Teaching Specialized Translation in the Machine Translation Era

The swift progress of neural machine translation (NMT) and the advent of tools such as Google Translate, DeepL, eTranslation and ChatGPT have considerably reshaped the world of translation. As a natural consequence, translation teaching has also been impacted by this paradigm shift. We invite participants interested in discussing any of the following: - Integrating NMT tools in specialized translation teaching - Teaching the limits and benefits, the pros and cons of NMT in specialized settings - Teaching NMT post-edition in specialized translation - Annotating errors in NMT post-edition of specialized texts - Defining quality with NMT tools (quality/quantity performance) - Comparing the use of NMT vs other tools by students (corpora, web search, terminological databases, translation memories, etc.) - Comparing NMT-aided translations with human specialized translations - Tendency to over-trust / mistrust NMT in specialized translation teaching / learning. Names and Affiliations of all Presenters:
1. Natalie Kübler, Alexandra Mestivier, Maud Bénard, CLILLAC-ARP, Université Paris Cité
2. Romane Bodart – Université Catholique de Louvain
3. Kiara Giancola – Université de Mons / Université Catholique de Louvain
4. Joëlle Popineau – UMR CNRS 7270, Université de Tours
5. Gaia Aragrande, Irene Frosi – University of Bologna
6. Célia Atzeni – CLILLAC-ARP Université de Paris, William Babonnaud – Talan’s Research and
Innovation Centre, Paris
7. Rossella Latorraca – University of Salerno
8. Radia Hannachi – University of Southern Brittany, Sílvia Araújo - University of Minho