  In this paper, we propose a new annotation scheme to classify different types
of clauses in Terms-and-Conditions contracts with the ultimate goal of
supporting legal experts to quickly identify and assess problematic issues in
this type of legal documents. To this end, we built a small corpus of
Terms-and-Conditions contracts and finalized an annotation scheme of 14
categories, eventually reaching an inter-annotator agreement of 0.92. Then, for
11 of them, we experimented with binary classification tasks using few-shot
prompting with a multilingual T5 and two fine-tuned versions of two BERT-based
LLMs for Italian. Our experiments showed the feasibility of automatic
classification of our categories by reaching accuracies ranging from .79 to .95
on validation tasks.
