Universität Wien
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340053 UE AI and Translation (2024W)

5.00 ECTS (2.00 SWS), SPL 34 - Translationswissenschaft
Continuous assessment of course work
Moodle
Mo 20.01. 13:15-14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

Languages: English, German

  • Monday 14.10. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 21.10. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 28.10. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 04.11. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 11.11. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 18.11. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 02.12. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 09.12. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 16.12. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Monday 13.01. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG

Information

Aims, contents and method of the course

Aims:

This course aims to provide basic knowledge and practical skills in the use of artificial intelligence (AI) in written translation. Upon completion of this course, students should:
• be familiar with principles and tools used by technical writers;
• be able to work with computer-assisted translation (CAT) tools;
• be familiar with the basics of controlled languages and be able to apply them to pre-editing;
• know the features of current neural machine translation (NMT) systems;
• know the difference between light and full post-editing;
• be able to post-edit effectively in their language pair;
• be familiar with the use of other relevant AI applications in translation (automatic speech recognition, automatic speech synthesis);
• be familiar with the basics of Large Language Models for language specialists and gain practical experience of prompt design.

Contents:
Overview of the state-of-the-art of neural machine translation and Large Language Models, and their use in translation; hands-on work with current AI systems and CAT tools.

Methods:
Input from the course instructors, presentations on relevant topics, independent work on practical tasks with relevant software, independent reading of relevant literature, discussion of the contents during the sessions.

Assessment and permitted materials

Reflections on coursework – 40%
Presentations – 30%
Portfolio – 30%

You may use any kind of resource to complete the tasks.

Minimum requirements and assessment criteria

Attendance and the submission of all assignments are minimum requirements for a positive grade. You are allowed to miss a maximum of two sessions.

Grades:
0-60 unsatisfactory (nicht genügend),
61-70 sufficient (genügend),
71-80 satisfactory (befriedigend),
81-90 good (gut),
91-100 very good (sehr gut).

Recommended skills: Basic use of the Windows operating system.

Recommended programmes: MS Office Word, MS Office Excel or equivalent OpenOffice programmes, file compression software (zip).

Examination topics

The topics of all three assessment components will be centred on the course contents.

Reading list

• Chan, S. (Hrsg.). (2023). Routledge Encyclopedia of Translation Technology (2. Aufl.). Routledge. https://doi.org/10.4324/9781003168348
• Guerberof Arenas, A. (2020). Pre-editing and post-editing. In E. Angelone, M. Ehrensberger-Dow, & G. Massey (Hrsg.), The Bloomsbury Companion to Language Industry Studies (S. 333–360). Bloomsbury.
• International Organization for Standardization. (2017). ISO 18587:2017 Translation services – Post-editing of machine translation output – Requirements. http://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/06/29/62970.html
• Kenny, D. (Hrsg.). (2022). Machine translation for everyone. Language Science Press. https://doi.org/10.5281/zenodo.6653406
• Koponen, M., Mossop, B., Robert, I. S., & Scocchera, G. (Hrsg.). (2020). Translation Revision and Post-editing: Industry Practices and Cognitive Processes. Routledge. https://www.routledge.com/Translation-Revision-and-Post-editing-Industry-Practices-and-Cognitive/Koponen-Mossop-Robert-Scocchera/p/book/9781138549715
• Moniz, H., & Parra Escartín, C. (Hrsg.). (2023). Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (Bd. 4). Springer International Publishing. https://doi.org/10.1007/978-3-031-14689-3
• Nitzke, J., & Hansen-Schirra, S. (2021). A short guide to post-editing. Language Science Press. https://doi.org/10.5281/zenodo.5646896
• Translation Centre for the Bodies of the European Union. (2019). Writing for translation. Publications Office. https://data.europa.eu/doi/10.2817/95648
• Translation Centre for the Bodies of the European Union. (2021). Writing for machine translation. Publications Office. https://data.europa.eu/doi/10.2817/191981

Association in the course directory

Last modified: We 18.09.2024 12:27