@inproceedings{andrews-etal-2025-bouquet,
title = "{BOUQ}u{ET} : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation",
author = "Andrews, Pierre and
Artetxe, Mikel and
Meglioli, Mariano Coria and
Costa-juss{\`a}, Marta R. and
Chuang, Joe and
Dale, David and
Duppenthaler, Mark and
Ekberg, Nathanial Paul and
Gao, Cynthia and
Licht, Daniel Edward and
Maillard, Jean and
Mourachko, Alexandre and
Ropers, Christophe and
Saleem, Safiyyah and
S{\'a}nchez, Eduardo and
Tsiamas, Ioannis and
Turkatenko, Arina and
Ventayol-Boada, Albert and
Yates, Shireen",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1400/",
pages = "27503--27523",
ISBN = "979-8-89176-332-6",
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 aim-ing at collecting a multi-way parallel corpus covering any written language. The dataset is freely available at https://huggingface.co/datasets/facebook/bouquet."
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<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 aim-ing at collecting a multi-way parallel corpus covering any written language. The dataset is freely available at https://huggingface.co/datasets/facebook/bouquet.</abstract>
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%0 Conference Proceedings
%T BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation
%A Andrews, Pierre
%A Artetxe, Mikel
%A Meglioli, Mariano Coria
%A Costa-jussà, Marta R.
%A Chuang, Joe
%A Dale, David
%A Duppenthaler, Mark
%A Ekberg, Nathanial Paul
%A Gao, Cynthia
%A Licht, Daniel Edward
%A Maillard, Jean
%A Mourachko, Alexandre
%A Ropers, Christophe
%A Saleem, Safiyyah
%A Sánchez, Eduardo
%A Tsiamas, Ioannis
%A Turkatenko, Arina
%A Ventayol-Boada, Albert
%A Yates, Shireen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F andrews-etal-2025-bouquet
%X 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 aim-ing at collecting a multi-way parallel corpus covering any written language. The dataset is freely available at https://huggingface.co/datasets/facebook/bouquet.
%U https://aclanthology.org/2025.emnlp-main.1400/
%P 27503-27523
Markdown (Informal)
[BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation](https://aclanthology.org/2025.emnlp-main.1400/) (Andrews et al., EMNLP 2025)
ACL
- 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, and Shireen Yates. 2025. BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 27503–27523, Suzhou, China. Association for Computational Linguistics.