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Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification

Yuan Wu

TL;DR

This work addresses the lack of theoretical guarantees in multi-domain text classification (MDTC) by reframing MDTC as a set of $M$ domain adaptation tasks and introducing margin discrepancy as the divergence measure, accompanied by a generalization bound based on Rademacher complexity. It then proposes MDAT, a margin-discrepancy-based adversarial training framework with a shared feature extractor, domain-specific extractors, and dual classifiers, optimized via a minimax objective that incorporates margin-driven alignment. The authors derive a formal MDTC generalization bound and demonstrate that MDAT achieves state-of-the-art results on two benchmarks (Amazon reviews and FDU-MTL), with robust ablation and sensitivity analyses. Overall, the paper provides both theoretical foundations and practical improvements for MDTC, offering a principled approach to designing domain-adaptive text classifiers with provable guarantees.

Abstract

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.

Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification

TL;DR

This work addresses the lack of theoretical guarantees in multi-domain text classification (MDTC) by reframing MDTC as a set of domain adaptation tasks and introducing margin discrepancy as the divergence measure, accompanied by a generalization bound based on Rademacher complexity. It then proposes MDAT, a margin-discrepancy-based adversarial training framework with a shared feature extractor, domain-specific extractors, and dual classifiers, optimized via a minimax objective that incorporates margin-driven alignment. The authors derive a formal MDTC generalization bound and demonstrate that MDAT achieves state-of-the-art results on two benchmarks (Amazon reviews and FDU-MTL), with robust ablation and sensitivity analyses. Overall, the paper provides both theoretical foundations and practical improvements for MDTC, offering a principled approach to designing domain-adaptive text classifiers with provable guarantees.

Abstract

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.
Paper Structure (25 sections, 9 theorems, 38 equations, 2 figures, 8 tables, 1 algorithm)

This paper contains 25 sections, 9 theorems, 38 equations, 2 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Domain Adaptation
  4. Multi-Domain Text Classification
  5. Preliminaries
  6. Learning Setup
  7. Adversarial Multi-Domain Text Classification
  8. Divergence Measurements
  9. Margin Discrepancy
  10. Methodology
  11. Theoretical Analysis
  12. Margin Discrepancy-based Adversarial Training
  13. Experiments
  14. Experimental Settings
  15. Results
  16. ...and 10 more sections

Key Result

Proposition 4.1

For any scoring function $f\in\mathcal{F}$, where $\lambda$ is a constant which is independent of $f$.

Figures (2)

  • Figure 1: The architecture of the MDAT method.
  • Figure 2: Parameter sensitivity analysis on the Amazon review dataset

Theorems & Definitions (11)

  • Proposition 4.1
  • Definition 4.2
  • Lemma 4.3
  • Theorem 4.4
  • Theorem 7.1
  • Definition 7.2
  • Lemma 7.3
  • Lemma 7.4
  • Lemma 7.5
  • Theorem 7.6
  • ...and 1 more