Neural Coreference Resolution based on Reinforcement Learning

• URL: http://arxiv.org/abs/2212.09028v1
• Date: Sun, 18 Dec 2022 07:36:35 GMT
• Title: Neural Coreference Resolution based on Reinforcement Learning
• Authors: Yu Wang and Hongxia Jin
• Abstract summary: Coreference resolution systems need to solve two subtasks. One task is to detect all of the potential mentions, the other is to learn the linking of an antecedent for each possible mention. We propose a reinforcement learning actor-critic-based neural coreference resolution system.
• Score: 53.73316523766183
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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