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