@InProceedings{zang-wan:2017:INLG2017,
  author    = {Zang, Hongyu  and  Wan, Xiaojun},
  title     = {Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores},
  booktitle = {Proceedings of the 10th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2017},
  address   = {Santiago de Compostela, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {168--177},
  abstract  = {Data-to-text generation is very essential and important in machine writing
	applications. The recent deep learning models, like Recurrent Neural Networks
	(RNNs), have shown a bright future for relevant text generation tasks. However,
	rare work has been done for automatic generation of long reviews from user
	opinions. In this paper, we introduce a deep neural network model to generate
	long Chinese reviews from aspect-sentiment scores representing users’
	opinions. We conduct our study within the framework of encoder-decoder
	networks, and we propose a hierarchical structure with aligned attention in the
	Long-Short Term Memory (LSTM) decoder. Experiments show that our model
	outperforms retrieval based baseline methods, and also beats the sequential
	generation models in qualitative evaluations.},
  url       = {http://www.aclweb.org/anthology/W17-3526}
}