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.

提出了一种基于图增强事件嵌入的共同多事件关系提取框架 GraphERE，通过使用静态 AMR 图和 IE 图来丰富事件嵌入以及事件参数和结构特征，并使用节点变换器和为每种类型的关系构建任务特定的动态事件图来共同提取多个事件关系，并使用多任务学习策略训练整个框架，实验证明 GraphERE 明显优于现有方法，进一步分析显示了图增强事件嵌入和共同提取策略的有效性。