Dataset Card for COPA-ca

Dataset Summary

The COPA-ca dataset (Choice of plausible alternatives in Catalan) is a professional translation of the English COPA dataset into Catalan, commissioned by BSC LangTech Unit. The dataset consists of 1000 premises, each given a question and two choices with a label encoding which of the choices is more plausible given the annotator.

The dataset is split into 400 training samples, 100 validation samples, and 500 test samples. It includes the following features: 'premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'.

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

Supported Tasks and Leaderboards

Commonsense reasoning, Language Model

Languages

The dataset is in Catalan (ca-ES).

Dataset Structure

Data Instances

Three JSON files, one for each split.

Example:

    
   {
      "premise": "El meu cos va dibuixar una ombra damunt l'herba.", 
      "choice1": "El sol estava sortint.", 
      "choice2": "L'herba estava tallada.", 
      "question": "cause", 
      "label": 0, 
      "idx": 1, 
      "changed": false
   }
   
   {
      "premise": "La dona va tolerar el comportament difícil de la seva amiga.", 
      "choice1": "La dona sabia que la seva amiga estava passant per un moment difícil.", 
      "choice2": "A la dona li va semblar que la seva amiga s'aprofitava de la seva amabilitat.", 
      "question": "cause", 
      "label": 0, 
      "idx": 2, 
      "changed": false
   }
  

Data Fields

• premise: a string feature.
• choice1: a string feature.
• choice2: a string feature.
• question: a string feature.
• label: a int64 feature.
• idx: a int32 feature.
• changed: a bool feature.

Data Splits

• copa-ca.train.jsonl: 400 examples
• copa-ca.val.jsonl: 100 examples
• copa-ca.test.jsonl: 500 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

COPA.

Initial Data Collection and Normalization

This dataset is a professional translation the English COPA dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.

Who are the source language producers?

For more information on how COPA was created, refer to the paper (Roemmele et al. 2011), or visit the COPA's webpage.

Annotations

Annotation process

[N/A]

Who are the annotators?

This is a professional translation of the English COPA dataset 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

This work is licensed under a Attribution-ShareAlike 4.0 International 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]