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In this paper, we explore how to utilize pre-trained language model toperform few-shot text classification where only a few annotated examples aregiven for each class. Since using traditional cross-entropy loss to fine-tunelanguage model under this scenario causes serious overfitting and leads tosub-optimal generalization of model, we adopt supervised contrastive learningon few labeled data and consistency-regularization on vast unlabeled data.Moreover, we propose a novel contrastive consistency to further boost modelperformance and refine sentence representation. After conducting extensiveexperiments on four datasets, we demonstrate that our model (FTCC) canoutperform state-of-the-art methods and has better robustness.

Few-shot Text Classification with Dual Contrastive Consistency