摘要
随着人工智能技术的发展,人工智能技术在生活中被广泛使用,并逐步深入到司法审理中.但在实际应用中存在着可解释性不足,不能有效的辅助审理这一问题.针对这一问题,本文结合刑事案件审理过程中依据犯罪构成采用的四要件理论,从犯罪构成的四要件角度,设计了构成要件识别任务.筛选了盗窃罪中一些构成要件,构建盗窃罪构成要件数据集.并基于预训练语言模型BERT(bidirectional encoder representations from transformers),设计了构成要件识别模型,对该模型在本文构建的数据集上进行测试,模型识别准确率达到93.54%.在构成要件基础上构建量刑辅助算法能提高现有算法的解释性,更有效的辅助法官审理案件.
With the development of artificial intelligence technology,it has been widely used in life and gradually penetrated judicial proceedings.However,there is insufficient interpretability in practical applications and thus it cannot effectively assist trials.In light of the four-element theory used in criminal case trials according to the constitution of a crime,this study addresses the above problem by designing an identification task of the four elements constituting a crime.Some constituent elements of crimes of theft are screened,and a data set of the constituent elements is constructed.Moreover,a constitutive elements identification model is developed on the basis of the pre-trained language model BERT and then tested on the data set constructed in this study,with the identification accuracy reaching 93.54%.Constructing an auxiliary sentencing algorithm based on the constituent elements can improve the interpretability of the existing algorithm and more effectively assist judges in hearing cases.
作者
费志伟
艾中良
张可
曹禹
FEI Zhi-Wei;AI Zhong-Liang;ZHANG Ke;CAO Yu(North China Institute of Computing Technology,Beijing 100083,China;China Justice Big Data Institute,Beijing 100043,China)
出处
《计算机系统应用》
2022年第4期229-237,共9页
Computer Systems & Applications
基金
国家重点研发计划(2018YFC0832306,2018YFC0831203,2018YFC0831206)。
关键词
四要件
盗窃罪
BERT
文本分类
深度学习
自然语言处理
four elements
crime of theft
bidirectional encoder representations from transformers(BERT)
text classification
deep learning
natural language processing(NLP)
