Statistical Response Method and Learning Data Acquisition using Gamified Crowdsourcing for a Non-task-oriented Dialogue Agent

  • Michimasa InabaEmail author
  • Naoyuki Iwata
  • Fujio Toriumi
  • Takatsugu Hirayama
  • Yu Enokibori
  • Kenichi Takahashi
  • Kenji Mase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)


This paper presents a proposal of a construction method for non-task-oriented dialogue agents (chatbots) that are based on the statistical response method. The method prepares candidate utterances in advance. From the data, it learns which utterances are suitable for context. Therefore, a dialogue agent constructed using our method automatically selects a suitable utterance depending on a context from candidate utterances. This paper also proposes a low-cost quality-assured method of learning data acquisition for the proposed response method. The method uses crowdsourcing and brings game mechanics to data acquisition.

Results of an experiment using learning data obtained using the proposed data acquisition method demonstrate that the appropriate utterance is selected with high accuracy.


Dialogue agent Chatbots Crowdsourcing Gamification. 


  1. 1.
    Zue, V., Seneff, S., Polifroni, J., Phillips, M., Pao, C., Goodine, D., Goddeau, D., Glass, J.: Pegasus: a spoken dialogue interface for on-line air travel planning. Speech Commun. 15(3–4), 331–340 (1994)CrossRefGoogle Scholar
  2. 2.
    Chu-Carroll, J., Nickerson, J.S.: Evaluating automatic dialogue strategy adaptation for a spoken dialogue system. In: Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference, pp. 202–209 (2000)Google Scholar
  3. 3.
    Bickmore, T., Cassell, J.: Relational agents: a model and implementation of building user trust. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 396–403 (2001)Google Scholar
  4. 4.
    Weizenbaum, J.: ELIZA-a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)CrossRefGoogle Scholar
  5. 5.
    Wallace, R.S.: The anatomy of alice, pp. 181–210. Parsing the Turing, Test (2009)Google Scholar
  6. 6.
    Worswick, S.: Mitsuku Chatbot (2013).
  7. 7.
    Veselov, V., Demchenko, E., Ulasen, S.: Eugene Goostman (2014).
  8. 8.
    Murao, H., Kawaguchi, N., Matsubara, S., Yamaguchi, Y., Inagaki, Y.: Example-based spoken dialogue system using woz system log. In: SIGdial Workshop on Discourse and Dialogue, pp. 140–148 (2003)Google Scholar
  9. 9.
    Banchs, R.E., Li, H.: Iris: a chat-oriented dialogue system based on the vector space model. In: Proceedings of the ACL 2012 System Demonstrations, pp. 37–42. Association for Computational Linguistics (2012)Google Scholar
  10. 10.
    Ritter, A., Cherry, C., Dolan, W.B.: Data-driven response generation in social media. In: Proceedings of the conference on empirical methods in natural language processing, pp. 583–593. Association for Computational Linguistics (2011)Google Scholar
  11. 11.
    Cao, Z., Qin, T., Liu, Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on Machine learning, pp. 129–136 (2007)Google Scholar
  12. 12.
    Plackett, R.L.: The analysis of permutations. Applied Statistics, pp. 193–202 (1975)Google Scholar
  13. 13.
    Luce, R.D.: Individual Choice Behavior: A Theoretical Analysis. Wiley, New York (1959)zbMATHGoogle Scholar
  14. 14.
    Ipeirotis, P.G., Provost, F., Wang, J.: Quality management on amazon mechanical turk. In: Proceedings of the ACM SIGKDD workshop on human computation, pp. 64–67. ACM (2010)Google Scholar
  15. 15.
    Lease, M.: On quality control and machine learning in crowdsourcing. In: Proceedings of the AAAI workshop on human computation, pp. 97–102 (2011)Google Scholar
  16. 16.
    Dekel, O., Shamir, O.: Vox populi: collecting high-quality labels from a crowd. In: Proceedings of the 22nd Annual Conference on Learning Theory (COLT) (2009)Google Scholar
  17. 17.
    Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 319–326. ACM (2004)Google Scholar
  18. 18.
    Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification. using game-design elements in non-gaming contexts. In: Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems, pp. 2425–2428. ACM (2011)Google Scholar
  19. 19.
    Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michimasa Inaba
    • 1
    Email author
  • Naoyuki Iwata
    • 2
  • Fujio Toriumi
    • 3
  • Takatsugu Hirayama
    • 2
  • Yu Enokibori
    • 2
  • Kenichi Takahashi
    • 1
  • Kenji Mase
    • 2
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan
  2. 2.Graduate School of Information SciencesNagoya UniversityNagoyaJapan
  3. 3.School of EngineeringUniversity of TokyoTokyoJapan

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