Data Augmentation for Deep Learning of Judgment Documents

Abstract

With the increasing number of machine learning parameters, the requirements on data quantity are getting higher and higher to train a good model. The choice of methods and the optimization of parameters can improve the model while the quality and quantity of the data determine the upper limit of the model. However, in realistic scenarios, it is quite challenging to get a lot of tag data. Therefore, it is natural to realize data augmentation by transforming the original data. We use three methods for data augmentation on different scales of original data in solving the crime prediction problem based on the description of the cases, and find that the effects of data augmentation are different for different models and different fundamental data quantities.