# Dual Adversarial Co-Learning for Multi-Domain Text Classification

arXiv: https://arxiv.org/abs/1909.08203 · HTML: https://mlsanity.com/paper/1909.08203/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)
- [A Robust Contrastive Alignment Method For Multi-Domain Text Classification](https://mlsanity.com/paper/2204.12125/) (2022)
- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)
- [Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification](https://mlsanity.com/paper/2104.08480/) (2021)
- [Co-Regularized Adversarial Learning for Multi-Domain Text Classification](https://mlsanity.com/paper/2201.12796/) (2022)
- [Maximum Batch Frobenius Norm for Multi-Domain Text Classification](https://mlsanity.com/paper/2202.00537/) (2022)
- [Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2402.14536/) (2024)
- [Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation](https://mlsanity.com/paper/2108.06583/) (2021)
- [Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning](https://mlsanity.com/paper/2309.10302/) (2023)
- [Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification](https://mlsanity.com/paper/2403.00888/) (2024)
- [Regularized Conditional Alignment for Multi-Domain Text Classification](https://mlsanity.com/paper/2312.11572/) (2023)
- [Transfer Learning for Evolving Domains](https://mlsanity.com/paper/2609.13039/) (2026)

## Abstract (arXiv)

In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). The approach learns shared-private networks for feature extraction and deploys dual adversarial regularizations to align features across different domains and between labeled and unlabeled data simultaneously under a discrepancy based co-learning framework, aiming to improve the classifiers' generalization capacity with the learned features. We conduct experiments on multi-domain sentiment classification datasets. The results show the proposed approach achieves the state-of-the-art MDTC performance.