Margin Discrepancy-based Adversarial Training for Multi-Domain Text
  Classification
Yuan Wu
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.