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Mitigating Gender Bias in Natural Language Processing: Literature Review | Synapse

January 1, 2019Open Access

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Mitigating Gender Bias in Natural Language Processing: Literature Review

TSTony SunColumbia University Irving Medical Center AGAndrew GautGeorgia Institute of Technology STShirlyn TangGeorgia Institute of Technology

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Resumen

Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

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Cite This Study

Sun et al. (Tue,) studied this question.

synapsesocial.com/papers/6a0679ced3fffcff0673ac73 https://doi.org/https://doi.org/10.18653/v1/p19-1159

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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Gender Bias in Contextualized Word Embeddings2019 · 37 citations
2Tie-breaker: Using language models to quantify gender bias in sports journalism2016 · 29 citations
3On Fairness and Calibration2017 · 290 citations
4Equality of Opportunity in Supervised Learning2016 · 1,926 citations
5Learning Gender-Neutral Word Embeddings2018 · 347 citations