Transfer Learning for Deontic Rule Classification: the Case Study of the GDPR

EasyChair Preprint 9170, version 1

Abstract

This work focuses on the automatic classification of deontic sentences. It presents a novel Machine Learning approach which combines the power of Transfer Learning with the information provided by two famous LegalXML formats. In particular, different BERT-like neural architectures have been fine-tuned on the downstream task of classifying rules from the European General Data Protection Regulation (GDPR) encoded in Akoma Ntoso and LegalRuleML. This work shows that fine-tuned language models can leverage the information provided in LegalXML documents to achieve automatic classification of deontic sentences and rules.

Keyphrases: AI&Law, Rule classification, deontic modality, legal knowledge representation

@booklet{EasyChair:9170,
  author    = {Davide Liga and Monica Palmirani},
  title     = {Transfer Learning for Deontic Rule Classification: the Case Study of the GDPR},
  howpublished = {EasyChair Preprint 9170},
  year      = {EasyChair, 2022}}