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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sid Ali Mahmoudi & Charles Condevaux & Guillaume Zambrano & Stéphane Mussard, 2024. "Article 700 Identification in Judicial Judgments: Comparing Transformers and Machine Learning Models," Stats, MDPI, vol. 7(4), pages 1-16, November.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:4:p:83-1436:d:1530023
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    References listed on IDEAS

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    1. Charles Condevaux, 2020. "Neural Legal Outcome Prediction with Partial Least Squares Compression," Stats, MDPI, vol. 3(3), pages 1-16, September.
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