Interpretable Model for Predicting Charges with Legal Provisions Incorporated
Charge prediction aims to infer and predict the crimes that a suspect may be charged with based on the relevant facts and ev-idence of the case.With the development of artificial intelligence technology,charge prediction models using deep learning methods have demonstrated excellent performance.However,in the legal field,the charge prediction models based on deep learning are easily challenged by legal experts due to the black-box nature behind them,which affects the reliability and credibility of the prediction re-sults.This paper proposes an interpretable charge prediction model with legal provisions incorporated.First,the representation learning of criminal legal instruments is performed by matching the legal provisions with the crime facts through a text-matching task that maps the factual descriptions and the legal provisions to the same space for feature alignment,so that the crime facts in the hidden space are clustered around the relevant legal provisions.The representation of factual descriptions is then further clustered to form concepts de-scribing different types of crimes and embedded into the hidden space of the prediction model as a decision base to guide the training process.During the prediction,the similarity between the sample representation to be tested and the concepts is computed,and the con-cept with the greatest similarity is used as the decision base to generate the prediction results.Two different types of experiments on the CAIL2018 dataset shows that the model prediction accuracy reaches 99.70％and 93.60％,outperforming the comparison model in terms of classification accuracy and model interpretability.
representation learning of legal documentscharge predictioninterpretabilitytext matching
NETL
NSTL
万方数据
