Events describe the state changes of entities. In a document, multiple events are connected by various relations (e.g., Coreference, Temporal, Causal, and Subevent). Therefore, obtaining the connections between events through Event-Event Relation Extraction (ERE) is critical to understand natural language. There are two main problems in the current ERE works: a. Only embeddings of the event triggers are used for event feature representation, ignoring event arguments (e.g., time, place, person, etc.) and their structure within the event. b. The interconnection between relations (e.g., temporal and causal relations usually interact with each other ) is ignored. To solve the above problems, this paper proposes a jointly multiple ERE framework called GraphERE based on Graph-enhanced Event Embeddings. First, we enrich the event embeddings with event argument and structure features by using static AMR graphs and IE graphs; Then, to jointly extract multiple event relations, we use Node Transformer and construct Task-specific Dynamic Event Graphs for each type of relation. Finally, we used a multi-task learning strategy to train the whole framework. Experimental results on the latest MAVEN-ERE dataset validate that GraphERE significantly outperforms existing methods. Further analyses indicate the effectiveness of the graph-enhanced event embeddings and the joint extraction strategy.

翻译：[translated abstract in Chinese] 事件描述了实体的状态变化。在一个文档中，多个事件通过多种关系（例如，共指关系、时序关系、因果关系和子事件关系）相互连接。因此，通过事件关系抽取（ERE）获取事件之间的联系对于理解自然语言至关重要。当前ERE工作存在两个主要问题：a. 仅使用事件触发词的嵌入进行事件特征表示，忽略了事件论元（例如，时间、地点、人物等）及其内部结构；b. 忽略了关系之间的相互关联（例如，时序关系和因果关系通常相互影响）。为解决上述问题，本文提出了一种基于图增强事件嵌入的联合多事件关系抽取框架GraphERE。首先，通过使用静态AMR图和IE图，利用事件论元和结构特征丰富事件嵌入；然后，为联合抽取多种事件关系，使用节点变换器（Node Transformer）并为每种关系类型构建任务特定的动态事件图；最后，采用多任务学习策略训练整个框架。在最新MAVEN-ERE数据集上的实验结果表明，GraphERE显著优于现有方法。进一步分析验证了图增强事件嵌入和联合抽取策略的有效性。