The content discusses the challenges in Multi-Domain Text Classification (MDTC) algorithms due to the absence of theoretical guarantees. It introduces a Margin Discrepancy-based Adversarial Training (MDAT) approach, supported by a comprehensive theoretical analysis. Empirical studies on two MDTC benchmarks demonstrate the superior performance of MDAT over existing methods. The paper bridges the gap between theory and practice in MDTC algorithms.
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