Datasets:

Multilinguality:
multilingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
multiun / README.md
albertvillanova's picture
Convert dataset to Parquet (#8)
489448d verified
metadata
annotations_creators:
  - found
language_creators:
  - found
language:
  - ar
  - de
  - en
  - es
  - fr
  - ru
  - zh
license:
  - unknown
multilinguality:
  - multilingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - translation
task_ids: []
paperswithcode_id: multiun
pretty_name: MultiUN (Multilingual Corpus from United Nation Documents)
config_names:
  - ar-de
  - ar-en
  - ar-es
  - ar-fr
  - ar-ru
  - ar-zh
  - de-en
  - de-es
  - de-fr
  - de-ru
  - de-zh
  - en-es
  - en-fr
  - en-ru
  - en-zh
  - es-fr
  - es-ru
  - es-zh
  - fr-ru
  - fr-zh
  - ru-zh
dataset_info:
  - config_name: ar-de
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - de
    splits:
      - name: train
        num_bytes: 94466261
        num_examples: 165090
    download_size: 41124373
    dataset_size: 94466261
  - config_name: ar-en
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - en
    splits:
      - name: train
        num_bytes: 4189844561
        num_examples: 9759125
    download_size: 1926776740
    dataset_size: 4189844561
  - config_name: ar-es
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - es
    splits:
      - name: train
        num_bytes: 4509667188
        num_examples: 10119379
    download_size: 2069474168
    dataset_size: 4509667188
  - config_name: ar-fr
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - fr
    splits:
      - name: train
        num_bytes: 4516842065
        num_examples: 9929567
    download_size: 2083442998
    dataset_size: 4516842065
  - config_name: ar-ru
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - ru
    splits:
      - name: train
        num_bytes: 5932858699
        num_examples: 10206243
    download_size: 2544128334
    dataset_size: 5932858699
  - config_name: ar-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ar
              - zh
    splits:
      - name: train
        num_bytes: 3781650541
        num_examples: 9832293
    download_size: 1829880809
    dataset_size: 3781650541
  - config_name: de-en
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - de
              - en
    splits:
      - name: train
        num_bytes: 76684413
        num_examples: 162981
    download_size: 35105094
    dataset_size: 76684413
  - config_name: de-es
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - de
              - es
    splits:
      - name: train
        num_bytes: 80936517
        num_examples: 162078
    download_size: 37042740
    dataset_size: 80936517
  - config_name: de-fr
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - de
              - fr
    splits:
      - name: train
        num_bytes: 81888299
        num_examples: 164025
    download_size: 37827000
    dataset_size: 81888299
  - config_name: de-ru
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - de
              - ru
    splits:
      - name: train
        num_bytes: 111517798
        num_examples: 164792
    download_size: 46723695
    dataset_size: 111517798
  - config_name: de-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - de
              - zh
    splits:
      - name: train
        num_bytes: 70534674
        num_examples: 176933
    download_size: 34964647
    dataset_size: 70534674
  - config_name: en-es
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - en
              - es
    splits:
      - name: train
        num_bytes: 4128132575
        num_examples: 11350967
    download_size: 2030826335
    dataset_size: 4128132575
  - config_name: en-fr
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - en
              - fr
    splits:
      - name: train
        num_bytes: 4678044616
        num_examples: 13172019
    download_size: 2312275443
    dataset_size: 4678044616
  - config_name: en-ru
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - en
              - ru
    splits:
      - name: train
        num_bytes: 5632653511
        num_examples: 11654416
    download_size: 2523567444
    dataset_size: 5632653511
  - config_name: en-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - en
              - zh
    splits:
      - name: train
        num_bytes: 2960368390
        num_examples: 9564315
    download_size: 1557547095
    dataset_size: 2960368390
  - config_name: es-fr
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - es
              - fr
    splits:
      - name: train
        num_bytes: 4454703338
        num_examples: 11441889
    download_size: 2187539838
    dataset_size: 4454703338
  - config_name: es-ru
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - es
              - ru
    splits:
      - name: train
        num_bytes: 5442647242
        num_examples: 10605056
    download_size: 2432480744
    dataset_size: 5442647242
  - config_name: es-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - es
              - zh
    splits:
      - name: train
        num_bytes: 3223863318
        num_examples: 9847770
    download_size: 1676774308
    dataset_size: 3223863318
  - config_name: fr-ru
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - fr
              - ru
    splits:
      - name: train
        num_bytes: 5979869673
        num_examples: 11761738
    download_size: 2690520032
    dataset_size: 5979869673
  - config_name: fr-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - fr
              - zh
    splits:
      - name: train
        num_bytes: 3241090573
        num_examples: 9690914
    download_size: 1693120344
    dataset_size: 3241090573
  - config_name: ru-zh
    features:
      - name: translation
        dtype:
          translation:
            languages:
              - ru
              - zh
    splits:
      - name: train
        num_bytes: 4233867889
        num_examples: 9557007
    download_size: 1984600328
    dataset_size: 4233867889
configs:
  - config_name: ar-de
    data_files:
      - split: train
        path: ar-de/train-*
  - config_name: ar-en
    data_files:
      - split: train
        path: ar-en/train-*
  - config_name: ar-es
    data_files:
      - split: train
        path: ar-es/train-*
  - config_name: ar-fr
    data_files:
      - split: train
        path: ar-fr/train-*
  - config_name: ar-ru
    data_files:
      - split: train
        path: ar-ru/train-*
  - config_name: ar-zh
    data_files:
      - split: train
        path: ar-zh/train-*
  - config_name: de-en
    data_files:
      - split: train
        path: de-en/train-*
  - config_name: de-es
    data_files:
      - split: train
        path: de-es/train-*
  - config_name: de-fr
    data_files:
      - split: train
        path: de-fr/train-*
  - config_name: de-ru
    data_files:
      - split: train
        path: de-ru/train-*
  - config_name: de-zh
    data_files:
      - split: train
        path: de-zh/train-*
  - config_name: en-es
    data_files:
      - split: train
        path: en-es/train-*
  - config_name: en-fr
    data_files:
      - split: train
        path: en-fr/train-*
  - config_name: en-ru
    data_files:
      - split: train
        path: en-ru/train-*
  - config_name: en-zh
    data_files:
      - split: train
        path: en-zh/train-*
  - config_name: es-fr
    data_files:
      - split: train
        path: es-fr/train-*
  - config_name: es-ru
    data_files:
      - split: train
        path: es-ru/train-*
  - config_name: es-zh
    data_files:
      - split: train
        path: es-zh/train-*
  - config_name: fr-ru
    data_files:
      - split: train
        path: fr-ru/train-*
  - config_name: fr-zh
    data_files:
      - split: train
        path: fr-zh/train-*
  - config_name: ru-zh
    data_files:
      - split: train
        path: ru-zh/train-*

Dataset Card for OPUS MultiUN

Table of Contents

Dataset Description

Dataset Summary

The MultiUN parallel corpus is extracted from the United Nations Website , and then cleaned and converted to XML at Language Technology Lab in DFKI GmbH (LT-DFKI), Germany. The documents were published by UN from 2000 to 2009.

This is a collection of translated documents from the United Nations originally compiled by Andreas Eisele and Yu Chen (see http://www.euromatrixplus.net/multi-un/).

This corpus is available in all 6 official languages of the UN consisting of around 300 million words per language

Supported Tasks and Leaderboards

The underlying task is machine translation.

Languages

Parallel texts are present in all six official languages: Arabic (ar), Chinese (zh), English (en), French (fr), Russian (ru) and Spanish (es), with a small part of the documents available also in German (de).

Dataset Structure

Data Instances

{
  "translation": {
    "ar": "قرار اتخذته الجمعية العامة",
    "de": "Resolution der Generalversammlung"
  }
}

Data Fields

  • translation (dict): Parallel sentences for the pair of languages.

Data Splits

The dataset contains a single "train" split for each language pair.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Original MultiUN source data: http://www.euromatrixplus.net/multi-unp

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

If you use this corpus in your work, please cite the paper:

@inproceedings{eisele-chen-2010-multiun,
    title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents",
    author = "Eisele, Andreas  and
      Chen, Yu",
    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
    month = may,
    year = "2010",
    address = "Valletta, Malta",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf",
    abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.",
}

If you use any part of the corpus (hosted in OPUS) in your own work, please cite the following article:

@inproceedings{tiedemann-2012-parallel,
    title = "Parallel Data, Tools and Interfaces in {OPUS}",
    author = {Tiedemann, J{\"o}rg},
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Do{\u{g}}an, Mehmet U{\u{g}}ur  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
    pages = "2214--2218",
    abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}

Contributions

Thanks to @patil-suraj for adding this dataset.