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

Sijia Wang, Mo Yu, Lifu Huang


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-xml-comments/2023.acl-short.111.pdf