  Contrastive learning (CL) has been successful as a powerful representation
learning method. In this paper, we propose a contrastive learning framework for
cross-domain sentiment classification. We aim to induce domain invariant
optimal classifiers rather than distribution matching. To this end, we
introduce in-domain contrastive learning and entropy minimization. Also, we
find through ablation studies that these two techniques behaviour differently
in case of large label distribution shift and conclude that the best practice
is to choose one of them adaptively according to label distribution shift. The
new state-of-the-art results our model achieves on standard benchmarks show the
efficacy of the proposed method.
