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。

一种双流 AMR 增强模型用于文档级事件论元抽取

A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

Deep learning has gained much success in sentence-level relation
classification. For example, convolutional neural networks (CNN) have delivered
competitive performance without much effort on feature engineering as the
conventional pattern-based methods. Thus a lot of works have been produced
based on CNN structures. However, a key issue that has not been well addressed
by the CNN-based method is the lack of capability to learn temporal features,
especially long-distance dependency between nominal pairs. In this paper, we
propose a simple framework based on recurrent neural networks (RNN) and compare
it with CNN-based model. To show the limitation of popular used SemEval-2010
Task 8 dataset, we introduce another dataset refined from MIMLRE(Angeli et al.,
2014). Experiments on two different datasets strongly indicates that the
RNN-based model can deliver better performance on relation classification, and
it is particularly capable of learning long-distance relation patterns. This
makes it suitable for real-world applications where complicated expressions are
often involved.

本文提出了一种基于循环神经网络的简单框架，并将其与基于卷积神经网络的模型进行比较。该模型在处理句子关系分类问题时具有学习长距离依赖关系模式的优点，并在使用 MIMLRE 数据集的实验中表现出优异的性能。