Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism

Qiang Zhao, Rundong Guo, Xiaowei Feng, Weifeng Hu, Siwen Zhao, Zihan Wang, Yujun Li and Yewen Cao
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Qiang Zhao: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Rundong Guo: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Xiaowei Feng: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Weifeng Hu: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Siwen Zhao: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Zihan Wang: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Yujun Li: School of Information Science and Engineering, Shandong University, Qingdao 266200, China
Yewen Cao: School of Information Science and Engineering, Shandong University, Qingdao 266200, China

Mathematics, 2022, vol. 10, issue 13, 1-22

Abstract: Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal inference and a multi-expert FTOPJUDGE mechanism. First, a causal inference algorithm was adopted to process unstructured text. This process did not require very much manual intervention to better mine the information in the text. Then, a neural network dedicated to each task was set up, and a neural network that simultaneously served multiple tasks was also set up. Finally, the pre-trained language model Lawformer was used to provide knowledge for downstream tasks. By using the public data set CAIL2018 and comparing it with current mainstream decision prediction models, it was shown that the model significantly improved the performance of downstream tasks and achieved great improvements in multiple indicators. Through ablation experiments, the effectiveness and rationality of each module of the proposed model were verified. The method proposed in this study achieved reasonably good performance in legal judgment prediction, which provides a promising solution for legal judgment prediction.

Keywords: deep neural network; legal judgment prediction; causal inference; data pre-training; multi-task learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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