Neural machine translation tools in the language learning classroom: Students' use, perceptions, and analyses

Abstract

Web-based machine translation (wbmt) tools have long been used by language learners, but until recently, their value as learning tools was limited by the limited accuracy of their outputs. In the past few years, however, the advent of neural machine translation has drastically improved the accuracy of wbmt, considerably 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 80 upperyear Korean-speaking university students regarding 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 reported 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. Furthermore, 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.