Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction

• URL: http://arxiv.org/abs/2310.10487v1
• Date: Mon, 16 Oct 2023 15:10:42 GMT
• Title: Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction
• Authors: Gang Zhao, Yidong Shi, Shudong Lu, Xinjie Yang, Guanting Dong, Jian Xu, Xiaocheng Gong, Si Li
• Abstract summary: Document-level event extraction faces two main challenges: arguments-scattering and multi-event. This paper proposes a novel-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.
• Score: 11.432496741340334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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