PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors

TL;DR

This paper introduces PyEuroVoc, a multilingual Transformer-based tool for classifying European Union legal documents using EuroVoc descriptors, significantly outperforming previous methods and providing open-source code and models.

Contribution

It presents a unified, fine-tuned Transformer framework for EuroVoc classification across 22 languages, improving upon existing tools like JEX.

Findings

Significant performance improvements over JEX

Effective multilingual classification with Transformer models

Open-source code and models provided

Abstract

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.