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 rewardrescaled max-margin objective. We find the latter to be more effective, resulting in significant improvements over the current state-ofthe-art on the English and Chinese portions of the CoNLL 2012 Shared Task.
For example, Clark and Manning REF applied deep reinforcement learning to directly optimize coreference evaluation metrics.
Deep Reinforcement Learning for Mention-Ranking Coreference Models
ArXiv
ArXiv
Computer Science