Most previous studies aim at extracting events from a single sentence, while
document-level event extraction still remains under-explored. In this paper, we
focus on extracting event arguments from an entire document, which mainly faces
two critical problems: a) the long-distance dependency between trigger and
arguments over sentences; b) the distracting context towards an event in the
document. To address these issues, we propose a Two-Stream Abstract meaning
Representation enhanced extraction model (TSAR). TSAR encodes the document from
different perspectives by a two-stream encoding module, to utilize local and
global information and lower the impact of distracting context. Besides, TSAR
introduces an AMR-guided interaction module to capture both intra-sentential
and inter-sentential features, based on the locally and globally constructed
AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the
boundary information for text spans explicitly. Extensive experiments
illustrate that TSAR outperforms previous state-of-the-art by a large margin,
with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents
datasets respectively, showing the superiority in the cross-sentence arguments
extraction. We release our code in this https URL PKUnlp-icler/TSAR.

本文提出了一个具有 AMR-guided interaction module 的 Two-Stream Abstract meaning Representation enhanced extraction model (TSAR)，通过双流编码模块从不同角度对整个文档进行编码，利用局部和全局信息，并采用边界损失增强边缘信息，取得了跨句子事件论元抽取方面的巨大进展。