基于BERT模型的多任务法律案件智能判决方法  

Multi task intelligent legal judgment method based on Bert model

作　　者：范阿曼 王延川 FAN Aman;WANG Yanchuan(Shaanxi Institute of International Trade and Commerce,XianYang 712000,China;Northwestern Polytechnical University,Xi’an 710000,China)

机构地区：[1]陕西国际商贸学院,陕西西安710000 [2]西北工业大学,陕西西安710000

出　　处：《微电子学与计算机》2022年第9期107-114,共8页Microelectronics & Computer

基　　金：陕西省社会科学基金项目(2018F028);国家重大社科基金项目(20&ZD177)。

摘　　要：近年来以深度学习和自然语言处理为代表的人工智能技术取得巨大突破,推动了司法智能化方法的发展。目前深度神经网络技术在法律审判领域应用受到广泛关注和发展.本文以BERT模型为基础,采用自然语言处理技术对于法律陈述事实文本进行学习,实现模型对于法律案例的智能分析能力.在模型中充分利用多层次的多头自注意力机制,从多个特征维度进一步理解了法律文本的语义信息,完成了罪名预测、法律条款推荐、刑期预测多个司法智能预测功能.通过智能司法判决的多任务学习,深入挖掘了各个子任务之间的相关性,提升了法律文本特征的提取能力,从而模型能够实现更好的泛化效果.使用公开数据集进行对比实验,验证了该方法的优异性能.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.

关 键 词：自然语言处理 判决预测 深度神经网络 多任务学习 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]