1887
Volume 36, Issue 2
  • ISSN 0924-1884
  • E-ISSN: 1569-9986
USD
Buy:$35.00 + Taxes

Abstract

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.1075/target.22127.kru
2024-04-09
2025-04-26
Loading full text...

Full text loading...

References

  1. 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 & Language491: 52–70. 10.1016/j.csl.2017.11.004
    https://doi.org/10.1016/j.csl.2017.11.004 [Google Scholar]
  2. Bloom, Benjamin S.
    1956 “Taxonomy of Educational Objectives: The Classification of Educational Goals; Handbook I: Cognitive Domain.” InTaxonomy of Educational Objectives: The Classification of Educational Goals; Handbook I: Cognitive Domain, edited byBenjamin S. Bloom, Max D. Engelhart, Edward J. Furst, Walker H. Hill, and David R. Krathwohl, 1–61. New York: Longman.
    [Google Scholar]
  3. Bowker, Lynne, and Jairo Buitrago Ciro
    2019Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley: Emerald Publishing.
    [Google Scholar]
  4. Bowker, Lynne
    2021 “Machine Translation Use Outside the Language Industries: A Comparison of Five Delivery Formats for Machine Translation Literacy Instruction.” InTRanslation and Interpreting Technology ONline. Proceedings of the Conference, edited byRuslan Mitkov, Vilhelmini Sosoni, Julie Christine Giguère, Elena Murgolo, and Elizabeth Deysel, 25–36. Shoumen: Incoma Ltd. triton-conference.org/proceedings/10.26615/978‑954‑452‑071‑7_004
    https://doi.org/10.26615/978-954-452-071-7_004 [Google Scholar]
  5. 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 Tradumàtica161: 125–133.
    [Google Scholar]
  6. Carl, Michael, and Barbara Dragsted
    2017 “Inside the Monitor Model: Processes of Default and Challenged Translation Production.” InCrossroads between Contrastive Linguistics, Translation Studies and Machine Translation: TC3-II, edited byOliver Czulo and Silvia Hansen-Schirra, 5–30. Berlin: Language Science Press. https://langsci-press.org/catalog/view/102/191/861-3
    [Google Scholar]
  7. Carl, Michael, and Moritz Schaeffer
    2017 “Sketch of a Noisy Channel Model for the Translation Process.” InEmpirical Modelling of Translation and Interpreting, edited bySilvia Hansen-Schirra, Oliver Czulo, and Sascha Hofmann, 71–116. Berlin: Language Science Press. https://langsci-press.org/catalog/book/132
    [Google Scholar]
  8. 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 Business561: 7–20. 10.7146/hjlcb.v0i56.97199
    https://doi.org/10.7146/hjlcb.v0i56.97199 [Google Scholar]
  9. Council of Europe
    Council of Europe 2020Common European Framework of Reference for Languages: Learning, Teaching, Assessment – Companion Volume. Strasbourg: Council of Europe Publishing.
    [Google Scholar]
  10. DataLitMT
    DataLitMT 2023 “DataLitMT – Teaching Data Literacy in the Context of Machine Translation Literacy.” Project website. https://itmk.github.io/The-DataLitMT-Project/
  11. Do Carmo, Félix, and Joss Moorkens
    2021 “Differentiating Editing, Post-Editing and Revision.” InTranslation, Revision and Post-Editing: Industry Practices and Cognitive Processes, edited byMaarit Koponen, Brian Mossop, Isabelle Robert, and Giovanna Scocchera, 35–49. Abingdon: Routledge.
    [Google Scholar]
  12. 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 Contexts8 (1): 49–66. 10.1075/ttmc.00080.dor
    https://doi.org/10.1075/ttmc.00080.dor [Google Scholar]
  13. ELIS Research
    ELIS Research 2022European Language Industry Survey 2022. https://elis-survey.org
    [Google Scholar]
  14. EMT
    EMT 2022 “European Master’s in Translation Competence Framework 2022.” Website of the DG Translation of the European Commission. https://commission.europa.eu/system/files/2022-11/emt_competence_fwk_2022_en.pdf
  15. European Commission
    European Commission. n. d. “Description of the Eight EQF Levels.” https://europa.eu/europass/en/description-eight-eqf-levels
  16. European Union Institutions
    European Union Institutions 2019EU Host Paper: New Technologies and Artificial Intelligence in the Field of Language and Conference Services. https://ec.europa.eu/education/knowledge-centre-interpretation/news/eu-host-paper-new-technologies-and-artificial-intelligence-field-language-and-conference_en
    [Google Scholar]
  17. Ginovart Cid, Clara, and Carme Colominas Ventura
    2021 “The MT Post-Editing Skill Set: Course Descriptions and Educators’ Thoughts.” InTranslation Revision and Post-Editing: Industry Practices and Cognitive Processes, edited byMaarit Koponen, Brian Mossop, Isabelle S. Robert, and Giovanna Scocchera, 226–246. Abingdon: Routledge.
    [Google Scholar]
  18. Göpferich, Susanne
    2008Translationsprozessforschung: Stand – Methoden – Perspektiven [Translation process research: State of the art – methods – perspectives]. Tübingen: Narr.
    [Google Scholar]
  19. 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. https://arxiv.org/abs/1803.05567
    [Google Scholar]
  20. Kenny, Dorothy, and Stephen Doherty
    2014 “Statistical Machine Translation in the Translation Curriculum: Overcoming Obstacles and Empowering Translators.” The Interpreter and Translator Trainer8 (2): 276–294. 10.1080/1750399X.2014.936112
    https://doi.org/10.1080/1750399X.2014.936112 [Google Scholar]
  21. Kenny, Dorothy
    2019 “Machine Translation.” InThe Routledge Handbook of Translation and Philosophy, edited byPiers Rawling and Philip Wilson, 428–445. Abingdon: Routledge.
    [Google Scholar]
  22. 2020 “Technology and Translator Training.” InThe Routledge Handbook of Translation and Technology, edited byMinako O’Hagan, 498–515. Abingdon: Routledge.
    [Google Scholar]
  23. 2022a “Introduction.” InKenny (2022b, v–viii).
    [Google Scholar]
  24. ed. 2022bMachine Translation for Everyone. Empowering Users in the Age of Artificial Intelligence. Berlin: Language Science Press.
    [Google Scholar]
  25. Kiraly, Don
    2013 “Towards a View of Translator Competence as an Emergent Phenomenon: Thinking Outside the Box(es) in Translator Education.” InNew Prospects and Perspectives for Educating Language Mediators, edited byDon Kiraly, Silvia Hansen-Schirra, and Karin Maksymski, 197–224. Tübingen: Narr.
    [Google Scholar]
  26. Koehn, Philipp
    2020Neural Machine Translation. Cambridge: Cambridge University Press. 10.1017/9781108608480
    https://doi.org/10.1017/9781108608480 [Google Scholar]
  27. Krüger, Ralph
    2022a “Integrating Professional Machine Translation Literacy and Data Literacy.” Lebende Sprachen67 (2): 247–282. 10.1515/les‑2022‑1022
    https://doi.org/10.1515/les-2022-1022 [Google Scholar]
  28. 2022b “Using Jupyter Notebooks as Didactic Instruments in Translation Technology Teaching.” The Interpreter and Translator Trainer16 (4): 503–523. 10.1080/1750399X.2021.2004009
    https://doi.org/10.1080/1750399X.2021.2004009 [Google Scholar]
  29. 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 Research671: 653–672. 10.1613/jair.1.11371
    https://doi.org/10.1613/jair.1.11371 [Google Scholar]
  30. Long, Duri, and Brian Magerko
    2020 “What is AI Literacy? Competencies and Design Considerations.” InCHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, edited byRegina Bernhaupt, Florian Mueller, David Verweij, and Josh Andres, 1–16. New York: Association for Computing Machinery. 10.1145/3313831.3376727
    https://doi.org/10.1145/3313831.3376727 [Google Scholar]
  31. Loock, Rudy, and Sophie Léchauguette
    2022 “Machine Translation Literacy and Undergraduate Students in Applied Languages: Report on an Exploratory Study.” Revista Tradumàtica191: 204–225.
    [Google Scholar]
  32. Misra, Archita
    2021 “Advancing Data Literacy in the Post-Pandemic World: A Primer to Catalyse Policy Dialogue and Action.” Draft discussion paper for the2021 PARIS21 Annual Meetings Data as a Public Good: Building Resilience for a Post-Pandemic World. PARIS21. https://paris21.org/sites/default/files/inline-files/DataLiteracy_Primer_0.pdf
    [Google Scholar]
  33. Moorkens, Joss, and Dave Lewis
    2019 “Research Questions and a Proposal for the Future Governance of Translation Data.” Journal of Specialised Translation321: 2–25.
    [Google Scholar]
  34. Moorkens, Joss, and Marta Rocchi
    2021 “Ethics in the Translation Industry.” InThe Routledge Handbook of Translation and Ethics, edited byKaisa Koskinen and Nike K. Pokorn, 320–337. Abingdon: Routledge.
    [Google Scholar]
  35. Moorkens, Joss
    2018 “What to Expect from Neural Machine Translation: A Practical In-Class Translation Evaluation Exercise.” The Interpreter and Translator Trainer12 (4): 375–387. 10.1080/1750399X.2018.1501639
    https://doi.org/10.1080/1750399X.2018.1501639 [Google Scholar]
  36. 2022 “Ethics and Machine Translation.” InKenny (2022b, 121–140).
    [Google Scholar]
  37. Nitzke, Jean, Silvia Hansen-Schirra, and Carmen Canfora
    2019 “Risk Management and Post-Editing Competence.” Journal of Specialised Translation311: 239–259.
    [Google Scholar]
  38. 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 Business561: 145–162. 10.7146/hjlcb.v0i56.97229
    https://doi.org/10.7146/hjlcb.v0i56.97229 [Google Scholar]
  39. Olohan, Maeve
    2017 “Technology, Translation and Society.” Target29 (2): 264–283. 10.1075/target.29.2.04olo
    https://doi.org/10.1075/target.29.2.04olo [Google Scholar]
  40. 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 Trainer12 (2): 111–131. 10.1080/1750399X.2018.1466093
    https://doi.org/10.1080/1750399X.2018.1466093 [Google Scholar]
  41. 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 Communications111: 1–15. 10.1038/s41467‑020‑18073‑9
    https://doi.org/10.1038/s41467-020-18073-9 [Google Scholar]
  42. Ramírez-Sánchez, Gema
    2022 “Custom Machine Translation.” InKenny (2022b, 165–186).
    [Google Scholar]
  43. Ridsdale, Chantel, James Rothwell, Mike Smit, Hossam Ali-Hassan, Michael Bliemel, Dean Irvine, Daniel Kelley, Stan Matwin, and Brad Wuetherick
    2015Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Dalhousie University. hdl.handle.net/10222/64578
    [Google Scholar]
  44. Rupcic, Kerstin
    2021Einsatzpotenziale maschineller Übersetzung in der juristischen Fachübersetzung [Potential uses of machine translation in legal translation]. Berlin: Frank & Timme.
    [Google Scholar]
  45. Schüller, Katharina
    2020Working Paper No. 53 – Future Skills: A Framework for Data Literacy. Essen: Hochschulforum Digitalisierung. https://hochschulforumdigitalisierung.de/de/future-skills-framework-data-literacy
    [Google Scholar]
  46. Stanovsky, Gabriel, Noah A. Smith, and Luke Zettlemoyer
    2019 “Evaluating Gender Bias in Machine Translation.” InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, edited byAnna Korhonen, David Traum, and Lluís Màrquez, 1679–1684. Florence: Association for Computational Linguistics. https://aclanthology.org/P19-1164/10.18653/v1/P19‑1164
    https://doi.org/10.18653/v1/P19-1164 [Google Scholar]
  47. TAUS
    TAUS 2018 “Translators in the Algorithmic Age: A Briefing Based on the Takeaways from TAUS Industry Leaders Forum ’18.” TAUS. https://info.taus.net/translators-in-the-algorithmic-age-no-time-to-read
  48. Toral, Antonio, and Víctor M. Sánchez-Cartagena
    2017 “A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions.” InProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, edited byMirella Lapata, Phil Blunsom, and Alexander Koller, 1063–1073. Valencia: Association for Computational Linguistics. https://aclanthology.org/E17-1100/10.18653/v1/E17‑1100
    https://doi.org/10.18653/v1/E17-1100 [Google Scholar]
  49. Toral, Antonio
    2019 “Post-Editese: An Exacerbated Translationese.” InProceedings of Machine Translation Summit XVII: Research Track, edited byMikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 273–281. Dublin: European Association for Machine Translation. https://aclanthology.org/W19-6627/
    [Google Scholar]
  50. Vanmassenhove, Eva, Dimitar Shterionov, and Matthew Gwilliam
    2021 “Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation.” InProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, edited byPaola Merlo, Jörg Tiedemann, and Reut Tsarfaty, 2203–2213. Online: Association for Computational Linguistics. 10.18653/v1/2021.eacl‑main.188
    https://doi.org/10.18653/v1/2021.eacl-main.188 [Google Scholar]
  51. Vanmassenhove, Eva, Dimitar Shterionov, and Andy Way
    2019 “Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation.” InProceedings of Machine Translation Summit XVII: Research Track, edited byMikel L. Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 222–232. Dublin: European Association for Machine Translation.
    [Google Scholar]
  52. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin
    2017 “Attention Is All You Need.” InAdvances in Neural Information Processing Systems 30 (NIPS 2017), edited byIsabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, 1–11. Long Beach: Curran Associates. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
    [Google Scholar]
  53. Vieira, Lucas Nunes, and Elisa Alonso
    2020 “Translating Perceptions and Managing Expectations: An Analysis of Management and Production Perspectives on Machine Translation.” Perspectives28 (2): 163–184. 10.1080/0907676X.2019.1646776
    https://doi.org/10.1080/0907676X.2019.1646776 [Google Scholar]
  54. Vygotsky, Lev S.
    1978Mind in Society: Development of Higher Psychological Processes. 14th ed. Harvard: Harvard University Press.
    [Google Scholar]
/content/journals/10.1075/target.22127.kru
Loading
/content/journals/10.1075/target.22127.kru
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error