Type-Aware Decoding Via Explicitly Aggregating Event Information for Document-Level Event Extraction
Gang Zhao, Yidong Shi, Shudong Lu, Xinjie Yang, Guanting Dong, Jian Xu, Xiaocheng Gong, Si Li
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.
BibTeX
@inproceedings{icassp2024_typeawaredecodin,
title = {Type-Aware Decoding Via Explicitly Aggregating Event Information for Document-Level Event Extraction},
author = {Gang Zhao and Yidong Shi and Shudong Lu and Xinjie Yang and Guanting Dong and Jian Xu and Xiaocheng Gong and Si Li},
booktitle = {ICASSP 2024},
year = {2024}
}