# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 4 Experiment
### Results and Analysis  
The experimental results on the criminal dataset are shown in Table 4. \(\dagger\) indicates significant improvements (\(p<0.05\)) compared with the best baseline. It can be observed that our model achieves better performance on three different-sized datasets, which showcases the robustness and wide applicability over various application scenarios of our proposed method for charge prediction. Our model is basically similar to other models on a certain metric of a dataset, such as the Acc (93.3) of LADAN and ours (93.5) are close to each other in Criminal-S. Through our annotation, it can be found that at least two indicators of our model are significantly improved in different datasets, especially in MR and F1. Thus, we can infer that the model in this paper significantly outperforms other baseline models on different datasets by looking at all the metrics for a particular dataset. In addition, the experimental results of different-sized datasets are compared horizontally, and it is found that the prediction results become more and more accurate with the increase of data volume, which proves that the amount of data can affect the training effect of the model, and the training data of large datasets are more adequate. Further comparing the experimental results, it can be observed that our model outperforms all the baseline models, and the existing models generally perform poorly on the F1 metric. This is mainly because of the imbalance of training samples among different charges and indicates the shortage of prediction for few-shot charges. In contrast, our model achieves an absolute improvement of 5.5%, 2.9%, and 3.1% in F1 over the current optimal model on the three datasets, respectively. It proves the effectiveness and robustness of the method in this paper under different scenarios (sufficient or insufficient data). Among these evaluation metrics, MR and F1 are the preferred evaluation metrics for classification problems, especially in the scenario of unbalanced data. The above analysis shows that our model is superior in F1. Therefore, we compared the performance with MR metric, and further compared the generalization performance of our model and the baseline model in a statistical sense. Firstly, we compared six different models on three datasets, and then sorted them on each dataset according to MR values and assigned ordinal values (1, 2,... ). As shown in Table 5, the last row is average ordinal values. Then, the \"Friedman test\" was used to judge whether the performance of these models was the same. Let \(r_{i}\) be the average ordinal values of the \(i\) - th models. We assumed a variable \(r_{\chi^{2}}=\frac{k-1}{k}\cdot\frac{12N}{k^{2}-1}\sum\limits_{i=1}^{k}(r_{i} -\frac{k+1}{2})^{2}\), where \(k=6\) is the number of models and \(N=3\) is the number of datasets. We obtained the \(r_{\chi^{2}}=14.619\) when \(k\) and \(N\) are large and \(r_{\chi^{2}}\) obeys distribution \(\chi^{2}\). Then, we used the variable \(r_{F}=\frac{(N-1)r_{\chi^{2}}}{N(k-1)-r_{\chi^{2}}}\), which follows the \(F\) distribution with degrees of freedom \((k-1)\) and \((k-1)(N-1)\). Then, we obtained the \(r_{F}=76.740\) by referring to the table of common critical values for the F test, when significance level \(\alpha=0.05\), \(r_{F}>3.217\). Therefore, the assumption that all models have the same performance was rejected. Then we used the \"Nemenyi follow-up test\" by referring the common critical values for the \"Nemenyi test\". We obtained the critical value \(q_{\alpha}=2.850\) when \(k=6\) and \(\alpha=0.05\). The \"Nemenyi test\" calculates the critical range \(CD=q_{\alpha}\sqrt{\frac{k(k+1)}{6N}}=3.522\) of the difference between the average ordinal values. Finally, we compared the average ordinal values gap and \(CD\):\(6-1>CD\);\(5-1>CD\). This shows that, compared with the ordinary neural network model (CNN and HAN), the performance of our model is particularly superior.