Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.

翻译：事件关系抽取旨在从文本中提取事件间的多种关系。然而，现有方法将事件关系简单分类为不同类别，未能充分捕捉这些关系的内在语义。为全面理解其内在语义，本文针对每类事件关系获取原型表示，并提出一种原型增强匹配框架（ProtoEM），用于联合提取多种事件关系。具体而言，ProtoEM 采用两步法进行事件关系抽取，即原型表示与原型匹配。第一步，为捕获不同事件关系的内涵，ProtoEM 利用示例表示对应这些关系的原型；随后，为捕捉事件关系间的相互依赖，它为这些关系对应的原型构建依赖图，并采用基于图神经网络（GNN）的模块进行建模。第二步，ProtoEM 获取新事件对的表示，并计算其与第一步所得原型的相似度，以判断它们所属的事件关系类型。在 MAVEN-ERE 数据集上的实验结果表明，所提出的 ProtoEM 框架能够有效表示事件关系的原型，并相较于基线模型取得显著性能提升。