Abstract:
In Legal Judgement Prediction (LJP), the primary aim is to anticipate the judgment outcome by
analyzing the factual description of criminal or civil cases, a field gaining increasing attention
within legal research. Typically, a LJP scenario include three core subtasks: predicting applica ble law articles, charges, and penalty terms. In real-world scenarios, both predicting charges and
legal articles necessitate multi-class classification combined with elements of multi-label learn ing. However, current methods frequently reduce these tasks to single-label learning paradigms
for multi-class categorization. Furthermore, these methods typically overlook the utilization
of pertinent keywords abundant in legal documents. The critical analysis of violated laws and
related articles to formulate a final judgement is a very time-consuming task. Hence, an au tomated prediction of judgement can fulfill huge amount of repetitive tasks in minimal time.
Therefore, we propose a model for the task of multi-class multi-label prediction formalizing
the dependencies among the sub tasks to improve the multi-task prediction efficiency using a
framework titled as Multi Task Learning for Legal Judgement Prediction. This framework lever ages a multi-layered attention network, machine learning and transformer models that integrates
legal keywords, fostering a comprehensive understanding of multiple LJP subtasks. Analyzing
and implementing the law heavily relies on legal texts, necessitating a thorough examination
of violated laws and relevant articles to reach a final judgment. Therefore, this research assist
judges in decision making process based on the valid references and an automated judgement
prediction simplifies the whole case proceedings.
