Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1631/FITEE.2100041 | Frontiers of Information Technology & Electronic Engineering |
Keywords | DocType | Volume |
Intelligent trial system, Smart court, Evidence analysis, Dialogue summarization, Focus of controversy, Automatic questioning, Judgment prediction, 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, TP391 | Journal | 23 |
Issue | ISSN | Citations |
2 | 2095-9184 | 1 |
PageRank | References | Authors |
0.41 | 14 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| baogang | 1 | 209 | 29.51 |
| Kun Kuang | 2 | 45 | 12.53 |
| Changlong Sun | 3 | 19 | 13.89 |
| Jun Feng | 4 | 29 | 3.20 |
| Y Zhang | 5 | 1 | 0.41 |
| X Zhu | 6 | 1 | 0.41 |
| Jianyi Zhou | 7 | 11 | 2.90 |
| Yun Zhai | 8 | 735 | 32.59 |
| Fei Wu | 9 | 2209 | 153.88 |