Chinese Event Argument Classification via Discourse-Level Graph Convolutional Network

Event Argument Extraction aims to extract various arguments of a specific event and assign the roles to them. Currently, most of previous work focused on sentence-level task, ignoring the whole contexts of the event mentions. Obviously, the event mentions in a discourse always are around a specific topic and have interior relations, especially those intra-sentence or adjacent-sentence event mentions. Therefore, this paper proposes an event argument extraction graph model on discourse level. It adds various event mentions, entity mentions and partial semantic dependencies into a graph, which aggregates the discourse contexts by event information, entity information and semantic dependency. The experimental results on the ACE2005 Chinese corpus show that our proposed model outperforms several strong baselines.