A full-process intelligent trial system for smart court

作     者：Bin WEI Kun KUANG Changlong SUN Jun FENG Yating ZHANG Xinli ZHU Jianghong ZHOU Yinsheng ZHAI Fei WU Bin WEI;Kun KUANG;Changlong SUN;Jun FENG;Yating ZHANG;Xinli ZHU;Jianghong ZHOU;Yinsheng ZHAI;Fei WU

作者机构：Guanghua Law SchoolZhejiang UniversityHangzhou 310008China College of Computer Science and TechnologyZhejiang UniversityHangzhou 310027China Alibaba GroupHangzhou 310099China State Grid Zhejiang Electric Power Co.Ltd.Hangzhou 310007China Zhejiang Higher People’s CourtHangzhou 310012China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2022年第23卷第2期

页      面：186-206页

核心收录：

学科分类：0710[理学-生物学] 1205[管理学-图书情报与档案管理] 081203[工学-计算机应用技术] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Key R&D Projects of the Ministry of Science and Technology of China(No.2020YFC0832500) the National Key Research and Development Program of China(No.2018AAA0101900) the National Social Science Foundation of China(No.20&ZD047) the National Natural Science Foundation of China(Nos.61625107 and 62006207) the Key R&D Project of Zhejiang Province,China(No.2020C01060) the Fundamental Research Funds for the Central Universities,China(Nos.LQ21F020020 and 2020XZA202) 

主　　题：Intelligent trial system Smart court Evidence analysis Dialogue summarization Focus of controversy Automatic questioning Judgment prediction 

摘      要：In constructing a smart court,to provide intelligent assistance for achieving more efficient,fair,and explainable trial proceedings,we propose a full-process intelligent trial system(FITS).In the proposed FITS,we introduce essential tasks for constructing a smart court,including information extraction,evidence classification,question generation,dialogue summarization,judgment prediction,and judgment document ***,the preliminary work involves extracting elements from legal texts to assist the judge in identifying the gist of the case *** the extracted attributes,we can justify each piece of evidence’s validity by establishing its consistency across all *** the trial process,we design an automatic questioning robot to assist the judge in presiding over the *** consists of a finite state machine representing procedural questioning and a deep learning model for generating factual questions by encoding the context of utterance in a court ***,FITS summarizes the controversy focuses that arise from a court debate in real time,constructed under a multi-task learning framework,and generates a summarized trial transcript in the dialogue inspectional summarization(DIS)*** support the judge in making a decision,we adopt first-order logic to express legal knowledge and embed it in deep neural networks(DNNs)to predict ***,we propose an attentional and counterfactual natural language generation(AC-NLG)to generate the court’s judgment.