Datasets:
Add acknowledgments
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README.md
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@@ -35,7 +35,7 @@ The sentences included in the dataset are in Spanish (ES) and Aragonese (AN).
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### Data Instances
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Two separate txt files are provided
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- es-an_corpus.es
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- es-an_corpus.an
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[Translation into Low-Resource Languages of Spain](https://www2.statmt.org/wmt24/romance-task.html).
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The corpus is the result of a thorough cleaning and preprocessing, as described in detail in the paper "Training and Fine-Tuning
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NMT Models for Low-Resource Languages using Apertium-Based Synthetic Corpora" (link to be added as soon as published).
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This dataset is mainly synthetic, generated using the rule-based translator [Apertium](https://www.apertium.org/).
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It contains synthetic Spanish, derived from the Aragonese [PILAR](https://github.com/transducens/PILAR) monolingual dataset.
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Language Technologies Unit at the Barcelona Supercomputing Center ([email protected]).
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### Licensing Information
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This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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### Citation Information
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### Data Instances
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Two separate txt files are provided:
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- es-an_corpus.es
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- es-an_corpus.an
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[Translation into Low-Resource Languages of Spain](https://www2.statmt.org/wmt24/romance-task.html).
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The corpus is the result of a thorough cleaning and preprocessing, as described in detail in the paper "Training and Fine-Tuning
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NMT Models for Low-Resource Languages using Apertium-Based Synthetic Corpora" (link to be added as soon as published).
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As no filtering based on alignment score was applied, the dataset may contain poorly aligned sentences.
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This dataset is mainly synthetic, generated using the rule-based translator [Apertium](https://www.apertium.org/).
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It contains synthetic Spanish, derived from the Aragonese [PILAR](https://github.com/transducens/PILAR) monolingual dataset.
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Language Technologies Unit at the Barcelona Supercomputing Center ([email protected]).
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This work is funded by the Ministerio para la Transformación Digital y de la Función Pública and Plan de Recuperación,
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Transformación y Resiliencia - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference
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2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335, 2022/TL22/00215334.
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The publication is part of the project PID2021-123988OB-C33, funded by MCIN/AEI/10.13039/501100011033/FEDER, EU.
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### Licensing Information
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This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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due to licence restrictions on part of the original data.
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### Citation Information
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