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Document-level event extraction aims to recognize event information from awhole piece of article. Existing methods are not effective due to twochallenges of this task: a) the target event arguments are scattered acrosssentences; b) the correlation among events in a document is non-trivial tomodel. In this paper, we propose Heterogeneous Graph-based Interaction Modelwith a Tracker (GIT) to solve the aforementioned two challenges. For the firstchallenge, GIT constructs a heterogeneous graph interaction network to captureglobal interactions among different sentences and entity mentions. For thesecond, GIT introduces a Tracker module to track the extracted events and hencecapture the interdependency among the events. Experiments on a large-scaledataset (Zheng et al., 2019) show GIT outperforms the previous methods by 2.8F1. Further analysis reveals GIT is effective in extracting multiple correlatedevents and event arguments that scatter across the document. Our code isavailable at https://github.com/RunxinXu/GIT.

Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker