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Interpretable prison term prediction with reinforce learning and attention

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Abstract

The task of prison term prediction is to predict the term of penalty based on the charge and the seriousness of the sentencing plot. Most existing methods focus on improving prediction accuracy but disregard interpretability, which yields unreliable judgment results. To address this problem, we propose an interpretable prison term prediction method. First, the prison term is divided into intervals according to the charge and sentencing plot. Second, we propose a reinforcement learning principle representation model combined with an attention mechanism for regression prediction (PRRP), which extracts phrase-level principles representation as the explanatory basis of prediction results, uses the principle in conjunction with the charge semantics to predict the interval value, and extracts the interval keywords as the sentencing plot. Third, we design a novel multiangle attention mechanism to capture the distinguishing features of cases from different aspects, and a feature fusion network is employed to more effectively stitch multiple pieces of information to learn the feature-enhanced fact representation. Last, the feature-enhanced fact representation is used to predict the prison term. Experimental results on real-work datasets show the interpretability and effectiveness of our method.

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  1. https://China.findlaw.cn/zuiming/

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Funding

This work is supported by the National Key R&D Program of China (Grant No. 2018YFB1601502) and the LiaoNing Revitalization Talents Program (Grant No. XLYC1902071).

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  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Peipeng Wang, Xiuguo Zhang, Han Yu & Zhiying Cao

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  1. Peipeng Wang
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  2. Xiuguo Zhang
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  4. Zhiying Cao
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Correspondence to Xiuguo Zhang or Zhiying Cao.

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Wang, P., Zhang, X., Yu, H. et al. Interpretable prison term prediction with reinforce learning and attention. Appl Intell 53, 1306–1323 (2023). https://doi.org/10.1007/s10489-022-03675-1

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