BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation
Nov 1, 2025·
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0 min read
Pierre Andrews
Mikel Artetxe
Mariano Coria Meglioli
Marta R. Costa-Jussà
Joe Chuang
David Dale
Mark Duppenthaler
Nathanial Paul Ekberg
Cynthia Gao
Daniel Edward Licht
Jean Maillard
Alexandre Mourachko
Christophe Ropers
Safiyyah Saleem
Eduardo Sánchez
Ioannis Tsiamas
Arina Turkatenko
Albert Ventayol-Boada
Shireen Yates
Abstract
BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages (i.e. Egyptian Arabic and Modern Standard Arabic, French, German, Hindi, Indonesian, Mandarin Chinese, Russian, and Spanish). Each of these source languages are representative of the most widely spoken ones and therefore they have the potential to serve as pivot languages that will enable more accurate translations. The dataset is multicentric to enforce representation of multilingual language features. In addition, the dataset goes beyond the sentence level, as it is organized in paragraphs of various lengths. Compared with related machine translation datasets, we show that BOUQuET has a broader representation of domains while simplifying the translation task for non-experts. Therefore, BOUQuET is specially suitable for crowd-source extension for which we are launching a call aiming at collecting a multi-way parallel corpus covering any written language. The dataset is freely available at https://huggingface.co/datasets/facebook/bouquet.
Type
Publication
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing