English-Indonesian crisis translation: accuracy and adequacy of Covid-19 terms translated by three MT tools
This study focuses on one basic question: how accurate and adequate are the three MT tools, namely Google Translate, Bing and Systran, in generating Covid-19 terms? It measures mainly the accuracy and adequacy of Covid-19 terms translated by three popular MT tools between English and Indonesian. Data analysis is conducted manually through human evaluation toward translation products by using a translation rubric. The assessment includes several samples covering the level of words, sentences and paragraphs. All samples are purposively retrieved from the Coronavirus Corpus and are translated by using the three MT tools. Two raters are involved to analyze texts at sentence and paragraph levels. The raters are used to provide the credibility of translation texts analysis. Results showed that the three MT tools produce different language accuracy and adequacy in revealing COVID-19 terms. Translating noun and pronoun in particular context from English into Indonesian language still remains unclear. This may affect paragraph cohesion. Furthermore, even though these MT tools successfully translate a number of English words into Indonesian, several of the words cited are officially absent in the Great Indonesian Dictionary. This gap raises confusion for Indonesian readers whose English is not sufficient to understand the lexical meaning. In this case, the study highlights the importance of updating the words data base. As this article implements an evaluation translation method, the goal is to produce some recommendations that may be useful for several parties: reader of target language, MT’s developer, linguist and government.
Nugraha, D. S. and Dewanti, R. (2022). English-Indonesian crisis translation: accuracy and adequacy of Covid-19 terms translated by three MT tools, Research Result. Theoretical and Applied Linguistics, 8 (1), 122-134. DOI: 10.18413/2313-8912-2022-8-1-0-8
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Asscher, O. and Glikson, E. (2021). Human evaluations of machine translation in an ethically charged situation, New Media and Society, available at: https://doi.org/10.1177/14614448211018833 (Accessed 6 July 2021). (In English)
Bowker, L. and Buitrago Ciro L. (2019). Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community, Emerald Publishing, Bingley, UK. (In English)
CDC (2021). Safety of Covid-19 Vaccines, available at: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/safety-of-vaccines.html (Accessed 9 July 2021). (In English)
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. https://doi.org/10.1016/j.system.2020.102408 (In English)
Das, P., Kuznetsova, A., Zhu, M. and Milanaik, R. (2018). Dangers of Machine Translation: The Need for Professionally Translated Anticipatory Guidance Resources for Limited English Proficiency Caregivers, Clinical Pediatrics, 58, 2, 247-249. https://doi.org/10.1177/0009922818809494 (In English)
Dew, K. N., Turner, A. M., Choi, Y. K., Bosold, A. and Kirchhoff, K. (2018). Development of machine translation technology for assisting health communication: A systematic review, Journal of Biomedical Informatics, 85, 56-67. https://doi.org/10.1016/j.jbi.2018.07.018 (In English)
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. http//doi.10.2174/1874944502114010094 (In English)
EMEA (2021). The European Medicines Agency, available at: https://opus.nlpl.eu/EMEA.php (Accessed 9 July 2021). (In English)
English-Corpora (2021). The Coronavirus Corpus, available at: https://www.english-corpora.org/corona/ (Accessed 9 July 2021). (In English)
Federici, F., O’Hagan, M., O’Brien, S. and Cadwell, P. (2019). Crisis Translation Training Challenges Arising from New Contexts of Translation, Cultus, 12, 246-279, available at: https://discovery.ucl.ac.uk/id/eprint/10085446/ (Accessed 9 July 2021). (In English)
Guzmán, F., Joty, S., Màrquez, L. and Nakov, P. (2017). Machine translation evaluation with neural networks, Computer Speech and Language, 45, 180-200, available at https://arxiv.org/abs/1710.02095 (Accessed 10 July 2021). (In English)
Koehn, P. (2010). Statistical Machine Translation, Cambridge University Press, Cambridge, UK. (In English)
Koehn, P. and Knowles, R. (2017). Six Challenges for Neural Machine Translation. Proceedings of the First Workshop on Neural Machine Translation, 28-39. http://doi.10.18653/v1/W17-3204 (In English)
Li, J., Xie, P., Ai, B. and Li, L. (2020). Multilingual communication experiences of international students during the COVID-19 Pandemic, Multilingua, 39, 5, 529-539 https://doi.org/10.1515/multi-2020-0116 (In English)
Maurya, K. K., Ravindran, R. P., Anirudh, C. R. and Murthy, K. N. (2020). Machine Translation Evaluation: Manual Versus Automatic – A Comparative Study, Advances in Intelligent Systems and Computing, 1079, 541–553. https://doi.org/10.1007/978-981-15-1097-7_45 (In English)
O’Brien, S. and Federici, F. M. (2019). Crisis translation: considering language needs in multilingual disaster settings, Disaster Prevention and Management: An International Journal, 29, 2, 129-143. https://doi.org/10.1108/DPM-11-2018-0373 (In English)
Raeisi, M., Dastjerdi, H. V. and Raeisi, M. (2019). Strategies used in the translation of scientific texts to fill the lexical gap, Research Result. Theoretical and Applied Linguistics, 5, 3, 116-123. (In English)
Saldanha, G. and O’Brien, S. (2014). Research Methodologies in Translation Studies, Routledge, New York, US. (In English)
Samir, A.-Y. M. (2020). Translation Quality Assessment Rubric: A Rasch Model-Based Validation, International Journal of Language Testing, 10, 2,101-128. (In English)
Sketch Engine (2021). Sketch Engine: Learn How Language Works, available at: https://www.sketchengine.eu (Accessed 9 July 2021). (In English)
Stankevičiūtė, G., Kasperė, R. and Horbačauskienė, J. (2017). Issues in Machine Translation, International Journal on Language, Literature and Culture in Education, 4, 1, 75-88. (In English)
TAUS and Systran (2021). Powering Automated Translation in Time of Corona Crisis, available at: https://www.systransoft.com/systran/news-and-events/specialized-corona-crisis-corpus-models/ (Accessed 9 July 2021). (In English)
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. https://doi.org/10.1080/1369118X.2020.1776370 (In English)
Williams, J. and Chesterman, A. (2011). The Map: A Beginer’s Guide to Doing Research in Translation, Routledge Taylor and Francis, Group London, UK. (In English)