# Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation

arXiv: https://arxiv.org/abs/2201.08702 · HTML: https://mlsanity.com/paper/2201.08702/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Large Margin Deep Networks for Classification](https://mlsanity.com/paper/1803.05598/) (2018)

## Built on by (most cited first, 23 of 23)

- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [ConDA: Contrastive Domain Adaptation for AI-generated Text Detection](https://mlsanity.com/paper/2309.03992/) (2023)
- [CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning](https://mlsanity.com/paper/2212.10341/) (2022)
- [CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning](https://mlsanity.com/paper/2409.17601/) (2024)
- [Transferring Backdoors between Large Language Models by Knowledge Distillation](https://mlsanity.com/paper/2408.09878/) (2024)
- [Positive Unlabeled Contrastive Learning](https://mlsanity.com/paper/2206.01206/) (2022)
- [Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification](https://mlsanity.com/paper/2405.11524/) (2024)
- [Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining](https://mlsanity.com/paper/2310.12172/) (2023)
- [Rank-Aware Negative Training for Semi-Supervised Text Classification](https://mlsanity.com/paper/2306.07621/) (2023)
- [MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2308.13139/) (2023)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching](https://mlsanity.com/paper/2207.02008/) (2022)
- [WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories](https://mlsanity.com/paper/2307.15293/) (2023)
- [Multi-label Sequential Sentence Classification via Large Language Model](https://mlsanity.com/paper/2411.15623/) (2024)
- [Sentiment Analysis of Movie Reviews Using BERT](https://mlsanity.com/paper/2502.18841/) (2025)
- [S2vNTM: Semi-supervised vMF Neural Topic Modeling](https://mlsanity.com/paper/2307.04804/) (2023)
- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)
- [Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content](https://mlsanity.com/paper/2505.23812/) (2025)
- [Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing](https://mlsanity.com/paper/2506.03501/) (2025)
- [Few-shot Text Classification with Dual Contrastive Consistency](https://mlsanity.com/paper/2209.15069/) (2022)
- [Improve Text Classification Accuracy with Intent Information](https://mlsanity.com/paper/2212.07649/) (2022)
- [Domain Lexical Knowledge-based Word Embedding Learning for Text Classification under Small Data](https://mlsanity.com/paper/2506.01621/) (2025)

## Abstract (arXiv)

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.