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。