We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, annotated with ~4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT's maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.
@article{arxiv.1906.02192,
title = {Large-Scale Multi-Label Text Classification on EU Legislation},
author = {Ilias Chalkidis and Manos Fergadiotis and Prodromos Malakasiotis and Ion Androutsopoulos},
journal= {arXiv preprint arXiv:1906.02192},
year = {2019}
}
Comments
9 pages, short paper at ACL 2019. arXiv admin note: text overlap with arXiv:1905.10892