Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

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).

Keywords

Break down, Design elements, Detection models, Empirical studies, Events detection, Large margins, Low-resource settings, Performance, Simple++, Unified framework

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Information Systems and Management

Publication

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023, Toronto, Canada, July 9-14

Volume

1

First Page

11211

Last Page

11236

ISBN

9781959429722

Publisher

ACL

City or Country

Texas

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