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- W4287777497 abstract "Recent research has demonstrated that vector space models of semantics can reflect undesirable biases in human culture. Our investigation of crosslinguistic word embeddings reveals that topical gender bias interacts with, and is surpassed in magnitude by, the effect of grammatical gender associations, and both may be attenuated by corpus lemmatization. This finding has implications for downstream applications such as machine translation." @default.
- W4287777497 created "2022-07-26" @default.
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- W4287777497 date "2020-05-18" @default.
- W4287777497 modified "2023-09-27" @default.
- W4287777497 title "Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings" @default.
- W4287777497 doi "https://doi.org/10.48550/arxiv.2005.08864" @default.
- W4287777497 hasPublicationYear "2020" @default.
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