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A Survey on Dialogue Systems: Recent Advances and New Frontiers
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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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“Key advances include the application of supervised machine learning for classification and information retrieval—enabling, for example, intent prediction and identification of user sentiment [20], which are critical to support task-oriented conversations.”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [20]
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Metadata
- Authors
- Chen, H., Liu, X., Yin, D., & Tang, J.
- Affiliations
- Jingdong (China) (CN)
- Michigan State University (US)
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2017-11-21
- Volume
- 19
- Issue
- 2
- Pages
- 25-35
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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