Large-Scale Multi-Label Text Classification on EU Legislation

Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos


Abstract
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EUR-LEX, 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.
Anthology ID:
P19-1636
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6314–6322
Language:
URL:
https://aclanthology.org/P19-1636
DOI:
10.18653/v1/P19-1636
Bibkey:
Cite (ACL):
Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, and Ion Androutsopoulos. 2019. Large-Scale Multi-Label Text Classification on EU Legislation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6314–6322, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Large-Scale Multi-Label Text Classification on EU Legislation (Chalkidis et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1636.pdf
Code
 iliaschalkidis/lmtc-eurlex57k
Data
EURLEX57KRCV1