Machine Translation (MT), the process by which a computer engine such as Google Translate or Bing automatically
translates a text from one language into another without any human involvement, is increasingly used in professional,
institutional and everyday contexts for a wide range of purposes. While a growing number of studies has looked at professional
translators and translation students, there is currently a lack of research on non-translator users and uses in multilingual
contexts.
This paper presents a survey examining how, when and why students at Leiden University’s Faculty of Humanities use
MT. A questionnaire was used to determine which MT engines students use and for what purposes, and gauge their awareness of issues
concerning privacy, academic integrity and plagiarism. The findings reveal a widespread adoption of Google Translate and indicate
that students use MT predominantly to look up single words, as an alternative to a dictionary. Many seemed sceptical about the
value of MT for educational purposes, and many assumed that the use of MT is not permitted by lecturers for graded assignments,
especially in courses focusing on language skills.
The results demonstrate a clear need for more MT literacy. Students may not need practical training in
how to use MT, but there is much room for improvement in terms of when and
why they use it.
Bowker, Lynne. 2020. “Machine
Translation Literacy Instruction for International Business Students and Business English
Instructors.” Journal of Business & Finance
Librarianship 25 (1–2): 25–43.
Bowker, Lynne, and Jairo Buitrago Ciro. 2019. Machine
Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly
Community. Bingley, UK: Emerald Publishing.
García, Ignacio. 2010. “Can
Machine Translation Help the Language Learner?” International Conference: “ICT for Language
Learning” 3rd Edition. [URL]
García, Ignacio, and María Isabel Pena. 2011. “Machine
Translation-Assisted Language Learning: Writing for Beginners.” Computer Assisted Language
Learning 24 (5): 471–487.
Gaspari, Federico. 2006. “Look
Who’s Translating. Impersonation, Chinese Whispers and Fun with Machine Translation on the
Internet.” In Proceedings of the 11th Annual Conference of the
European Association for Machine Translation, ed. by European Association for
Machine
Translation, 149–158. Oslo: European Association for Machine Translation. [URL]
Gaspari, Federico, Hala Almaghout, and Stephen Doherty. 2015. “A
Survey of Machine Translation Competences: Insights for Translation Technology Educators and
Practicioners.” Perspectives 23 (3): 333–358.
Hassan, Hany, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018. “Achieving
Human Parity on Automatic Chinese to English News Translation.” Computing Research
Repository. [URL]
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): 276–294.
Läubli, Samuel, and David Orrego-Carmona. 2017. “When
Google Translate is Better than Some Human Colleagues, Those People are no Longer
Colleagues.” In Proceedings of the 39th Conference Translating and
the Computer, ed. by João Esteves-Ferreira, Juliet Macan, Ruslan Mitkov, and Olaf-Michael Stefanov, 59–69. Geneva: Tradulex.
Learning@Leiden. About the Vision on Teaching and
Learning. Accessed October 12,
2021. [URL]
Learning@Leiden. Application of
Technology. Accessed October 12,
2021. [URL]
Lee, Sangmin-Michelle. 2019. “The
Impact of Using Machine Translation on EFL Students’ Writing.” Computer Assisted Language
Learning 33 (3): 157–175.
Massey, Gary. 2021. “Re-Framing
Conceptual Metaphor Translation Research in the Age of Neural Machine Translation: Investigating Translators’ Added Value with
Products and Processes.” Training, Language and
Culture 5 (1): 37–56.
Moorkens, Joss. 2018. “What
to Expect from Neural Machine Translation: A Practical In-Class Translation Evaluation
Exercise.” The Interpreter and Translator
Trainer 12 (4): 375–387.
Mundt, Klaus, and Michael Groves. 2016. “A
Double-Edged Sword: The Merits and the Policy Implications of Google Translate in Higher
Education.” European Journal of Higher
Education 6 (4): 387–401.
Niño, Ana. 2008. “Evaluating
the Use of Machine Translation Post-Editing in the Foreign Language Class.” Computer Assisted
Language
Learning 21 (1): 29–49.
Niño, Ana. 2009. “Machine
Translation in Foreign Language Learning: Language Learners’ and Tutors’ Perceptions of its Advantages and
Disadvantages.” ReCALL 21 (2): 241–258.
O’Brien, Sharon, and Maureen Ehrensberger-Dow. 2020. “MT
Literacy – a Cognitive View.” Translation, Cognition &
Behavior 3 (2): 145–164.
O’Brien, Sharon, and Federico Marco Federici. 2020. “Crisis
Translation: Considering Language Needs in Multilingual Disaster Settings.” Disaster Prevention
and
Management 29 (2): 129–143.
Rossi, Caroline. 2017. “Introducing
Statistical Machine Translation in Translator Training: From Uses and Perceptions to Course Design, and Back
Again.” Revista
Traducmàtica 151: 48–62.
Saldanha, Gabriela, and Sharon O’Brien. 2013. Research
Methodologies in Translation Studies. Manchester: St. Jerome Publishing.
Sánchez-Gijón, Pilar, Joss Moorkens, and Andy Way. 2019. “Post-Editing
Neural Machine Translation versus Translation Memory Segments.” Machine
Translation 331: 31–59.
Statistica. 2021. Social Media Usage
in The Netherlands – Statistics & Facts. Accessed October 12, 2021. [URL]
Cited by (12)
Cited by 12 other publications
Chen, Hua
2024. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), ► pp. 1640 ff.
Grieve, Averil, Amir Rouhshad, Elpida Petraki, Alan Bechaz & David Wei Dai
2024. Nursing and midwifery students’ ethical views on the acceptability of using AI machine translation software to write university assignments: A deficit-oriented or translanguaging perspective?. Journal of English for Academic Purposes 70 ► pp. 101379 ff.
Gu, Longmei
2024. Multi-strategy KOA Algorithm for Optimizing Gated Recurrent Cell Networks in Automatic Writing Scoring Method Design. ICST Transactions on Scalable Information Systems 11:5
High, Michael D., Andrew McIntosh, Shuhan Li & Yu Ji
2024. 2024 International Conference on Data Science and Network Security (ICDSNS), ► pp. 1 ff.
Lo, Siowai
2024. The effects of NMT as a de facto dictionary on vocabulary learning: a comparison of three look-up conditions. Computer Assisted Language Learning► pp. 1 ff.
Yang, Yanxia
2024. Understanding machine translation fit for language learning: The mediating effect of machine translation literacy. Education and Information Technologies 29:15 ► pp. 20163 ff.
Semenova, Sofiia Novikovna, Anna Vitalevna Zhandarova & Denis Nikolaevich Sekunov
2023. Cognitive and pragmatic characteristic of S. King’s work “Pet Cematary”: experience in comparative analysis of original and translated texts. Ethnic Culture 5:3 ► pp. 49 ff.
Yang, Yanxia, Xiangqing Wei, Ping Li & Xuesong Zhai
2023. Assessing the effectiveness of machine translation in the Chinese EFL writing context: A replication of Lee (2020). ReCALL 35:2 ► pp. 211 ff.
Liu, Kanglong, Ho Ling Kwok, Jianwen Liu & Andrew K.F. Cheung
2022. Sustainability and Influence of Machine Translation: Perceptions and Attitudes of Translation Instructors and Learners in Hong Kong. Sustainability 14:11 ► pp. 6399 ff.
This list is based on CrossRef data as of 11 december 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
Any errors therein should be reported to them.