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A Survey On Dialogue Systems: Recent Advances And New Frontiers

Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang . ACM SIGKDD Explorations Newsletter 2017 – 396 citations

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3d Representation Applications Compositional Generalization Content Enrichment Dialogue & Multi Turn Fine Tuning Image Text Integration Interactive Environments Interdisciplinary Approaches KDD Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Survey Paper Variational Autoencoders Visual Contextualization Visual Question Answering

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 non-task-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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