Cited by 580Speech and dialogue systemsAI in Service InteractionsTopic Modeling

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A Survey on Dialogue Systems: Recent Advances and New Frontiers

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Chen, Hongshen, Liu, Xiaorui, Yin, Dawei, Tang, Jiliang·2017·ACM SIGKDD Explorations Newsletter

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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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Authors: Chen, H., Liu, X., Yin, D., & Tang, J.
Affiliations: Jingdong (China) (CN)
Michigan State University (US)
ORCID: Chen, Hongshen
Liu, Xiaorui
Yin, Dawei
Tang, Jiliang
Venue: ACM SIGKDD Explorations Newsletter
Publisher: Association for Computing Machinery (ACM)
Published: 2017-11-21
Volume: 19
Issue: 2
Pages: 25-35
ISSN: 1931-0145 (ISSN-L)
1931-0153
DOI: 10.1145/3166054.3166058
OpenAlex: W4290742115
Cited by: 579
References: 134
Citations by year: 2026: 28 · 2025: 64 · 2024: 77 · 2023: 104 · 2022: 97 · 2021: 83 · 2020: 55 · 2019: 56 · 2018: 15
FWCI: 20.57
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM SIGKDD Explorations Newsletter (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: dialogue systems, deep learning, task-oriented dialogue systems, response generation, feature representation learning, chatbots

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A Survey on Dialogue Systems: Recent Advances and New Frontiers

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