README.md

metadata

dataset_info:
  features:
    - name: br
      dtype: string
    - name: fr
      dtype: string
  splits:
    - name: train
      num_bytes: 101142
      num_examples: 882
  download_size: 60945
  dataset_size: 101142
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - translation
language:
  - br
  - fr
multilinguality:
  - multilingual

Description

Paires breton/français du jeu de données QED disponible sur OPUS.

⚠ Attention ⚠ : il y a des problèmes d'alignement. Ce jeu de données n'est donc pas utilisbale tel quel et un post-processing serait à effectuer.

Citations

QED

@inproceedings{abdelali-etal-2014-amara,
    title = "The {AMARA} Corpus: Building Parallel Language Resources for the Educational Domain",
    author = "Abdelali, Ahmed  and  Guzman, Francisco  and Sajjad, Hassan  and Vogel, Stephan",
    editor = "Calzolari, Nicoletta  and       Choukri, Khalid  and       Declerck, Thierry  and       Loftsson, Hrafn  and       Maegaard, Bente  and       Mariani, Joseph  and       Moreno, Asuncion  and       Odijk, Jan  and       Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf",
    pages = "1856--1862",
    abstract = "This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.",
}

OPUS

@inbook{4992de1b5fb34f3e9691772606b36edf,
title = "News from OPUS - A Collection of Multilingual Parallel Corpora with Tools and Interfaces",
author = "J{\"o}rg Tiedemann",
year = "2009",
language = "odefinierat/ok{\"a}nt",
volume = "V",
pages = "237--248",
editor = "N. Nicolov and K. Bontcheva and G. Angelova and R. Mitkov",
booktitle = "Recent Advances in Natural Language Processing",

}