Paper ID: 2406.15032

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

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.

Submitted: Jun 21, 2024