Universität Wien

340236 UE Machine translation (2022S)

3.00 ECTS (2.00 SWS), SPL 34 - Translationswissenschaft
Continuous assessment of course work
MIXED
Moodle

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. 25 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 09.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 16.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 23.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 30.03. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 06.04. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 04.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 11.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 18.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 25.05. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 01.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 08.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 15.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 22.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG
  • Wednesday 29.06. 13:15 - 14:45 Medienlabor II ZfT Gymnasiumstraße 50 4.OG

Information

Aims, contents and method of the course

UE Maschinelle Translation (English Version):

Goals:
Students will apply elements of the three main machine translation (MT) paradigms to a range of tasks, ranging from automatic translation between closely-related languages, language variants or dialects to building a new machine translation engine from scratch.

Using state-of-the-art technologies, students will learn to design a machine translation engine, gather training data, clean the data, train at least one engine, evaluate it, and improve it.

In addition, students will also use controlled language and technical writing principles and techniques in order to author or edit content for the purpose of improving the quality of raw MT output. Post-editing MT (PEMT) standards and best practices will be applied to practical PEMT tasks, which will give students the opportunity to fine-tune their skills identifying and annotating subtle MT errors using popular industry annotation frameworks.

Content:
• Rule-based (RBMT), statistical (SMT) and neural machine translation (NMT): current 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

Didactic approach:
Students will need to complete assignments involving a wide range of technologies involved in the process of building MT engines, estimating the quality of their output, and comparing the results of these automatic estimations to other MT evaluation techniques.

Students will also gain experience of post-editing MT output.

Prof. Ciobanu will teach two seminar groups in English and Miss Wiesinger will teach one seminar group in German. In the English seminar, whenever possible, you will have access to simultaneous (but automatic, machine-generated) translation into German which, although not perfect, should still give students broad access to the live discussions and so that they have the live experience of machine translation applied in the course itself.

Assessment and permitted materials

Continuous evaluation:
- attendance, seminar presentation and weekly reflections count for 20% of the mark.
- student portfolio counts for 40% of the mark.
- final reflective essay counts for 40% of the mark.

Minimum requirements and assessment criteria

MT UE marking map

excellent - sehr gut (1)
good - gut (2)
average - befriedigend (3)
sufficient - genügend (4)
insufficient - nicht genügend (5)

Examination topics

Continuous evaluation

Reading list

- 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. 2020. Neural Machine Translation. Cambridge University Press
- 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
- ASTM F2575 Standard Guide for Quality Assurance in Translation

Association in the course directory

Last modified: Fr 26.07.2024 00:18