Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Author:

Zheng Min1,Liu Bo1ORCID,Sun Le2ORCID

Affiliation:

1. Hubei University of Science and Technology, Xianning, Hubei, China

2. Nanjing University of Information Science and Technology, Nanjing, China

Abstract

Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Judgment prediction from legal documents using Texas wolf optimization based deep BiLSTM model;Intelligent Decision Technologies;2024-06-07

2. Retracted: Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era;Computational Intelligence and Neuroscience;2023-07-12

3. Artificial Intelligence in the Judiciary System of Saudi Arabia: A Literature Review;2023 International Conference On Cyber Management And Engineering (CyMaEn);2023-01-26

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