  Events are inter-related in documents. Motivated by the
one-sense-per-discourse theory, we hypothesize that a participant tends to play
consistent roles across multiple events in the same document. However recent
work on document-level event argument extraction models each individual event
in isolation and therefore causes inconsistency among extracted arguments
across events, which will further cause discrepancy for downstream applications
such as event knowledge base population, question answering, and hypothesis
generation. In this work, we formulate event argument consistency as the
constraints from event-event relations under the document-level setting. To
improve consistency we introduce the Event-Aware Argument Extraction (EA$^2$E)
model with augmented context for training and inference. Experiment results on
WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA$^2$E
compared to baseline methods.
