@InProceedings{tran-nguyen:2018:C18-1,
  author    = {Tran, Van-Khanh  and  Nguyen, Le-Minh},
  title     = {Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {1205--1217},
  abstract  = {Domain Adaptation arises when we aim at learning from source domain a model that can perform acceptably well on a different target domain. It is especially crucial for Natural Language Generation (NLG) in Spoken Dialogue Systems when there are sufficient annotated data in the source domain, but there is a limited labeled data in the target domain. How to effectively utilize as much of existing abilities from source domains is a crucial issue in domain adaptation. },
  url       = {http://www.aclweb.org/anthology/C18-1103}
}