Lune

EMNLP2025Top-tier venue

BOUQuET : dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation

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

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. 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 . Column A: Lang-ID This column should have the same 3-lowercase-letter code representing the source language of the sentences being created followed by an underscore character ( ) and a 4-letter code representing the script. Column B: Domain This is 1 of the 8 domains represented in the dataset (see Section 3.1).

Ask Lune

Ask anything. The answer quotes the paper.

Lune reads all of it and the papers around it, then shows you the passages behind every answer.

Questions to start from

Lune answers from

the full paper · related work

Get the answer

Free to start. No credit card required.

Builds on5

  1. NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each BenchmarkOscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz et al.EMNLP Findings 2023 · 85 citations
  2. CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information RetrievalShuo Sun, Kevin DuhEMNLP 2020 · 53 citations
  3. Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary ResourcesXinyan Yu, Trina Chatterjee, Akari Asai, Junjie Hu et al.EMNLP Findings 2022 · 15 citations
  4. COMET-QE and Active Learning for Low-Resource Machine TranslationEverlyn Chimoto, Bruce A. BassettEMNLP Findings 2022 · 5 citations
  5. Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningShivalika Singh, Freddie Vargus, Daniel D'souza, Börje Karlsson et al.ACL 2024

Related papers

  1. WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & DialectsDaniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein et al.ACL Findings 2025 · 6 citations
  2. Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented DialogueNikita Moghe, Evgeniia Razumovskaia, Liane Guillou, Ivan Vulic et al.ACL Findings 2023 · 12 citations
  3. Generative Language Models for Paragraph-Level Question GenerationAsahi Ushio, Fernando Alva-Manchego, José Camacho-ColladosEMNLP 2022 · 30 citations
  4. LangMark: A Multilingual Dataset for Automatic Post-EditingDiego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin et al.ACL 2025
  5. An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo et al.ACL 2025
Dusk over the sea between two cliffs drawn in fine vertical lines