Datasets:

Languages:
Catalan
Multilinguality:
monolingual
Size Categories:
unknown
Annotations Creators:
professional translators
ArXiv:
License:

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Dataset Card for XNLI-ca

Dataset Summary

Professional translation into Catalan of the Cross-lingual Natural Language Inference XNLI dataset, an evaluation corpus for language transfer and cross-lingual sentence classification.

XNLI-ca is a collection of 7,500 sentence pairs annotated with textual entailment.

XNLI is restricted to only non-commercial research purposes under the Creative Commons Attribution Non-commercial 4.0 International Public License.

Supported Tasks and Leaderboards

Textual entailment, Text classification, Language Model

Languages

The dataset is in Catalan (ca-ES).

Dataset Structure

Data Instances

Two JSON files, one for each split.

Example:

    
   {
    "label": "contradiction",
    "premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
    "hypothesis": "No he tornat a parlar amb ell."
  },
  {
    "label": "entailment",
    "premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
    "hypothesis": "Estava tan molest que vaig començar a parlar amb ell de nou."
  },
  {
    "label": "neutral",
    "premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
    "hypothesis": "Vam tenir una gran xerrada."
  }
  

Data Fields

  • premise: text
  • hypothesis: text related to the premise
  • label: relation between premise and hypothesis:
    • 0: entailment
    • 1: neutral
    • 2: contradiction

Data Splits

  • dev.json: 2490 examples
  • test.json: 5010 examples

Dataset Creation

Curation Rationale

We created this dataset to contribute to the development of language models in Catalan, a low-resource language.

Source Data

XNLI.

Initial Data Collection and Normalization

This dataset is a professional translation of XNLI into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.

Who are the source language producers?

For more information on how XNLI was created, refer to the paper XNLI: Evaluating Cross-lingual Sentence Representations, or visit the XNLI's webpage.

Annotations

Annotation process

[N/A]

Who are the annotators?

This is a professional translation of the XNLI corpus and its annotations.

Personal and Sensitive Information

No personal or sensitive information included.

Considerations for Using the Data

Social Impact of Dataset

We hope this dataset contributes to the development of language models in Catalan, a low-resource language.

Discussion of Biases

[N/A]

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)

This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.

Licensing Information

XNLI is restricted to only non-commercial research purposes under the Creative Commons Attribution Non-commercial 4.0 International Public License.

Citation Information

@inproceedings{gonzalez-agirre-etal-2024-building-data,
    title = "Building a Data Infrastructure for a Mid-Resource Language: The Case of {C}atalan",
    author = "Gonzalez-Agirre, Aitor  and
      Marimon, Montserrat  and
      Rodriguez-Penagos, Carlos  and
      Aula-Blasco, Javier  and
      Baucells, Irene  and
      Armentano-Oller, Carme  and
      Palomar-Giner, Jorge  and
      Kulebi, Baybars  and
      Villegas, Marta",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.231",
    pages = "2556--2566",
}

DOI

Contributions

[N/A]

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