340104 VO Machine translation (2023S)
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).
Details
max. 1000 participants
Language: English
Examination dates
Tuesday
27.06.2023
09:45 - 11:15
Digital
Tuesday
03.10.2023
09:45 - 11:15
Digital
Tuesday
21.11.2023
09:45 - 11:15
Digital
Tuesday
23.01.2024
09:45 - 11:15
Digital
Lecturers
Classes (iCal) - next class is marked with N
You will have the opportunity to evaluate this course in our session on 6. June 2023.
Tuesday
07.03.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
14.03.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
21.03.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
28.03.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
18.04.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
02.05.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
09.05.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
16.05.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
23.05.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
06.06.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
13.06.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Tuesday
20.06.
09:45 - 11:15
Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
Students will need to use all the knowledge and experience regarding a wide range of aspects connected with machine translation which they have acquired during the course to answer a series of questions about the topics covered during the course.The exam will be held as a multiple-choice test in a digital format via Moodle.It will be an open book exam, which means that the course materials can be used in the exam.
Minimum requirements and assessment criteria
MT VO marking mapAt least 90% of the points – excellent (sehr gut, 1)
At least 80% of the points – good (gut, 2)
At least 70% of the points – average (befriedigend, 3)
At least 60% of the points – sufficient (genügend, 4)
Less than 60% of the points – insufficient (nicht genügend, 5)To pass the exam, a minimum of 60% of the points is required.
At least 80% of the points – good (gut, 2)
At least 70% of the points – average (befriedigend, 3)
At least 60% of the points – sufficient (genügend, 4)
Less than 60% of the points – insufficient (nicht genügend, 5)To pass the exam, a minimum of 60% of the points is required.
Examination topics
The questionnaire used for the assessment will be based on the information shared in and discussions held in the live course sessions.
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 PressAdditional 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.
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – Requirements
- 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 PressAdditional 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.
- BS EN ISO 17100:2015: Translation Services. Requirements for translation services
- ISO/DIS 18587 Translation services - Post-editing of machine translation output – Requirements
Association in the course directory
Last modified: Tu 07.11.2023 14:28
Students will understand the evolution of the main machine translation (MT) paradigms (rule-based, statistical, and neural) and the extent to which each of them (or the tools used to enable them) is/are still applicable today for a range of language processing tasks. The analysis of the detailed process of building, evaluating and fine-tuning an MT system will focus on the neural paradigm.
Using examples from both production and experimental engines, students will be introduced to the variety of scenarios in which machine translation proves to be effective, as well as specifically how MT engines can be integrated in localization workflows and computer-assisted translation (CAT)/translation environment tools (TEnT). These scenarios will also cover a range of current challenges including, but not limited to, using MT for technical v creative texts, or text-to-text v speech-to-speech translation. The topics of and techniques for machine translation evaluation, as well as machine translation quality estimation will also be illustrated with examples.
However, this is not a module focusing just on the technology behind and around machine translation. The ethics of using machine translation will also be discussed, alongside bias in MT and challenges of identifying and correcting it. International standards which govern the use of machine translation and translation technologies will be introduced and discussed. Post-editing best practices will be discussed and productivity myths regarding the effectiveness of MT will be challenged. Cognitive ergonomics and alternative ways of working with MT besides the current post-edit-inside-CAT-tools will be discussed.Content:
• Rule-based (RBMT), statistical (SMT), and neural machine translation (NMT): history and 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 linguists, the language services industry, natural languages, and the environment.Didactic approach:
The content of this course is acquired by students in an interactive and blended way between home assignments studying the research literature, reflecting on concrete scenarios of applying machine translation systems to a variety of business problems, and the joint discussion and assessment of their applicability.
The course is held in English.