Multi-granular Legal Topic Classification on Greek Legislation

TL;DR

This paper introduces a new Greek legal text dataset and evaluates various machine learning models, finding that domain-specific RNNs perform well and that multilingual transformers outperform monolingual models, challenging common assumptions.

Contribution

It provides the first open research dataset for Greek legal text classification and compares multiple models, highlighting the effectiveness of domain-specific RNNs and the competitive performance of multilingual transformers.

Findings

Recurrent models with domain-specific embeddings outperform some transformer models.

Multilingual transformers perform on par or better than monolingual models.

First open dataset for Greek legal text classification.

Abstract

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.