面向法律文书的量刑预测方法研究

Automatic Sentencing Prediction for Legal Texts

大规模法律文书数据为智能司法审判研究提供了重要的数据基础。量刑预测是智能司法审判中的一个关键环节,对维护司法审判的公平与公正具有重要意义。该文首先基于区间划分和多模型投票方法进行了量刑预测初探,发现区间划分策略可以有效缓解刑期类别众多和数据不平衡问题;在此基础上,又采用基于量刑属性的预测方法来充分理解量刑情节。在CAIL2018评测数据上的实验表明:该文所提出的两种方法,性能明显超过其他基线系统。

Large-scale legal documents provide a kind of data for Intelligent Judicial Adjudication research. This paper probes into the prediction for measurement of penalty based on interval partition and multi-models voting method, revealing that the strategy can effectively alleviate the issue of excessive types of penalty and data imbalance. Further, we explore the prediction for measurement of penalty based on case attributes to fully capture the factors in the circumstances of human judgment. Experiments on the dataset provided by 2018 Competition of AI and Law in China(CAIL2018) show the better performance of above models than the baselines.