The General Data Protection Regulation (GDPR) is an European regulation on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA), and for all foreign subjects dealing with European citizens data. Therefore, the GDPR has important legislation implications that hold beyond EU member states. In this paper, we address the problem of GDPR article retrieval through the use of pre-trained language models (PLMs). Our approach features several key aspects, which include both domain-general and domain-specific pre-trained BERT models, further powered by self-supervised task-adaptive pre-training stages, with or without data enrichment based on recitals. Our study endeavors to demonstrate the potential of PLMs in addressing the challenges posed by the GDPR’s intricate legal framework, thus ultimately facilitating efficient access to GDPR provisions for government agencies, law firms, legal professionals, and citizens alike.
GDPR Article Retrieval based on Domain-adaptive and Task-adaptive Legal Pre-trained Language Models.
BibTeX Entry and Citation Info
@inproceedings{SimeriGDPRLirai2023,
author = {Andrea Simeri and
Andrea Tagarelli},
title = {{GDPR Article Retrieval based on Domain-adaptive and Task-adaptive Legal Pre-trained Language Models}},
booktitle = {Proceedings of the 1st Legal Information Retrieval meets Artificial Intelligence Workshop LIRAI 2023 co-located with the 34th ACM Hypertext Conference HT 2023},
series = {{CEUR} Workshop Proceedings},
volume = {3594},
pages = {63-76},
year = {2023},
url = {https://ceur-ws.org/Vol-3594/paper5.pdf}
}
References
Simeri, A. and Tagarelli, A. (2023b). GDPR Article Retrieval based on Domain- adaptive and Task-adaptive Legal Pre-trained Language Models. In Proceedings of the 1st Legal Information Retrieval meets Artificial Intelligence Workshop LIRAI 2023 co-located with the 34th ACM Hypertext Conference HT 2023, volume 3594 of CEUR Workshop Proceedings, pages 63–76.
