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Register a new userAutomated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language
استخراج آلي من قرارات الأحكام من قضايا المحكمة في اللغة العبرية
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نقدم مهمة استخراج العقوبة الآلي (القرد) في قرارات الأحكام من قضايا المحكمة الجنائية في العبرية.سيمكن معالجة القرد من تحديد أنماط الحكم وتشكل حجر خطوة هامة للعديد من تطبيقات NLP القانونية المتابعة باللغة العبرية، بما في ذلك التنبؤ بقرارات الأحكام.نحن نحذر مجموعة بيانات من قرارات الحكم الجنسي للاعتداء الجنسي ومجموعة بيانات التقييم المشروح يدويا، وتنفيذ النماذج القائمة على القواعد والإشراف.نجد أنه في حين أن النماذج الخاضعة للإشراف يمكن أن تحدد الجملة التي تحتوي على العقوبة بدقة جيدة، فإن النهج القائمة على القواعد تتفوق عليها في مهمة القرد الكامل.نستنتج من خلال تقديم أول تحليل لأنماط الأحكام في DataSet وتحليل أخطاء النماذج المشتركة، مما يشير إلى طرق العمل في المستقبل، مثل التمييز بين الاختبار وعقوبة السجن الفعلية.سنقوم بإتاحة جميع مواردنا عند الطلب، بما في ذلك البيانات والتصفية والنماذج القياسية الأولى.
We present the task of Automated Punishment Extraction (APE) in sentencing decisions from criminal court cases in Hebrew. Addressing APE will enable the identification of sentencing patterns and constitute an important stepping stone for many follow up legal NLP applications in Hebrew, including the prediction of sentencing decisions. We curate a dataset of sexual assault sentencing decisions and a manually-annotated evaluation dataset, and implement rule-based and supervised models. We find that while supervised models can identify the sentence containing the punishment with good accuracy, rule-based approaches outperform them on the full APE task. We conclude by presenting a first analysis of sentencing patterns in our dataset and analyze common models' errors, indicating avenues for future work, such as distinguishing between probation and actual imprisonment punishment. We will make all our resources available upon request, including data, annotation, and first benchmark models.
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