CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-08-03
Cited: 0
Clicked: 6990
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, 2022, 23(2): 186-206.
@article{title="A full-process intelligent trial system for smart court",
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"
}
%0 Journal Article
%T A full-process intelligent trial system for smart court
%A Bin WEI
%A Kun KUANG
%A Changlong SUN
%A Jun FENG
%A Yating ZHANG
%A Xinli ZHU
%A Jianghong ZHOU
%A Yinsheng ZHAI
%A Fei WU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 2
%P 186-206
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100041
TY - JOUR
T1 - A full-process intelligent trial system for smart court
A1 - Bin WEI
A1 - Kun KUANG
A1 - Changlong SUN
A1 - Jun FENG
A1 - Yating ZHANG
A1 - Xinli ZHU
A1 - Jianghong ZHOU
A1 - Yinsheng ZHAI
A1 - Fei WU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 2
SP - 186
EP - 206
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
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.
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