  Event argument extraction (EAE) aims to identify the arguments of an event
and classify the roles that those arguments play. Despite great efforts made in
prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the
long-range dependency, specifically, the connection between an event trigger
and a distant event argument. (3) Integrating event trigger information into
candidate argument representation. For (1), we explore using unlabeled data in
different ways. For (2), we propose to use a syntax-attending Transformer that
can utilize dependency parses to guide the attention mechanism. For (3), we
propose a trigger-aware sequence encoder with several types of
trigger-dependent sequence representations. We also support argument extraction
either from text annotated with gold entities or from plain text. Experiments
on the English ACE2005 benchmark show that our approach achieves a new
state-of-the-art.
