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• W2972572477 abstract "Bias in word representations, such as Word2Vec, has been widely reported and investigated, and efforts made to debias them. We apply the debiasing conceptor for post-processing both traditional and contextualized word embeddings. Our method can simultaneously remove racial and gender biases from word representations. Unlike standard debiasing methods, the debiasing conceptor can utilize heterogeneous lists of biased words without loss in performance. Finally, our empirical experiments show that the debiasing conceptor diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017)." @default.
• W2972572477 created "2019-09-19" @default.
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• W2972572477 date "2019-01-01" @default.
• W2972572477 modified "2023-09-26" @default.
• W2972572477 title "Conceptor Debiasing of Word Representations Evaluated on WEAT" @default.
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• W2972572477 doi "https://doi.org/10.18653/v1/w19-3806" @default.
• W2972572477 hasPublicationYear "2019" @default.
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