Objectives of Programme's Learning Outcomes

• Thematic Skills
• thoroughly understand the fundamental principles of artificial intelligence in the field of languages, literature and translation;
• understand the epistemological approaches and methodological tools relating to AI issues in the field of languages, literature and translation;
• apply the relevant analytical approaches and appropriate methodological tools for rigorous, in-depth and comparative analysis of problems related to AI, particularly generative AI, in written and audiovisual translation;
• Ethical and Professional Skills
• take a critical and ethical look at the challenges of AI, particularly with regard to data protection and confidentiality;
• combine reflection, research and action in order to design and carry out a professional project structured around an innovative project.
• Technological Skills
• use familiar translation tools and applications specific to their sub-fields;
• adapt to new tools and evaluate their possibilities and limitations;
• update their technological know-how and use it for professional purposes.

Learning Outcomes of UE

By the end of this module, students will be able to apply various AI tools, in particular machine translation based on neural networks (NMT) and large language models (LLM), to localisation contents (websites, video games, software, etc.). They will thus be able to identify the pitfalls of these tools and the characteristic elements of the types of texts being processed that make certain parts impervious to processing by AI. In this way, they will be able to determine the most effective working methods. In addition, research in AI applied to localisation will be introduced (methods, reasoning, research papers, presentation of results), as well as basic programming skills enabling learners to work with the different types of localisation packages used by clients.

UE Content: description and pedagogical relevance

Introduction to AI applied to localisation, presentation of several NMT and LLM translation tools, highlighting of the particularities of different localisation content types, identification of the pitfalls specific to different text types, qualitative comparison between several AI tools depending on the documents to be localised, identification and reading of relevant scientific resources in the field of AI applied to localisation, introduction to the process and presentation of qualitative and/or quantitative scientific work on AI applied to localisation, key elements for localisation quality assessment, presentation of the latest work in the field.

Prior Experience

-French: C2 level (CEFR)
-English: C1 level (CEFR)
-Ability to work with the main CAT tools (SDL Trados / Phrase / MemoQ)