论文标题
基于演员批评学习的混合规则 - 神经核心分辨率系统
Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning
论文作者
论文摘要
核心分辨率系统是将所有提及在给定上下文中引用相同实体的提及。所有核心分辨率系统都需要解决两个主要任务：一项任务是检测所有潜在提及，另一个任务是学习每次可能提及的先决条件的链接。在本文中，我们提出了一个基于参与者 - 批判性学习的混合规则 - 性核心分辨率系统，以便通过利用启发式规则和神经会议模型的优势来实现更好的核心绩效。该端到端系统还可以通过利用联合培训算法来执行提及检测和解决。我们在BERT模型上实验以生成输入跨度表示。我们使用BERT SPAN表示的模型可以在Conll-2012共享任务英语测试集上的模型中实现最先进的性能。
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.
