Mitigating Gender Bias in Natural Language Processing: Literature Review
Mitigating Gender Bias in Natural Language Processing: Literature Review
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DOI：
10.18653/v1/p19-1159
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Tony Sun;Andrew Gaut;Shirlyn Tang;Yuxin Huang;Mai Elsherief;Jieyu Zhao;Diba Mirza;E. Belding-Royer;Kai-Wei Chang;William Yang Wang
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文献类型：
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作者：
Tony Sun;Andrew Gaut;Shirlyn Tang;Yuxin Huang;Mai Elsherief;Jieyu Zhao;Diba Mirza;E. Belding-Royer;Kai-Wei Chang;William Yang Wang
随着自然语言处理(NLP)和机器学习(ML)工具的普及，认识到它们在形成社会偏见和刻板印象中所起的作用变得越来越重要。尽管自然语言处理模型在模拟各种应用方面取得了成功，但它们传播甚至可能放大了文本语料库中的性别偏见。虽然对人工智能中的性别偏见的研究并不新鲜，但减轻自然语言处理中的性别偏见的方法还相对较新。在这篇文章中，我们回顾了当代关于认识和减轻自然语言中的性别偏见的研究。我们基于四种表征偏见的形式讨论了性别偏见，并分析了性别偏见的识别方法。此外，我们还讨论了现有的性别去偏见方法的优缺点。最后，我们讨论了未来认识和减轻NLP中的性别偏见的研究。
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.