340007 UE Machine translation (2021W)
Continuous assessment of course work
Labels
REMOTE
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 Fr 10.09.2021 09:00 to Fr 24.09.2021 17:00
- Registration is open from Mo 11.10.2021 09:00 to Fr 15.10.2021 17:00
- Deregistration possible until Su 31.10.2021 23:59
Details
max. 25 participants
Language: German, English
Lecturers
Classes
This course will be taught fully online.
Information
Aims, contents and method of the course
Assessment and permitted materials
Continuous evaluation:
- attendance, seminar presentation and weekly reflections count for 15% of the mark.
- student portfolio counts for 45% of the mark.
- final reflective essay counts for 40% of the mark.
- attendance, seminar presentation and weekly reflections count for 15% of the mark.
- student portfolio counts for 45% of the mark.
- final reflective essay counts for 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 60 points.
MT UE marking map
90 – 100 excellent - sehr gut (1)
80 – 89 good - gut (2)
70 – 79 average - befriedigend (3)
60 – 69 sufficient - genügend (4)
0 – 59 insufficient - nicht genügend (5)
In order to pass this module, a student needs to reach the threshold of 60 points.
MT UE marking map
90 – 100 excellent - sehr gut (1)
80 – 89 good - gut (2)
70 – 79 average - befriedigend (3)
60 – 69 sufficient - genügend (4)
0 – 59 insufficient - nicht genügend (5)
Examination topics
Continuous evaluation
Reading list
- 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.
- Koehn, P. 2020. Neural Machine Translation. Cambridge University Press
- 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
- ASTM F2575 Standard Guide for Quality Assurance in Translation
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.
- Koehn, P. 2020. Neural Machine Translation. Cambridge University Press
- 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
- ASTM F2575 Standard Guide for Quality Assurance in Translation
Association in the course directory
Last modified: Mo 07.02.2022 10:10
Students will apply elements of the three main machine translation (MT) paradigms to a range of tasks, ranging from automatic translation between closely-related languages, language variants or dialects to building a new machine translation engine from scratch.Using state-of-the-art technologies, students will learn to design a machine translation engine, gather training data, clean the data, train at least one engine, evaluate it, and improve it.In addition, students will also use controlled language and technical writing principles and techniques in order to author or edit content for the purpose of improving the quality of raw MT output. Post-editing MT (PEMT) standards and best practices will be applied to practical PEMT tasks, which will give students the opportunity to fine-tune their skills identifying and annotating subtle MT errors using popular industry annotation frameworks.Content:
• Rule-based (RBMT), statistical (SMT) and neural machine translation (NMT): current applications
• NMT architectures
• MT evaluation metrics
• MT quality estimation (QE) practices
• 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:
Students will need to complete assignments involving a wide range of technologies involved in the process of building MT engines, estimating the quality of their output, and comparing the results of these automatic estimations to other MT evaluation techniques.Students will also gain experience of post-editing MT output.
Depending on the lecturer available, the seminar will be held in German or in English. If it is held in English, it will have, whenever possible, simultaneous (but automatic, machine-generated) translation into German which, although not perfect, should still give students broad access to the live discussions and so that they have the live experience of machine translation applied in the
course itself.