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    <abstract>In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framework, where test samples are first assigned pseudo-labels and are then used to train a neural text classifier. In most previous work, the pseudo-labeling step is dependent on obtaining seed words that best capture the relevance of each class label. We present LIME, a framework for weakly-supervised text classification that entirely replaces the brittle seed-word generation process with entailment-based pseudo-classification. We find that combining weakly-supervised classification and textual entailment mitigates shortcomings of both, resulting in a more streamlined and effective classification pipeline. With just an off-the-shelf textual entailment model, LIME outperforms recent baselines in weakly-supervised text classification and achieves state-of-the-art in 4 benchmarks.</abstract>
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%0 Conference Proceedings
%T LIME: Weakly-Supervised Text Classification without Seeds
%A Park, Seongmin
%A Lee, Jihwa
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F park-lee-2022-lime
%X In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framework, where test samples are first assigned pseudo-labels and are then used to train a neural text classifier. In most previous work, the pseudo-labeling step is dependent on obtaining seed words that best capture the relevance of each class label. We present LIME, a framework for weakly-supervised text classification that entirely replaces the brittle seed-word generation process with entailment-based pseudo-classification. We find that combining weakly-supervised classification and textual entailment mitigates shortcomings of both, resulting in a more streamlined and effective classification pipeline. With just an off-the-shelf textual entailment model, LIME outperforms recent baselines in weakly-supervised text classification and achieves state-of-the-art in 4 benchmarks.
%U https://aclanthology.org/2022.coling-1.91
%P 1083-1088

Markdown (Informal)

[LIME: Weakly-Supervised Text Classification without Seeds](https://aclanthology.org/2022.coling-1.91) (Park & Lee, COLING 2022)

• LIME: Weakly-Supervised Text Classification without Seeds (Park & Lee, COLING 2022)

ACL

• Seongmin Park and Jihwa Lee. 2022. LIME: Weakly-Supervised Text Classification without Seeds. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1083–1088, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.