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Effect of Cultural Misunderstanding Warning in MT-Mediated Communication

  • Mondheera PituxcoosuvarnEmail author
  • Yohei Murakami
  • Donghui Lin
  • Toru Ishida
Conference paper
  • 193 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12324)

Abstract

Thanks to today’s technologies, the world’s borders have been fading away and intercultural collaboration has become easier and easier. Language and cultural differences are common problems in inter-cultural collaboration. Machine translation (MT) is now available to overcome the language barrier, so people can easily express and understand messages in different languages. However, misunderstandings often plague users from different cultures, especially in MT-mediated communication. To communicate productively, it is important to avoid such misunderstandings. One existing work proposed the idea of using automated cultural difference detection to warn the users of misunderstanding. However, no study has examined how such warnings affect the communication. To eliminate this gap, we conduct a controlled experiment on how users react to the warnings and what are the results in terms of communication. The results show that, with the data from cultural difference detection, warning the user of cultural misunderstanding can help reduce misunderstandings and increase awareness of cultural differences. The results of this experiment confirm the effectiveness of cultural misunderstanding alerts and suggest new directions in multilingual chat design.

Keywords

Intercultural collaboration Machine translation Cultural misunderstanding 

Notes

Acknowledgments

This research was partially supported by a Grant-in-Aid for Scientific Research (A) (17H00759, 2017–2020), a Grant-in-Aid for Scientific Research (B) (18H03341, 2018–2020), and a Grant-in-Aid Young Scientists (A) (17H04706, 2017–2020) from the Japan Society for the Promotion of Science (JSPS).

References

  1. 1.
    Bailey, S.: Academic Writing: A Handbook for International Students. Taylor & Francis (2003). https://books.google.co.jp/books?id=SUeRAgAAQBAJ
  2. 2.
    Cho, H., Ishida, T., Yamashita, N., Inaba, R., Mori, Y., Koda, T.: Culturally-situated pictogram retrieval. In: Ishida, T., Fussell, S.R., Vossen, P.T.J.M. (eds.) IWIC 2007. LNCS, vol. 4568, pp. 221–235. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74000-1_17CrossRefGoogle Scholar
  3. 3.
    Deutscher, G.: Through the Language Glass: Why the World Looks Different in Other Languages. Metropolitan Books, New York (2010)Google Scholar
  4. 4.
    Gookin, D.: Word 2013 For Dummies. For Dummies, Wiley (2013). https://books.google.co.jp/books?id=gxd6bMou76EC
  5. 5.
    Herring, C.: Does diversity pay?: race, gender, and the business case for diversity. Am. Sociol. Rev. 74(2), 208–224 (2009)CrossRefGoogle Scholar
  6. 6.
    Hofstede, G.: Cultural dimensions in management and planning. Asia Pac. J. Manage. 1(2), 81–99 (1984)CrossRefGoogle Scholar
  7. 7.
    Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., Kanzaki, K.: Development of the Japanese wordnet. In: Sixth International Conference on Language Resources and Evaluation (2008)Google Scholar
  8. 8.
    Ishida, T., Murakami, Y., Lin, D., Nakaguchi, T., Otani, M.: Language service infrastructure on the web: the language grid. Computer 51(6), 72–81 (2018)CrossRefGoogle Scholar
  9. 9.
    Kelley, J.F.: An iterative design methodology for user-friendly natural language office information applications. ACM Trans. Inf. Syst. (TOIS) 2(1), 26–41 (1984)CrossRefGoogle Scholar
  10. 10.
    Lafferty, J.C., Pond, A.W.: The Desert Survival Situation: Problem: a Group Decision Making Experience for Examining and Increasing Individual and Team Effectiveness. Human Synergistics (1974)Google Scholar
  11. 11.
    Miller, G.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  12. 12.
    Nishimura, I., Murakami, Y., Pituxcoosuvarn, M.: Image-based detection criteria for cultural differences in translation. In: International Conference on Collaboration and Technology. Springer (2020)Google Scholar
  13. 13.
    Pituxcoosuvarn, M., Ishida, T.: Multilingual communication via best-balanced machine translation. New Gener. Comput. 36(4), 349–364 (2018)CrossRefGoogle Scholar
  14. 14.
    Pituxcoosuvarn, M., Ishida, T., Yamashita, N., Takasaki, T., Mori, Y.: Machine translation usage in a children’s workshop. In: Egi, H., Yuizono, T., Baloian, N., Yoshino, T., Ichimura, S., Rodrigues, A. (eds.) CollabTech 2018. LNCS, vol. 11000, pp. 59–73. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-98743-9_5CrossRefGoogle Scholar
  15. 15.
    Pituxcoosuvarn, M., Lin, D., Ishida, T.: A method for automated detection of cultural difference based on image similarity. In: Nakanishi, H., Egi, H., Chounta, I.-A., Takada, H., Ichimura, S., Hoppe, U. (eds.) CRIWG+CollabTech 2019. LNCS, vol. 11677, pp. 129–143. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-28011-6_9CrossRefGoogle Scholar
  16. 16.
    Yamashita, N., Inaba, R., Kuzuoka, H., Ishida, T.: Difficulties in establishing common ground in multiparty groups using machine translation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 679–688. ACM (2009)Google Scholar
  17. 17.
    Yoshino, R., Hayashi, C.: An overview of cultural link analysis of national character. Behaviormetrika 29(2), 125–141 (2002)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Yoshino, T., Miyabe, M., Suwa, T.: A proposed cultural difference detection method using data from Japanese and Chinese wikipedia. In: 2015 International Conference on Culture and Computing (Culture Computing), pp. 159–166. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Information Science and TechnologyRitsumeikan UniversityShigaJapan
  2. 2.Department of Social InformaticsKyoto UniversityKyotoJapan
  3. 3.School of Creative Science and EngineeringWaseda UniversityTokyoJapan

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