  Class imbalance naturally exists when train and test models in different
domains. Unsupervised domain adaptation (UDA) augments model performance with
only accessible annotations from the source domain and unlabeled data from the
target domain. However, existing state-of-the-art UDA models learn
domain-invariant representations and evaluate primarily on class-balanced data
across domains. In this work, we propose an unsupervised domain adaptation
approach via reinforcement learning that jointly leverages feature variants and
imbalanced labels across domains. We experiment with the text classification
task for its easily accessible datasets and compare the proposed method with
five baselines. Experiments on three datasets prove that our proposed method
can effectively learn robust domain-invariant representations and successfully
adapt text classifiers on imbalanced classes over domains. The code is
available at https://github.com/woqingdoua/ImbalanceClass.
