The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-specific attributes. Additionally, they employ adversarial training to align marginal feature distributions. Nevertheless, these methodologies encounter two primary challenges: (1) Neglecting class-aware information during adversarial alignment poses a risk of misalignment; (2) The limited availability of labeled data across multiple domains fails to ensure adequate discriminative capacity for the model. To tackle these issues, we propose a method called Regularized Conditional Alignment (RCA) to align the joint distributions of domains and classes, thus matching features within the same category and amplifying the discriminative qualities of acquired features. Moreover, we employ entropy minimization and virtual adversarial training to constrain the uncertainty of predictions pertaining to unlabeled data and enhance the model's robustness. Empirical results on two benchmark datasets demonstrate that our RCA approach outperforms state-of-the-art MDTC techniques.

我们提出了一种称为正则化条件对齐 (RCA) 的方法，通过对领域和类别的联合分布进行对齐，来匹配相同类别中的特征并增强获取的特征的判别能力，同时还利用熵最小化和虚拟对抗训练来限制未标记数据的预测不确定性并增强模型的鲁棒性。在两个基准数据集上的实证结果表明，我们的RCA方法优于最先进的多领域文本分类技术。

多领域文本分类的正则化条件对齐