Differentiating between machine translation and student translation: red flags and salient lexicogrammatical features.

Andrew Richard Burns Innes

doi:10.17951/lsmll.2019.43.4.1-13

Andrew Richard Burns Innes

Open Access

https://doi.org/10.17951/lsmll.2019.43.4.1-13

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Abstract

ABSTRACTMachine translation enables students to produce work in the target L2 which may be superior to that which they could produce otherwise. The present study examines whether use of machine translation can be detected by teachers. Seventeen native teachers compared and assessed the authorship of five human translations (HT) and five machine translations (MT) of Japanese news stories. Native teachers were able to accurately detect the difference in 74.04% of cases due to increased passive clauses (a ratio of 1 to 2.5), and inappropriate pronoun use (a ratio of 1 to 6.5) when MT was used.

Highlights

Negative sentiments of machine translation (MT) are borne out by Van Praagwho view it as achallenge to [their] knowledge and expertise, and anuisance and distraction
While exact figures pertaining to student use of MT will perhaps be confounded by worries around personal disclosure, a study at Duke University found that more than 88% of L2 students admitted to having used it, with 77% of instructors being opposed to its use (Clifford, Merschel, & Reisinger, 2013, p. 44)
Purposes of the present study The purpose of the present study is to investigate the extent to which native speakers of English can differentiate between a work produced by students, and a work produced via Google Translate

Summary

Introduction

Negative sentiments of machine translation (MT) are borne out by Van Praagwho view it as achallenge to [their] knowledge and expertise, and anuisance and distraction. Banning MT has been found to be ineffective as students will use it regardless (Kazemzadeh & Kashani, 2014). 13) found that 57.5% of Korean students strongly agreed with the statement, `I do not need to learn to write in English because [online] translators can do the work for me. Using a five-point Likert Scale ranging from strongly disagree to strongly agree, White and Heidrich (2013) received a mean of 3.59 when asking eighteen German students the degree to which they agreed with the statement, “I feel like I might have cheated” Using a five-point Likert Scale ranging from strongly disagree to strongly agree, White and Heidrich (2013) received a mean of 3.59 when asking eighteen German students the degree to which they agreed with the statement, “I feel like I might have cheated” (p. 241)

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