The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
Citations Over TimeTop 1% of 2022 papers
Abstract
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 fully aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.
Related Papers
- → A Benchmark Test Structure for Experimental Dynamic Substructuring(2011)9 cited
- → Solutions to the Third Benchmark Control Problem(1991)3 cited
- Theoretical Analysis of the Benchmark for Choosing Manipulative Instruments of Monetary Policies(2009)
- → Exploring disk performance benchmarks(2017)
- → Support Structure Performance Benchmark(2023)