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340370 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 04.12.2023 09:00 to Fr 15.12.2023 09:00
- Deregistration possible until Fr 05.01.2024 23:59
Details
max. 25 participants
Language: German, English
Lecturers
Classes (iCal) - next class is marked with N
- Friday 12.01. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 12.01. 11:30 - 13:00 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 19.01. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 19.01. 11:30 - 13:00 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 02.02. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 02.02. 11:30 - 13:00 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Thursday 08.02. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Thursday 08.02. 11:30 - 13:00 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 09.02. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Friday 09.02. 11:30 - 13:00 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Saturday 10.02. 09:45 - 11:15 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Saturday 10.02. 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, exercise reports and in-class participation count for 30% of the mark.
- MT fine-tuning task deliverables and seminar paper - 35% of the mark.
- MT evaluation, annotation and post-editing task deliverables and seminar paper - 35% of the mark.
- attendance, exercise reports and in-class participation count for 30% of the mark.
- MT fine-tuning task deliverables and seminar paper - 35% of the mark.
- MT evaluation, annotation and post-editing task deliverables and seminar paper - 35% 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)
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
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – RequirementsAdditional recommended resources:
- Alammar, J. 2018a. The Illustrated Transformer. Retrieved from https://jalammar.github.io/illustrated-transformer/ (Accessed on: February 1, 2020)
- Alammar, J. 2018b. Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention). Retrieved from https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ (Accessed on: February 1, 2020)
- Bird, S., Klein, E., & Loper, E. 2009. NLTK Book. Natural Language Processing with Python. O’Reilly Media Inc. Retrieved from http://www.nltk.org/book/ (Accessed on: September 14, 2019)
- Bawden, R. 2018. Going beyond the sentence : Contextual Machine Translation of Dialogue. Université Paris-Saclay. Retrieved from https://tel.archives-ouvertes.fr/tel-02004683 (Accessed on: March 18, 2020)
- Brown et al. 2020. Language Models are Few-Shot Learners. Retrieved from https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
-Deutscher Terminologie-Tag e.V. (Hg.). 2014. Terminology Work: Best Practices 2.0. Cologne: Dt. Terminologie-Tag.
- 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.
- Koehn, P. 2010. Statistical Machine Translation. Cambridge: Cambridge University Press.
- Lang, C. 2020. Neural Machine Translation - How machines learn to translate patent language - An overview, evaluation and tutorial. Master’s thesis, University of Vienna.
- Lo Presti, R. 2016. Menschliche und automatische Evaluation von Übersetzungen von Fachtexten in Google Translate. Master’s thesis, University of Vienna.
- Luong, M.-T. 2016. Neural Machine Translation - A Dissertation. Standford University. Retrieved from https://github.com/lmthang/thesis
- 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
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – RequirementsAdditional recommended resources:
- Alammar, J. 2018a. The Illustrated Transformer. Retrieved from https://jalammar.github.io/illustrated-transformer/ (Accessed on: February 1, 2020)
- Alammar, J. 2018b. Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention). Retrieved from https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ (Accessed on: February 1, 2020)
- Bird, S., Klein, E., & Loper, E. 2009. NLTK Book. Natural Language Processing with Python. O’Reilly Media Inc. Retrieved from http://www.nltk.org/book/ (Accessed on: September 14, 2019)
- Bawden, R. 2018. Going beyond the sentence : Contextual Machine Translation of Dialogue. Université Paris-Saclay. Retrieved from https://tel.archives-ouvertes.fr/tel-02004683 (Accessed on: March 18, 2020)
- Brown et al. 2020. Language Models are Few-Shot Learners. Retrieved from https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
-Deutscher Terminologie-Tag e.V. (Hg.). 2014. Terminology Work: Best Practices 2.0. Cologne: Dt. Terminologie-Tag.
- 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.
- Koehn, P. 2010. Statistical Machine Translation. Cambridge: Cambridge University Press.
- Lang, C. 2020. Neural Machine Translation - How machines learn to translate patent language - An overview, evaluation and tutorial. Master’s thesis, University of Vienna.
- Lo Presti, R. 2016. Menschliche und automatische Evaluation von Übersetzungen von Fachtexten in Google Translate. Master’s thesis, University of Vienna.
- Luong, M.-T. 2016. Neural Machine Translation - A Dissertation. Standford University. Retrieved from https://github.com/lmthang/thesis
- 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: Fr 29.12.2023 10:06
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 and adapt 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. Students are also encouraged to explore the use of modern generative large language models for translation and other tasks.Content:
- Computer-Assisted Translation (CAT) tools
- Rule-based (RBMT), statistical (SMT) and deep learning-based machine translation (NMT and generative language models): brief overview and current applications
- NMT architectures and fine-tuning pre-trained models
- MT evaluation metrics and annotation tools and techniques
- MT in professional workflows
- MT in the language industry
- 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:
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 German, with some opportunities for interaction in English. Based on student request, an automatic, machine-generated translation into other languages of the recording of the sessions can be provided. The recording does not replace the need for physical presence during the exercise.