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- W4385570650 abstract "While CLIP models are useful for zero-shot vision-and-language (VL) tasks or computer vision tasks, little attention has been paid to the application of CLIP for language tasks. Intuitively, CLIP model have a rich representation pre-trained with natural language supervision, in which we argue that it is useful for language tasks. Hence, this work bridge this gap by investigating a CLIP model for zero-shot text classification. Specifically, we introduce CLIPText, a novel paradigm for zero-shot text classification, which reformulates zero-shot text classification into a text-image matching problem that CLIP can be applied to. In addition, we further incorporate prompt into CLIPText (Prompt-CLIPText) to better derive knowledge from CLIP. Experimental results on seven publicly available zero-shot text classification datasets show that both CLIPText and Prompt-CLIPText attain promising performance. Besides, extensive analysis further verifies that knowledge from CLIP can benefit zero-shot text classification task. We hope this work can attract more breakthroughs on applying VL pre-trained models for language tasks." @default.
- W4385570650 created "2023-08-05" @default.
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- W4385570650 date "2023-01-01" @default.
- W4385570650 modified "2023-09-24" @default.
- W4385570650 title "CLIPText: A New Paradigm for Zero-shot Text Classification" @default.
- W4385570650 doi "https://doi.org/10.18653/v1/2023.findings-acl.69" @default.
- W4385570650 hasPublicationYear "2023" @default.
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