DORE: A Dataset For Portuguese Definition Generation
This addresses a gap for Portuguese language processing, benefiting over 200 million speakers, but is incremental as it extends existing methods to a new language.
The paper tackles the lack of a definition modelling dataset for Portuguese by introducing DORE, the first such dataset containing over 100,000 definitions, and evaluates deep learning models on it.
Definition modelling (DM) is the task of automatically generating a dictionary definition for 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.