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Open Access

KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

Hunan Key Laboratory of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
Big Data Institute, Central South University, Changsha 410083, China
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Abstract

Event Extraction (EE) is a key task in information extraction, which requires high-quality annotated data that are often costly to obtain. Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations. While recent approaches have endeavored to address EE through a more data-efficient generative process, they often overlook event keywords, which are vital for EE. To tackle these challenges, we introduce KeyEE, a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE). We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language model. With the auxiliary sub-prompt, KeyEE learns event keywords knowledge implicitly, thereby reducing the dependence on annotated data. Furthermore, we investigate and analyze various EKE sub-prompt strategies to encourage further research in this area. Our experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

Keywords

natural language processingEvent Extraction (EE)Multi-Prompt Learning (MPL)low-resource

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Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 547-560
DOI: 10.26599/BDMA.2023.9020036
Cite this article:
Duan J, Liao X, An Y, et al. KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt. Big Data Mining and Analytics, 2024, 7(2): 547-560. https://doi.org/10.26599/BDMA.2023.9020036

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Received: 26 July 2023
Revised: 06 November 2023
Accepted: 28 November 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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