Event argument extraction (EAE) aims to identify the arguments of an event
and classify the roles that those arguments play. Despite great efforts made in
prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the
long-range dependency, specifically, the connection between an event trigger
and a distant event argument. (3) Integrating event trigger information into
candidate argument representation. For (1), we explore using unlabeled data in
different ways. For (2), we propose to use a syntax-attending Transformer that
can utilize dependency parses to guide the attention mechanism. For (3), we
propose a trigger-aware sequence encoder with several types of
trigger-dependent sequence representations. We also support argument extraction
either from text annotated with gold entities or from plain text. Experiments
on the English ACE2005 benchmark show that our approach achieves a new
state-of-the-art.

该研究提出了利用语法引导 transformer 的长距离依存关系，来提高事件面向序列编码的实体识别性能。在 ACE2005 数据集上，该方法取得了新的最高性能。

事件论元抽取的资源增强神经模型

Resource-Enhanced Neural Model for Event Argument Extraction

Standard methods in deep learning for natural language processing fail to
capture the compositional structure of human language that allows for
systematic generalization outside of the training distribution. However, human
learners readily generalize in this way, e.g. by applying known grammatical
rules to novel words. Inspired by work in neuroscience suggesting separate
brain systems for syntactic and semantic processing, we implement a
modification to standard approaches in neural machine translation, imposing an
analogous separation. The novel model, which we call Syntactic Attention,
substantially outperforms standard methods in deep learning on the SCAN
dataset, a compositional generalization task, without any hand-engineered
features or additional supervision. Our work suggests that separating syntactic
from semantic learning may be a useful heuristic for capturing compositional
structure.

本文提出一种基于神经机器翻译的新模型 Syntactic Attention，采用分离语法与语义学习的方式训练，拥有捕捉人类语言组成性结构的能力，相较于标准深度学习方法在具有组成性泛化任务的数据集 SCAN 上表现显著优异。