Title: A concurrent prediction of criminal law charge and sentence using twin convolutional neural networks

Authors: Tong-Ying Juang; Chih-Shun Hsu; Yuh-Shyan Chen; Wan-Chun Chen

Addresses: Department of Computer Science and Information Engineering, National Taipei University, New Taipei 237, Taiwan ' Department of Information Management, Shih Hsin University, Taipei 116, Taiwan ' Department of Computer Science and Information Engineering, National Taipei University, New Taipei 237, Taiwan ' Department of Computer Science and Information Engineering, National Taipei University, New Taipei 237, Taiwan

Abstract: An intelligent law article prediction scheme, which solves the law articles imbalance problem and the missing value problem of the judgement, is proposed in this paper. This paper applies the law article description as the label attribute. Based on the property of the vector space, the missing value problem can be got over by learning a representative embedding vector through the vector similarity weighted mechanism. For the imbalance problem, we use a weight sharing classification layer which classifies the label according to the relevance between the fact vector and the law article vector of the vector space. We also use the transfer learning to train the model by the high-frequency law articles first, then share the weight as the prior knowledge to the low-frequency one to improve the classification performance. The proposed approach outperforms the performance on few-shot law article prediction.

Keywords: natural language processing; NLP; deep learning; few-shot learning; law article prediction; legal intelligent.

DOI: 10.1504/IJAHUC.2022.125038

International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.41 No.1, pp.29 - 43

Received: 18 Jun 2021
Accepted: 29 Sep 2021
Published online: 23 Aug 2022 *

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