A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".

Overview

Dual-Contrastive-Learning

GitHub

PWC

PWC

PWC

A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".

You can download the paper via: [ArXiv] [PapersWithCode].

One-Sentence Summary

This paper proposes a novel contrastive learning framework for supervised classification tasks by simultaneously learning the features of input samples and the parameters of classifiers in the same space.

method

Abstract

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.

Requirement

  • Python >= 3.7
  • torch >= 1.9.0
  • numpy >= 1.17.2
  • transformers >= 4.15.0

Preparation

Clone

git clone https://github.com/hiyouga/Dual-Contrastive-Learning.git

Create an anaconda environment:

conda create -n dualcl python=3.7
conda activate dualcl
pip install -r requirements.txt

Usage

python main_polarity.py

Citation

If this work is helpful, please cite as:

@article{chen2022dual,
  title={Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation},
  author={Qianben Chen and Richong Zhang and Yaowei Zheng and Yongyi Mao},
  journal={arXiv preprint},
  year={2022}
}

Contact

hiyouga [AT] buaa [DOT] edu [DOT] cn

License

MIT

Issues
  • Some questions with baselines

    Some questions with baselines

    Your work is very good and effective. But I have some questions about the baseline approach. I tried different hyperparameters to adjust supervised contrastivelearning or unsupervised contrastive learning to fine-tune BERT, and then to classify. But I've never been able to do anything better than just Cross-Entropy. I wonder what I didn't take into account? I've seen a lot of papers that contrastive learning can help improve classification results, but here I always get the opposite. Maybe I want to know the hyperparameters you set when you ran the comparison.

    opened by TaoCesc 3
  • Some logical problems

    Some logical problems

    Using the calculation comparison loss method in the source code, the calculated loss may be negative, My input is(batchsizedim, batchsizeclass_num*dim, class_num),And Lz and Lθ may be negative at the same time

    opened by Hlw20171113 0
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