@InProceedings{dong-EtAl:2017:EACLlong,
  author    = {Dong, Li  and  Huang, Shaohan  and  Wei, Furu  and  Lapata, Mirella  and  Zhou, Ming  and  Xu, Ke},
  title     = {Learning to Generate Product Reviews from Attributes},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {623--632},
  abstract  = {Automatically generating product reviews is a meaningful, yet not well-studied
	task in sentiment analysis. Traditional natural language generation methods
	rely extensively on hand-crafted rules and predefined templates. This paper
	presents an attention-enhanced attribute-to-sequence model to generate product
	reviews for given attribute information, such as user, product, and rating. The
	attribute encoder learns to represent input attributes as vectors. Then, the
	sequence decoder generates reviews by conditioning its output on these vectors.
	We also introduce an attention mechanism to jointly generate reviews and align
	words with input attributes. The proposed model is trained end-to-end to
	maximize the likelihood of target product reviews given the attributes. We
	build a publicly available dataset for the review generation task by leveraging
	the Amazon book reviews and their metadata. Experiments on the dataset show
	that our approach outperforms baseline methods and the attention mechanism
	significantly improves the performance of our model.},
  url       = {http://www.aclweb.org/anthology/E17-1059}
}