  Coreference resolution systems are typically trained with heuristic loss
functions that require careful tuning. In this paper we instead apply
reinforcement learning to directly optimize a neural mention-ranking model for
coreference evaluation metrics. We experiment with two approaches: the
REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective.
We find the latter to be more effective, resulting in significant improvements
over the current state-of-the-art on the English and Chinese portions of the
CoNLL 2012 Shared Task.
