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+language:
+- es
+- ast
+pretty_name: ES-AST Parallel Corpus
+task_categories:
+- translation
+size_categories:
+- size category
+---
+
+# Dataset Card for ES-AST Parallel Corpus
+
+## Dataset Description
+
+- **Point of Contact:** langtech@bsc.es
+
+
+### Dataset Summary
+
+The ES-AN Parallel Corpus is a Spanish-Asturian dataset created to support the use of under-resourced languages from Spain, 
+such as Asturian, in NLP tasks, specifically Machine Translation.
+
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train Bilingual Machine Translation models between Asturian and Spanish in any direction, as well as Multilingual Machine Translation models.
+
+### Languages
+
+The sentences included in the dataset are in Spanish (ES) and Asturian (AST).
+
+## Dataset Structure
+
+### Data Instances
+
+Two separate txt files are provided:
+
+- es-ast_corpus.es
+- es-ast_corpus.ast
+
+The dataset is additionally provided in parquet format: es-ast_corpus.parquet.
+
+The parquet file contains two columns of parallel text obtained from the two original text files. 
+Each row in the file represents a pair of parallel sentences in the two languages of the dataset.
+
+
+### Data Fields
+
+[N/A]
+    
+### Data Splits
+
+The dataset contains a single split: `train`.
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset is aimed at promoting the development of Machine Translation between Spanish and under-resourced languages from Spain, specifically Asturian.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+This dataset was created as part of the participation of Language Technologies Unit at BSC in the WMT24 Shared Task: 
+[Translation into Low-Resource Languages of Spain](https://www2.statmt.org/wmt24/romance-task.html). 
+The corpus is the result of a thorough cleaning and preprocessing, as described in detail in the paper "Training and Fine-Tuning 
+NMT Models for Low-Resource Languages using Apertium-Based Synthetic Corpora" (link to be added as soon as published). 
+As no filtering based on alignment score was applied, the dataset may contain poorly aligned sentences.
+
+This dataset aggregates both synthetic and authentic data. 
+The authentic parallel data come from [OPUS](https://opus.nlpl.eu/) (Spanish-Asturian), while the synthetic data consist of Spanish translations generated from the Asturian monolingual corpus of the [PILAR](https://github.com/transducens/PILAR) dataset. 
+To create the synthetic Spanish we used the rule-based [Apertium](https://www.apertium.org/) translator.
+
+#### Who are the source language producers?
+
+[Opus](https://opus.nlpl.eu/)
+
+[PILAR](https://github.com/transducens/PILAR)
+
+[WMT24](https://www2.statmt.org/wmt24/romance-task.html)
+
+
+### Annotations
+
+#### Annotation process
+
+The dataset does not contain any annotations.
+
+#### Who are the annotators?
+
+[N/A]
+
+### Personal and Sensitive Information
+
+Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied, 
+personal and sensitive information may be present in the data. This needs to be considered when using the data for training models.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+By providing this resource, we intend to promote the use of Asturian across NLP tasks, 
+thereby improving the accessibility and visibility of the Asturian language.
+
+### Discussion of Biases
+
+No specific bias mitigation strategies were applied to this dataset. 
+Inherent biases may exist within the data.
+
+### Other Known Limitations
+
+The dataset contains data of a general domain. Applications of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
+
+## Additional Information
+
+### Dataset Curators
+
+Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es).
+
+This work is funded by the Ministerio para la Transformación Digital y de la Función Pública and Plan de Recuperación, 
+Transformación y Resiliencia - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 
+2022/TL22/00215337, 2022/TL22/00215336, 2022/TL22/00215335, 2022/TL22/00215334.
+
+The publication is part of the project PID2021-123988OB-C33, funded by MCIN/AEI/10.13039/501100011033/FEDER, EU.
+
+
+### Licensing Information
+
+This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) 
+due to licence restrictions on part of the original data.
+
+### Citation Information
+
+[N/A]
+
+### Contributions
+
+[N/A]