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Machine translation and other NLP systems often contain significant biases regarding sensitive attributes, such as gender or race, that worsen system performance and perpetuate harmful stereotypes.
Topics to avoid: Demoting latent confounds in text classification
Sachin Kumar, Shuly Wintner, Noah A. Smith, and Yulia Tsvetkov. 2019 · 1909
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Findings of the 2014 workshop on statistical machine translation
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Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. 2019 · 2014
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
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Data decisions and theoretical implications when adversarially learning fair representations
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
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The trouble with bias
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2018 · 2018
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018 · 2018
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Contestability in algorithmic systems
Kristen Vaccaro, Karrie Karahalios, Deirdre K. Mulligan, Daniel Kluttz, and Tad Hirsch. 2019 · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
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Toward gender-inclusive coreference resolution
Yang Trista Cao and Hal Daumé III. 2020 · 2020
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Gender coreference and bias evaluation at WMT 2020
Tom Kocmi, Tomasz Limisiewicz, and Gabriel Stanovsky. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Reducing gender bias in neural machine translation as a domain adaptation problem
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How stereotypes are shared through language: a review and introduction of the social categories and stereotypes communication (SCSC) framework
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Equalizing gender bias in neural machine translation with word embeddings techniques
Joel Escudé Font and Marta R. Costa-jussà. 2019 · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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Danielle Saunders and Bill Byrne. 2020 · 2020
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Neural machine translation doesn’t translate gender coreference right unless you make it
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2020 · 2020
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Mitigating gender bias in machine translation with target gender annotations
Artūrs Stafanovičs, Toms Bergmanis, and Mārcis Pinnis. 2020 · 2020
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Demoting racial bias in hate speech detection
Mengzhou Xia, Anjalie Field, and Yulia Tsvetkov. 2020 · 2020
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