A Judicial Sentencing Method Based on Fused Deep Neural Networks

Abstract

Nowadays, the judicial system has been hard to satisfy the growing judicial needs of the people. Therefore, the introduction of artificial intelligence into the judicial field is an inevitable trend. This paper incorporates deep learning into intelligent judicial sentencing and proposes a comprehensive network fusion model based on massive legal documents. The proposed method combines multiple networks, e.g., recurrent neural network and convolutional neural network, in the procedure of sentencing prediction. Specially, we use text classification and post-classification regression to predict the defendant’s conviction, articles of law related to the case and prison term. Moreover, we use the simulated gradient descent method to build a fusion model. Experimental results on legal documents datasets justify the effectiveness of the proposed method in sentencing prediction. The fused network model outperforms each individual model in terms of higher accuracy and stability when predicting the conviction, law article and prison term.