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New Generation Computing

, Volume 36, Issue 4, pp 349–364 | Cite as

Multilingual Communication via Best-Balanced Machine Translation

  • Mondheera Pituxcoosuvarn
  • Toru Ishida
Research Paper
  • 126 Downloads

Abstract

It is known that creative ideas are often generated by correspondents from different cultures, but it remains true that distance still matters due to the language barrier. To enhance multilingual communication, this paper proposes the model of best-balanced machine translation. Our model is based on the quality of messages among participants (assumed to have different levels of language skill) and takes not only machine translation quality but also users’ language skill into account. We provide a method to select languages to be used with machine translators, and a way of creating the best-balanced communication environment. Many studies have addressed machine translation with the goal of helping the non-native speaker to understand what was said. Our approach is totally differently, since we focus on helping the non-native speaker by enhancing the opportunity to join in the conversation. We conduct an experiment and find that this model allows machine translation technologies to benefit multilingual communication while making best use of the participants’ different language skills. The proposed model addresses the talkativeness of the participants. It also improves communication by reducing serious machine translation errors and the number of conversation breakdowns.

Keywords

Communication support environment Intercultural collaboration Multilingual communication Usability of machine translation 

Notes

Acknowledgements

This research was partially supported by a Grant-in-Aid for Scientific Research (S) (24220002, 2012–2016) 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.

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Copyright information

© Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan

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