research-article

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Authors:

Jiliang TangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 19, Issue 2

Pages 25 - 35

https://doi.org/10.1145/3166054.3166058

Published: 21 November 2017 Publication History

Abstract

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 19, Issue 2

December 2017

46 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3166054

Editors:
Charu Aggarwal
IBM T.J. Watson
,
Haixun Wang
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Hanghang Tong
Arizona State University

Issue’s Table of Contents

Copyright © 2017 Authors.

Publisher

Association for Computing Machinery

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Published: 21 November 2017

Published in SIGKDD Volume 19, Issue 2

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