Abstract
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to their outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. We speculate that this work is a good starting point for academics who are new to the dialogue systems or those who want to quickly grasp up-to-date techniques in this area.
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Notes
Statistic source: https://markets.businessinsider.com.
Statistic source: https://outgrow.co.
The quality of being logical and consistent not only between words/subwords but also between responses of different timesteps.
Template filling is an efficient approach to extract and structure complex information from text to fill in a pre-defined template. They are mostly used in task-oriented dialogue systems.
Stochastic gradient ascent simply uses the negated objective function of stochastic gradient descent.
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This research/project is supported by A*STAR under its Industry Alignment Fund (LOA Award I1901E0046).
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Ni, J., Young, T., Pandelea, V. et al. Recent advances in deep learning based dialogue systems: a systematic survey. Artif Intell Rev 56, 3055–3155 (2023). https://doi.org/10.1007/s10462-022-10248-8
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DOI: https://doi.org/10.1007/s10462-022-10248-8