@InProceedings{mrkvsic-EtAl:2017:Long,
  author    = {Mrk\v{s}i\'{c}, Nikola  and  \'{O} S\'{e}aghdha, Diarmuid  and  Wen, Tsung-Hsien  and  Thomson, Blaise  and  Young, Steve},
  title     = {Neural Belief Tracker: Data-Driven Dialogue State Tracking},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1777--1788},
  abstract  = {One of the core components of modern spoken dialogue systems is the belief
	tracker, which estimates the user's goal at every step of the dialogue.
	However, most current approaches have difficulty scaling to larger, more
	complex dialogue domains. This is due to their dependency on either: a) Spoken
	Language Understanding models that require large amounts of annotated training
	data; or b) hand-crafted lexicons for capturing some of the linguistic
	variation in users' language. We propose a novel Neural Belief Tracking (NBT)
	framework which overcomes these problems by building on recent advances in
	representation learning. NBT models reason over pre-trained word vectors,
	learning to compose them into distributed representations of user utterances
	and dialogue context. Our evaluation on two datasets shows that this approach
	surpasses past limitations, matching the performance of state-of-the-art models
	which rely on hand-crafted semantic lexicons and outperforming them when such
	lexicons are not provided.},
  url       = {http://aclweb.org/anthology/P17-1163}
}