Deep Reinforcement Learning For Mention-ranking Coreference Models | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A continuously updated collection of research papers on LLMs. Maintained by Sean Moran.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Deep Reinforcement Learning For Mention-ranking Coreference Models

Kevin Clark, Christopher D. Manning . Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 2016 – 44 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Reinforcement Learning

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.

Similar Work