陈俊帆
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陈俊帆
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论文
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
发布时间：2025-10-22点击次数：
发表刊物：
Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), CCF-A
摘要：
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.
合写作者：
陈俊帆,张日崇, Yongyi Mao, Jie Xu
论文类型：
国际学术会议
页面范围：
10492-10500
是否译文：
否
发表时间：
2022-01-01