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In this work, we study the task of classifying legal texts written in theGreek language. We introduce and make publicly available a novel dataset basedon Greek legislation, consisting of more than 47 thousand official, categorizedGreek legislation resources. We experiment with this dataset and evaluate abattery of advanced methods and classifiers, ranging from traditional machinelearning and RNN-based methods to state-of-the-art Transformer-based methods.We show that recurrent architectures with domain-specific word embeddings offerimproved overall performance while being competitive even to transformer-basedmodels. Finally, we show that cutting-edge multilingual and monolingualtransformer-based models brawl on the top of the classifiers' ranking, makingus question the necessity of training monolingual transfer learning models as arule of thumb. To the best of our knowledge, this is the first time the task ofGreek legal text classification is considered in an open research project,while also Greek is a language with very limited NLP resources in general.

Multi-granular Legal Topic Classification on Greek Legislation