Methods › Natural Language Processing › Text Classification Models › DualCL

Dual Contrastive Learning

DualCL

2 papers tagged archive 2025-07-28

Introduced by Qianben Chen et al. in Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Classification1
Contrastive Learning1
Data Augmentation1
Reinforcement Learning (RL)1
Representation Learning1
Sentiment Analysis1
Subjectivity Analysis1
Text Classification1
Zero-shot Generalization1
text-classification1

Usage over time archive 2025-07-28

Papers per year tagged with DualCL: 2022 to 2023, peak 1 1 0 2022: 1 paper 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Text Classification Models

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