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: 5824
Citations: Bibtex RefMan EndNote GB/T7714
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,in press.https://doi.org/10.1631/FITEE.2100041 @article{title="A full-process intelligent trial system for smart court", %0 Journal Article TY - JOUR
一种智慧法院的全流程智能化审判系统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。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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