Semantic Scholar Open Access 2019 630 sitasi

Mitigating Gender Bias in Natural Language Processing: Literature Review

Tony Sun Andrew Gaut Shirlyn Tang Yuxin Huang Mai Elsherief +5 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (10)

Tony Sun

Andrew Gaut

Shirlyn Tang

Yuxin Huang

Mai Elsherief

Jieyu Zhao

Diba Mirza

E. Belding-Royer

Kai-Wei Chang

William Yang Wang

Format Sitasi

APA MLA BibTeX

Sun, T., Gaut, A., Tang, S., Huang, Y., Elsherief, M., Zhao, J. et al. (2019). Mitigating Gender Bias in Natural Language Processing: Literature Review. https://doi.org/10.18653/v1/P19-1159

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/P19-1159

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 630×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/P19-1159
Akses: Open Access ✓