We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection. The experimental results demonstrate that a well-defined and comprehensive event type prompt can significantly improve the performance of event detection, especially when the annotated data is scarce (few-shot event detection) or not available (zero-shot event detection). By leveraging the semantics of event types, our unified framework shows up to 24.3\% F-score gain over the previous state-of-the-art baselines.
- Date of publication:
- April 14, 2022
- Cornell University
- Publication note:
Sijia Wang, Mo Yu, Lifu Huang: The Art of Prompting: Event Detection based on Type Specific Prompts. CoRR abs/2204.07241 (2022)