JuriBERT: A Masked-Language Model Adaptation for French Legal Text
This helps law professionals by providing a tailored language model for French legal text, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the lack of domain-specific BERT models for French legal text by creating JuriBERT, showing that domain-specific pre-trained models outperform generalized ones in legal tasks.
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.