Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

@inproceedings{Zhao2018GenderBI,
  title={Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  author={Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai-Wei Chang},
  booktitle={North American Chapter of the Association for Computational Linguistics},
  year={2018}
}
In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. 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… 

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