Using Machine Translation Engines in the Classroom - A Survey of Translation Students’ Performance

Oct 15, 2026

The paper reports an experimental study on how machine translation engines affect the performance of translation students. MT engines are described as fully automatic systems translating source texts into target languages, classified by algorithm and customisation level. Forty-eight undergraduate translation majors completed two translation tasks: one group used Microsoft Translator in English→Ukrainian, while the other translated by hand. Results show lower text quality with MT without prior post-editing training and a tendency to accept MT output uncritically, indicating a need for instruction in MT use and post-editing methodology.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Defining MT and MT engines

Classification by algorithm

Classification by customisation

Need for translator training

USING MACHINE TRANSLATION ENGINES IN THE CLASSROOM: A SURVEY OF TRANSLATION STUDENTS’ PERFORMANCE Alla Olkhovska, Iryna Frolova V. N. Karazin Kharkiv National University, Kharkiv, Ukraine [transengl@karazin.ua](transengl@karazin.ua) 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. Keywords: machine translation; machine translation engine; post-editing; students majoring in translation; translation pedagogy; machine translation evaluation. Introduction The way translators work has changed drastically in recent decades due to the extensive development of technologies. The modern translation market demands translating large volumes of texts within the shortest possible time. The requirement could be fulfilled only with the help of translation technologies, CAT-tools being in the top of the list. However, nowadays the situation on the technology market shifts as machine translation (MT) is gaining more and more popularity due to the significant improvement of its quality and the prospects of translation costs reduction. MT can be defined as “a sub-field of computational linguistics (CL) or natural language processing (NLP) that investigates the use of software to translate text or speech from one natural language to another”(Qun and Xiaojun, 2015), while an MT engine refers to the software developed to translate source texts into target languages in a fully automatic mode without any human assistance (What is Machine Translation?) . The terms “MT” and “MT engine” are often used interchangeably in the scientific papers, for example,“MT output” in fact means “the output produced by an MT engine”. The main criteria used to classify MT and MT engines include their algorithm and the level of customisation. According to the algorithm, MT technologies include (Hutchins and Somers, 1992): E

FAQs

What was the goal of the study on machine translation engines?

To explore how using machine translation engines impacts the performance of translation students.

How were the students grouped in the experiment?

One group translated the same English text into Ukrainian using an MT engine, while the other translated it by hand.

What main conclusion does the study reach about using MT without training?

Without prior training in post-editing, students’ MT-assisted translations were of poorer quality than translations done without MT.

Oct 15, 2026

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