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        <title>Few-shot Event Detection: An Empirical Study and a Unified View</title>
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    <abstract>Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along several dimensions and build a unified framework on prototype-based methods. Under such unified view, each prototype-method can be viewed a combination of different modules from these design elements. We further combine all advantageous modules and propose a simple yet effective baseline, which outperforms existing methods by a large margin (e.g., 2.7% F1 gains under low-resource setting).</abstract>
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%0 Conference Proceedings
%T Few-shot Event Detection: An Empirical Study and a Unified View
%A Ma, Yubo
%A Wang, Zehao
%A Cao, Yixin
%A Sun, Aixin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ma-etal-2023-shot
%X Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along several dimensions and build a unified framework on prototype-based methods. Under such unified view, each prototype-method can be viewed a combination of different modules from these design elements. We further combine all advantageous modules and propose a simple yet effective baseline, which outperforms existing methods by a large margin (e.g., 2.7% F1 gains under low-resource setting).
%R 10.18653/v1/2023.acl-long.628
%U https://aclanthology.org/2023.acl-long.628
%U https://doi.org/10.18653/v1/2023.acl-long.628
%P 11211-11236

Markdown (Informal)

[Few-shot Event Detection: An Empirical Study and a Unified View](https://aclanthology.org/2023.acl-long.628) (Ma et al., ACL 2023)

• Few-shot Event Detection: An Empirical Study and a Unified View (Ma et al., ACL 2023)

ACL

• Yubo Ma, Zehao Wang, Yixin Cao, and Aixin Sun. 2023. Few-shot Event Detection: An Empirical Study and a Unified View. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11211–11236, Toronto, Canada. Association for Computational Linguistics.