340320 VO Maschinelle Translation (2021W)
Labels
DIGITAL
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
Details
max. 1000 Teilnehmer*innen
Sprache: Englisch
Prüfungstermine
- Mittwoch 26.01.2022 09:45 - 11:15 Digital
- Mittwoch 02.03.2022 09:45 - 11:15 Digital
- Mittwoch 27.04.2022 09:45 - 11:15 Digital
- Mittwoch 29.06.2022 09:45 - 11:15 Digital
Lehrende
Termine
MI wtl von 13.10.2021 bis 26.01.2022 09.45-11.15 Ort: digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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 write a comprehensive, accurate and helpful response to a fictitious client's case study.
Mindestanforderungen und Beurteilungsmaßstab
MT VO pass mark
In order to pass this module, a student needs to reach the threshold of 60 points in the final examination.MT VO marking map
90 – 100 excellent - sehr gut (1)
80 – 89 good - gut (2)
70 – 79 average - befriedigend (3)
60 – 69 sufficient - genügend (4)
0 – 59 insufficient - nicht genügend (5)The mark will take into account: the student's general understanding of the topic of MT; MT from the perspective of the solutions architect (technical knowledge); MT from the perspective of the language services provider (LSP); MT from the perspective of the linguist; coherence, analysis and detail.
In order to pass this module, a student needs to reach the threshold of 60 points in the final examination.MT VO marking map
90 – 100 excellent - sehr gut (1)
80 – 89 good - gut (2)
70 – 79 average - befriedigend (3)
60 – 69 sufficient - genügend (4)
0 – 59 insufficient - nicht genügend (5)The mark will take into account: the student's general understanding of the topic of MT; MT from the perspective of the solutions architect (technical knowledge); MT from the perspective of the language services provider (LSP); MT from the perspective of the linguist; coherence, analysis and detail.
Prüfungsstoff
The fictitious client's case study will allow students to use all the information they have acquired throughout the course to advise the client regarding the feasibility of implementing MT services in the client's particular settings.
Literatur
- Carstensen, K-U. (2017) Sprachtechnologie - Ein Überblick. http://kai-uwe-carstensen.de/
Publikationen/Sprachtechnologie.pdf
- Chan, S.-W. (2015) Routledge encyclopedia of translation technology Abingdon, Oxon : Routledge.
- Depraetere, I. (2011) Perspectives on translation quality. Berlin: de Gruyter Mouton
- Hausser, R. (2000) Grundlagen der Computerlinguistik - Mensch-Maschine-
Kommunikation in natürlicher Sprache (mit 772 – Übungen). Springer.
- Kockaert, H. J. & Steurs, F. (2015) Handbook of terminology. Amsterdam; Philadelphia: John Benjamins Publishing Company.
- Koehn, P. (2020) Neural Machine Translation. Cambridge University Press
- Munday, J. (2012) Evaluation in translation: critical points of translator decision-making: Routledge.
- O'Hagan, M. (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. & 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
- ASTM F2575 Standard Guide for Quality Assurance in Translation
Publikationen/Sprachtechnologie.pdf
- Chan, S.-W. (2015) Routledge encyclopedia of translation technology Abingdon, Oxon : Routledge.
- Depraetere, I. (2011) Perspectives on translation quality. Berlin: de Gruyter Mouton
- Hausser, R. (2000) Grundlagen der Computerlinguistik - Mensch-Maschine-
Kommunikation in natürlicher Sprache (mit 772 – Übungen). Springer.
- Kockaert, H. J. & Steurs, F. (2015) Handbook of terminology. Amsterdam; Philadelphia: John Benjamins Publishing Company.
- Koehn, P. (2020) Neural Machine Translation. Cambridge University Press
- Munday, J. (2012) Evaluation in translation: critical points of translator decision-making: Routledge.
- O'Hagan, M. (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. & 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
- ASTM F2575 Standard Guide for Quality Assurance in Translation
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 12.05.2023 00:25
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. 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 linguistsDidactic 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 with, whenever possible, simultaneous (but automatic, machine-generated) translation into German which, although not perfect, should still give students broad access to the contents of the course and so that they have the live experience of machine translation applied in the course itself.