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2018 | OriginalPaper | Buchkapitel

A View of the State of the Art of Dialogue Systems

verfasst von : Leire Ozaeta, Manuel Graña

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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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approaches based on traditional artificial intelligence techniques, such as ontologies and tree based search, have been superseded by machine learning approaches and, more recently, deep learning.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.

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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_35CrossRef

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_8CrossRef

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)MATH

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)

10.

Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., Jurafsky, D.: Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016)

11.

Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., Jurafsky, D.: Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547 (2017)

12.

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)CrossRef

13.

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)

14.

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)

15.

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)

16.

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)

17.

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)

18.

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)

19.

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)

20.

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)

21.

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)

22.

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)

Titel: A View of the State of the Art of Dialogue Systems
verfasst von: Leire Ozaeta
Manuel Graña
Verlag: Springer International Publishing
Buch: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-319-92638-4

Electronic ISBN: 978-3-319-92639-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-92639-1_59

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