  Most previous event extraction studies have relied heavily on features
derived from annotated event mentions, thus cannot be applied to new event
types without annotation effort. In this work, we take a fresh look at event
extraction and model it as a grounding problem. We design a transferable neural
architecture, mapping event mentions and types jointly into a shared semantic
space using structural and compositional neural networks, where the type of
each event mention can be determined by the closest of all candidate types . By
leveraging (1)~available manual annotations for a small set of existing event
types and (2)~existing event ontologies, our framework applies to new event
types without requiring additional annotation. Experiments on both existing
event types (e.g., ACE, ERE) and new event types (e.g., FrameNet) demonstrate
the effectiveness of our approach. \textit{Without any manual annotations} for
23 new event types, our zero-shot framework achieved performance comparable to
a state-of-the-art supervised model which is trained from the annotations of
500 event mentions.
