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

MT Literacy—A cognitive view

ORCID logo | Dublin City University
ORCID logo | 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
DOI logo
https://doi.org/10.1075/tcb.00038.obr
References (58)
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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Cadwell, Patrick. 2019. “Trust, Distrust and Translation in a Disaster.” Disaster Prevention and Management 29 (2): 157–174. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. [URL]
Earley, P. Christopher, and Soon Ang. 2003. Cultural Intelligence. Individual interactions across cultures. Stanford, CA: Stanford Business Books. DOI logoGoogle Scholar
Ehrensberger-Dow, Maureen, and Sharon O’Brien. 2015. “Ergonomics of the Translation Workplace: Potential for cognitive friction.” Translation Spaces 4 (1): 98–118. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Language Industry Survey. 2019. Expectations and Concerns of the European Language Industry. Accessed April 19, 2020. [URL]
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. DOI logoGoogle 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. [URL]
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. [URL]
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. [URL]
Massey, Gary, and Maureen Ehrensberger-Dow. 2017. “Machine Learning—Implications for translator education.” Lebende Sprache 62 (2): 300–312. DOI logoGoogle 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. DOI logoGoogle Scholar
Muñoz Martín, Ricardo. 2016. Of Minds and Men—Computers and translators. Poznań Studies in Contemporary Linguistics 52 (2): 351–381. DOI logoGoogle 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. [URL]
. 2020. “Raw Machine Translation Use by Patent Professionals. A case of distributed cognition.” Translation, Cognition & Behavior 3 (1): 100–121. DOI logoGoogle Scholar
O’Brien, Sharon. 2005. “Methodologies for Measuring the Correlations between Post-Editing Effort and Machine Translatability.” Machine Translation 191: 37–58. DOI logoGoogle Scholar
. 2006. “Pauses as Indicators of Cognitive Effort in Post-editing Machine Translation Output.” Across Languages and Cultures 7 (1): 1–21. DOI logoGoogle 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. [URL]
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. DOI logoGoogle 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. DOI logoGoogle 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. [URL]
Seel, Norbet M. ed. 2012. Encyclopedia of the Sciences of Learning. New York: Springer. DOI logoGoogle 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. DOI logoGoogle Scholar
Somers, Harold. 1997. “A Practical Approach to Using Machine Translation Software.” The Translator 3 (2): 193–212. DOI logoGoogle 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. [URL]
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). DOI logoGoogle Scholar
Ståhl, Tore. 2017. “How ICT Savvy are Digital Natives Actually?” Nordic Journal of Digital Literacy 12 (3): 89–108. DOI logoGoogle Scholar
Sweller, John. 1988. “Cognitive Load during Problem Solving: Effects on learning.” Cognitive Science 12 (2): 257–285. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
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