Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System
Svetlana Stoyanchev, Suraj Pandey, Simon Keizer, Norbert Braunschweiler, Rama Doddipatla · 2022
Users of interactive search dialogue systems specify their preferences with natural language utterances.However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content.In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences.Using unsupervised semantic similarity metrics and text snippets associated with the search items, the system identifies suitable items for the user's unconstrained natural language query.In a crowd-sourced evaluation, the users were asked to chat with our extended restaurant search system.Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using this unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.