@InProceedings{perez-liu:2017:EACLlong,
  author    = {Perez, Julien  and  Liu, Fei},
  title     = {Dialog state tracking, a machine reading approach using Memory Network},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {305--314},
  abstract  = {In an end-to-end dialog system, the aim of dialog state tracking is to
	accurately estimate a compact representation of the current dialog status from
	a sequence of noisy observations produced by the speech recognition and the
	natural language understanding modules. This paper introduces a novel method of
	dialog state tracking based on the general paradigm of machine reading and
	proposes to solve it using an End-to-End Memory Network, MemN2N, a
	memory-enhanced neural network architecture. We evaluate the proposed approach
	on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has
	been converted for the occasion in order to frame the hidden state variable
	inference as a question-answering task based on a sequence of utterances
	extracted from a dialog. We show that the proposed tracker gives encouraging
	results. Then, we propose to extend the DSTC-2 dataset with specific reasoning
	capabilities requirement like counting, list maintenance, yes-no question
	answering and indefinite knowledge management. Finally, we present encouraging
	results using our proposed MemN2N based tracking model.},
  url       = {http://www.aclweb.org/anthology/E17-1029}
}