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Çevirmenlerin Nöral Makine Çevirisi Hizmetlerini Kullanma Davranışları ve Tercihleri

Year 2025, Issue: Çeviribilim Özel Sayısı II, 726 - 740, 25.03.2025
https://doi.org/10.29110/soylemdergi.1601714

Abstract

Çeviri sektörü, makine çevirisi hizmetlerinin ortaya çıkmasıyla birlikte derin
bir dönüşüm geçirmiştir. Bu hizmetler, sundukları fırsatlar ve zorluklarla
çevirmenlerin rollerini ve iş süreçlerini değiştirmiştir. Bu nedenle, bu çalışma,
İngilizce-Türkçe dil çiftinde çalışan çevirmenlerin davranışlarını ve tercihlerini,
nöral makine hizmetlerini profesyonel yaşamlarına ne ölçüde entegre ettikleri
açısından araştırmayı amaçlamaktadır. Çalışma, en sık kullanılan platformlar,
nöral makine çevirisi hizmetlerinin ücretli veya ücretsiz sürümleri için tercihler ve
seçimlerinin ardındaki belirli nedenler de dahil olmak üzere, bu hizmetlerin seçim
kriterlerine ilişkin temel soruları ele almaktadır. Veri toplamak için toplam 23
sorudan oluşan hem açık uçlu hem de evet/hayır sorularını içeren ayrıntılı bir
çevrim içi anket kullanılmıştır. Katılımcılar, ortalama 8 yıllık deneyime sahip ana
dili Türkçe olan 14 çevirmenden oluşmaktadır. Ayrıca, katılımcı grubu coğrafi
konum, yaş ve uzmanlık açısından çeşitlilik göstermektedir. Katılımcıların
yanıtlarındaki örüntüleri belirlemek için tematik analiz uygulanmıştır. Bu çalışma,
nöral ağ kullanan makine çevirisi hizmetlerinin Türkiye'deki çevirmenlerin
profesyonel yaşamlarına entegrasyonu ve kullanım kolaylığı, hız, doğruluk ve
erişilebilirlik gibi tercihlerini etkileyen faktörler hakkında önemli bilgiler
sunmakta ve böylece çevirmenler ile mevcut makine çevirisi hizmetleri arasında
gelişen ilişkinin anlaşılmasına katkı sağlamaktadır.

References

  • Clarke, Victoria & Braun, Virginia (2016). Thematic analysis. The Journal of Positive Psychology, 12(3), 297–298. https://doi.org/10.1080/17439760.2016.1262613
  • Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319.
  • DeepL. (2022, May 25). DeepL welcomes Turkish and Indonesian. Retrieved from https://www.deepl.com/en/blog/deepl-welcomes-turkish-and-indonesian (Access Date: 05.07.2024)
  • Esperança-Rodier, Emmanuelle, & Frankowski, Damian. (2021). DeepL vs Google Translate: who’s the best at translating MWEs from French into Polish? A multidisciplinary approach to corpora creation and quality translation of MWEs. 43rd Translating and the Computer Conference, Asling.
  • Hidalgo-Ternero, Carlos Manuel (2021). Google Translate vs. DeepL: Analysing neural machine translation performance under the challenge of phraseological variation. MonTI. Monographs in Translation and Interpreting, 154-177. https://doi.org/10.6035/MonTI.2020.ne6.5
  • Jiang, Zhaokun et al. (2023). Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach. arXiv. https://doi.org/10.48550/arXiv.2312.10750
  • Kamocki, Pawel & O’Regan, Jim (2016). Privacy issues in online machine translation services -European perspective. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 4458–4462). European Language Resources Association (ELRA).
  • Koskinen, Kaisa & Ruokonen, Minna (2017). Love letter or hate mail? Translators‘ technology acceptance in the light of their emotional narratives. In K. Dorothy (Ed.), Human issues in translation technology (pp. 8-14). Routledge.
  • Mohamed, Shereen A. et al. (2021). Neural machine translation: past, present, and future. Neural Comput & Applic 33, 15919–15931. https://doi.org/10.1007/s00521-021-06268-0
  • Öner, Işın & Bengi, Zehra Bengi (2024). Temel mi yoksa modası geçmiş mi? Teknoloji odaklı dil hizmetleri sektöründe insan yeterliliklerinin rolü. TransLogos Çeviri Çalışmaları Dergisi, 7 (1), 78–104. https://doi.org/10.29228/transLogos.66
  • Peng, Keqin, Ding et al. (2023). Towards Making the Most of ChatGPT for Machine Translation. arXiv. https://doi.org/10.48550/arXiv.2303.13780
  • Pitman, Jeff (2021, April 28). Google Translate: One billion installs, one billion stories. The Keyword. Retrieved from https://blog.google/products/translate/one-billion-installs (Access Date: 05.07.2024)
  • Sakamoto, Akiko (2019). Why do many translators resist post-editing? A sociological analysis using Bourdieu’s concepts. The Journal of Specialised Translation, 31, 201-216.
  • Salloum, S.A., Aljanada, R.A., Alfaisal, A.M., Al Saidat, M.R., Alfaisal, R. (2024). Exploring the acceptance of ChatGPT for translation: An extended TAM model approach. In: Al-Marzouqi, A., Salloum, S.A., Al-Saidat, M., Aburayya, A., Gupta, B. (Eds) Artificial Intelligence in education: The power and dangers of ChatGPT in the classroom. Studies in Big Data, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-52280-2_33
  • Stahlberg, Felix (2020). Machine translation: A review and survey. University of Cambridge. https://arxiv.org/pdf/1912.02047.pdf (Access Date: 07.07.2024)
  • Wadhwani, Preeti (2023). Machine translation market size - By technology (SMT, RBMT, NMT, HMT, EBMT), deployment model (On-premises, cloud), application (automotive, bfsi, e-commerce, electronics, healthcare, it & telecommunications, military & defense) & global forecast, 2023 – 2032, Report ID: GMI159. Retrieved from https://www.gminsights.com/industry-analysis/machine-translation-market-size (Access Date: 30.06.2024)
  • Yang, Yanxia, & Wang, Xiangling (2019). Modeling the intention to use machine translation for student translators: An extension of technology acceptance model. Computers & Technology, 133, 116-126. https://doi.org/10.1016/j.compedu.2019.01.015
  • Yonghui Wu et al. (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv. https://doi.org/10.48550/arXiv.1609.08144
  • Yaman, İsmail (2023). Deepl Translate ve Google Translate sistemlerinin İngilizce-Türkçe ve Türkçe-İngilizce çeviri performanslarının karşılaştırılması. Söylem Filolojisi, Çeviribilim Özel Sayısı, 29, 29-41. https://doi.org/10.29110/soylemdergi.1187172 (Access Date: 30.06.2024)
  • Zaretskaya, Anna (2015, June). The use of machine translation among professional translators. Paper presented at the EXPERT Scientific and Technological Workshop, Malaga, Spain.

Translators' Usage Behaviors and Preferences of Neural Machine Translation Services

Year 2025, Issue: Çeviribilim Özel Sayısı II, 726 - 740, 25.03.2025
https://doi.org/10.29110/soylemdergi.1601714

Abstract

The translation industry has undergone a deep transformation with the
emergence of machine translation services. These services have changed the roles
and work process of translators with the opportunities and challenges they offer.
Therefore, the present study seeks to investigate the behaviours and preferences
of translators working in the English-Turkish language pair in terms of the extent
to which they have integrated neural machine services into their professional lives.
The study addresses key questions regarding the selection criteria of these
services, including the most frequently used platforms, preferences for paid or free
versions of the neural machine translation services and the specific reasons behind
their choices. The methodological approach involves a detailed online
questionnaire with both yes/no and open-ended questions including a total of 23
items. The participants include 14 translators, whose native language were Turkish
and had an average experience of 8 years. Furthermore, the participant group
presents diversity in geographic location, age, and specialization. Thematic
analysis has been applied to identify patterns in participants' responses. This study
provides valuable insights into the integration of machine translation services
which use neural network into the professional lives of translators in Türkiye and
the factors influencing their choices such as ease of use, speed, accuracy and
accessibility, thereby providing an understanding of the evolving relationship
between translators and current machine translation technologies based on neural
network.

References

  • Clarke, Victoria & Braun, Virginia (2016). Thematic analysis. The Journal of Positive Psychology, 12(3), 297–298. https://doi.org/10.1080/17439760.2016.1262613
  • Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319.
  • DeepL. (2022, May 25). DeepL welcomes Turkish and Indonesian. Retrieved from https://www.deepl.com/en/blog/deepl-welcomes-turkish-and-indonesian (Access Date: 05.07.2024)
  • Esperança-Rodier, Emmanuelle, & Frankowski, Damian. (2021). DeepL vs Google Translate: who’s the best at translating MWEs from French into Polish? A multidisciplinary approach to corpora creation and quality translation of MWEs. 43rd Translating and the Computer Conference, Asling.
  • Hidalgo-Ternero, Carlos Manuel (2021). Google Translate vs. DeepL: Analysing neural machine translation performance under the challenge of phraseological variation. MonTI. Monographs in Translation and Interpreting, 154-177. https://doi.org/10.6035/MonTI.2020.ne6.5
  • Jiang, Zhaokun et al. (2023). Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach. arXiv. https://doi.org/10.48550/arXiv.2312.10750
  • Kamocki, Pawel & O’Regan, Jim (2016). Privacy issues in online machine translation services -European perspective. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 4458–4462). European Language Resources Association (ELRA).
  • Koskinen, Kaisa & Ruokonen, Minna (2017). Love letter or hate mail? Translators‘ technology acceptance in the light of their emotional narratives. In K. Dorothy (Ed.), Human issues in translation technology (pp. 8-14). Routledge.
  • Mohamed, Shereen A. et al. (2021). Neural machine translation: past, present, and future. Neural Comput & Applic 33, 15919–15931. https://doi.org/10.1007/s00521-021-06268-0
  • Öner, Işın & Bengi, Zehra Bengi (2024). Temel mi yoksa modası geçmiş mi? Teknoloji odaklı dil hizmetleri sektöründe insan yeterliliklerinin rolü. TransLogos Çeviri Çalışmaları Dergisi, 7 (1), 78–104. https://doi.org/10.29228/transLogos.66
  • Peng, Keqin, Ding et al. (2023). Towards Making the Most of ChatGPT for Machine Translation. arXiv. https://doi.org/10.48550/arXiv.2303.13780
  • Pitman, Jeff (2021, April 28). Google Translate: One billion installs, one billion stories. The Keyword. Retrieved from https://blog.google/products/translate/one-billion-installs (Access Date: 05.07.2024)
  • Sakamoto, Akiko (2019). Why do many translators resist post-editing? A sociological analysis using Bourdieu’s concepts. The Journal of Specialised Translation, 31, 201-216.
  • Salloum, S.A., Aljanada, R.A., Alfaisal, A.M., Al Saidat, M.R., Alfaisal, R. (2024). Exploring the acceptance of ChatGPT for translation: An extended TAM model approach. In: Al-Marzouqi, A., Salloum, S.A., Al-Saidat, M., Aburayya, A., Gupta, B. (Eds) Artificial Intelligence in education: The power and dangers of ChatGPT in the classroom. Studies in Big Data, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-52280-2_33
  • Stahlberg, Felix (2020). Machine translation: A review and survey. University of Cambridge. https://arxiv.org/pdf/1912.02047.pdf (Access Date: 07.07.2024)
  • Wadhwani, Preeti (2023). Machine translation market size - By technology (SMT, RBMT, NMT, HMT, EBMT), deployment model (On-premises, cloud), application (automotive, bfsi, e-commerce, electronics, healthcare, it & telecommunications, military & defense) & global forecast, 2023 – 2032, Report ID: GMI159. Retrieved from https://www.gminsights.com/industry-analysis/machine-translation-market-size (Access Date: 30.06.2024)
  • Yang, Yanxia, & Wang, Xiangling (2019). Modeling the intention to use machine translation for student translators: An extension of technology acceptance model. Computers & Technology, 133, 116-126. https://doi.org/10.1016/j.compedu.2019.01.015
  • Yonghui Wu et al. (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv. https://doi.org/10.48550/arXiv.1609.08144
  • Yaman, İsmail (2023). Deepl Translate ve Google Translate sistemlerinin İngilizce-Türkçe ve Türkçe-İngilizce çeviri performanslarının karşılaştırılması. Söylem Filolojisi, Çeviribilim Özel Sayısı, 29, 29-41. https://doi.org/10.29110/soylemdergi.1187172 (Access Date: 30.06.2024)
  • Zaretskaya, Anna (2015, June). The use of machine translation among professional translators. Paper presented at the EXPERT Scientific and Technological Workshop, Malaga, Spain.
There are 20 citations in total.

Details

Primary Language English
Subjects Translation and Interpretation Studies
Journal Section ARAŞTIRMA MAKALELERİ
Authors

Ozan Abat 0009-0008-5025-0186

Zeynep Başer 0000-0003-4391-4075

Early Pub Date March 23, 2025
Publication Date March 25, 2025
Submission Date December 15, 2024
Acceptance Date March 11, 2025
Published in Issue Year 2025 Issue: Çeviribilim Özel Sayısı II

Cite

APA Abat, O., & Başer, Z. (2025). Translators’ Usage Behaviors and Preferences of Neural Machine Translation Services. Söylem Filoloji Dergisi(Çeviribilim Özel Sayısı II), 726-740. https://doi.org/10.29110/soylemdergi.1601714