Skip to main content
SpringerLink
Log in

Incorporating Social Practices in Dialogue Systems

  • Conference paper
  • First Online:
Chatbot Research and Design (CONVERSATIONS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13171))

Included in the following conference series:

Abstract

Current dialogue management systems do not take social concepts such as norms, conventions, roles etc. into account when managing dialogues. Neither do they keep track of the personal (mental) state such as goals, needs, etc. While the data-driven approaches work quite well in some cases, they are usually domain/user dependent and not transparent. On the other hand, the rule-based methods can only work on the predefined scenarios and are not flexible in that sense. In addition, these approaches are limited to modeling only the dialogue system and do not include the human participant as part of the overall dialogue. This makes the current dialogue systems not well suited for complex and natural dialogues. In this paper, we present a dialogue management system framework that incorporates the notion of social practices as a first step to extend the type of dialogues that can be supported. The use of social practices is meant to give structure to the dialogue without restricting it to a fixed protocol. We demonstrate the use of the proposed system on a scenario between the doctor and patient roles where the doctor is a medical student and the patient is simulated by the dialogue management system.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

References

  1. Andrew, G., Gao, J.: Scalable training of L1-regularized log-linear models. In: Proceedings of the 24th International Conference on Machine Learning, pp. 33–40 (2007)

    Google Scholar 

  2. Bickmore, T., Cassell, J.: How about this weather? Social dialogue with embodied conversational agents. In: Proceedings of the AAAI Fall Symposium on Socially Intelligent Agents (2000)

    Google Scholar 

  3. Chen, H., Liu, X., Yin, D., Tang, J.: A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explor. Newsl. 19(2), 25–35 (2017)

    Article  Google Scholar 

  4. Chizhik, A., Zherebtsova, Y.: Challenges of building an intelligent chatbot. In: IMS, pp. 277–287 (2020)

    Google Scholar 

  5. Clark, H.H.: Using Language. Cambridge University Press, Cambridge (1996)

    Book  Google Scholar 

  6. Dautenhahn, K.: Socially intelligent robots: dimensions of human-robot interaction. Philos. Trans. Royal Soc. B Biol. Sci. 362(1480), 679–704 (2007)

    Article  Google Scholar 

  7. Dignum, F.: Interactions as social practices: towards a formalization. arXiv preprint arXiv:1809.08751 (2018)

  8. Goel, R., Paul, S., Hakkani-Tür, D.: Hyst: A hybrid approach for flexible and accurate dialogue state tracking. arXiv preprint arXiv:1907.00883 (2019)

  9. Hellström, T., Dignum, V., Bensch, S.: Bias in machine learning - what is it good for? In: Proceedings of the First International Workshop on New Foundations for Human-Centered AI (NeHuAI) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020). CEUR Workshop Proceedings, vol. 2659, pp. 3–10 (2020)

    Google Scholar 

  10. Klüwer, T.: “I Like Your Shirt’’ - dialogue acts for enabling social talk in conversational agents. In: Vilhjálmsson, H.H., Kopp, S., Marsella, S., Thórisson, K.R. (eds.) IVA 2011. LNCS (LNAI), vol. 6895, pp. 14–27. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23974-8_2

    Chapter  Google Scholar 

  11. Lee, H., Lee, S.Y., Choi, J., Sung, J.E., Lim, H., Lim, Y.: Analyzing the rules of social dialogue and building a social dialogue model in human-robot interaction

    Google Scholar 

  12. Levin, E., Pieraccini, R., Eckert, W.: Using Markov decision process for learning dialogue strategies. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 (Cat. No. 98CH36181), vol. 1, pp. 201–204. IEEE (1998)

    Google Scholar 

  13. Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055 (2015)

  14. Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016)

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

  16. Lison, P.: A hybrid approach to dialogue management based on probabilistic rules. Comput. Speech Lang. 34(1), 232–255 (2015)

    Article  Google Scholar 

  17. Lucas, G.M., et al.: Culture, errors, and rapport-building dialogue in social agents. In: Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 51–58 (2018)

    Google Scholar 

  18. Lucas, G.M., et al.: Getting to know each other: the role of social dialogue in recovery from errors in social robots. In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 344–351 (2018)

    Google Scholar 

  19. Marge, M., Miranda, J., Black, A.W., Rudnicky, A.: Towards improving the naturalness of social conversations with dialogue systems. In: Proceedings of the SIGDIAL 2010 Conference, pp. 91–94 (2010)

    Google Scholar 

  20. Mrkšić, N., Séaghdha, D.O., Wen, T.H., Thomson, B., Young, S.: Neural belief tracker: data-driven dialogue state tracking. arXiv preprint arXiv:1606.03777 (2016)

  21. Mrkšić, N., Vulić, I.: Fully statistical neural belief tracking. arXiv preprint arXiv:1805.11350 (2018)

  22. Prakash, A., Brockett, C., Agrawal, P.: Emulating human conversations using convolutional neural network-based IR. arXiv preprint arXiv:1606.07056 (2016)

  23. Ramadan, O., Budzianowski, P., Gašić, M.: Large-scale multi-domain belief tracking with knowledge sharing. arXiv preprint arXiv:1807.06517 (2018)

  24. Reckwitz, A.: Toward a theory of social practices: a development in culturalist theorizing. Eur. J. Soc. Theory 5(2), 243–263 (2002)

    Article  Google Scholar 

  25. Serban, I., Sordoni, A., Bengio, Y., Courville, A., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  26. Sidnell, J., Stivers, T.: The Handbook of Conversation Analysis, vol. 121. Wiley, New York (2012)

    Book  Google Scholar 

  27. Sordoni, A., et al.: A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714 (2015)

  28. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)

  29. Weizenbaum, J.: Eliza-a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)

    Article  Google Scholar 

  30. Williams, J.D.: Web-style ranking and SLU combination for dialog state tracking. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp. 282–291 (2014)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Umeå University, Umeå, Sweden

    Eren Yildiz, Suna Bensch & Frank Dignum

Authors
  1. Eren Yildiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suna Bensch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank Dignum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eren Yildiz .

Editor information

Editors and Affiliations

  1. SINTEF, Oslo, Norway

    Asbjørn Følstad

  2. VU Amsterdam, Amsterdam, The Netherlands

    Theo Araujo

  3. CERTH-ITI, Thessaloniki, Greece

    Symeon Papadopoulos

  4. Durham University, Durham, UK

    Effie L.-C. Law

  5. University of Edinburgh, Edinburgh, UK

    Ewa Luger

  6. Center for Artificial Intelligence Research, University of Agder, Kristiansand, Norway

    Morten Goodwin

  7. University of Oslo, Oslo, Norway

    Petter Bae Brandtzaeg

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yildiz, E., Bensch, S., Dignum, F. (2022). Incorporating Social Practices in Dialogue Systems. In: Følstad, A., et al. Chatbot Research and Design. CONVERSATIONS 2021. Lecture Notes in Computer Science(), vol 13171. Springer, Cham. https://doi.org/10.1007/978-3-030-94890-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94890-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94889-4

  • Online ISBN: 978-3-030-94890-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation