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        <title>PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification</title>
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        <namePart type="given">Yau-Shian</namePart>
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        <namePart type="given">Ta-Chung</namePart>
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        <namePart type="given">Ruohong</namePart>
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    <name type="personal">
        <namePart type="given">Yiming</namePart>
        <namePart type="family">Yang</namePart>
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    <abstract>We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text retrieval problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label retrieval, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.</abstract>
    <identifier type="citekey">wang-etal-2023-pesco</identifier>
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%0 Conference Proceedings
%T PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification
%A Wang, Yau-Shian
%A Chi, Ta-Chung
%A Zhang, Ruohong
%A Yang, Yiming
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-pesco
%X We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text retrieval problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label retrieval, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.
%R 10.18653/v1/2023.acl-long.832
%U https://aclanthology.org/2023.acl-long.832
%U https://doi.org/10.18653/v1/2023.acl-long.832
%P 14897-14911

Markdown (Informal)

[PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification](https://aclanthology.org/2023.acl-long.832) (Wang et al., ACL 2023)

• PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification (Wang et al., ACL 2023)

ACL

• Yau-Shian Wang, Ta-Chung Chi, Ruohong Zhang, and Yiming Yang. 2023. PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14897–14911, Toronto, Canada. Association for Computational Linguistics.