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.- Anthology ID:
- 2023.acl-short.111
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1286–1299
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.111
- DOI:
- 10.18653/v1/2023.acl-short.111
- Cite (ACL):
- Sijia Wang, Mo Yu, and Lifu Huang. 2023. The Art of Prompting: Event Detection based on Type Specific Prompts. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1286–1299, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The Art of Prompting: Event Detection based on Type Specific Prompts (Wang et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.acl-short.111.pdf