Article 700 Identification in Judicial Judgments: Comparing Transformers and Machine Learning Models

Sid Ali Mahmoudi, Charles Condevaux, Guillaume Zambrano and Stéphane Mussard ()
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Sid Ali Mahmoudi: Université Nîmes Chrome, Avenue du Dr. Georges Salan, 30000 Némes, France
Charles Condevaux: Université Nîmes Chrome, Avenue du Dr. Georges Salan, 30000 Némes, France
Guillaume Zambrano: Université Nîmes Chrome, Avenue du Dr. Georges Salan, 30000 Némes, France
Stéphane Mussard: Université Nîmes Chrome, Avenue du Dr. Georges Salan, 30000 Némes, France

Stats, 2024, vol. 7, issue 4, 1-16

Abstract: Predictive justice, which involves forecasting trial outcomes, presents significant challenges due to the complex structure of legal judgments. To address this, it is essential to first identify all claims across different categories before attempting to predict any result. This paper focuses on a classification task based on the detection of Article 700 in judgments, which is a rule indicating whether the plaintiff or defendant is entitled to reimbursement of their legal costs. Our experiments show that conventional machine learning models trained on word and document frequencies can be competitive. However, using transformer models specialized in legal language, such as Judicial CamemBERT , also achieves high accuracies.

Keywords: CamemBERT; text classification; predictive justice; TF-IDF (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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