@InProceedings{tran-nguyen:2017:CoNLL,
  author    = {Tran, Van-Khanh  and  Nguyen, Le-Minh},
  title     = {Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {442--451},
  abstract  = {Natural language generation (NLG) is a critical component in a spoken dialogue
	system. 
	This paper presents a Recurrent Neural Network based Encoder-Decoder
	architecture, in which an LSTM-based decoder is introduced to select, aggregate
	semantic elements produced by an attention mechanism over the input elements,
	and to produce the required utterances.
	The proposed generator can be jointly trained both sentence planning and
	surface realization to produce natural language sentences.
	The proposed model was extensively evaluated on four different NLG datasets.
	The experimental results showed that the proposed generators not only
	consistently outperform the previous methods across all the NLG domains but
	also show an ability to generalize from a new, unseen domain and learn from
	multi-domain datasets.},
  url       = {http://aclweb.org/anthology/K17-1044}
}