  Many text classification tasks are domain-dependent, and various domain
adaptation approaches have been proposed to predict unlabeled data in a new
domain. Domain-adversarial neural networks (DANN) and their variants have been
used widely recently and have achieved promising results for this problem.
However, most of these approaches assume that the label proportions of the
source and target domains are similar, which rarely holds in most real-world
scenarios. Sometimes the label shift can be large and the DANN fails to learn
domain-invariant features. In this study, we focus on unsupervised domain
adaptation of text classification with label shift and introduce a domain
adversarial network with label proportions estimation (DAN-LPE) framework. The
DAN-LPE simultaneously trains a domain adversarial net and processes label
proportions estimation by the confusion of the source domain and the
predictions of the target domain. Experiments show the DAN-LPE achieves a good
estimate of the target label distributions and reduces the label shift to
improve the classification performance.
