Charge Prediction for Multi-defendant Cases with Multi-scale Attention
The charge prediction task for multi-defendant cases is to determine appropriate charges for a specific defendant according to its name and its fact description. This task is not trivial since it is hard to recognize fact descriptions for different defendants. Therefore, we propose a multi-scale attention model for this problem. We employ local attention, which is highly related to the position of the specific defendant’s name appear in the fact description, to restrict our model to the description for a specific defendant and employ global attention, which is calculated by a charge prediction model for single-defendant cases, to supplement the model with global information of the case. We collect about 160,000 indictments for experiments. After data preprocessing, we choose the two most common charge pairs which are Theft with Concealment of Crime-related Income, and Open Casinos with Gamble for experiments. Experimental results show the effectiveness of our model, the multi-scale attention model does benefit from the global information from the complete case compared to the local attention model.
KeywordsLegal intelligence Charge prediction Attention
This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFC0381402 and the project of Guangdong Provincial Joint Laboratory of Natural Language Processing and Machine Learning.
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