Few-Shot Charge Prediction with Multi-Grained Features and M utual I nformation (2021)

Abstract: Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charges has achieved promising results but that on few-shot chargesis still challenging. In this work we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically we extract coarse- and fine-grained features to enhance the model’s capability on representation based on which the few-shot chargescan be better distinguished. Furthermore we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides our method can work wellon tiny datasets and has better efficiency in the training which provides better applicability in realscenarios.

## introduction charge prediction aims to determine the final charge (e.g., manslaughter, traffic offence, or theft) for a case by analyzing the textual fact description of the defendants' behavior. as a subtask of legal judgment prediction (ljp), charge prediction plays an important role in legal assistance systems, thus has been widely applied in real scenarios. on the one hand, a charge prediction system can provide legal professionals, such as judges and lawyers, a quick and effective reference to improve their work efficiency. dong has been relying on the private loan usury contract projects. between 2012 and 2015, ... to raise funds and make a profit, dong own directly or through others to high interest rates, ... borrow or take a stake in the way of sharing bonus to their friends and family and the society to absorb funds... as a private developer, ... he absorbs a deposit of x yuan illegally... ## methodology we propose a multi-task framework based on multi-grained features and mutual information (mfmi) for few-shot charge prediction. the structure of our mfmi is shown in figure 2 . in general, mfmi considers three different kinds of features (namely coarse-grained features, fine-grained features, and attribute features) to represent a case and predict the charge based on the fused representations. to facilitate the few-shot charge prediction, we propose a loss function based on mutual information, with which the few-shot charges can contribute more on the model optimization, so the overall performance can be improved. besides, similar to @xcite , we also add a supplementary task to predict whether the predefined attributes are contained in the fact, which is reported to be helpful in improving c c l 2 0 2 1 the performance. as a result, the whole model is trained with both our proposed mutual information loss on charge prediction and a cross-entropy loss on attribute prediction. ## conclusion in this work, we studied the problem of few-shot charge prediction. we proposed a multi-task learning framework, where both the charge prediction and attribute prediction are learned simultaneously. we extracted fine-grained and coarse-grained features to enhance the model's capability of representation, which are helpful for distinguishing the few-shot charges. besides, we also proposed a loss function based on mutual information to enhance the learning on few-shot charges. experimental results demonstrated the effectiveness of proposed methods on charge prediction, especially on the few-shot charges. moreover, further experiments showed that our method has better scalability and efficiency. in the future, we will apply our method to other legal judgement prediction tasks and leverage the knowledge from other tasks to further improve the performance.