RCEAE: A Role Correlation-enhanced Model for Event Argument Extraction

Event argument extraction (EAE) is an important information extraction task, which aims to retrieve arguments from texts and classify them into predefined argument roles. In practice, argument roles are usually annotated with numerous types, which often arises schema-specific nature and long-tail type nature. Existing studies explore the role correlations without full utilization, which may limit the capability of argument extraction. This paper investigates two crucial correlations potentially benefiting to the task, namely intra- and inter-event role correlations. The intra-event role correlations consider the role dependency within an event, thus helping to capture event schema-specific nature. The inter-event role correlations leverage the relevance among roles across events, thus helping to learn beneficial features from other roles. To achieve the above ideas, we propose RCEAE from a new role correlation-enhanced perspective. Particularly, for intra-event role correlations, we devise a prompt-based cross encoder to capture role correlations from an event prompt, and retrieve arguments considering event schema information. For inter-event role correlations, we devise a multi-view graph-based role encoder to build relevance for role representations, accessing to beneficial features not only from their training data but also from their related roles across different event types. By incorporating both the correlation knowledge, we predict event arguments with a role-specific interactive decoder. We conduct experiments on three public benchmarks, ACE, RAMS and WIKIEVENTS. Empirical results show RCEAE achieves state-of-the-art F1 on all benchmarks and demonstrates the effectiveness of incorporating both intra- and inter-event role correlations.