  Traditional event detection methods heavily rely on manually engineered rich
features. Recent deep learning approaches alleviate this problem by automatic
feature engineering. But such efforts, like tradition methods, have so far only
focused on single-token event mentions, whereas in practice events can also be
a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to
detect events that can be either words or phrases. To the best our knowledge,
this is one of the first efforts to handle multi-word events and also the first
attempt to use RNNs for event detection. Experimental results demonstrate that
FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the
Rich ERE 2015 event detection tasks.
