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Neural Coreference Resolution based on Reinforcement Learning

2022-12-18 07:36:35
Yu Wang, Hongxia Jin

Abstract

The target of a coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to solve two subtasks; one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this paper, we propose a reinforcement learning actor-critic-based neural coreference resolution system, which can achieve both mention detection and mention clustering by leveraging an actor-critic deep reinforcement learning technique and a joint training algorithm. We experiment on the BERT model to generate different input span representations. Our model with the BERT span representation achieves the state-of-the-art performance among the models on the CoNLL-2012 Shared Task English Test Set.

Abstract (translated)

URL

https://arxiv.org/abs/2212.09028

PDF

https://arxiv.org/pdf/2212.09028.pdf


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