340007 UE Machine translation (2022W)
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 12.09.2022 09:00 to Fr 23.09.2022 17:00
- Registration is open from Mo 10.10.2022 09:00 to Fr 14.10.2022 17:00
- Deregistration possible until Mo 31.10.2022 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 -
Tuesday
11.10.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
18.10.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
25.10.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
08.11.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
15.11.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
29.11.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
06.12.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
13.12.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
10.01.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
17.01.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
24.01.
11:30 - 13:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Tuesday
31.01.
11:30 - 13:00
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 20% of the mark.
- MT fine-tuning task deliverables and seminar paper - 40% of the mark.
- MT annotation and post-editing task deliverables and seminar paper - 40% of the mark.
- attendance, weekly reflections and in-class participation count for 20% of the mark.
- MT fine-tuning task deliverables and seminar paper - 40% of the mark.
- MT annotation and post-editing task deliverables and 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
- MT fine-tuning
- MT evaluation
- MT post-editing
- 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
- 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
- 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 05.10.2022 10:30
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
• MT in more complex systems (e.g. speech-to-speech translation)
• Controlled language; pre-editing
• 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.