The Art of Prompting: Event Detection based on Type Specific Prompts
Sijia Wang, Mo Yu, Lifu Huang

TL;DR
This paper introduces a unified prompt-based framework for event detection that leverages event type semantics, significantly improving performance in supervised, few-shot, and zero-shot scenarios, especially with limited or no annotated data.
Contribution
It proposes a novel unified framework using event type prompts, achieving substantial performance gains over previous methods in various data-scarcity settings.
Findings
Up to 24.3% F-score improvement over baselines
Effective in few-shot and zero-shot event detection
Highlights importance of event type semantics
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 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.
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Topic Modeling
