Enhancing Participation Balance in Intercultural Collaboration

  • Mondheera PituxcoosuvarnEmail author
  • Toru Ishida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10397)


In multilingual collaboration, a paucity of shared language and gaps in the language backgrounds of group members could bring about imbalanced participation, which is likely to hinder problem solving, idea generation and collaborative learning. This paper proposes a model of best balanced communication based on the Quality of Messages among participants using various languages. We describe a method for selecting the languages to be used with machine translators, and how to create the best balanced communication environment. Currently, many studies on machine translators and balancing conversations have been published, but none have attempted to balance asymmetric participation in multilingual groups. Our vision allows machine translation technologies to enhance the communication between humans with different language backgrounds in terms of balancing their participation. We conduct controlled experiments and find the proposed method successfully enables users to interact and communicate with better equality while minimizing the problems that can arise from machine translation usage.


Communication support environment Intercultural collaboration Multilingual communication Usability of machine translation 



This research was partially supported by a Grant-in-Aid for Scientific Research (A) (17H00759, 2017-2020) from Japan Society for the Promotion of Science (JSPS), and the Leading Graduates Schools Program, “Collaborative Graduate Program in Design” by the Ministry of Education, Culture, Sports, Science and Technology, Japan.


  1. 1.
    Ishida, T.: Intercultural collaboration and support systems: a brief history. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (eds.) PRIMA 2016. LNCS, vol. 9862, pp. 3–19. Springer, Cham (2016). doi: 10.1007/978-3-319-44832-9_1 CrossRefGoogle Scholar
  2. 2.
    David, C.: English as a Global Language. Cambridge University Press, Cambridge (1997)Google Scholar
  3. 3.
    Henderson, J.K.: Language diversity in international management teams. Int. Stud. Manag. Organ. 35, 66–82 (2005)Google Scholar
  4. 4.
    Morita, D., Ishida T.: Collaborative translation by monolinguals with machine translators. In: 14th International Conference on Intelligent User Interfaces (IUI 2009), pp. 361–366. ACM, New York (2009). doi: 10.1145/1502650.1502701
  5. 5.
    Yamashita, N., Echenique, A., Ishida, T., Hautasaari, A.: Lost in transmittance: how transmission lag enhances and deteriorates multilingual collaboration. In: 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013), pp. 923–934. ACM, Texas (2013). doi: 10.1145/2441776.2441881
  6. 6.
    Gao, G., Yamashita, N., Hautasaari, A.M., Fussell, S.R.: Improving multilingual collaboration by displaying how non-native speakers use automated transcripts and bilingual dictionaries. In: 33rd Annual ACM Conf. on Human Factors in Computing Systems, pp. 3463–3472. ACM, Seoul (2015). doi: 10.1145/2702123.2702498
  7. 7.
    Nikolova, S., Ma, X., Tremaine, M., Cook, P.: Vocabulary navigation made easier. In: 15th International Conference on Intelligent User Interfaces (IUI 2010), pp. 361–364. ACM, Hong Kong (2010). doi: 10.1145/1719970.1720031
  8. 8.
    Toyama, T., Sonntag, D., Dengel, A., Matsuda, T., Iwamura, M., Kise, K.: A mixed reality head-mounted text translation system using eye gaze input. In: 15th International Conference on Intelligent User Interfaces (IUI 2010), pp. 329–334. ACM, Haifa (2014). doi: 10.1145/2557500.2557528
  9. 9.
    Aiken, M.: Transterpreting multilingual electronic meetings. Int. J. Manag. Inf. Syst. 13(1), 35–46 (2009). doi: 10.19030/ijmis.v13i1.4940 MathSciNetGoogle Scholar
  10. 10.
    Fellbaum, C.: WordNet. In: Poli, R. (ed.) Theory and applications of ontology: computer applications, pp. 231–234. Springer, Dordrecht (2010). doi: 10.1007/978-90-481-8847-5_10 CrossRefGoogle Scholar
  11. 11.
    Yamashita, N., Ishida, T.: Effects of machine translation on collaborative work. In: Computer Supported Cooperative Work and Social Computing 2006 (CSCW 2006), pp. 515–524. ACM, Alberta (2006). doi: 10.1145/1180875.1180955
  12. 12.
    Sarda, S., et al.: Real-Time Feedback System for Monitoring and Facilitating Discussions. In: Mariani, J., Rosset, S., Garnier-Rizet, M., Devillers, L. (eds.) Natural Interaction with Robots, pp. 375–387. Knowbots and Smartphones. Springer, New York (2014). doi: 10.1007/978-1-4614-8280-2_34 CrossRefGoogle Scholar
  13. 13.
    DiMicco, J.M., Pandolfo, A., Bender, W.: Influencing group participation with a shared display. In: Computer Supported Cooperative Work and Social Computing 2004 (CSCW 2004), pp. 614–623. ACM, Illinois (2004). doi: 10.1145/1031607.1031713
  14. 14.
    DiMicco, J.M., Hollenbach, K.J., Pandolfo, A., Bender, W.: The impact of increased awareness while face-to-face. Hum. Comput. Interact. 22(1), 47–96 (2007)Google Scholar
  15. 15.
    Bachour, K., Kaplan, F., Dillenbourg, P.: An interactive table for supporting participation balance in face-to-face collaborative learning. IEEE Trans. Learn. Technol. 3, 203–213 (2010). doi: 10.1109/tlt.2010.18 CrossRefGoogle Scholar
  16. 16.
    Bramantoro, A., Ishida, T.: User-centered QoS in combining web services for interactive domain. In: 5th International Conference on Semantics, Knowledge and Grid, pp. 41–48. IEEE Press, Guangdong (2009). doi: 10.1109/skg.2009.106
  17. 17.
    Lafferty, J.C., Eady, P.M., Elmers, J.: The desert survival problem. In: Experimental Learning Methods, Michigan (1974)Google Scholar
  18. 18.
    Fermilab Project ARISE.
  19. 19.
  20. 20.
    Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., Schroeder, J.: (Meta-) evaluation of machine translation. In: 2nd Workshop on Statistical Machine Translation, pp. 136–158. ACM, Prague (2007). doi: 10.3115/1626394.1626403
  21. 21.
    Linguistic Data Consortium: Linguistic data annotation specification: assessment of fluency and adequacy in translations. Technical report (2005)Google Scholar
  22. 22.
    Ishida, T. (ed.): The Language Grid: Service-Oriented Collective Intelligence for Language Resource Interoperability. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21178-2_1 Google Scholar
  23. 23.
    Yamashita, N., Inaba, R., Kuzuoka, K., Ishida, T.: Difficulties in establishing common ground in multiparty groups using machine translation. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 679–688. ACM, Massachusetts (2009). doi: 10.1145/1518701.1518807

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

Personalised recommendations