  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 https://github.com/RunxinXu/GIT.

Computation and Language;Artificial Intelligence