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2025-2026 / TRAD0175-1

Machine translation and post-editing, Introduction to interpreting and not-taking

Introduction to interpreting and note-taking

Machine translation and post-editing

Duration

Introduction to interpreting and note-taking : 30h Th
Machine translation and post-editing : 30h Th

Number of credits

 Bachelor in translation and interpretation5 crédits 

Lecturer

Introduction to interpreting and note-taking : Bénédicte Klinkenberg, Muriel Mercier, Valéria Nagy
Machine translation and post-editing : Perrine Schumacher

Coordinator

Perrine Schumacher

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Introduction to interpreting and note-taking

(1) Presentation of the profession of conference interpreter, the different types of interpreting techniques and the general competences required and practical exercises. (2) Introduction to the consecutive interpretation technique and more specifically to note taking. This part of the course will begin by focusing on public speaking, discourse analysis and memory exercises. After the presentation of recommendations for note taking for consecutive interpreting, students will start developing their own technique before rendering consecutively simple oral interventions.

Machine translation and post-editing

This course is designed to introduce third-year (B3) translation and interpreting students to machine translation (MT) and post-editing (PE). It provides them with the theoretical and practical foundations needed to understand, assess, and critically use MT tools. No technical prerequisites.

The course will cover the following topics:

  • The history of MT research
  • The evolution of MT paradigms
  • MT quality assessment
  • Post-editing: definition, pedagogical and professional challenges
  • The latest technological advances (Gen AI tools)

Learning outcomes of the learning unit

Introduction to interpreting and note-taking

By the end of the course, students will have gained basic knowledge and practice regarding the content of the course (see above). They will have a command of essential public speaking skills, be able to identify the main and secondary ideas in a speech in French, be able to do a consecutive interpretation of a very short and simple speech in French into French, using rewording and synonymy, as well as simple consecutive interpretation from their 2 foreign languages into French. 

Machine translation and post-editing

By the end of the course, students will have:

  • gained technological knowledge valued in the professional market;
  • developed an awareness of media and marketing discourse that tends to overestimate AI performance;
  • explored a range of features offered by MT tools and generative tools (chatbots).
They will also be able to:

  • critically evaluate the benefits and limitations of AI tools;
  • leverage their human strengths (reasoning, critical thinking, cultural sensitivity, creativity, etc.);
  • enhance their adaptability and critical thinking skills.

Prerequisite knowledge and skills

Machine translation and post-editing

Very good command of French

Planned learning activities and teaching methods

Introduction to interpreting and note-taking

Practical course with some theory. Course given by the trainer.

Machine translation and post-editing

Theoretical presentations, practical exercises, critical discussions.

Mode of delivery (face to face, distance learning, hybrid learning)

Introduction to interpreting and note-taking

Intensive face-to-face course during Q2

Machine translation and post-editing

Face-to-face course

Course materials and recommended or required readings

Introduction to interpreting and note-taking

News of the last few months.

Machine translation and post-editing

Platform(s) used for course materials:
- eCampus
- MyULiège


Further information:

Course materials (PPTs, videos, readings, podcasts) will be posted on eCampus

 

Recommended Readings

Corteel M. (2025). Ni Dieu ni IA. Une philosophie sceptique de l'intelligence artificielle. Paris : La Découverte, 240 p.

EMT (European Master's in Translation). (2022). Référentiel de compétences 2022. Bruxelles, Commission européenne. Disponible sur https://commission.europa.eu/document/download/b482a2c0-42df-4291-8bf8-923922ddc6e1_fr?filename=emt_competence_fwk_2022_fr.pdf

Levin, F. et É. Ollion. (2024). Ce qui échappe à l'intelligence artificielle. Hermann

Pérez-Ortiz, J. A., Forcada, M.L., et Sánchez-Martínez, F. (2022). How neural machine translation works. In D.Kenny (Ed.), Machine translation for everyone: Empowering users in the age of artificial intelligence (pp. 141-164). Language Science Press. https://doi.org/10.5281/zenodo.6760020

Poibeau, T. (2019). Babel 2.0 : Où va la traduction automatique ? Odile Jacob.

Université de Liège. (déc. 2023). Charte ULiège d'utilisation des intelligences artificielles génératives dans les travaux universitaires. Université de Liège. https://tinyurl.com/yzs65bkw

Introduction to interpreting and note-taking

Exam(s) in session

Any session

- In-person

oral exam


Further information:

Final exam consisting of:
- Two consecutive interpretings of a very short speech from the foreign languages into French.

Machine translation and post-editing

Exam(s) in session

Any session

- In-person

written exam

Written work / report


Further information:

Les étudiantes sont tenues de prendre connaissance de la Charte ULiège d'utilisation de l'intelligence artificielle générative dans les travaux universitaires afin d'en faire un usage réfléchi, responsable, critique et transparent : https://www.student.uliege.be/cms/c_19230399/fr/faq-student-charte-uliege-d-utilisation-des-intelligences-artificielles-generatives-dans-les-travaux-universitaires.

Si l'étudiante utilise l'intelligence artificielle*, il ou elle devra alors renseigner précisément son usage de l'IA dans une section dédiée : outil utilisé, prompt, type d'aide demandée (reformulation, synthèse, analyse, correction linguistique, etc...), résultats obtenus.

*Par « IA », nous entendons les moteurs de traduction automatique (DeepL, GoogleTraduction...) et les agents conversationnels (ChatGPT, Copilot, Gemini, DeepSeek, etc.), ainsi que tout autre outil d'IA permettant de rédiger, synthétiser, analyser des données, etc.

Work placement(s)

Introduction to interpreting and note-taking

None

Organisational remarks and main changes to the course

Introduction to interpreting and note-taking

Students will have at least one notebook (squared or blank shorthand notebook type), rough paper, pens.

Contacts

Introduction to interpreting and note-taking

muriel.mercier@uliege.be

nagyvaleriaeszter@gmail.com


b.klinkenberg@uliege.be
 

Machine translation and post-editing

Perrine Schumacher

p.schumacher@uliege.be

Association of one or more MOOCs