---
YAML tags: null
language:
- es
- oc
pretty_name: ES-OC Parallel Corpus
task_categories:
- translation
size_categories:
- size category
---

# Dataset Card for ES-OC Parallel Corpus

## Dataset Description

- **Point of Contact:** langtech@bsc.es


### Dataset Summary

The ES-OC Parallel Corpus is a Spanish-Aranese dataset created to support the use of under-resourced languages from Spain, 
such as Aranese, in NLP tasks, specifically Machine Translation.


### Supported Tasks and Leaderboards

The dataset can be used to train Bilingual Machine Translation models between Aranese and Spanish in any direction, as well as Multilingual Machine Translation models.

### Languages

The sentences included in the dataset are in Spanish (ES) and Aranese (OC). 
Aranese is a variant of Occitan spoken in the Val d'Aran, in northwestern Catalonia, 
where it is one of the three official languages, along with Catalan and Spanish.

## Dataset Structure

### Data Instances

Two separate txt files are provided:

- es-arn_corpus.es
- es-arn_corpus.arn

The dataset is additionally provided in parquet format: es-arn_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 Aranese.

### 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 is mainly synthetic, generated using the rule-based translator [Apertium](https://www.apertium.org/). 
It contains synthetic Spanish, derived from the Aranese [PILAR](https://github.com/transducens/PILAR) monolingual dataset. 
It also includes synthetic Aranese, obtained by translating the Spanish side of the Spanish-Aranese pairs from [OPUS](https://opus.nlpl.eu/). 
Additionally, it contains synthetic Spanish translated from monolingual Aragonese text extracted from the document Diccionari_der_Aranés.pdf, provided by the shared-task organizers.

#### 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 Aranese across NLP tasks, 
thereby improving the accessibility and visibility of the Aranese 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.

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]