Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| languages: | |
| - bg | |
| - cs | |
| - da | |
| - de | |
| - el | |
| - en | |
| - es | |
| - et | |
| - fi | |
| - fr | |
| - ga | |
| - hu | |
| - is | |
| - it | |
| - lt | |
| - lv | |
| - mt | |
| - nl | |
| - 'no' | |
| - pl | |
| - pt | |
| - ro | |
| - sk | |
| - sl | |
| - sv | |
| licenses: | |
| - cc-by-sa-4-0 | |
| multilinguality: | |
| - translation | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - conditional-text-generation | |
| task_ids: | |
| - machine-translation | |
| # Dataset Card for [Dataset Name] | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory) | |
| - **Paper:** [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0) | |
| - **Point of Contact:** [Ralf Steinberger](mailto:Ralf.Steinberger@jrc.ec.europa.eu) | |
| ### Dataset Summary | |
| In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. | |
| ECDC-TM covers 25 languages: the 23 official languages of the EU plus Norwegian (Norsk) and Icelandic. ECDC-TM was created by translating from English into the following 24 languages: Bulgarian, Czech, Danish, Dutch, English, Estonian, Gaelige (Irish), German, Greek, Finnish, French, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian (NOrsk), Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish. | |
| All documents and sentences were originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. | |
| To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek: | |
| `dataset = load_dataset("europa_ecdc_tm", language_pair=("cs", "el"))` | |
| ### Supported Tasks and Leaderboards | |
| - `conditional-text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu). | |
| ### Languages | |
| All documents and sentences were originally written in English (`en`). They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. | |
| Translations are available in these languages: `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`. | |
| ## Dataset Structure | |
| ### Data Instances | |
| ``` | |
| { | |
| "translation": { | |
| "<source_language>":"Sentence to translate", | |
| "<target_language>": "Translated sentence", | |
| }, | |
| } | |
| ``` | |
| ### Data Fields | |
| - `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`. | |
| ### Data Splits | |
| The data is not splitted (only the `train` split is available). | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The ECDC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of public health. Also, it includes translation units for the languages Irish (Gaelige, GA), Norwegian (Norsk, NO) and Icelandic (IS). | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| ECDC-TM was built on the basis of the website of the European Centre for Disease Prevention and Control (ECDC). The major part of the documents talks about health-related topics (anthrax, botulism, cholera, dengue fever, hepatitis, etc.), but some of the web pages also describe the organisation ECDC (e.g. its organisation, job opportunities) and its activities (e.g. epidemic intelligence, surveillance). | |
| #### Who are the source language producers? | |
| All documents and sentences were originally written in English, by the ECDC website content producers. | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed] | |
| #### Who are the annotators? | |
| All documents and sentences were thus originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg. | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| Contains translations of sentences in the public healthcare domain, including technical terms (disease and treatment names for example). | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| Copyright © EU / ECDC, 2020 | |
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| ### Citation Information | |
| ``` | |
| @Article{Steinberger2014, | |
| author={Steinberger, Ralf | |
| and Ebrahim, Mohamed | |
| and Poulis, Alexandros | |
| and Carrasco-Benitez, Manuel | |
| and Schl{\"u}ter, Patrick | |
| and Przybyszewski, Marek | |
| and Gilbro, Signe}, | |
| title={An overview of the European Union's highly multilingual parallel corpora}, | |
| journal={Language Resources and Evaluation}, | |
| year={2014}, | |
| month={Dec}, | |
| day={01}, | |
| volume={48}, | |
| number={4}, | |
| pages={679-707}, | |
| issn={1574-0218}, | |
| doi={10.1007/s10579-014-9277-0}, | |
| url={https://doi.org/10.1007/s10579-014-9277-0} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset. |