Extreme Multi-Label Legal Text Classification: A case study in EU Legislation
This work addresses legal text classification for EU legislation, providing a larger dataset and improved methods, but it is incremental as it builds on existing neural approaches.
The authors tackled extreme multi-label text classification in the legal domain by releasing a new dataset of 57k EU legislative documents and showing that BIGRUs with self-attention outperform current state-of-the-art methods, achieving the best overall performance when replacing CNNs with BIGRUs in label-wise attention networks.
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.