  Event detection (ED) aims at detecting event trigger words in sentences and
classifying them into specific event types. In real-world applications, ED
typically does not have sufficient labelled data, thus can be formulated as a
few-shot learning problem. To tackle the issue of low sample diversity in
few-shot ED, we propose a novel knowledge-based few-shot event detection method
which uses a definition-based encoder to introduce external event knowledge as
the knowledge prior of event types. Furthermore, as external knowledge
typically provides limited and imperfect coverage of event types, we introduce
an adaptive knowledge-enhanced Bayesian meta-learning method to dynamically
adjust the knowledge prior of event types. Experiments show our method
consistently and substantially outperforms a number of baselines by at least 15
absolute F1 points under the same few-shot settings.
