AWEJ for Translation & Literary Studies, Volume 7, Number 1. February 2023 Pp.208-219
Google Translate Errors in Legal Texts: Machine Translation Quality Assessment
Eman Rashed Alkatheery
English Language Department, College of Languages Studies
King Saud University, Riyadh, Kingdom of Saudi Arabia
Received: 11/07/2022 Accepted: 01/31/2023 Published: 02/24/2023
Machine translation received intensive research in different language pairs; yet, the quality of specialized translation; e.g., legal translation, from Arabic into English received little attention. The present study investigates the quality of machine translation of legal texts from Arabic into English. The paper aims at examining the errors found in the machine translation of legal texts from Arabic into English. It also studies the legal discourse features in machine-translation output. The research questions tackle the accuracy of machine translation of legal discourse and error categories and frequencies in machine translation. The researcher evaluated several factors to assess the quality of Google Translate; i.e., lexical, syntactic, and register-related errors. The study data consists of five legislative texts. The researcher conducted a manual error assessment and classification. To ensure the reliability of the error analysis, an existing human translation of the documents was used as a reference to ensure the reliability of the MT quality assessment and post-editing process. Later, the errors were classified, and their percentages and frequencies were calculated. A few examples of errors in each category were discussed and analysed. The highest error category was lexical errors scoring 43.4%. The last detected error category was deletion with a percentage of 1.7%. Syntactic errors constituted one-fourth of the errors found in the data. Legal register-related errors scored 30.2%. The subcategories of legal register-related errors varied in their occurrence; e.g., one-third of the errors related to legal discourse were legal terms. The study concluded that machine translation; though it provided a comprehensible output, could not translate legal structures and terminology perfectly.
Keywords: Machine Translation, Translation Quality Assessment, Legal Translation, Translation Error Typology, Google Translate
Cite as: Alkatheery, E.R. (2023). Google Translate Errors in Legal Texts: Machine Translation Quality Assessment. Arab World English Journal for Translation & Literary Studies 7 (1): 208-219. DOI: http://dx.doi.org/10.24093/awejtls/vol7no1.16
Adly, N., & Al Ansary, S. (2009). Evaluation of Arabic machine translation system based on the universal networking language. In International Conference on Application of Natural Language to Information Systems (pp. 243–257), Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12550-8_20
Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. Routledge. https://doi.org/10.4324/9781003191018
Alqudsi, A., Omar, N., & Shaker, K. (2012). Arabic machine translation: A survey. Artificial Intelligence Review, 42(4), 549–572.Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. Routledge. https://doi.org/10.4324/9781003191018
Al-Sukhni, E., Al-Kabi, M. N., & Alsmadi, I. M. (2016). An automatic evaluation for online machine translation: Holy Quran case study. International Journal of Advanced Computer Science and Applications, 7(6), 118–123. DOI: 10.14569/IJACSA.2016.070614
Castilho, S., Doherty, S., Gaspari, F., Moorkens, J. (2018). Approaches to Human and Machine Translation Quality Assessment. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty, (eds.), Translation Quality Assessment. Machine Translation: Technologies and Applications, 1, 9-38. Springer, Cham. https://doi.org/10.1007/978-3-319-91241-7_2
El-Farahaty, H. (2014). Arabic-English-Arabic Legal Translation. Routledge. https://doi.org/10.4324/9781315745893
Foley, R. (2002). Legislative Language in the EU: the Crucible’. International Journal for the Semiotics of Law, 15(4), 361–374. https://doi.org/10.1023/A:1021203529151
Hijazi, B. (2013). Assessment of Google’s Translation of Legal Texts, (Unpublished Master’s Thesis). University of Petra, Amman, Jordan.
Hadla, L., Hailat, T., & Al-Kabi, M. (2014). Evaluating Arabic to English Machine Translation. International Journal of Advanced Computer Science and Applications (IJACSA), 5(11), 68 – 73. DOI: 10.14569/IJACSA.2014.051112#sthash.NW2l6vl5.dpuf
Hutchins, W. J. (2000) Early Years in Machine Translation, Amsterdam and Philadelphia: John Benjamins Publishing Company.
Izwaini, S. (2006). Problems of Arabic machine translation: Evaluation of three systems. Retrieved from www.mt-archive.info/BCS-2006-Izwaini.pdf
Jabak, O. (2019). Assessment of Arabic-English translation produced by Google Translate. International Journal of Linguistics, Literature and Translation, 2(4), 238-247.
Killman, J. (2014). Vocabulary accuracy of statistical machine translation in the legal context. In O,Brien, S., Simard, M., & Specia, L (Eds.), Third Workshop on Post-Editing Technology and Practice (WPTP-3), The 11th Conference of the Association for Machine Translation in the Americas (pp. 22-26).Vancouver: AMTA. https://www.amtaweb.org/AMTA2014Proceedings/AMTA2014Proceedings_PEWorkshop_final.pdf
Sin-wai, C. (2016). The Future of Translation Technology: Towards a World without Babel (1st ed.). Routledge. https://doi.org/10.4324/9781315731865
Way, A. (2018). Quality Expectations of Machine Translation. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty, (eds.), Translation Quality Assessment. Machine Translation: Technologies and Applications, 1,159-178. Springer, Cham. https://doi.org/10.1007/978-3-319-91241-7_8
Wisemann, E. (2019). Machine Translation in the Field of Law: a Study of the Translation of Italian Legal Texts into German. Comparative Legilinguistics, 37(4), 117-153. http://dx.doi.org/10.14746/cl.2019.37.4