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• W2949969209 abstract "Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: feminine, masculine, gender-neutral and stereotypical, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information." @default.
• W2949969209 created "2019-06-27" @default.
• W2949969209 creator A5005531754 @default.
• W2949969209 creator A5073503574 @default.
• W2949969209 date "2019-01-01" @default.
• W2949969209 modified "2023-10-05" @default.
• W2949969209 title "Gender-preserving Debiasing for Pre-trained Word Embeddings" @default.
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• W2949969209 doi "https://doi.org/10.18653/v1/p19-1160" @default.
• W2949969209 hasPublicationYear "2019" @default.
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