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

摘要In 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.
著者Baolin Peng, micheal Seltzer, YC Ju, Geoffrey Zweig, Kam-Fai Wong
會議名稱European Chapter of the Association for Computational Linguistics Valencia
會議論文集題名Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics
出版社Association for Computational Linguistics
頁次450 - 459

上次更新時間 2018-20-01 於 18:59