Deep Reinforcement Learning for Mention-Ranking Coreference Models

EMNLP 2016  ·  Kevin Clark, Christopher D. Manning ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Coreference Resolution OntoNotes Reward Rescaling F1 65.73 # 24

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