From terminological inconsistencies to major mistranslations
A qualitative analysis of NMT errors in public health communication
DOI:
https://doi.org/10.62408/ai-ling.v1i1.15Keywords:
Neural Machine Translation, Public Health Communication, COVID-19, Machine Translation Literacy, Error AnalysisAbstract
This article presents a qualitative analysis of frequent NMT errors in public health communication. Its primary aim is to sensitise users and decision-makers to common issues and raise public awareness for potential pitfalls and limits of current state-of-the-art NMT systems. In this context, the article also addresses the usability of raw NMT output in emergency situations. The investigation itself focuses on pandemic-related WHO texts and consists of a fine-grained manual error analysis encompassing three languages (English, French, and Spanish) and two NMT systems (DeepL and Google Translate). The five most frequent error types observed in this investigation included mistranslations, inconsistent use of terminology, unidiomatic or awkward style, untranslated text, and other internal inconsistencies. These error types are illustrated with examples and analysed in terms of their severity, their underlying causes, and their potential consequences. The findings show that raw NMT output is useful only to a very limited extent and that the risks and benefits associated with its use should be assessed extremely carefully.
References
Almahasees, Zakaryia & Meqdadi, Samah & Albudairi, Yousef. 2021. Evaluation of Google Translate in rendering English Covid-19 texts into Arabic. Journal of Language and Linguistic Studies 17(4). 2065–2080. doi:10.52462/jlls.149.
Araabi, Ali & Monz, Christof & Niculae, Vlad. 2022. How effective is Byte Pair encoding for out-of-vocabulary words in Neural Machine Translation? In Duh, Kevin & Guzmán, Francisco (eds), Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (1). Association for Machine Translation in the Americas (Orlando, September 12-16, 2022). 117–130. doi:10.48550/arXiv.2208.05225.
Bowker, Lynne & Ciro, Jairo. 2019. Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley: Emerald Publishing. doi:10.1108/9781787567214.
Haddow, Barry & Birch, Alexandra & Heafield, Kenneth. 2021. Machine translation in healthcare. In Susam-Saraeva, Şebnem & Spišiaková, Eva (eds), The Routledge Handbook of Translation and Health, 108–129. London: Routledge. doi:10.4324/9781003167983-10.
Isabelle, Pierre & Cherry, Colin & Foster, George. 2017. A challenge set approach to evaluating Machine Translation. In Palmer, Martha & Hwa, Rebecca & Riedel, Sebastian (eds), Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Copenhagen, September 7-11, 2017), Association for Computational Linguistics. 2486–2496. doi:10.18653/v1/D17-1263.
Khoong, Elaine & Steinbrook, Eric & Brown, Cortlyn & Fernandez, Alicia. 2019. Assessing the use of Google Translate for Spanish and Chinese translations of emergency Department Discharge Instructions. JAMA Internal Medicine (179). 580–582. doi:10.1001/jamainternmed.2018.7653.
Läubli, Samuel & Sennrich, Rico & Volk, Martin. 2018. Has Machine Translation achieved human parity? A case for document-level evaluation. In Riloff, Ellen & Chiang, David & Hockenmaier, Julia & Tsujiii, Jun’ichi (eds), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Brussels, October 31-November 1, 2018). Association for Computational Linguistics. 4791–4796. doi:10.18653/v1/D18-1512.
Lommel, Arle & Burchardt, Aljoscha & Uszkoreit, Hans. 2014. Multidimensional Quality Metrics (MQM): A framework for declaring and describing Translation Quality Metrics. Tradumàtica: tecnologies de la traducció (12). 455–463. doi:10.5565/rev/tradumatica.77.
Lommel, Arle & Görög, Attila & Melby, Alan & Uszkoreit, Hans & Burchardt, Aljoscha & Popović, Maja (2015): QT21: Deliverable 3.1 – Harmonised Metric. European Commission. doi:10.3030/645452.
Popović, Maja. 2018. Error classification and analysis for Machine Translation quality assessment. In Moorkens, Joss & Castilho, Sheila & Gaspari, Federico & Doherty, Stephen (eds), Translation Quality Assessment. Machine Translation: Technologies and Applications (1). Cham: Springer. doi:10.1007/978-3-319-91241-7_7.
Pym, Anthony & Ayvazyan, Nune & Prioleau, Jonathan. 2022. Should raw machine translation be used for public-health information? Suggestions for a multilingual communication policy in Catalonia. Just. Journal of Language Rights & Minorities, 1 (1-2). 71–99. doi:10.7203/Just.1.24880.
Šorak, Vanessa. 2025. Neural Machine Translation in pandemic-related health communication: A case study on risks and potentials in the context of the Covid-19 pandemic. In Atayan, Vahram & Choffat, Delphine & Czachur, Waldemar & Felder, Ekkehard & Pasques, Delphine (eds), Diskursanalytische Perspektiven auf medizinische Fachkommunikation im europäischen Kontext, 271–300. Heidelberg: Winter.
Vieira, Lucas Nunes & O’Hagan, Minako & O’Sullivan, Carol. 2020. Understanding the societal impacts of Machine Translation: A critical review of the literature on medical and legal use cases. Information, Communication & Society 24 (11). 1515–1532. doi:10.1080/1369118X.2020.1776370.
Zappatore, Marco & Ruggieri, Gilda. 2024. Adopting machine translation in the healthcare sector: A methodological multi-criteria review. Computer Speech & Language (84). 31–34. doi:10.1016/j.csl.2023.101582.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Vanessa Šorak

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.