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.

翻译：本文提出一种新的标注方案，用于对《服务条款》合同中的各类条款进行分类，其最终目标是辅助法律专家快速识别并评估此类法律文件中存在的潜在问题。为此，我们构建了一个小规模的《服务条款》合同语料库，并确定了包含14个类别的标注体系，最终标注者间一致性达到0.92。随后，针对其中11个类别，我们采用多语言T5模型进行少样本提示学习，并结合两个基于BERT架构、针对意大利语优化的微调大语言模型，进行了二元分类任务的实验验证。实验结果表明，所提类别体系具备自动分类的可行性，在验证任务中取得了0.79至0.95的准确率范围。