@InProceedings{zhou-huang-zhu:2016:COLING,
  author    = {Zhou, Hao  and  Huang, Minlie  and  zhu, xiaoyan},
  title     = {Context-aware Natural Language Generation for Spoken Dialogue Systems},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2032--2041},
  abstract  = {Natural language generation (NLG) is an important component of question
	answering(QA) systems which has a significant impact on system quality. Most
	tranditional QA systems based on templates or rules tend to generate rigid and
	stylised responses without the natural variation of human language.
	Furthermore, such methods need an amount of work to generate the templates or
	rules. To address this problem, we propose a Context-Aware LSTM model for NLG.
	The model is completely driven by data without manual designed templates or
	rules. In addition, the context information, including the question to be
	answered, semantic values to be addressed in the response, and the dialogue act
	type during interaction, are well approached in the neural network model, which
	enables the model to produce variant and informative responses. The
	quantitative evaluation and human evaluation show that CA-LSTM obtains
	state-of-the-art performance.
	Author{3}{Affiliation}},
  url       = {http://aclweb.org/anthology/C16-1191}
}