Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction
This work improves document-level event extraction for natural language processing applications, representing an incremental advance over prior methods.
The paper tackles document-level event extraction by addressing interference from event-unrelated sentences and between different event roles, proposing a Schema-based Explicitly Aggregating (SEA) model that aggregates event information into type and role representations. Experimental results show SEA outperforms state-of-the-art methods on ChFinAnn and DuEE-fin datasets.
Document-level event extraction (DEE) faces two main challenges: arguments-scattering and multi-event. Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction. Therefore, this paper proposes a novel Schema-based Explicitly Aggregating~(SEA) model to address these limitations. SEA aggregates event information into event type and role representations, enabling the decoding of event records based on specific type-aware representations. By detecting each event based on its event type representation, SEA mitigates the interference caused by event-unrelated information. Furthermore, SEA extracts arguments for each role based on its role-aware representations, reducing mutual interference between different roles. Experimental results on the ChFinAnn and DuEE-fin datasets show that SEA outperforms the SOTA methods.