@inproceedings{dimas-furtado-etal-2024-dore,
    title = "{DORE}: A Dataset for {P}ortuguese Definition Generation",
    author = "Dimas Furtado, Anna Beatriz  and
      Ranasinghe, Tharindu  and
      Blain, Frederic  and
      Mitkov, Ruslan",
    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.473",
    pages = "5315--5322",
    abstract = "Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for \textbf{D}efinition M\textbf{O}delling for Po\textbf{R}tugu\textbf{E}se containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.",
}

Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for \textbf{D}efinition M\textbf{O}delling for Po\textbf{R}tugu\textbf{E}se containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.
Dimas Furtado, Anna Beatriz Ranasinghe, Tharindu Blain, Frederic Mitkov, Ruslan
DORE: A Dataset for Portuguese Definition Generation