In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.
REF propose to jointly learn domain-independent sentence embeddings by auxiliary tasks to predict sentiment scores of domain-independent words.
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification
IJCNLP
Computer Science