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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14457 |
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| _version_ | 1866914812408102912 |
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| author | Bizzaro, Pietro Giovanni Della Valentina, Elena Napolitano, Maurizio Mana, Nadia Zancanaro, Massimo |
| author_facet | Bizzaro, Pietro Giovanni Della Valentina, Elena Napolitano, Maurizio Mana, Nadia Zancanaro, Massimo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts Bizzaro, Pietro Giovanni Della Valentina, Elena Napolitano, Maurizio Mana, Nadia Zancanaro, Massimo Computation and Language 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. |
| title | Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.14457 |