Lightweight neural translation technologies for low-resource languages

Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz, Víctor Sánchez-Cartagena, Andrés Lou, Cristian García-Romero, Aarón Galiano-Jiménez, Miquel Esplà-Gomis


Abstract
The LiLowLa (“Lightweight neural translation technologies for low-resource languages”) project aims to enhance machine translation (MT) and translation memory (TM) technologies, particularly for low-resource language pairs, where adequate linguistic resources are scarce. The project started in September 2022 and will run till August 2025.
Anthology ID:
2024.eamt-2.3
Volume:
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
Month:
June
Year:
2024
Address:
Sheffield, UK
Editors:
Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation (EAMT)
Note:
Pages:
4–5
Language:
URL:
https://aclanthology.org/2024.eamt-2.3
DOI:
Bibkey:
Cite (ACL):
Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz, Víctor Sánchez-Cartagena, Andrés Lou, Cristian García-Romero, Aarón Galiano-Jiménez, and Miquel Esplà-Gomis. 2024. Lightweight neural translation technologies for low-resource languages. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 4–5, Sheffield, UK. European Association for Machine Translation (EAMT).
Cite (Informal):
Lightweight neural translation technologies for low-resource languages (Sánchez-Martínez et al., EAMT 2024)
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PDF:
https://aclanthology.org/2024.eamt-2.3.pdf