Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
This addresses the challenge of low sample diversity in event detection for real-world applications where labeled data is scarce.
The paper tackles the problem of few-shot event detection by introducing external knowledge and dynamically adjusting it with Bayesian meta-learning, achieving at least 15 absolute F1 points improvement over baselines.
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.