We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts. It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation. We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages.
@article{arxiv.1909.01296,
title = {PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking},
author = {Matthew Henderson and Ivan Vulić and Iñigo Casanueva and Paweł Budzianowski and Daniela Gerz and Sam Coope and Georgios Spithourakis and Tsung-Hsien Wen and Nikola Mrkšić and Pei-Hao Su},
journal= {arXiv preprint arXiv:1909.01296},
year = {2019}
}