Multi-task Legal Judgement Prediction Combining a Subtask of the Seriousness of Charges

Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.

[1]  Zhiyuan Liu,et al.  Legal Judgment Prediction via Topological Learning , 2018, EMNLP.

[2]  Xin Jiang,et al.  Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions , 2018, NAACL.

[3]  Deng Cai,et al.  Charge-Based Prison Term Prediction with Deep Gating Network , 2019, EMNLP.

[4]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[5]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[6]  Josh Blackman,et al.  Predicting the Behavior of the Supreme Court of the United States: A General Approach , 2014, ArXiv.

[7]  Zhiyuan Liu,et al.  Few-Shot Charge Prediction with Discriminative Legal Attributes , 2018, COLING.

[8]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[9]  Dongyan Zhao,et al.  Learning to Predict Charges for Criminal Cases with Legal Basis , 2017, EMNLP.

[10]  Maosong Sun,et al.  Punctuation as Implicit Annotations for Chinese Word Segmentation , 2009, CL.

[11]  Chao-Lin Liu,et al.  Exploring Phrase-Based Classification of Judicial Documents for Criminal Charges in Chinese , 2006, ISMIS.

[12]  Weijia Jia,et al.  Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network , 2019, IJCAI.

[13]  Xiaoyan Wang,et al.  Distinguish Confusing Law Articles for Legal Judgment Prediction , 2020, ACL.

[14]  Zhiyuan Liu,et al.  CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction , 2018, ArXiv.

[15]  Maosong Sun,et al.  Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction , 2020, AAAI.

[16]  Yi-Hung Liu,et al.  Predicting associated statutes for legal problems , 2015, Inf. Process. Manag..

[17]  D. Katz,et al.  A general approach for predicting the behavior of the Supreme Court of the United States , 2016, PloS one.