Counterfactual Representation Augmentation for Cross-Domain Sentiment Analysis

Abstract

Cross-domain sentiment analysis aims to adapt sentiment classification models trained on one or more source domains to the target domain, which can effectively alleviate the problem of insufficient labelled data in specific domains. Unlike most previous methods of adjusting models based on observed data, we propose a novel counterfactual representation augmentation (CRA) method, which aims to improve target-domain generalization by constructing new counterfactual representations for training. Specifically, we train a domain discriminator to learn the domain discrepancy between the source and target domains on unlabeled data, and use a gradient editing method to directly construct counterfactual representations, which reduces the inductive bias of the source domain and augments the training data. Moreover, we further leverage an ensemble-based training method to indirectly encourage the target-domain classifier to rely more on robust features for prediction. Extensive experiments on a widely-used cross-domain sentiment classification benchmark dataset show that our method consistently surpasses different baseline methods on different tasks, demonstrating the strong ability to improve domain generalization. We also find that our model can effectively adjust the decision boundary to make the classifier more robust and generalized, through extensive qualitative and quantitative analysis.