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--- |
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YAML tags: null |
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language: |
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- es |
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- ast |
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pretty_name: ES-AST Parallel Corpus |
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task_categories: |
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- translation |
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size_categories: |
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- size category |
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--- |
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# Dataset Card for ES-AST Parallel Corpus |
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## Dataset Description |
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- **Point of Contact:** [email protected] |
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### Dataset Summary |
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The ES-AST Parallel Corpus is a Spanish-Asturian dataset created to support the use of under-resourced languages from Spain, |
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such as Asturian, in NLP tasks, specifically Machine Translation. |
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### Supported Tasks and Leaderboards |
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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. |
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### Languages |
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The sentences included in the dataset are in Spanish (ES) and Asturian (AST). |
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## Dataset Structure |
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### Data Instances |
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Two separate txt files are provided: |
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- es-ast_corpus.es |
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- es-ast_corpus.ast |
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The dataset is additionally provided in parquet format: es-ast_corpus.parquet. |
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The parquet file contains two columns of parallel text obtained from the two original text files. |
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Each row in the file represents a pair of parallel sentences in the two languages of the dataset. |
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### Data Fields |
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[N/A] |
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### Data Splits |
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The dataset contains a single split: `train`. |
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## Dataset Creation |
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### Curation Rationale |
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This dataset is aimed at promoting the development of Machine Translation between Spanish and under-resourced languages from Spain, specifically Asturian. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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This dataset was created as part of the participation of Language Technologies Unit at BSC in the WMT24 Shared Task: |
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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 aggregates both synthetic and authentic data. |
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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. |
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To create the synthetic Spanish we used the rule-based [Apertium](https://www.apertium.org/) translator. |
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#### Who are the source language producers? |
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[Opus](https://opus.nlpl.eu/) |
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[PILAR](https://github.com/transducens/PILAR) |
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[WMT24](https://www2.statmt.org/wmt24/romance-task.html) |
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### Annotations |
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#### Annotation process |
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The dataset does not contain any annotations. |
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#### Who are the annotators? |
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[N/A] |
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### Personal and Sensitive Information |
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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, |
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personal and sensitive information may be present in the data. This needs to be considered when using the data for training models. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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By providing this resource, we intend to promote the use of Asturian across NLP tasks, |
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thereby improving the accessibility and visibility of the Asturian language. |
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### Discussion of Biases |
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No specific bias mitigation strategies were applied to this dataset. |
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Inherent biases may exist within the data. |
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### Other Known Limitations |
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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. |
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## Additional Information |
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### Dataset Curators |
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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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[N/A] |
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### Contributions |
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[N/A] |
