Learning and Reusing Dialog for Repeated Interactions with a Situated Social Agent

  • James Kennedy
  • Iolanda Leite
  • André Pereira
  • Ming Sun
  • Boyang Li
  • Rishub Jain
  • Ricson Cheng
  • Eli Pincus
  • Elizabeth J. Carter
  • Jill Fain Lehman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10498)

Abstract

Content authoring for conversations is a limiting factor in creating verbal interactions with intelligent virtual agents. Building on techniques utilizing semi-situated learning in an incremental crowdworking pipeline, this paper introduces an embodied agent that self-authors its own dialog for social chat. In particular, the autonomous use of crowdworkers is supplemented with a generalization method that borrows and assesses the validity of dialog across conversational states. We argue that the approach offers a community-focused tailoring of dialog responses that is not available in approaches that rely solely on statistical methods across big data. We demonstrate the advantages that this can bring to interactions through data collected from 486 conversations between a situated social agent and 22 users during a 3 week long evaluation period.

Keywords

Verbal chat Social robot Repeated interactions Borrowing dialog 

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References

  1. 1.
    Aylett, R.S., Louchart, S., Dias, J., Paiva, A., Vala, M.: FearNot! – an experiment in emergent narrative. In: Panayiotopoulos, T., Gratch, J., Aylett, R., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS, vol. 3661, pp. 305–316. Springer, Heidelberg (2005). doi:10.1007/11550617_26 CrossRefGoogle Scholar
  2. 2.
    Bohus, D., Rudnicky, A.: The RavenClaw dialog management framework: Architecture and systems. Computer Speech & Language 23(3), 332–361 (2009)CrossRefGoogle Scholar
  3. 3.
    Guo, S., Lenchner, J., Connell, J., Dholakia, M., Muta, H.: Conversational bootstrapping and other tricks of a concierge robot. In: Proc. of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 73–81. ACM (2017)Google Scholar
  4. 4.
    Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N.: An affective guide robot in a shopping mall. In: 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 173–180. IEEE (2009)Google Scholar
  5. 5.
    Lasecki, W., Wesley, R., Nichols, J., Kulkarni, A., Allen, J., Bigham, J.: Chorus: a crowd-powered conversational assistant. In: UIST 2013, pp. 151–162. ACM (2013)Google Scholar
  6. 6.
    Leite, I., Pereira, A., Funkhouser, A., Li, B., Lehman, J.F.: Semi-situated learning of verbal and nonverbal content for repeated human-robot interaction. In: ICMI 2016, pp. 13–20. ACM, New York (2016)Google Scholar
  7. 7.
    Matsuyama, Y., Bhardwaj, A., Zhao, R., Romero, O.J., Akoju, S.A., Cassell, J.: Socially-aware animated intelligent personal assistant agent. In: 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 224 (2016)Google Scholar
  8. 8.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  9. 9.
    Mori, H., Araki, M.: Selection method of an appropriate response in chat-oriented dialogue systems. In: 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 228 (2016)Google Scholar
  10. 10.
    Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proc. of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)Google Scholar
  11. 11.
    Shang, L., Lu, Z., Li, H.: Neural Responding Machine for Short-Text Conversation (2015). CoRR abs/1503.02364Google Scholar
  12. 12.
    Vinyals, O., Le, Q.: A neural conversational model (2015). arXiv preprint arxiv:1506.05869
  13. 13.
    Young, S., Gašić, M., Thomson, B., Williams, J.D.: POMDP-based statistical spoken dialog systems: A review. Proc. of the IEEE 101(5), 1160–1179 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • James Kennedy
    • 1
  • Iolanda Leite
    • 1
    • 2
  • André Pereira
    • 1
  • Ming Sun
    • 1
  • Boyang Li
    • 1
  • Rishub Jain
    • 1
    • 3
  • Ricson Cheng
    • 1
    • 3
  • Eli Pincus
    • 1
    • 4
  • Elizabeth J. Carter
    • 1
  • Jill Fain Lehman
    • 1
  1. 1.Disney ResearchPittsburghUSA
  2. 2.KTH Royal Institute of TechnologyStockholmSweden
  3. 3.Carnegie Mellon UniversityPittsburghUSA
  4. 4.USC Institute for Creative TechnologiesLos AngelesUSA

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