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Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men.
Feminine formation in modern Hebrew
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Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2018 · 2018
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Paul Michel and Graham Neubig. 2018 · 2018
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Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019 · 2019
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Joel Escudé Font and Marta R. Costa-jussà. 2019 · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
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Ethics guidelines for trustworthy AI
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Multi-representation ensembles and delayed SGD updates improve syntax-based NMT
Danielle Saunders, Felix Stahlberg, Adrià de Gispert, and Bill Byrne. 2018 · 2018
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Filling gender & number gaps in neural machine translation with black-box context injection
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Assessing gender bias in machine translation: a case study with google translate
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Mitigating gender bias in natural language processing: Literature review
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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 · 2019
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Overcoming catastrophic forgetting during domain adaptation of neural machine translation
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