LegalHANOpt: A Hierarchical Attention Network with BOHB Optimization for Predicting and Explaining Legal Case Decisions

Abstract

Legal documents are often lengthy and complex, making it challenging and time-consuming for experts to accurately predict case outcomes. Older methods are not well-suited to the structure and language used in legal texts. This paper proposes a model called LegalHANOpt (Legal Hierarchical Attention Network with Optimized Parameters) to make accurate predictions about legal case decisions and explain how those decisions are made. LegalHANOpt utilizes a Hierarchical Attention Network (HAN) that analyzes legal documents by examining individual words and sentences, much like lawyers typically do. To improve the model's performance, Bayesian Optimization with Hyperband (BOHB) is utilized. This sophisticated method automatically determines the optimal settings for training the model, such as learning rate and dropout. The LegalHANOpt is trained on an extensive collection of past legal cases, including relevant facts, laws, and decisions. Results show that LegalHANOpt gives more accurate predictions than older methods achieving superior performance with an accuracy of 0.91%, macro F1-score of 0.83%, and AUC-ROC of 0.92%. It also highlights essential parts of the text, helping users understand why the model made a particular decision. In short, LegalHANOpt is a valuable and explainable tool to support legal experts in making better and faster decisions.