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.

翻译：共同参考解析系统将所有提及特定背景下同一实体的词组组合在一起。所有共同参考解析系统都需要处理两个主要任务:一项任务是检测所有潜在提及的内容,另一项任务是学习每个可能提及的内容的预兆的链接。在本文中,我们提议基于行为体-批评学习的混合规则-神经共参考解析系统,这样它就能通过利用超自然规则和神经会议模式的优势实现更好的共同参照性能。这个端对端系统也可以通过利用联合培训算法进行提及探测和解析。我们在BERT模型上进行实验,以生成输入跨区域表。我们与BERT代表的模型实现了CONLL-2012共享任务英语测试组模型中的最新性能。