MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona De Gibert, Shaoxiong Ji, Niki Andreas Loppi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
Abstract
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 at https://github.com/Helsinki-NLP/mammoth.- Anthology ID:
- 2024.eacl-demo.14
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Nikolaos Aletras, Orphee De Clercq
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 127–136
- Language:
- URL:
- https://aclanthology.org/2024.eacl-demo.14
- DOI:
- Cite (ACL):
- Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona De Gibert, Shaoxiong Ji, Niki Andreas Loppi, Alessandro Raganato, Raúl Vázquez, and Jörg Tiedemann. 2024. MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 127–136, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki (Mickus et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.eacl-demo.14.pdf