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

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations