At UCLA-NLP, our mission is to develop fair, accountable, robust natural language processing technology to benefit everyone. We will present papers at ACL 2020 on the following topics.

Fairness in Natural Language Processing

Natural Language Processing (NLP) models are widely used in our daily lives. Despite these methods achieve high performance in various applications, they run the risk of exploiting and reinforcing the societal biases (e.g. gender bias) that are present in the underlying data. At ACL, we present our studies on 1) how gender bias is propagated in cross-lingual transfer, 2) how bias is amplified in the distribution of model predictions, and 3) gender bias in relation extraction.

[1], [2], [3]

  1. 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.
    QA Sessions: 6A Ethics, 10B Ethics Paper link in the virtual conference
    Full Text Slides BibTeX Details 
    Multilingual 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}
}

    Related Publications

    1. Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

      Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, and Aram Galstyan, in ACL Finding, 2022.
      Full Text Abstract BibTeX Details 
      Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model’s biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal – modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT-2models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.
      @inproceedings{gupta2022equitable,
  title = {Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal},
  author = {Gupta, Umang and Dhamala, Jwala and Kumar, Varun and Verma, Apurv and Pruksachatkun, Yada and Krishna, Satyapriya and Gupta, Rahul and Chang, Kai-Wei and Steeg, Greg Ver and Galstyan, Aram},
  booktitle = {ACL Finding},
  year = {2022}
}
      Details

    2. 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 Details 
      Gender 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}
}
      Details

    3. 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 Details 
      Multilingual 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}
}
      Details

    4. 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 Details 
      Recent 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}
}
      Details

    5. 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 Details 
      In 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}
}
      Details

    6. 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 Details 
      Despite 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}
}
      Details

    7. 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 Details 
      Word 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}
}
      Details

    8. 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 16 
      The 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}
}
      Details
    Details

  2. 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.
    QA Sessions: 6A Ethics, 7B Ethics Paper link in the virtual conference
    Full Text BibTeX Details 
    Recent 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}
}

    Related Publications

    1. Measuring Fairness of Text Classifiers via Prediction Sensitivity

      Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, and Kai-Wei Chang, in ACL, 2022.
      Full Text Abstract BibTeX Details 
      With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.
      @inproceedings{krishna2022measuring,
  title = {Measuring Fairness of Text Classifiers via Prediction Sensitivity},
  author = {Krishna, Satyapriya and Gupta, Rahul and Verma, Apurv and Dhamala, Jwala and Pruksachatkun, Yada and Chang, Kai-Wei},
  booktitle = {ACL},
  year = {2022}
}
      Details

    2. 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 Details 
      Existing 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}
}
      Details

    3. LOGAN: Local Group Bias Detection by Clustering

      Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
      Full Text Code Abstract BibTeX Details 
      Machine 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}
}
      Details

    4. 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 Details 
      Recent 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}
}
      Details

    5. 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 Details 
      Advanced 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}
}
      Details

    6. 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 Details 
      As 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}
}
      Details

    7. 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 18 
      In 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}
}
      Details

    8. 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 17 
      Language 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}
}
      Details
    Details

  3. Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

    Shengyu Jia, Tao Meng, Jieyu Zhao, and Kai-Wei Chang, in ACL (short), 2020.
    QA Sessions: 6A Ethics, 10B Ethics Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    Advanced 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}
}

    Related Publications

    1. Measuring Fairness of Text Classifiers via Prediction Sensitivity

      Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, and Kai-Wei Chang, in ACL, 2022.
      Full Text Abstract BibTeX Details 
      With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.
      @inproceedings{krishna2022measuring,
  title = {Measuring Fairness of Text Classifiers via Prediction Sensitivity},
  author = {Krishna, Satyapriya and Gupta, Rahul and Verma, Apurv and Dhamala, Jwala and Pruksachatkun, Yada and Chang, Kai-Wei},
  booktitle = {ACL},
  year = {2022}
}
      Details

    2. 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 Details 
      Existing 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}
}
      Details

    3. LOGAN: Local Group Bias Detection by Clustering

      Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
      Full Text Code Abstract BibTeX Details 
      Machine 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}
}
      Details

    4. 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 Details 
      Recent 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}
}
      Details

    5. 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 Details 
      Advanced 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}
}
      Details

    6. 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 Details 
      As 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}
}
      Details

    7. 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 18 
      In 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}
}
      Details

    8. 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 17 
      Language 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}
}
      Details
    Details

Analyze and Understand NLP Models

It is essential to analyze and understand the capability of NLP technology. At ACL, we present the following papers on 1) analyzing the robustness of contextualized language encoders against grammatical errors, 2) understanding what are captured by pre-trained visually grounded language models like VisualBERT, and 3) benchmarking transformer-based approaches for source code summarization.

[1], [2], [3]

  1. On the Robustness of Language Encoders against Grammatical Errors

    Fan Yin, Quanyu Long, Tao Meng, and Kai-Wei Chang, in ACL, 2020.
    QA Sessions: 6B Interpretability, 8A Interpretability Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    We 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}
}

    Related Publications

    1. VideoCon: Robust video-language alignment via contrast captions

      Hritik Bansal, Yonatan Bitton, Idan Szpektor, Kai-Wei Chang, and Aditya Grover, in CVPR, 2024.
      Full Text Code Demo Abstract BibTeX Details Best paper at DPFM workshop at ICLR 
      Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations.
      @inproceedings{bansal2023videocon,
  author = {Bansal, Hritik and Bitton, Yonatan and Szpektor, Idan and Chang, Kai-Wei and Grover, Aditya},
  title = {VideoCon: Robust video-language alignment via contrast captions},
  booktitle = {CVPR},
  year = {2024}
}
      Details

    2. Red Teaming Language Model Detectors with Language Models

      Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh, in TACL, 2023.
      Full Text Code Abstract BibTeX Details 
      The prevalence and high capacity of large language models (LLMs) present significant safety and ethical risks when malicious users exploit them for automated content generation. To prevent the potentially deceptive usage of LLMs, recent works have proposed several algorithms to detect machine-generated text. In this paper, we systematically test the reliability of the existing detectors, by designing two types of attack strategies to fool the detectors: 1) replacing words with their synonyms based on the context; 2) altering the writing style of generated text. These strategies are implemented by instructing LLMs to generate synonymous word substitutions or writing directives that modify the style without human involvement, and the LLMs leveraged in the attack can also be protected by detectors. Our research reveals that our attacks effectively compromise the performance of all tested detectors, thereby underscoring the urgent need for the development of more robust machine-generated text detection systems.
      @inproceedings{shi2023red,
  author = {Shi, Zhouxing and Wang, Yihan and Yin, Fan and Chen, Xiangning and Chang, Kai-Wei and Hsieh, Cho-Jui},
  title = {Red Teaming Language Model Detectors with Language Models},
  booktitle = {TACL},
  year = {2023}
}
      Details

    3. CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning

      Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, and Kai-Wei Chang, in ICCV, 2023.
      Full Text Code Abstract BibTeX Details Best Paper Award at ICLR Workshop, Oral at ICCV (195 out of 8088 submissions, top 2.5%) 
      Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have revealed that such models are vulnerable to backdoor attacks. Specifically, when trained on backdoored examples, CLIP learns spurious correlations between the embedded backdoor trigger and the target label, aligning their representations in the joint embedding space. Injecting even a small number of poisoned examples, such as 75 examples in 3 million pretraining data, can significantly manipulate the model’s behavior, making it difficult to detect or unlearn such correlations. To address this issue, we propose CleanCLIP, a finetuning framework that weakens the learned spurious associations introduced by backdoor attacks by independently re-aligning the representations for individual modalities. We demonstrate that unsupervised finetuning using a combination of multimodal contrastive and unimodal self-supervised objectives for individual modalities can significantly reduce the impact of the backdoor attack. We show empirically that CleanCLIP maintains model performance on benign examples while erasing a range of backdoor attacks on multimodal contrastive learning.
      @inproceedings{bansal2023cleanclip,
  author = {Bansal, Hritik and Singhi, Nishad and Yang, Yu and Yin, Fan and Grover, Aditya and Chang, Kai-Wei},
  title = {CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning},
  booktitle = {ICCV},
  year = {2023}
}
      Details

    4. ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation

      Fan Yin, Yao Li, Cho-Jui Hsieh, and Kai-Wei Chang, in EMNLP, 2022.
      Full Text Abstract BibTeX Details 
      Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) community. Despite the surge of new AED methods, our studies show that existing methods heavily rely on a shortcut to achieve good performance. In other words, current search-based adversarial attacks in NLP stop once model predictions change, and thus most adversarial examples generated by those attacks are located near model decision boundaries. To surpass this shortcut and fairly evaluate AED methods, we propose to test AED methods with Far Boundary (FB) adversarial examples. Existing methods show worse than random guess performance under this scenario. To overcome this limitation, we propose a new technique, ADDMU, adversary detection with data and model uncertainty, which combines two types of uncertainty estimation for both regular and FB adversarial example detection. Our new method outperforms previous methods by 3.6 and 6.0 AUC points under each scenario. Finally, our analysis shows that the two types of uncertainty provided by ADDMU can be leveraged to characterize adversarial examples and identify the ones that contribute most to model’s robustness in adversarial training.
      @inproceedings{yin2022addmu,
  title = {ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation},
  author = {Yin, Fan and Li, Yao and Hsieh, Cho-Jui and Chang, Kai-Wei},
  booktitle = {EMNLP},
  year = {2022}
}
      Details

    5. Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers

      Jieyu Zhao, Xuezhi Wang, Yao Qin, Jilin Chen, and Kai-Wei Chang, in EMNLP-Finding (short), 2022.
      Full Text BibTeX Details 
      @inproceedings{zhao2022investigating,
  title = {	Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers},
  author = {Zhao, Jieyu and Wang, Xuezhi and Qin, Yao and Chen, Jilin and Chang, Kai-Wei},
  booktitle = {EMNLP-Finding (short)},
  year = {2022}
}
      Details

    6. Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations

      Kuan-Hao Huang, Varun Iyer, Anoop Kumar, Sriram Venkatapathy, Kai-Wei Chang, and Aram Galstyan, in EMNLP-Finding (short), 2022.
      Full Text BibTeX Details 
      @inproceedings{huang2022unsupervised,
  title = {Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations},
  author = {Huang, Kuan-Hao and Iyer, Varun and Kumar, Anoop and Venkatapathy, Sriram and Chang, Kai-Wei and Galstyan, Aram},
  booktitle = {EMNLP-Finding (short)},
  year = {2022}
}
      Details

    7. Improving the Adversarial Robustness of NLP Models by Information Bottleneck

      Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, and Cho-Jui Hsieh, in ACL-Finding, 2022.
      Full Text Abstract BibTeX Details 
      Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the previously reported defense methods while suffering almost no performance drop in clean accuracy on SST-2, AGNEWS and IMDB datasets.
      @inproceedings{zhang2022improving,
  title = {Improving the Adversarial Robustness of NLP Models by Information Bottleneck},
  author = {Zhang, Cenyuan and Zhou, Xiang and Wan, Yixin and Zheng, Xiaoqing and Chang, Kai-Wei and Hsieh, Cho-Jui},
  booktitle = {ACL-Finding},
  year = {2022}
}
      Details

    8. 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 Details 
      Recent 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}
}
      Details

    9. 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 Details 
      Deep 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}
}
      Details

    10. 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 Details 
      Although 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}
}
      Details

    11. 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 Details 
      Robustness 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}
}
      Details

    12. 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 Details 
      Linear 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}
}
      Details

    13. 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 Details 
      We 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}
}
      Details

    14. 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 Details 
      Robustness 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}
}
      Details

    15. 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 Details 
      Adversarial 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}
}
      Details

    16. 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 Details 
      Contextualized 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}
}
      Details

    17. 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 18 
      Deep 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}
}
      Details
    Details

  2. 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.
    QA Sessions: 9A THEME-1, 10A THEME-2 Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    Pre-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}
}
    See the full version of this paper.

    Related Publications

    1. 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 Details 
      Commonsense 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}
}
      Details

    2. 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 Details 
      Pre-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}
}
      Details

    3. 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 Details 
      Pre-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}
}
      Details

    4. 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 Details 
      We 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}
}
      Details
    Details

  3. A Transformer-based Approach for Source Code Summarization

    Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang, in ACL (short), 2020.
    QA Sessions: 9A Summarization, 10B Summarization Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    Generating 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}
}

    Related Publications

    1. 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 Details 
      Generating 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}
}
      Details

    2. Context Attentive Document Ranking and Query Suggestion

      Wasi Ahmad, Kai-Wei Chang, and Hongning Wang, in SIGIR, 2019.
      Full Text Slides Code Abstract BibTeX Details 
      We 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}
}
      Details

    3. 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 Details 
      Sequence-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}
}
      Details

    4. 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 Details 
      We 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}
}
      Details

    5. 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 Details 
      Modern 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}
}
      Details

    6. 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 Details 
      Automated 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}
}
      Details

    7. 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 Details 
      The 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}
}
      Details
    Details

Energy Efficient Pre-Training

Contextual representation models greatly improve various NLP tasks. However they are difficult to train due to their large parameter size and high computational complexity. We present a paper to drastically reduce the trainable parameters and training time.

[1]

  1. Efficient Contextual Representation Learning With Continuous Outputs

    Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, and Kai-Wei Chang, in TACL, 2019.
    QA Sessions: 4B Machine Learning, 5B Machine Learning Paper link in the virtual conference
    Full Text Slides BibTeX Details 
    Contextual 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}
}

    Related Publications

    1. 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 Details 
      Designing 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}
}
      Details

    2. 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 Details 
      We 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}
}
      Details

    3. 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 Details 
      Contextual 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}
}
      Details

    4. 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 Details 
      We 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}
}
      Details

    5. 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 Details 
      Many 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}
}
      Details

    6. 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 Details 
      Methods 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}
}
      Details

    7. Structural Learning with Amortized Inference

      Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, and Dan Roth, in AAAI, 2015.
      Full Text Poster Abstract BibTeX Details 
      Training 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}
}
      Details
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Enhance Contextulaized Encoder

We present the following two papers to enhance contextualized encoders by 1) injecting pronunciation embedding for Pun Recognition, and 2) by incorporating tree structure to capture compositional sentiment semantics for sentiment analysis.

[1], [2]

  1. SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics

    Da Yin, Tao Meng, and Kai-Wei Chang, in ACL, 2020.
    QA Sessions: 6B Sentiment Analysis, 8A Sentiment Analysis Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    We 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}
}

    Related Publications

    1. 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 Details 
      Answering 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}
}
      Details

    2. 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 Details 
      Various 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}
}
      Details

    3. 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 Details 
      Paraphrase 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}
}
      Details

    4. 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 Details 
      There  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}
}
      Details

    5. 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 Details 
      Privacy 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}
}
      Details

    6. 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 20 
      Graph 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}
}
      Details

    7. 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 Details 
      We 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}
}
      Details

    8. 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 Details 
      Text 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}
}
      Details

    9. 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 Details 
      Automatically 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}
}
      Details
    Details

  2. "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition

    Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, and Wei Wang, in ACL, 2020.
    QA Sessions: 1B Application, 5B Application Paper link in the virtual conference
    Full Text Slides Code BibTeX Details 
    Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.
    @inproceedings{zhou2020boating,
  author = {Zhou, Yichao and Jiang, Jyun-Yu and Zhao, Jieyu and Chang, Kai-Wei and Wang, Wei},
  title = {"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition},
  booktitle = {ACL},
  presentation_id = {https://virtual.acl2020.org/paper_main.75.html},
  year = {2020}
}

    Related Publications

    1. "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition

      Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, and Wei Wang, in ACL, 2020.
      Full Text Slides Video Code Abstract BibTeX Details 
      Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.
      @inproceedings{zhou2020boating,
  author = {Zhou, Yichao and Jiang, Jyun-Yu and Zhao, Jieyu and Chang, Kai-Wei and Wang, Wei},
  title = {"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition},
  booktitle = {ACL},
  presentation_id = {https://virtual.acl2020.org/paper_main.75.html},
  year = {2020}
}
      Details
    Details