Çeviribiliminin Yapay Zeka (YZ) ile Geliştirilmesi: Zorluklar, İmkânlar ve Öneriler

Yıl 2023, Cilt: 5 Sayı: 2, 177 - 191, 30.12.2023

Fırat Soysal

https://doi.org/10.55036/ufced.1402649

Cited By: 1

Öz

Bu kuramsal çalışma, Çeviribilim ve Yapay Zeka (YZ) arasındaki simbiyotik ilişkiyi inceleyerek bu iki alan arasındaki iş birliğinin önemini vurgulamaktadır. Çalışma, YZ‘nin çeviri uygulamalarına entegrasyonunun çeviri verimliliğini artırma, dil engellerini aşma ve bilgiye erişimi genişletme potansiyelini araştırmaktadır. Bu doğrultuda çalışma, Çeviribilim alanındaki YZ entegrasyonunda insan uzmanlığının rolü, çevirilerin doğruluğu ve kültürel uygunluğu ve de YZ‘nin işgücü üzerindeki etkisi gibi önemli etik konuları ele almaktadır. Çalışma, Çeviribilim (veya Mütercim ve Tercümanlık) programlarının müfredatına YZ ile ilgili konuların dahil edilmesinin ehemmiyetini vurgulamakta, akademisyenler ile YZ geliştiriciler arasında iş birliğine dayalı araştırma projelerinin teşvik edilmesini savunmakta ve YZ‘nin IQ (Zeka Seviyesi/Katsayısı) ve EQ (Duygusal Zeka Seviyesi/Katsayısı) yetenekleri arasındaki boşluğu kapatma ihtiyacına dikkat çekmektedir. Çeviribilim ve Yapay Zeka (YZ) arasındaki iş birliği, teknik olarak isabetli ve kültürel olarak hassas çeviriler sunarak bireylerin ve işletmelerin ihtiyaçlarını karşılayan yüksek kaliteli çevirilerin gerçekleşmesini sağlayabilir. Söz konusu iş birliği, YZ'nin çeviri faaliyetlerindeki kalitesini ve etkisini artırarak daha güvenilir ve uygun çevirilerin ortaya çıkmasına olanak tanıyabilir. Bu nedenle, mevcut çalışma Çeviribilim ve YZ arasındaki iş birliğinin öneminin altını çizerek çeviri hizmetlerinin kalitesinin artırılması ve kültürel olarak hassas çevirilerin yaygınlaşmasının teşvik edilmesi gibi hususlara dikkat çekmektedir.

Anahtar Kelimeler

Çeviribilim, Yapay Zeka (YZ), Etik, Zeka (IQ) ve Duygusal Zeka (EQ), Kesişim, Müfredat

Enhancing Translation Studies with Artificial Intelligence (AI): Challenges, Opportunities, and Proposals

Öz

This theoretical study delves into the symbiotic relationship between Translation Studies and Artificial Intelligence (AI), emphasizing the need for collaboration between these two fields. The study explores the challenges and opportunities for developing Translation Studies with AI and presents proposals for advancing the integration of AI in the field. The integration of AI in translation practices has the potential to enhance translation efficiency, overcome language barriers, and expand access to the information. However, this integration also raises the important ethical considerations, such as the role of human expertise in translation, the accuracy and cultural appropriateness of translations, and the impact of AI on the workforce. The study highlights the importance of integrating AI-related topics into the curriculum of Translation Studies programs, fostering collaborative research projects between scholars and AI developers, and addressing the need to bridge the gap between AI's IQ and EQ capabilities. Translation Studies can play a crucial role in improving AI systems' accuracy and cultural sensitivity in translation by providing valuable insights into the cultural nuances, context, and ethical considerations. By leveraging the expertise of Translation Studies, AI developers and researchers can enhance the performance of AI-based translation systems, ultimately improving the quality and impact of AI in translation. Therefore, this study supports the collaboration between Translation Studies and AI to improve the quality of translation services and promote the widespread use of culturally sensitive translations.

Anahtar Kelimeler

Translation Studies, Artificial Intelligence (AI), Ethics, Intelligence Quotient (IQ) and Emotional Quotient (EQ), Intersection, Curriculum

Kaynakça

• Amedior, N. C. (2023). Ethical Implications of Artificial Intelligence in the Healthcare Sector. Advances in Multidisciplinary and Scientific Research Journal Publication, 36, 1-12. https://doi.org/10.22624/AIMS-/ACCRABESPOKE2023P1.
• Asan, O., & Choudhury, A. (2021). Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review. JMIR Human Factors, 8(2), 1-21. https://doi.org/10.2196/28236.
• Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154.
• Avanzo, M., Trianni, A., Botta, F., Talamonti, C., Stasi, M., & Iori, M. (2021). Artificial Intelligence and the Medical Physicist: Welcome to the Machine. Applied Sciences, 11(4), 1-17.
• Baker, M., & Saldanha, G. (2009). Routledge encyclopedia of translation studies (2nd ed.). Routledge.
• Bakola, L. N., Drigas, A., & Skianis, C. (2022). Inteligência Emocional vs. Inteligência Artificial: A interação da inteligência humana na robótica evolutiva. Research, Society and Development, 11(16), e72111636919. https://doi.org/10.33448/rsd-v11i16.36919.
• Bossen, C., & Pine, K. H. (2023). Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists. ACM Transactions on Computer-Human Interaction, 30(2), 1-29. https://doi.org/10.1145/3569892.
• Bundgaard, K. (2017). Translator Attitudes towards Translator-Computer Interaction - Findings from a Workplace Study. HERMES - Journal of Language and Communication in Business. (56), 125-144. https://doi.org/10.7146/hjlcb.v0i56.97228.
• Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., . . . Terry, M. (2019). Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. In S. Brewster (Ed.), ACM Digital Library, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (ss. 1-14). New York,NY,United States: Association for Computing Machinery. https://doi.org/10.1145/3290605.3300234.
• Casey, A., Ansari, S., Nakisa, B., Kelly, B., Brown, P., Cooper, P., Muhammad, I., Livingstone, S., Reddy, S., & Makinen, V.-P. (2023). Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care, 1-20. https://doi.org/10.1101/2023.02.23.23286374.
• Chetouani, M., Dignum, V., Lukowicz, P., & Sierra, C. (2023). Human-centered artificial intelligence: Advanced lectures. Lecture notes in computer science: Vol. 13500. Springer. https://doi.org/10.1007/978-3-031-24349-3.
• Christoforaki, M., & Beyan, O. (2022). AI Ethics-A Bird’s Eye View. Applied Sciences, 12(9), 1-17. https://doi.org/10.3390/app12094130.
• Cozendey-Silva, E. N., da Silva, C. R., Larentis, A. L., Wasserman, J. C., Rozemberg, B., & Teixeira, L. R. (2016). Cross-cultural adaptation of an environmental health measurement instrument: Brazilian version of the health-care waste management • rapid assessment tool. BMC Public Health, 16(1), 1-12. https://doi.org/10.1186/s12889-016-3618-4.
• Ding, C., Draffan, E. A., & Wald, M. (2020). AI and Global AAC Symbol Communication. In K. Miesenberger, R. Manduchi, M. Covarrubias Rodriguez, & P. Peňáz (Eds.), LNCS Sublibrary: SL3 - Information systems and applications, incl. Internet/web, and HCI: 12376-12377. Computers Helping People with Special Needs: 17th International Conference, ICCHP 2020, Lecco, Italy, September 9-11, 2020, Proceedings (Vol. 12376, pp. 59-66). Springer. https://doi.org/10.1007/978-3-030-58796-3_8.
• Efe, A. (2022). The Impact of Artificial Intelligence on Social Problems and Solutions: An Analysis on The Context of Digital Divide and Exploitation. Yeni Medya Dergisi. Advance online publication. https://doi.org/10.55609/yenimedya.1146586.
• Eszenyi, R., Bednárová-Gibová, K., & Robin, E. (2023). Ezikov Svyat volume 21 issue 2. Ezikov Svyat (Orbis Linguarum). (ezs.swu.v21i2), 102-113. https://doi.org/10.37708/ezs.swu.bg.v21i2.13.
• Eweje, F. R., Byun, S., Chandra, R., Hu, F., Kamel, I., Zhang, P., Jiao, Z., & Bai, H. X. (2022). Translatability Analysis of National Institutes of Health-Funded Biomedical Research That Applies Artificial Intelligence. JAMA Network Open, 5(1), e2144742. https://doi.org/10.1001/jamanetworkopen.2021.44742.
• Freitag, M., Foster, G., Grangier, D., Ratnakar, V., Tan, Q., & Macherey, W. (2021). Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation. Transactions of the Association for Computational Linguistics, 9, 1460-1474.
• Handayani, W., Rozimela, Y., Thahar, H. E., Ramadhan, S., Agustina, A., & Zaim, M. (2018). Recent Technology For Translation Study. In Proceedings of the Seventh International Conference on Languages and Arts (ICLA 2018) (ss. 699-704). Paris, France: Atlantis Press. https://doi.org/10.2991/icla-18.2019.115.
• Heer, J. (2019). Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences of the United States of America, 116(6), 1844-1850. https://doi.org/10.1073/pnas.1807184115.
• Hou, Q., & Zhang, L. (2022). Design and Implementation of Interactive English Translation System in Internet of Things Auxiliary Information Processing. Wireless Communications and Mobile Computing, 2022, 1-12. https://doi.org/10.1155/2022/3987970.
• Houssami, N., Kirkpatrick-Jones, G., Noguchi, N., & Lee, C. I. (2019). Artificial Intelligence (AI) for the early detection of breast cancer: A scoping review to assess AI's potential in breast screening practice. Expert Review of Medical Devices, 16(5), 351–362. https://doi.org/10.1080/17434440.2019.1610387.
• Huriye, A. Z. (2023). The Ethics of Artificial Intelligence: Examining the Ethical Considerations Surrounding the Development and Use of AI. American Journal of Technology, 2(1), 37-45. https://doi.org/10.58425/ajt.v2i1.142.
• Jia, Z. (2022). Analysis Methods for the Planning and Dissemination Mode of Radio and Television Assisted by Artificial Intelligence Technology. Mathematical Problems in Engineering, 2022, 1-11. https://doi.org/10.1155/2022/7538692.
• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2.
• Kamalov, F., Thabtah, F., & Gurrib, I. (2021). Autocorrelation for time series with linear trend. In 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 181-185). IEEE. https://doi.org/10.1109/3ICT53449.2021.9581809.
• Karamanis, N., Luz, S., & Doherty, G. (2011). Translation practice in the workplace: contextual analysis and implications for machine translation. Machine Translation, 25(1), 35-52. https://doi.org/10.1007/s10590-011-9093-x.
• Killman, J. (2015). Context as Achilles’ heel of translation technologies. Translation and Interpreting Studies, 10(2), 203-222. https://doi.org/10.1075/tis.10.2.03kil.
• Koehn, P. (2010). Statistical machine translation. Cambridge University Press.
• Kulkarni, S. B., Deshmukh, P. D., & Kale, K. V. (2013). Syntactic and Structural Divergence in English-to-Marathi Machine Translation, 191-194. https://doi.org/10.1109/iscbi.2013.46.
• Kunst, J. R., & Bierwiaczonek, K. (2023). Utilizing AI questionnaire translations in cross-cultural and intercultural research: Insights and recommendations, 1-29. https://doi.org/10.31234/osf.io/sxcyk.
• Lai, V., Smith-Renner, A., Zhang, K., Cheng, R., Zhang, W., Tetreault, J., & Jaimes-Larrarte, A. (2022). An Exploration of Post-Editing Effectiveness in Text Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 502-519). https://doi.org/10.18653/v1/2022.naacl-main.35.
• Little, M. M., St Hill, C. A., Ware, K. B., Swanoski, M. T., Chapman, S. A., Lutfiyya, M. N., & Cerra, F. B. (2017). Team science as interprofessional collaborative research practice: A systematic review of the science of team science literature. Journal of İnvestigative Medicine : The Official Publication of the American Federation for Clinical Research, 65(1), 15-22. https://doi.org/10.1136/jim-2016-000216.
• Low, D. S., Mcneill, I., & Day, M. J. (2022). Endangered Languages: A Sociocognitive Approach to Language Death, Identity Loss, and Preservation in the Age of Artificial Intelligence. Sustainable Multilingualism, 21(1), 1-25. https://doi.org/10.2478/sm-2022-0011.
• Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., . . . Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236.
• Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mökander, J., & Floridi, L. (2021). Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. Minds and Machines, 31(2), 239-256. https://doi.org/10.1007/s11023-021-09563-w.
• Morley, J., Kinsey, L., Elhalal, A., Garcia, F., Ziosi, M., & Floridi, L. (2023). Operationalising AI ethics: barriers, enablers and next steps. AI & SOCIETY, 38(1), 411-423. https://doi.org/10.1007/s00146-021-01308-8.
• Murakami, K. (2015). Non-coding RNAs and hypertension-unveiling unexpected hypertension mechanisms by the genome's dark matter. Current Hypertension Reviews, 11(2), 80-90. https://doi.org/10.2174/1573402111666150401105317.
• Nanomi Arachchige, I. A., Suraweera, S., & Herath, D. (2022). Transformer-based Language models for the Identification of Idiomatic Expressions. In Proceedings of the International Conference EUROPHRAS 2022 (short papers, posters and MUMTTT workshop contributions) (ss. 119-127). Incoma Ltd. Shoumen, Bulgaria. https://doi.org/10.26615/978-954-452-080-9_015.
• Papastratis, I., Chatzikonstantinou, C., Konstantinidis, D., Dimitropoulos, K., & Daras, P. (2021). Artificial Intelligence Technologies for Sign Language. Sensors (Basel, Switzerland), 21(17). https://doi.org/10.3390/s21175843.
• Rafiq, F., Dogra, N., Adil, M., & Wu, J.-Z. (2022). Examining Consumer’s Intention to Adopt AI-Chatbots in Tourism Using Partial Least Squares Structural Equation Modeling Method. Mathematics, 10(13), 1-15. https://doi.org/10.3390/math10132190.
• Ryan, M., & Stahl, B. C. (2021). Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications. Journal of Information, Communication and Ethics in Society, 19(1), 61-86. https://doi.org/10.1108/JICES-12-2019-0138.
• Sartori, L., & Theodorou, A. (2022). A sociotechnical perspective for the future of AI: narratives, inequalities, and human control. Ethics and Information Technology, 24(1). https://doi.org/10.1007/s10676-022-09624-3.
• Shao, Y. (2022). Human-Computer Interaction Environment Monitoring and Collaborative Translation Mode Exploration Using Artificial Intelligence Technology. Journal of Environmental and Public Health, 2022, 4702003. https://doi.org/10.1155/2022/4702003.
• Sounderajah, V., Ashrafian, H., Rose, S., Shah, N. H., Ghassemi, M., Golub, R., . . . Darzi, A. (2021). A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: Quadas-AI. Nature Medicine, 27(10), 1663-1665. https://doi.org/10.1038/s41591-021-01517-0.
• Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. https://doi.org/10.48550/ARXIV.1409.3215.
• Toh, T. S., Dondelinger, F., & Wang, D. (2019). Looking beyond the hype: Applied AI and machine learning in translational medicine. EBioMedicine, 47, 607-615. https://doi.org/10.1016/j.ebiom.2019.08.027.
• van Rosse, F., Bruijne, M. de, Suurmond, J., Essink-Bot, M.-L., & Wagner, C. (2016). Language barriers and patient safety risks in hospital care. A mixed methods study. International Journal of Nursing Studies, 54, 45-53. https://doi.org/10.1016/j.ijnurstu.2015.03.012.
• Vereschak, O., Bailly, G., & Caramiaux, B. (2021). How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–39. https://doi.org/10.1145/3476068.
• Vieira, L. N. (2020). Post-editing of machine translation. In M. O'Hagan (Ed.), Routledge handbooks in translation and interpreting studies. The Routledge handbook of translation and technology (pp. 319-336). Routledge Taylor & Francis Group. https://doi.org/10.4324/9781315311258-19.
• Wang, L. (2023). The Impacts and Challenges of Artificial Intelligence Translation Tool on Translation Professionals. SHS Web of Conferences, 163, 1-6. https://doi.org/10.1051/shsconf/202316302021.
• Weisz, J. D., Muller, M., Houde, S., Richards, J., Ross, S. I., Martinez, F., Agarwal, M., & Talamadupula, K. (2021). Perfection Not Required? Human-AI Partnerships in Code Translation. In T. Hammond (Ed.), ACM Conferences, 26th International Conference on Intelligent User Interfaces (pp. 402-412). Association for Computing Machinery. https://doi.org/10.1145/3397481.3450656.
• Yang, H., & Kyun, S. (2022). The current research trend of artificial intelligence in language learning: A systematic empirical literature review from an activity theory perspective. Australasian Journal of Educational Technology, 180-210. https://doi.org/10.14742/ajet.7492.
• Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020). Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. In R. Bernhaupt, F. '. Mueller, D. Verweij, J. Andres, J. McGrenere, A. Cockburn, . . . F. Mueller (Eds.), CHI'20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems : April 25-30, 2020, Honolulu, HI, USA (ss. 1-13). New York, New York: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376301.
• Zhao, X., & Jin, X. (2022). A Comparative Study of Text Genres in English-Chinese Translation Effects Based on Deep Learning LSTM. Computational and Mathematical Methods in Medicine, 2022, 1-9. https://doi.org/10.1155/2022/7068406.
• Zhong, W., & Chin, T. (2015). The role of translation in cross-cultural knowledge transfer within a MNE’s business networks. Chinese Management Studies, 9(4), 589-610. https://doi.org/10.1108/cms-06-2015-0114.
• Zhou, L., Gao, J., Di Li, & Shum, H.-Y. (2020). The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. Computational Linguistics, 46(1), 53-93. https://doi.org/10.1162/coli_a_00368.
• Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M. (2018). Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation. In 2018 IEEE Conference on Computational Intelligence and Games (CIG 2018): Maastricht, Netherlands, 14-17 August 2018 (ss. 1-8). Piscataway, NJ: IEEE. https://doi.org/10.1109/CIG.2018.8490433.
• https://www.howitstart.com/technology-defined-understanding-its-nature-and-exploring-examples/ (accessed: December 15, 2023).
• https://www.techmediatoday.com/bartosz-ciesielski-ai-wont-replace-writers/ (accessed: December 10, 2023).
• https://marktine.com/ai-and-natural-language-processing/ (accessed: December 2, 2023). https://devlearn.com/program/pre-conference-activities/colocated-events/ai-and-learning-symposium/ (accessed: 23.11.2023).
• https://tohoku.pure.elsevier.com/ja/publications/deep-reinforcement-learning-with-hidden-layers-on-future-states (accessed: 10.12.2023).