340100 UE Machine translation (2023W)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 11.09.2023 09:00 to Fr 22.09.2023 17:00
- Registration is open from Mo 09.10.2023 09:00 to Fr 13.10.2023 17:00
- Deregistration possible until Tu 31.10.2023 23:59
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 -
Please note: The double session which should have taken place on the 20th of November between 1.15 and 4.30pm will take place on the 13th of November between 1.15 and 4.30pm in Media Lab I (across the hall from Media Lab II).- Monday 09.10. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 09.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 23.10. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 23.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 06.11. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 06.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 04.12. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 04.12. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 08.01. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Monday 08.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Information
Aims, contents and method of the course
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.
- 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 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)
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
- 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 servicesAdditional 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.
- 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 servicesAdditional 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 27.09.2023 11:09
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 linguistsDidactic 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 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. 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.