Extremely Weakly-supervised Text Classification with Wordsets Mining and Sync-Denoising

Lysa Xiao


Abstract
Extremely weakly-supervised text classification aims to classify texts without any labeled data, but only relying on class names as supervision. Existing works include prompt-based and seed-based methods. Prompt-based methods prompt language model with instructions, while seed-based methods generate pseudo-labels with word matching. Both of them have significant flaws, including zero-shot instability and context-dependent ambiguities. This paper introduces SetSync, which follows a new paradigm, i.e. wordset-based, which can avoid the above problems. In SetSync, a class is represented with wordsets, and pseudo-labels are generated with wordsets matching. To facilitate this, we propose to use information bottleneck to identify class-relevant wordsets. Moreover, we regard the classifier training as a hybrid learning of semi-supervised and noisy-labels, and propose a new training strategy, termed sync-denoising. Extensive experiments on 11 datasets show that SetSync outperforms all existing prompt and seed methods, exceeding SOTA by an impressive average of 8 points.
Anthology ID:
2024.naacl-long.397
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7160–7172
Language:
URL:
https://aclanthology.org/2024.naacl-long.397
DOI:
Bibkey:
Cite (ACL):
Lysa Xiao. 2024. Extremely Weakly-supervised Text Classification with Wordsets Mining and Sync-Denoising. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7160–7172, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Extremely Weakly-supervised Text Classification with Wordsets Mining and Sync-Denoising (Xiao, NAACL 2024)
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https://aclanthology.org/2024.naacl-long.397.pdf
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