Research
NLP
Beyond English-Centric Multilingual Machine Translation
October 18, 2020
Abstract
Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model here.
AUTHORS
Written by
Ahmed El-Kishky
Siddharth Goyal
Mandeep Baines
Guillaume Wenzek
Vishrav Chaudhary
Naman Goyal
Tom Birch
Sergey Edunov
Publisher
arXiv
Research Topics
Related Publications
March 17, 2026
NLP
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Niyati Bafna, Ioannis Tsiamas, Mark Duppenthaler, Albert Ventayol-Boada, Alexandre Mourachko, Andrea Caciolai, Arina Turkatenko, Artyom Kozhevnikov, Belen Alastruey, Charles-Eric Saint-James, Chierh CHENG, Christophe Ropers, Cynthia Gao, David Dale, Edan Toledo, Eduardo Sánchez, Gabriel Mejia Gonzalez, Holger Schwenk, Jean Maillard, Joe Chuang, João Maria Janeiro, Kevin Heffernan, Marta R. Costa-jussa, Mary Williamson, Nate Ekberg, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot, Rashel Moritz, Shireen Yates, Surya Parimi
March 17, 2026
March 17, 2026
Speech & Audio
Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Omnilingual SONAR Team, Ioannis Tsiamas, Yen Meng, Vivek Iyer, Guillem Ramirez, Jaehyeong Jo, Alexandre Mourachko, Yu-An Chung, Artyom Kozhevnikov, Belen Alastruey, Christophe Ropers, David Dale, Holger Schwenk, João Maria Janeiro, Kevin Heffernan, Loic Barrault, Marta R. Costa-jussa, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot
March 17, 2026
February 27, 2026
Human & Machine Intelligence
Unified Vision–Language Modeling via Concept Space Alignment
Yifu Qiu, Holger Schwenk, Paul-Ambroise Duquenne
February 27, 2026
November 10, 2025
Speech & Audio
Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages
Omnilingual ASR team, Skyler Wang, Ife Adebara, Michael Auli, Kaushik Ram Sadagopan, Zheng-Xin Yong, Albert Ventayol-Boada, Alexandre Mourachko, Alexander Erben, Yu-An Chung, Arina Turkatenko, Artyom Kozhevnikov, Caley Drooff, Can Balioglu, Chierh Cheng, Christophe Ropers, Cynthia Gao, Gabriel Mejia Gonzalez, Gil Keren, Jean Maillard, Joe Chuang, Kehan Lyu, Kevin Chan, Mark Duppenthaler, Mary Williamson, Matthew Setzler, Paul-Ambroise Duquenne, Rashel Moritz, Safiyyah Saleem, Sagar Miglani, Shireen Yates, Vineel Pratap, Yen Meng
November 10, 2025
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
March 14, 2019
NLP
On the Pitfalls of Measuring Emergent Communication | Facebook AI Research
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
March 14, 2019
January 13, 2020
NLP
Scaling up online speech recognition using ConvNets | Facebook AI Research
Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
January 13, 2020
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026