A full-process intelligent trial system for smart court

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

doi:10.1631/FITEE.2100041

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A full-process intelligent trial system for smart court

Regular Papers | Updated：2022-04-19

- A full-process intelligent trial system for smart court
  Enhanced Publication
- 一种智慧法院的全流程智能化审判系统
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 2, Pages: 186-206(2022)
- Affiliations：
  
  1.Guanghua Law School, Zhejiang University, Hangzhou310008, China
  2.College of Computer Science and Technology, Zhejiang University, Hangzhou310027, China
  3.Alibaba Group, Hangzhou310099, China
  4.State Grid Zhejiang Electric Power Co., Ltd., Hangzhou310007, China
  5.Zhejiang Higher People's Court, Hangzhou310012, China
- Author bio：
  
  E-mail: srsysj@zju.edu.cn;
  E-mail: kunkuang@zju.edu.cn;
  E-mail: changlong.scl@taobao.com;
  E-mail: JuneFeng.81@gmail.com;
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2100041
  CLC： TP391
- Received：24 January 2021，
  
  Accepted：03 August 2021，
  
  Published：0 February 2022
- Accepted：
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Bin WEI, Kun KUANG, Changlong SUN, et al. A full-process intelligent trial system for smart court[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 186-206.
DOI：

Bin WEI, Kun KUANG, Changlong SUN, et al. A full-process intelligent trial system for smart court[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 186-206. DOI： 10.1631/FITEE.2100041.

摘要

在智慧法院建设中，为实现更高效、公平和可解释的审判程序，我们提出一种全流程智能化审判系统（FITS）来提供智能化协助。在所提FITS中，介绍了对构建智慧法院至关重要的任务，包括信息抽取、证据分类、问题生成、对话摘要、判决预测和判决文书生成。具体而言，准备工作是从法律文本中抽取要素，从而帮助法官高效地确定案情。利用提取的属性，通过在所有证据中确认一致性等标准来证实每条证据的有效性。在庭审过程中，设计了自动发问机器人，协助法官主持庭审。它由一个表示程序性发问的有限状态机和一个通过对法庭辩论中的话语上下文编码进而生成事实问题的深度学习模型组成。此外，FITS还在多任务学习框架下，实时总结法庭辩论中产生的争议焦点，并在对话检查摘要（DIS）模块中生成摘要审判记录。为支持法官决策，采用了一阶逻辑来表达法律知识，并将其嵌入深度神经网络（DNN）来预测判决。最后，提出一种基于注意力和反事实的自然语言生成（AC-NLG）方法生成法院判决。

Abstract

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

Keywords

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