README.md

---
license: cc-by-4.0
task_categories:
- translation
language:
- va
- es
size_categories:
- 100K<n<1M
dataset_info:
- config_name: parallel-va-es
  features:
  - name: VA
    dtype: string
  - name: ES
    dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_examples: 738777
  download_size: 272825842
configs:
- config_name: parallel-va-es
  sep: "\t"
  data_files:
  - split: train
    path: parallel/va-es.tsv
---

# AMIC_PARALLEL Dataset

## Dataset Summary

AMIC_PARALLEL is a parallel dataset for Valencian (VA) to Spanish (ES) translation. It consists of sentence pairs in Valencian and Spanish, along with the source file from which the data was extracted. This dataset is designed to support machine translation tasks and linguistic research.

## Dataset Structure

Each row in the dataset includes the following columns:

- **VA**: A sentence in Valencian.
- **ES**: The corresponding translation of the Valencian sentence in Spanish.
- **Source**: The name of the source file from which the sentence pair was extracted.
## Dataset Creation

### Curation Rationale

This dataset is aimed at promoting the development of Machine Translation
between Valencian and Spanish, supporting research in multilingual NLP
and facilitating the development of translation systems for these language pairs.

### Source Data

The parallel data in this dataset is extracted from web documents published by the [Associació de Mitjans d'Informació i Comunicació (AMIC).](https://www.amic.media/mitjans_digitals.php?sel_provincia=9&amp;sel_comarca=0).

### Data Filtering and Normalization

All data underwent rigorous filtering and normalization:
- **Alignment filtering**: Sentence- and paragraph-level alignments were calculated with the [`gplsi
translation-alignment` tool](https://github.com/gplsi/translation-alignment).
- **Language identification**: Valencian documents are filtered using a private discriminative tool to differentiate them from Catalan.
- **Deduplication:** The filtered datasets were deduplicated to remove redundant sentence pairs

The filtered and normalized datasets were then concatenated to form the final corpus.

## Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA.

<!-- ## Acknowledgments

We would like to express our gratitude to all individuals and institutions that have contributed to the development of this work.

Special thanks to:

- [Data providers]
- [Technological support providers]

We also acknowledge the financial, technical, and scientific support of the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA. -->

## Reference

Please cite this dataset using the following BibTeX format:

```
@misc{amic2025parallel,
  author       = {Espinosa Zaragoza, Sergio and Sep{\'u}lveda Torres, Robiert and Mu{\~n}oz Guillena, Rafael and Consuegra-Ayala, Juan Pablo},
  title        = {AMIC\_PARALLEL Dataset},
  year         = {2025},
  institution  = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
  howpublished = {\url{https://huggingface.co/datasets/gplsi/amic_parallel}}
}
```

## Disclaimer

Be aware that the data may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this data, or use the data themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence.
The University of Alicante, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.

## License

This work is licensed under a [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) licence.