@InProceedings{yu-jiang:2017:I17-1,
  author    = {Yu, Jianfei  and  Jiang, Jing},
  title     = {Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {654--663},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/I17-1066}
}