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The Art of Prompting: Event Detection based on Type Specific Prompts

Sijia Wang,Mo Yu,Lifu Huang

2022 · DOI: 10.48550/arXiv.2204.07241
Annual Meeting of the Association for Computational Linguistics · 28 Citations

TLDR

The experimental results demonstrate that a well-defined and comprehensive event type prompt can significantly improve event detection performance, especially when the annotated data is scarce (few- shot event detection) or not available (zero-shot event detection).

Abstract

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 event detection performance, 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 22.2% F-score gain over the previous state-of-the-art baselines.