Deep Reinforcement Learning for Mention-Ranking Coreference Models
2016-09-27EMNLP 2016Code Available0· sign in to hype
Kevin Clark, Christopher D. Manning
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- github.com/clarkkev/deep-corefOfficialIn papernone★ 0
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| OntoNotes | Reward Rescaling | F1 | 65.73 | — | Unverified |