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.
翻譯成其他語言
從原文內容
arxiv.org
深入探究