RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

• URL: http://arxiv.org/abs/2206.03377v1
• Date: Tue, 7 Jun 2022 15:11:42 GMT
• Title: RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction
• Authors: Yuan Liang, Zhuoxuan Jiang, Di Yin, Bo Ren
• Abstract summary: We propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE) 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.
• Score: 16.87868728956481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-of-the-art performance on two public datasets. Our code is available at https://github. com/TencentYoutuResearch/RAAT.

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