Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings

Katherine McCurdy; Oguz Serbetci

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arXiv · 2005.08864

Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings

Katherine McCurdy·Oguz Serbetci

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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.

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Katherine McCurdy, Oguz Serbetci. 2020-05-18. Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings. https://arxiv.org/abs/2005.08864

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