Abstract
Dialogue systems are becoming central tools in human computer interface systems. New interaction systems, e.g. Siri, Echo and others, are proposed by the day, and new features are added to these systems at breathtaking pace. The conventional approaches based on traditional artificial intelligence techniques, such as ontologies and tree based search, have been superseded by machine learning approaches and, more recently, deep learning. In this paper we give a view of the current state of dialogue systems, describing the areas of application, as well as the current technical approaches and challenges. We propose two emerging domains of application of dialogue systems that may be highly influential in the near future: storytelling and therapeutic systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baumann, T., Kennington, C., Hough, J., Schlangen, D.: Recognising conversational speech: what an incremental ASR should do for a dialogue system and how to get there. In: Jokinen, K., Wilcock, G. (eds.) Dialogues with Social Robots. LNEE, vol. 999, pp. 421–432. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2585-3_35
Bordes, A., Boureau, Y.-L., Weston, J.: Learning end-to-end goal-oriented dialog. arXiv preprint arXiv:1605.07683 (2016)
Bowden, K.K., Oraby, S., Misra, A., Wu, J., Lukin, S.: Data-driven dialogue systems for social agents. arXiv preprint arXiv:1709.03190 (2017)
Cuayáhuitl, H.: SimpleDS: a simple deep reinforcement learning dialogue system. In: Jokinen, K., Wilcock, G. (eds.) Dialogues with Social Robots. LNEE, vol. 999, pp. 109–118. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2585-3_8
Cuayahuitl, H., et al.: Deep reinforcement learning for conversational robots playing games (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)
Henderson, M.: Machine learning for dialog state tracking: a review. In: Proceedings of The First International Workshop on Machine Learning in Spoken Language Processing (2015)
Jurafsky, D., Martin, J.H.: Dialog Systems and Chatbots. Speech and Language Processing, 3 (2014)
Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., Jurafsky, D.: Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016)
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., Jurafsky, D.: Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547 (2017)
Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., Ward, R.: Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(4), 694–707 (2016)
Sainath, T.N., Vinyals, O., Senior, A., Sak, H.: Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4580–4584. IEEE (2015)
Serban, I.V., Lowe, R., Henderson, P., Charlin, L., Pineau, J.: A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742 (2015)
Serban, I.V., Sordoni, A., Bengio, Y., Courville, A.C., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: AAAI, vol. 16, pp. 3776–3784 (2016)
Serban, I.V., Sordoni, A., Lowe, R., Charlin, L., Pineau, J., Courville, A.C., Bengio, Y.: A hierarchical latent variable encoder-decoder model for generating dialogues. In: AAAI, pp. 3295–3301 (2017)
Singh, S.P., Kearns, M.J., Litman, D.J., Walker, M.A.: Reinforcement learning for spoken dialogue systems. In: Advances in Neural Information Processing Systems, pp. 956–962 (2000)
Strub, F., De Vries, H., Mary, J., Piot, B., Courville, A., Pietquin, O.: End-to-end optimization of goal-driven and visually grounded dialogue systems. arXiv preprint arXiv:1703.05423 (2017)
Su, P.H., Gasic, M., Mrksic, N., Rojas-Barahona, L., Ultes, S., Vandyke, D., Wen, T.H., Young, S.: On-line active reward learning for policy optimisation in spoken dialogue systems. arXiv preprint arXiv:1605.07669 (2016)
Su, P.H., Vandyke, D., Gasic, M., Mrksic, N., Wen, T.H., Young, S.: Reward shaping with recurrent neural networks for speeding up on-line policy learning in spoken dialogue systems. arXiv preprint arXiv:1508.03391 (2015)
Wen, T.H., Vandyke, D., Mrksic, N., Gasic, M., Rojas-Barahona, L.M., Su, P.H., Ultes, S., Young, S.: A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016)
Wu, J., Li, M., Lee, C.H.: An entropy minimization framework for goal-driven dialogue management. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Acknowledgments
Leire Ozaeta has been supported by a Predoctoral grant from the Basque Government. This work has been partially supported by the EC through project CybSPEED funded by the MSCA-RISE grant agreement No 777720.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ozaeta, L., Graña, M. (2018). A View of the State of the Art of Dialogue Systems. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)