Capsule Network with Identifying Transferable Knowledge for Cross-Domain Sentiment Classification

Domain adaptation tasks have raised much attention in recent years, especially, the task of cross-domain sentiment classification, and remarkable success has been achieved on specific domains with large amounts of labeled data. However, annotating enough data in each domain is still expensive and time-consuming, which will produce difficulty in the application of domain adaptation. In this paper, we proposed a Capsule network method with Identifying Transferable Knowledge (CITK) as common knowledge for cross-domain sentiment classification. CITK model uses capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge, which bridges the knowledge gap between the source and target domains. In addition, we use Bidirectional Encoder Representations from Transformers (BERT) to convert sentences to equal length, which is called pre-training, in order to obtain more complete semantic embedded representation, so that Significant Consistent Polarity (SCP) words can be more accurate. Extensive experiments are conducted to evaluate the effectiveness of the proposed CITK model on a real world data set of four domains. Experimental results demonstrate that CITK can significantly outperform the state-of-the-art methods for the cross-domain sentiment classification task.