İngilizce Olarak Yayınlanan Akademik Dergilerin Kapsayıcılığını Artırmada Makine Çevirisinin Kullanımı

Year 2025, Volume: 3 Issue: 1, 1 - 8, 07.05.2025

Gökhan Ural

Abstract

Akademik yayıncılıkta İngilizcenin artan hâkimiyeti, özellikle uluslararası dergilerde yayımlanma konusunda ana dili İngilizce olmayan akademisyenler için çeşitli zorluklar doğurmaktadır. Bu çalışma, nöral makine çevirisinin (NMÇ), bu akademisyenler için akademik yazımı kolaylaştırıcı bir araç olarak potansiyelini incelemektedir. Makine çevirisi (MÇ) tarihsel olarak şüpheyle karşılanmış olsa da, NMÇ'deki gelişmeler çeviri kalitesini önemli ölçüde artırmış ve böylece akademik yazımı destekleyici umut vadeden bir araç hâline gelmiştir.

Bu makale, makine çevirisinin çeviri iş akışındaki rolünü, ana dili İngilizce olmayan akademisyenlerin yayımlama sürecinde karşılaştıkları engelleri ve NMÇ’nin akademik yazıma son okuma ve düzenleme (post-editing) desteğiyle entegre edilme olasılığını ele almaktadır. Akademisyenler, kendi uzmanlık alanlarındaki bilgi birikimlerinden faydalanarak, makine tarafından üretilen çevirileri hedefe yönelik son düzenlemelerle iyileştirebilir ve böylece profesyonel dil düzeltme ve çeviri hizmetleriyle ilişkili maliyetleri ve zaman kaybını azaltabilirler. Çalışma, özellikle alan odaklı terminolojinin belirgin olduğu teknik ve bilimsel alanlarda, stratejik bir şekilde kullanıldığında NMT’nin uluslararası akademik yayıncılığa erişimi artırabileceğini öne sürmektedir. Bulgular, NMT’nin etkinliğini en üst düzeye çıkarmak için yapılandırılmış son okuma ve düzenleme yönergelerinin gerekliliğini vurgulamaktadır. Sonuç olarak, bu çalışma, akademisyenler ve çevirmenlerin iş birliği içinde çalışarak makine çeviri çıktılarının optimize edilmesini savunmakta ve böylece küresel akademik söylemde kapsayıcılığın ve daha geniş katılımın teşvik edilebileceğini öne sürmektedir.

Keywords

makine çevirisi, akademik yazım, kapsayıcılık, çeviri, makine çevirisi sonrası düzenleme

The Prospective Use of Machine Translation in Promoting Inclusivity of Academic Journals Published in English

Abstract

The increasing dominance of English in academic publishing presents challenges for non-native English-speaking scholars, particularly in achieving publication in international journals. This study explores the potential of neural machine translation (NMT) as a tool to facilitate academic writing for these scholars. While machine translation (MT) has historically been met with skepticism, advancements in NMT have significantly improved translation quality and thus it has turned into a prospective tool in aiding academic writing. The paper examines the role of MT in translation workflows, the barriers faced by non-native scholars in publishing, and the possibility of integrating NMT into academic writing with post-editing support. By leveraging their expertise in their respective fields, scholars can refine machine-generated translations with targeted post-editing, potentially reducing costs and time associated with professional proofreading and translation services. The study suggests that NMT, when used strategically, can enhance accessibility to international academic publishing, particularly in technical and scientific fields where domain-specific terminology is well-defined. The findings emphasize the need for structured post-editing guidelines to maximize NMT effectiveness. Ultimately, this study advocates for a collaborative approach where scholars and translators work together to optimize machine translation outputs, therefore inclusivity and broader participation in global academic discourse can be promoted.

Keywords

machine translation, academic writing, inclusivity, translation, post-editing

References

• Bowker, L., & Buitrago Ciro, J. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Publishing Limited.
• Castilho, S., Doherty, S., Gaspari, F., & Moorkens, J. (2017). Approaches to human and machine translation quality assessment. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation Quality Assessment: From Principles to Practice (pp. 9–38). Springer.
• Castilho, S., Gaspari, F., Moorkens, J., & Way, A. (2018). Translation Quality Assessment: From Principles to Practice. Springer. https://doi.org/10.1007/978-3-319-91241-7
• Curry, M. J., & Lillis, T. (2004). Multilingual scholars and the imperative to publish in English: Negotiating interests, demands, and rewards. TESOL Quarterly, 38(4), 663–688. https://doi.org/10.2307/3588284
• Doherty, S. (2016). The impact of translation technologies on the process and product of translation. International Journal of Communication, (10), 947–969.
• Flowerdew, J. (2000). Discourse community, legitimate peripheral participation, and the nonnative‐English‐speaking scholar. TESOL Quarterly, 34(1), 127–150. https://doi.org/10.2307/3588099
• Gaspari, F., & Hutchins, J. (2007). Online and free! Ten years of online machine translation: Origins, developments, current use and future prospects. MT Summit XI, 199–206.
• Hutchins, W. J., & Somers, H. L. (1992). An introduction to machine translation. Academic Press.
• Hutchins, J. (2005). Current commercial machine translation systems and computer-based translation tools: System types and their uses. International Journal of Translation, 17(1–2), 5–38.
• Kituku, B., Muchemi, L., & Nganga, W. (2016). A review on machine translation approaches. Indonesian Journal of Electrical Engineering and Computer Science, 1(1), 182–190. https://doi.org/10.11591/ijeecs.v1.i1.pp182-190 Koehn, P. (2009). Statistical machine translation. Cambridge University Press.
• Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? A case for document-level evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4791–4796). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1512
• Lillis, T., & Curry, M. J. (2006). Professional academic writing by multilingual scholars: Interactions with literacy brokers in the production of English-medium texts. Written Communication, 23(1), 3–35. https://doi.org/10.1177/0741088305283754
• McGrail, M. R., Rickard, C. M., & Jones, R. (2006). Publish or perish: A systematic review of interventions to increase academic publication rates. Higher Education Research & Development, 25(1), 19–35. https://doi.org/10.1080/07294360500453053
• Moorkens, J., Castilho, S., Gaspari, F., & Doherty, S. (Eds.). (2018). Human issues in translation technology: The IATIS yearbook. Routledge.
• O’Brien, S. (2011). Towards predicting post-editing productivity. Machine Translation, 25(3), 197–215. https://doi.org/10.1007/s10590-011-9096-7
• O’Hagan, M., & Mangiron, C. (2013). Game Localization: Translating for the Global Digital Entertainment Industry. John Benjamins Publishing Company.
• Quah, C. K. (2006). Translation and technology. Palgrave Macmillan. https://doi.org/10.1007/978-1-4039-1831-4
• Pym, A. (2020). Translation solutions for many languages: Histories of a flawed dream. Bloomsbury Academic.
• Ragni, V., & Vieira, L. N. (2022). What has changed with neural machine translation? A critical review of human factors. Perspectives, 30(1), 137–158. https://doi.org/10.1080/0907676X.2021.1889005
• Vieira, L. N., & Alonso, E. (2020). Translating perceptions and managing expectations in translation technology adoption. Perspectives, 28(2), 163–183. https://doi.org/10.1080/0907676X.2019.1677735
• Way, A. (2018). Quality expectations of machine translation. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation Quality Assessment: From Principles to Practice (pp. 159–178). Springer.
• 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.