Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning

• URL: http://arxiv.org/abs/2212.10087v1
• Date: Tue, 20 Dec 2022 08:55:47 GMT
• Title: Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning
• Authors: Yu Wang and Hongxia Jin
• Abstract summary: Coreference resolution systems need to tackle two main tasks. 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 hybrid rule-neural coreference resolution system based on actor-critic learning.
• Score: 53.73316523766183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 tackle two main tasks: 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 hybrid rule-neural coreference resolution system based on actor-critic learning, such that it can achieve better coreference performance by leveraging the advantages from both the heuristic rules and a neural conference model. This end-to-end system can also perform both mention detection and resolution by leveraging a joint training algorithm. We experiment on the BERT model to generate 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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