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2 claims from 1 paper

1. Computer Science › Topic Modeling

   PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification

   Wang, Chi, Zhang and Yang · Annual Meeting of the Association for Computational Linguistics (ACL) · 2023

   The paper presents PESCO, a contrastive learning method that treats text classification as matching documents to labels, and reports strong zero-shot results, including 98.5% accuracy on DBpedia.

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   1. UncheckedPESCO reports state-of-the-art results on four benchmark text classification datasets without using labelled training data.“PESCO achieves state-of-the-art performance on four benchmark text classification datasets.”
   2. UncheckedThe paper reports that each component of PESCO is needed for its improved zero-shot text classification performance.“Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.”