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.13304
|
2023-05-22T17:58:10Z
|
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
|
[
"Wangchunshu Zhou",
"Yuchen Eleanor Jiang",
"Peng Cui",
"Tiannan Wang",
"Zhenxin Xiao",
"Yifan Hou",
"Ryan Cotterell",
"Mrinmaya Sachan"
] |
The fixed-size context of Transformer makes GPT models incapable of
generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a
language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is
built upon a large language model (LLM) such as ChatGPT and uses natural
language to simulate the Long Short-Term Memory mechanism in an LSTM. At each
timestep, RecurrentGPT generates a paragraph of text and updates its
language-based long-short term memory stored on the hard drive and the prompt,
respectively. This recurrence mechanism enables RecurrentGPT to generate texts
of arbitrary length without forgetting. Since human users can easily observe
and edit the natural language memories, RecurrentGPT is interpretable and
enables interactive generation of long text. RecurrentGPT is an initial step
towards next-generation computer-assisted writing systems beyond local editing
suggestions. In addition to producing AI-generated content (AIGC), we also
demonstrate the possibility of using RecurrentGPT as an interactive fiction
that directly interacts with consumers. We call this usage of generative models
by ``AI As Contents'' (AIAC), which we believe is the next form of conventional
AIGC. We further demonstrate the possibility of using RecurrentGPT to create
personalized interactive fiction that directly interacts with readers instead
of interacting with writers. More broadly, RecurrentGPT demonstrates the
utility of borrowing ideas from popular model designs in cognitive science and
deep learning for prompting LLMs. Our code is available at
https://github.com/aiwaves-cn/RecurrentGPT and an online demo is available at
https://www.aiwaves.org/recurrentgpt.
|
[
"cs.CL",
"cs.LG"
] | true |
2305.13504
|
2023-05-22T21:43:12Z
|
Neural Machine Translation for Code Generation
|
[
"Dharma KC",
"Clayton T. Morrison"
] |
Neural machine translation (NMT) methods developed for natural language
processing have been shown to be highly successful in automating translation
from one natural language to another. Recently, these NMT methods have been
adapted to the generation of program code. In NMT for code generation, the task
is to generate output source code that satisfies constraints expressed in the
input. In the literature, a variety of different input scenarios have been
explored, including generating code based on natural language description,
lower-level representations such as binary or assembly (neural decompilation),
partial representations of source code (code completion and repair), and source
code in another language (code translation). In this paper we survey the NMT
for code generation literature, cataloging the variety of methods that have
been explored according to input and output representations, model
architectures, optimization techniques used, data sets, and evaluation methods.
We discuss the limitations of existing methods and future research directions
|
[
"cs.CL",
"cs.LG",
"A.1"
] | false |
2305.13530
|
2023-05-22T22:52:47Z
|
The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian
Language
|
[
"Daria Stetsenko",
"Inez Okulska"
] |
This paper provides an overview of a text mining tool the StyloMetrix
developed initially for the Polish language and further extended for English
and recently for Ukrainian. The StyloMetrix is built upon various metrics
crafted manually by computational linguists and researchers from literary
studies to analyze grammatical, stylistic, and syntactic patterns. The idea of
constructing the statistical evaluation of syntactic and grammar features is
straightforward and familiar for the languages like English, Spanish, German,
and others; it is yet to be developed for low-resource languages like
Ukrainian. We describe the StyloMetrix pipeline and provide some experiments
with this tool for the text classification task. We also describe our package's
main limitations and the metrics' evaluation procedure.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.13535
|
2023-05-22T23:19:01Z
|
Improving Classifier Robustness through Active Generation of Pairwise
Counterfactuals
|
[
"Ananth Balashankar",
"Xuezhi Wang",
"Yao Qin",
"Ben Packer",
"Nithum Thain",
"Jilin Chen",
"Ed H. Chi",
"Alex Beutel"
] |
Counterfactual Data Augmentation (CDA) is a commonly used technique for
improving robustness in natural language classifiers. However, one fundamental
challenge is how to discover meaningful counterfactuals and efficiently label
them, with minimal human labeling cost. Most existing methods either completely
rely on human-annotated labels, an expensive process which limits the scale of
counterfactual data, or implicitly assume label invariance, which may mislead
the model with incorrect labels. In this paper, we present a novel framework
that utilizes counterfactual generative models to generate a large number of
diverse counterfactuals by actively sampling from regions of uncertainty, and
then automatically label them with a learned pairwise classifier. Our key
insight is that we can more correctly label the generated counterfactuals by
training a pairwise classifier that interpolates the relationship between the
original example and the counterfactual. We demonstrate that with a small
amount of human-annotated counterfactual data (10%), we can generate a
counterfactual augmentation dataset with learned labels, that provides an
18-20% improvement in robustness and a 14-21% reduction in errors on 6
out-of-domain datasets, comparable to that of a fully human-annotated
counterfactual dataset for both sentiment classification and question
paraphrase tasks.
|
[
"cs.CL",
"cs.LG"
] | false |
2305.18322
|
2023-05-22T22:40:11Z
|
REFinD: Relation Extraction Financial Dataset
|
[
"Simerjot Kaur",
"Charese Smiley",
"Akshat Gupta",
"Joy Sain",
"Dongsheng Wang",
"Suchetha Siddagangappa",
"Toyin Aguda",
"Sameena Shah"
] |
A number of datasets for Relation Extraction (RE) have been created to aide
downstream tasks such as information retrieval, semantic search, question
answering and textual entailment. However, these datasets fail to capture
financial-domain specific challenges since most of these datasets are compiled
using general knowledge sources such as Wikipedia, web-based text and news
articles, hindering real-life progress and adoption within the financial world.
To address this limitation, we propose REFinD, the first large-scale annotated
dataset of relations, with $\sim$29K instances and 22 relations amongst 8 types
of entity pairs, generated entirely over financial documents. We also provide
an empirical evaluation with various state-of-the-art models as benchmarks for
the RE task and highlight the challenges posed by our dataset. We observed that
various state-of-the-art deep learning models struggle with numeric inference,
relational and directional ambiguity.
|
[
"cs.CL",
"cs.AI"
] | false |
2305.12628
|
2023-05-22T01:39:40Z
|
Duplex Diffusion Models Improve Speech-to-Speech Translation
|
[
"Xianchao Wu"
] |
Speech-to-speech translation is a typical sequence-to-sequence learning task
that naturally has two directions. How to effectively leverage bidirectional
supervision signals to produce high-fidelity audio for both directions?
Existing approaches either train two separate models or a multitask-learned
model with low efficiency and inferior performance. In this paper, we propose a
duplex diffusion model that applies diffusion probabilistic models to both
sides of a reversible duplex Conformer, so that either end can simultaneously
input and output a distinct language's speech. Our model enables reversible
speech translation by simply flipping the input and output ends. Experiments
show that our model achieves the first success of reversible speech translation
with significant improvements of ASR-BLEU scores compared with a list of
state-of-the-art baselines.
|
[
"cs.CL",
"cs.LG",
"cs.SD",
"eess.AS"
] | false |
2305.12647
|
2023-05-22T02:43:15Z
|
Reflective Linguistic Programming (RLP): A Stepping Stone in
Socially-Aware AGI (SocialAGI)
|
[
"Kevin A. Fischer"
] |
This paper presents Reflective Linguistic Programming (RLP), a unique
approach to conversational AI that emphasizes self-awareness and strategic
planning. RLP encourages models to introspect on their own predefined
personality traits, emotional responses to incoming messages, and planned
strategies, enabling contextually rich, coherent, and engaging interactions. A
striking illustration of RLP's potential involves a toy example, an AI persona
with an adversarial orientation, a demon named `Bogus' inspired by the
children's fairy tale Hansel & Gretel. Bogus exhibits sophisticated behaviors,
such as strategic deception and sensitivity to user discomfort, that
spontaneously arise from the model's introspection and strategic planning.
These behaviors are not pre-programmed or prompted, but emerge as a result of
the model's advanced cognitive modeling. The potential applications of RLP in
socially-aware AGI (Social AGI) are vast, from nuanced negotiations and mental
health support systems to the creation of diverse and dynamic AI personas. Our
exploration of deception serves as a stepping stone towards a new frontier in
AGI, one filled with opportunities for advanced cognitive modeling and the
creation of truly human `digital souls'.
|
[
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | false |
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