  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.
