@InProceedings{li-EtAl:2017:I17-11,
  author    = {Li, Xiujun  and  Chen, Yun-Nung  and  Li, Lihong  and  Gao, Jianfeng  and  Celikyilmaz, Asli},
  title     = {End-to-End Task-Completion Neural Dialogue Systems},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {733--743},
  abstract  = {One of the major drawbacks of modularized task-completion dialogue systems is
	that each module is trained individually, which presents several challenges.
	For example, downstream modules are affected by earlier modules, and the
	performance of the entire system is not robust to the accumulated errors. This
	paper presents a novel end-to-end learning framework for task-completion
	dialogue systems to tackle such issues.Our neural dialogue system can directly
	interact with a structured database to assist users in accessing information
	and accomplishing certain tasks. The reinforcement learning based dialogue
	manager offers robust capabilities to handle noises caused by other components
	of the dialogue system. Our experiments in a movie-ticket booking domain show
	that our end-to-end system not only outperforms modularized dialogue system
	baselines for both objective and subjective evaluation, but also is robust to
	noises as demonstrated by several systematic experiments with different error
	granularity and rates specific to the language understanding module.},
  url       = {http://www.aclweb.org/anthology/I17-1074}
}