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arXiv · 2110.01485

JuriBERT: A Masked-Language Model Adaptation for French Legal Text

Stella Douka·Hadi Abdine·Michalis Vazirgiannis·Rajaa El Hamdani·David Restrepo Amariles
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Abstract

Language models have proven to be very useful when adapted to specific domains. Nonetheless, little research has been done on the adaptation of domain-specific BERT models in the French language. In this paper, we focus on creating a language model adapted to French legal text with the goal of helping law professionals. We conclude that some specific tasks do not benefit from generic language models pre-trained on large amounts of data. We explore the use of smaller architectures in domain-specific sub-languages and their benefits for French legal text. We prove that domain-specific pre-trained models can perform better than their equivalent generalised ones in the legal domain. Finally, we release JuriBERT, a new set of BERT models adapted to the French legal domain.

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Stella Douka, Hadi Abdine, Michalis Vazirgiannis, Rajaa El Hamdani, David Restrepo Amariles. 2021-10-04. JuriBERT: A Masked-Language Model Adaptation for French Legal Text. https://arxiv.org/abs/2110.01485

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