Our long-term research goal is to develop models, algorithms, and learning protocols for fair, accountable, and robust language processing technology. Please see our recent publications on these topics.
- Multimodal LLM
- Trustworthy NLP (Fairness, Robustness, Accountability)
- Learning and Inference in Natural Language Processing
- NLP for Social Good Applications
Multimodal Large Language Models
Governing Societal Bias in NLP Models
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Societal Biases in Language Generation: Progress and Challenges
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng, in ACL, 2021.
Full Text Abstract BibTeX DetailsDetailsTechnology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.
@inproceedings{sheng2021societal, title = {Societal Biases in Language Generation: Progress and Challenges}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Prem and Peng, Nanyun}, booktitle = {ACL}, year = {2021} } -
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang, in ACL-Finding, 2021.
Full Text Code Abstract BibTeX DetailsDetailsExisting bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions. While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input. In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts.
@inproceedings{pruksachatkun2021robustness, title = {Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification}, author = {Pruksachatkun, Yada and Krishna, Satyapriya and Dhamala, Jwala and Gupta, Rahul and Chang, Kai-Wei}, booktitle = {ACL-Finding}, year = {2021} } -
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng, in NAACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsAd hominem attacks are those that target some feature of a person’s character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person’s credibility. Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses. To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts. We specifically compare responses to Twitter topics about marginalized communities (#BlackLivesMatter, #MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations. Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated. Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities, 2) different quantities of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can use constrained decoding techniques to reduce ad hominems in generated dialogue responses.
@inproceedings{sheng2021nice, title = {"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses}, booktitle = {NAACL}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Prem and Peng, Nanyun}, presentation_id = {https://underline.io/events/122/sessions/4137/lecture/19854-%27nice-try,-kiddo%27-investigating-ad-hominems-in-dialogue-responses}, year = {2021} } -
BOLD: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta, in FAccT, 2021.
Full Text Code Abstract BibTeX DetailsDetailsRecent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to automatic storytelling, they have been shown to generate texts that exhibit social biases. To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23,679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology. We also propose new automated metrics for toxicity, psycholinguistic norms, and text gender polarity to measure social biases in open-ended text generation from multiple angles. An examination of text generated from three popular language models reveals that the majority of these models exhibit a larger social bias than human-written Wikipedia text across all domains. With these results we highlight the need to benchmark biases in open-ended language generation and caution users of language generation models on downstream tasks to be cognizant of these embedded prejudices.
@inproceedings{dhamala2021bold, author = {Dhamala, Jwala and Sun, Tony and Kumar, Varun and Krishna, Satyapriya and Pruksachatkun, Yada and Chang, Kai-Wei and Gupta, Rahul}, title = {BOLD: Dataset and metrics for measuring biases in open-ended language generation}, booktitle = {FAccT}, year = {2021} } -
LOGAN: Local Group Bias Detection by Clustering
Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
Full Text Code Abstract BibTeX DetailsDetailsMachine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.
@inproceedings{zhao2020logan, author = {Zhao, Jieyu and Chang, Kai-Wei}, title = {LOGAN: Local Group Bias Detection by Clustering}, booktitle = {EMNLP (short)}, presentation_id = {https://virtual.2020.emnlp.org/paper_main.2886.html}, year = {2020} } -
Towards Controllable Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP-Finding, 2020.
Full Text Code Abstract BibTeX DetailsDetailsWe present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
@inproceedings{sheng2020towards, title = {Towards Controllable Biases in Language Generation}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun}, booktitle = {EMNLP-Finding}, year = {2020} } -
Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang, in ACL, 2020.
Full Text Abstract BibTeX DetailsDetailsRecent developments in Neural Relation Extraction (NRE) have made significant strides towards automated knowledge base construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birth date or birth place. We also analyze two existing bias mitigation techniques, word embedding debiasing and data augmentation. Unfortunately, due to NRE models relying heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in relation extraction.
@inproceedings{gaut2020towards, author = {Gaut, Andrew and Sun, Tony and Tang, Shirlyn and Huang, Yuxin and Qian, Jing and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Belding, Elizabeth and Chang, Kai-Wei and Wang, William Yang}, title = {Towards Understanding Gender Bias in Relation Extraction}, booktitle = {ACL}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.265.html} } -
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
Shengyu Jia, Tao Meng, Jieyu Zhao, and Kai-Wei Chang, in ACL (short), 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsAdvanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., Zhao et al. (2017) show that these techniques inadvertently capture the societal bias hiddenin the corpus and further amplify it. However,their analysis is conducted only on models’ top predictions. In this paper, we investigate thegender bias amplification issue from the distribution perspective and demonstrate that thebias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.
@inproceedings{jia2020mitigating, author = {Jia, Shengyu and Meng, Tao and Zhao, Jieyu and Chang, Kai-Wei}, title = {Mitigating Gender Bias Amplification in Distribution by Posterior Regularization}, booktitle = {ACL (short)}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.264.html} } -
The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP (short), 2019.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsWe present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.
@inproceedings{sheng2019woman, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun}, title = {The Woman Worked as a Babysitter: On Biases in Language Generation}, booktitle = {EMNLP (short)}, vimeo_id = {426366363}, year = {2019} } -
Mitigating Gender in Natural Language Processing: Literature Review
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Kai-Wei Chang, and William Yang Wang, in ACL, 2019.
Full Text Slides Video Abstract BibTeX DetailsDetailsAs Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.
@inproceedings{sun2019mitigating, author = {Sun, Tony and Gaut, Andrew and Tang, Shirlyn and Huang, Yuxin and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Chang, Kai-Wei and Wang, William Yang}, title = {Mitigating Gender in Natural Language Processing: Literature Review}, booktitle = {ACL}, vimeo_id = {384482151}, year = {2019} } -
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2018.
Full Text Poster Code Abstract BibTeX Details Top-10 cited paper at NAACL 18DetailsIn this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.
@inproceedings{zhao2018gender, author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, booktitle = {NAACL (short)}, press_url = {https://www.stitcher.com/podcast/matt-gardner/nlp-highlights/e/55861936}, year = {2018} } -
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in EMNLP, 2017.
Full Text Slides Code Abstract BibTeX Details EMNLP 2017 Best Long Paper Award; Top-10 cited paper at EMNLP 17DetailsLanguage is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occuring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, but a trained model amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for the resulting inference problems. Our method results in no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 33.3% and 44.9% for multilabel classification and visual semantic role labeling, respectively.
@inproceedings{zhao2017men, author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei}, title = {Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints}, booktitle = {EMNLP}, year = {2017} }
Fairness, Ethics, Accountability, and Transparency (FEAT) in NLP
Our group contributed to the first few studies concerning algorithmic fairness and robustness in NLP.
Governing Societal Bias in NLP Models
-
Societal Biases in Language Generation: Progress and Challenges
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng, in ACL, 2021.
Full Text Abstract BibTeX DetailsDetailsTechnology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.
@inproceedings{sheng2021societal, title = {Societal Biases in Language Generation: Progress and Challenges}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Prem and Peng, Nanyun}, booktitle = {ACL}, year = {2021} } -
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang, in ACL-Finding, 2021.
Full Text Code Abstract BibTeX DetailsDetailsExisting bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions. While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input. In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts.
@inproceedings{pruksachatkun2021robustness, title = {Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification}, author = {Pruksachatkun, Yada and Krishna, Satyapriya and Dhamala, Jwala and Gupta, Rahul and Chang, Kai-Wei}, booktitle = {ACL-Finding}, year = {2021} } -
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng, in NAACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsAd hominem attacks are those that target some feature of a person’s character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person’s credibility. Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses. To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts. We specifically compare responses to Twitter topics about marginalized communities (#BlackLivesMatter, #MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations. Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated. Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities, 2) different quantities of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can use constrained decoding techniques to reduce ad hominems in generated dialogue responses.
@inproceedings{sheng2021nice, title = {"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses}, booktitle = {NAACL}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Prem and Peng, Nanyun}, presentation_id = {https://underline.io/events/122/sessions/4137/lecture/19854-%27nice-try,-kiddo%27-investigating-ad-hominems-in-dialogue-responses}, year = {2021} } -
BOLD: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta, in FAccT, 2021.
Full Text Code Abstract BibTeX DetailsDetailsRecent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to automatic storytelling, they have been shown to generate texts that exhibit social biases. To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23,679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology. We also propose new automated metrics for toxicity, psycholinguistic norms, and text gender polarity to measure social biases in open-ended text generation from multiple angles. An examination of text generated from three popular language models reveals that the majority of these models exhibit a larger social bias than human-written Wikipedia text across all domains. With these results we highlight the need to benchmark biases in open-ended language generation and caution users of language generation models on downstream tasks to be cognizant of these embedded prejudices.
@inproceedings{dhamala2021bold, author = {Dhamala, Jwala and Sun, Tony and Kumar, Varun and Krishna, Satyapriya and Pruksachatkun, Yada and Chang, Kai-Wei and Gupta, Rahul}, title = {BOLD: Dataset and metrics for measuring biases in open-ended language generation}, booktitle = {FAccT}, year = {2021} } -
LOGAN: Local Group Bias Detection by Clustering
Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
Full Text Code Abstract BibTeX DetailsDetailsMachine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.
@inproceedings{zhao2020logan, author = {Zhao, Jieyu and Chang, Kai-Wei}, title = {LOGAN: Local Group Bias Detection by Clustering}, booktitle = {EMNLP (short)}, presentation_id = {https://virtual.2020.emnlp.org/paper_main.2886.html}, year = {2020} } -
Towards Controllable Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP-Finding, 2020.
Full Text Code Abstract BibTeX DetailsDetailsWe present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
@inproceedings{sheng2020towards, title = {Towards Controllable Biases in Language Generation}, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun}, booktitle = {EMNLP-Finding}, year = {2020} } -
Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang, in ACL, 2020.
Full Text Abstract BibTeX DetailsDetailsRecent developments in Neural Relation Extraction (NRE) have made significant strides towards automated knowledge base construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birth date or birth place. We also analyze two existing bias mitigation techniques, word embedding debiasing and data augmentation. Unfortunately, due to NRE models relying heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in relation extraction.
@inproceedings{gaut2020towards, author = {Gaut, Andrew and Sun, Tony and Tang, Shirlyn and Huang, Yuxin and Qian, Jing and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Belding, Elizabeth and Chang, Kai-Wei and Wang, William Yang}, title = {Towards Understanding Gender Bias in Relation Extraction}, booktitle = {ACL}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.265.html} } -
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
Shengyu Jia, Tao Meng, Jieyu Zhao, and Kai-Wei Chang, in ACL (short), 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsAdvanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., Zhao et al. (2017) show that these techniques inadvertently capture the societal bias hiddenin the corpus and further amplify it. However,their analysis is conducted only on models’ top predictions. In this paper, we investigate thegender bias amplification issue from the distribution perspective and demonstrate that thebias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.
@inproceedings{jia2020mitigating, author = {Jia, Shengyu and Meng, Tao and Zhao, Jieyu and Chang, Kai-Wei}, title = {Mitigating Gender Bias Amplification in Distribution by Posterior Regularization}, booktitle = {ACL (short)}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.264.html} } -
The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP (short), 2019.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsWe present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.
@inproceedings{sheng2019woman, author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun}, title = {The Woman Worked as a Babysitter: On Biases in Language Generation}, booktitle = {EMNLP (short)}, vimeo_id = {426366363}, year = {2019} } -
Mitigating Gender in Natural Language Processing: Literature Review
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Kai-Wei Chang, and William Yang Wang, in ACL, 2019.
Full Text Slides Video Abstract BibTeX DetailsDetailsAs Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.
@inproceedings{sun2019mitigating, author = {Sun, Tony and Gaut, Andrew and Tang, Shirlyn and Huang, Yuxin and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Chang, Kai-Wei and Wang, William Yang}, title = {Mitigating Gender in Natural Language Processing: Literature Review}, booktitle = {ACL}, vimeo_id = {384482151}, year = {2019} } -
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2018.
Full Text Poster Code Abstract BibTeX Details Top-10 cited paper at NAACL 18DetailsIn this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.
@inproceedings{zhao2018gender, author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, booktitle = {NAACL (short)}, press_url = {https://www.stitcher.com/podcast/matt-gardner/nlp-highlights/e/55861936}, year = {2018} } -
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in EMNLP, 2017.
Full Text Slides Code Abstract BibTeX Details EMNLP 2017 Best Long Paper Award; Top-10 cited paper at EMNLP 17DetailsLanguage is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occuring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, but a trained model amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for the resulting inference problems. Our method results in no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 33.3% and 44.9% for multilabel classification and visual semantic role labeling, respectively.
@inproceedings{zhao2017men, author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei}, title = {Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints}, booktitle = {EMNLP}, year = {2017} }
Governing Societal Bias in Representation
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Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff Phillips, and Kai-Wei Chang, in EMNLP, 2021.
Full Text Slides Poster Abstract BibTeX DetailsDetailsGender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.
@inproceedings{dev2021harms, title = {Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies}, author = {Dev, Sunipa and Monajatipoor, Masoud and Ovalle, Anaelia and Subramonian, Arjun and Phillips, Jeff and Chang, Kai-Wei}, presentation_id = {https://underline.io/events/192/sessions/7788/lecture/37320-harms-of-gender-exclusivity-and-challenges-in-non-binary-representation-in-language-technologies}, blog_url = {https://uclanlp.medium.com/harms-of-gender-exclusivity-and-challenges-in-non-binary-representation-in-language-technologies-5f89891b5aee}, booktitle = {EMNLP}, year = {2021} } -
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer
Jieyu Zhao, Subhabrata Mukherjee, Saghar Hosseini, Kai-Wei Chang, and Ahmed Hassan Awadallah, in ACL, 2020.
Full Text Slides Video Abstract BibTeX DetailsDetailsMultilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence on the bias in transfer learning. We further provide recommendations for using the multilingual word representations for downstream tasks.
@inproceedings{zhao2020gender, author = {Zhao, Jieyu and Mukherjee, Subhabrata and Hosseini, Saghar and Chang, Kai-Wei and Awadallah, Ahmed Hassan}, title = {Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer}, booktitle = {ACL}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.260.html} } -
Examining Gender Bias in Languages with Grammatical Gender
Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen, Ryan Cotterell, and Kai-Wei Chang, in EMNLP, 2019.
Full Text Poster Code Abstract BibTeX DetailsDetailsRecent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embeddings under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches effectively reduce the gender bias while preserving the utility of the embeddings.
@inproceedings{zhou2019examining, author = {Zhou, Pei and Shi, Weijia and Zhao, Jieyu and Huang, Kuan-Hao and Chen, Muhao and Cotterell, Ryan and Chang, Kai-Wei}, title = {Examining Gender Bias in Languages with Grammatical Gender}, booktitle = {EMNLP}, year = {2019} } -
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez, in ICCV, 2019.
Full Text Code Demo Abstract BibTeX DetailsDetailsIn this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables –such as gender– in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network – and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.
@inproceedings{wang2019balanced, author = {Wang, Tianlu and Zhao, Jieyu and Yatskar, Mark and Chang, Kai-Wei and Ordonez, Vicente}, title = {Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations}, booktitle = {ICCV}, year = {2019} } -
Gender Bias in Contextualized Word Embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2019.
Full Text Slides Video Abstract BibTeX DetailsDetailsDespite the great success of contextualized word embeddings on downstream applications, these representations potentially embed the societal biases exhibited in their training corpus. In this paper, we quantify, analyze and mitigate the gender bias exhibited in ELMo contextualized word vectors. We first demonstrate that the vectors encode and propagate information about genders unequally and then conduct a principal component analysis to visualize the geometry of the gender information in the embeddings. Then we show that ELMo works unequally well for men and women in down-stream tasks. Finally, we explore a variety of methods to remove such gender bias and demonstrate that it can be reduced through data augmentation.
@inproceedings{zhao2019gender, author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Cotterell, Ryan and Ordonez, Vicente and Chang, Kai-Wei}, title = {Gender Bias in Contextualized Word Embeddings}, booktitle = {NAACL (short)}, year = {2019} } -
Learning Gender-Neutral Word Embeddings
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang, in EMNLP (short), 2018.
Full Text Code Abstract BibTeX DetailsDetailsWord embeddings have become a fundamental component in a wide range of Natu-ral Language Processing (NLP) applications.However, these word embeddings trained onhuman-generated corpora inherit strong gen-der stereotypes that reflect social constructs.In this paper, we propose a novel word em-bedding model, De-GloVe, that preserves gen-der information in certain dimensions of wordvectors while compelling other dimensions tobe free of gender influence. Quantitative andqualitative experiments demonstrate that De-GloVe successfully isolates gender informa-tion without sacrificing the functionality of theembedding model.
@inproceedings{zhao2018learning, author = {Zhao, Jieyu and Zhou, Yichao and Li, Zeyu and Wang, Wei and Chang, Kai-Wei}, title = {Learning Gender-Neutral Word Embeddings}, booktitle = {EMNLP (short)}, year = {2018} } -
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai, in NeurIPS, 2016.
Full Text Code Abstract BibTeX Details Top-10 cited paper at NeurIPS 16DetailsThe blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.
@inproceedings{bolukbasi2016man, author = {Bolukbasi, Tolga and Chang, Kai-Wei and Zou, James and Saligrama, Venkatesh and Kalai, Adam}, title = {Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings}, booktitle = {NeurIPS}, year = {2016} }
Robustness in NLP
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On the Transferability of Adversarial Attacks against Neural Text Classifier
Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, and Kai-Wei Chang, in EMNLP, 2021.
Full Text Abstract BibTeX DetailsDetailsDeep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can fool another model. In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples. Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. Such adversarial examples reflect the defects of the learning process and the data bias in the training set. Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.
@inproceedings{yuan2021on, title = {On the Transferability of Adversarial Attacks against Neural Text Classifier}, author = {Yuan, Liping and Zheng, Xiaoqing and Zhou, Yi and Hsieh, Cho-Jui and Chang, Kai-Wei}, presentation_id = {https://underline.io/events/192/posters/8223/poster/38067-on-the-transferability-of-adversarial-attacks-against-neural-text-classifier}, booktitle = {EMNLP}, year = {2021} } -
Searching for an Effiective Defender: Benchmarking Defense against Adversarial Word Substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, and Cho-Jui Hsieh, in EMNLP, 2021.
Full Text Abstract BibTeX DetailsDetailsRecent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets by a significant margin.
@inproceedings{li2021searching, title = {Searching for an Effiective Defender: Benchmarking Defense against Adversarial Word Substitution}, author = {Li, Zongyi and Xu, Jianhan and Zeng, Jiehang and Li, Linyang and Zheng, Xiaoqing and Zhang, Qi and Chang, Kai-Wei and Hsieh, Cho-Jui}, presentation_id = {https://underline.io/events/192/posters/8225/poster/38025-searching-for-an-effective-defender-benchmarking-defense-against-adversarial-word-substitution}, booktitle = {EMNLP}, year = {2021} } -
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, and Xuanjing Huang, in ACL, 2021.
Full Text Code Abstract BibTeX DetailsDetailsAlthough deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitutionbased attacks. During training, DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. In such a way, the model is robust to adversarial attacks while maintaining the performance on the original clean data. DNE is agnostic to the network architectures and scales to large models (e.g., BERT) for NLP applications. Through extensive experimentation, we demonstrate that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.
@inproceedings{zhou2021defense, title = {Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble}, author = {Zhou, Yi and Zheng, Xiaoqing and Hsieh, Cho-Jui and Chang, Kai-Wei and Huang, Xuanjing}, booktitle = {ACL}, year = {2021} } -
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation
Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, and Cho-Jui Hsieh, in NAACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsRobustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation results keep the same? In this paper, we propose a "double perturbation" framework to uncover model weaknesses beyond the test dataset. The framework first perturbs the test dataset to construct abundant natural sentences similar to the test data, and then diagnoses the prediction change regarding a single-word substitution. We apply this framework to study two perturbation-based approaches that are used to analyze models’ robustness and counterfactual bias in English. (1) For robustness, we focus on synonym substitutions and identify vulnerable examples where prediction can be altered. Our proposed attack attains high success rates (96.0%-99.8%) in finding vulnerable examples on both original and robustly trained CNNs and Transformers. (2) For counterfactual bias, we focus on substituting demographic tokens (e.g., gender, race) and measure the shift of the expected prediction among constructed sentences. Our method is able to reveal the hidden model biases not directly shown in the test dataset.
@inproceedings{zhang2021double, title = { Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation}, booktitle = {NAACL}, author = {Zhang, Chong and Zhao, Jieyu and Zhang, Huan and Chang, Kai-Wei and Hsieh, Cho-Jui}, year = {2021}, presentation_id = {https://underline.io/events/122/sessions/4229/lecture/19609-double-perturbation-on-the-robustness-of-robustness-and-counterfactual-bias-evaluation} } -
Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh, in NeurIPS, 2020.
Full Text Code Abstract BibTeX DetailsDetailsLinear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. The majority of LiRPA-based methods only consider simple feed-forward networks and it needs particular manual derivations and implementations when extended to other architectures. In this paper, we develop an automatic framework to enable perturbation analysis on any neural network structures, by generalizing exiting LiRPA algorithms such as CROWN to operate on general computational graphs. The flexibility, differentiability and ease of use of our framework allow us to obtain state-of-the-art results on LiRPA based certified defense on fairly complicated networks like DenseNet, ResNeXt and Transformer that are not supported by prior work. Our framework also enables loss fusion, a technique that significantly reduces the computational complexity of LiRPA for certified defense. For the first time, we demonstrate LiRPA based certified defense on Tiny ImageNet and Downscaled ImageNet where previous approaches cannot scale to due to the relatively large number of classes. Our work also yields an open-source library for the community to apply LiRPA to areas beyond certified defense without much LiRPA expertise, e.g., we create a neural network with a provably flat optimization landscape. Our open source library is available at https://github.com/KaidiXu/auto_LiRPA
@inproceedings{xu2020provable, author = {Xu, Kaidi and Shi, Zhouxing and Zhang, Huan and Wang, Yihan and Chang, Kai-Wei and Huang, Minlie and Kailkhura, Bhavya and Lin, Xue and Hsieh, Cho-Jui}, title = {Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs}, booktitle = {NeurIPS}, year = {2020} } -
On the Robustness of Language Encoders against Grammatical Errors
Fan Yin, Quanyu Long, Tao Meng, and Kai-Wei Chang, in ACL, 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsWe conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use this approach to facilitate debugging models on downstream applications. Results confirm that the performance of all tested models is affected but the degree of impact varies. To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors. We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions. We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context. Our results shed light on understanding the robustness and behaviors of language encoders against grammatical errors.
@inproceedings{yin2020robustness, author = {Yin, Fan and Long, Quanyu and Meng, Tao and Chang, Kai-Wei}, title = {On the Robustness of Language Encoders against Grammatical Errors}, booktitle = {ACL}, presentation_id = {https://virtual.acl2020.org/paper_main.310.html}, year = {2020} } -
Robustness Verification for Transformers
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh, in ICLR, 2020.
Full Text Video Code Abstract BibTeX DetailsDetailsRobustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees. However, previous methods are usually limited to relatively simple neural networks. In this paper, we consider the robustness verification problem for Transformers. Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous work. We resolve these challenges and develop the first verification algorithm for Transformers. The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation. These bounds also shed light on interpreting Transformers as they consistently reflect the importance of words in sentiment analysis.
@inproceedings{shi2020robustness, author = {Shi, Zhouxing and Zhang, Huan and Chang, Kai-Wei and Huang, Minlie and Hsieh, Cho-Jui}, title = {Robustness Verification for Transformers}, booktitle = {ICLR}, year = {2020} } -
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang, and Wei Wang, in EMNLP, 2019.
Full Text Code Abstract BibTeX DetailsDetailsAdversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel framework, learning to DIScriminate Perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models. To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations. For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search. DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure. Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification. In addition, in-depth analysis shows the robustness of DISP across different situations.
@inproceedings{zhou2019learning, author = {Zhou, Yichao and Jiang, Jyun-Yu and Chang, Kai-Wei and Wang, Wei}, title = {Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification}, booktitle = {EMNLP}, year = {2019} } -
Retrofitting Contextualized Word Embeddings with Paraphrases
Weijia Shi, Muhao Chen, Pei Zhou, and Kai-Wei Chang, in EMNLP (short), 2019.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsContextualized word embedding models, such as ELMo, generate meaningful representations of words and their context. These models have been shown to have a great impact on downstream applications. However, in many cases, the contextualized embedding of a word changes drastically when the context is paraphrased. As a result, the downstream model is not robust to paraphrasing and other linguistic variations. To enhance the stability of contextualized word embedding models, we propose an approach to retrofitting contextualized embedding models with paraphrase contexts. Our method learns an orthogonal transformation on the input space, which seeks to minimize the variance of word representations on paraphrased contexts. Experiments show that the retrofitted model significantly outperforms the original ELMo on various sentence classification and language inference tasks.
@inproceedings{shi2019retrofitting, author = {Shi, Weijia and Chen, Muhao and Zhou, Pei and Chang, Kai-Wei}, title = {Retrofitting Contextualized Word Embeddings with Paraphrases}, booktitle = {EMNLP (short)}, vimeo_id = {430797636}, year = {2019} } -
Generating Natural Language Adversarial Examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang, in EMNLP (short), 2018.
Full Text Code Abstract BibTeX Details Top-10 cited paper at EMNLP 18DetailsDeep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the network to misclassify. In the image domain, these perturbations can often be made virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a population-based optimization algorithm to generate semantically and syntactically similar adversarial examples. We demonstrate via a human study that 94.3% of the generated examples are classified to the original label by human evaluators, and that the examples are perceptibly quite similar. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.
@inproceedings{alzanto2018generating, author = {Alzantot, Moustafa and Sharma, Yash and Elgohary, Ahmed and Ho, Bo-Jhang and Srivastava, Mani and Chang, Kai-Wei}, title = {Generating Natural Language Adversarial Examples}, booktitle = {EMNLP (short)}, year = {2018} }
Learning and Inference in Natural Language Processing
We contributed to building fundamental statistical tools for processing text at scale.
Efficient Joint Prediction Models
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Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization
Ching-pei Lee and Kai-Wei Chang, in Machine Learning Journal, 2019.
Full Text Code Abstract BibTeX DetailsDetailsDesigning distributed algorithms for empirical risk minimization (ERM) has become an active research topic in recent years because of the practical need to deal with the huge volume of data. In this paper, we propose a general framework for training an ERM model via solving its dual problem in parallel over multiple machines. Our method provides a versatile approach for many large-scale machine learning problems, including linear binary/multi-class classification, regression, and structured prediction. Comparing with existing approaches, we show that our method has faster convergence under weaker conditions both theoretically and empirically.
@inproceedings{LD17, author = {Lee, Ching-pei and Chang, Kai-Wei}, title = {Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization}, booktitle = {Machine Learning Journal}, year = {2019} } -
Robust Text Classifier on Test-Time Budgets
Md Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, and Venkatesh Saligrama, in EMNLP (short), 2019.
Full Text Slides Code Abstract BibTeX DetailsDetailsWe propose a generic and interpretable learning framework for building robust text classification model that achieves accuracy comparable to full models under test-time budget constraints. Our approach learns a selector to identify words that are relevant to the prediction tasks and passes them to the classifier for processing. The selector is trained jointly with the classifier and directly learns to incorporate with the classifier. We further propose a data aggregation scheme to improve the robustness of the classifier. Our learning framework is general and can be incorporated with any type of text classification model. On real-world data, we show that the proposed approach improves the performance of a given classifier and speeds up the model with a mere loss in accuracy performance.
@inproceedings{parvez2019robust, author = {Parvez, Md Rizwan and Bolukbasi, Tolga and Chang, Kai-Wei and Saligrama, Venkatesh}, title = {Robust Text Classifier on Test-Time Budgets}, booktitle = {EMNLP (short)}, year = {2019} } -
Efficient Contextual Representation Learning With Continuous Outputs
Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, and Kai-Wei Chang, in TACL, 2019.
Full Text Slides Video Abstract BibTeX DetailsDetailsContextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.
@inproceedings{li2019efficient, author = {Li, Liunian Harold and Chen, Patrick H. and Hsieh, Cho-Jui and Chang, Kai-Wei}, title = {Efficient Contextual Representation Learning With Continuous Outputs}, booktitle = {TACL}, year = {2019} } -
Structured Prediction with Test-time Budget Constraints
Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, and Venkatesh Saligrama, in AAAI, 2017.
Full Text Slides Abstract BibTeX DetailsDetailsWe study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two real-world structured prediction tasks, optical character recognition (OCR) and dependency parsing. For OCR our method cuts the feature acquisition time by half coming within a 1% margin of top accuracy. For dependency parsing we realize an overall runtime gain of 20% without significant loss in performance.
@inproceedings{bolukbasi2017structured, author = {Bolukbasi, Tolga and Chang, Kai-Wei and Wang, Joseph and Saligrama, Venkatesh}, title = {Structured Prediction with Test-time Budget Constraints}, booktitle = {AAAI}, year = {2017} } -
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He, Hal Daume III, John Langford, and Stephane Ross, in NeurIPS, 2016.
Full Text Code Abstract BibTeX DetailsDetailsMany machine learning applications involve jointly predicting multiple mutually dependent output variables. Learning to search is a family of methods where the complex decision problem is cast into a sequence of decisions via a search space. Although these methods have shown promise both in theory and in practice, implementing them has been burdensomely awkward. In this paper, we show the search space can be defined by an arbitrary imperative program, turning learning to search into a credit assignment compiler. Altogether with the algorithmic improvements for the compiler, we radically reduce the complexity of programming and the running time. We demonstrate the feasibility of our approach on multiple joint prediction tasks. In all cases, we obtain accuracies as high as alternative approaches, at drastically reduced execution and programming time.
@inproceedings{chang2016credit, author = {Chang, Kai-Wei and He, He and III, Hal Daume and Langford, John and Ross, Stephane}, title = {A Credit Assignment Compiler for Joint Prediction}, booktitle = {NeurIPS}, year = {2016} } -
Learning to Search Better Than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume; III, and John Langford, in ICML, 2015.
Full Text Video Code Abstract BibTeX DetailsDetailsMethods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.
@inproceedings{chang2015learninh, author = {Chang, Kai-Wei and Krishnamurthy, Akshay and Agarwal, Alekh and III, Hal Daume; and Langford, John}, title = {Learning to Search Better Than Your Teacher}, booktitle = {ICML}, year = {2015} } -
Structural Learning with Amortized Inference
Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, and Dan Roth, in AAAI, 2015.
Full Text Poster Abstract BibTeX DetailsDetailsTraining a structured prediction model involves performing several loss-augmented inference steps. Over the lifetime of the training, many of these inference problems, although different, share the same solution. We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the training process by exploiting this redundancy of solutions, without compromising the performance of the model. We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entity-relation extraction task. Our method learns the same model as an exact training algorithm would, but call the inference engine only in 10% . 24% of the inference problems encountered during training. We observe similar gains on a multi-label classification task and with a Structured Perceptron model for the entity-relation task.
@inproceedings{chang2015structural, author = {Chang, Kai-Wei and Upadhyay, Shyam and Kundu, Gourab and Roth, Dan}, title = {Structural Learning with Amortized Inference}, booktitle = {AAAI}, year = {2015} }
Learning with Auxiliary Supervision and Word Knowledge
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DEGREE: A Data-Efficient Generative Event Extraction Model
I.-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, and Nanyun Peng, in NAACL, 2022.
Full Text Abstract BibTeX DetailsDetailsEvent extraction (EE), the task that identifies event triggers and their arguments in text, is usually formulated as a classification or structured prediction problem. Such models usually reduce labels to numeric identifiers, making them unable to take advantage of label semantics (e.g. an event type named Arrest is related to words like arrest, detain, or apprehend). This prevents the generalization to new event types. In this work, we formulate EE as a natural language generation task and propose GenEE, a model that not only captures complex dependencies within an event but also generalizes well to unseen or rare event types. Given a passage and an event type, GenEE is trained to generate a natural sentence following a predefined template for that event type. The generated output is then decoded into trigger and argument predictions. The autoregressive generation process naturally models the dependencies among the predictions – each new word predicted depends on those already generated in the output sentence. Using carefully designed input prompts during generation, GenEE is able to capture label semantics, which enables the generalization to new event types. Empirical results show that our model achieves strong performance on event extraction tasks under all zero-shot, few-shot, and high-resource scenarios. Especially, in the high-resource setting, GenEE outperforms the state-of-the-art model on argument extraction and gets competitive results with the current best on end-to-end EE tasks.
@inproceedings{hsu2021degree, title = {DEGREE: A Data-Efficient Generative Event Extraction Model}, author = {Hsu, I-Hung and Huang, Kuan-Hao and Boschee, Elizabeth and Miller, Scott and Natarajan, Prem and Chang, Kai-Wei and Peng, Nanyun}, booktitle = {NAACL}, year = {2022} } -
Relation-Guided Pre-Training for Open-Domain Question Answering
Ziniu Hu, Yizhou Sun, and Kai-Wei Chang, in EMNLP-Finding, 2021.
Full Text Abstract BibTeX DetailsDetailsAnswering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We then pre-train a QA model to infer the latent relations from the question, and then conduct extractive QA to get the target answer entity. We demonstrate that by pretraining with propoed RGPT-QA techique, the popular open-domain QA model, Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions. Particularly, we show that RGPT-QA improves significantly on questions with long-tail relations
@inproceedings{hu2021relation, title = {Relation-Guided Pre-Training for Open-Domain Question Answering}, author = {Hu, Ziniu and Sun, Yizhou and Chang, Kai-Wei}, presentation_id = {https://underline.io/events/192/sessions/7932/lecture/38507-relation-guided-pre-training-for-open-domain-question-answering}, booktitle = {EMNLP-Finding}, year = {2021} } -
An Integer Linear Programming Framework for Mining Constraints from Data
Tao Meng and Kai-Wei Chang, in ICML, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsVarious structured output prediction problems (e.g., sequential tagging) involve constraints over the output space. By identifying these constraints, we can filter out infeasible solutions and build an accountable model. To this end, we present a general integer linear programming (ILP) framework for mining constraints from data. We model the inference of structured output prediction as an ILP problem. Then, given the coefficients of the objective function and the corresponding solution, we mine the underlying constraints by estimating the outer and inner polytopes of the feasible set. We verify the proposed constraint mining algorithm in various synthetic and real-world applications and demonstrate that the proposed approach successfully identifies the feasible set at scale. In particular, we show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules. We also demonstrate results on hierarchical multi-label classification and conduct a theoretical analysis on how close the mined constraints are from the ground truth.
@inproceedings{meng2020integer, author = {Meng, Tao and Chang, Kai-Wei}, title = {An Integer Linear Programming Framework for Mining Constraints from Data}, booktitle = {ICML}, year = {2021} } -
Intent Classification and Slot Filling for Privacy Policies
Wasi Ahmad, Jianfeng Chi, Tu Le, Thomas Norton, Yuan Tian, and Kai-Wei Chang, in ACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsUnderstanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent text spans convey further specific information about that practice. We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling. In this work, we propose PolicyIE, a corpus consisting of 5,250 intent and 11,788 slot annotations spanning 31 privacy policies of websites and mobile applications. PolicyIE corpus is a challenging benchmark with limited labeled examples reflecting the cost of collecting large-scale annotations. We present two alternative neural approaches as baselines: (1) formulating intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task. Experiment results show that both approaches perform comparably in intent classification, while the Seq2Seq method outperforms the sequence tagging approach in slot filling by a large margin. Error analysis reveals the deficiency of the baseline approaches, suggesting room for improvement in future works. We hope the PolicyIE corpus will stimulate future research in this domain.
@inproceedings{ahmad2021intent, title = {Intent Classification and Slot Filling for Privacy Policies}, author = {Ahmad, Wasi and Chi, Jianfeng and Le, Tu and Norton, Thomas and Tian, Yuan and Chang, Kai-Wei}, booktitle = {ACL}, year = {2021} } -
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs
Kuan-Hao Huang and Kai-Wei Chang, in EACL, 2021.
Full Text Slides Poster Code Abstract BibTeX DetailsDetailsParaphrase generation plays an essential role in natural language process (NLP), and it has many downstream applications. However, training supervised paraphrase models requires many annotated paraphrase pairs, which are usually costly to obtain. On the other hand, the paraphrases generated by existing unsupervised approaches are usually syntactically similar to the source sentences and are limited in diversity. In this paper, we demonstrate that it is possible to generate syntactically various paraphrases without the need for annotated paraphrase pairs. We propose Syntactically controlled Paraphrase Generator (SynPG), an encoder-decoder based model that learns to disentangle the semantics and the syntax of a sentence from a collection of unannotated texts. The disentanglement enables SynPG to control the syntax of output paraphrases by manipulating the embedding in the syntactic space. Extensive experiments using automatic metrics and human evaluation show that SynPG performs better syntactic control than unsupervised baselines, while the quality of the generated paraphrases is competitive. We also demonstrate that the performance of SynPG is competitive or even better than supervised models when the unannotated data is large. Finally, we show that the syntactically controlled paraphrases generated by SynPG can be utilized for data augmentation to improve the robustness of NLP models.
@inproceedings{huang2021generating, author = {Huang, Kuan-Hao and Chang, Kai-Wei}, title = {Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs}, booktitle = {EACL}, year = {2021} } -
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
Yichao Zhou, Yu Yan, Rujun Han, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, and Wei Wang, in AAAI, 2021.
Full Text Code Abstract BibTeX DetailsDetailsThere has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. However, existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among theevents. In this paper, we propose Clinical Temporal Relation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG), a novel method to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methodsfor temporal relation extraction.
@inproceedings{zhou2021clinical, author = {Zhou, Yichao and Yan, Yu and Han, Rujun and Caufield, J. Harry and Chang, Kai-Wei and Sun, Yizhou and Ping, Peipei and Wang, Wei}, title = {Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference}, booktitle = {AAAI}, year = {2021} } -
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Wasi Ahmad, Jianfeng Chi, Yuan Tian, and Kai-Wei Chang, in EMNLP-Finding (short), 2020.
Full Text Code Abstract BibTeX DetailsDetailsPrivacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.
@inproceedings{ahmad2020policyqa, author = {Ahmad, Wasi and Chi, Jianfeng and Tian, Yuan and Chang, Kai-Wei}, title = {PolicyQA: A Reading Comprehension Dataset for Privacy Policies}, booktitle = {EMNLP-Finding (short)}, year = {2020} } -
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun, in KDD, 2020.
Full Text Video Code Abstract BibTeX Details Top-10 cited paper at KDD 20DetailsGraph neural networks (GNNs) have been demonstrated to besuccessful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned knowledge to downstream models. In this paper, we present the GPT-GNN’s framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN,which allows the GNN to capture the intrinsic structural and semantic properties of the graph. We factorize the likelihood of graph generation into two components: 1) attribute generation, and 2) edgegeneration. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on thebillion-scale academic graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art base GNN models without pre-training by up to 9.1% across different downstream tasks.
@inproceedings{hu2020gptgnn, author = {Hu, Ziniu and Dong, Yuxiao and Wang, Kuansan and Chang, Kai-Wei and Sun, Yizhou}, title = {GPT-GNN: Generative Pre-Training of Graph Neural Networks}, booktitle = {KDD}, slide_url = {https://acbull.github.io/pdf/gpt.pptx}, year = {2020} } -
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
Da Yin, Tao Meng, and Kai-Wei Chang, in ACL, 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsWe propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics. The model incorporates contextualized representation with binary constituency parse tree to capture semantic composition. Comprehensive experiments demonstrate that SentiBERT achieves competitive performance on phrase-level sentiment classification. We further demonstrate that the sentiment composition learned from the phrase-level annotations on SST can be transferred to other sentiment analysis tasks as well as related tasks, such as emotion classification tasks. Moreover, we conduct ablation studies and design visualization methods to understand SentiBERT. We show that SentiBERT is better than baseline approaches in capturing negation and the contrastive relation and model the compositional sentiment semantics.
@inproceedings{yin2020sentibert, author = {Yin, Da and Meng, Tao and Chang, Kai-Wei}, title = {SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics}, booktitle = {ACL}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.341.html} } -
Building Language Models for Text with Named Entities
Md Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in ACL, 2018.
Full Text Poster Code Abstract BibTeX DetailsDetailsText in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 40.3% on code generation than state-of-the-art language models.
@inproceedings{parvez2018building, author = {Parvez, Md Rizwan and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei}, title = {Building Language Models for Text with Named Entities}, booktitle = {ACL}, year = {2018} } -
Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems
Shyam Upadhyay, Ming-Wei Chang, Kai-Wei Chang, and Wen-tau Yih, in EMNLP, 2016.
Full Text Abstract BibTeX DetailsDetailsAutomatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG-514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%. Dataset
@inproceedings{BCWS16, author = {Upadhyay, Shyam and Chang, Ming-Wei and Chang, Kai-Wei and Yih, Wen-tau}, title = {Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems}, booktitle = {EMNLP}, year = {2016} }
Crosslingual Transfer Learning
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Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
Kuan-Hao Huang, Wasi Ahmad, Nanyun Peng, and Kai-Wei Chang, in EMNLP, 2021.
Full Text Code Abstract BibTeX DetailsDetailsPre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.
@inproceedings{huang2021improving, title = {Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training}, author = {Huang, Kuan-Hao and Ahmad, Wasi and Peng, Nanyun and Chang, Kai-Wei}, presentation_id = {https://underline.io/events/192/posters/7783/poster/40656-improving-zero-shot-cross-lingual-transfer-learning-via-robust-training}, booktitle = {EMNLP}, year = {2021} } -
Syntax-augmented Multilingual BERT for Cross-lingual Transfer
Wasi Ahmad, Haoran Li, Kai-Wei Chang, and Yashar Mehdad, in ACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsIn recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences across languages, the cross-lingual transfer is challenging. Nevertheless, language syntax, e.g., syntactic dependencies, can bridge the typological gap. Previous works have shown that pretrained multilingual encoders, such as mBERT (Devlin et al., 2019), capture language syntax, helping cross-lingual transfer. This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. We perform rigorous experiments on four NLP tasks, including text classification, question answering, named entity recognition, and taskoriented semantic parsing. The experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks, such as PAWS-X and MLQA, by 1.4 and 1.6 points on average across all languages. In the generalized transfer setting, the performance boosted significantly, with 3.9 and 3.1 points on average in PAWS-X and MLQA.
@inproceedings{ahmad2021syntax, title = {Syntax-augmented Multilingual BERT for Cross-lingual Transfer}, author = {Ahmad, Wasi and Li, Haoran and Chang, Kai-Wei and Mehdad, Yashar}, booktitle = {ACL}, year = {2021} } -
Evaluating the Values of Sources in Transfer Learning
Md Rizwan Parvez and Kai-Wei Chang, in NAACL, 2021.
Full Text Video Code Abstract BibTeX DetailsDetailsTransfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP). However, when training a transfer model over multiple sources, not every source is equally useful for the target. To better transfer a model, it is essential to understand the values of the sources. In this paper, we develop SEAL-Shap, an efficient source valuation framework for quantifying the usefulness of the sources (e.g., domains/languages) in transfer learning based on the Shapley value method. Experiments and comprehensive analyses on both cross-domain and cross-lingual transfers demonstrate that our framework is not only effective in choosing useful transfer sources but also the source values match the intuitive source-target similarity.
@inproceedings{parvez2021evaluating, title = {Evaluating the Values of Sources in Transfer Learning}, author = {Parvez, Md Rizwan and Chang, Kai-Wei}, booktitle = {NAACL}, presentation_id = {https://underline.io/events/122/sessions/4261/lecture/19707-evaluating-the-values-of-sources-in-transfer-learning}, year = {2021} } -
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction
Wasi Ahmad, Nanyun Peng, and Kai-Wei Chang, in AAAI, 2021.
Full Text Code Abstract BibTeX DetailsDetailsPrevalent approaches in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic representations such that models trained on one language can be applied to other languages. However, GCNs lack in modeling long-range dependencies or disconnected words in the dependency tree. To address this challenge, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words at different syntactic distances. We introduce GATE, a \bf Graph \bf Attention \bf Transformer \bf Encoder, and test its cross-lingual transferability on relation and event extraction tasks. We perform rigorous experiments on the widely used ACE05 dataset that includes three typologically different languages: English, Chinese, and Arabic. The evaluation results show that GATE outperforms three recently proposed methods by a large margin. Our detailed analysis reveals that due to the reliance on syntactic dependencies, GATE produces robust representations that facilitate transfer across languages.
@inproceedings{ahmad2021gate, author = {Ahmad, Wasi and Peng, Nanyun and Chang, Kai-Wei}, title = {GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction}, booktitle = {AAAI}, year = {2021} } -
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, and Xuanjing Huang, in EMNLP-Finding, 2020.
Full Text Abstract BibTeX DetailsDetailsWe propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.
@inproceedings{liu2020cross-lingual, author = {Liu, Lu and Zhou, Yi and Xu, Jianhan and Zheng, Xiaoqing and Chang, Kai-Wei and Huang, Xuanjing}, title = {Cross-Lingual Dependency Parsing by POS-Guided Word Reordering}, booktitle = {EMNLP-Finding}, year = {2020} } -
Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages
Wasi Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, and Nanyun Peng, in CoNLL, 2019.
Full Text Poster Code Abstract BibTeX DetailsDetailsCross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.
@inproceedings{ahmad2019crosslingual, author = {Ahmad, Wasi and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun}, title = { Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages}, booktitle = {CoNLL}, year = {2019} } -
Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing
Tao Meng, Nanyun Peng, and Kai-Wei Chang, in EMNLP, 2019.
Full Text Poster Code Abstract BibTeX DetailsDetailsPrior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlooks the potential of leveraging linguistic properties of the languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-wise constraints to guide the inference process during the test time. We adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization inference improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially significant for target languages that have different word order features from the source language.
@inproceedings{meng2019target, author = {Meng, Tao and Peng, Nanyun and Chang, Kai-Wei}, title = {Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing}, booktitle = {EMNLP}, year = {2019} } -
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng, in NAACL, 2019.
Full Text Video Code Abstract BibTeX DetailsDetailsDifferent languages might have different wordorders. In this paper, we investigate cross-lingual transfer and posit that an order-agnostic model will perform better when trans-ferring to distant foreign languages. To test ourhypothesis, we train dependency parsers on anEnglish corpus and evaluate their transfer per-formance on 30 other languages. Specifically,we compare encoders and decoders based onRecurrent Neural Networks (RNNs) and mod-ified self-attentive architectures. The formerrelies on sequential information while the lat-ter is more flexible at modeling word order.Rigorous experiments and detailed analysisshows that RNN-based architectures transferwell to languages that are close to English,while self-attentive models have better overallcross-lingual transferability and perform espe-cially well on distant languages.
@inproceedings{ahmad2019difficulties, author = {Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Hovy, Eduard and Chang, Kai-Wei and Peng, Nanyun}, title = {On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing}, booktitle = {NAACL}, year = {2019} }
Representation Learning in NLP
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Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu, Ting Chen, Kai-Wei Chang, and Yizhou Sun, in ACL, 2019.
Full Text Poster Code Abstract BibTeX DetailsDetailsExisting approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. It is challenging to learn accurate representations of these words with only a few observations. In this paper, we formulate the learning of OOV embeddings as a few-shot regression problem, and address it by training a representation function to predict the oracle embedding vector (defined as embedding trained with abundant observations) based on limited observations. Specifically, we propose a novel hierarchical attention-based architecture to serve as the neural regression function, with which the context information of a word is encoded and aggregated from K observations. Furthermore, our approach can leverage Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing accurate embeddings for OOV words, and improves downstream tasks where these embeddings are utilized.
@inproceedings{hu2019fewshot, author = {Hu, Ziniu and Chen, Ting and Chang, Kai-Wei and Sun, Yizhou}, title = {Few-Shot Representation Learning for Out-Of-Vocabulary Words}, booktitle = {ACL}, year = {2019} } -
Learning Word Embeddings for Low-resource Languages by PU Learning
Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, and Kai-Wei Chang, in NAACL, 2018.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsWord embedding has been used as a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is very sparse because many word pairs are not observed to co-occur. In contrast to existing approaches, we argue that the zero entries in the co-occurrence matrix also provide valuable information and design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix. The experimental results demonstrate that the proposed approach requires a smaller amount of training text to obtain a reasonable word embedding model.
@inproceedings{jiang2018learning, author = {Jiang, Chao and Yu, Hsiang-Fu and Hsieh, Cho-Jui and Chang, Kai-Wei}, title = {Learning Word Embeddings for Low-resource Languages by PU Learning}, booktitle = {NAACL}, vimeo_id = {277670013}, year = {2018} } -
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, and Carlo Zaniolo, in IJCAI, 2018.
Full Text Slides Code Abstract BibTeX DetailsDetailsMultilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge enabled with cross-lingual inferences that benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.
@inproceedings{chen2018multilingual, author = {Chen, Muhao and Tian, Yingtao and Chang, Kai-Wei and Skiena, Steven and Zaniolo, Carlo}, title = {Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment}, booktitle = {IJCAI}, year = {2018} } -
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, and James Zou, in ACL RepL4NLP Workshop, 2017.
Full Text Abstract BibTeX Details Best Paper AwardDetailsWord embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.
@inproceedings{upadhyay2017beyond, author = {Upadhyay, Shyam and Chang, Kai-Wei and Taddy, Matt and Kalai, Adam and Zou, James}, title = {Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context}, booktitle = {ACL RepL4NLP Workshop}, year = {2017} }
Multi-modal Representaiton
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DesCo: Learning Object Recognition with Rich Language Descriptions
Liunian Harold Li, Zi-Yi Dou, Nanyun Peng, and Kai-Wei Chang, in NeurIPS, 2023.
Full Text Abstract BibTeX Details Ranks 1st at the #OmniLabel Challenge of CVPR2023DetailsRecent development in vision-language approaches has instigated a paradigm shift in learning visual recognition models from language supervision. These approaches align objects with language queries (e.g. "a photo of a cat") and improve the models’ adaptability to identify novel objects and domains. Recently, several studies have attempted to query these models with complex language expressions that include specifications of fine-grained semantic details, such as attributes, shapes, textures, and relations. However, simply incorporating language descriptions as queries does not guarantee accurate interpretation by the models. In fact, our experiments show that GLIP, the state-of-the-art vision-language model for object detection, often disregards contextual information in the language descriptions and instead relies heavily on detecting objects solely by their names. To tackle the challenges, we propose a new description-conditioned (DesCo) paradigm of learning object recognition models with rich language descriptions consisting of two major innovations: 1) we employ a large language model as a commonsense knowledge engine to generate rich language descriptions of objects based on object names and the raw image-text caption; 2) we design context-sensitive queries to improve the model’s ability in deciphering intricate nuances embedded within descriptions and enforce the model to focus on context rather than object names alone. On two novel object detection benchmarks, LVIS and OminiLabel, under the zero-shot detection setting, our approach achieves 34.8 APr minival (+9.1) and 29.3 AP (+3.6), respectively, surpassing the prior state-of-the-art models, GLIP and FIBER, by a large margin.
@inproceedings{li2023desco, author = {Li, Liunian Harold and Dou, Zi-Yi and Peng, Nanyun and Chang, Kai-Wei}, title = {DesCo: Learning Object Recognition with Rich Language Descriptions}, booktitle = {NeurIPS}, year = {2023} } -
AVATAR: A Parallel Corpus for Java-Python Program Translation
Wasi Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, and Kai-Wei Chang, in ACL-Finding (short), 2023.
Full Text Code Abstract BibTeX DetailsDetailsProgram translation refers to migrating source code from one programming language to another. It has a tremendous practical value in software development as porting software across different languages is time-consuming and costly. Automating program translation is of paramount importance in software migration, and recently researchers explored unsupervised approaches due to the unavailability of parallel corpora. However, the availability of pre-trained language models for programming languages enable supervised fine-tuning with a small amount of labeled examples. In this work, we present a corpus of 8,475 programming problems and their solutions written in two popular languages, Java and Python. We collect the dataset from competitive programming sites, online platforms, and open source repositories. We present several baselines, including models trained from scratch or pre-trained on large-scale source code collection and fine-tuned on our proposed dataset. Experiment results show that while the models perform relatively well in terms of the lexical match, they lack in generating code that is accurate in terms of syntax and data-flow match.
@inproceedings{ahmad2021avatar, title = {AVATAR: A Parallel Corpus for Java-Python Program Translation}, author = {Ahmad, Wasi and Tushar, Md Golam Rahman and Chakraborty, Saikat and Chang, Kai-Wei}, booktitle = {ACL-Finding (short)}, year = {2023} } -
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning
Da Yin, Liunian Harold Li, Ziniu Hu, Nanyun Peng, and Kai-Wei Chang, in EMNLP, 2021.
Full Text Code Abstract BibTeX DetailsDetailsCommonsense is defined as the knowledge that is shared by everyone. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenarios of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors. Such regional characteristics, however, are generally omitted in prior work. In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense. In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard multimodal commonsense benchmark with images primarily from Western regions. We then evaluate how well the trained models can generalize to answering the questions in GD-VCR. We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region. We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition.
@inproceedings{yin2021broaden, title = { Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning}, author = {Yin, Da and Li, Liunian Harold and Hu, Ziniu and Peng, Nanyun and Chang, Kai-Wei}, booktitle = {EMNLP}, presentation_id = {https://underline.io/events/192/sessions/7790/lecture/37514-broaden-the-vision-geo-diverse-visual-commonsense-reasoning}, year = {2021} } -
Retrieval Augmented Code Generation and Summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in EMNLP-Finding, 2021.
Full Text Abstract BibTeX DetailsDetailsSoftware developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. To mimic developers’ code or summary generation behavior, we propose a retrieval augmented framework, \tool, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models. \tool has a couple of uniqueness. First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries. Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs). We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.
@inproceedings{parvez2021retrieval, title = {Retrieval Augmented Code Generation and Summarization}, author = {Parvez, Md Rizwan and Ahmad, Wasi and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei}, booktitle = {EMNLP-Finding}, presentation_id = {https://underline.io/events/192/sessions/7923/lecture/38314-retrieval-augmented-code-generation-and-summarization}, year = {2021} } -
Unified Pre-training for Program Understanding and Generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in NAACL, 2021.
Full Text Video Code Abstract BibTeX Details Top-10 cited paper at NAACL 21DetailsCode summarization nd generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART’s effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., if block inside an else block is equivalent to else if block) that are crucial to program semantics and thus excels even with limited annotations.
@inproceedings{ahmad2021unified, title = {Unified Pre-training for Program Understanding and Generation}, author = {Ahmad, Wasi and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei}, booktitle = {NAACL}, presentation_id = {https://underline.io/events/122/sessions/4197/lecture/20024-unified-pre-training-for-program-understanding-and-generation}, year = {2021} } -
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
Liunian Harold Li, Haoxuan You, Zhecan Wang, Alireza Zareian, Shih-Fu Chang, and Kai-Wei Chang, in NAACL, 2021.
Full Text Video Abstract BibTeX DetailsDetailsPre-trained contextual vision-and-language (V&L) models have brought impressive performance improvement on various benchmarks. However, the paired text-image data required for pre-training are hard to collect and scale up. We investigate if a strong V&L representation model can be learned without text-image pairs. We propose Weakly-supervised VisualBERT with the key idea of conducting "mask-and-predict" pre-training on language-only and image-only corpora. Additionally, we introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. Evaluation on four V&L benchmarks shows that Weakly-supervised VisualBERT achieves similar performance with a model pre-trained with paired data. Besides, pre-training on more image-only data further improves a model that already has access to aligned data, suggesting the possibility of utilizing billions of raw images available to enhance V&L models.
@inproceedings{li2021unsupervised, author = {Li, Liunian Harold and You, Haoxuan and Wang, Zhecan and Zareian, Alireza and Chang, Shih-Fu and Chang, Kai-Wei}, title = {Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions}, booktitle = {NAACL}, presentation_id = {https://underline.io/events/122/sessions/4269/lecture/19725-unsupervised-vision-and-language-pre-training-without-parallel-images-and-captions}, year = {2021} } -
What Does BERT with Vision Look At?
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang, in ACL (short), 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsPre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear. In this work, we demonstrate that certain attention heads of a visually grounded language model actively ground elements of language to image regions. Specifically, some heads can map entities to image regions, performing the task known as entity grounding. Some heads can even detect the syntactic relations between non-entity words and image regions, tracking, for example, associations between verbs and regions corresponding to their arguments. We denote this ability as \emphsyntactic grounding. We verify grounding both quantitatively and qualitatively, using Flickr30K Entities as a testbed.
@inproceedings{li2020what, author = {Li, Liunian Harold and Yatskar, Mark and Yin, Da and Hsieh, Cho-Jui and Chang, Kai-Wei}, title = {What Does BERT with Vision Look At?}, booktitle = {ACL (short)}, presentation_id = {https://virtual.acl2020.org/paper_main.469.html}, year = {2020} } -
VisualBERT: A Simple and Performant Baseline for Vision and Language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang, in Arxiv, 2019.
Full Text Code Abstract BibTeX DetailsDetailsWe propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.
@inproceedings{li2019visualbert, author = {Li, Liunian Harold and Yatskar, Mark and Yin, Da and Hsieh, Cho-Jui and Chang, Kai-Wei}, title = {VisualBERT: A Simple and Performant Baseline for Vision and Language}, booktitle = {Arxiv}, year = {2019} }
NLP for Social Good Applications
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A Transformer-based Approach for Source Code Summarization
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in ACL (short), 2020.
Full Text Slides Video Code Abstract BibTeX DetailsDetailsGenerating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens’ position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available to facilitate future research.
@inproceedings{ahmad2020transformer, author = {Ahmad, Wasi and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei}, title = {A Transformer-based Approach for Source Code Summarization}, booktitle = {ACL (short)}, year = {2020}, presentation_id = {https://virtual.acl2020.org/paper_main.449.html} } -
Context Attentive Document Ranking and Query Suggestion
Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in SIGIR, 2019.
Full Text Slides Code Abstract BibTeX DetailsDetailsWe present a context-aware neural ranking model to exploit users’ on-task search activities and enhance retrieval performance. Inparticular, a two-level hierarchical recurrent neural network isintroduced to learn search context representation of individualqueries, search tasks, and corresponding dependency structure byjointly optimizing two companion retrieval tasks: document rank-ing and query suggestion. To identify variable dependency structurebetween search context and users’ ongoing search activities, at-tention at both levels of recurrent states are introduced. Extensiveexperiment comparisons against a rich set of baseline methods andan in-depth ablation analysis confirm the value of our proposedapproach for modeling search context buried in search tasks.
@inproceedings{ahmad2019context, author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning}, title = {Context Attentive Document Ranking and Query Suggestion}, booktitle = {SIGIR}, year = {2019} } -
Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN
Muhao Chen, Chelsea J.-T. Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang, in ISMB, 2019.
Full Text Code Abstract BibTeX DetailsDetailsSequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information. Hence, we present an end-to-end framework, Lasagna, for PPI predictions using only the primary sequences of a protein pair. Lasagna incorporates a deep residual recurrent convolutional neural network in the Siamese learning architecture, which leverages both robust local features and contextualized information that are significant for capturing the mutual influence of protein sequences. Our framework relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that Lasagna outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.
@inproceedings{chen2019multifaceted, author = {Chen, Muhao and Ju, Chelsea J.-T. and Zhou, Guangyu and Chen, Xuelu and Zhang, Tianran and Chang, Kai-Wei and Zaniolo, Carlo and Wang, Wei}, title = {Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN}, booktitle = {ISMB}, year = {2019} } -
Multi-Task Learning for Document Ranking and Query Suggestion
Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in ICLR, 2018.
Full Text Code Abstract BibTeX DetailsDetailsWe propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommen tracks users’ query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users’ underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
@inproceedings{ahmad2018multitask, author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning}, title = {Multi-Task Learning for Document Ranking and Query Suggestion}, booktitle = {ICLR}, year = {2018} } -
Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search
Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in SIGIR, 2018.
Full Text Code Abstract BibTeX DetailsDetailsModern web search engines exploit users’ search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk of privacy infringement and raises serious public concerns. In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario. In our solution, each user query is submitted with l additional cover queries and corresponding clicks, which act as decoys to mask users’ genuine search intent from a search engine. The cover queries are sequentially sampled from a set of hierarchically organized language models to ensure the coherency of fake search intents in a cover search task. Our approach emphasizes the plausibility of generated cover queries, not only to the current genuine query but also to previous queries in the same task, to increase the complexity for a search engine to identify a user’s true intent. We also develop two new metrics from an information theoretic perspective to evaluate the effectiveness of provided privacy protection. Comprehensive experiment comparisons with state-of-the-art query obfuscation techniques are performed on the public AOL search log, and the propitious results substantiate the effectiveness of our solution.
@inproceedings{ahmad2018intent, author = {Ahmad, Wasi and Chang, Kai-Wei and Wang, Hongning}, title = {Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search}, booktitle = {SIGIR}, year = {2018} } -
Counterexamples for Robotic Planning Explained in Structured Language
Lu Feng, Mahsa Ghasemi, Kai-Wei Chang, and Ufuk Topcu, in ICRA, 2018.
Full Text Abstract BibTeX DetailsDetailsAutomated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations. However, such artifacts may be too complex for humans to understand, because existing representations of counterexamples typically include a large number of paths or a complex automaton. To help improve the interpretability of counterexamples, we define a notion of explainable counterexample, which includes a set of structured natural language sentences to describe the robotic behavior that lead to a requirement violation in an MDP model of robotic mission plan. We propose an approach based on mixed-integer linear programming for generating explainable counterexamples that are minimal, sound and complete. We demonstrate the usefulness of the proposed approach via a case study of warehouse robots planning.
@inproceedings{feng2018conterexamples, author = {Feng, Lu and Ghasemi, Mahsa and Chang, Kai-Wei and Topcu, Ufuk}, title = {Counterexamples for Robotic Planning Explained in Structured Language}, booktitle = {ICRA}, year = {2018} } -
Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions
Dat Duong, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, and Jingyi Jessica Li, in Journal of Computational Biology, 2018.
Full Text Code Abstract BibTeX DetailsDetailsThe Gene Ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. Under this paradigm, the locations of two GO terms in the tree dictate their similarity score. In this paper, we introduce two new solutions for this problem, by focusing instead on the definitions of the GO terms. We apply neural network based techniques from the natural language processing (NLP) domain. The first method does not rely on the GO tree, whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO definitions by treating them as two unordered sets of words. The word similarity is estimated by a word embedding model that maps words into an N-dimensional space. In our second approach, we account for the word-ordering within a sentence. We use a sentence encoder to embed GO definitions into vectors and estimate how likely one definition entails another. We validate our methods in two ways. In the first experiment, we test the model’s ability to differentiate a true protein-protein network from a randomly generated network. In the second experiment, we test the model in identifying orthologs from randomly-matched genes in human, mouse, and fly. In both experiments, a hybrid of NLP and GO-tree based method achieves the best classification accuracy.
@inproceedings{DAECL18, author = {Duong, Dat and Ahmad, Wasi Uddin and Eskin, Eleazar and Chang, Kai-Wei and Li, Jingyi Jessica}, title = {Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions}, booktitle = {Journal of Computational Biology}, year = {2018} }