يتكون تنبؤ الحكم القانوني (LJP) عادة في مهمة تصنيف النص يهدف إلى التنبؤ بالحكم على أساس وصف الحقيقة.يظهر الأدب أن استخدام المقالات كميزات الإدخال يساعد على تحسين أداء التصنيف.في هذا العمل، صممنا مهمة تنبؤ بالحكم بناء على نزاعات مستأجرة المالك وقمنا بتطبيق النماذج القائمة على بيرت التي تغذيها من مختلف الميزات القائمة على المادة.على الرغم من أن النتائج التي تم الحصول عليها تتفق مع الأدبيات، إلا أن التحسينات المتعلقة بالمقالات التي يتم الحصول عليها في الغالب مع الملصقات الأكثر شيوعا، مما يشير إلى أن النماذج القائمة على المحولات المدربة ومضبوطة مسبقا وغير قابلة للتحجيم كما هو الحال بالنسبة للمنطق القانوني في سيناريوهات الحياة الحقيقيةإنهم سيحلون فقط في التنبؤ بدقة أكبر الأحكام المتكررة على حساب النتائج القانونية الأخرى.
Legal judgment prediction (LJP) usually consists in a text classification task aimed at predicting the verdict on the basis of the fact description. The literature shows that the use of articles as input features helps improve the classification performance. In this work, we designed a verdict prediction task based on landlord-tenant disputes and we applied BERT-based models to which we fed different article-based features. Although the results obtained are consistent with the literature, the improvements with the articles are mostly obtained with the most frequent labels, suggesting that pre-trained and fine-tuned transformer-based models are not scalable as is for legal reasoning in real life scenarios as they would only excel in accurately predicting the most recurrent verdicts to the detriment of other legal outcomes.
References used
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