mlsanityData to 7 Oct 2026

Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings

arXiv 2005.08864 ↗First posted 18 May 2020cs.CLcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 May 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

9 papers.