Mitigating Gender Bias in Natural Language Processing: Literature Review

@inproceedings{Sun2019MitigatingGB,
  title={Mitigating Gender Bias in Natural Language Processing: Literature Review},
  author={Tony Sun and Andrew Gaut and Shirlyn Tang and Yuxin Huang and Mai ElSherief and Jieyu Zhao and Diba Mirza and Elizabeth M. Belding-Royer and Kai-Wei Chang and William Yang Wang},
  booktitle={ACL},
  year={2019}
}
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… CONTINUE READING
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