We present ShopTalk, a multi-turn conversational faceted search system for shopping that is designed to handle large and complex schemas that are beyond the scope of state of the art slot-filling systems. ShopTalk decouples dialog management from fulfillment, thereby allowing the dialog understanding system to be domain-agnostic and not tied to the particular shopping application. The dialog understanding system consists of a deep-learned Contextual Language Understanding module, which interprets user utterances, and a primarily rules-based Dialog-State Tracker (DST), which updates the dialog state and formulates search requests intended for the fulfillment engine. The interface between the two modules consists of a minimal set of domain-agnostic "intent operators," which instruct the DST on how to update the dialog state. ShopTalk was deployed in 2020 on the Google Assistant for Shopping searches.
@article{arxiv.2109.00702,
title = {ShopTalk: A System for Conversational Faceted Search},
author = {Gurmeet Manku and James Lee-Thorp and Bhargav Kanagal and Joshua Ainslie and Jingchen Feng and Zach Pearson and Ebenezer Anjorin and Sudeep Gandhe and Ilya Eckstein and Jim Rosswog and Sumit Sanghai and Michael Pohl and Larry Adams and D. Sivakumar},
journal= {arXiv preprint arXiv:2109.00702},
year = {2021}
}