Comparative Analysis of Online Translators in the Machine Translation System
DOI:
https://doi.org/10.18662/rrem/16.3/885Keywords:
online translator, machine translation, translation activity, information and communication technologiesAbstract
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
How to Cite
Issue
Section
License
Copyright (c) 2024 The Authors & LUMEN Publishing House
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant this journalright of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work, with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as an earlier and greater citation of published work (See The Effect of Open Access).
Revista Romaneasca pentru Educatie Multidimensionala Journal has an Attribution-NonCommercial-NoDerivs
CC BY-NC-ND