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A Proposal for the Development of Lifelong Dialog Systems

  • David GriolEmail author
  • Araceli Sanchis
  • Jose Manuel Molina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

In this paper we describe a proposal that employs Soft Computing techniques for developing intelligent dialog systems that can improve over time. To do this, our proposal merges statistical dialog management methodologies, intentional and emotional information in order to make dialog managers more efficient and adaptive. The prediction of the user intention and emotion is carried out for each user turn in the dialog by means of specific modules that are conceived as an intermediate phase between natural language understanding and dialog management in the architecture of these systems. We have applied and evaluated our method in the UAH system, for which the evaluation results show that merging both sources of information improves system performance as well as its perceived quality.

Keywords

Conversational interfaces Spoken interaction Evolving classifiers Fuzzy-rule based systems Human-machine interaction 

References

  1. 1.
    Bangalore, S., DiFabbrizio, G., Stent, A.: Learning the structure of task-driven human-human dialogs. IEEE Trans. Audio Speech Lang. Process. 16(7), 1249–1259 (2008)CrossRefGoogle Scholar
  2. 2.
    Barnard, E., Halberstadt, A., Kotelly, C., Phillips, M.: A consistent approach to designing spoken-dialog systems. In: Proceedings of ASRU 1999, pp. 1173–1176 (1999)Google Scholar
  3. 3.
    Burkhardt, F., van Ballegooy, M., Engelbrecht, K., Polzehl, T., Stegmann, J.: Emotion detection in dialog systems - Usecases, strategies and challenges. In: Proceedings of ACII 2009 (2009)Google Scholar
  4. 4.
    Callejas, Z., López-Cózar, R.: Influence of contextual information in emotion annotation for spoken dialogue systems. Speech Commun. 50(5), 416–433 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, Z., Liu, B.: Lifelong Machine Learning. Morgan & Claypool, San Rafael (2018)Google Scholar
  6. 6.
    Engelbrecht, K.: Estimating Spoken Dialog System Quality with User Models. Springer, Heidelberg (2012)Google Scholar
  7. 7.
    Griol, D., Sanchis, A., Molina, J.: FRB-dialog: a toolkit for automatic learning of fuzzy-rule based (FRB) dialog managers. In: Proceedings of HAIS 2017, pp. 306–317 (2017)Google Scholar
  8. 8.
    Mazumder, S., Ma, N., Liu, B.: Towards a continuous knowledge learning engine for chatbots. Comput. Res. Repos. 2, 1–11 (2018)Google Scholar
  9. 9.
    McTear, M.F., Callejas, Z., Griol, D.: The Conversational Interface. Talking to Smart Devices. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  10. 10.
    Pieraccini, R., Suendermann, D., Dayanidhi, K., Liscombe, J.: Are we there yet? Research in commercial spoken dialog systems. LNCS 5729, 3–13 (2009)Google Scholar
  11. 11.
    Ravi, K., Ravi, V., Prasad, P.S.R.K.: Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl. Soft Comput. 60, 786–807 (2017)CrossRefGoogle Scholar
  12. 12.
    Schatzmann, J., Weilhammer, K., Stuttle, M., Young, S.: A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. Knowl. Eng. Rev. 21(2), 97–126 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • David Griol
    • 1
    Email author
  • Araceli Sanchis
    • 1
  • Jose Manuel Molina
    • 1
  1. 1.Department of Computer ScienceUniversidad Carlos III de MadridLeganésSpain

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