Document-level event extraction aims to recognize event information from a
whole piece of article. Existing methods are not effective due to two
challenges of this task: a) the target event arguments are scattered across
sentences; b) the correlation among events in a document is non-trivial to
model. In this paper, we propose Heterogeneous Graph-based Interaction Model
with a Tracker (GIT) to solve the aforementioned two challenges. For the first
challenge, GIT constructs a heterogeneous graph interaction network to capture
global interactions among different sentences and entity mentions. For the
second, GIT introduces a Tracker module to track the extracted events and hence
capture the interdependency among the events. Experiments on a large-scale
dataset (Zheng et al., 2019) show GIT outperforms the previous methods by 2.8
F1. Further analysis reveals GIT is effective in extracting multiple correlated
events and event arguments that scatter across the document. Our code is
available at this https URL

本文提出了一种名为 GIT 的异构图交互模型，该模型引入跟踪器模块解决文献中目标事件参数分布广泛、事件之间关联复杂的问题，并且在大规模数据集上实验表明，GIT 在提取多个相关事件和事件参数方面表现良好，F1 值比先前的方法提高了 2.8。