  Using the shared-private paradigm and adversarial training has significantly
improved the performances of multi-domain text classification (MDTC) models.
However, there are two issues for the existing methods. First, instances from
the multiple domains are not sufficient for domain-invariant feature
extraction. Second, aligning on the marginal distributions may lead to fatal
mismatching. In this paper, we propose a mixup regularized adversarial network
(MRAN) to address these two issues. More specifically, the domain and category
mixup regularizations are introduced to enrich the intrinsic features in the
shared latent space and enforce consistent predictions in-between training
instances such that the learned features can be more domain-invariant and
discriminative. We conduct experiments on two benchmarks: The Amazon review
dataset and the FDU-MTL dataset. Our approach on these two datasets yields
average accuracies of 87.64\% and 89.0\% respectively, outperforming all
relevant baselines.
