Role-Pair Event Tracing Graph for Joint Document-Level Event Extraction

Existing studies mostly decompose the document-level event extraction task into several sub-tasks, namely entity extraction, event type detection, and multi-event extraction under recognized event types. These sub-tasks are then executed independently in a step-by-step manner. This induces certain error propagation. Meanwhile, the associated semantics of entities functioning as arguments in multiple events have not been fully exploited, and the path expansion strategy relies heavily on the predefined role order. To address these issues, this paper proposes a novel strategy for joint document-level event extraction. First, inspired by the path expansion strategy, we devise a Role-Pair Event Tracing Graph (RP-ETG) to achieve the multi-event extraction sub-task. In this graph, tokens serve as nodes, with <role, role> pairs designated as the forward edge types and tracing identifiers as the backward edge types. The forward edges serve to identify the roles that token nodes play in an event, while the backward edges are used to track which token nodes belong to the same event. Next, to determine the event type, this paper sequentially assigns numbers to the role-pair labels across different event types, ensuring that the labels for each event type fall into a distinct numerical interval. Thus, RP-ETG not only enables the accurate decoding of all events within a document, but also directly clarifies the argument roles that tokens assume in specific events under given event types. It can also capture the associated semantics of tokens when they act as arguments across multiple events. Leveraging this graph structure, we develop a graph-enhanced model for joint document-level event extraction. We evaluate the efficacy of our proposed method through exhaustive experiments on two public datasets. The results manifest its superiority over state-of-the-art baselines, validating the significant advancements enabled by our approach.