PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text
  Classification
Yau-Shian Wang Ta-Chung Chi Ruohong Zhang Yiming Yang
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 matching 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 matching, 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.