摘要在法律文书智能化处理过程中，针对罪名预测、法条推荐、刑期预测，该文提出了一种长文本分类的混合深度神经网络模型HAC（hybrid attention and CNN model），该模型利用残差网络融合了改进的层次注意力网络（iHAN）和深度金字塔卷积神经网络（DPCNN）。在"中国法研杯"司法人工智能挑战赛（CAIL-2018）的测试数据集上，该模型对罪名的预测与相关法条的推荐的F1-Score（Micro-F1和Macro-F1的均值）分别为85%和87%。对于刑期的预测，由于地区、年代、法院、法官、被告人的态度等方面的差异会导致刑期预测难度加大。该模型具有优良的预测性能和泛化能力，能够很好地适应这些差异。同时，将该模型在罪名预测和法条推荐的输出结果加入到刑期预测任务的输入中，并使用分类方法对刑期进行预测，进一步提升了模型的效果，最终在刑期预测任务中F1-Score超过77%，获得CAIL-2018刑期预测优秀成绩。
Abstract：This article presents a neural network model for crime prediction, legal article recommendation, and sentence prediction from judicial documents. The model is based on a hybrid attention and CNN model which combines the improved hierarchical attention network (iHAN) and the deep pyramid convolutional neural network (DPCNN) by ResNet. The F1-Scores (mean value of Micro-F1 and Macro-F1) for the crime prediction and related law samples from CAIL-2018 were 85% and 87%. The sentence prediction accuracy is impacted by differences in locations, dates, courts, judges, and defendant attitudes. The model adjusts well to these differences because of its high predictive ability and model generalization. The model prediction outputs for the recommended crime prediction and law items were then added to the model input for the sentence prediction task to further improve the model performance. The model got an excellent result in the sentence prediction task (CAIL-2018) with an F1-Score of over 77%.
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2019, Vol. 59 
