ShopTalk: A System for Conversational Faceted Search
Gurmeet Manku, James Lee-Thorp, Bhargav Kanagal, Joshua Ainslie,, Jingchen Feng, Zach Pearson, Ebenezer Anjorin, Sudeep Gandhe, Ilya Eckstein,, Jim Rosswog, Sumit Sanghai, Michael Pohl, Larry Adams, D. Sivakumar

TL;DR
ShopTalk is a conversational search system for shopping that manages complex schemas and separates dialog understanding from fulfillment, enabling domain-agnostic multi-turn interactions.
Contribution
It introduces a decoupled architecture with deep learning-based understanding and rule-based dialog state tracking for complex shopping schemas.
Findings
Deployed on Google Assistant for Shopping in 2020.
Handles large, complex schemas beyond traditional slot-filling systems.
Achieves domain-agnostic dialog management.
Abstract
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…
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Taxonomy
TopicsSpeech and dialogue systems · Topic Modeling · Natural Language Processing Techniques
MethodsDynamic Sparse Training
