Universität Wien

340248 UE Machine translation (2024S)

3.00 ECTS (2.00 SWS), SPL 34 - Translationswissenschaft
Continuous assessment of course work
Moodle
Mo 27.05. 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

Monday 11.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 18.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 08.04. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 15.04. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 29.04. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 06.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 13.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 03.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 10.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday 17.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG

Information

Aims, contents and method of the course

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
• Neural MT 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 ranging from CAT tools to solutions 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 in English.

Assessment and permitted materials

Continuous evaluation:
• weekly reflections and in-class participation: 30% of the mark
• short group presentation: 30 % of the mark
• MT portfolio (comprising alignment, MT, and annotation/post-editing files, as well as a short commentary): 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

Attendance is mandatory — two absences are allowed.

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
- Moniz, H., & Parra Escartín, C. (Eds.). (2023). Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (Vol. 4). Springer International Publishing. https://doi.org/10.1007/978-3-031-14689-3
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – Requirements
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- Rothwell, Andrew, Joss Moorkens, María Fernández-Parra, Joanna Drugan and Frank Austermuehl. 2023. Translation Tools and Technologies (1st ed.). London/New York: Routledge https://doi.org/10.4324/9781003160793

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.- 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: Tu 05.03.2024 09:47