Bir meslek olarak çevirinin sürdürülebilirliği: Makine çevirisindeki gelişmeler ışığında çevirmenlerin değişen rolleri

Öz

Son yıllarda makine çevirisindeki gelişmelerin bir meslek olarak çeviri üzerinde ciddi etkileri olmuştur. Bu gelişmeler ışığında çevirmenlerin yeni roller üstlenmesi ve yeni beceriler edinmesi beklenmektedir. Çevirmenler artık sadece kaynak bir metin üzerinde çalışmamaktadır. Çevirmenler artık çoğu çeviri iş akış şemasında ham makine çevirisi çıktılarıyla karşı karşıya kalmaktadır. Bu yüzde makine çevirisiyle desteklenen böyle bir çeviri bağlamında çevirmenlerin bu ham makine çıktılarını önceden belirlenmiş bazı kriterlere göre düzeltmeleri beklenmektedir. Bu durum çevirmenlerin bir kaynak metni sıfırdan çevirdiği alışılagelmiş çeviri iş akış şemasına göre oldukça farklı görünmektedir. Makine çevirisini düzeltme işlemi müşterinin beklentilerine ve metnin amacına göre farklı seviyelerde olabilmektedir. Böyle bir durumda çevirmenlerin sahip olması gereken beceri ve edinçler önemli hale gelmektedir. Bu bakımdan bu çalışma makine çevirisinde görülen gelişmeler ışığında ve çevirmen edinçleri kapsamında çevirmenlerin değişen rollerini incelemeyi amaçlamaktadır. Bu amaçla ilk olarak makine çevirisindeki bu gelişmeler ve bunların bir meslek olarak çeviri üzerine etkileri çevirmen edinçleri kapsamında ele alınmıştır. Daha sonra PACTE ve EMT tarafından tasarlanan edinç şemalarına dayanarak çevirmenlerden ve makine çevirisi düzeltmenlerinden beklenen edinçler karşılaştırılmıştır. Çalışma makine çevirisinin baskın olduğu bu çağda çevirmenlerin değişen rolleri dikkate alınarak çevirmen edinçlerinin yeniden tanımlanması sonucuna varmıştır.

Anahtar Kelimeler

• Makine Çevirisi
• çeviride sürdürülebilirlik
• çevirmenlerin değişen rolleri
• çeviri edinci

Sustainability of translation as a profession: Changing roles of translators in light of the developments in machine translation systems

Abstract

Translation as a profession has been radically affected by the developments in machine translation systems in recent years. In light of these developments, translators are expected to assume new roles and acquire new skills. Translators no longer work on only a source text. They are faced with raw machine translation outputs in many translation workflows. Thus, in a setting supported by machine translation, the translators are required to post-edit these outputs according to some pre-defined criteria, which sounds very different compared to traditional translation workflow in which translators translate a source text from scratch. Post-editing can be at different levels depending on the expectations of the customer and the intended purpose of the text. As such, the skills and competences that translators must have become prominent. In this regard, this study aims to address the changing role of translators within the scope of translator competences considering the developments seen in machine translation systems. To this end, initially, the developments in machine translation systems and their effects on translation as a profession are discussed with an emphasis on translator competences. Moreover, post-editing levels and criteria for these levels are also addressed with regard to these skills and competences. Then, the competences required of translators and post-editors are compared building on the competence frameworks designed by PACTE and EMT groups. The research concludes that the translator competences should be redefined considering the changing roles of translators in an era dominated by machine translation systems.

Keywords

• Machine Translation
• sustainability in translation
• changing role of translators
• translator competence

References

1. Albir, A. H. (2007). Competence-based Curriculum Design for Training Translators. The Interpreter and Translator Trainer, 1(2), 163–195. https://doi.org/10.1080/1750399X.2007.10798757
2. Alcina, A. (2008). Translation technologies: Scope, tools, and resources. Target: International Journal on Translation Studies, 20(1), 79–102.
3. Allen, J. (2003). Post-Editing. In H. Somers (Ed.), Computers and Translation (pp. 297–319). Amsterdam: John Benjamins Publishing Company.
4. Almeida, G. de. (2013). Translating the post-editor: An investigation of post-editing changes and correlations with professional experience across two Romance languages (Ph.D. Thesis). Dublin City University, Dublin.
5. Arianna, L. P. (2018). Determining translators’ perception, productivity and post-editing effort when using SMT and NMT systems. 21st Annual Conference of the European Association for Machine Translation. Presented at the Alacant, Spain. Alacant, Spain: Universitat d’Alacant Alacant.
6. Austermühl, F. (2011). On clouds and crowds: Current developments in translation technology. Translation in Transition, 9, 1–26.
7. Austermühl, F. (2013). Future (and not-so-future) trends in the teaching of translation technology. Revista Tradumàtica: Tecnologies de La Traducció, (11), 326–337.
8. Bowker, L. (2002). Computer-aided translation technology: A practical introduction. Ottawa: University of Ottawa Press.

9. Bowker, L. (2003). Terminology tools for translators. In H. Somers (Ed.), Computers and Translation (pp. 297–319). Amsterdam: John Benjamins Publishing Company.
10. Bowker, L. (2005). Productivity vs quality? A pilot study on the impact of translation memory systems. Localization Focus, 4(1), 13–20.
11. Bowker, L., & Fisher, D. (2010). Computer-aided translation. In Y. Gambier & L. Van Doorslaer (Eds.), Handbook of translation studies. Vol. 1. Amsterdam: Benjamins.
12. Çetiner, C., & İşisağ, K. U. (2019). Undergraduate level translation students’ attitudes towards machine translation post-editing training. International Journal of Languages’ Education and Teaching, 7(1), 110–120.
13. Christensen, T., & Schjoldager, A. (2010). Translation-Memory (TM) Research: What Do We Know and How Do We Know It? Hermes – Journal of Language and Communication Studies, 44.
14. Ehrensberger-Dow, M., & Massey, G. (2013). Indicators of translation competence: Translators’ self-concepts and the translation of titles. Journal of Writing Research, 5(1), 103–131.
15. EMT. (2009). Competences for professional translators, experts in multilingual and multimedia communication. Brussels: European Commission. Retrieved from European Commission website: https://ec.europa.eu/info/sites/default/files/emt_competences_translators_en.pdf
16. EMT Board. (2017). European master’s in the translation competence framework. Brussels: European Commission.
17. Fiederer, R., & O’Brien, S. (2009). Quality and Machine Translation: A realistic objective? The Journal of Specialised Translation, 11(1), 52–74.
18. Flanagan, M., & Christensen Paulsen, T. (2014). Testing post-editing guidelines: How translation trainees interpret them and how to tailor them for translator training purposes. The Interpreter and Translator Trainer, 8(2), 257–275.
19. Forcada, M. L. (2017). Making sense of neural machine translation. TS Translation Spaces, 6(2), 291–309.
20. Fullford, H. (2002). Freelance translators and machine translation: An investigation of perceptions, uptake, experience and training needs. Teaching Machine Translation, 117–122. Manchester.
21. García, I. (2006). Translators on translation memories: A blessing or a curse? Translation Technology and Its Teaching, Tarragona: Intercultural Studies Group, 97–105.
22. Garcia, I. (2009). Beyond translation memory: Computers and the professional translator. The Journal of Specialised Translation, 12(12), 199–214.
23. Gaspari, F., Toral, A., Naskar, S. K., Groves, D., & Way, A. (2014). Perception vs reality: Measuring machine translation post-editing productivity. In S. O’Brien, M. Simard, & L. Specia (Eds.), Proceedings of the Third Workshop on Post-editing Techniques and Practices (WPTP-3): Canada.
24. Göpferich, S. (2009). Towards a model of translation competence and its acquisition: The longitudinal study Transcomp. In S. Göpferich, A. L. Jakobsen, & I. M. Mees (Eds.), Behind the Mind: Methods, Models and Results in Translation Process Research (pp. 11–37). Samfundslitteratur.
25. Gordin, M. D. (2016). The dostoevsky machine in georgetown: Scientific translation in the cold war. Annals of Science., 208–223.
26. Hutchins, J., & Somers, H. L. (1992). An introduction to machine translation. Manchester: Academic Press.
27. Korošec, M. K. (2011). Applicability and Challenges of Using Machine Translation in Translator Training. ELOPE: English Language Overseas Perspectives and Enquiries, 8(2), 7–18.
28. Krings, H. P. (2001). Repairing Texts: Empirical Investigations of Machine Translation Post-editing Processes. Ohio: The Kent State University Press.
29. Lauffer, S. C. (2006). Translating with computer-assisted technology: A study of translator experience. (Master Thesis). Library and Archives Canada, Ottawa.
30. Melby, A. (1992). The translator workstation. In J. Newton (Ed.), Computers in Translation A Practical Appraisal. London: Routledge.
31. Mellinger, C. D. (2014). Computer-Assisted Translation: An Empirical Investigation of Cognitive Effort (Ph.D. Thesis). Kent State University, Ohio.
32. Mellinger, C. D. (2017). Translators and machine translation: Knowledge and skills gaps in translator pedagogy. Interpreter and Translator Trainer, 11(14), 280–293.
33. Nitzke, J., Hansen-Schirra, S., & Canfora, C. (2019). Risk management and post-editing competence. The Journal of Specialised Translation, 31, 239–259.
34. Olohan, M. (2021). Post-editing: A genealogical perspective on translation practice. In M. Bisiada (Ed.), Empirical studies in translation and discourse (Translation and Multilingual Natural Language Processing 14) (pp. 1–25). Berlin: Language Science Press.
35. PACTE. (2003). Building a translation competence model. In F. Alves (Ed.), Triangulating Translation: Perspectives in process oriented research (Vol. 45, pp. 43–68). John Benjamins.
36. Pym, A. (2003). Redefining translation competence in an electronic age. In defence of a minimalist approach. Meta: Journal Des Traducteurs/Meta: Translators’ Journal, 48(4), 481–497.
37. Pym, A. (2011). What technology does to translating. The International Journal for Translation & Interpreting Research, 3(1), 1–9.
38. Quah, C. K. (2006). Translation and technology. New York: Palgrave Macmillan.
39. Reichert, C. (2016, September 28). Google announces Neural Machine Translation to improve Google Translate. Retrieved November 19, 2018, from https://www.zdnet.com/article/google-announces-neural-machine-translation-to-improve-google-translate/
40. Rico, C., & Torrejón, E. (2012). Skills and Profile of the New Role of the Translator as MT Post-editor. Revista Tradumàtica: Tecnologies de La Traducció, (10), 166–178.
41. Robert, I. S., Remael, A., & Ureel, J. J. J. (2017). Towards a model of translation revision competence. The Interpreter and Translator Trainer, 11(1), 1–19. https://doi.org/10.1080/1750399X.2016.1198183
42. Şahin, M. (2015). Çevirmen adaylarının gözünden i̇ngilizce-türkçe bilgisayar çevirisi ve bilgisayar destekli çeviri: Google deneyi. Hacettepe Üniversitesi Çeviribilim ve Uygulamaları Dergisi, 21, 43–60.
43. Silva, R. (2014). Integrating post-editing mt in a professional translation workflow. In S. O’Brien, L. Winther, M. Carl, M. Simard, & L. Specia (Eds.), Post-editing of machine translation: Processes and applications (pp. 2–24). Cambridge: Cambridge Scholars Publishing.
44. Sin-wai, C. (2015). The development of translation technology. In C. Sin-wai (Ed.), The Routledge encyclopedia of translation technology (pp. 3–32). New York: Routledge.
45. Taravella A & Villeneuve A.O. (2013). Acknowledging the needs of computer-assisted translation tools users: The human perspective in human-machine translation. Journal of Specialised Translation, (19), 62–74.
46. Temizöz, Ö. (2016). Postediting machine translation output: Subject-matter experts versus professional translators. Perspectives, 24(4), 646–665. https://doi.org/10.1080/0907676X.2015.1119862
47. Torron-Sanchez, M., & Koehn, P. (2016). Machine Translation Quality and Post-Editor Productivity. AMTA 2016, 16, 16–26. Austin.
48. Witczak, O. (2016). Incorporating post-editing into a computer-assisted translation course. A study of student attitudes. Journal of Translator Education and Translation Studies, 1(1), 33–55.
49. 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(1), 49–67.
50. Yuste, E. (2001). Making MT commonplace in translation training curricula—Too many misconceptions, so much potential. Teaching MT Workshop. Presented at the Machine Translation Summit VII, Santiago de Compostela, Spain.