PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
September 03, 2019 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
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Authors
Matthew Henderson, Ivan Vuliฤ, Iรฑigo Casanueva, Paweล Budzianowski, Daniela Gerz, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkลกiฤ, Pei-Hao Su
arXiv ID
1909.01296
Category
cs.CL: Computation & Language
Citations
15
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
Abstract
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.
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