Datasets:
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)
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configs:
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data_files:
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path: ar-de/train-*
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data_files:
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data_files:
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path: ar-es/train-*
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path: ar-fr/train-*
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path: ar-ru/train-*
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path: ar-zh/train-*
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path: de-es/train-*
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path: de-fr/train-*
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data_files:
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path: de-ru/train-*
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data_files:
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path: de-zh/train-*
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data_files:
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path: en-es/train-*
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data_files:
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path: en-fr/train-*
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data_files:
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path: en-ru/train-*
- config_name: en-zh
data_files:
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path: en-zh/train-*
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data_files:
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path: es-fr/train-*
- config_name: es-ru
data_files:
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path: es-ru/train-*
- config_name: es-zh
data_files:
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path: es-zh/train-*
- config_name: fr-ru
data_files:
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path: fr-ru/train-*
- config_name: fr-zh
data_files:
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Dataset Card for OPUS MultiUN
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://opus.nlpl.eu/MultiUN/corpus/version/MultiUN
- Repository: More Information Needed
- Paper: https://aclanthology.org/L10-1473/
- Leaderboard: More Information Needed
- Point of Contact: More Information Needed
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.