Cover not available
Article published In:
Target
Vol. 36:2 (2024) ► pp.245–275

A competence matrix for machine translation-oriented data literacy teaching

ORCID logo | TH Köln – University of Applied Sciences
ORCID logo | Ghent University
This article presents a matrix of competence descriptors aimed at machine translation-oriented data literacy teaching. This competence matrix constitutes the didactics-facing side of the DataLitMT project, which develops learning resources for teaching relevant components of data literacy in their translation-specific form of professional machine translation (MT) literacy to BA and MA students in translation and specialised communication. After highlighting the increasing relevance of both professional MT literacy and data literacy in the context of Translation Studies and professional translation, the article presents and discusses a professional MT literacy framework and an MT-specific data literacy framework, which serve to structure the two frames of reference relevant to this article. Then, the article provides a detailed discussion of the competence matrix developed based on the two frameworks sketched previously. This discussion is intended to show how the individual dimensions and sub-dimensions of data literacy were linked to relevant (sub-)dimensions of professional MT literacy and translated into corresponding competence descriptors. To conclude, the article presents an example of a learning resource for MT-oriented data literacy teaching developed based on the descriptors of the competence matrix.
Keywords: translation didactics, machine translation, professional machine translation literacy, data literacy, competence matrix, competence descriptors
Article outline
  • 1.Introduction
  • 2.Providing internal structure to the concepts of professional MT literacy and data literacy
    • 2.1A professional MT literacy framework
    • 2.2An MT-specific data literacy framework
  • 3.Combining professional MT literacy and data literacy in the DataLitMT competence matrix
    • 3.1Competence descriptors at the interface of data context and MT
    • 3.2Competence descriptors at the interface of data planning and MT
    • 3.3Competence descriptors at the interface of data collection/production and MT
    • 3.4Competence descriptors at the interface of data evaluation and MT
    • 3.5Competence descriptors at the interface of data use and MT
  • 4.Example of a learning resource of the DataLitMT project
  • 5.Conclusions
  • Acknowledgements
  • Notes
  • References
Published online: 9 April 2024
DOI logo
https://doi.org/10.1075/target.22127.kru
References (54)
References
Bentivogli, Luisa, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2018. “Neural versus Phrase-Based MT Quality: An in-Depth Analysis on English–German and English–French.” Computer Speech & Language 491: 52–70. DOI logoGoogle Scholar
Bloom, Benjamin S. 1956. “Taxonomy of Educational Objectives: The Classification of Educational Goals; Handbook I: Cognitive Domain.” In Taxonomy of Educational Objectives: The Classification of Educational Goals; Handbook I: Cognitive Domain, edited by Benjamin S. Bloom, Max D. Engelhart, Edward J. Furst, Walker H. Hill, and David R. Krathwohl, 1–61. New York: Longman.Google 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
Bowker, Lynne. 2021. “Machine Translation Use Outside the Language Industries: A Comparison of Five Delivery Formats for Machine Translation Literacy Instruction.” In TRanslation and Interpreting Technology ONline. Proceedings of the Conference, edited by Ruslan Mitkov, Vilhelmini Sosoni, Julie Christine Giguère, Elena Murgolo, and Elizabeth Deysel, 25–36. Shoumen: Incoma Ltd. [URL] DOI logo
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.” Revista Tradumàtica 161: 125–133.Google Scholar
Carl, Michael, and Barbara Dragsted. 2017. “Inside the Monitor Model: Processes of Default and Challenged Translation Production.” In Crossroads between Contrastive Linguistics, Translation Studies and Machine Translation: TC3-II, edited by Oliver Czulo and Silvia Hansen-Schirra, 5–30. Berlin: Language Science Press. [URL]
Carl, Michael, and Moritz Schaeffer. 2017. “Sketch of a Noisy Channel Model for the Translation Process.” In Empirical Modelling of Translation and Interpreting, edited by Silvia Hansen-Schirra, Oliver Czulo, and Sascha Hofmann, 71–116. Berlin: Language Science Press. [URL]
Christensen, Tina Paulsen, Marian Flanagan, and Anne Schjoldager. 2017. “Mapping Translation Technology Research in Translation Studies: An Introduction to the Thematic Section.” Hermes – Journal of Language and Communication in Business 561: 7–20. DOI logoGoogle Scholar
Council of Europe. 2020. Common European Framework of Reference for Languages: Learning, Teaching, Assessment – Companion Volume. Strasbourg: Council of Europe Publishing.Google Scholar
DataLitMT. 2023. “DataLitMT – Teaching Data Literacy in the Context of Machine Translation Literacy.” Project website. [URL]
Do Carmo, Félix, and Joss Moorkens. 2021. “Differentiating Editing, Post-Editing and Revision.” In Translation, Revision and Post-Editing: Industry Practices and Cognitive Processes, edited by Maarit Koponen, Brian Mossop, Isabelle Robert, and Giovanna Scocchera, 35–49. Abingdon: Routledge.Google Scholar
Dorst, Aletta G., Susana Valdez, and Heather Bouman. 2022. “Machine Translation in the Multilingual Classroom: How, When and Why Do Humanities Students at a Dutch University Use Machine Translation?” Translation and Translanguaging in Multilingual Contexts 8 (1): 49–66. DOI logoGoogle Scholar
ELIS Research. 2022. European Language Industry Survey 2022. [URL]
EMT. 2022. “European Master’s in Translation Competence Framework 2022.” Website of the DG Translation of the European Commission. [URL]
European Commission. n. d. “Description of the Eight EQF Levels.” [URL]
European Union Institutions. 2019. EU Host Paper: New Technologies and Artificial Intelligence in the Field of Language and Conference Services. [URL]
Ginovart Cid, Clara, and Carme Colominas Ventura. 2021. “The MT Post-Editing Skill Set: Course Descriptions and Educators’ Thoughts.” In Translation Revision and Post-Editing: Industry Practices and Cognitive Processes, edited by Maarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 226–246. Abingdon: Routledge.Google Scholar
Göpferich, Susanne. 2008. Translationsprozessforschung: Stand – Methoden – Perspektiven [Translation process research: State of the art – methods – perspectives]. Tübingen: Narr.Google Scholar
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.” arXiv. [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. DOI logoGoogle Scholar
Kenny, Dorothy. 2019. “Machine Translation.” In The Routledge Handbook of Translation and Philosophy, edited by Piers Rawling and Philip Wilson, 428–445. Abingdon: Routledge.Google Scholar
. 2020. “Technology and Translator Training.” In The Routledge Handbook of Translation and Technology, edited by Minako O’Hagan, 498–515. Abingdon: Routledge.Google Scholar
. 2022a. “Introduction.” In Kenny (2022b, v–viii).Google Scholar
, ed. 2022b. Machine Translation for Everyone. Empowering Users in the Age of Artificial Intelligence. Berlin: Language Science Press.Google Scholar
Kiraly, Don. 2013. “Towards a View of Translator Competence as an Emergent Phenomenon: Thinking Outside the Box(es) in Translator Education.” In New Prospects and Perspectives for Educating Language Mediators, edited by Don Kiraly, Silvia Hansen-Schirra, and Karin Maksymski, 197–224. Tübingen: Narr.Google Scholar
Koehn, Philipp. 2020. Neural Machine Translation. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Krüger, Ralph. 2022a. “Integrating Professional Machine Translation Literacy and Data Literacy.” Lebende Sprachen 67 (2): 247–282. DOI logoGoogle Scholar
. 2022b. “Using Jupyter Notebooks as Didactic Instruments in Translation Technology Teaching.” The Interpreter and Translator Trainer 16 (4): 503–523. DOI logoGoogle Scholar
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
Long, Duri, and Brian Magerko. 2020. “What is AI Literacy? Competencies and Design Considerations.” In CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, edited by Regina Bernhaupt, Florian Mueller, David Verweij, and Josh Andres, 1–16. New York: Association for Computing Machinery. DOI logoGoogle Scholar
Loock, Rudy, and Sophie Léchauguette. 2022. “Machine Translation Literacy and Undergraduate Students in Applied Languages: Report on an Exploratory Study.” Revista Tradumàtica 191: 204–225.Google Scholar
Misra, Archita. 2021. “Advancing Data Literacy in the Post-Pandemic World: A Primer to Catalyse Policy Dialogue and Action.” Draft discussion paper for the 2021 PARIS21 Annual Meetings Data as a Public Good: Building Resilience for a Post-Pandemic World . PARIS21. [URL]
Moorkens, Joss, and Dave Lewis. 2019. “Research Questions and a Proposal for the Future Governance of Translation Data.” Journal of Specialised Translation 321: 2–25.Google Scholar
Moorkens, Joss, and Marta Rocchi. 2021. “Ethics in the Translation Industry.” In The Routledge Handbook of Translation and Ethics, edited by Kaisa Koskinen and Nike K. Pokorn, 320–337. Abingdon: Routledge.Google Scholar
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. DOI logoGoogle Scholar
. 2022. “Ethics and Machine Translation.” In Kenny (2022b, 121–140).Google Scholar
Nitzke, Jean, Silvia Hansen-Schirra, and Carmen Canfora. 2019. “Risk Management and Post-Editing Competence.” Journal of Specialised Translation 311: 239–259.Google Scholar
O’Brien, Sharon, Maureen Ehrensberger-Dow, Marcel Hasler, and Megan Conolly. 2017. “Irritating CAT Tool Features that Matter to Translators.” Hermes – Journal of Language and Communication in Business 561: 145–162. DOI logoGoogle Scholar
Olohan, Maeve. 2017. “Technology, Translation and Society.” Target 29 (2): 264–283. DOI logoGoogle Scholar
PACTE Group (Amparo Hurtado Albir, Anabel Galán-Mañas, Anna Kuznik, Christian Olalla-Soler, Patricia Rodríguez-Inés, and Lupe Romero). 2018. “Competence Levels in Translation: Working Towards a European Framework.” The Interpreter and Translator Trainer 12 (2): 111–131. DOI logoGoogle Scholar
Popel, Martin, Marketa Tomkova, Jakub Tomek, Łukasz Kaiser, Jakob Uszkoreit, Ondřej Bojar, and Zdeněk Žabokrtský. 2020. “Transforming Machine Translation: A Deep Learning System Reaches News Translation Quality Comparable to Human Professionals.” Nature Communications 111: 1–15. DOI logoGoogle Scholar
Ramírez-Sánchez, Gema. 2022. “Custom Machine Translation.” In Kenny (2022b, 165–186).Google Scholar
Ridsdale, Chantel, James Rothwell, Mike Smit, Hossam Ali-Hassan, Michael Bliemel, Dean Irvine, Daniel Kelley, Stan Matwin, and Brad Wuetherick. 2015. Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Dalhousie University. [URL]
Rupcic, Kerstin. 2021. Einsatzpotenziale maschineller Übersetzung in der juristischen Fachübersetzung [Potential uses of machine translation in legal translation]. Berlin: Frank & Timme.Google Scholar
Schüller, Katharina. 2020. Working Paper No. 53 – Future Skills: A Framework for Data Literacy. Essen: Hochschulforum Digitalisierung. [URL]
Stanovsky, Gabriel, Noah A. Smith, and Luke Zettlemoyer. 2019. “Evaluating Gender Bias in Machine Translation.” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, edited by Anna Korhonen, David Traum, and Lluís Màrquez, 1679–1684. Florence: Association for Computational Linguistics. [URL] DOI logo
TAUS. 2018. “Translators in the Algorithmic Age: A Briefing Based on the Takeaways from TAUS Industry Leaders Forum ’18.” TAUS. [URL]
Toral, Antonio, and Víctor M. Sánchez-Cartagena. 2017. “A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions.” In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, edited by Mirella Lapata, Phil Blunsom, and Alexander Koller, 1063–1073. Valencia: Association for Computational Linguistics. [URL] DOI logo
Toral, Antonio. 2019. “Post-Editese: An Exacerbated Translationese.” In Proceedings of Machine Translation Summit XVII: Research Track, edited by Mikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 273–281. Dublin: European Association for Machine Translation. [URL]
Vanmassenhove, Eva, Dimitar Shterionov, and Matthew Gwilliam. 2021. “Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation.” In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, edited by Paola Merlo, Jörg Tiedemann, and Reut Tsarfaty, 2203–2213. Online: Association for Computational Linguistics. DOI logoGoogle Scholar
Vanmassenhove, Eva, Dimitar Shterionov, and Andy Way. 2019. “Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation.” In Proceedings of Machine Translation Summit XVII: Research Track, edited by Mikel L. Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 222–232. Dublin: European Association for Machine Translation.Google Scholar
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” In Advances in Neural Information Processing Systems 30 (NIPS 2017), edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, 1–11. Long Beach: Curran Associates. [URL]
Vieira, Lucas Nunes, and Elisa Alonso. 2020. “Translating Perceptions and Managing Expectations: An Analysis of Management and Production Perspectives on Machine Translation.” Perspectives 28 (2): 163–184. DOI logoGoogle Scholar
Vygotsky, Lev S. 1978. Mind in Society: Development of Higher Psychological Processes. 14th ed. Harvard: Harvard University Press.Google Scholar