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

Yuan Liang, Zhuoxuan Jiang, Di Yin, Bo Ren
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引用次数: 8

Abstract

In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multipleevents may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of greatsignificance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, calledRelation-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.
文档级事件抽取中关系建模的关系增强注意转换器
在文档级事件提取(DEE)任务中,事件参数总是分散在句子中(跨句子问题),多个事件可能位于一个文档中(多事件问题)。本文认为事件参数的关系信息对于解决上述两个问题具有重要意义,并提出了一种可以对关系依赖进行建模的新的事件抽取框架——关系增强文档级事件抽取(ReDEE)。更具体地说,这个框架的特点是一个新颖的定制变压器,称为关系增强注意力变压器(RAAT)。RAAT是可伸缩的,可以捕获多尺度和多数量的参数关系。为了进一步利用关系信息,我们引入了单独的事件关系预测任务,并采用多任务学习方法显式提高了事件提取性能。大量的实验证明了该方法的有效性,该方法可以在两个公共数据集上达到最先进的性能。我们的代码可在https://github.com/TencentYoutuResearch/RAAT上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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