  Event extraction is a fundamental task for natural language processing.
Finding the roles of event arguments like event participants is essential for
event extraction. However, doing so for real-life event descriptions is
challenging because an argument's role often varies in different contexts.
While the relationship and interactions between multiple arguments are useful
for settling the argument roles, such information is largely ignored by
existing approaches. This paper presents a better approach for event extraction
by explicitly utilizing the relationships of event arguments. We achieve this
through a carefully designed task-oriented dialogue system. To model the
argument relation, we employ reinforcement learning and incremental learning to
extract multiple arguments via a multi-turned, iterative process. Our approach
leverages knowledge of the already extracted arguments of the same sentence to
determine the role of arguments that would be difficult to decide individually.
It then uses the newly obtained information to improve the decisions of
previously extracted arguments. This two-way feedback process allows us to
exploit the argument relations to effectively settle argument roles, leading to
better sentence understanding and event extraction. Experimental results show
that our approach consistently outperforms seven state-of-the-art event
extraction methods for the classification of events and argument role and
argument identification.
