Niki A. Loppi


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
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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.