MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch
Gema Ramírez-Sánchez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Caroline Rossi, Dorothy Kenny, Riccardo Superbo, Pilar Sánchez-Gijón, Olga Torres-Hostench
Abstract
The MultiTraiNMT Erasmus+ project aims at developing an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource that can support citizens in their attempt to acquire and develop language skills if they are trained in an informed and critical way. Machine translation could thus help tackle the mismatch between the desired EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which citizens generally fall far short of this objective. The training materials consists of an open-access coursebook, an open-source NMT web application called MutNMT for training purposes, and corresponding activities.- Anthology ID:
- 2021.triton-1.21
- Volume:
- Proceedings of the Translation and Interpreting Technology Online Conference
- Month:
- July
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Vilelmini Sosoni, Julie Christine Giguère, Elena Murgolo, Elizabeth Deysel
- Venue:
- TRITON
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 181–186
- Language:
- URL:
- https://aclanthology.org/2021.triton-1.21
- DOI:
- Cite (ACL):
- Gema Ramírez-Sánchez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Caroline Rossi, Dorothy Kenny, Riccardo Superbo, Pilar Sánchez-Gijón, and Olga Torres-Hostench. 2021. MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch. In Proceedings of the Translation and Interpreting Technology Online Conference, pages 181–186, Held Online. INCOMA Ltd..
- Cite (Informal):
- MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch (Ramírez-Sánchez et al., TRITON 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.triton-1.21.pdf