USING MACHINE TRANSLATION ENGINES IN THE CLASSROOM: A SURVEY OF TRANSLATION STUDENTS' PERFORMANCE - Alla Olkhovska; Iryna Frolova (National Technical University of Ukraine "Kyiv Polytechnic Institute", 2020)
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.