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Academic Journal of Humanities & Social Sciences, 2023, 6(24); doi: 10.25236/AJHSS.2023.062417.

Accuracy and Fluency of High-frequency COVID-19 Words Translated by Three Machine Translation(MT) Tools


Zhang Li, Chi Lixia, Yang Wenyi

Corresponding Author:
Zhang Li

Institute of Disaster Prevention, Sanhe, China, 065201


This study aims to investigate accuracy and fluency of COVID-19 words generated by the three MT tools, namely, Google Translate, Baidu Translate and DeepL Translator. Data analysis is done through human evaluation toward translation texts by using a translation rubric at the word, sentence and paragraph level. To ensure the credibility of translation texts analysis, ten raters are involved to analyse the translation texts at sentence and paragraph levels. It is found that the three machine translation tools can maintain a high accuracy rate in the translation of simple words and short sentences, but the collection of new words is relatively slow and the translation of longer sentences or paragraphs is not adequate and fluent enough. Further, there are language problems such as grammar and logical coherence.


Accuracy; Fluency; COVID-19 high-frequency words; MT

Cite This Paper

Zhang Li, Chi Lixia, Yang Wenyi. Accuracy and Fluency of High-frequency COVID-19 Words Translated by Three Machine Translation(MT) Tools. Academic Journal of Humanities & Social Sciences (2023) Vol. 6, Issue 24: 98-103. https://doi.org/10.25236/AJHSS.2023.062417.


[1] Chon, Y.V., Shin D. and Kim G. E. (2021). Comparing L2 Learners’ Writing against Parallel Machine-Translated Texts: Raters’ Assessment, Linguistic Complexity and Errors. System, 96. 

[2] Dahal,S. B., Auon, M.(2023). Exploring the Role of Machine Translation in Improving Health Information Access for Linguistically Diverse Populations, Journal of Intelligent Information Systems, 8(2):4-6.

[3] Dreisbach, J. L. and Mendoza-Dreisbach, S. (2021). Unity in Adversity: Multilingual Crisis Translation and Emergency Linguistics in the COVID-19 Pandemic, The Open Public Health Journal, 14, 1, 94–97.

[4] Koehn, P. & Knowles, R. (2017). Six Challenges for Neural Machine Translation[J]. Proceedings of the First Workshop on Neural Machine Translation, 28-39. 

[5] Koehn, P. (2010). Statistical Machine Translation[M]. New York, the United States of America by Cambridge University Press, 217-235.

[6] Nugraha, D. S. and Dewanti,R. (2022). English-Indonesian crisis translation: accuracy and adequacy of Covid-19 terms translated by three MT tools, Theoretical and Applied Linguistics, 8 (1), 122-134. 

[7] Siu, S.C.(2023). COVID-19 MT Evaluator:A Platform for the Evaluation of Machine Translation of Public Health Information Related to COVID-19, published in Translation and Interpreting in the Age of COVID-19, edited by Liu, K., Cheung, A.K.F., Springer. 

[8] Vieira, L. N., O’Hagan, M. and O’Sullivan, C. (2020). Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases, Information, Communication and Society, 24, 11, 1515-1532.