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Dataset Card for CatalanQA

Dataset Summary

This dataset can be used to build extractive-QA and Language Models. It is an aggregation and balancing of 2 previous datasets: VilaQuAD and ViquiQuAD.

Splits have been balanced by kind of question, and unlike other datasets like SQuAD, it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.

This dataset was developed by BSC TeMU as part of Projecte AINA, to enrich the Catalan Language Understanding Benchmark (CLUB).

This work is licensed under a Attribution-ShareAlike 4.0 International License.

Supported Tasks and Leaderboards

Extractive-QA, Language Model.


The dataset is in Catalan (ca-ES).

Dataset Structure

Data Instances

      "title": "Els 521 policies espanyols amb més mala nota a les oposicions seran enviats a Catalunya",
      "paragraphs": [
          "context": "El Ministeri d'Interior espanyol enviarà a Catalunya els 521 policies espanyols que han obtingut més mala nota a les oposicions. Segons que explica El País, hi havia mig miler de places vacants que s'havien de cobrir, però els agents amb més bones puntuacions han elegit destinacions diferents. En total van aprovar les oposicions 2.600 aspirants. D'aquests, en seran destinats al Principat 521 dels 560 amb més mala nota. Per l'altra banda, entre els 500 agents amb més bona nota, només 8 han triat Catalunya. Fonts de la policia espanyola que esmenta el diari ho atribueixen al procés d'independència, al Primer d'Octubre i a la 'situació social' que se'n deriva.",
          "qas": [
              "question": "Quants policies enviaran a Catalunya?",
              "id": "0.5961700408283691",
              "answers": [
                  "text": "521",
                  "answer_start": 57

Data Fields

Follows (Rajpurkar, Pranav et al., 2016) for SQuAD v1 datasets:

  • id (str): Unique ID assigned to the question.
  • title (str): Title of the article.
  • context (str): Article text.
  • question (str): Question.
  • answers (list): Answer to the question, containing:
    • text (str): Span text answering to the question.
    • answer_start Starting offset of the span text answering to the question.

Data Splits

  • train.json: 17135 question/answer pairs
  • dev.json: 2157 question/answer pairs
  • test.json: 2135 question/answer pairs

Dataset Creation

Curation Rationale

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

Source Data

Initial Data Collection and Normalization

This dataset is a balanced aggregation from ViquiQuAD and VilaQuAD datasets.

Who are the source language producers?

Volunteers from Catalan Wikipedia and professional journalists from VilaWeb.


Annotation process

We did an aggregation and balancing from ViquiQuAD and VilaQuAD datasets.

To annotate those datasets, we commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 (Rajpurkar, Pranav et al., 2016).

For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.

Who are the annotators?

Annotation was commissioned by a specialized company that hired a team of native language speakers.

Personal and Sensitive Information

No personal or sensitive information is included.

Considerations for Using the Data

Social Impact of Dataset

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

Discussion of Biases


Other Known Limitations


Additional Information

Dataset Curators

Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Licensing Information

This work is licensed under a Attribution-ShareAlike 4.0 International License.

Citation Information

    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 = "",
    pages = "2556--2566",



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