Advertisement

Dialogue Management for User-Centered Adaptive Dialogue

Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

A novel approach for introducing adaptivity to user satisfaction into dialogue management is presented in this work. In general, rendering the dialogue adaptive to user satisfaction enables the dialogue system to improve the course of the dialogue or to handle problematic situations better. In this contribution, the theoretical aspects of rendering the dialogue cycle adaptive are outlined. Furthermore, a detailed description of how an existing dialogue management component is extended to adapting to user satisfaction is described. The approach is validated by presenting an actual implementation. For a proof-of-concept, the resulting dialogue manager is applied in an experiment comparing different confirmation strategies. Having a simple dialogue, the adaptive strategy performs successful and as good as the best strategy.

References

  1. 1.
    Fraser NM (1994) The sundial speech understanding and dialogue project: results and implications for translation. In: Aslib proceedings, vol 46. MCB UP Ltd, pp 141–148Google Scholar
  2. 2.
    Gnjatović M, Rösner D (2008) Adaptive dialogue management in the nimitek prototype system. In: PIT ’08: Proceedings of the 4th IEEE tutorial and research workshop on perception and interactive technologies for speech-based systems. Springer, Berlin, pp 14–25. doi: 10.1007/978-3-540-69369-7_3
  3. 3.
    Heinroth T, Denich D, Schmitt A (2010) Owlspeak—adaptive spoken dialogue within intelligent environments. In: IEEE PerCom workshop proceedings. Presented as part of SmartE workshopGoogle Scholar
  4. 4.
    Hone KS, Graham R (2000) Towards a tool for the subjective assessment of speech system interfaces (sassi). Nat Lang Eng 6(3–4):287–303. doi: 10.1017/s1351324900002497 CrossRefGoogle Scholar
  5. 5.
    Larsson S, Traum DR (2000) Information state and dialogue management in the trindi dialogue move engine. Nat Lang Eng Spec Issue 6:323–340. http://www.ling.gu.se/~sl/nle.ps
  6. 6.
    Litman D, Pan S (2002) Designing and evaluating an adaptive spoken dialogue system. User Model User-Adap Inter 12(2–3):111–137. doi: 10.1023/a:1015036910358
  7. 7.
    Nothdurft F, Honold F, Kurzok P (2012) Using explanations for runtime dialogue adaptation. In: Proceedings of the 14th ACM international conference on multimodal interaction. ACM, pp 63–64Google Scholar
  8. 8.
    Oshry M, Auburn R, Baggia P, Bodell M, Burke D, Burnett D, Candell E, Carter J, Mcglashan S, Lee A, Porter B, Rehor K (2007) Voice extensible markup language (voicexml) version 2.1. Tech. rep. W3C—Voice Browser Working GroupGoogle Scholar
  9. 9.
    Raux A, Bohus D, Langner B, Black AW, Eskenazi M (2006) Doing research on a deployed spoken dialogue system: one year of let’s go! experience. In: Proceedings of the international conference on speech and language processing (ICSLP)Google Scholar
  10. 10.
    Schmitt A, Schatz B, Minker W (2011) Modeling and predicting quality in spoken human-computer interaction. In: Proceedings of the SIGDIAL 2011 conference. Association for Computational Linguistics, Portland, pp 173–184Google Scholar
  11. 11.
    Schmitt A, Ultes S, Minker W (2012) A parameterized and annotated spoken dialog corpus of the cmu let’s go bus information system. In: International conference on language resources and evaluation (LREC), pp 3369–337Google Scholar
  12. 12.
    Ultes S, Heinroth T, Schmitt A, Minker W (2011) A theoretical framework for a user-centered spoken dialog manager. In: Proceedings of the paralinguistic information and its integration in spoken dialogue systems workshop. Springer, pp 241–246Google Scholar
  13. 13.
    Ultes S, Minker W (2014) Managing adaptive spoken dialogue for intelligent environments. J Ambient Intell Smart Environ 6(5):523–539. doi: 10.3233/ais-140275 Google Scholar
  14. 14.
    Ultes S, Schmitt A, Minker W (2012) Towards quality-adaptive spoken dialogue management. In: NAACL-HLT Workshop on future directions and needs in the spoken dialog community: tools and data (SDCTD 2012). Association for Computational Linguistics, Montréal, pp 49–52. http://www.aclweb.org/anthology/W12-1819
  15. 15.
    Ultes S, Schmitt A, Minker W (2013) On quality ratings for spoken dialogue systems—experts vs. users. In: Proceedings of the 2013 conference of the north american chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics, pp 569–578Google Scholar
  16. 16.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Communications TechnologyUlmGermany

Personalised recommendations