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A review of dialogue systems: current trends and future directions

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Abstract

Advances in dialogue systems have recently been made in various fields as an easy to use and inexpensive option to support or replace workers. However, developing dialogue systems that produce satisfactory responses to user queries on par with human workers still presents significant challenges. The primary purpose of this review is to analyse prominent studies on dialogue systems in the literature. Comparison frameworks were developed to perform an in-depth analysis in terms of approaches, data sets and evaluation metrics. Unlike previous reviews, we thoroughly examined how reinforcement learning is applied to dialogue systems. We also analysed studies attempting to interleave the two main types of dialogue systems (i.e. open-domain dialogue and task-oriented dialogue). We present some open-source platforms for developing dialogue systems. Finally, we identified research gaps and discussed potential research directions.

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Notes

  1. https://openai.com/blog/chatgpt.

  2. https://bard.google.com/.

  3. https://pydial.cs.hhu.de.

  4. https://github.com/thu-coai/ConvLab-2.

  5. https://parl.ai.

  6. https://rasa.com.

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Acknowledgements

The authors wish to acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for providing the facilities to carry out this research. Many thanks are due to the anonymous referees for their detailed and helpful comments.

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  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Atheer Algherairy & Moataz Ahmed

  2. Department of Computer Science, Imam Abdulrahman Bin Faisal University, 34212, Dammam, Saudi Arabia

    Atheer Algherairy

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Algherairy, A., Ahmed, M. A review of dialogue systems: current trends and future directions. Neural Comput & Applic 36, 6325–6351 (2024). https://doi.org/10.1007/s00521-023-09322-1

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