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We introduce MULTI-EURLEX, a new multilingual dataset for topicclassification of legal documents. The dataset comprises 65k European Union(EU) laws, officially translated in 23 languages, annotated with multiplelabels from the EUROVOC taxonomy. We highlight the effect of temporal conceptdrift and the importance of chronological, instead of random splits. We use thedataset as a testbed for zero-shot cross-lingual transfer, where we exploitannotated training documents in one language (source) to classify documents inanother language (target). We find that fine-tuning a multilingually pretrainedmodel (XLM-ROBERTA, MT5) in a single source language leads to catastrophicforgetting of multilingual knowledge and, consequently, poor zero-shot transferto other languages. Adaptation strategies, namely partial fine-tuning,adapters, BITFIT, LNFIT, originally proposed to accelerate fine-tuning for newend-tasks, help retain multilingual knowledge from pretraining, substantiallyimproving zero-shot cross-lingual transfer, but their impact also depends onthe pretrained model used and the size of the label set.

MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer