Abstract
Most of the existing machine learning methods for charge prediction adopt the training mechanism of supervised learning. These algorithms have high requirements for the number of training samples corresponding to each crime. However, few or no cases are corresponding to some crimes in real scenarios, which leads to the poor performance of these models in practice. To alleviate this problem, we propose a novel Zero-Shot Learning (ZSL) based method for legal charge prediction tasks. Specifically, we define a set of semantic attributes to represent the domain knowledge of charges, which enables the model to migrate knowledge from seen charges to unseen charges. In this way, with the help of the ZSL mechanism, unseen charges and charges with a small number of training samples could be relatively predicted accurately. We evaluate the performance of the proposed method on a dataset collected from China Judgements Online, and the experimental results show that our method obtains \(32.4\%\) accuracy for the unseen charges and can largely retain the predictive power for the seen charges.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (61836005 and 62106150), Guangdong Basic and Applied Basic Research Foundation (2019A1515011577), Stable Support Programs of Shenzhen City (20200810150421002), CCF-NSFOCUS (2021001), and CAAC Key Laboratory of Civil Aviation Wide Survellence and Safety Operation Management & Control Technology (202102).
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Zhou, C., Cao, W., Xu, Z. (2022). Charge Prediction for Criminal Law with Semantic Attributes. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_19
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