NLP
BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation
February 07, 2025
Abstract
This paper presents BOUQuET, a multicentric and multi-register/domain dataset and benchmark, and its broader collaborative extension initiative. This dataset is handcrafted in non-English languages first, each of these source languages being represented among the 23 languages commonly used by half of the world’s population and therefore having the potential to serve as pivot languages that will enable more accurate translations. The dataset is specially designed to avoid contamination and be multicentric, so as 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 (MT) 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 the open initiative and call for translation participation that we are launching to extend it to a multi-way parallel corpus to any written language
AUTHORS
Written by
The Omnilingual MT Team
Mikel Artetxe
Albert Ventayol
Alexandre Mourachko
Arina Turkatenko
Christophe Ropers
Cynthia Gao
David Dale
Eduardo Sánchez
Jean Maillard
Joe Chuang
Mariano Coria Meglioli
Marta R. Costa-jussa
Pierre Andrews
Safiyyah Saleem
Shireen Yates
Yiannis Tsiamas
Publisher
arXiv
Research Topics
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