Login/Register

AWEJ for Translation & Literary Studies, Volume 8, Number 2 May 2024 Pp. 165-182

Enhancing Post-Editing Machine Translation Skills Among Iraqi Undergraduate Students

Huda Saad Mudheher Al Sammarraie

College of Dentistry
Al-Mustansiriyah University, Baghdad, Iraq

School of Languages, Literacies and Translation
Universiti Sains Malaysia, Penang, Malaysia
Corresponding Author: mozhgan.ghassemi@gmail.com

Abstract:

Post-editing machine translation often results in discrepancies between industry expectations and the actual quality of the final product. Despite the usually, low-quality outcomes associated with machine translation, it remains underutilized, even when there are opportunities for improvement through post-editing jobs. This study focuses on the impact of technology-assisted training on English proficiency among students. It investigates a standard error observed among Iraqi students during the post-editing of machine translations using Google Translate deciding whether to edit the machine translation output or to translate from scratch and understand the source text. This research involved 20 undergraduates from Al-Mustansiriyah University and employed a mixed methods approach, collecting data through an online questionnaire completed by students in the undergraduate translation program. The findings indicate a significant improvement in the frequency of post-editing skills among Iraqi students, serving as a distinguishing factor between the two groups. It also highlighted differences in translation abilities among students in Iraqi higher education, with notable improvements observed in the experimental group. In summary, this study supports previous research from the last five years, indicating a more favourable attitude toward machine translation and suggesting that post-editing is considered an essential sub-competency in translation education.

Cite as:

Mudheher, H.S.,  & Ghassemiazghandi, M.  (2024). Enhancing Post-Editing Machine Translation Skills Among Iraqi Undergraduate Students. Arab World English Journal for Translation & Literary Studies 8 (1) 165-182.

References:

Abdelaal, N. M., & Alazzawie, A. (2020). Machine Translation: The Case of Arabic-English Translation of News Texts. Theory & Practice in Language Studies, 10(4). https://doi.org/10.17507/tpls.1004.09

Allen, J. (2003). Post-editing. Benjamins Translation Library, 35, 297-318. https://doi.org/10.1075/btl.35.19all 

Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. Routledge. https://doi.org/10.4324/9781003191018

Arenas, A. G. (2019). Pre-editing and post-editing. The Bloomsbury companion to language industry studies, 333. https://doi.org/10.5040/9781350024960.0019

Cheng, Y., Yue, S., Li, J., Deng, L., & Quan, Q. (2021). Errors of machine translation of terminology in the patent text from English into Chinese. ASP Transactions on Computers, 1(1), 12-17. https://doi.org/10.52810/tc.2021.100022

Cid, C. G., Colominas, C., & Oliver, A. (2020). Language industry views on the profile of the post-editor. Translation Spaces, 9(2), 283-313. https://doi.org/10.1075/ts.19010.cid

Cid-Leal, P., Espín-García, M. C., & Presas, M. (2019). Traducción automática y posedición: perfiles y competencias en los programas de formación de traductores. https://doi.org/10.6035/monti.2019.11.7

Daems, J., Carl, M., Vandepitte, S., Hartsuiker, R., & Macken, L. (2016). The effectiveness of consulting external resources during translation and post-editing of general text types. New Directions in Empirical Translation Process Research: Exploring the CRITT TPR-DB, 111-133. https://doi.org/10.1007/978-3-319-20358-4_6

De Grez, L., Valcke, M., & Roozen, I. (2009). The impact of goal orientation, self-reflection, and personal characteristics on acquiring oral presentation skills. European journal of psychology of education, 24(3), 293-306. https://doi.org/10.1007/bf03174762

Depraetere, I. (2010, May). What counts as useful advice in a university post-editing training context? Report on a case study. In Annual conference of the European Association for Machine Translation, (pp. endling). https://doi.org/10.3115/980491.980542  

Diab, N. (2021). Out of the BLEU: An error analysis of statistical and neural machine translation of Wiki How articles from English into Arabic. CDELT Occasional Papers in the Development of English Education, 75(1), 181-211. https://doi.org/10.21608/opde.2021.208437

Fakih, A., Ghassemiazghandi, M., Fakih, A. H., & Singh, M. K. (2024). Evaluation of Instagram’s Neural Machine Translation for Literary Texts: An MQM-Based Analysis.  https://doi.org/10.17576/gema-2024-2401-13 

Farhana, B. C. D., Baharuddin, W. A. L., & Farmasari, S. (2023). Academic text quality improvement by English department students at the University of Mataram: A study on pre-editing of Google neural machine translation. Jurnal Ilmiah Profesi Pendidikan, 8(1), 247-254. http://doi.org/10.29303/jipp.v8i1.1186

Ghassemiazghandi, M. (2023). Machine Translation of Selected Ghazals of Hafiz from Persian into English. AWEJ for Translation & Literary Studies, 7(1).  https://doi.org/10.24093/awejtls/vol7no1.17

Green, S., Heer, J., & Manning, C. D. (2013, April). The efficacy of human post-editing for language translation. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 439-448). https://doi.org/10.1145/2470654.2470718

Guerberof, A. (2008). Productivity and quality in the post-editing of outputs from translation memories and machine translation. Localisation Focus. The International Journal of Localisation, 7(1), 11-21.  https://doi.org/10.1515/scp-2017-0007 

Guerberof, 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/10.18298/ijlet.3242 

Hussain, A. E., & Khuddro, A. (2016). English Arabic cultural effect in translation: A relevance theory perspective. International Journal of English Language and Linguistics Research, 4(1), 31-44. https://doi.org/10.7575/aiac.ijalel.v.5n.3p.212 

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. The Journal of Specialised Translation, 31(1), 60-86. https://doi.org/10.1007/s10590-019-09229-6 

Kenny, D. (2020). Machine Translation. In M. Baker & G. Saldanha (eds.), Routledge Encyclopedia of Translation Studies (3rd ed., pp. 305-310). Routledge. https://doi.org/10.4324/9781315678627-65 

Kiraly, D. (2014). A social constructivist approach to translator education: Empowerment from theory to practice. Routledge. https://doi.org/10.4324/9781315760186 

Klimova, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez-Stockhammer, C. (2023). Neural machine translation can be done foreign language teaching and learning: A systematic review. Education and Information Technologies, 28(1), 663-682. http://doi.org/10.1007/s10639-022-11194-2

Krings, H. P. (2001). Repairing texts: Empirical investigations of machine translation post-editing processes (Vol. 5). Kent State University Press. https://doi.org/10.7202/008026ar 

Lee, S-M. (2023). The effectiveness of machine translation in foreign language education: A systematic review and meta-analysis. Computer Assisted Language Learning, 36(1-2), 103-125. https://doi.org/10.1080/095882 21.2021.1901745

Li, D. (2013). Teaching Business Translation: A task-based method. https://doi.org/10.1080/13556509.2013.10798841

Liang, Y., & Han, W. (2022). Source text pre-editing versus target text post-editing in using Google Translate to provide health services to culturally and linguistically diverse clients. Science, Engineering and Health Studies, 16, 1-5. https://doi.org/10.14456/sehs.2022.25

Lihua, Z. (2022). The Relationship between Machine Translation and Human Translation under the Influence of Artificial Intelligence Machine Translation. Mobile Information Systems, 2022.  https://doi.org/10.1155/2022/9121636 

Mohammed, A. S., & Jamal, M. (2023). Post-Editing of Neural Machine Translation of the Novel “Murder of The Bookseller” from Arabic into English. https://doi.org/10.36777/ijollt2023.6.2.080

Moorkens, J. (2018). What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise. The Interpreter and Translator Trainer, 12(4), 375-387. https://doi.org/10.1080/1750399x.2018.1501639 

Munday, J., & Vasserman, E. (2022). The name and nature of translation studies: A reappraisal. Translation and Translanguaging in Multilingual Contexts, 8(2), 101-113. https://doi.org/10.1075/ttmc.00089.mun 

Nunes Vieira, L., & Alonso, E. (2020). Translating perceptions and managing expectations: an analysis of management and production perspectives on machine translation. Perspectives, 28(2), 163-184. https://doi.org/10.1080/0907676x.2019.1646776 

O’Brien, S. (2005). Methodologies for measuring the correlations between post-editing effort and machine translatability. Machine translation, 19, 37-58. https://www.jstor.org/stable/20060468

O’Brien, S. (2011). Towards predicting post-editing productivity. Machine translation, 25, 197-215. https://doi.org/10.1007/s10590-011-9096-7 

Okpor, M. D. (2014). Machine translation approaches: issues and challenges. International Journal of Computer Science Issues (IJCSI), 11(5), 159. https://doi.org/10.20943/01201702.5457 

Organització Internacional per a la Normalització. (2017). ISO 18587: Translation Services: Post-editing of Machine Translation Output: Requirements. ISO. https://doi.org/10.3403/30279909u 

Pérez, C. R. (2024). Re-thinking Machine Translation Post-Editing Guidelines. The Journal of Specialised Translation, (41), 26-47. https://doi.org/10.26034/cm.jostrans.2024.4696 

Plitt, M., & Masselot, F. (2010). A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context. Prague Bull. Math. Linguistics, 93, 7-16. https://doi.org/10.2478/v10108-010-0010-x 

Popović, M. (2018). Error classification and analysis for machine translation quality assessment. Translation quality assessment: From principles to practice, 129-158.  https://doi.org/10.1007/978-3-319-91241-7_7 

Pym, A. (2013). Translation skill sets in a machine-translation age. Meta, 58(3), 487-503. https://doi.org/10.7202/1025047ar  

Qianqian. S, )2023(. Development of post-editing skills in machine translation. Region – Educational Research and Reviews, 5(3), 25-130. https://doi.org/10.32629/rerr.v5i3.1325   

Rico Pérez, C., & Torrejón, E. (2012). Skills and Profile of the New Role of the Translator as MT Post-editor. Tradumàtica, (10), 0166-178. https://doi.org/10.5565/rev/tradumatica.18 

Sabtan, Y. M. N. (2018). Towards Corpus-Based Stemming for Arabic Texts. International Journal of Linguistics, Literature and Translation, 1(4), 119-129. https://doi.org/10.4324/9781351063388-14 

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). https://doi.org/10.18298/ijlet.3242

Sánchez-Gijón, P., Moorkens, J., & Way, A. (2019). Post-editing neural machine translation versus translation memory segments. Machine Translation, 33(1-2), 31-59. https://doi.org/10.1007/s10590-019-09232-x   

Schulz, B. (2008). The importance of soft skills: Education beyond academic knowledge. https://doi.org/10.1080/03797720802522627   

Sin-wai, C. (2016). The future of translation technology: Towards a world without Babel. Taylor & Francis. Routledge. https://doi.org/10.4324/9781315731865 https://doi.org/10.4324/9781315731865

Subramaniam, I., & Zainal, E. Z. (2023). An Analysis of Translation Procedures of Cultural-specific Items from English into Arabic Applied in “Sunshine in the Rain: A Maid’s Courage.” International Journal of Language Education and Applied Linguistics13(2), 19-27. https://doi.org/10.15282/ijleal.v13i2.9632

Sudoh, K., Takahashi, K., & Nakamura, S. (2021, April). Is this translation error critical? Classification-based human and automatic machine translation evaluation focusing on critical errors. In Proceedings of the Workshop on Human Evaluation of NLP Systems (pp. 46-55). https://doi.org/10.18653/v1/2021.eval4nlp-1.15  

Tsuji, K. (2024). Identifying MT Errors for Higher-Quality Second Language Text. International Journal of Translation, Interpretation, and Applied Linguistics (IJTIAL)6(1), 1-12. https://doi.org/10.4018/ijtial.335899  

Vilar, D., Xu, J., Luis Fernando, D. H., & Ney, H. (2006, May). Error Analysis of Statistical Machine Translation Output. In LREC (pp. 697-702).  https://doi.org/10.3115/1654650.1654652 

Wagner, E. (1985). Post-editing Systran, a challenge for Commission Translators. Terminologies et reduction, (3), 1-7. https://doi.org/10.7202/037047ar 

Yamada, M. (2015). Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings. Machine Translation, 29, 49-67. https://doi.org/10.1007/s10590-014-9167-7 

Yamada, M. (2021). Post-editing and a sustainable future for translators. Journal of Foreign Language Studies, 24, 83-105. https://doi.org/10.32286/00023085

Yang, Z., & Mustafa, H. R. (2022). On Postediting of machine translation and workflow for undergraduate translation program in China. Human Behavior and Emerging Technologies2022, 1-11. https://doi.org/10.1155/2022/5793054 

Yehia Emara, N. (2023). Using Machine Translation Error Identification to Improve Translation Students’ Post-Editing Skills. Transcultural Journal of Humanities and Social Sciences4(1).  https://doi.org/10.21608/tjhss.2023.289357 

Zheng, Y., Peng, C., & Mu, Y. (2022). Designing controlled Chinese rules for MT pre-editing of product description text. International Journal of Translation, Interpretation, and Applied Linguistics (IJTIAL), 4(2), 1-13. http://doi.org/10.4018/IJTIAL.313919

Facebook
Twitter
LinkedIn
Tumblr
Reddit
Email
StumbleUpon
Digg

Huda Saad Mudheher Al Sammarraie is a Ph.D. candidate at the School of Languages, Literacies, and Translation at Universiti Sains Malaysia (USM). She is currently working as an assistant lecturer in English Language at the College of Dentistry, Al-Mustansiriyah University, Baghdad, Iraq. Her areas of interest include the translation of technology, eye tracking, post-editing of machine translation, and Arabic-English translation. ORCid ID: https://orcid.org/0009-0007-1747-1887

 Mozhgan Ghassemiazghandi holds a Ph.D. in Translation and is currently a senior lecturer at the School of Languages, Literacies, and Translation at Universiti Sains Malaysia (USM). Her areas of interest include translation technology, machine translation, and audiovisual translation. Mozhgan is also an experienced translator and subtitler with over a decade of experience. ORCID ID: https://orcid.org/0000-0002-3038-3124