Study on Prediction of Legal Judgments Based on the CNN-BiGRU Model

Abstract

As the cases exploded, leading legal judgment prediction becomes a promising application of artificial intelligence techniques in the legal field. The goal of legal judgment prediction is to predict the judgment results based on the facts information of a case. However, the classifier of the traditional method has poor accuracy performance and cost large computational time. The commonly used deep learning models are CNN and RNN. In this paper, CNN-BiGRU was established and analyzed, which combined the good extraction ability of CNN for local feature information and RNN for long-term dependencies information of the text. Compared with the CAIL 2018 dataset, the prediction accuracy of the charges, law articles and the terms of penalty are 94.8%, 93.6%, and 73.4%, respectively. Results showed that CNN-BiGRU has a higher prediction accuracy than CNN or RNN alone and a good training efficiency over baselines. The effectiveness and practicability of this model are validated.