mlsanityData to 7 Oct 2026

Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

arXiv 1804.06876 ↗First posted 18 Apr 2018cs.CL cs.AIcited by 657

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Abstract (arXiv)

We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.

Key facts. Cited by 657 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2018.

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Citing papers per year: 2021: 75; 2022: 90; 2023: 128; 2024: 155; 2025: 136; 2026: 73.

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