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.

Assesing the Accuracy of Translation Result of Kataku Version 1.1 and Transtool 10 from English to Indonesian and Its Implication on Language Teaching

“…This is to prevent students from getting wrong translation that may lead to a wrong understanding of the content of a reading material, when machine translations are the sole reference there is. This is in accordance with the result of the study that Briggs (2018) conducted. The study intended to figure out the quality of the web translation, another type of machine translation, and its implication on Korean students in learning English as a foreign language.…”

“…Actually, a research on the contribution of machine translation on language education as well as the quality of the output has been done (Briggs, 2018). However, the translation quality that is assessed in the study is only acceptability.…”

“…As already explained in the introduction, the progress of language education has seen machine translation as a pedagogical tool (Pan & Pan, 2012; Dagilienė, 2012); Giannetti, 2016;Briggs, 2018). Machine translation has been often utilized in EFL classes.…”

Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

“…Educators must not overlook the significance of this relatively recent shift in MT technology, particularly because studies show that its use is already widespread among students (i.e. Alhaisoni & Alhaysony, 2017;Briggs, 2018). To date, NMT provides students with access to instant translations of the learners' first language (referred to hereafter as L1), which can be remarkably accurate depending on the nature of the user's input.…”

An Analysis of the Errors in the Auto-Generated Captions of University Commencement Speeches on YouTube

“…This refers to the generating of verb tenses necessary for translation of English into Spanish, or the word order used when translating from Chinese into English. In a research conducted by Briggs (2018), he discovered that the error rate of web-based machine translation systems was significant when used as an assistive learning tool for Korean university students. In a survey involving 80 students who regularly used it, most of them reported a low degree of trust in the data output.…”