Machine translation and post-editing, Introduction to interpreting and notE-taking

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 (subject to change):

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

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 on AI.

• 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.

Machine translation and post-editing

Very good command of French

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 (post-editing exercises EN-FR), critical discussions.

Introduction to interpreting and note-taking

Intensive face-to-face course during Q2

Machine translation and post-editing

Face-to-face course

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

Alombert, A. (2024). La face cachée de l'« intelligence artificielle » : enjeux écologiques, psychiques et politiques des automates numériques. Les Temps qui restent, 3(3), 149-178. https://doi.org/10.3917/tqr.003.0149

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:

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.

Each learning activity (AA) accounts for 50% of the final grade for the course unit (UE).
 
This course unit ("Unité d'enseignement," or UE) is subject to the principle of the "note absorbante," which means that the student having obtained a mark below 8/20 for one of the learning activities ("activités d'apprentissage," or AA) will receive a failing grade for the entire unit and will have to retake the failed AA in the second session regardless of the mathematical average of the two AA grades

 

Students are required to read the ULiège charter on the use of AI https://www.student.uliege.be/cms/c_19230399/fr/faq-student-charte-uliege-d-utilisation-des-intelligences-artificielles-generatives-dans-les-travaux-universitaires. Please note that the students have to comply to the guidelines specified by the teacher.

Machine translation and post-editing

Exam(s) in session

Any session

- In-person

written exam

Written work / report


Further information:

A creative project accompanied by a short oral presentation to the class, worth 25% of the final grade.

In-session written exam (75%)
 
Each learning activity (AA) accounts for 50% of the final grade for the course unit (UE).
 
The course unit ("Unité d'enseignement," or UE) is subject to the principle of the "note absorbante," which means that the student having obtained a mark below 8/20 for one of the learning activities ("activités d'apprentissage," or AA) will receive a failing grade for the entire unit and will have to retake the failed AA in the second session regardless of the mathematical average of the two AA grades
 
Les étudiant.es sont tenu.es 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'étudiant.e 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. Toute omission ou dissimulation d'un recours à l'IA pourra être considérée comme contraire aux règles d'intégrité académique.

*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.

Introduction to interpreting and note-taking

None

Introduction to interpreting and note-taking

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

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