Abstract
EFL university students in general and Iranian EFL university learners in particular, specifically in the commencement of their study at bachelor degree prefer to have their assigned English texts translated into mother tongue since they do not have a good command of the language. Machine translation (MT) has recently turned into a favorite tool for all students including EFL ones since it is free and readily available. The aim of this study, therefore, was to evaluate the effect of MT output on such students. To achieve this goal, two sample texts in students’ mother tongue were selected and translated into English by a proficient human translator whose native language was English and by an MT system (Google Translate). Next, 167 homogenous EFL freshman students were given a proficiency test and 152 homogenous ones randomly assigned to two groups, namely, control and experimental groups. The control group was given the human translation (HT) and the experimental group was exposed to machine translation. Using an independent t-test, indicated that there was a negligible difference between mentioned groups. That is, MT has improved to such an extent that it can compete with the HT. It can be concluded that due to its great improvements in the last few years and its widespread use among university students, EFL educators should accept the presence of this tool and try to implement it effectively in their teaching instead of banning students from using it.
Highlights
The link between translation and Foreign Language reading comprehension has a timehonored history in English as a Foreign Language tradition, as old as the Grammar
The findings of this study confirm the suitability or usefulness of mt system has been measured based on informativeness, comprehensiveness, and fluency of its output via a reading comprehension test
78 to be inconsistent with that some previous research, for example Mitkov (2004), the way Maghsoudi & Mirzaei: Machine versus human translation outputs mt output was investigated in the present study was unlike the previous studies devising artificial-intelligence-oriented approaches (e.g. Bahdanau et al, 2014; Bojar et al, 2016; Jean et al, 2015; Koehn & Knowles, 2017); instead of using mathematical and automated methods to evaluate mt output, the researcher focused on mt output quality defined in terms of its usefulness
Summary
To fill the gap in the existing body of empirical knowledge, this study aimed at comparing the participants’ reading comprehension of the mt versus ht texts
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