Comparative Analysis of Online Translators in the Machine Translation System

Authors

  • Lesia Matviienko Ph.D. in Pedagogics, Associate Professor of the Department of Humanities and Social Sciences, Poltava State Agrarian University, Poltava, Ukraine.
  • Liubov Khomenko Ph.D. in Physical and Mathematical Sciences, Associate Professor of the Department of Theory and Methods of Technological Education, Poltava V. G. Korolenko National Pedagogical University, Poltava, Ukraine
  • Iryna Denysovets PhD in Philology, Associate Professor of the Department of Ukrainian Studies, Culture and Documentation, National University «Yuri Kondratyuk Poltava Polytechnic», Poltava, Ukraine
  • Kateryna Horodenska Doctor of Philological Sciences, Professor, Head of the Department of Grammar and Scientific Terminology, National Academy of Science of Ukraine, Institute for Ukrainian Language, Kyiv, Ukraine
  • Tetyana Nikolashyna PhD in Philology, Associate Professor of the Department of Ukrainian Language, Poltava V. G. Korolenko National Pedagogical University, Poltava, Ukraine
  • Iryna Pavlova PhD in Philology, Аssociate Professor, Head of the Department of Ukrainian Language, Poltava V. G. Korolenko National Pedagogical University, Poltava, Ukraine

DOI:

https://doi.org/10.18662/rrem/16.3/885

Keywords:

online translator, machine translation, translation activity, information and communication technologies

Abstract

The article presents a comparative analysis of various online translators in the machine translation system. The author of the study focused on finding out the effectiveness and accuracy of the translation, identifying the strengths and weaknesses of the three most common online translators: Google Translate, DeepL and Microsoft Translator. The article begins with an overview of current trends in the field of machine translation and the need for online translations based on this context. The research methodology is selected in the article, in particular, the selection of individual online translators for comparison and the criteria for evaluating their work. The researcher analyzes the quality of translations for various types of texts, including common phrases, technical terms, complex sentences, etc. Attention is also paid to different language pairs and ensuring the availability of rarer languages in electronic translation. The article reveals such aspects as accuracy of translation, speed of work, recognition of context and idiomatic expressions, availability of additional functions and capabilities that ensure better translation quality. The author thoroughly highlights the strengths and weaknesses of each system, provides clear information about the use of integrations, available functions and additional capabilities of each translator. In the final part of the article, the author provides conclusions and recommendations regarding the use of online translators in the machine translation system. The research highlights that each electronic translator has its own advantages and limitations, and the choice depends on the specific needs of the user. In addition, the author emphasizes the need for constant updating and improvement of online translators to increase their accuracy and efficiency. This comparative analysis will help simplify the selection of the optimal translation software for users who require high-quality machine translation for their professional activities or everyday needs.

References

Balahur, A., & Turchi, M. (2014). Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28(1), 56-75. https://doi.org/10.1016/j.csl.2013.03.004

Chen, C.,W.,Y. (2020). Using Google Translate in an authentic translation task: process, refinement efforts, and students’ perceptions. Current Trands in Translation Teaching and Learning E. 7:213-218. http://www.cttl.org/uploads/5/2/4/3/5243866/cttl_e_2020_7_cheryl_chen_wei-yu.pdf

Filipova, L., Oliinyk, O., Shelestova, A. (2022). Assessing the application of online computer translators while learning English in Ukraine (KhSAC). International Journal of Computer Science and Network Security. 22:475-484. http://paper.ijcsns.org/07_book/202205/20220567.pdf

Fitria, T. N. (2021). A review of machine translation tools: the translation’s ability. Language Circle Journal of Language and Literature. 16(1):162-176. https://doi.org/10.15294/lc.v16i1.30961

Garcia, I. (2010). Is machine translation ready yet?. Target-international Journal of Translation Studies. 22:7-21. https://doi.org/10.1075/target.22.1.02gar

Haque, R., Hasanuzzaman, M., & Way, A. (2020). Analysing terminology translation errors in statistical and neural machine translation. Machine Translation, 34, 149-195. https://doi.org/10.1007/s10590-020-09251-z

Koehn, P. & Knowles R. (2017). Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation. 31(1-2):28-39. doi: 10.18653/v1/W17-3204. https://aclanthology.org/W17-3204

Li, H., Zhu, Y. (2016). Classifying Commas for Patent Machine Translation. In: Yang, M., Liu, S. (eds) Machine Translation. CWMT 2016. Communications in Computer and Information Science (vol. 668, pp.91-101). Springer. https://doi.org/10.1007/978-981-10-3635-4_8

Rikters, M. (2019). Hybrid machine translation by combining output from multiple machine translation systems. Baltic Journal of Modern Computing, 7(3), 301-341. https://doi.org/10.22364/bjmc.2019.7.3.01

Roiss, S., & Zimmermann González, P. (2021). DeepL y su potencial para el desarrollo de la capacidad de análisis crítico en la clase de Traducción inversa. Hermēneus. Revista De traducción E interpretación, (22), 363–382. https://doi.org/10.24197/her.22.2020.363-382

Saffari, M., Sajjadi, S., Mohammadi, M. (2017). Evaluation of machine translation (Google Translate vs. Bing Translator) from English into Persian across academic fields. Modern Journal of Language Teaching Methods. 7:429-442. http://dx.doi.org/10.26655/mjltm.2017.8.1

Shterionov, D., Carmo, F. D., Moorkens, J., Hossari, M., Wagner, J., Paquin, E., Schmidtke, D., Groves, D., & Way, A. (2020). A roadmap to neural automatic post-editing: an empirical approach. Machine translation : MT, 34(2), 67–96. https://doi.org/10.1007/s10590-020-09249-7

Specia, L., Scarton, C., Paetzold, G.H. (2018). Quality Estimation for MT at Subsentence Level. In Quality Estimation for Machine Translation. Synthesis Lectures on Human Language Technologies. Springer, Cham (pp: 5-41). https://doi.org/10.1007/978-3-031-02168-8_2

Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2022). Progress in machine translation. Engineering, 18, 143-153. https://doi.org/10.1016/j.eng.2021.03.023

Zhang, C. X., Marshall, J., Bernard, A., & Walker-Smith, K. (2020). Development and evaluation of interprofessional e-learning for speech pathologists, interpreters and translators. Translation & Interpreting: The International Journal of Translation and Interpreting Research, 12(1), 142-158. doi: 10.12807/ti.112201.2020.a09. https://www.trans-int.org/index.php/transint/article/view/1155

Downloads

Published

2024-08-12

How to Cite

Matviienko, L., Khomenko, L., Denysovets, I., Horodenska, K., Nikolashyna, T., & Pavlova, I. (2024). Comparative Analysis of Online Translators in the Machine Translation System. Revista Romaneasca Pentru Educatie Multidimensionala, 16(3), 101-118. https://doi.org/10.18662/rrem/16.3/885

Issue

Section

Reform, Change and Innovation in Education

Most read articles by the same author(s)


Publish your work at the Scientific Publishing House LUMEN

It easy with us: publish now your work, novel, research, proceeding at Lumen Scientific Publishing House

Send your manuscript right now