# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 2 Related Work  
With the rapid development of artificial intelligence technology, some artificial intelligence applications such as intelligent justice [1; 3; 4; 5] and intelligent transportation [11] have become research hotspots. Charge prediction is the key link of intelligent judicial trial. Firstly, our work is relevant to the existing few-shot and zero-shot classification. In recent years, some researchers have put forward solutions to this problem. For example, [PERSON] et al. [12] proposed a hierarchical attention prototype network, designed feature-level, word-level and instance-level multiple cross-attentions, and made full use of the few-shot information. [PERSON] et al. [13] proposed a two-step framework that combines multiple semantic knowledges to solve the zero-shot text classification. [PERSON] et al. [14] combined a dynamic routing algorithm with a meta-learning framework to simulate human generalization ability and effectively improve the classification effect of small samples. [PERSON] et al. [15] used the meta-learning framework to classify low-frequency text, and leverage the distributional signatures of words, which encode pertinent word occurrence patterns. These works provide a good academic reference for the few-shot charge prediction task in this paper. In addition, this paper works on the task of charge prediction in the LJP. For this task, many researchers have proposed different methods to improve the prediction effect. [PERSON] et al. [16], for the multi-label charge prediction problem, using label co-occurrence, predicted multiple charges with article information on imbalanced data occasion. [PERSON] et al. [17] proposed a deep neural framework to extract short but charge-decisive texts as the interpretation of charge prediction, which improved the interpretability of prediction results. [PERSON] et al. [18] proposed a multi-scale attention model for different defendants and introduced Gaussian function into the model, effectively solving the problem of multiple defendants in charge prediction. [PERSON] et al. [19] followed two essential principles in LJP, Presumption of Innocence and Elemental Trial, and used the reinforcement learning method to extract the interpretation principles of cases so as to improve the interpretability  
Figure 1: Charge hierarchy label structure. of predicted results. [PERSON] et al. (2019) designed a multi-perspective and bi-feedback neural network to analyze the LJP subtasks' dependence and predicted the charge with legal articles and imprisonment. However, only a few works focus on few-shot charges or confusing charges. The above methods are only good at predicting when the amount of data is sufficient, and the model performance decreases when the training data are unbalanced or the samples are small. Few-shot and confusing charge problems in charge prediction are also the focus of this study. [PERSON] et al. (2020) used legal articles as external knowledge and proposed a neural network with an attention mechanism to predict the charges, which improved the charge prediction results and provided legal bias. [PERSON] et al. (2020) constructed ten discriminative attributes of charges for few-charge prediction. [PERSON] et al. (2020) used a seq2 seq model and designed an attention-based seq2 seq model to predict the charge. [PERSON] et al. (2020) used a graph neural network to solve the confusing crime problem in LJP. However, the above methods do not deeply analyze its high-order representation of fact description, and the advantages are not obvious when there are few cases of some confusing charges. Therefore, it is necessary to enhance the data for low-frequency charges. [PERSON] et al. (2020) proposed a knowledge-aware model in which the graph convolution neural network is used to extract the fact description features, the legal schematic knowledge is regarded as a tree structure, and the self-attention network is used to model the hierarchical relationship.