  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. Replacing CNNs with BIGRUs in
label-wise attention networks leads to the best overall performance.
