NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. In this paper, we present the MAMMOTH toolkit: a framework designed for training massively multilingual modular machine translation systems at scale, initially derived from OpenNMT-py and then adapted to ensure efficient training across computation clusters. We showcase its efficiency across clusters of A100 and V100 NVIDIA GPUs, and discuss our design philosophy and plans for future information. The toolkit is publicly available online.
@article{arxiv.2403.07544,
title = {MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki},
author = {Timothee Mickus and Stig-Arne Grönroos and Joseph Attieh and Michele Boggia and Ona De Gibert and Shaoxiong Ji and Niki Andreas Lopi and Alessandro Raganato and Raúl Vázquez and Jörg Tiedemann},
journal= {arXiv preprint arXiv:2403.07544},
year = {2024}
}