Universität Wien

340234 UE Machine translation (2023S)

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

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

- This course will be taught in person in the ZTW computer lab, without a hybrid or online option -

Tuesday 07.03. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 14.03. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 21.03. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 28.03. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 18.04. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 02.05. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 09.05. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 16.05. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 23.05. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 06.06. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 13.06. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday 20.06. 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 and evaluate them using automatic and manual metrics. Students will also experience translating with the MT engine integrated into a popular CAT tool, annotate their output using an industry annotation framework, and post-edit MT output according to ISO standards.

Content:
 Computer-Assisted Translation (CAT) tools
 Neural Machine Translation (NMT): current applications and integration into CAT tools
 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:

This course will be team-taught, with each team member focusing on one or more relevant areas. To the students, this course is likely to appear as a simulated, technology-intensive internship with a language service provider (LSP).
Students will need to complete assignments involving a 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. If it is held in English, based on student request, it can have, whenever possible, simultaneous (but automatic, machine-generated) translations into German and other languages which, although not perfect, should still give students broad access to the live discussions alongside a deeper understanding of the applicability of MT to live communication.

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.
- seminar paper: 40% of the mark.

Minimum requirements and assessment criteria

MT UE pass markIn 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 annotation
– 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
- 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.
- 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: We 01.03.2023 12:49