Few-shot Text Classification with Dual Contrastive Consistency

• URL: http://arxiv.org/abs/2209.15069v1
• Date: Thu, 29 Sep 2022 19:26:23 GMT
• Title: Few-shot Text Classification with Dual Contrastive Consistency
• Authors: Liwen Sun, Jiawei Han
• Abstract summary: In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification. We adopt supervised contrastive learning on few labeled data and consistency-regularization on vast unlabeled data.
• Score: 31.141350717029358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification where only a few annotated examples are given for each class. Since using traditional cross-entropy loss to fine-tune language model under this scenario causes serious overfitting and leads to sub-optimal generalization of model, we adopt supervised contrastive learning on few labeled data and consistency-regularization on vast unlabeled data. Moreover, we propose a novel contrastive consistency to further boost model performance and refine sentence representation. After conducting extensive experiments on four datasets, we demonstrate that our model (FTCC) can outperform state-of-the-art methods and has better robustness.

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