  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.
