Research

NLP

The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

June 1, 2021

Abstract

One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages,consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a highquality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.

Download the Paper

AUTHORS

Written by

Naman Goyal

Cynthia Gao

Vishrav Chaudhary

Peng-Jen Chen

Guillaume Wenzeky

Da Ju

Sanjana Krishnan

Marc’Aurelio Ranzato

Francisco Guzmán

Angela Fan

Related Publications

April 17, 2025

Human & Machine Intelligence

Conversational AI

Collaborative Reasoner: Self-improving Social Agents with Synthetic Conversations

Ansong Ni, Ruta Desai, Yang Li, Xinjie Lei, Dong Wang, Ramya Raghavendra, Gargi Ghosh, Daniel Li (FAIR), Asli Celikyilmaz

April 17, 2025

March 17, 2025

NLP

reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs

Zhaofeng Wu, Michihiro Yasunaga, Andrew Cohen, Yoon Kim, Asli Celikyilmaz, Marjan Ghazvininejad

March 17, 2025

February 06, 2025

NLP

Brain-to-Text Decoding: A Non-invasive Approach via Typing

Jarod Levy, Mingfang (Lucy) Zhang, Svetlana Pinet, Jérémy Rapin, Hubert Jacob Banville, Stéphane d'Ascoli, Jean Remi King

February 06, 2025

February 06, 2025

NLP

From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production

Mingfang (Lucy) Zhang, Jarod Levy, Stéphane d'Ascoli, Jérémy Rapin, F.-Xavier Alario, Pierre Bourdillon, Svetlana Pinet, Jean Remi King

February 06, 2025

April 30, 2018

NLP

Speech & Audio

Identifying Analogies Across Domains | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

November 01, 2018

NLP

Computer Vision

Non-Adversarial Unsupervised Word Translation | Facebook AI Research

Yedid Hoshen, Lior Wolf

November 01, 2018

December 02, 2018

NLP

Computer Vision

One-Shot Unsupervised Cross Domain Translation | Facebook AI Research

Sagie Benaim, Lior Wolf

December 02, 2018

June 30, 2019

NLP

Variational Training for Large-Scale Noisy-OR Bayesian Networks | Facebook AI Research

Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth

June 30, 2019

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.