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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 10: AAAI-22 Technical Tracks 10

ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

February 1, 2023

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Abstract:

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 task-level 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.

Authors

Junfan Chen

SKLSDE, School of Computer Science and Engineering, Beihang University, Beijing, China


Richong Zhang

SKLSDE, School of Computer Science and Engineering, Beihang University, Beijing, China


Yongyi Mao

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada


Jie Xu

Department of Computer Science, University of Leeds, UK


DOI:

10.1609/aaai.v36i10.21292


Topics: AAAI

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HOW TO CITE:

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2022) 10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification AAAI 2022, 10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu (2022). ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36 2022 p.10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. 2022. ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification. "Proceedings of the AAAI Conference on Artificial Intelligence, 36". 10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. (2022) "ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification", Proceedings of the AAAI Conference on Artificial Intelligence, 36, p.10492-10500

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu, "ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification", AAAI, p.10492-10500, 2022.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. "ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2022, p.10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. "ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 36, (2022): 10492-10500.

Junfan Chen||Richong Zhang||Yongyi Mao||Jie Xu. ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification. AAAI[Internet]. 2022[cited 2023]; 10492-10500.


ISSN: 2374-3468


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