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Deep Learning in Natural Language Processing pp 49–78Cite as

Deep Learning in Spoken and Text-Based Dialog Systems

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Abstract

Last few decades have witnessed substantial breakthroughs on several areas of speech and language understanding research, specifically for building human to machine conversational dialog systems. Dialog systems, also known as interactive conversational agents, virtual agents or sometimes chatbots, are useful in a wide range of applications ranging from technical support services to language learning tools and entertainment. Recent success in deep neural networks has spurred the research in building data-driven dialog models. In this chapter, we present state-of-the-art neural network architectures and details on each of the components of building a successful dialog system using deep learning. Task-oriented dialog systems would be the focus of this chapter, and later different networks are provided for building open-ended non-task-oriented dialog systems. Furthermore, to facilitate research in this area, we have a survey of publicly available datasets and software tools suitable for data-driven learning of dialog systems. Finally, appropriate choice of evaluation metrics are discussed for the learning objective.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/mobile/experiences/cortana/.

  2. 2.

    http://www.apple.com/ios/siri/.

  3. 3.

    https://developer.amazon.com/alexa.

  4. 4.

    https://madeby.google.com/home.

  5. 5.

    https://developers.facebook.com/blog/post/2016/04/12/bots-for-messenger/.

  6. 6.

    We refer the reader to the “Deep Learning in Conversational Language Understanding” chapter in this book for more details in discussing this issue.

  7. 7.

    https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/.

  8. 8.

    http://camdial.org/~mh521/dstc/.

  9. 9.

    http://www.colips.org/workshop/dstc4/.

  10. 10.

    http://workshop.colips.org/dstc5/.

  11. 11.

    https://datasets.maluuba.com/Frames.

  12. 12.

    https://github.com/facebookresearch/ParlAI.

  13. 13.

    https://github.com/rkadlec/ubuntu-ranking-dataset-creator.

  14. 14.

    https://github.com/plison/opendial.

  15. 15.

    https://github.com/facebookresearch/ParlAI.

  16. 16.

    https://github.com/UFAL-DSG/alex.

  17. 17.

    http://ufal.mff.cuni.cz/.

  18. 18.

    https://github.com/cuayahuitl/SimpleDS.

  19. 19.

    https://github.com/gunthercox/ChatterBot.

  20. 20.

    https://github.com/pender/chatbot-rnn.

  21. 21.

    http://meta-guide.com/software-meta-guide/100-best-github-chatbot.

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Author information

Authors and Affiliations

  1. Microsoft Research, Redmond, WA, USA

    Asli Celikyilmaz

  2. Citadel, Chicago & Seattle, USA

    Li Deng

  3. Google, Mountain View, CA, USA

    Dilek Hakkani-Tür

Authors
  1. Asli Celikyilmaz
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  2. Li Deng
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  3. Dilek Hakkani-Tür
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Correspondence to Asli Celikyilmaz .

Editor information

Editors and Affiliations

  1. AI Research at Citadel , Chicago, Illinois, USA

    Li Deng

  2. Tsinghua University , Beijing, China

    Yang Liu

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Celikyilmaz, A., Deng, L., Hakkani-Tür, D. (2018). Deep Learning in Spoken and Text-Based Dialog Systems. In: Deng, L., Liu, Y. (eds) Deep Learning in Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5209-5_3

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  • DOI: https://doi.org/10.1007/978-981-10-5209-5_3

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