Universität Wien

340320 VO Machine translation (2025W)

4.00 ECTS (2.00 SWS), SPL 34 - Translationswissenschaft
Moodle
Tu 16.12. 15:00-16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50

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Details

max. 1000 participants
Language: English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 14.10. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 21.10. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 28.10. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 04.11. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 11.11. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 18.11. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 02.12. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 09.12. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 13.01. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Tuesday 20.01. 15:00 - 16:30 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50

Information

Aims, contents and method of the course

Goals:

Students will understand the evolution of the main machine translation (MT) paradigms (rule-based, statistical, and neural) and the extent to which each of them (or the tools used to enable them) is/are still applicable today for a range of language processing tasks. The analysis of the detailed process of building, evaluating and fine-tuning an MT model will focus on the neural paradigm.

Using examples from both production and experimental models, students will be introduced to the variety of scenarios in which machine translation proves to be effective, as well as specifically how MT engines can be integrated in localization workflows and computer-assisted translation (CAT)/translation environment tools (TEnT). The topics of and techniques for machine translation evaluation, as well as machine translation quality estimation will also be discussed.

However, this is not a course focusing just on the technology behind and around machine translation. The ethics of using machine translation will also be discussed, alongside bias in MT and challenges of identifying and correcting it. International standards which govern the use of machine translation and translation technologies will be introduced. Post-editing best practices will be discussed and productivity myths regarding the effectiveness of MT will be challenged. Alternative ways of working with MT besides the current post-edit-inside-CAT-tools will be presented.
In addition to these MT-related aspects, the course will feature a general introduction to technology use in translation.

Content:
• Rule-based (RBMT), statistical (SMT), and neural machine translation (NMT): history and applications
• NMT architectures
• MT evaluation metrics
• MT quality estimation (QE) practices
• MT in professional workflows
• Controlled language; pre-editing
• Post-editing machine translation (PEMT) standards and best practices
• Ethics of using MT and impact of MT on freelance linguists, the language services industry, natural languages, and the environment
• General information on the translation industry and major technologies used by professionals

Didactic approach:
The content of this course is acquired by students in an interactive and blended way by studying the research literature, reflecting on concrete scenarios of applying machine translation models, as well as the lecturers’ presentation and joint discussions in class. The course is held in English.

Assessment and permitted materials

Students will need to use all the knowledge and experience regarding a wide range of aspects connected with machine translation and other translation tools which they have acquired during the course to answer a series of questions about the topics covered during the course.

The exam will be held as an in-person open-book multiple-choice exam without access to any electronic resources. This means that notes and print-outs can be used during the exam, but no electronic devices (which also excludes the use of artificial intelligence tools in the exam).

Minimum requirements and assessment criteria

MT VO marking map

At least 90% of the points – excellent (sehr gut, 1)
At least 80% of the points – good (gut, 2)
At least 70% of the points – average (befriedigend, 3)
At least 60% of the points – sufficient (genügend, 4)
Less than 60% of the points – insufficient (nicht genügend, 5)

To pass the exam, a minimum of 60% of the points is required.

Examination topics

The questionnaire used for the assessment will be based on the information shared in and discussions held in the live course sessions.

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
- 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
- Walker, C., & Lambert, J. (Eds). (2025). The Routledge Handbook of the Translation Industry (1st edn). Routledge. https://doi.org/10.4324/9781003381303

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.

- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – Requirements

Association in the course directory

Last modified: Tu 02.12.2025 11:07