Skip to main content
SpringerLink
Log in

An Approach to Teach Nao Dialogue Skills

  • Conference paper
  • First Online:
Understanding the Brain Function and Emotions (IWINAC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11486))

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

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.youtube.com/watch?v=RbfM-9gaxzY.

References

  1. 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

    Chapter  Google Scholar 

  2. 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)

    Google Scholar 

  3. Bordes, A., Boureau, Y.L., Weston, J.: Learning end-to-end goal-oriented dialog. arXiv preprint arXiv:1605.07683 (2016)

  4. Bowden, K.K., Oraby, S., Misra, A., Wu, J., Lukin, S.: Data-driven dialogue systems for social agents. arXiv preprint arXiv:1709.03190 (2017)

  5. 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

    Google Scholar 

  6. 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

    Chapter  Google Scholar 

  7. Dodge, J., et al. Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931 (2015)

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  11. Jurafsky, D., Martin, J.H.: Dialog systems and chatbots. In: Speech and Language Processing, vol. 3. Pearson, London (2014)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

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

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

  15. 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)

  16. 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)

    Article  Google Scholar 

  17. 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

    Chapter  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Pennisi, P., et al.: Autism and social robotics: a systematic review. Autism Res. 9(2), 165–183 (2016)

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

  22. 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)

    Google Scholar 

  23. Serban, I.V., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues. In: AAAI, pp. 3295–3301 (2017)

    Google Scholar 

  24. 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)

  25. Su, P.-H., et al.: On-line active reward learning for policy optimisation in spoken dialogue systems. arXiv preprint arXiv:1605.07669 (2016)

  26. 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)

  27. Trieu, H.-L., Iida, H., Bao, N.P.H., Nguyen, L.-M.: Towards developing dialogue systems with entertaining conversations. ICAART 2, 511–518 (2017)

    Google Scholar 

  28. Triguero, A., Graña, M.: Teaching NAO to answer. https://doi.org/10.5281/zenodo.2567595. February 2019

  29. Wen, T.-H., et al.: A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016)

Download references

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

  1. Computational Intelligence Group, Department of CCIA, University of the Basque Country, San Sebastián, Spain

    Manuel Graña & Alexander Triguero

Authors
  1. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Triguero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Graña .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Madrid, Spain

    Félix de la Paz López

  4. Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics

Search

Navigation