  We propose P4E, an identify-and-localize event detection framework that
integrates the best of few-shot prompting and structured prediction. Our
framework decomposes event detection into an identification task and a
localization task. For the identification task, which we formulate as
multi-label classification, we leverage cloze-based prompting to align our
objective with the pre-training task of language models, allowing our model to
quickly adapt to new event types. We then employ an event type-agnostic
sequence labeling model to localize the event trigger conditioned on the
identification output. This heterogeneous model design allows P4E to quickly
learn new event types without sacrificing the ability to make structured
predictions. Our experiments demonstrate the effectiveness of our proposed
design, and P4E shows superior performance for few-shot event detection on
benchmark datasets FewEvent and MAVEN and comparable performance to SOTA for
fully-supervised event detection on ACE.
