Discriminative Feature Adaptation via Conditional Mean Discrepancy for Cross-Domain Text Classification

This paper concerns the problem of Unsupervised Domain Adaptation (UDA) in text classification, aiming to transfer the knowledge from a source domain to a different but related target domain. Previous methods learn the discriminative feature of target domain in terms of noisy pseudo labels, which inevitably produces negative effects on training a robust model. In this paper, we propose a novel criterion Conditional Mean Discrepancy (CMD) to learn the discriminative features by matching the conditional distributions across domains. CMD embeds both the conditional distributions of source and target domains into tensor-product Hilbert space and computes Hilbert-Schmidt norm instead. We shed a new light on discriminative feature adaptation: the collective knowledge of discriminative features of different domains is naturally discovered by minimizing CMD. We propose Aligned Adaptation Networks (AAN) to learn the domain-invariant and discriminative features simultaneously based on Maximum Mean Discrepancy (MMD) and CMD. Meanwhile, to trade off between the marginal and conditional distributions, we further maximize both MMD and CMD criterions using adversarial strategy to make the features of AAN more discrepancy-invariant. To the best of our knowledge, this is the first work to definitely evaluate the shifts in the conditional distributions across domains. Experiments on cross-domain text classification demonstrate that AAN achieves better classification accuracy but less convergence time compared to the state-of-the-art deep methods.