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 

机构地区：[1]Guanghua Law School,Zhejiang University,Hangzhou 310008,China [2]College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China [3]Alibaba Group,Hangzhou 310099,China [4]State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310007,China [5]Zhejiang Higher People’s Court,Hangzhou 310012,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2022年第2期186-206,共21页信息与电子工程前沿（英文版）

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)。

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 generation.Specifically,the preliminary work involves extracting elements from legal texts to assist the judge in identifying the gist of the case efficiently.With the extracted attributes,we can justify each piece of evidence’s validity by establishing its consistency across all evidence.During the trial process,we design an automatic questioning robot to assist the judge in presiding over the trial.It 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 debate.Furthermore,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)module.To 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 judgments.Finally,we propose an attentional and counterfactual natural language generation(AC-NLG)to generate the court’s judgment.

关 键 词：Intelligent trial system Smart court Evidence analysis Dialogue summarization Focus of controversy Automatic questioning Judgment prediction 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]