A Comparison of the Performance of Google Translate in 2018 and 2023
DOI:
https://doi.org/10.14456/jlapsu.2024.5Keywords:
Google Translate, Translation, Lexical errors, Syntactic errors, Discourse errorsAbstract
Google Translate has evolved into an indispensable tool for Thai readers seeking to comprehend English texts. While it may not be flawless, it offers remarkable features that facilitate readers in grasping the overall meaning. Furthermore, its continuous annual progress necessitates ongoing studies. Therefore, this article sets out to compare Google Translate's machine translation errors in two online news articles retrieved from both 2018 and 2023, from an English-to-Thai perspective. One example from beginner-level reading comprehension materials was also included in the analysis. These texts underwent meticulous qualitative and quantitative analyses to identify errors introduced by Google Translate. The findings of this study unveiled the inevitability of errors in Google Translate's translations. These errors predominantly fell into three major categories: lexical, syntactic, and discourse. Notably, Google Translate exhibited a penchant for making lexical errors in the translated texts in both 2018 and 2023. The frequency of errors in Google Translate was 87% in 2018 and decreased to 39% in 2023. From the total errors, Google Translate made lexical errors in 2018 for 55%, syntactic errors for 30%, and discourse errors for 20%. In contrast, the error rate improved in 2023: lexical errors decreased to 25%, syntactical errors to 10%, and discourse errors to 10%, indicating advancements over the past half-decade. Despite the prevalence of errors, this study aims to provide explanations and practical implications to enhance future use. While Google Translate's errors may occasionally hinder a reader's comprehension, the software still holds the potential to offer a general understanding of a text. Recognizing the reliance on translation tools and understanding the types of errors are critical steps for readers to employ these tools more effectively.
References
Abraham, L. B. (2009). Web-based translation for promoting language awareness: Evidence from Spanish. In Abraham, L. B. & Williams, L. (eds.), Electronic discourse in language learning and language teaching. Amsterdam: John Benjamins Publisher, (65–84). https://doi.org/10.1075/lllt.25.06abrl
Balk, M.E. et al. (2012). Accuracy of data extraction of non-English language trials with Google Translate. Agency for Healthcare Research and Quality, Rockville, Maryland, USA.
Balk, M.E. et al. (2013). Data extraction from machine-translated versus original language randomized trial reports: A comparative study. Systematic Reviews, 2, 97. https://pubmed.ncbi.nlm.nih.gov/24199894/
Bavendiek, U. (2022). Using machine translation as a parallel text to access literature for modern language learning. In C. Hampton & S. Salin (Eds), Innovative language teaching and learning at university: facilitating transition from and to higher education (pp. 57-67). Research-publishing.net. https://doi.org/10.14705/rpnet.2022.56.1373
Bojar, O. (2011). Analyzing error types in English-Czech machine translation. The Prague Bulletin of Mathematical Linguistics, 95, 63-76.
Bowker, L., & Ciro, J. B. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Bingley, UK: Emerald Publishing.
Briggs, N. (2018). Neural machine translation tools in the language learning classroom: Students’ use, perceptions, and analyses. The JALT CALL Journal, 14(1), 3–24. https://doi.org/10.29140/jaltcall.v14n1.221
Caswell, I., & Liang, B. (2020, June 8). Recent advances in Google Translate. Retrieved from https://blog.research.google/2020/06/recent-advances-in-google translate. html?m=1
Chimsuk, T. (2010). A Framework for implementation of Thai to English machine translation systems. [Doctoral Dissertation, School of Applied Statistics, National Institute of Development Administration]. Nida Wisdom Repository. http://repository.nida.ac.th/handle/662723737/273.
Clifford, J., Merschel, L., & Munné, J. (2013). Surveying the landscape: What is the role of machine translation in language learning? The Acquisition of Second Languages and Innovative Pedagogies, 10, 108-121. https://doi.org/10.7203/attic.10.2228
Coady, J., & Huckin, T. (1997). Second language vocabulary acquisition. UK: Cambridge: Cambridge University Press.
Corder, S. P. (1967). The significance of learners’ errors. International Review of Applied Linguistics in Language Teaching, 5, 161-170. http://dx.doi.org/10.1515/iral.1967.5.1-4.161
Correa, M. (2014). Leaving the “peer” out of peer-editing: Online translators as a pedagogical tool in the Spanish as a second language classroom. Latin American Journal of Content and Language Integrated Learning, 7(1), 1–20. https://doi.org/10. 5294/laclil.2014.7.1.1
Costa, Â., Ling, W., Luís, T., Correia, R., & Coheur, L. (2015). A linguistically motivated taxonomy for Machine Translation error analysis. Machine Translation, 29(2), 127-161.
Delorme Benites, A., Cotelli Kureth, S., Lehr, C., & Steele, E. (2021). Machine translation literacy: A panoramaof practices at Swiss universities and implications for language teaching. In A. Zoghlami, C. Brudermann, C. Sarré, M. Grosbois, L. Bradley, & S. Thoësny (Eds.), CALL and professionalization: Short papers from EUROCALL 2021 (pp. 80–87). Research‐publishing.net. https://doi.org/10.14705/rpnet.2021.54.1313
Ducar, C., & Schocket, D. H. (2018). Machine translation and the L2 classroom: Pedagogical solutions for making peace with Google translate. Foreign Language Annals, 51(4), 779-795.
Elliott, D., Hartley, A., & Atwell, E. (2004). A fluency error categorization scheme to guide automated machine translation evaluation. In: Frederking, R.E., Taylor, K.B. (eds) Machine Translation: From Real Users to Research. AMTA 2004. Lecture Notes in Computer Science. (Vol. 3265, pp. 64-73). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30194-3_8
Fem, S. (2011). An Analysis of the Translation from English Sentences to Indonesian Sentences by Using “Google Translate”. [Bachelor Degree Thesis, Soegirapranata Catholic University]. Unika Repository. http://repository.unika.ac.id/19032.
Filbeck, D. (1973). The passive in Thai. Anthropological Linguistics, 15(1), 33–4.
Fredholm, K. (2015). Online translation use in Spanish as a foreign language essay writing: Effects on fluency, complexity and accuracy. Revista Nebrija de Lingüística Aplicada a la Ensenanza de las Lenguas, 18, 7–24.
Goulet, M.-J., Simard, M., Parra, E. C., & O’Brien, S. (2017). Using machine translation for academic writing in English as a second language: Results of an exploratory study on linguistic quality. ASp: la revue du GERAS, 72, 5-28. https://doi.org/10.4000/asp.5045
Grammar Bank. (2018). Beginners reading comprehension. grammar bank. Retrieved January,2018, from https://www.grammarbank.com/reading-comprehension-test.html
Grace, C. A. (1998). Retention of word meanings inferred from context and sentence‐level translations: Implications for the design of beginning‐level CALL software. The Modern Language Journal, 82(4), 533-544.
Hutchins, J. (2003). Machine translation: General overview. In R. Mitkov (Ed.), The Oxford handbook of computational linguistics (pp. 482-490). Oxford University Press.
Hutchins, W. J. & Somers, H. L. (1992). An introduction to machine translation. Cambridge: Cambridge University Press.
James, C. (1998). Errors in language learning and use: Exploring error analysis. London: Longman.
Kim, S. (2019) Playing with machine translation in language classroom: Affordances and constraints. Multimedia-Assisted Language Learning, 22(2), 9-28.
Kok Wei, L. (2021). The use of Google Translate in English language learning: How students view it. International Journal of Advanced Research in Education and Society, 3(1), 47-53.
Kumnoed, P. (2018a, April 27-29). An analysis of English reading comprehension and reading strategies through Google Translator of Loei Rajabhat University students [Paper Presentation]. The Asian Conference on Language Learning (ACLL 2018), Surviving and Thriving: Education in Times of Change, Kobe, Japan.
Kumnoed, P. (2018b). An analysis of English reading comprehension and reading strategies through Google Translator of Loei Rajabhat University students [Unpublished Research Report]. Loei Rajabhat University.
Lee, S.-M. (2022). An investigation of machine translation output quality and the influencing factors of source texts. ReCALL, 34(1), 81–94. doi:10.1017/S0958344021000124
Llitjós, A. F., Carbonell, J. G., & Lavie, A. (2005). A framework for interactive and automatic refinement of transfer-based machine translation [Conference session]. European Association for Machine Translation Conferences/Workshops, Budapest, Hungary.
Lyons, S. (2016). A survey of the use of mobile technology and translation tools by students at secondary school in Thailand. Payap University Journal, 26(1), 35-57.
Mala, D. (2016, August 7). Students to get sex education. Bangkok Post. Retrieved January, 2018, from https://www.bangkokpost. com/thailand/general/1055009/students-to-get-sex-education
Modhiran, T., Kosawat, K., Klaithin, S., Boriboon, M., & Supnithi, T. (2005, September 12-16). Parasite: An online Thai-English machine translation [Paper presentation]. The 10th Machine Translation Summit, Pathum Thani, Thailand. Newmark, P. (1988). A Textbook of Translation. Prentice Hall, New York, USA.
Newmark, P. (2001). Approaches to translation. Shanghai: Shanghai Foreign Language Education Press.
Nida, E. A. (1991). Theories of translation. TTR: Traduction, Terminologie, Rédaction, 4(1), 19-32.
Nunan, D. (2001). Teaching English to speakers of other languages. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511667206
O’Brien, S., Simard, M., & Goulet, M.-J. (2018) Machine translation and self-post-editing for Academic writing support: Quality explorations. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (eds.), Translation quality assessment: From principles
to practice. Cham: Springer (237–262). https://doi.org/10.1007/978-3-319-91241-7_11
Park, J. (2017) Analysis of the types of translation errors by the Google Translator. Studies on English Language & Literature, 59(4), 279-299.
Perfetti, C. A. (1988). Verbal efficiency in reading ability. In M. Daneman, G. E. Mackinnon, & T. G. Waller (Eds.), Reading research: Advances in theory and practice (pp. 109–143). New York: Academic Press.
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