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Çeviri Odaklı Teknolojik Yeterlilik: Gelenek ve Yenilik Arasındaki Köprü

Year 2025, Volume: 49 Issue: 1, 85 - 95, 27.03.2025

Haldun Vural

Abstract

Bir zamanlar geleneksel bir dilbilim disiplini olan çeviri, teknolojik gelişmelerle şekillenen çok disiplinli bir mesleğe dönüşmüştür. Çevirmenler tarihsel olarak diller ve kültürler arasında iletişimi kolaylaştırmışlardır ve bugün alan çeşitli teorileri içermekte ve uygulama ve eğitimdeki teknoloji odaklı değişikliklere uyum sağlamaktadır. Bu çalışma, teknolojinin çevirmenlerin üretkenliğini, doğruluğunu ve verimliliğini nasıl artırdığını vurgulayarak çeviri odaklı teknolojik yeterliliği araştırmaktadır. Facebook, Instagram ve WhatsApp gibi platformlar da dahil olmak üzere dijital medyanın ve Web 2.0 teknolojilerinin yükselişi, iletişimi devrim niteliğinde değiştirmiş ve yazılım, mobil uygulamalar ve akıllı cihazlar aracılığıyla etkileşimli çeviri çözümleri sunmuştur. Ancak yapay zeka destekli makine çevirisi, karmaşık metinleri yorumlamak için gerekli olan duygusal zekadan ve nüansları anlayıştan yoksundur ve bu da insan çevirmenlerin vazgeçilmez rolünü pekiştirir. Çevirmenler, kalite ve özgünlüğü korumada teknolojik yeterliliğin önemini vurgulayarak, makine tarafından oluşturulan çevirileri gözden geçirmeye ve iyileştirmeye giderek daha fazla odaklanmaktadır. Dijital araçlarda ustalaşarak ve bunların sınırlamalarını tanıyarak, çevirmenler performansı optimize edebilir, hassasiyeti garanti edebilir ve küreselleşmiş bir dünyanın taleplerini karşılayabilir. Bu makale, çevirmenin dijital çağda kritik bir arabulucu olarak rolünün altını çizerek, devam eden beceri geliştirme ve gelişen çeviri uygulamalarına uyum sağlamayı savunurken, kültürel ve bağlamsal olarak uygun çeviriler sunmada insan unsurunun yeri doldurulamaz değerini yeniden teyit etmektedir.

Keywords

çeviride yeterlik teknolojik gelişmeler makine çevirisi yapay zeka web 2.0 araçları

References

  • Albir, A. H., Beeby, A., Fernández, M., Fox, O., KUZNIK, A., Serra, W. N., ... & Wimmer, S. (2011). Results of the validation of the PACTE translation competence model: Translation project and dynamic translation index. In
  • Cognitive explorations of translation (pp. 30-53).
  • Austermuhl, F. (2014). Electronic tools for translators. London: Routledge. https://doi.org/10.4324/9781315760353
  • Baker, M. (1995). Corpora in translation studies: An overview and some suggestions for future research. Target. International Journal of Translation Studies, 7(2), 223-243.
  • Bassnett, S. (2013). Translation studies. Routledge.
  • Bowker, L. (2002). Computer-aided translation technology: A practical introduction. University of Ottawa Press.
  • Bowker, L., & Fisher, D. (2010). Computer-aided translation. Handbook of translation studies, 1, 60-65.
  • Candel-Mora, M. Á. (2017). Criteria for the integration of term banks in the professional translation environment . Sendebar, 28, 243-260.
  • Chan, S. W. (Ed.). (2018). The human factor in machine translation. Routledge.
  • Cronin, M. (2013). Translation and globalization. Routledge.
  • Daems J, Vandepitte S, Hartsuiker RJ and Macken L (2017). Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort. Front. Psychol. 8, 1282. doi: 10.3389/fpsyg.2017.01282
  • Froeliger, N., Krause, A., & Salmi, L. (2023). Institutional translation–EMT Competence Framework and beyond. Institutional Translator Training, Routledge, 13-29.
  • Gouadec, D. (2007). Translation As A Profession, Amsterdam/ Philadelphia: John
  • Heletka, M. L. (2022). The concept of modern electronic translation dictionaries. Publishing House “Baltija Publishing”.
  • Heyn, M. (2016). Translation memories: Insights and prospects. In Unity in diversity (pp. 123-136). Routledge.
  • Hu, K. (2016). Introducing corpus-based translation studies. Berlin: Springer.
  • Hutchins, J. & Somers, H. (1992). An Introduction to Machine Translation, London: Academic Pres Ltd.
  • Kabát, M., & Koscelníková, M. (2022). Localization and Its Place in Translation Studies. L10N Journal, 1(1), 4-26.
  • Kenny, D. (2018). Machine translation. In The Routledge handbook of translation and philosophy (pp. 428-445). Routledge.
  • Kiraly, D. (2014). A social constructivist approach to translator education: Empowerment from theory to practice. Routledge.
  • Koponen, M. (2016). Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. The Journal of Specialised Translation, 25(2), 131-148.
  • Krüger, R. (2022). Using Jupyter notebooks as didactic instruments in translation technology teaching. The Interpreter and Translator Trainer, 16(4), 503-523.
  • Nagrama, N. D. C., Lingating, M. L. D., Calleno, J. T., Rato, R. K. A., Catungal, M. L. P., & Encarnacion, P. C. (2024). Web-based Document Management System. International Journal, 13(3).
  • Negri, M., Turchi, M., Bertoldi, N., & Federico, M. (2018). Online neural automatic post-editing for neural machine translation. In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018).
  • O’Brien, S., Simard, M., & Goulet, M. J. (2018). Machine translation and self-post-editing for academic writing support: Quality explorations. Translation quality assessment: From principles to practice, 237-262.
  • O'Hagan, M., & Ashworth, D. (2002). Translation-mediated communication in a digital world: Facing the challenges of globalization and localization (Vol. 23). Multilingual Matters.
  • O'Hagan, M. (Ed.). (2019). The Routledge handbook of translation and technology. Taylor & Francis.
  • O’Neill, E. M. (2019). Training students to use online translators and dictionaries: The impact on second language writing scores. International Journal of Research Studies in Language Learning, 8(2), 47-65.
  • PACTE. (2017). PACTE translation competence model: A holistic, dynamic model of translation competence. In A. Hurtado Albir (Ed.), Researching translation competence by PACTE group (pp. 35-42). Amsterdam/Philadelphia: John Benjamins Publishing Co. https://doi.org/10.1075/btl.127.02pac
  • Peris, Á, Domingo, M., & Casacuberta, F. (2017). Interactive neural machine translation. Computer Speech & Language, 45, 201-220.
  • Pym, A. (2011). What technology does to translating. Translation & Interpreting: The International Journal of Translation and Interpreting Research, 3(1), 1-9.
  • Pym, A. (2023). Exploring translation theories. Third edition. New York: Routledge. Ramos, S., & del Mar, M. (2016). Community healthcare translator training and ad hoc corpora. Current trends in translation teaching and learning, 3, 119-139.
  • Reinke, U. (2018). State of the art in translation memory technology. Language Technologies for a Multilingual Europe; Rehm, G., Stein, D., Sasaki, F., Witt, A., Eds, 55-84.
  • Rodríguez-Inés, P., & Albir, A. H. (2012). Assessing competence in using electronic corpora in translator training. Global Trends in Translator and Interpreter Training: Mediation and Culture, 96-126.
  • Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J., & Austermuehl, F. (2023). Translation tools and technologies. Routledge.
  • Shuttleworth, M. (2014). Translation management systems. In Routledge encyclopedia of translation technology (pp. 678-691). Routledge.
  • Sikora, I. (2014). The need for CAT training within translator training programmes. TRAlinea Special Issue: Challenges in Translation Pedagogy, 1-6.
  • Sin-wai, C. (2014). The development of translation technology 1967–2013. In Routledge Encyclopedia of Translation Technology (pp. 2-31). Routledge.
  • Stevenson, A. (2010). Oxford dictionary of English. Oxford University Press.
  • Vu, T. T., & Haffari, R. (2018). Automatic post-editing of machine translation: A neural programmer-interpreter approach. In Empirical Methods in Natural Language Processing 2018 (pp. 3048-3053). Association for Computational Linguistics (ACL).
  • Wright, S. E. (2019). Standards for the language, translation and localization industry. In The Routledge handbook of translation and technology (pp. 21-44). Routledge.
  • Yves, G. (2019). Impact of technology on Translation and Translation Studies. Russian Journal of Linguistics, 23(2), 344-361.

TRANSLATION-FOCUSED TECHNOLOGICAL COMPETENCE: TRADITION AND INNOVATION

Year 2025, Volume: 49 Issue: 1, 85 - 95, 27.03.2025

Haldun Vural

Abstract

Translation has become a multidisciplinary profession which is influenced by technological advances rather than being a traditional linguistic discipline. Translators have long acted as intermediaries between languages and cultures, facilitating communication across boundaries. The field of translation studies has broadened in the contemporary age, including theories from other fields and adjusting to technological advancements that are changing the nature of translation both in practice and education. The idea of translation-focused technological competency is examined in this study, with an emphasis on how technology improves the productivity, accuracy, and efficiency of translators. Instant information sharing via Facebook, Instagram, WhatsApp, YouTube, and other platforms has been made possible by the emergence of digital media and Web 2.0 technologies, which have completely changed communication. By providing interactive communication and translation solutions via software, mobile applications, and smart devices, these innovations have also revolutionized translation methods. Machine translation enabled by artificial intelligence lacks emotional intelligence and nuanced comprehension required for precise interpretation of complicated texts. Therefore, human translators are still essential, even if technology helps to increase accessibility and decrease workload. Consequently, human translators are moving into positions that concentrate on correcting and improving translations produced by machines. This change emphasizes how crucial it is for translators to be technologically proficient since it enables them to use digital tools efficiently without sacrificing the quality and authenticity of their translations. Translators may improve their performance, ensure accuracy, and satisfy the needs of a globalized society by being proficient with technology tools and being aware of their limitations. This paper positions translators as crucial mediators in the digital era by highlighting the necessity of ongoing adaptation and skill improvement in translation methods. In the end, the human element is still essential to producing translations that are suitable for the target culture and setting.

Keywords

translation competence technological innovation digital media machine translation Web 2.0 artificial intelligence

References

  • Albir, A. H., Beeby, A., Fernández, M., Fox, O., KUZNIK, A., Serra, W. N., ... & Wimmer, S. (2011). Results of the validation of the PACTE translation competence model: Translation project and dynamic translation index. In
  • Cognitive explorations of translation (pp. 30-53).
  • Austermuhl, F. (2014). Electronic tools for translators. London: Routledge. https://doi.org/10.4324/9781315760353
  • Baker, M. (1995). Corpora in translation studies: An overview and some suggestions for future research. Target. International Journal of Translation Studies, 7(2), 223-243.
  • Bassnett, S. (2013). Translation studies. Routledge.
  • Bowker, L. (2002). Computer-aided translation technology: A practical introduction. University of Ottawa Press.
  • Bowker, L., & Fisher, D. (2010). Computer-aided translation. Handbook of translation studies, 1, 60-65.
  • Candel-Mora, M. Á. (2017). Criteria for the integration of term banks in the professional translation environment . Sendebar, 28, 243-260.
  • Chan, S. W. (Ed.). (2018). The human factor in machine translation. Routledge.
  • Cronin, M. (2013). Translation and globalization. Routledge.
  • Daems J, Vandepitte S, Hartsuiker RJ and Macken L (2017). Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort. Front. Psychol. 8, 1282. doi: 10.3389/fpsyg.2017.01282
  • Froeliger, N., Krause, A., & Salmi, L. (2023). Institutional translation–EMT Competence Framework and beyond. Institutional Translator Training, Routledge, 13-29.
  • Gouadec, D. (2007). Translation As A Profession, Amsterdam/ Philadelphia: John
  • Heletka, M. L. (2022). The concept of modern electronic translation dictionaries. Publishing House “Baltija Publishing”.
  • Heyn, M. (2016). Translation memories: Insights and prospects. In Unity in diversity (pp. 123-136). Routledge.
  • Hu, K. (2016). Introducing corpus-based translation studies. Berlin: Springer.
  • Hutchins, J. & Somers, H. (1992). An Introduction to Machine Translation, London: Academic Pres Ltd.
  • Kabát, M., & Koscelníková, M. (2022). Localization and Its Place in Translation Studies. L10N Journal, 1(1), 4-26.
  • Kenny, D. (2018). Machine translation. In The Routledge handbook of translation and philosophy (pp. 428-445). Routledge.
  • Kiraly, D. (2014). A social constructivist approach to translator education: Empowerment from theory to practice. Routledge.
  • Koponen, M. (2016). Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. The Journal of Specialised Translation, 25(2), 131-148.
  • Krüger, R. (2022). Using Jupyter notebooks as didactic instruments in translation technology teaching. The Interpreter and Translator Trainer, 16(4), 503-523.
  • Nagrama, N. D. C., Lingating, M. L. D., Calleno, J. T., Rato, R. K. A., Catungal, M. L. P., & Encarnacion, P. C. (2024). Web-based Document Management System. International Journal, 13(3).
  • Negri, M., Turchi, M., Bertoldi, N., & Federico, M. (2018). Online neural automatic post-editing for neural machine translation. In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018).
  • O’Brien, S., Simard, M., & Goulet, M. J. (2018). Machine translation and self-post-editing for academic writing support: Quality explorations. Translation quality assessment: From principles to practice, 237-262.
  • O'Hagan, M., & Ashworth, D. (2002). Translation-mediated communication in a digital world: Facing the challenges of globalization and localization (Vol. 23). Multilingual Matters.
  • O'Hagan, M. (Ed.). (2019). The Routledge handbook of translation and technology. Taylor & Francis.
  • O’Neill, E. M. (2019). Training students to use online translators and dictionaries: The impact on second language writing scores. International Journal of Research Studies in Language Learning, 8(2), 47-65.
  • PACTE. (2017). PACTE translation competence model: A holistic, dynamic model of translation competence. In A. Hurtado Albir (Ed.), Researching translation competence by PACTE group (pp. 35-42). Amsterdam/Philadelphia: John Benjamins Publishing Co. https://doi.org/10.1075/btl.127.02pac
  • Peris, Á, Domingo, M., & Casacuberta, F. (2017). Interactive neural machine translation. Computer Speech & Language, 45, 201-220.
  • Pym, A. (2011). What technology does to translating. Translation & Interpreting: The International Journal of Translation and Interpreting Research, 3(1), 1-9.
  • Pym, A. (2023). Exploring translation theories. Third edition. New York: Routledge. Ramos, S., & del Mar, M. (2016). Community healthcare translator training and ad hoc corpora. Current trends in translation teaching and learning, 3, 119-139.
  • Reinke, U. (2018). State of the art in translation memory technology. Language Technologies for a Multilingual Europe; Rehm, G., Stein, D., Sasaki, F., Witt, A., Eds, 55-84.
  • Rodríguez-Inés, P., & Albir, A. H. (2012). Assessing competence in using electronic corpora in translator training. Global Trends in Translator and Interpreter Training: Mediation and Culture, 96-126.
  • Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J., & Austermuehl, F. (2023). Translation tools and technologies. Routledge.
  • Shuttleworth, M. (2014). Translation management systems. In Routledge encyclopedia of translation technology (pp. 678-691). Routledge.
  • Sikora, I. (2014). The need for CAT training within translator training programmes. TRAlinea Special Issue: Challenges in Translation Pedagogy, 1-6.
  • Sin-wai, C. (2014). The development of translation technology 1967–2013. In Routledge Encyclopedia of Translation Technology (pp. 2-31). Routledge.
  • Stevenson, A. (2010). Oxford dictionary of English. Oxford University Press.
  • Vu, T. T., & Haffari, R. (2018). Automatic post-editing of machine translation: A neural programmer-interpreter approach. In Empirical Methods in Natural Language Processing 2018 (pp. 3048-3053). Association for Computational Linguistics (ACL).
  • Wright, S. E. (2019). Standards for the language, translation and localization industry. In The Routledge handbook of translation and technology (pp. 21-44). Routledge.
  • Yves, G. (2019). Impact of technology on Translation and Translation Studies. Russian Journal of Linguistics, 23(2), 344-361.
There are 42 citations in total.

Details

Primary Language English
Subjects Linguistics (Other)
Journal Section Articles
Authors

Haldun Vural 0000-0002-4638-4084

Publication Date March 27, 2025
Submission Date January 14, 2025
Acceptance Date February 13, 2025
Published in Issue Year 2025 Volume: 49 Issue: 1

Cite

APA Vural, H. (2025). TRANSLATION-FOCUSED TECHNOLOGICAL COMPETENCE: TRADITION AND INNOVATION. Cumhuriyet Üniversitesi Fen-Edebiyat Fakültesi Sosyal Bilimler Dergisi, 49(1), 85-95.
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