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

Abstract

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.

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

“…Work [ 14 ] builds the first Legal Judgment Prediction (LJP) model for UK court cases by creating a labeled dataset of UK court decisions and subsequently applying the machine learning model with high performance and experimentally demonstrating the high performance capabilities of the proposed LJP model. Work [ 15 ] presents a multitask Legal Judgment Prediction model that combines the subtask of allegation severity with the defendant's position, enabling it to focus on contextual information about the defendant. Experiments show that the model achieves better performance on the public CAIL2018 dataset.…”

Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction

“…Li et al [21] proposed a multi-channel attention neural network to jointly predict charges, relevant law articles, and the term of penalty. Xu et al [3] proposed LADAN, which also addressed the above three tasks jointly by multi-task learning. LADAN can effectively distinguish subtle differences between confusing law articles with a community-based graph neural network.…”

“…LJP has been studied for decades, and most existing works regard this task as a text classification problem. Researchers have proposed various methods based on machine learning and deep learning models and made significant progress in LJP tasks like like predicting charges [1], relevant law articles [2], and the term of penalty [3]. Some of the existing works have already achieved an accuracy of over 90% on predicting charges and relevant law articles, which is very close to human judges.…”

ADAN: An Intelligent Approach Based on Attentive Neural Network and Relevant Law Articles for Charge Prediction

“…Li et al [18] designed a multichannel neural network model framework with attention mechanism to complete entire LJP tasks. Xu et al [19] proposed a new unified LJP model which capture the attention weights of different terms of penalty and the position of defendant. Yang et.al [20] presented a multi-perspective bi-feedback network with the word collocation attention mechanism for LJP task.…”