Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components

Raúl Vázquez, Michele Boggia, Alessandro Raganato, Niki A. Loppi, Stig-Arne Grönroos, Jörg Tiedemann


Abstract
We describe the enhancement of a multilingual NMT toolkit developed as part of the FoTran project. We devise our modular attention-bridge model, which connects language-specific components through a shared network layer. The system now supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture. The model enables the study of emerging language-agnostic representations and also provides a modular toolkit for efficient machine translation.
Anthology ID:
2022.eamt-1.45
Volume:
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Month:
June
Year:
2022
Address:
Ghent, Belgium
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
311–312
Language:
URL:
https://aclanthology.org/2022.eamt-1.45
DOI:
Bibkey:
Cite (ACL):
Raúl Vázquez, Michele Boggia, Alessandro Raganato, Niki A. Loppi, Stig-Arne Grönroos, and Jörg Tiedemann. 2022. Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 311–312, Ghent, Belgium. European Association for Machine Translation.
Cite (Informal):
Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components (Vázquez et al., EAMT 2022)
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PDF:
https://aclanthology.org/2022.eamt-1.45.pdf