Universität Wien

340007 UE Machine translation (2025W)

3.00 ECTS (2.00 SWS), SPL 34 - Translationswissenschaft
Continuous assessment of course work
Moodle
Tu 09.12. 15:00-16:30 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: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 14.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 21.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 28.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 04.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 11.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 18.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 02.12. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 16.12. 15:00 - 16:30 Digital
    Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 13.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Tuesday 20.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG

Information

Aims, contents and method of the course

- This course will be taught in person in the ZTW computer lab, without a hybrid or online option -
Goals:
Students will acquire hands-on computer-assisted translation (CAT) tool knowledge alongside machine translation (MT) integration, customisation, annotation, and post-editing expertise.
Using state-of-the-art technologies, students will learn to fine-tune pre-trained MT models, evaluate them using automatic and manual metrics, integrate them into a popular CAT tool, annotate their output using popular industry annotation frameworks, and post-edit MT output according to ISO standards.
Content:
• Computer-Assisted Translation (CAT) tools
• Rule-based (RBMT), statistical (SMT) and neural machine translation (NMT): current applications
• NMT architectures and fine-tuning pre-trained models
• MT evaluation metrics and annotation tools and techniques
• MT in professional workflows
• Post-editing machine translation (PEMT) standards and best practices
• Ethics of using MT and impact of MT on freelance linguists
Didactic approach:
Students will need to complete assignments involving a wide range of technologies for fine-tuning, integrating, evaluating, and improving MT output. Students will also gain experience of post-editing MT output according to ISO standards.
The course will be taught mainly in English, with some opportunities for interaction in German. .

Assessment and permitted materials

Continuous evaluation:
- Attendance, weekly reflections and in-class participation count for 30% of the mark.
- MT portfolio (comprising fine-tuning, annotation, and post-editing task deliverables): 30% of the mark.
- Team presentation: 40% of the mark.

All media and tools are allowed, but their use needs to be referenced. Furthermore, if using AI Tools such as ChatGPT you also need to provide, in a footnote, the prompt used. Generally, we follow the principles described here: https://libguides.brown.edu/c.php?g=1338928&p=9868287

Minimum requirements and assessment criteria

MT UE pass mark
In order to pass this module, a student needs to reach the threshold of 4.

MT UE marking map
excellent - sehr gut (1)
good - gut (2)
average - befriedigend (3)
sufficient - genügend (4)
insufficient - nicht genügend (5)

Examination topics

– CAT tool use
- MT fine-tuning
- MT evaluation
- MT post-editing

Reading list

Core texts:
- Kenny, Dorothy. 2022. Machine translation for everyone: Empowering users in the age of artificial intelligence. (Translation and Multilingual Natural Language Processing 18). Berlin: Language Science Press. DOI: 10.5281/zenodo.6653406 (url: https://langsci-press.org/catalog/book/342)
- Koehn, P. 2020. Neural Machine Translation. Cambridge University Press
-Moorkens, J., Way, A., & S. Lankford. (2025). Automating Translation. Abingdon: Routledge
- Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J. & F. Austermuehl. (2023). Translation Tools and Technologies (1st ed.). London: Routledge
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – Requirements
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services

Additional recommended resources:
- Globally Speaking: A podcast for and by localization professionals. https://www.globallyspeakingradio.com/

- Carstensen, K-U. 2017. Sprachtechnologie - Ein Überblick. http://kai-uwe-carstensen.de/
Publikationen/Sprachtechnologie.pdf
- Chan, Sin-Wai. Ed. 2015. Routledge encyclopedia of translation technology Abingdon, Oxon : Routledge.
- Depraetere, I. Ed. 2011. Perspectives on translation quality. Berlin: de Gruyter Mouton
- Hausser, Roland. 2000. Grundlagen der Computerlinguistik - Mensch-Maschine-Kommunikation in natürlicher Sprache (mit 772 – Übungen). Springer.
- Kockaert, H. J. and Steurs, F. Eds. 2015. Handbook of terminology. Amsterdam; Philadelphia: John Benjamins Publishing Company.
- Moniz, Helena and Parra Escartín, Carla. Eds. 2023. Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation. Springer Verlag.
- Munday, J. 2012. Evaluation in translation: critical points of translator decision-making: Routledge.
- O'Hagan, M. Ed. 2019. The Routledge Handbook of Translation and Technology. Abingdon: Routledge
- Waibel, A. 2015. Sprachbarrieren durchbrechen: Traum oder Wirklichkeit? Nova Acta Leopoldina NF 122, Nr. 410, 101–123. https://isl.anthropomatik.kit.edu/downloads/
NAL_Bd122_Nr410_101-124_Waibel_low_res.pdf
- Wright, S. E. and Budin, G. 1997/2001. The Handbook of Terminology Management. Two volumes. Amsterdam/Philadelphia: John Benjamins Publishing Company.

Association in the course directory

Last modified: Th 09.10.2025 13:07