Duration
Introduction to interpreting and note-taking : 30h Th
Machine translation and post-editing : 30h Th
Number of credits
| Bachelor in translation and interpretation | 5 crédits |
Lecturer
Introduction to interpreting and note-taking : Bénédicte Klinkenberg, Muriel Mercier, Valéria Nagy
Machine translation and post-editing : Perrine Schumacher
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
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).
- 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