Toward Gender-Inclusive Coreference Resolution Yang Trista Cao Hal Daume´ III University of Maryland University of Maryland
[email protected] Microsoft Research
[email protected] Abstract crimination in trained coreference systems, show- ing that current systems over-rely on social stereo- Correctly resolving textual mentions of people types when resolving HE and SHE pronouns1 (see fundamentally entails making inferences about those people. Such inferences raise the risk of §2). Contemporaneously, critical work in Human- systemic biases in coreference resolution sys- Computer Interaction has complicated discussions tems, including biases that can harm binary around gender in other fields, such as computer and non-binary trans and cis stakeholders. To vision (Keyes, 2018; Hamidi et al., 2018). better understand such biases, we foreground Building on both lines of work, and inspired by nuanced conceptualizations of gender from so- Keyes’s (2018) study of vision-based automatic ciology and sociolinguistics, and develop two gender recognition systems, we consider gender new datasets for interrogating bias in crowd annotations and in existing coreference reso- bias from a broader conceptual frame than the bi- lution systems. Through these studies, con- nary “folk” model. We investigate ways in which ducted on English text, we confirm that with- folk notions of gender—namely that there are two out acknowledging and building systems that genders, assigned at birth, immutable, and in per- recognize the complexity of gender, we build fect correspondence