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.
[Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components](https://aclanthology.org/2022.eamt-1.45) (Vázquez et al., EAMT 2022)