Share this page:

Societal Biases in Language Generation: Progress and Challenges

Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.

Download the full text


Abstract


Bib Entry

@inproceedings{sheng2021societal,
  title = {Societal Biases in Language Generation: Progress and Challenges},
  author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
  booktitle = {Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2021}
}

Related Publications

  • Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

    Jiao Sun and Nanyun Peng, in Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
    Full Text BibTeX Details
    @inproceedings{sun2021men,
      title = {Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia},
      author = {Sun, Jiao and Peng, Nanyun},
      booktitle = {Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)},
      year = {2021}
    }
    
    Details
  • Societal Biases in Language Generation: Progress and Challenges

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
    Full Text BibTeX Details
    @inproceedings{sheng2021societal,
      title = {Societal Biases in Language Generation: Progress and Challenges},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {Proceedings of the Conference of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)},
      year = {2021}
    }
    
    Details
  • "Nice Try, Kiddo": Ad Hominems in Dialogue Systems

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in The 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.
    Full Text Video Code Abstract BibTeX Details
    Ad hominem attacks are those that attack some feature of a person’s character instead of the position the person is maintaining. As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations. Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses. In this work, we propose categories of ad hominems that allow us to analyze human and dialogue system responses to Twitter posts. We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH). Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity to apply soft constraints to top-k sampling and can decrease the amount of ad hominems generated by dialogue systems. Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics, 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems.
    @inproceedings{sheng2021nice,
      title = {"Nice Try, Kiddo": Ad Hominems in Dialogue Systems},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {The 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
      publisher = {Association for Computational Linguistics},
      pages = {750--767},
      year = {2021}
    }
    
    Details
  • Towards Controllable Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, long, 2020.
    Full Text Poster Code 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 = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, long},
      year = {2020}
    }
    
    Details
  • Man is to person as woman is to location: Measuring gender bias in named entity recognition

    Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, and Aram Galstyan, in 31st ACM Conference on Hypertext and Social Media (HT’20), 2020.
    Full Text BibTeX Details
    @inproceedings{mehrabi2020man,
      title = {Man is to person as woman is to location: Measuring gender bias in named entity recognition},
      author = {Mehrabi, Ninareh and Gowda, Thamme and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
      booktitle = {31st ACM Conference on Hypertext and Social Media (HT’20)},
      year = {2020}
    }
    
    Details
  • The Woman Worked as a Babysitter: On Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short, 2019.
    Full Text BibTeX Details
    @inproceedings{sheng2019woman,
      title = {The Woman Worked as a Babysitter: On Biases in Language Generation},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short},
      year = {2019}
    }
    
    Details
  • Debiasing Community Detection: The Importance of Lowly-Connected Nodes

    Ninareh Mehrabi, Fred Morstatter, Nanyun Peng, and Aram Galstyan, in The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), 2019.
    Full Text BibTeX Details
    @inproceedings{mehrabi2019debiasing,
      title = {Debiasing Community Detection: The Importance of Lowly-Connected Nodes},
      author = {Mehrabi, Ninareh and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
      booktitle = {The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
      year = {2019}
    }
    
    Details