Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
340370 UE Maschinelle Translation (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 16.09.2024 09:00 bis Fr 27.09.2024 17:00
- Anmeldung von Mo 14.10.2024 09:00 bis Fr 18.10.2024 17:00
- Abmeldung bis Do 31.10.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- N Mittwoch 16.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 23.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 30.10. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 06.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 13.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 20.11. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 04.12. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 11.12. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 08.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 15.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
- Mittwoch 22.01. 15:00 - 16:30 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.
• team presentation: 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.
• team presentation: 40% of the mark.
Mindestanforderungen und Beurteilungsmaßstab
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)
Prüfungsstoff
• CAT tool use
• MT fine-tuning
• MT evaluation
• MT post-editing
• MT fine-tuning
• MT evaluation
• MT post-editing
Literatur
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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 03.09.2024 12:26
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:
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.