Papers › Large-Scale Multi-Label Text Classification on EU Legislation

Large-Scale Multi-Label Text Classification on EU Legislation

5 Jun 2019ACL 2019 7arXiv:1906.02192archive 2025-07-28

Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos

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.

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iliaschalkidis/lmtc-eurlex57k officialmentioned in papermentioned on GitHubtfApache-2.0 report

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Tasks

Classification General Classification Multi Label Text Classification Multi-Label Text Classification Text Classification Zero-Shot Learning text-classification

Datasets

Introduced by this paper, per the archive.

EURLEX57K

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multi-Label Text Classification	EUR-Lex	bert-base	Micro F1	73.2	#1 of 3	Archive leaderboard	report
Multi-Label Text Classification	EUR-Lex	bert-base	P@5	68.7	#1 of 3	Archive leaderboard	report
Multi-Label Text Classification	EUR-Lex	bert-base	RP@5	79.6	#1 of 3	Archive leaderboard	report
Multi-Label Text Classification	EUR-Lex	bert-base	nDCG@5	82.3	#1 of 3	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT BiLSTM Dense Connections Dropout ELMo LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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