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