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 this http URL

本研究介绍了一个新的基准数据集 WinoBias，用于解决与性别偏见有关的指代消解问题。通过数据增强方法，结合现有的词嵌入去偏置技术，可以消除核心指代消解系统对 WinoBias 数据集中男女刻板定型实体的偏见，同时不会显著影响系统的表现。