EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition

Zixiao Zhu, Junlang Qian, Zijian Feng, Hanzhang Zhou, Kezhi Mao


Abstract
Few-shot text classification has seen significant advancements, particularly with entailment-based methods, which typically use either class labels or intensional definitions of class labels in hypotheses for label semantics expression. In this paper, we propose EDEntail, a method that employs extensional definition (EDef) of class labels in hypotheses, aiming to express the semantics of class labels more explicitly. To achieve the above goal, we develop an algorithm to gather and select extensional descriptive words of class labels and then order and format them into a sequence to form hypotheses. Our method has been evaluated and compared with state-of-the-art models on five classification datasets. The results demonstrate that our approach surpasses the supervised-learning methods and prompt-based methods under the few-shot setting, which underlines the potential of using an extensional definition of class labels for entailment-based few-shot text classification. Our code is available at https://github.com/MidiyaZhu/EDEntail.
Anthology ID:
2024.findings-naacl.71
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
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Publisher:
Association for Computational Linguistics
Note:
Pages:
1124–1137
Language:
URL:
https://aclanthology.org/2024.findings-naacl.71
DOI:
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Cite (ACL):
Zixiao Zhu, Junlang Qian, Zijian Feng, Hanzhang Zhou, and Kezhi Mao. 2024. EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1124–1137, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition (Zhu et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.71.pdf
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