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Events are inter-related in documents. Motivated by theone-sense-per-discourse theory, we hypothesize that a participant tends to playconsistent roles across multiple events in the same document. However recentwork on document-level event argument extraction models each individual eventin isolation and therefore causes inconsistency among extracted argumentsacross events, which will further cause discrepancy for downstream applicationssuch as event knowledge base population, question answering, and hypothesisgeneration. In this work, we formulate event argument consistency as theconstraints from event-event relations under the document-level setting. Toimprove consistency we introduce the Event-Aware Argument Extraction (EA$^2$E)model with augmented context for training and inference. Experiment results onWIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA$^2$Ecompared to baseline methods.

EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction