  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 which 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.
