Neural Machine Translation Tools in the Language Learning Classroom: Students’ Use, Perceptions, and Analyses

  • Neil Briggs Hannam University
Keywords: Web-based machine translation, English as a foreign language, Neural machine translation, Korean students, analysis of translator output

Abstract

Web-based machine translation (WBMT) tools have long been used by language learners, but until recently, their value has been limited by the poor accuracy of their outputs. In the past few years, however, the advent of neural machine translation has drastically improved the accuracy of WBMT, drastically increasing their attractiveness to language learners. Accordingly, the present exploratory study seeks to delve into students’ attitudes and beliefs regarding the use of WBMT tools for English language learning. Surveys are used to collect data from eighty upper-year Korean-speaking university students pertaining to their use of and attitudes toward using WBMT tools. The results indicate that the majority of students use them to support their language studies both at home and at school, and for a range of purposes. Most students report having limited trust in the accuracy of the outputs, but in general, the results reveal disparities among students in terms of their dependency upon and perceived value of such tools. Finally, the students evaluated the output of two popular WBMT tools, revealing evidence of struggle in terms of their ability to critically analyze their outputs. The pedagogical implications associated with this issue are discussed.

Author Biography

Neil Briggs, Hannam University

Neil Briggs is an assistant professor at Hannam University in Daejeon, South Korea. He holds a master’s degree in TESL and a PhD in English education. His research interests include computer-assisted language learning and holistic, Activity Theoretical analysis of the communicative language learning classroom. 

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Published
2018-04-19
Section
Regular Articles