In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-theart methods on five benchmark datasets.
Yu REF used two auxiliary tasks to help induce sentence embeddings that work well across different domains.
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification
EMNLP
Computer Science