May I take your order? A Neural Model for Extracting Structured Information from Conversations
Refereed conference paper presented and published in conference proceedings

Other information
AbstractIn this paper we tackle a unique and im- portant 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 un- der development to assist in the order tak- ing 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 re- ceipt. This problem is critically differ- ent from other tasks like machine trans- lation where sequence-to-sequence mod- els have been used: the input includes two sides of a conversation; the out- put is highly structured; and logical ma- nipulations must be performed, for ex- ample when the customer changes his mind while ordering. We present a novel sequence-to-sequence model that incorpo- rates a special attention-memory gating mechanism and conversational role mark- ers. The proposed model improves per- formance over both a phrase-based ma- chine translation approach and a standard sequence-to-sequence model.
All Author(s) ListBaolin Peng, micheal Seltzer, YC Ju, Geoffrey Zweig, Kam-Fai Wong
Name of ConferenceEuropean Chapter of the Association for Computational Linguistics Valencia
Start Date of Conference03/04/2017
End Date of Conference07/04/2017
Place of ConferenceValencia
Country/Region of ConferenceSpain
Proceedings TitleProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics
Volume Number1
PublisherAssociation for Computational Linguistics
Pages450 - 459
LanguagesEnglish-United States

Last updated on 2018-20-01 at 18:59