# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 1 Introduction  
Charge prediction refers to determining the final charge of the case (such as \"theft\" and \"robbery\") according to the text description of the case. It plays an irreplaceable role in the judicial system. It not only provides convenient reference for legal experts, but also provides important legal advice for ordinary people who are not familiar with legal knowledge [1]. Automatic charge prediction has been studied for decades. Early researchers used statistical methods to analyze the factual factors affecting decision making and predict charges [2]. It was only effective for small-scale data and cases with obvious characteristics, which was difficult to extend to general cases. Later, with the development of machine learning algorithms, researchers [3, 4, 5] further extracted effective features from fact description and made predictions by machine learning methods, such as Naive Bayesian Model, SVMs, or K-NN. However, these methods rely heavily on manual features, which are difficult to gather on a bigger dataset. In recent years, with the successful usage of deep learning methods on speech, computer vision and natural language processing, the neural network is used to model legal documents [1]. However, charge prediction is not a simple task. There are still two main challenges in the real scene: few-shot charges and confusing charges. Additionally, some few-shot charges often have the characteristics of confusing, which undoubtedly adds new difficulties to the research. On the one hand, in the real datasets, the number of cases ofvarious charges is extremely unbalanced, and the ten most common charges cover 78% of cases; the uncommon 50 charges cover less than 0.5% of the cases. In case of insufficient training data, the general deep learning model performs poorly. To solve this problem, [PERSON] et al. [1] used different convolution neural networks to extract fine-grained features and used capsule network for feature fusion. [PERSON] et al. [6] effectively used focal loss function to predict few-shot charges. However, there are still some limitations in this study: (1) There is still a lack of sufficient data to train the neural network model, and the fine-grained features extracted by the model cannot be used to distinguish some confusing charges [7]. (2) Many studies do not consider the effect of the prior distribution of the charges on the cross-entropy loss function, so it is difficult to classify few-shot charges effectively. On the other hand, there are many confusing charges in the real dataset, such as robbery and snatch. The cases of these charges are similar, but the judgment results are different. How to capture the subtle differences in the cases is a challenging task. [PERSON] et al. [8] proposed a novel graph neural network to automatically extract distinguishable features between legal articles. Some researchers considered introducing external knowledge, for example, [PERSON] et al. [9] introduced 10 discriminative attributes of charges and used the attention mechanism to obtain fact descriptions related to specific attributes, which offered signals for distinguishing confusing charges. However, summaries and notes require a lot of manual labeling, which relies too much on expert knowledge to extract sufficient distinguishable features when training data is small. In practice, we hope that even in the case of data imbalance, the model can still effectively extract distinguishable features and accurately predict the charges. Therefore, we propose a novel model with data augmentation and feature augmentation for few-shot charge prediction. From the perspective of data augmentation, different from the existing fine-grained feature extraction methods, this paper draws on the Mixup data augmentation method in image classification [10].