Multi-domain text classification (MDTC) endeavors to harness available resources from correlated domains to enhance the classification accuracy of the target domain. Presently, most MDTC approaches that embrace adversarial training and the shared-private paradigm exhibit cutting-edge performance. Unfortunately, these methods face a non-negligible challenge: the absence of theoretical guarantees in the design of MDTC algorithms. The dearth of theoretical underpinning poses a substantial impediment to the advancement of MDTC algorithms. To tackle this problem, we first provide a theoretical analysis of MDTC by decomposing the MDTC task into multiple domain adaptation tasks. We incorporate the margin discrepancy as the measure of domain divergence and establish a new generalization bound based on Rademacher complexity. Subsequently, we propose a margin discrepancy-based adversarial training (MDAT) approach for MDTC, in accordance with our theoretical analysis. To validate the efficacy of the proposed MDAT method, we conduct empirical studies on two MDTC benchmarks. The experimental results demonstrate that our MDAT approach surpasses state-of-the-art baselines on both datasets.

多领域文本分类（MDTC）是利用相关领域的可用资源来提高目标领域的分类准确性。本研究提供了关于MDTC的理论分析，将其分解为多个领域适应任务，并以边缘差异作为领域差异的度量，建立了一个基于Rademacher复杂度的新的泛化界限，并提出了基于边缘差异的对抗训练（MDAT）方法以验证其有效性。实验证明，我们的MDAT方法在两个MDTC基准上超过了先前的方法。

基于边缘差异的域间文本分类对抗训练