  Contrastive learning (CL) has been successful as a powerful representation
learning method. In this work we propose CLIM: Contrastive Learning with mutual
Information Maximization, to explore the potential of CL on cross-domain
sentiment classification. To the best of our knowledge, CLIM is the first to
adopt contrastive learning for natural language processing (NLP) tasks across
domains. Due to scarcity of labels on the target domain, we introduce mutual
information maximization (MIM) apart from CL to exploit the features that best
support the final prediction. Furthermore, MIM is able to maintain a relatively
balanced distribution of the model's prediction, and enlarges the margin
between classes on the target domain. The larger margin increases our model's
robustness and enables the same classifier to be optimal across domains.
Consequently, we achieve new state-of-the-art results on the Amazon-review
dataset as well as the airlines dataset, showing the efficacy of our proposed
method CLIM.
