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.

翻译：在文件级事件提取(DEE)任务中,事件争论总是分散在各句(交叉问题)和多个事件之间(多事件问题),在这份文件中,我们争辩说,事件争论的关联信息对于解决上述两个问题具有重大意义,并提出一个新的DEE框架,可以模拟关系依赖关系,称为 " 关系强化文件提取(ReDEE) " 。更具体地说,这个框架有一个新颖和量身定制的变压器,名为 " Relation-Augment Toolence Tranger(RAAT) " 。RAAT可以缩放多尺度和多容量的参数关系。为了进一步利用关系信息,我们引入了单独的事件关联预测任务,并采用多任务学习方法明确提高事件提取性能。广泛的实验表明拟议方法的有效性,可以在两个公共数据集上实现状态-艺术性表现。我们的代码可以在 https://github. com/TentYouturesearch/RAAT上查阅。