Multi task intelligent legal judgment method based on Bert model

In recent years, artificial intelligence technology represented by deep learning and natural language processing has made great breakthroughs, which has promoted the development of judicial intelligence. Deep neural network technology has been widely used in the legal intelligence. Based on the Bert (bidirectional encoder representation from transformers) model, this paper uses natural language processing technology to learn the legal facts to realize the intelligent analysis in legal cases. Through the self-attention mechanism in BERT module, further understand the semantic information of the legal text from multiple feature dimensions to complete multiple judicial intelligent prediction functions such as crime prediction, legal clause recommendation and sentence prediction. With the multi-task learning, the correlation between various tasks is analyzed, which improves the extraction ability of legal text features, so that our model can achieve better generalization effect. Comparative experiments using public data sets verify the excellent performance of the method in the paper.