@InProceedings{peng-EtAl:2017:EACLlong2,
  author    = {Peng, Baolin  and  Seltzer, Michael  and  Ju, Y.C.  and  Zweig, Geoffrey  and  Wong, Kam-Fai},
  title     = {May I take your order? A Neural Model for Extracting Structured Information from Conversations},
  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     = {450--459},
  abstract  = {In this paper we tackle a unique and important problem of extracting a
	structured order from the conversation a customer has with an order taker at a
	restaurant. This is motivated by an actual system under development to assist
	in the order taking process. We develop a sequence-to-sequence model that is
	able to map from unstructured conversational input to the structured form that
	is conveyed to the kitchen and appears on the customer receipt. This problem is
	critically different from other tasks like machine translation where
	sequence-to-sequence models have been used: the input includes two sides of a
	conversation; the output is highly structured; and logical manipulations must
	be performed, for example when the customer changes his mind while ordering. We
	present a novel sequence-to-sequence model that incorporates a special
	attention-memory gating mechanism and conversational role markers. The proposed
	model improves performance over both a phrase-based machine translation
	approach and a standard sequence-to-sequence model.},
  url       = {http://www.aclweb.org/anthology/E17-1043}
}