At UCLA-NLP, our mission is to develop fair, accountable, robust natural language processing technology to benefit everyone. We will present papers at EMNLP 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 EMNLP, we present our studies on 1) how to detect bias in a local region of instances, 2) how to control bias in language generation.

[1], [2]
  • LOGAN: Local Group Bias Detection by Clustering

    Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
    QA Sessions: Gather-1I: Nov 17, 02:00-04:00 UTC / 18:00-20:00 PST -1d Paper link in the virtual conference
    Full Text 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}
    }
    

    Related Publications

    • LOGAN: Local Group Bias Detection by Clustering

      Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
      Full Text 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
    • Towards Controllable Biases in Language Generation

      Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP-Finding, 2020.
      Full Text Abstract BibTeX Details
      We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
      @inproceedings{sheng2020towards,
        title = {Towards Controllable Biases in Language Generation},
        author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
        booktitle = {EMNLP-Finding},
        year = {2020}
      }
      
      Details
    • 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
    • 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
    • The Woman Worked as a Babysitter: On Biases in Language Generation

      Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP (short), 2019.
      Full Text Slides Video Code Abstract BibTeX Details
      We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.
      @inproceedings{sheng2019woman,
        author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
        title = {The Woman Worked as a Babysitter: On Biases in Language Generation},
        booktitle = {EMNLP (short)},
        vimeo_id = {426366363},
        year = {2019}
      }
      
      Details
    • Debiasing 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{sun2019debiasing,
        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 = {Debiasing Gender in Natural Language Processing: Literature Review},
        booktitle = {ACL},
        vimeo_id = {384482151},
        year = {2019}
      }
      
      Details
    • 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
      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
    • 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
      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
  • Towards Controllable Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP-Finding, 2020.
    Full Text BibTeX Details
    We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
    @inproceedings{sheng2020towards,
      title = {Towards Controllable Biases in Language Generation},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {EMNLP-Finding},
      year = {2020}
    }
    

    Related Publications

    • LOGAN: Local Group Bias Detection by Clustering

      Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
      Full Text 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
    • Towards Controllable Biases in Language Generation

      Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP-Finding, 2020.
      Full Text Abstract BibTeX Details
      We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
      @inproceedings{sheng2020towards,
        title = {Towards Controllable Biases in Language Generation},
        author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
        booktitle = {EMNLP-Finding},
        year = {2020}
      }
      
      Details
    • 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
    • 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
    • The Woman Worked as a Babysitter: On Biases in Language Generation

      Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in EMNLP (short), 2019.
      Full Text Slides Video Code Abstract BibTeX Details
      We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.
      @inproceedings{sheng2019woman,
        author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
        title = {The Woman Worked as a Babysitter: On Biases in Language Generation},
        booktitle = {EMNLP (short)},
        vimeo_id = {426366363},
        year = {2019}
      }
      
      Details
    • Debiasing 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{sun2019debiasing,
        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 = {Debiasing Gender in Natural Language Processing: Literature Review},
        booktitle = {ACL},
        vimeo_id = {384482151},
        year = {2019}
      }
      
      Details
    • 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
      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
    • 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
      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

Cross-Lingual Transfer

[1]
  • Cross-Lingual Dependency Parsing by POS-Guided Word Reordering

    Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, and Xuanjing Huang, in EMNLP-Finding, 2020.
    Full Text BibTeX Details
    We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.
    @inproceedings{liu2020cross-lingual,
      author = {Liu, Lu and Zhou, Yi and Xu, Jianhan and Zheng, Xiaoqing and Chang, Kai-Wei and Huang, Xuanjing},
      title = {Cross-Lingual Dependency Parsing by POS-Guided Word Reordering},
      booktitle = {EMNLP-Finding},
      year = {2020}
    }
    

    Related Publications

    • GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

      Wasi Ahmad, Nanyun Peng, and Kai-Wei Chang, in AAAI, 2021.
      Full Text Abstract BibTeX Details
      Prevalent approaches in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic representations such that models trained on one language can be applied to other languages. However, GCNs lack in modeling long-range dependencies or disconnected words in the dependency tree. To address this challenge, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words at different syntactic distances. We introduce GATE, a \bf Graph \bf Attention \bf Transformer \bf Encoder, and test its cross-lingual transferability on relation and event extraction tasks. We perform rigorous experiments on the widely used ACE05 dataset that includes three typologically different languages: English, Chinese, and Arabic. The evaluation results show that GATE outperforms three recently proposed methods by a large margin. Our detailed analysis reveals that due to the reliance on syntactic dependencies, GATE produces robust representations that facilitate transfer across languages.
      @inproceedings{ahmad2020gatf,
        author = {Ahmad, Wasi and Peng, Nanyun and Chang, Kai-Wei},
        title = {GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction},
        booktitle = {AAAI},
        year = {2021}
      }
      
      Details
    • Cross-Lingual Dependency Parsing by POS-Guided Word Reordering

      Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, and Xuanjing Huang, in EMNLP-Finding, 2020.
      Full Text Abstract BibTeX Details
      We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.
      @inproceedings{liu2020cross-lingual,
        author = {Liu, Lu and Zhou, Yi and Xu, Jianhan and Zheng, Xiaoqing and Chang, Kai-Wei and Huang, Xuanjing},
        title = {Cross-Lingual Dependency Parsing by POS-Guided Word Reordering},
        booktitle = {EMNLP-Finding},
        year = {2020}
      }
      
      Details
    • Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages

      Wasi Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, and Nanyun Peng, in CoNLL, 2019.
      Full Text Poster Code Abstract BibTeX Details
      Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages.  One of the fundamental techniques to transfer across languages is learning language-agnostic representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations  Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages.  Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings.  We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.  
      @inproceedings{ahmad2019crosslingual,
        author = {Ahmad, Wasi and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
        title = {  Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
        booktitle = {CoNLL},
        year = {2019}
      }
      
      Details
    • Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

      Tao Meng, Nanyun Peng, and Kai-Wei Chang, in EMNLP, 2019.
      Full Text Poster Code Abstract BibTeX Details
      Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlooks the potential of leveraging linguistic properties of the languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-wise constraints to guide the inference process during the test time. We adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization inference improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially significant for target languages that have different word order features from the source language.
      @inproceedings{meng2019target,
        author = {Meng, Tao and Peng, Nanyun and Chang, Kai-Wei},
        title = {Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing},
        booktitle = {EMNLP},
        year = {2019}
      }
      
      Details
    • On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing

      Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng, in NAACL, 2019.
      Full Text Video Code Abstract BibTeX Details
      Different languages might have different wordorders. In this paper, we investigate cross-lingual transfer and posit that an order-agnostic model will perform better when trans-ferring to distant foreign languages. To test ourhypothesis, we train dependency parsers on anEnglish corpus and evaluate their transfer per-formance on 30 other languages. Specifically,we compare encoders and decoders based onRecurrent Neural Networks (RNNs) and mod-ified self-attentive architectures. The formerrelies on sequential information while the lat-ter is more flexible at modeling word order.Rigorous experiments and detailed analysisshows that RNN-based architectures transferwell to languages that are close to English,while self-attentive models have better overallcross-lingual transferability and perform espe-cially well on distant languages.
      @inproceedings{ahmad2019difficulties,
        author = {Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Hovy, Eduard and Chang, Kai-Wei and Peng, Nanyun},
        title = {On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing},
        booktitle = {NAACL},
        year = {2019}
      }
      
      Details

    Details

NLP for Social Good

[1]
  • 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 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}
    }
    

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