Mind the GAP: A balanced corpus of gendered ambiguous pronouns
Open accessAbstract
Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.
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All GAP examples contain two names of the same gender and an ambiguous pronoun, though not all follow Winograd schemas.
“Not all examples follow Winograd schemas, but they all contain two names of the same gender and an ambiguous pronoun.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Webster et al. 2018
GAP introduces 8,908 ambiguous pronoun-name pairs for coreference resolution to measure gender bias.
“GAP (Webster et al. 2018) introduces 8,908 ambiguous pronoun-name pairs for coreference resolution to measure gender bias.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Webster et al. 2018
GAP contains an equal number of masculine and feminine instances.
“The dataset contains an equal number of masculine and feminine instances.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Webster et al. 2018
GAP is derived from Wikipedia to represent more realistic use cases.
“To represent more realistic use cases, this dataset is derived from Wikipedia.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Webster et al. 2018
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- Authors
- Webster, K., Recasens, M., Axelrod, V., & Baldridge, J.
- Affiliations
- Google (United States) (US)
- ORCID
- Baldridge, Jason
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 6
- Pages
- 605-617
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2920114910
- Cited by
- 223
- References
- 5
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 21 · 2023: 34 · 2022: 30 · 2021: 39 · 2020: 42 · 2019: 35
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- coreference resolution, ambiguous pronouns, gender bias, masculine bias, syntactic structure
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