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

arXiv: https://arxiv.org/abs/2201.12796 · HTML: https://mlsanity.com/paper/2201.12796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 30 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Dual Mixup Regularized Learning for Adversarial Domain Adaptation](https://mlsanity.com/paper/2007.03141/) (2020)
- [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017)
- [Multinomial Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/1802.05694/) (2018)
- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [Dual Adversarial Co-Learning for Multi-Domain Text Classification](https://mlsanity.com/paper/1909.08203/) (2019)
- [Multiple Source Domain Adaptation with Adversarial Training of Neural Networks](https://mlsanity.com/paper/1705.09684/) (2017)
- [Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks](https://mlsanity.com/paper/1804.08139/) (2018)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)
- [Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation](https://mlsanity.com/paper/2108.06583/) (2021)

## Built on by (most cited first, 3 of 3)

- [Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification](https://mlsanity.com/paper/2402.18825/) (2024)
- [A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking](https://mlsanity.com/paper/2210.15147/) (2022)
- [Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification](https://mlsanity.com/paper/2403.00888/) (2024)

## Abstract (arXiv)

Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recently, many MDTC methods adopt adversarial learning, shared-private paradigm, and entropy minimization to yield state-of-the-art results. However, these approaches face three issues: (1) Minimizing domain divergence can not fully guarantee the success of domain alignment; (2) Aligning marginal feature distributions can not fully guarantee the discriminability of the learned features; (3) Standard entropy minimization may make the predictions on unlabeled data over-confident, deteriorating the discriminability of the learned features. In order to address the above issues, we propose a co-regularized adversarial learning (CRAL) mechanism for MDTC. This approach constructs two diverse shared latent spaces, performs domain alignment in each of them, and punishes the disagreements of these two alignments with respect to the predictions on unlabeled data. Moreover, virtual adversarial training (VAT) with entropy minimization is incorporated to impose consistency regularization to the CRAL method. Experiments show that our model outperforms state-of-the-art methods on two MDTC benchmarks.