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Article published In:
Babel
Vol. 69:5 (2023) ► pp.666–703

Technology preparedness and translator training

Implications for curricula

 | University of Macau
With increasing acknowledgment of enhanced quality now achievable by Machine Translation, new possibilities have emerged through collaboration between human and machine in the translation process, including providing varying qualities of translation in response to quality/efficiency requirements. This paper presents surveys of post-graduate students of translation conducted over four consecutive years to examine if their awareness and preparedness have kept pace with these possibilities. It is found that respondents across the years generally perceive their awareness as lacking, are hesitant in employing MT, and show marked reservations when reconsidering issues such as quality and the preeminent position of the human translator. A review of existing research in translator training points towards a lopsided emphasis on linguistic competence and standalone courses for introducing technology as the primary cause behind low adoption. The need of the hour is translator training that fully integrates technology in the translation process and also provides a clear framework to adjust quality/efficiency is important to ensure preparedness. A repeat survey of students from 2021 who were trained under this model shows an increase in willingness to use MT and to consider quality as dependent on intended use. The focus here is on Chinese-English translation, but the discussion may find resonance with other language pairs.
Keywords: translator training, computer-assisted translation, Machine Translation, translation pedagogy, Chinese-English translation
Article outline
  • 1.Introduction
  • 2.The surveys
    • 2.1Results
      • 2.1.1Awareness of machine translation
      • 2.1.2Preparedness in integrating MT into workflow and attitude toward future prospects
      • 2.1.3Usefulness of available technology
    • 2.2Discussion
  • 3.Review of translator training
    • 3.1Lack of exposure to translation technology in training
    • 3.2Standalone treatments
  • 4.Implications for translator training
    • 4.1Integrating machine translation into the translation process
    • 4.2Customization (Varying Qualities)
    • 4.3Integrating other CAT tools
  • 5.Exit survey
  • 6.Conclusion
  • Notes
  • References
Published online: 22 September 2023
DOI logo
https://doi.org/10.1075/babel.00335.ven
References (50)
References
Allen, Jeffrey. 2003. “Post-Editing.” In Computers and Translation: A Translator’s Guide, edited by Harold Somers, 297–317. Benjamins Translation Library 35. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Bittner, Hansjörg. 2020. Evaluating the Evaluator: A Novel Perspective on Translation Quality Assessment. New York: Routledge.Google Scholar
Bowker, Lynne. 2005. “What Does It Take to Work in the Translation Profession in Canada in the 21st Century?: Exploring a Database of Job Advertisements.” Meta 49 (4): 960–972. DOI logoGoogle Scholar
Bowker, Lynne, and Jairo Buitrago Ciro. 2019. Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley: Emerald Publishing.Google Scholar
Buysschaert, Joost, María Fernández-Parra, Koen Kerremans, Maarit Koponen, and Gys-Walt Van Egdom. 2018. “Embracing Digital Disruption in Translator Training: Technology Immersion in Simulated Translation Bureaus.” Tradumàtica: Tecnologies de La Traducció 161 (December): 125. DOI logoGoogle Scholar
Chang, Daphne Qi-rong, Samuel Ju-hsin Yang, and Tracy Jr-yun Wang. 2019. “Dongji! Wo xuyao dongji! Ronghe diannao fuzhu fanyi de daxue fanyi ke ” 動機！我需要動機！融合電腦輔助翻譯的大學翻譯課 [Motivation! I need motivation! Incorporation of CAT into university translation courses]. Fanyixue yanjiu jikan 翻譯學研究集刊 [Studies of translation and interpretation] 231 (November): 129–156.Google Scholar
Chung, Eun Seon. 2020. “The Effect of L2 Proficiency on Post-Editing Machine Translated Texts.” The Journal of AsiaTEFL 17 (1): 182–193. DOI logoGoogle Scholar
Cui, Qiliang 崔启亮. 2014. “Lun jiqi fanyi de yihou bianji” 论机器翻译的译后编辑 [On post-editing of machine translation].” Zhongguo fanyi 中国翻译 [Chinese translators journal] (6): 68–73.Google Scholar
Cui, Qiliang. 2019a. “MTI Programs: Employment Investigation.” In Restructuring Translation Education: Implications from China for the Rest of the World, edited by Feng Yue et al., 55–68. Singapore: Springer Singapore. DOI logoGoogle Scholar
. 2019b. “MTI Programs: Teaching and Learning.” In Restructuring Translation Education: Implications from China for the Rest of the World, edited by Feng Yue et al., 41–54. Singapore: Springer Singapore. DOI logoGoogle Scholar
Doherty, Stephen. 2016. “The Impact of Translation Technologies on the Process and Product of Translation.” International Journal of Communication 101 (February): 969.Google Scholar
Drugan, Joanna. 2013. Quality in Professional Translation: Assessment and Improvement. London and New York: Bloomsbury.Google Scholar
“EMT Competence Framework 2017.” 2017. European Master’s in Translation (EMT). 2017. [URL]
“EMT Competence Framework 2022.” 2022. European Master’s in Translation (EMT). 2022. [URL]
Escartín, Carla Parra, and Marie-Josée Goulet. 2020. “When the Post-Editor Is Not a Translator.” In Translation Revision and Post-Editing, edited by Maarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 89–106. London and New York : Rutledge, 2020.: Routledge. DOI logoGoogle Scholar
Garcia, Ignacio. 2011. “Translating by Post-Editing: Is It the Way Forward?” Machine Translation 25 (3): 217–237. DOI logoGoogle Scholar
Gaspari, Federico, Hala Almaghout, and Stephen Doherty. 2015. “A Survey of Machine Translation Competences: Insights for Translation Technology Educators and Practitioners.” Perspectives 23 (3): 333–358. DOI logoGoogle Scholar
Grace, Katja, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans. 2018. “When Will AI Exceed Human Performance? Evidence from AI Experts.” ArXiv:1705.08807 [Cs], May. [URL] DOI logo
Hassan, Hany, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, et al. 2018. “Achieving Human Parity on Automatic Chinese to English News Translation.” DOI logoGoogle Scholar
Hutchins, William John, and Harold L. Somers. 1997. An Introduction to Machine Translation. 21. printing. London: Academic Press.Google Scholar
“ISO 18587:2017 Translation Services – Post-Editing of Machine Translation Output – Requirements.” 2017. International Organization for Standardization. 2017. [URL]
Jia, Yanfang, Michael Carl, and Xiangling Wang. 2019. “Post-Editing Neural Machine Translation versus Phrase-Based Machine Translation for English-Chinese.” Machine Translation 33 (1–2): 9–29. DOI logoGoogle Scholar
Kenny, Dorothy, and Stephen Doherty. 2014. “Statistical Machine Translation in the Translation Curriculum: Overcoming Obstacles and Empowering Translators.” The Interpreter and Translator Trainer 8 (2): 276–94. DOI logoGoogle Scholar
Killman, Jeffrey. 2018. “A Context-Based Approach to Introducing Translation Memory in Translator Training.” In Translation, Globalization and Translocation, edited by Concepción B. Godev, 137–159. Cham: Springer International Publishing. DOI logoGoogle Scholar
Konttinen, Kalle, Leena Salmi, and Maarit Koponen. 2020. “Revision and Post-Editing Competences in Translator Education.” In Translation Revision and Post-Editing, edited by Maarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 187–202. London and New York: Rutledge. DOI logoGoogle Scholar
Kornacki, Michał. 2018. Computer-Assisted Translation (CAT) Tools in the Translator Training Process. Berlin and New York: Peter Lang GmbH, Internationaler Verlag der Wissenschaften. DOI logoGoogle Scholar
Läubli, Samuel, Rico Sennrich, and Martin Volk. 2018. “Has Machine Translation Achieved Human Parity? A Case for Document-Level Evaluation.” ArXiv:1808.07048 [Cs], August. [URL] DOI logo
Man, Deliang, Aiping Mo, Meng Huat Chau, John Mitchell O’Toole, and Charity Lee. 2020. “Translation Technology Adoption: Evidence from a Postgraduate Programme for Student Translators in China.” Perspectives 28 (2): 253–270. DOI logoGoogle Scholar
Massardier-Kenney, Françoise. 2017. “An MA in Translation.” In Teaching Translation: Programs, Courses, Pedagogies, edited by Lawrence Venuti, 32–38. London and New York: Routledge.Google Scholar
Massardo, Isabella, Jap van der Meer, Sharon O’Brian, Fred Hollowood, Nora Aranberri, and Katrin Drescher. 2016. “TAUS Post-Editing Guidelines.” TAUS. 2016. [URL]
Massey, Gary, and Maureen Ehrensberger-Dow. 2017. “Machine Learning: Implications for Translator Education.” Lebende Sprachen 62 (2): 300–312. DOI logoGoogle Scholar
Mellinger, Christopher D. 2017. “Translators and Machine Translation: Knowledge and Skills Gaps in Translator Pedagogy.” The Interpreter and Translator Trainer 11 (4): 280–93. DOI logoGoogle Scholar
2018. “Problem-Based Learning in Computer-Assisted Translation Pedagogy.” HERMES – Journal of Language and Communication in Business, no. 57 (June): 195–208. DOI logoGoogle Scholar
Pym, Anthony. 2012. On Translator Ethics: Principles for mediation between cultures. Benjamins Translation Library, v. 1041. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Qin, Ying 秦颖. 2018. “Jiyu shenjing wangluo de jiqi fanyi zhiliang pingxi ji dui fanyi jiaoxue deyingxiang” 基于神经网络的机器翻译质量评析及对翻译教学的影响 [An analytical study of neural network machine translation and its impacts on translation teaching].” Waiyu dianhua jiaoxue 外语电化教学 [Translation teaching and research] 1801 (April): 51–56.Google Scholar
Qu, Shaobing. 2020. Zhongguo yuyan fuwu fazhan baogao (2020) 中国语言服务发展报告(2020) [Language service development in China 2020]. Beijing: Shangwu yinshu guan.Google Scholar
Rodríguez de Céspedes, Begoña. 2019. “Translator Education at a Crossroads:The Impact of Automation.” Lebende Sprachen 64 (1): 103–121. DOI logoGoogle Scholar
Sarti, Gabriele, Arianna Bisazza, Ana Guerberof Arenas, and Antonio Toral. 2022. “DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages.” In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 7795-7816. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. DOI logoGoogle Scholar
Somers, Harold, ed. 2003. Computers and Translation: A Translator’s Guide. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Van Wyke, Ben. 2017. “An Undergraduate Certificate in Translation Studies.” In Teaching Translation: Programs, Courses, Pedagogies, edited by Lawrence Venuti, 17–24. London and New York: Routledge.Google Scholar
Venkatesan, Hari. 2009. “Teaching Translation Memory Systems: SDL Trados 2007.” Journal of Translation Studies 13 (1–2): 71–81.Google Scholar
. 2021. “The Fourth Dimension in Translation: Time and Disposability.” Perspectives 30 (4): 662–677. DOI logoGoogle Scholar
Von Flotow, Luise. 2017. “A Doctoral Program in Translation Studies.” In Teaching Translation: Programs, Courses, Pedagogies, edited by Lawrence Venuti, 46–52. London and New York: Routledge.Google Scholar
Wang, Huashu 王华树. 2013. “Yuyan fuwu hangye jishu shiyuxia de MTI jishu kecheng tixi goujian” 语言服务行业技术视域下的MTI技术课程体系构建 [A constructive technology curriculum for MTI education from the perspective of language service industry technologies]. Zhongguo fanyi 中国翻译 [Chinese translator’s journal] (6): 23–28.Google Scholar
Wang, Xiangling, Tingting Wang, Ricardo Muñoz Martín, and Yanfang Jia. 2021. “Investigating Usability in Postediting Neural Machine Translation: Evidence from Translation Trainees’ Self-Perception and Performance:” Across Languages and Cultures 22 (1): 100–123. DOI logoGoogle Scholar
Way, Andy. 2018. “Quality Expectations of Machine Translation.” In Translation Quality Assessment, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 11:159–78. Machine Translation: Technologies and Applications. Cham: Springer International Publishing. DOI logoGoogle Scholar
Wu, Di, Lawrence Jun Zhang, and Lan Wei. 2019. “Developing Translator Competence: Understanding Trainers’ Beliefs and Training Practices.” The Interpreter and Translator Trainer 13 (3): 233–54. DOI logoGoogle Scholar
Wu, Yonghui, et al. 2016. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.” ArXiv:1609.08144 [Cs], October. [URL]
Xu, Mianjun, and Xiaoye You. 2021. “Translation Practice of Master of Translation and Interpreting (MTI) Teachers in China: An Interview-Based Study.” The Interpreter and Translator Trainer 15 (3): 343–59. DOI logoGoogle Scholar
Zhang, Xiaochun, and Lucas Nunes Vieira. 2021. “CAT Teaching Practices: An International Survey.” JoSTrans: The Journal of Specialised Translation 361: 99–142.Google Scholar