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Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support.
Bleu: a method for automatic evaluation of machine translation
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Gender-aware natural language translation
James Kuczmarski and Melvin Johnson. 2018 · 2018
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Welcome, singular "they"
Chelsea Lee. 2019 · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 2019
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Mitigating gender bias in natural language processing: Literature review
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Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But Do Not Remove Them
Hila Gonen and Yoav Goldberg. 2019 · 2019
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019 · 2019
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Language (technology) is power: A critical survey of "bias" in nlp
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Stereoset: Measuring stereotypical bias in pretrained language models
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