@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",
    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.",
}

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