Abstract
Endowing social robots with natural interaction abilities, such as following a dialogue that gives the human user a sense of natural interaction, is a current interest in many areas. We are interested in developing the dialogue skills of the Nao robot for its potential use in treatment of children with special educational needs and elder people at risk of isolation. Corpora based dialog system development approaches are not adequate for personalization. In our approach we propose a teacher and introspection approach that may be able to produce highly personalized and entertaining dialog systems. The introspection module would run in the background using generative randomized systems creating new dialog pathways from the patterns learnt by direct teaching interaction.
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
Similar content being viewed by others
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
Bertero, D., Fung, P.: A long short-term memory framework for predicting humor in dialogues. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 130–135 (2016)
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.: Deep reinforcement learning for conversational robots playing games. In: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 771–776, November 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
Dodge, J., et al. Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931 (2015)
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)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jurafsky, D., Martin, J.H.: Dialog systems and chatbots. In: Speech and Language Processing, vol. 3. Pearson, London (2014)
Khouzaimi, H., Laroche, R., Lefèvre, F.: A methodology for turn-taking capabilities enhancement in spoken dialogue systems using reinforcement learning. Comput. Speech Lang. 47, 93–111 (2018)
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., Ritter, A.,Jurafsky, D.: Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547 (2017)
Lowe, R., Pow, N., Serban, I., Pineau, J.: The Ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909 (2015)
Cruz, A.M., Rincon, A.M.R., Duenas, W.R.R., Torres, D.A.Q., Bohorquez-Heredia, A.F.: What does the literature say about using robots on children with disabilities? Disabil. Rehabil. Assist. Technol. 12(5), 429–440 (2017)
Ozaeta, L., Graña, M.: A view of the state of the art of dialogue systems. In: de Cos Juez, F.J., et al. (eds.) Hybrid Artificial Intelligent Systems, pp. 706–715. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92639-1_59
Palangi, H., et al.: Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ACM Transact. Audio Speech Lang. Process. (TASLP) 24(4), 694–707 (2016)
Pennisi, P., et al.: Autism and social robotics: a systematic review. Autism Res. 9(2), 165–183 (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., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues. In: AAAI, pp. 3295–3301 (2017)
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., et al.: 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)
Trieu, H.-L., Iida, H., Bao, N.P.H., Nguyen, L.-M.: Towards developing dialogue systems with entertaining conversations. ICAART 2, 511–518 (2017)
Triguero, A., Graña, M.: Teaching NAO to answer. https://doi.org/10.5281/zenodo.2567595. February 2019
Wen, T.-H., et al.: A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016)
Acknowledgments
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
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Graña, M., Triguero, A. (2019). An Approach to Teach Nao Dialogue Skills. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-19591-5_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19590-8
Online ISBN: 978-3-030-19591-5
eBook Packages: Computer ScienceComputer Science (R0)