Navigating the Gap: A Lexico-Syntactic Error Analysis of Human and AI-Powered Translation in English-Indonesia ELT Journal

Immatul Jannah Alimir, Veni Roza, Widya Syafitri

Abstract


This study investigates lexico-syntactic errors in Indonesian translations of English-language ELT journal abstracts, comparing outputs produced by human translators and AI-powered systems. The corpus consists of ten English abstracts and their corresponding translations, resulting in twenty texts analyzed in total. Human translations were prepared by graduate students trained in translation studies, while AI translations were generated automatically using neural machine translation tools. Each abstract was examined line by line, and translation segments were coded for lexical choice errors, syntactic errors, omissions, and additions. The analysis revealed sixteen dominant error instances, with human translations exhibiting a higher frequency of lexical and syntactic deviations, whereas AI translations were generally smoother but occasionally inaccurate in terminology and clause-level alignment. No omissions or additions were observed across the corpus, indicating that deviations were confined to word selection and sentence construction rather than content completeness. These findings highlight the importance of precise lexical choices and syntactic structuring in academic translation and emphasize the need for human post-editing when utilizing AI-assisted translation in the context of English Language Teaching. 


Keywords


Lexical errors; Syntatic errors; ELT abstract; Human translation; AI translation

Full Text:

PDF

References


Ardhito, A., Purwarianti, A., & Mahendra, R. (2020). Neural machine translation for Indonesian: Challenges and preliminary results. Proceedings of the 2020 International Conference on Asian Language Processing, 45–50.

Baker, M. (1992). In other words: A coursebook on translation. Routledge.

Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: A case study. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 257–267.

Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J., & Way, A. (2017). Is neural machine translation the new state of the art? The Prague Bulletin of Mathematical Linguistics, 108(1), 109–120.

Corder, S. P. (1967). The significance of learners' errors. International Review of Applied Linguistics in Language Teaching, 5(4), 161–170.

Dardjowidjojo, S. (2003). English language teaching in Indonesia. EA Journal, 21(1), 22–30.

Dulay, H., Burt, M., & Krashen, S. (1982). Language two. Oxford University Press.

Emilia, E. (2008). Menulis tesis dan disertasi. Alfabeta.

House, J. (1997). Translation quality assessment: A model revisited. Gunter Narr Verlag.

Hyland, K. (2000). Disciplinary discourses: Social interactions in academic writing. Longman.

Koehn, P. (2017). Neural machine translation. Cambridge University Press.

Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. Proceedings of the First Workshop on Neural Machine Translation, 28–39.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.

Lauder, A. (2008). The status and function of English in Indonesia: A review of key factors. MAKARA Sosial Humaniora, 12(1), 9–20.

Mackey, A., & Gass, S. M. (2005). Second language research: Methodology and design. Lawrence Erlbaum Associates.

Nord, C. (1997). Translating as a purposeful activity: Functionalist approaches explained. St. Jerome Publishing.

Popovic, M., & Ney, H. (2007). Word error rates: Decomposition over POS classes and applications for error analysis. Proceedings of the Second Workshop on Statistical Machine Translation, 48–55.

Pym, A. (1992). Translation error analysis and the interface with language teaching. In C. Dollerup & A. Loddegaard (Eds.), Teaching translation and interpreting (pp. 279–288). John Benjamins.

Setiawan, B., Wulandari, D., & Permadi, A. (2021). Machine translation accuracy in Indonesian academic texts: A comparative study. BAHTERA: Jurnal Pendidikan Bahasa dan Sastra, 20(2), 112–125.

Shreve, G. M., & Angelone, E. (Eds.). (2010). Translation and cognition. American Translators Association.

Sneddon, J. N., Adelaar, A., Djenar, D. N., & Ewing, M. C. (2010). Indonesian: A comprehensive grammar (2nd ed.). Routledge.

Swales, J. M. (1990). Genre analysis: English in academic and research settings. Cambridge University Press.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.

Vilar, D., Xu, J., D'Haro, L. F., & Ney, H. (2006). Error analysis of statistical machine translation output. Proceedings of LREC 2006, 697–702.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., & Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.




DOI: https://doi.org/10.31764/telaah.v11i2.40598

Refbacks

  • There are currently no refbacks.


_________________________________________________________

Jurnal Ilmiah Telaah
ISSN (Online) 2620-6226 | ISSN (Print) 2477-2429
Email: [email protected]
Tel / fax : (0370)-633723 / (0370)-641906

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