---
YAML tags: null
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-AST 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]