Effects of machine translation on collaborative work

Abstract

Even though multilingual communities that use machine translation to overcome language barriers are increasing, we still lack a complete understanding of how machine translation affects communication. In this study, eight pairs from three different language communities-China, Korea, and Japan-worked on referential tasks in their shared second language (English) and in their native languages using a machine translation embedded chat system. Drawing upon prior research, we predicted differences in conversational efficiency and content, and in the shortening of referring expressions over trials. Quantitative results combined with interview data show that lexical entrainment was disrupted in machine translation-mediated communication because echoing is disrupted by asymmetries in machine translations. In addition, the process of shortening referring expressions is also disrupted because the translations do not translate the same terms consistently throughout the conversation. To support natural referring behavior in machine translation-mediated communication, we need to resolve asymmetries and inconsistencies caused by machine translations.

Understanding informal communication in multilingual contexts

“…Displaying a collage of pictures and text relevant to ongoing conversation might contextualize cultural referents and provide information to help resolve ambiguity, translation errors, and other fluency issues. Another way to address fluency issues would be to exploit real-time machine translation tools that allow people to converse in their native languages [54,55]. And, though we have focused on CMC, physical spaces might also be augmented with a combination of speech recognition and ambient displays to present icebreakers and conversational referents.…”

How beliefs about the presence of machine translation impact multilingual collaborations

“…Yamashita and colleagues [28,29,30], for example, have shown how the use of MT, as opposed to a shared second language, created confusion about the meaning of referring expressions between multilingual speakers. Wang et al [27] examined multilingual brainstorming conversations and found that both native and non-native English speakers viewed MTmediated messages as less comprehensible than English messages.…”

A Multilingual Chat System with Image Presentation for Detecting Mistranslation

“…Yamashita and Ishida [8] have investigated and reported the effects of machine translation on collaborative work. In their study, eight pairs from China, Korea, and Japan worked on referential tasks in English and in their native languages using a machine translation embedded chat system.…”