| dc.description.abstract | In recent years, the use of machine learning and deep learning methods for tagging legal terminology, classifying legal chapter names, and predicting legal sentencing ranges in legal texts written in Traditional Chinese, Simplified Chinese, and Englis- h has gradually become a topic of interest. This has led to the extension of the AI judge issue in domestic circles due to the issue of citizen judges. The hope is to us- e more rational, objective, and standardized logic processing capabilities to make a- ccurate and consistent judgments in legal cases.According to tests conducted by O- penAI Laboratory, the Generative Pre-trained Transformer (GPT) model can achie- ve high-level scores, placing it in the top 10% on the New York State Bar Exam. This indicates that the model has achieved excellent legal knowledge results, dem- onstrating its effectiveness in the application of transformer models in legal doc- uments.In the application of machine learning and deep learning in the Chinese le- gal field, the first challenge encountered is the problem of legal data structuring. The legal system in the Republic of China (Taiwan) is not easy to handle in terms of data structuring. Due to the vast amount of legal data and the complexity of the relationships and hierarchies between different types of legal provisions, effective data structuring and organization is necessary. In this study of the AI judge model, the model architecture is divided into three stages: data preprocessing, feature sele- ction for the AI judge system′s discretion, and model training and prediction. In the data preprocessing stage, the national court′s judgment data in JSON open data format from January 1996 to August 2022 is downloaded from the Judicial Depart- met Open Data Platform. The Bidirectional Encoder Representations from Trans- formers (BERT) is used to process the text of the judgments. By training on the text of legal judgments, the model learns the semantic information and vocabulary relationships within the judgments and converts the judgment contents into vector representations. Relevant features are extracted from the dataset for cases involve- ng drunk driving. Finally, the model is trained and predicted using multi-task lear- ning and automatic loss weight adjustment to obtain both the prediction results for the criminal liability and the sentence length, with an accuracy rate of 0.95. | en_US |