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.
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Notes
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Note that the translation output is likely to change from time to time since MT is always developing and the output also depends on the services selected.
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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).
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Pituxcoosuvarn, M., Murakami, Y., Lin, D., Ishida, T. (2020). Effect of Cultural Misunderstanding Warning in MT-Mediated Communication. In: Nolte, A., Alvarez, C., Hishiyama, R., Chounta, IA., Rodríguez-Triana, M., Inoue, T. (eds) Collaboration Technologies and Social Computing . CollabTech 2020. Lecture Notes in Computer Science(), vol 12324. Springer, Cham. https://doi.org/10.1007/978-3-030-58157-2_8
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