  Few-shot text classification has recently been promoted by the meta-learning
paradigm which aims to identify target classes with knowledge transferred from
source classes with sets of small tasks named episodes. Despite their success,
existing works building their meta-learner based on Prototypical Networks are
unsatisfactory in learning discriminative text representations between similar
classes, which may lead to contradictions during label prediction. In addition,
the tasklevel and instance-level overfitting problems in few-shot text
classification caused by a few training examples are not sufficiently tackled.
In this work, we propose a contrastive learning framework named ContrastNet to
tackle both discriminative representation and overfitting problems in few-shot
text classification. ContrastNet learns to pull closer text representations
belonging to the same class and push away text representations belonging to
different classes, while simultaneously introducing unsupervised contrastive
regularization at both task-level and instance-level to prevent overfitting.
Experiments on 8 few-shot text classification datasets show that ContrastNet
outperforms the current state-of-the-art models.
