Deep Reinforcement Learning for Mention-Ranking Coreference Models

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.

• Publication year

  2016

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  2016-09-27

• Fields of study

  Computer Science

• Identifiers
  DOI 10.18653/v1/D16-1245arXiv 1609.08667
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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