USING MACHINE TRANSLATION ENGINES IN THE CLASSROOM: A SURVEY OF TRANSLATION STUDENTS’ PERFORMANCE

Alla Olkhovska,Iryna Frolova

doi:10.20535/2410-8286.197812

Abstract

This paper outlines the results of the experimental study aiming to explore the impact of using machine translation engines on the performance of translation students. Machine translation engines refer to the software developed to translate source texts into target languages in a fully automatic mode which can be classified according to the algorithm (example-based, rule-based, statistical, pragmatics-based, neural, hybrid) and the level of customisation (generic, customised, adaptive). The study was carried out in the form of a translation test held during the first semester of 2019/2020 academic year (September). The subjects included 48 undergraduate students of the School of Foreign Languages of V. N. Karazin Kharkiv National University (42 women and 6 men aged from 19 to 22) majoring in translation. They were subdivided into two sample groups: sample group 1 performed translation from English into Ukrainian with the help of the MT engine while sample group 2 translated the same text from English into Ukrainian by hand. The MT engine chosen for conducting the research was Microsoft Translator for personal use. The research was arranged into the following stages: preliminary (designing the experiment), main (implementing the experiment into life), conclusive (processing the results and interpreting them). The comparison of the students’ average results allowed us to come to the conclusion that the hypothesis preconceived in the beginning of the study was confirmed: the quality of the text translated by the students who received no previous training in post-editing with the use of a modern machine translation engine was of a poorer quality compared to the translation of the same text made by the students without the use of a machine translation engine. The students using the machine translation engine showed the tendency not to treat critically the output of the machine translation engine, thus scoring more penal points than the students translating on their own. The evidence from this study implies the necessity of teaching students to use machine translation in their work and to post-edit texts by means of developing the appropriate cross-curricular methodology of teaching.

Highlights

The way translators work has changed drastically in recent decades due to the extensive development of technologies
In order to conduct our study, we developed a preconceived hypothesis: translation students who received no previous training in post-editing texts translated with the use of a modern machine translation (MT) engine are likely to show a poorer quality
In this article, we have studied the impact of MT on the performance of translation students in terms of quality

Summary

Introduction

The way translators work has changed drastically in recent decades due to the extensive development of technologies. MT engines can be of real help, but they are unable to replace a human translator, their use is subject to some restrictions and limitations while the results (translated texts) need human control and correction. They influence the translation process and the way professional translators work and imply some training in their professional use to be introduced into the curriculum of students majoring in translation.

Objectives

Methods

Discussion

Conclusion