SentATN: learning sentence transferable embeddings for cross-domain sentiment classification

Cross-domain Sentiment Classification (CDSC) aims to exploit useful knowledge from the source domain to obtain a high-performance classifier on the target domain. Most of the existing methods for CDSC mainly concentrate on extracting domain-shared features, while ignoring the importance of domain-specific features. Besides, these approaches focus on reducing the discrepancy of the source domain and target domain on the word-level. As a result, they cannot fully capture the whole meaning of a sentence, which makes these methods unable to learn enough transferable features. To address these issues, we present a Sentence-level Attention Transfer Network (SentATN) for CDSC, with two distinctive characteristics. Firstly, we design an efficient encoder unit to extract domain-specific features of a sentence. Secondly, SentATN provides a sentence-level adversarial training method, which can better transfer sentiment across domains by capturing complete semantic information of a sentence. Comprehensive experiments have been conducted on extended Amazon review datasets, and the results show that the proposed SentATN performs significantly better than state-of-the-art methods.