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A Spoken Dialogue System for the EMPATHIC Virtual Coach

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9th International Workshop on Spoken Dialogue System Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 579))

Abstract

The EMPATHIC project is devoted to the development of future generations of personalised virtual coaches to help elderly people to live independently. In this paper we describe a proposal to deal with the Dialogue Management of the EMPATHIC Virtual Coach. The paper describes a DM system capable of dealing with both long-term goals and well-being plans, that can implement an effective motivational model. The system to be put into practice aims for high level healthy-ageing, utilising expressive multi-modal dialogue tailored for each specific user, working in tandem with short-term goal-oriented dialogue.

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References

  1. Bohus D, Rudnicky AI (2009) The RavenClaw dialog management framework: architecture and systems. Comput Speech Lang 23(3):332–361

    Article  Google Scholar 

  2. Bordes A, Boureau YL, Weston J (2017) Learning end-to-end goal oriented dialog. In: International conference of learning representations

    Google Scholar 

  3. ci cek FJ, Thomson B, Young S (2012) Reinforcement learning for parameter estimation in statistical spoken dialogue systems. Comput Speech Lang 26(3), 168–192

    Google Scholar 

  4. Eskénazi M, Black AW, Raux A, Langner B (2008) Let’s go lab: a platform for evaluation of spoken dialog systems with real world users. In: INTERSPEECH, p 219. ISCA

    Google Scholar 

  5. Ghigi F, Eskenazi M, Torres MI, Lee S (2014) Incremental dialog processing in a task-oriented dialog. In: InterSpeech, pp 308–312

    Google Scholar 

  6. Hurtado LF, Planells J, Segarra E, Sanchis E (2016) Spoken dialog systems based on online generated stochastic finite-state transducers. Speech Commun 83:81–93. https://doi.org/10.1016/j.specom.2016.07.011

    Article  Google Scholar 

  7. Kim S, D’Haro LF, Banchs RE, Williams JD, Henderson M (2017) The fourth dialog state tracking challenge. In: Dialogues with social robots - enablements, analyses, and evaluation, seventh international workshop on spoken dialogue systems, IWSDS 2016, Saariselkä, Finland, 13–16 Jan 2016, pp 435–449. https://doi.org/10.1007/978-981-10-2585-3_36

    Google Scholar 

  8. Levin E, Pieraccini R, Eckert W (2000) A stochastic model of human-machine interaction for learning dialog strategies. IEEE Trans Speech Audio Process 8(1):11–23

    Article  Google Scholar 

  9. Martínez FF, López JF, de Córdoba Herralde R, Martínez JMM, Hernández RSS, Muñoz JMP (2009) A bayesian networks approach for dialog modeling: the fusion bn. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing ICASSP 2009. IEEE, New Jersey, EEUU. http://oa.upm.es/5579/

  10. Olaso J, Torres MI (2017) User experience evaluation of a conversational bus information system in spanish. In: 8th IEEE international conference on cognitive infocommunications, Debrecen, Hungary, September 2017

    Google Scholar 

  11. Olaso JM, Milhorat P, Himmelsbach J, Boudy J, Chollet G, Schlögl S, Torres MI (2017) A multi-lingual evaluation of the vAssist spoken dialog system. Comparing Disco and RavenClaw. Springer, Singapore, pp 221–232

    Google Scholar 

  12. Serban IV, Sordoni A, Bengio Y, Courville A, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, AAAI’16. AAAI Press, pp 3776–3783. http://dl.acm.org/citation.cfm?id=3016387.3016435

  13. Serras M, Perez N, Torres MI, Del Pozo A, Justo R (2015) Topic classifier for customer service dialog systems. Springer International Publishing, Cham, pp 140–148. https://doi.org/10.1007/978-3-319-24033-6_16

    Chapter  Google Scholar 

  14. Serras M, Torres MI, Del Pozo A (2017) Online learning of attributed bi-automata for dialogue management in spoken dialogue systems. Springer International Publishing, Cham, pp 22–31. https://doi.org/10.1007/978-3-319-58838-4_3

    Chapter  Google Scholar 

  15. Su PH, Vandyke D, Gasic M, Kim D, Mrksic N, Wen TH, Young S (2015) Learning from real users: rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems. In: InterSpeech, pp 2007–2011

    Google Scholar 

  16. Torres MI (2013) Stochastic bi-languages to model dialogs. In: International conference on finite state methods and natural language processing, pp 9–17

    Google Scholar 

  17. Walker M (2000) An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email. J Artif Intell Res 12:387–416

    Article  Google Scholar 

  18. Williams JD (2016) End-to-end deep learning of task-oriented dialog systems. In: Keynote in future and emerging trends in language technologies FETLT, Seville

    Google Scholar 

  19. Williams JD, Asadi K, Zweig G (2017) Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. In: ACL (1). Association for Computational Linguistics, pp 665–677

    Google Scholar 

  20. Williams JD, Young S (2007) Partially observable Markov decision processes for spoken dialog systems. Comput Speech Lang 21(2):393–422

    Article  Google Scholar 

  21. Young S (2000) Probabilistic methods in spoken dialogue systems. Philos Trans R Soc Lond

    Google Scholar 

  22. Young S, Gašić M, Thomson B, Williams JD (2013) POMDP-based statistical spoken dialog systems: a review. Proc IEEE 101(5):1160–1179

    Article  Google Scholar 

  23. Zhao T, Eskénazi M (2016) Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning. In: Proceedings of the SIGDIAL 2016 conference, The 17th annual meeting of the special interest group on discourse and dialogue, 13–15 Septr 2016, Los Angeles, CA, USA, pp 1–10 (2016)

    Google Scholar 

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Acknowledgements

This work is founded by the European Commission H2020 SC1-PM15 program under RIA grant 769872.

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Correspondence to M. Inés Torres .

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Torres, M.I., Olaso, J.M., Glackin, N., Justo, R., Chollet, G. (2019). A Spoken Dialogue System for the EMPATHIC Virtual Coach. In: D'Haro, L., Banchs, R., Li, H. (eds) 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-13-9443-0_22

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