RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction
Liang Yuan, Zhuoxuan Jiang, Di Yin, Bo Ren · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issue).In this paper, we argue that the relation information of event arguments is of great significance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE).More specifically, this framework features a novel and tailored transformer, named as Relation-augmented Attention Transformer (RAAT).RAAT is scalable to capture multi-scale and multi-amount argument relations.To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.Extensive experiments demonstrate the effectiveness of the proposed method, which can achieve state-ofthe-art performance on two public datasets.