Article published in:
Translation, Cognition & Behavior
Vol. 3:2 (2020) ► pp. 145–164

MT Literacy—A cognitive view

| Dublin City University
| Zurich University of Applied Sciences

MT literacy means knowing how MT works, how the technology can be useful in a particular context, and what the implications are of using it for various purposes. As MT usage grows, the necessity for MT literacy also grows. This knowledge forms part of the greater need for digital literacies. In this contribution, we relate MT literacy to the concept of cognitive load in professional translation production and in translator training scenarios. We then move beyond the sphere of translation studies to examine other use-case settings—crisis communication, academic writing and patent publishing—to consider how MT can offer solutions and how MT literacy can impact cognitively in those settings. We discuss how training in MT literacy can empower language professionals and present two proposals for course content designed for MT users in other sectors.

Keywords: MT literacy, cognitive load, use-case scenarios, digital literacies
Article outline
  • 1.What is machine translation literacy?
  • 2.Why MT literacy is important
  • 3.MT literacy and cognitive load in translation production
  • 4.MT literacy and cognitive load in other use-case scenarios
    • 4.1MT literacy and crisis communication
    • 4.2Academic writing and MT literacy
    • 4.3MT literacy in the patent process
  • 5.Training and empowerment
  • 6.Conclusion
  • Notes
  • References
Published online: 10 November 2020
https://doi.org/10.1075/tcb.00038.obr
References
Alexander, David, and Gianluca Pescaroli
2019 “The Role of Translators and Interpreters in Cascading Crises and Disasters: Towards a framework for confronting the challenges.” Disaster Prevention and Management 29 (1): 144–156. CrossrefGoogle Scholar
Bayer-Hohenwarter, Gerrit
2012 Translatorische Kreativität: Definition—Messung—Entwicklung [Translational Creativity: Definition—Measurement—Development]. Tübingen: Narr.Google Scholar
Bennett, Karen
2013 “English as a Lingua Franca in Academia. Combating epistemicide through translator training.” The Interpreter and Translator Trainer 7 (2): 169–193. CrossrefGoogle Scholar
2014 “The Political and Economic Infrastructure of Academic Practice: The ‘semi-periphery’ as a category for social and linguistic analysis.” In The Semi-periphery of Academic Writing: Discourses, communities and practices. Edited by K. Bennett, 1–12. London: Palgrave Macmillan. CrossrefGoogle Scholar
2015 “Towards an Epistemological Monoculture: Mechanisms of epistemicide in European research publication. In English as an Academic and Research Language (English in Europe Vol. 2). Edited by R. Plo Alastrué, and C. Pérez-Llantada, 9–35. Berlin: De Gruyter Mouton.Google Scholar
Bowker, Lynne, and Jairo Buitrago Ciro Ciro
2019 Machine Translation and Global Research: Towards improved machine translation literacy in the scholarly community. Bingley: Emerald Publishing. CrossrefGoogle Scholar
Breuer, Esther O.
2015 First Language versus Foreign Language. Fluency, errors and revision processes in foreign language academic writing. Frankfurt am Main: Peter Lang. CrossrefGoogle Scholar
Cadwell, Patrick
2019 “Trust, Distrust and Translation in a Disaster.” Disaster Prevention and Management 29 (2): 157–174. CrossrefGoogle Scholar
Cadwell, Patrick, Claudia Bollig, and Juliane Ried
2019 “Management and Training of Linguistic Volunteers: A case study of translation at Cochrane Germany.” In Translation in Cascading Crises. Edited by F. M. Federici, and S. O’Brien, 152–173. London: Routledge. CrossrefGoogle Scholar
Cadwell, Patrick, Sharon O’Brien, and Carlos S. C. Teixeira
2018 “Resistance and Accommodation: Factors for the (non-) adoption of machine translation among professional translators.” Perspectives 26 (3): 301–321. CrossrefGoogle Scholar
Cadwell, Patrick, Sheila Castilho, Sharon O’Brien, and Linda Mitchell
2016 “Human Factors in Machine Translation and Post-editing among Institutional Translators.” Translation Spaces 5 (2): 222–243. CrossrefGoogle Scholar
Chen, Fang, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, and Dan Conway
2016 Robust Multimodal Cognitive Load Measurement. Cham: Springer. CrossrefGoogle Scholar
Ciriello, Livia
2019 Post-Editing und Kreativität [Post-editing and Creativity]. Master’s Thesis, ZHAW Zurich University of Applied Sciences.Google Scholar
Coiro, Julie, Michele Knobel, Colin Lankshear, and Donald J. Leu
2014 “Central Issues in New Literacies and New Literacies Research.” In The Handbook of Research on New Literacies. Edited by J. Coiro, M. Knobel, C. Lankshear, and D. J. Leu, 1–21. New York; NY: Taylor & Francis. CrossrefGoogle Scholar
Cooper, Alan
2004 The Inmates are Running the Asylum: Why hi-tech products drive us crazy and how to restore the sanity. Indianapolis: Sams Publishing.Google Scholar
Depraetere, Ilse
2010 “What Counts as Useful Advice in a University Post-Editing Training Context? Report on a case study.” 14th Annual Meeting of the European Association for Machine Translation. Accessed April 19, 2020. http://​www​.mt​-archive​.info​/10​/EAMT​-2010​-Depraetere​-2​.pdf
Earley, P. Christopher, and Soon Ang
2003 Cultural Intelligence. Individual interactions across cultures. Stanford, CA: Stanford Business Books.Google Scholar
Ehrensberger-Dow, Maureen, and Sharon O’Brien
2015 “Ergonomics of the Translation Workplace: Potential for cognitive friction.” Translation Spaces 4 (1): 98–118. CrossrefGoogle Scholar
Elming, Jakob, Laura W. Balling, and Michael Carl
2014 “Investigating User Behaviour in Post-editing and Translation using the CASMACAT Workbench.” In Post-editing of Machine Translation: Processes and applications. Edited by S. O’Brien, L. W. Balling, M. Carl, M. Simard, and L. Specia, 147–169. Newcastle upon Tyne: Cambridge Scholars.Google Scholar
EMT
2017 European Master’s in Translation Competence Framework 2017. Brussels: European Commission.Google Scholar
Federici, Federico M., and Christophe Declercq
eds. 2019 Intercultural Crisis Communication. London: Bloomsbury.Google Scholar
Forcada, Mikel L.
2017 “Making Sense of Neural Machine Translation.” Translation Spaces 6 (2): 291–309. CrossrefGoogle Scholar
Goulet, Marie-Josée, Michel Simard, Carla Parra Escartín, and Sharon O’Brien
2017 “La traduction automatique comme outil d’aide à la rédaction scientifique en anglais langue seconde: résultats d’une étude exploratoire sur la qualité linguistique. [Machine Translation as an aid for ESL Academic Writing: Results of an exploratory study on linguistic quality].” La revue du GERAS 721: 5–28. CrossrefGoogle Scholar
Green, Spence, Jeffrey Heer, and Christopher D. Manning
2013 “The Efficacy of Human Post-editing for Language Translation.” Proceedings of the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 439–448. CrossrefGoogle Scholar
Kappus, Martin, and Maureen Ehrensberger-Dow
forthcoming. “The Ergonomics of Translation Tools: Understanding When Less is Actually More.” The Interpreter and Translator Trainer.
Krings, Hans P.
2001 Repairing Texts: Empirical Investigations of machine translation post-editing processes. Edited by G. S. Koby. Kent, OH: Kent State University Press.Google Scholar
Langset, Inger Dagrunn, Dan Yngve Jakobsen, and Halvdan Haugsbakken
2018 “Digital Professional Development: Towards a collaborative learning approach for taking higher education into the digitalized age.” Nordic Journal of Digital Literacy 13 (1): 24–39. CrossrefGoogle Scholar
Language Industry Survey
2019 Expectations and Concerns of the European Language Industry. Accessed April 19, 2020. https://​euatc​.org​/wp​-content​/uploads​/2019​/11​/2019​-Language​-Industry​-Survey​-Report​.pdf
Läubli, Samuel, Sheila Castilho, Graham Neubig, Rico Sennrich, Qinlan Shen, and Antonio Toral
2020 “A Set of Recommendations for Assessing Human–Machine Parity in Language Translation.” Journal of Artificial Intelligence Research 671: 653–672. CrossrefGoogle Scholar
Lewis, William
2010 “Haitian Creole: How to build and ship an MT engine from scratch in 4 days, 17 hours, & 30 minutes.” 14th Annual Meeting of the EAMT. Accessed April 19, 2020. https://​pdfs​.semanticscholar​.org​/9083​/5bc87814208e303ebb3e8b58eed03aa4063e​.pdf​?_ga​=2​.126917512​.445420217​.1587735964​-1033436181​.1587330375
Lewis, William, Rob Munro, and Stephan Vogel
2011 “Crisis MT: Developing a Cookbook for MT in Crisis Situations.” Proceedings of the 6th Workshop on Statistical Machine Translation 501–511. Accessed April 19, 2020. https://​www​.aclweb​.org​/anthology​/W11​-21
Lillis, Theresa, and Mary Jane Curry
2010 Academic Writing in a Global Context: The politics and practices of publishing in English. London: Routledge.Google Scholar
Martindale, Marianna J., and Marine Carpuat
2018 “Fluency over Accuracy: A pilot study in measuring user trust in imperfect MT.” Proceedings of AMTA 2018 11: 13–25. Accessed April 19, 2020. http://​aclweb​.org​/anthology​/W18​-1803
Massey, Gary, and Maureen Ehrensberger-Dow
2017 “Machine Learning—Implications for translator education.” Lebende Sprache 62 (2): 300–312. CrossrefGoogle Scholar
Mellinger, Christopher D.
2014 Computer-assisted Translation: An empirical investigation of cognitive effort. Doctoral Dissertation, Kent State University.Google Scholar
Moorkens, Joss, Antonio Toral, Sheila Castilho, and Andy Way
2018 “Translators’ Perceptions of Literary Post-editing Using Statistical and Neural Machine Translation.” Translation Spaces 7 (2): 240–262. CrossrefGoogle Scholar
Muñoz Martín, Ricardo
2016 Of Minds and Men—Computers and translators. Poznań Studies in Contemporary Linguistics 52 (2): 351–381. CrossrefGoogle Scholar
Nakamura, Jeanne, and Mihaly Csikszentmihalyi
2002 “Flow Theory and Research.” In The Oxford Handbook of Positive Psychology. Edited by C. R. Snyder, and S. J. Lopez, 89–105. Oxford: Oxford University Press.Google Scholar
Nitzke, Jean, Silvia Hansen-Schirra, and Carmen Canfora
2019 “Risk Management and Post-editing Competence.” The Journal of Specialised Translation 311: 239–259.Google Scholar
Nurminen, Mary
2019 “Decision-making, Risk, and Gist Machine Translation in the Work of Patent Professionals.” Proceedings of the 8th Workshop on Patent and Scientific Literature Translation. Accessed April 19, 2020. https://​www​.aclweb​.org​/anthology​/W19​-7204​.pdf
2020 “Raw Machine Translation Use by Patent Professionals. A case of distributed cognition.” Translation, Cognition & Behavior 3 (1): 100–121. CrossrefGoogle Scholar
O’Brien, Sharon
2005 “Methodologies for Measuring the Correlations between Post-Editing Effort and Machine Translatability.” Machine Translation 191: 37–58. CrossrefGoogle Scholar
2006 “Pauses as Indicators of Cognitive Effort in Post-editing Machine Translation Output.” Across Languages and Cultures 7 (1): 1–21. CrossrefGoogle Scholar
O’Brien, Sharon, Maureen Ehrensberger-Dow, Marcel Hasler, and Megan Connolly
2017 “Irritating CAT Tool Features that Matter to Translators.” Hermes Journal of Language and Communication in Business 561: 145–162. Accessed April 19, 2020. https://​tidsskrift​.dk​/her​/article​/view​/97229​/146028
O’Brien, Sharon, Michel Simard, and Marie-Josée Goulet
2018 “Machine Translation and Self-post-editing for Academic Writing Support: Quality explorations.” In Translation Quality Assessment: From Principles to Practice. Edited by J. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, 237–262. Cham: Springer. CrossrefGoogle Scholar
Paas, Fred, Juhani E. Tuovinen, Huib Tabbers, and Pascal W. M. Van Gerven
2003 “Cognitive Load Measurement as a Means to Advance Cognitive Load Theory.” Educational Psychologist 38 (1): 63–71. CrossrefGoogle Scholar
Rossetti, Alessandra, Sharon O’Brien, and Patrick Cadwell
2020 “Comprehension and Trust in Crises: Investigating the impact of machine translation and post-editing.” Proceedings of EAMT 2020. https://​www​.aclweb​.org​/anthology​/2020​.eamt​-1​.2​.pdf
Seel, Norbet M.
ed. 2012 Encyclopedia of the Sciences of Learning. New York: Springer. CrossrefGoogle Scholar
Shopova, Tatiana
2014 “Digital Literacy of Students and Its Improvement at the University.” Journal on Efficiency and Responsibility in Education and Science 7 (2): 26–32. CrossrefGoogle Scholar
Somers, Harold
1997 “A Practical Approach to Using Machine Translation Software.” The Translator 3 (2): 193–212. CrossrefGoogle Scholar
Soricut, Radu, and Abdessamad Echihabi
2010 “TrustRank: Inducing trust in automatic translations via ranking.” 48th Annual Meeting of the Association for Computational Linguistics: 612–621. Accessed April 19, 2020. https://​www​.aclweb​.org​/anthology​/P10​-1063​.pdf
Spante, Maria, Sylvana S. Hashemi, Mona Lundin, and Anne Algers
2018 “Digital Competence and Digital Literacy in Higher Education Research: Systematic review of concept use.” Cogent Education 5 (1). CrossrefGoogle Scholar
Ståhl, Tore
2017 “How ICT Savvy are Digital Natives Actually?” Nordic Journal of Digital Literacy 12 (3): 89–108. CrossrefGoogle Scholar
Sweller, John
1988 “Cognitive Load during Problem Solving: Effects on learning.” Cognitive Science 12 (2): 257–285. CrossrefGoogle Scholar
Toral, Antonio, Sheila Castilho, Ke Hu, and Andy Way
2018 “Attaining the Unattainable? Reassessing claims of human parity in neural machine translation.” Accessed April 19, 2020. arXiv:1808.10432.Google Scholar
van Laar, Esther, Alexander J. A. M. van Deursen, Johannes A. G. M. van Dijk, and Jos de Haan
2017 “The Relation between 21st-century Skills and Digital Skills: A systematic literature review.” Computers in Human Behavior 721: 577–588. CrossrefGoogle Scholar
Wahler, Madison Elizabeth
2018 “A Word is Worth a Thousand Words: Legal implications of relying on machine translation technology.” Stetson Law Review 481: 109–139.Google Scholar
Willey, Ian, and Kimie Tanimoto
2015 “ ‘We’re Drifting into Strange Territory Here’: What think-aloud protocols reveal about convenience editing.” Journal of Second Language Writing 271: 63–83. CrossrefGoogle Scholar
Cited by

Cited by 1 other publications

Paulsen Christensen, Tina, Kristine Bundgaard, Anne Schjoldager & Helle Dam Jensen
2021. What motor vehicles and translation machines have in common - a first step towards a translation automation taxonomy. Perspectives ► pp. 1 ff. Crossref logo

This list is based on CrossRef data as of 27 april 2021. 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.