Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization

Existing language models usually require large amount of labeled data and are severely challenged by domain shift. In this work we propose a novel model for cross-domain sentiment classification - CLIM - Contrastive Learning with mutual Information Maximization, to explore the potential of contrastive learning for learning domain-invariant and task-discriminative features. To the best of our knowledge, CLIM is the first to investigate contrastive learning for cross-domain sentiment classification. Due to the scarcity of labels on the target domain, we introduce mutual information maximization (MIM) to explore the features that best support the final prediction. Furthermore, MIM is able to maintain a relatively balanced distribution of the model’s prediction, and enlarge the margin between classes on the target, which increases the model 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, demonstrating the efficacy of our methods.