Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning
Zhenrui Yue Huimin Zeng Mengfei Lan Heng Ji Dong Wang
With emerging online topics as a source for numerous new events, detecting
unseen / rare event types presents an elusive challenge for existing event
detection methods, where only limited data access is provided for training. To
address the data scarcity problem in event detection, we propose MetaEvent, a
meta learning-based framework for zero- and few-shot event detection.
Specifically, we sample training tasks from existing event types and perform
meta training to search for optimal parameters that quickly adapt to unseen
tasks. In our framework, we propose to use the cloze-based prompt and a
trigger-aware soft verbalizer to efficiently project output to unseen event
types. Moreover, we design a contrastive meta objective based on maximum mean
discrepancy (MMD) to learn class-separating features. As such, the proposed
MetaEvent can perform zero-shot event detection by mapping features to event
types without any prior knowledge. In our experiments, we demonstrate the
effectiveness of MetaEvent in both zero-shot and few-shot scenarios, where the
proposed method achieves state-of-the-art performance in extensive experiments
on benchmark datasets FewEvent and MAVEN.