2023
ACL
ACL 2023
The Art of Prompting: Event Detection based on Type Specific Prompts
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Learning Paradigms > Few-Shot Learning
Machine Learning > Learning Paradigms > Zero-Shot Learning
Artificial Intelligence > Core AI > Information Extraction
Natural Language Processing > Resources & Methods > Prompt Engineering