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The task of event detection involves identifying and categorizing eventtriggers. Contextual information has been shown effective on the task. However,existing methods which utilize contextual information only process the contextonce. We argue that the context can be better exploited by processing thecontext multiple times, allowing the model to perform complex reasoning and togenerate better context representation, thus improving the overall performance.Meanwhile, dynamic memory network (DMN) has demonstrated promising capabilityin capturing contextual information and has been applied successfully tovarious tasks. In light of the multi-hop mechanism of the DMN to model thecontext, we propose the trigger detection dynamic memory network (TD-DMN) totackle the event detection problem. We performed a five-fold cross-validationon the ACE-2005 dataset and experimental results show that the multi-hopmechanism does improve the performance and the proposed model achieves best$F_1$ score compared to the state-of-the-art methods.

Exploiting Contextual Information via Dynamic Memory Network for Event Detection