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 RanzatoFrancisco GuzmánAngela Fan

Related Publications

September 10, 2019

NLP

Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling | Facebook AI Research

A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user’s query. On the other hand, many NLP problems, such as question answering and paraphrase identification, can be considered…

Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

September 10, 2019

June 11, 2019

NLP

COMPUTER VISION

Adversarial Inference for Multi-Sentence Video Description | Facebook AI Research

While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the…

Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach

June 11, 2019

May 17, 2019

NLP

Unsupervised Hyper-alignment for Multilingual Word Embeddings | Facebook AI Research

We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This…

Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin

May 17, 2019

July 27, 2019

NLP

Unsupervised Question Answering by Cloze Translation | Facebook AI Research

Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is…

Patrick Lewis, Ludovic Denoyer, Sebastian Riedel

July 27, 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.