GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions
Giulio Salierno, Rosamaria Bertè, Luca Attias, Carla Morrone, Dario Pettazzoni, Daniela Battisti
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Recent advances in Natural Language Processing have demonstrated the effectiveness of pretrained language models like BERT for a variety of downstream tasks. We present GiusBERTo, the first BERT-based model specialized for anonymizing personal data in Italian legal documents. GiusBERTo is trained on a large dataset of Court of Auditors decisions to recognize entities to anonymize, including names, dates, locations, while retaining contextual relevance. We evaluate GiusBERTo on a held-out test set and achieve 97% token-level accuracy. GiusBERTo provides the Italian legal community with an accurate and tailored BERT model for de-identification, balancing privacy and data protection.