ACL 2023short28 citations
The Art of Prompting: Event Detection based on Type Specific Prompts
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.
BibTeX
@inproceedings{wang-etal-2023-art,
title = "The Art of Prompting: Event Detection based on Type Specific Prompts",
author = "Wang, Sijia and
Yu, Mo and
Huang, Lifu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.111/",
doi = "10.18653/v1/2023.acl-short.111",
pages = "1286--1299"
}