The Art of Prompting: Event Detection based on Type Specific Prompts
About
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.
Sijia Wang, Mo Yu, Lifu Huang• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Event Detection | MAVEN (test) | F1 Score68.8 | 26 | |
| Event Detection | ERE | F1 Score63.4 | 23 | |
| Event Detection | ACE05-E+ (Evaluation) | F1 Score74.9 | 23 |
Showing 3 of 3 rows