Full Text:   <1668>

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CLC number: TP391

On-line Access: 2022-02-28

Received: 2021-01-24

Revision Accepted: 2022-04-22

Crosschecked: 2021-08-03

Cited: 0

Clicked: 3884

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Bin WEI

https://orcid.org/0000-0002-6895-7007

Fei WU

https://orcid.org/0000-0003-2139-8807

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.186-206

http://doi.org/10.1631/FITEE.2100041


A full-process intelligent trial system for smart court


Author(s):  Bin WEI, Kun KUANG, Changlong SUN, Jun FENG, Yating ZHANG, Xinli ZHU, Jianghong ZHOU, Yinsheng ZHAI, Fei WU

Affiliation(s):  Guanghua Law School, Zhejiang University, Hangzhou 310008, China; more

Corresponding email(s):   srsysj@zju.edu.cn, kunkuang@zju.edu.cn, changlong.scl@taobao.com, JuneFeng.81@gmail.com, wufei@zju.edu.cn

Key Words:  Intelligent trial system, Smart court, Evidence analysis, Dialogue summarization, Focus of controversy, Automatic questioning, Judgment prediction


Bin WEI, Kun KUANG, Changlong SUN, Jun FENG, Yating ZHANG, Xinli ZHU, Jianghong ZHOU, Yinsheng ZHAI, Fei WU. A full-process intelligent trial system for smart court[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 186-206.

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author="Bin WEI, Kun KUANG, Changlong SUN, Jun FENG, Yating ZHANG, Xinli ZHU, Jianghong ZHOU, Yinsheng ZHAI, Fei WU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="2",
pages="186-206",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100041"
}

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A1 - Jianghong ZHOU
A1 - Yinsheng ZHAI
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DOI - 10.1631/FITEE.2100041


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.

一种智慧法院的全流程智能化审判系统

魏斌1,况琨2,孙常龙2,3,冯珺4,张雅婷3,朱新力5,周江洪2,翟寅生5,吴飞2
1浙江大学光华法学院,中国杭州市,310008
2浙江大学计算机科学与技术学院,中国杭州市,3100273阿里巴巴达摩院,中国杭州市,310099
4国家电网浙江省电力有限公司,中国杭州市,310007
5浙江省高级人民法院,中国杭州市,310012
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。

关键词:智能化审判系统;智慧法院;证据分析;对话摘要;争议焦点;自动发问;判决预测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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