DORE: A Dataset for Portuguese Definition Generation

Anna Beatriz Dimas Furtado, Tharindu Ranasinghe, Frederic Blain, Ruslan Mitkov

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 Definition MOdelling for PoRtuguEse 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.
Anthology ID:
2024.lrec-main.473
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
5315–5322
Language:
URL:
https://aclanthology.org/2024.lrec-main.473
DOI:
Bibkey:
Cite (ACL):
Anna Beatriz Dimas Furtado, Tharindu Ranasinghe, Frederic Blain, and Ruslan Mitkov. 2024. DORE: A Dataset for Portuguese Definition Generation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5315–5322, Torino, Italia. ELRA and ICCL.
Cite (Informal):
DORE: A Dataset for Portuguese Definition Generation (Dimas Furtado et al., LREC-COLING 2024)
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