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

Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection