How Training Enhances Post-Editing Speed In Iranian BA Translation Students
A Case of Google Translated Financial Texts
DOI:
https://doi.org/10.7146/hjlcb.vi66.156454Keywords:
Financial Text, Google Translate, Machine Translation, Post-Editing, Translation TrainingAbstract
The increasing adoption of machine translation post-editing (PE) in the language industry underscores the need for effective training to improve PE speed. However, empirical evidence on the effectiveness of PE training remains limited, particularly for the English–Persian language pair. This study investigated the effect of a guideline-based training program on PE speed among undergraduate English Translation students in Iran. Participants were randomly assigned to either an experimental group (n = 10), which received a structured 16-session PE training program based on the TAUS (2017) PE guidelines, or a control group (n = 10), which received no formal PE instruction. Before the intervention, participants completed a 15-item Likert-scale questionnaire assessing their perceptions of PE speed and efficiency. Following the training period, participants post-edited the same 400-word English–Persian financial text, pre-translated using Google Translate, on the MateCat platform. PE speed was measured using actual task completion time and words per minute (WPM), calculated from the MateCat editing log. The experimental group completed the task significantly faster than the control group (M = 44.20 vs. 68.90 min), Welch's t(15.61) = −7.28, p < .001, d = 3.25, and achieved a significantly higher WPM rate (9.20 vs. 5.89), Welch's t(14.97) = 7.13, p < .001, d = 3.19. Participants in the experimental group also reported significantly more positive perceptions of PE speed and efficiency, controlling for pre-test scores (ANCOVA, p = .002). These findings support the integration of guideline-based PE training into translator education.
References
Al Sammarraie, H. S. M. (2025). Challenges and strategies in post-editing English into Arabic Neural Machine Translations of movie subtitles. Journal of Modern Languages, 35(1), 139-164. https://doi.org/10.22452/jml.vol35no1.7
Al Sammarraie, H. S. M., & Ghassemiazghandi, M. (2024). Enhancing post-editing machine translation skills among Iraqi undergraduate students. Arab World English Journal for Translation & Literar Studies, 8(2), 165–182. https://doi.org/10.24093/awejtls/vol8no2.12
Ali, A. F., & Alhebshi, S. H. S. (2026). The Use of AI in Translating Economic and Financial Texts. In D. Chen, A. Sun & D. Li (Eds.), Translating Finance and Economics: Theory, Practice, Training and Technology (pp. 99-128). Springer Nature Singapore.
Allen, J. (2001). Postediting: An integrated part of a translation software program. Language International, 13(2), 26–29.
Alvarez-Vidal, S., & Oliver, A. (2023). Assessing MT with measures of PE effort. Ampersand, 11, Article 100125. https://doi.org/10.1016/j.amper.2023.100125
Aranberri, N. (2017). What do professional translators do when post-editing for the first time? First insight into the Span-ish-Basque language pair. HERMES – Journal of Language and Communication in Business, (56), 89–110. https://doi.org/10.7146/hjlcb.v0i56.97235
Arjani, S. H., & Jamshidiha, M. (2024). A comparison of the quality and speed of post-edited translation and human-initiated translation by novice translators. Iranian Journal of Translation Studies, 21(84), 54-73. https://journal.translationstudies.ir/ts/article/view/1074
Asadi, V., Shokpour, N., O’Neill, S., Dann, C., & Wang, J. (2023). Neural machine translation and post-editing: Improv-ing translation of domain specific English terms for a low resource language [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3145838/v1
Bar-Hillel, Y. (1960). The present status of automatic translation of languages. Advances in Computers, 1, 91–163. https://doi.org/10.1016/S0065-2458(08)60607-5
Bastan, M., Khadivi, S., & Homayounpour, M. M. (2017). Neural machine translation on scarce-resource condition: A case study on Persian-English. In 2017 Iranian Conference on Electrical Engineering (ICEE) (pp. 1485-1490). IEEE. https://doi.org/10.1109/iraniancee.2017.7985278
Blagodarna, O. (2018). Enhancement of post-editing performance: Introducing machine translation post-editing in transla-tor training [Doctoral dissertation, Universitat Autònoma de Barcelona]. UAB. http://hdl.handle.net/10803/666847
Blagodarna, O. (2020). Acquisition of post-editing competence: Training proposal and its outcomes. In C. Szabó, M. Fisch-er, & R. Besznyák (Eds.), Fit-for-market translator and interpreter training in a digital age (pp. 85–102). Vernon Press.
Borucinsky, M., Kegalj, J., & Vukalović, N. (2022). A review of computer-assisted translation (CAT) tools used by Croa-tian translators. Hieronymus: Journal of Translation Studies and Terminology, (9), 83–105. https://doi.org/10.17234/Hieronymus.9.4
Bowker, L. (2019). Fit-for-purpose translation. In M. O’Hagan (Ed.), The Routledge handbook of translation and technol-ogy (1st ed., pp. 453–468). Routledge.
Carl, M., Dragsted, B., Elming, J., Hardt, D., & Jakobsen, A. L. (2011). The process of post-editing: A pilot study. Copen-hagen Studies in Language, 41(1), 131–142.
Çetiner, C., & İşisağ, K. (2019). Undergraduate-level translation students' attitudes towards machine translation post-editing training. International Journal of Languages' Education and Teaching, 7(1), 110–120.
Costa, F. A., Ferreira, T. C., Pagano, A. S., & Meira, W. (2021). Exploring semantic annotations to measure post-editing quality. In M. Ji & M. P. Oakes (Eds.), Corpus exploration of lexis and discourse in translation (pp. 83–104). Routledge.
de Almeida, G., & O'Brien, S. (2010). Analysing post-editing performance: Correlations with years of translation experi-ence. In Proceedings of the 14th Annual Conference of the European Association for Machine Translation (pp. 27–32). European Association for Machine Translation.
Daems, J., Vandepitte, S., Hartsuiker, R. J., & Macken, L. (2017). Identifying the machine translation error types with the greatest impact on post-editing effort. Frontiers in Psychology, 8, 1282. https://doi.org/10.3389/fpsyg.2017.01282
Dede, V., & Antonova-Ünlü, E. (2022). Does a formal post-editing training affect the performance of novice post-editors? An experimental study. Cankaya University Journal of Humanities and Social Sciences, 16(2), 131–148. https://doi.org/10.47777/cankujhss.1106326
Devatine, N., & Abraham, L. (2024). Assessing human editing effort on LLM-generated texts via compression-based edit distance (arXiv:2412.17321). arXiv. https://doi.org/10.48550/arXiv.2412.17321
Deoghare, S., Kanojia, D., & Bhattacharyya, P. (2024). Together we can: Multilingual automatic post-editing for low-resource languages (arXiv:2410.17973). arXiv. https://doi.org/10.48550/arXiv.2410.17973
Doherty, S., & Kenny, D. (2014). The design and evaluation of a statistical machine translation syllabus for translation students. The Interpreter and Translator Trainer, 8(2), 295–315. https://doi.org/10.1080/1750399x.2014.937571
Eljazouli, A., & Azmi, N. (2024). Linguistic and terminological complexities in post-editing English-Arabic machine trans-lations. International Journal of Language and Literary Studies, 6(3), 16–29. https://doi.org/10.36892/ijlls.v6i3.1775
Emara, N. Y. (2023). Using Machine Translation Error Identification to Improve Translation Students’ Post-Editing Skills. Transcultural Journal of Humanities and Social Sciences, 4(1), 345–357. https://doi.org/10.21608/tjhss.2023.289357
Garcia, I. (2011). Translating by post-editing: Is it the way forward? Machine Translation, 25, 217–237. https://doi.org/10.1007/s10590-011-9115-8
Goldfarb, S. (2024, February 2). Treasury yields rebound after Fed signals higher rates ahead. The Wall Street Journal. https://www.wsj.com/economy/treasury-yields-rebound-after-fed-signals-higher-rates-ahead-b12dd481
González-Pastor, D. (2024). La traducción automática y la formación de traductores en España: Perspectivas desde la indus-tria y el ámbito académico. Mutatis Mutandis: Revista Latinoamericana de Traducción, 17(1), 117-142.
Google. (n.d.). Google Translate [Software]. Retrieved September 25, 2024, from https://translate.google.com/
Green, S., Heer, J., & Manning, C. D. (2013). The efficacy of human post-editing for language translation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 439–448). ACM. https://doi.org/10.1145/2470654.2470718
Gu, Q., Wang, S., Ren, X., & Ji, X. (2025). Post-editing of AI-assisted Translation in Translator Education: A Genre-based Classroom Study. Journal of Historical, Cultural and Social Sciences, 1(2), 10-22.
Guerberof Arenas, A., & Moorkens, J. (2019). Machine translation and post-editing training as part of a master's program. JoSTrans: The Journal of Specialised Translation, (31), 217–238. https://doi.org/10.26034/cm.jostrans.2019.184
Harto, S., Hamied, F. A., Musthafa, B., & Setyarini, S. (2022). Exploring undergraduate students' experiences in dealing with post-editing of machine translation. Indonesian Journal of Applied Linguistics, 11(3), 696–707. https://doi.org/10.17509/ijal.v11i3.42825
Jia, Y., Carl, M., & Wang, X. (2019). How does the post-editing of neural machine translation compare with from-scratch translation? A product and process study. JosTrans. The Journal of Specialised Translation, (31), 60–86. https://doi.org/10.26034/cm.jostrans.2019.177
Jia, Y., & Sun, S. (2023). Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing. Perspectives, 31(5), 950–968. https://doi.org/10.1080/0907676X.2022.2129028
Jovein, S. K., Sharifabad, E. D., & Yazdani, M. (2024). On the relationship between university translator training programs and the translation market requirements: The case of English translation graduates and postgraduates of Imam Reza In-ternational University. Journal of Critical Studies in Language and Literature, 5(6), 1-13.
Kenny, D. (2022). Human and machine translation. In D. Kenny (Ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence (pp. 23–49). Language Science Press. https://doi.org/10.5281/zenodo.6759976
Khoury, O., Alkhawaja, L., Ghnaim, F., Awwad, S., & Dudeen, H. (2024). Post-editing a Google Translate output: Experi-enced translators vs. trainees. Theory and Practice in Language Studies, 14(6), 1745–1756.
Kim, H. S., & Cha, Y. (2023). Post-editing of machine translation while reading on English proficiency levels. Linguistic Research, 40(3), 517–541.
Koehn, P. (2009). A process study of computer-aided translation. Machine Translation, 23(4), 241–263. https://doi.org/10.1007/s10590-010-9076-3
Koglin, A. (2015). An empirical investigation of the cognitive effort required to post-edit machine-translated metaphors compared to the translation of metaphors. Translation & Interpreting: The International Journal of Translation and In-terpreting Research, 7(1), 126–141.
Koponen, M., Salmi, L., & Nikulin, M. (2019). A product and process analysis of post-editor corrections on neural, statisti-cal and rule-based machine translation output. Machine Translation, 33(1–2), 61–90. https://doi.org/10.1007/s10590-019-09228-7
Koskinen, K., & Ruokonen, M. (2017). Love letters or hate mail? Translators' technology acceptance in the light of their emotional narratives. In Kenny, D. (ed), Human issues in translation technology (pp. 26–42). Routledge.
Krings, H. P. (2001). Repairing texts: Empirical investigations of machine translation post-editing processes. Kent State University Press.
Kriston, A. (2017). Machine Translation in Translating Business Texts. Professional Communication and Translation Studies, (10), 126-136.
Läubli, S., Amrhein, C., Düggelin, P., Gonzalez, B., Zwahlen, A., & Volk, M. (2019). Post-editing productivity with neural machine translation: An empirical assessment of speed and quality in the banking and finance domain. In Proceedings of Machine Translation Summit XVII: Research Track (pp. 267–272). European Association for Machine Translation.
MateCat. (n.d.). MateCat [Software]. Retrieved September 25, 2024, from https://www.matecat.com
Moorkens, J., & O'Brien, S. (2015). Post-editing evaluations: Trade-offs between novice and professional participants. In Proceedings of the 18th Annual Conference of the European Association for Machine Translation (75-81). European Association for Machine Translation.
Nitzke, J. (2019). Problem-solving activities in post-editing and translation from scratch: A multi-method study. Language Science Press.
O'Brien, S. (2011). Towards predicting post-editing productivity. Machine Translation, 25(3), 197–215.
O'Brien, S. (2021). Post-editing. In L. van Doorslaer & Y. Gambier (eds.), Handbook of Translation Studies (pp. 178-184). John Benjamins.
Ortiz-Boix, C., & Matamala, A. (2017). Assessing the quality of post-edited wildlife documentaries. Perspectives, 25(4), 571–593.
Pérez, C. R. (2024). Re-thinking machine translation post-editing guidelines. JosTrans. The Journal of Specialised Transla-tion, (41), 26–47. https://doi.org/10.26034/cm.jostrans.2024.4696
Pilevar, M. T., & Faili, H. (2010). PersianSMT: A first attempt to English-Persian statistical machine translation. In Pro-ceedings of the 10th International Conference on Statistical Analysis of Textual Data (JADT 2010) (pp. 1101–1112).
Plitt, M., & Masselot, F. (2010). A productivity test of statistical machine translation post-editing in a typical localisation context. Prague Bulletin of Mathematical Linguistics, (93), 7–16.
Rakhimova, D., Karibayeva, A., & Turarbek, A. (2024). The task of post-editing machine translation for the low-resource language. Applied Sciences, 14(2), Article 486. https://doi.org/10.3390/app14020486
Samman, H. M. (2022). Evaluating machine translation post-editing training in undergraduate translation programs: An exploratory study in Saudi Arabia (Doctoral dissertation). University of Southampton.
Sanayai Meetei, L., Singh, T. D., Bandyopadhyay, S., Vela, M., & van Genabith, J. (2020). English to Manipuri and Mizo post-editing effort and its impact on low-resource machine translation. In Proceedings of the 17th International Confer-ence on Natural Language Processing (ICON) (pp. 50–59). NLP Association of India.
Sánchez Ramos, M. M. (2025). Machine translation post-editing through emotional narratives: A methodological approach. Translation and Translanguaging in Multilingual Contexts, 11(1), 31–47.
Saricaoglu, A., & Dinçer, B. H. (2025). Machine translation post-editing behavior of undergraduate EFL students across two genres. CALICO Journal, 42(1), 121–142. https://doi.org/10.3138/calico-2024-1225
Schäfer, F. (2003). MT post-editing: how to shed light on the “unknown task”. In Experiences at SAP.EAMT Workshop: Improving MT through other language technology tools: resources and tools for building MT.
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., & Makhoul, J. (2006). A study of translation edit rate with targeted human annotation. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers (pp. 223–231). AMTA.
Stasimioti, M., & Sosoni, V. (2019). Undergraduate translation students' performance and attitude vis-à-vis machine transla-tion and post-editing: Does training play a role? In Proceedings of the Translating and the Computer Conference (Vol. 41, pp. 125–136). AsLing.
TAUS. (2017). TAUS quality evaluation using an error typology approach. https://cdn2.hubspot.net/hubfs/2734675/Reports,%20ebooks/Error%20Typology%20Best%20Practices%20Guidelines.pdf
Temnikova, I. P. (2010). Cognitive evaluation approach for a controlled language post-editing experiment. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10).
Terribile, S. (2023). Is post-editing really faster than human translation? Translation Spaces, 13(2), 171–199. https://doi.org/10.1075/ts.22044.ter
Toral, A., Wieling, M., & Way, A. (2018). Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5, Article 9. https://doi.org/10.3389/fdigh.2018.00009
Torrejón, E., & Rico, C. (2002). Controlled translation: a new teaching scenario tailor-made for the translation industry. In Proceedings of the 6th EAMT Workshop: Teaching Machine Translation (107-116). EAMT.
Vashee, K. (2013). Understanding the economics of machine translation. Translation Spaces, 2(1), 125–149. https://doi.org/10.1075/ts.2.07vas
Wang, L., & Wang, X. (2021). Building virtual communities of practice in post-editing training: A mixed-method quasi-experimental study. JosTrans. The Journal of Specialised Translation, (36), 193-219. https://doi.org/10.26034/cm.jostrans.2021.041
Yamada, M. (2019). The impact of Google neural machine translation on post-editing by student translators. JosTrans. The Journal of Specialised Translation, (31), 87–106. https://doi.org/10.26034/cm.jostrans.2019.178
Yngve, V. H. (1954). The machine and the man. Mechanical Translation and Computational Linguistics, 1(2), 20–22.
Zhang, H., & Torres-Hostench, O. (2022). Training in machine translation post-editing for foreign language students. Lan-guage Learning & Technology, 26(1), 1–17. https://doi.org/10.64152/10125/73466
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