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

• URL: http://arxiv.org/abs/2106.03193v1
• Date: Sun, 6 Jun 2021 17:58:12 GMT
• Title: The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
• Authors: Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc'Aurelio Ranzato, Francisco Guzman, Angela Fan
• Abstract summary: In this work, we introduce the FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages.
• Score: 21.55080388385458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.

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