Skip to main content
SpringerLink
Log in

Adaptation and user expertise modelling in AthosMail

  • LONG PAPER
  • Published:
Universal Access in the Information Society Aims and scope Submit manuscript

Abstract

This article describes the User Model component of AthosMail, a speech-based interactive e-mail application developed in the context of the EU project DUMAS. The focus is on the system’s adaptive capabilities and user expertise modelling, exemplified through the User Model parameters dealing with initiative and explicitness of the system responses. The purpose of the conducted research was to investigate how the users could interact with a system in a more natural way, and the two aspects that mainly influence the system’s interaction capabilities, and thus the naturalness of the dialogue as a whole, are considered to be the dialogue control and the amount of information provided to the user. The User Model produces recommendations of the system’s appropriate reaction depending on the user’s observed competence level, monitored and computed on the basis of the user’s interaction with the system. The article also discusses methods for the evaluation of adaptive user models and presents results from the AthosMail evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Allwood J, Traum D, Jokinen K (2001) Cooperation, dialogue and ethics. Int J Hum-Comput Stud 53:871–914

    Article  Google Scholar 

  2. Benyon D, Murray D (1993) Developing adaptive systems to fit individual aptitudes. Intell User Interfaces, 115–121

  3. Berglund A, Johansson P (2004) Using speech and dialogue for interactive TV navigation. Univ Access Inf Soc 3(3–4):224–238

    Article  Google Scholar 

  4. Brusilovsky P, Maybury MT (2002) From adaptive hypermedia to the adaptive Web. Commun ACM 45(5):30–33

    Article  Google Scholar 

  5. Chesnais P, Mucklo M, Sheena JA (1995) The Fishwrap personalized news system, IEEE Second International Workshop on Community Networking Integrating Multimedia Services to the Home. Princeton, NJ, USA

    Book  Google Scholar 

  6. Chin D (1989) KNOME: modeling what the user knows in UC. In: Kobsa A, Wahlster W (eds) User Modeling in Dialogue Systems. Springer, Berlin Heidelberg New York, pp 74–107

    Google Scholar 

  7. Chu-Carroll J (2000) MIMIC: an adaptive mixed initiative spoken dialogue system for information queries. In: Proceedings of ANLP 6, pp 97–104

  8. Chu-Carroll J, Nickerson JS (2000) Evaluating automatic dialogue strategy adaptation for a spoken dialogue system. In: Proceedings of NAACL 1, pp 202–209

  9. Dai W, Cohen R (2003) Dynamic personalized TV recommendation system. Proceedings of the UM 2003 Workshop on Personalization in Future TV, Pittsburgh, US, pp 12–21

  10. Danieli M, Gerbino E (1995) Metrics for evaluating dialogue strategies in a spoken language system. In: Proceedings of the AAAI Spring Symposium on Empirical Methods in Discourse Interpretation and Generation, pp 34–39

  11. Dreyfus HL, Dreyfus SE (1986) Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. The Free Press, New York

    Google Scholar 

  12. Dybkjær L, Bernsen NO, Dybkjær H (1997) Designing co-operativity in spoken human-machine dialogues. In: Varghese K, Pfleger S (eds) Human Comfort and Security of Information Systems. Advanced Interfaces for the Information Society. Springer, Berlin Heidelberg New York, pp 104–124

    Google Scholar 

  13. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):51–60

    Article  Google Scholar 

  14. Grice HP (1975) Logic and Conversation. In: Cole P, Morgan J (eds) Syntax and Semantics. vol 3. Academic, New York, pp 43–58

  15. Höök K (2000) Steps to take before intelligent user interfaces become real. J Interact Computers 12(4):409–426

    Article  Google Scholar 

  16. Jameson A, Schwarzkopf E (2002) Pros and Cons of Controllability:An Empirical Study. Lect Notes Comput Sci 2347:193–202

    Article  Google Scholar 

  17. Jokinen K (1996) Goal formulation based on communicative principles. In: Proceedings of the 16th International Conference on Computational Linguistics, Copenhagen, Denmark, pp 598–603

  18. Jokinen K (2003) Natural interaction in spoken dialogue systems. In: Stephanidis C, Jacko J (eds) Human–Computer Interaction: Theory and Practice (Part II), vol 4. Lawrence Erlbaum Associates, Mahwah, pp 730–734

  19. Jokinen K, Gambäck B (2004) DUMAS—adaptation and robust information processing for mobile speech interfaces. In: Proceedings of the First Baltic Conference “Human Language Technologies – The Baltic Perspective”, Riga, Latvia, pp 115–120

  20. Jokinen K, Kanto K (2004) User expertise modelling and adaptivity in a speech-based e-mail system. In: Proceedings of the ACL-04, Barcelona, Spain, pp 88–95

  21. Jokinen K, Kanto K, Kerminen A, Rissanen J (2004) Evaluation of adaptivity and user expertise in a speech based e-mail system. In: Proceedings of the COLING Satellite Workshop Robust and Adaptive Information Processing for Mobile Speech Interfaces, Geneva, Switzerland, pp 44–52

  22. Jokinen K, Kanto K, Rissanen J (2004) Adaptive user modelling in Athosmail. In: Stary C, Stephanidis C (eds) UI4All, Lecture Notes in Computer Science 3196, pp 149–158 Springer, Berlin Heidelberg New York. Also available online: http://www.springerlink.com/index/DJTGK90N40EU80UW

  23. Jokinen K, Rissanen J, Keränen H, Kanto K (2002) Learning interaction patterns for adaptive user interfaces. In: Adjunct Proceedings of the Seventh ERCIM Workshop User Interfaces for All, Paris, France, pp 53–58

  24. Jokinen K, Wilcock G (2003) Adaptivity and response generation in a spoken dialogue system. In: van Kuppevelt J, Smith RW (eds) Current and New Directions in Discourse and Dialogue. Kluwer Academic, Dordrecht, pp 213–234

    Google Scholar 

  25. Kamm C, Litman L, Walker M (1998) From novice to expert: the effect of tutorials on user expertise with spoken dialogue systems. In: Proceedings of the Fifth International Conference on Spoken Language Processing (ICSLP98), pp 1211–1212

  26. Kanerva P, Kristoferson J, Holst A (2000) Random indexing of text samples for latent semantic analysis. In: Proceedings of the 22nd Annual Conference of the Cognitive Science Society. Lawrence Erlbaum Associates, Mahwah, p 1036

  27. Kanto K, Cheadle M, Gambäck B, Hansen P, Jokinen K, Keränen H, Rissanen J (2003) Multi-session group scenarios for speech interface design. In: Stephanidis C, Jacko J (eds) Human-Computer Interaction: Theory and Practice (Part II), vol 2. Lawrence Erlbaum Associates, Mahwah, pp 676–680

  28. Karlgren J, Sahlgren J (2001) From Words to Understanding. In: Uesaka Y, Kanerva P, Asoh H (eds) 2001. Foundations of Real-World Intelligence. CSLI Publications, Stanford, pp 294–308

    Google Scholar 

  29. Kay J (2001) Learner control. User Model User-Adapted Interact 11:111–127

    Article  MATH  Google Scholar 

  30. Krahmer E, Swerts M, Theune M, Weegels M (1999) Problem spotting in human–machine interaction. In: Proceedings of Eurospeech ’99, vol 3. Budapest, Hungary, pp 1423–1426

  31. Litman DJ, Pan S (2002) Designing and evaluating an adaptive spoken dialogue system. User Model User-Adapted Interact 12(2/3):111–137

    Article  MATH  Google Scholar 

  32. Malone T, Grant K, Turbak F, Brobst S, Cohen M (1987) Intelligent information-sharing systems. Commun ACM 30(5):390–402

    Article  Google Scholar 

  33. Moukas A, Maes P (1998) Amalthaea: an evolving multi-agent information filtering and discovery system for the WWW. Autonomous Agents Multi-Agent Syst 1:(1):59–88

    Article  Google Scholar 

  34. Möller S (2002) A new taxonomy for the quality of telephone services based on spoken dialogue systems. In: Jokinen K, McRoy S (eds) Proceedings of the Third SIGDial Workshop, Philadelphia, US, pp 142–153

  35. Oppermann R (1994) Adaptively supported adaptability. Int J Hum Comput Stud 40:455–472

    Article  Google Scholar 

  36. Paramythis A, Totter A, Stephanidis C (2001) A modular approach to the evaluation of adaptive user interfaces. In: Proceedings of the eight international conference on User Modeling, Sonthofen, Germany, pp 9–24

  37. Paris C (1988) Tailoring Descriptions to a User’s Level of Expertise. J Comput Linguist 14(3):64–78

    Google Scholar 

  38. Smith RW (1993) Effective spoken natural language dialog requires variable initiative behavior: an empirical study. In: Proceedings of the AAAI Fall Symposium on Human–Computer Collaboration: Reconciling Theory, Synthesizing Practice

  39. Smith RW, Hipp DR (1994) Spoken Natural Language Dialog Systems—A Practical Approach. Oxford University Press, Oxford

    Google Scholar 

  40. Turunen M, Salonen E-P, Hartikainen M, Hakulinen J, Black WJ, Ramsay A, Funk A, Conroy A, Thompson P, Stairmand M, Jokinen K, Rissanen J, Kanto K, Kerminen A, Gambäck B, Cheadle M, Olsson F, Sahlgren M (2004) AthosMail—a multilingual adaptive spoken dialogue system for e-mail domain. In: Proceedings of the COLING Workshop Robust and Adaptive Information Processing for Mobile Speech Interfaces, Geneva, Switzerland, pp 78–86

  41. Walker MA, Langkilde I, Wright J, Gorin A, Litman DJ (2000) Learning to predict problematic situations in a spoken dialogue system: experiments with How May I Help You? In: Proceedings of NAACL’00, Seattle, US, pp 210–217

  42. Walker M, Litman DJ, Kamn CA, Abella A (1998) Evaluating spoken dialogue agents with PARADISE: two case studies. Comput Speech Lang 12(3)

    Article  Google Scholar 

  43. Yankelovich N (1996) How do users know what to say? Interactions 3(6):32–43

    Article  Google Scholar 

Download references

Acknowledgements

The research was carried out within the EU project DUMAS (Dynamic Universal Mobility for Adaptive Speech Interfaces), IST-2000–29452 (http://www.sics.se/dumas), and the financial support of the EU is gratefully acknowledged. The author would also like to express her thanks to all the project participants for their cooperation and inspiring discussions concerning the AthosMail system and her research group at the University of Art and Design for carrying out the implementation and experiments with the user model components. The author would especially like to thank Kari Kanto for his contributions to the design and evaluation of the Cooperativity Model.

Author information

Authors and Affiliations

  1. University of Helsinki, Helsinki, Finland

    Kristiina Jokinen

Authors
  1. Kristiina Jokinen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kristiina Jokinen.

Additional information

The research was done while the author was affiliated with the University of Art and Design Helsinki as the scientific coordinator of the DUMAS project.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jokinen, K. Adaptation and user expertise modelling in AthosMail. Univ Access Inf Soc 4, 374–392 (2006). https://doi.org/10.1007/s10209-005-0002-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10209-005-0002-z

Keywords

Search

Navigation