Automatic Legal Judgment Prediction via Large Amounts of Criminal Cases

Abstract

We research how automatically predict the charges and relevant law articles of criminal cases in our work. At first, the distributions of charges, relevant law articles and fact description of criminal cases are analyzed based on CAIL2018. CAIL2018, the first large-scale dataset for legal judgment prediction in China, contains large amounts of criminal cases collected from the Supreme People’s Court of China. By our analysis, we find the distribution of criminal cases is typical 8020 distribution. Then we present our framework to predict criminal cases automatically. In our framework, data enhancement, oversampling, key word extraction are used to optimize data quality, and deep learning is employed to predict charges and relevant articles. In the prediction, single deep learning model is tested firstly, then ensemble of different deep learning models are compared to achieve better performance than that of single model. In our work, we find data enhancement and ensemble strategy can improve the performance of judgment prediction. More differences of joint models and data, better performance of ensemble strategy.