May I take your order? A Neural Model for Extracting Structured Information from Conversations

Baolin Peng, Michael Seltzer, Y.C. Ju, Geoffrey Zweig, Kam-Fai Wong

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.
Anthology ID:
E17-1043
Volume:
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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
450–459
Language:
URL:
https://aclanthology.org/E17-1043
DOI:
Bibkey:
Cite (ACL):
Baolin Peng, Michael Seltzer, Y.C. Ju, Geoffrey Zweig, and Kam-Fai Wong. 2017. May I take your order? A Neural Model for Extracting Structured Information from Conversations. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 450–459, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
May I take your order? A Neural Model for Extracting Structured Information from Conversations (Peng et al., EACL 2017)
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https://preview.aclanthology.org/ingestion-script-update/E17-1043.pdf
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