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        <title>Mitigating Gender Bias Amplification in Distribution by Posterior Regularization</title>
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        <namePart type="given">Shengyu</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models’ top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias 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.</abstract>
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%0 Conference Proceedings
%T Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
%A Jia, Shengyu
%A Meng, Tao
%A Zhao, Jieyu
%A Chang, Kai-Wei
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F jia-etal-2020-mitigating
%X Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models’ top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias 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.
%R 10.18653/v1/2020.acl-main.264
%U https://aclanthology.org/2020.acl-main.264
%U https://doi.org/10.18653/v1/2020.acl-main.264
%P 2936-2942

Markdown (Informal)

[Mitigating Gender Bias Amplification in Distribution by Posterior Regularization](https://aclanthology.org/2020.acl-main.264) (Jia et al., ACL 2020)

• Mitigating Gender Bias Amplification in Distribution by Posterior Regularization (Jia et al., ACL 2020)

ACL

• Shengyu Jia, Tao Meng, Jieyu Zhao, and Kai-Wei Chang. 2020. Mitigating Gender Bias Amplification in Distribution by Posterior Regularization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2936–2942, Online. Association for Computational Linguistics.