# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 4 Experiment
### Results and Analysis
In addition, compared with other legal judgment prediction models, our model predicts a higher MR value, which is slightly better than other models in statistical sense. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{4}{c}{**Criminal-S**} & \multicolumn{4}{c}{**Criminal-M**} & \multicolumn{4}{c}{**Criminal-L**} \\ \hline
**Metrics** & **Acc.** & **MP** & **MR** & **F1** & **Acc.** & **MP** & **MR** & **F1** & **Acc.** & **MP** & **MR** & **F1** \\ \hline CNN & 91.9 & 50.5 & 44.9 & 43.5 & 93.5 & 57.6 & 48.1 & 50.5 & 93.9 & 66.0 & 50.3 & 54.7 \\ \hline HAN & 92.2 & 52.7 & 49.2 & 44.9 & 93.7 & 61.1 & 55.3 & 54.6 & 95.1 & 68.2 & 57.3 & 58.6 \\ \hline Few-attribute & 92.8 & 57.0 & 53.9 & 53.4 & 94.7 & 66.7 & 60.4 & 61.8 & 95.7 & 73.3 & 67.1 & 68.6 \\ \hline MPBFN & 93.3 & 63.4 & 57.9 & 56.2 & 94.7 & 69.1 & 68.2 & 66.9 & 95.8 & 76.6 & 73.6 & 72.5 \\ \hline LADAN & 93.3 & 63.7 & 57.2 & 56.6 & **94.9** & 69.7 & 67.2 & 67.1 & 95.5 & 76.8 & 72.9 & 73.1 \\ \hline Our model & **93.5** & **66.2 \({}^{\dagger}\)** & **63.8 \({}^{\dagger}\)** & **62.1 \({}^{\dagger}\)** & **94.9** & **70.3 \({}^{\dagger}\)** & **73.5 \({}^{\dagger}\)** & **70.0 \({}^{\dagger}\)** & **95.9** & **78.6 \({}^{\dagger}\)** & **77.1 \({}^{\dagger}\)** & **76.2 \({}^{\dagger}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Charge prediction results of three datasets. By comparing the ordinary neural network model and the charge prediction model, it is found that the charge prediction model is significantly better than the ordinary neural network in various indexes, which indicates that in-depth analysis of various factors affecting LJP can effectively improve the prediction effect by introducing external knowledge or multi-task joint learning. Comparing on three different size datasets, the advantage of our model is particularly obvious on the Criminal-S dataset, mainly due to the adoption of the Mixup method in this paper. The two most basic metrics in the classification domain are Acc. and MR, and, comparing our model with LADAN and HAN, the accuracy is basically close, but the recall differs greatly. The main reason is that low-frequency and confusing charges account for a lower proportion, while the LADAN model can effectively distinguish confusing charges through DGCN, so the MR is improved. Through data enhancement and feature enhancement, our model can not only distinguish easily confused charges, but also has a good prediction effect on few-shot charges. To further demonstrate the advantage of our model in the case of few-shot charges, MPBFN and LADAN models, which are more effective in the baseline model, were compared with our model under different frequency charges. Here, according to the frequency of the charge, we divided them into three parts: the number of cases included in the low-frequency charges is less than 10 and the number of cases included in the high-frequency charges is more than 100. As shown in Table 6, the effects of the three models in high-frequency charges are close, but compared with the baseline model, our model improves 3.95% and 4.74% in low-frequency charges, respectively, which fully proves the advantages of our model in predicting few-shot charges. In addition, it can be seen from Figure 6 that the charge distribution of data in the legal field is imbalanced. In order to prove the effectiveness of this model in an unbalanced data scenario, we select a new evaluation metrics for comparative experiments. As shown in Table 4, in the ordinary neural network model the prediction effect of HAN is better than the CNN. In the LJP models, the Few-attribute model is specially used to solve the few-shot charge problem. Therefore, we selected HAN and Few-attribute from the baseline model. At the same time, the loss function of our model was replaced by the ordinary cross entropy; the variant is named cross-loss. We compared the above three models with our model on the Criminal-L set with sufficient data.