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.

通过引入包含超过 100,000 个定义的 DORE 数据集，并对其进行评估，本研究填补了葡萄牙语领域中的定义建模数据集的空白，并报道了基于深度学习的定义建模模型的结果，该数据集和本文的发现将促进对葡萄牙语的更广泛的研究和学习。