  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.
