340217 VU Basics in Machine Translation (2024S)
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 12.02.2024 09:00 to Fr 23.02.2024 17:00
- Registration is open from Mo 11.03.2024 09:00 to Fr 15.03.2024 17:00
- Deregistration possible until Su 31.03.2024 23:59
Details
max. 40 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Monday
11.03.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
18.03.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
08.04.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
15.04.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
29.04.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
06.05.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
13.05.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
N
Monday
27.05.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
03.06.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
10.06.
16:45 - 19:00
Medienlabor II ZfT Gymnasiumstraße 50 4.OG
Monday
17.06.
16:45 - 19:00
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.
- NMT code base 40%
- NMT fine-tuning, annotation, and post-editing task deliverables - 20% of the mark.
- NMT course report - 20% of the mark.All media and tools are allowed, but their use needs to be referenced. Furthermore, if using AI Tools such as ChatGPT you also need to provide, in a footnote, the prompt used. Generally, we follow the principles described here: https://libguides.brown.edu/c.php?g=1338928&p=9868287
- attendance, weekly reflections, and in-class participation count for 20% of the mark.
- NMT code base 40%
- NMT fine-tuning, annotation, and post-editing task deliverables - 20% of the mark.
- NMT course report - 20% of the mark.All media and tools are allowed, but their use needs to be referenced. Furthermore, if using AI Tools such as ChatGPT you also need to provide, in a footnote, the prompt used. Generally, we follow the principles described here: https://libguides.brown.edu/c.php?g=1338928&p=9868287
Minimum requirements and assessment criteria
In order to pass this module, a student needs to reach the threshold of 4.MT marking map
excellent - sehr gut (1)
good - gut (2)
average - befriedigend (3)
sufficient - genügend (4)
insufficient - nicht genügend (5)
excellent - sehr gut (1)
good - gut (2)
average - befriedigend (3)
sufficient - genügend (4)
insufficient - nicht genügend (5)
Examination topics
- MT training
- MT fine-tuning
- MT evaluation
- MT post-editing
- MT fine-tuning
- 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
- Moniz, H., & Parra Escartín, C. (Eds.). (2023). Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (Vol. 4). Springer International Publishing. https://doi.org/10.1007/978-3-031-14689-3
- Pilehvar, Mohammad Taher and José Camacho-Collados. 2020. Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. Synthesis Lectures on Human Language Technologies (http://josecamachocollados.com/book_embNLP_draft.pdf).
- 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:- The Illustrated Word2vec. https://jalammar.github.io/illustrated-word2vec/
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention). https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
- 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
- Moniz, H., & Parra Escartín, C. (Eds.). (2023). Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (Vol. 4). Springer International Publishing. https://doi.org/10.1007/978-3-031-14689-3
- Pilehvar, Mohammad Taher and José Camacho-Collados. 2020. Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. Synthesis Lectures on Human Language Technologies (http://josecamachocollados.com/book_embNLP_draft.pdf).
- 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:- The Illustrated Word2vec. https://jalammar.github.io/illustrated-word2vec/
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention). https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
- 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 28.02.2024 11:47
Students will acquire practical machine translation (MT), customisation, annotation, and post-editing expertise within computer-assisted translation (CAT) tools.
Using state-of-the-art technologies, students will learn to fine-tune pre-trained MT models, evaluate them using automatic and manual metrics, integrate MT into CAT tools, annotate their output using standard industry annotation frameworks, and post-edit MT output according to ISO standards.Content:
- Rule-Based (RBMT), Statistical (SMT), and neural machine translation (NMT)
- NMT encoder decoder architecture
- NMT transformer architecture and fine-tuning pre-trained models
- MT automatic evaluation metrics and manual annotation of error typologies - Post-editing machine translation (PEMT) standards and best practicesDidactic approach:This course will be team-taught, with each team member focusing on one or more relevant topics.
Students will need to complete practical 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 in English, with some opportunities for using other languages to complete the coursework.