340236 UE Maschinelle Translation (2023S)
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 06.02.2023 09:00 bis Fr 17.02.2023 17:00
- Anmeldung von Mo 06.03.2023 09:00 bis Fr 10.03.2023 17:00
- Abmeldung bis Fr 31.03.2023 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Deutsch, Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- This course will be taught in person in the ZTW computer lab, without a hybrid or online option -
Montag
06.03.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
20.03.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
27.03.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
17.04.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
08.05.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
15.05.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
22.05.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
05.06.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
12.06.
15:00 - 16:30
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Montag
19.06.
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.
- 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.
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 annotation
– MT post-editing
– MT fine-tuning
– MT annotation
– 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
- 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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 01.03.2023 15:49
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 and evaluate them using automatic and manual metrics. Students will also experience translating with the MT engine integrated into a popular CAT tool, annotate their output using an industry annotation framework, and post-edit MT output according to ISO standards.Content:
Computer-Assisted Translation (CAT) tools
Neural Machine Translation (NMT): current applications and integration into CAT tools
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 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.