@inproceedings{vazquez-etal-2022-latest,
    title = "Latest Development in the {F}o{T}ran Project {--} Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Boggia, Michele  and
      Raganato, Alessandro  and
      Loppi, Niki A.  and
      Gr{\"o}nroos, Stig-Arne  and
      Tiedemann, J{\"o}rg},
    editor = {Moniz, Helena  and
      Macken, Lieve  and
      Rufener, Andrew  and
      Barrault, Lo{\"i}c  and
      Costa-juss{\`a}, Marta R.  and
      Declercq, Christophe  and
      Koponen, Maarit  and
      Kemp, Ellie  and
      Pilos, Spyridon  and
      Forcada, Mikel L.  and
      Scarton, Carolina  and
      Van den Bogaert, Joachim  and
      Daems, Joke  and
      Tezcan, Arda  and
      Vanroy, Bram  and
      Fonteyne, Margot},
    booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
    month = jun,
    year = "2022",
    address = "Ghent, Belgium",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2022.eamt-1.45/",
    pages = "311--312",
    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."
}