Extreme Multi-Label Legal Text Classification: A case study in EU Legislation

Understand

We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain.

• We release a new dataset of 57k legislative documents from EURLEX, the European Union's public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus.
• The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning.
• Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention.