@inproceedings{sun-etal-2019-mitigating,
    title = "Mitigating Gender Bias in Natural Language Processing: Literature Review",
    author = "Sun, Tony  and
      Gaut, Andrew  and
      Tang, Shirlyn  and
      Huang, Yuxin  and
      ElSherief, Mai  and
      Zhao, Jieyu  and
      Mirza, Diba  and
      Belding, Elizabeth  and
      Chang, Kai-Wei  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1159",
    pages = "1630--1640",
    abstract = "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.",
}