Integrating Multi-Layer Structure Information of Law for Legal Judgement Prediction

doi:10.3778/j.issn.1002-8331.2108-0340

Abstract

Abstract: In recent years, the intellectualization of the judicial field has attracted extensive attention of academic circles. This paper chooses legal judgment prediction which is an important task in the judicial field as this research topic. And the legal judgment prediction includes three sub-tasks：law article recommendation, charge prediction and term of penalty prediction. With the wide application of deep learning in various fields, some researchers have introduced the deep learning method into the task of legal judgment prediction and have achieved good results. The existing methods of legal judgment prediction based on deep learning usually improve the model prediction ability by building the attention between case description and law articles or improve the overall performance by using the relationship of the sub-tasks. But these works do not consider the multi-layer structured information in the law. To solve the problem, this paper introduces the multi-layer structured information of legal texts to the task of legal judgment prediction. Specifically, this paper preprocesses the multi-layer structured information of legal texts and integrates the information of the law into the case description by using the co-attention mechanism and obtains the representation of the case description of each sub-task which integrates different levels of law information, so as to improve the performance of legal judgment prediction. Then this paper conducts experiments on the real public dataset of legal judgment prediction. The results show that the proposed method is better than the best model in the legal judgment prediction task. Finally, the development of legal judgment is discussed.

Key words: legalal judgement prediction, multi-layer structure information of law, deep learning

摘要： 近年来，法律领域的智能化引起了学界的广泛关注。选取法律领域中十分重要的法律判决预测任务作为研究重点，法律判决预测包含推荐相关法条、定罪和刑期预测等三个子任务。随着深度学习在各个领域的广泛应用，一些研究者将深度学习方法引入法律判决预测任务并取得了较好的效果。现有基于深度学习的法律判决预测方法通常是通过构建案情描述和法条之间的注意力来提升模型预测能力，或者利用三个法律判决预测子任务间的关系来提升整体的性能。但是这些工作未考虑法律文本中的多层层次化信息，如刑法第三百九十七条包含职务侵占罪和玩忽职守罪，其法条大类是渎职罪，并且每个罪行有不同的刑期。针对该问题，考虑引入法律文本的多层层次化信息用于法律判决预测任务。具体来说，对法律文本的多层结构信息进行预处理，并利用协同注意力机制将法条的多层信息融入到案情描述中，得到每个子任务的融合不同层次的法律信息的案情描述表示，从而提升司法判决预测任务的性能。在真实的法律判决预测任务公开的数据集上进行了实验，结果显示提出的融合法律文本多层结构信息的模型在法律判决预测任务上优于当前最好的模型。对法律智能化的未来和发展进行了展望。

关键词: 法律判决预测, 法律多层结构信息, 深度学习

ZHANG Han, ZHENG Weihao, DOU Zhicheng, WEN Jirong. Integrating Multi-Layer Structure Information of Law for Legal Judgement Prediction[J]. Computer Engineering and Applications, 2023, 59(3): 253-263.

张晗, 郑伟昊, 窦志成, 文继荣. 融合法律文本结构信息的刑事案件判决预测[J]. 计算机工程与应用, 2023, 59(3): 253-263.

References

[1] ZHONG H，XIAO C，TU C，et al.How does NLP benefit legal system：a summary of legal artificial intelligence[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics，2020.
[2] 高格.定罪与量刑[M].北京：中国方正出版社，1999.
GAO G.Conviction and sentencing[M].Beijing：China Fangzheng Press，1999.
[3] 洪星.定罪基础理论和实践问题研究[D].武汉：武汉大学，2014.
HONG X.Study on the theory and practiceof conviction：on the perspective of uncertainty type[D].Wuhan：Wuhan University，2014.
[4] 赵廷光.量刑概念新论[J].法学，1995（12）：27-28.
ZHAO T G.A new view of sentencing concept[J].Law Science，1995（12）：27-28.
[5] KEOWN R.Mathematical models for legal prediction[J].The John Marshall Journal of Information Technology & Privacy Law，1980，2（1）.
[6] NAGEL S S.Applying correlation analysis to case prediction[J].Texas Law Review，1964，42（7）：1006-1017.
[7] SEGAL J A.Predicting supreme court cases probabilistically：the search and seizure cases，1962-1981[J].American Political Science Review，1984，78（4）：891-900.
[8] LAUDERDALE B E，CLARK T S.The supreme court’s many median justices[J].American Political Science Review，2012，106（4）：847-866.
[9] ULMER S S.Quantitative analysis of judicial processes：some practical and theoretical applications[J].Law & Contemp Probs，1963，28：164.
[10] 林琬真，郭宗廷，张桐嘉，等.利用机器学习与中文法律文件之标记，案件分类及量刑预测[J].中文计算语言学期刊，2012，17（4）：49-67.
LIN W C，KUO T T，CHANG T J，et al.Exploiting machine learning models for Chinese legal documents labeling，case classification，and sentencing prediction[J].International Journal of Computational Linguistics & Chinese Language Processing，2012，17（4）：49-67.
[11] 高菲.基于机器学习的计算机辅助量刑初探[D].上海：华东政法大学，2005.
GAO F.A primary research on machine learning based computer aided measure of penalty[D].Shanghai：East China University of Political Science and Law，2005.
[12] LUO B，FENG Y，XU J，et al.Learning to predict charges for criminal cases with legal basis[J].arXiv：1707.09168，2017.
[13] HU Z，LI X，TU C，et al.Few-shot charge prediction with discriminative legal attributes[C]//International Conference on Computational Linguistics，2018：487-498.
[14] ZHONG H X，GUO Z P，TU C C，et al.Legal judgment prediction via topological learning[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing，2018：3540-3549.
[15] XU N，WANG P，CHEN L，et al.Distinguish confusing law articles for legal judgment prediction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics，2020.
[16] FAWEI B，PAN J Z，KOLLINGBAUM M，et al.A methodology for a criminal law and procedure ontology for legal question answering[C]//Joint International Semantic Technology Conference.Cham：Springer，2018：198-214.
[17] LONG S，TU C，LIU Z，et al.Automatic judgment prediction via legal reading comprehension[C]//China National Conference on Chinese Computational Linguistics.Cham：Springer，2019：558-572.
[18] XIAO G，MO J，CHOW E，et al.Multi-task CNN for classification of Chinese legal questions[C]//2017 IEEE 14th International Conference on e-Business Engineering（ICEBE），2017：84-90.
[19] MERCHANT K，PANDE Y.NLP based latent semantic analysis for legal text summarization[C]//2018 International Conference on Advances in Computing，Communications and Informatics（ICACCI），2018：1803-1807.
[20] KANAPALA A，PAL S，PAMULA R.Text summarization from legal documents：a survey[J].Artificial Intelligence Review，2019，51（3）：371-402.
[21] JIANG X，YE H，LUO Z C，et al.Interpretable rationale augmented charge prediction system[C]//Proceedings of the 27th International Conference on Computational Linguistics：System Demonstrations，2018：146-151.
[22] YE H，JIANG X，LUO Z C，et al.Interpretable charge predictions for criminal cases：learning to generate court views from fact descriptions[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies（Volume 1：Long Papers），2018：1854-1864.
[23] LIU Z，TU C，SUN M.Legal cause prediction with inner descriptions and outer hierarchies[C]//China National Conference on Chinese Computational Linguistics.Cham：Springer，2019：573-586.
[24] SULEA O M，ZAMPIERI M，MALMASI S，et al.Exploring the use of text classification in the legal domain[J].arXiv：1710.09306，2017.
[25] SULEA O M，ZAMPIERI M，VELA M，et al.Predicting the law area and decisions of french supreme court cases[C]//Proceedings of the International Conference Recent Advances in Natural Language Processing，2017：716-722.
[26] WANG P，FAN Y，NIU S，et al.Hierarchical matching network for crime classification[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval，2019：325-334.
[27] CHEN H，CAI D，DAI W，et al.Charge-based prison term prediction with deep gating network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing（EMNLP-IJCNLP），2019：6362-6367.
[28] XIAO C，ZHONG H，GUO Z，et al.CAIL2018：a large-scale legal dataset for judgment prediction[J].arXiv：1807.02478，2018.
[29] YANG W，JIA W，ZHOU X，et al.Legal judgment prediction via multi-perspective bi-feedback network[J].Twenty-Eighth International Joint Conference on Artificial Intelligence，2019.
[30] 孙茂松，陈新雄，张开旭.THULAC：一个高效的中文词法分析工具包[R].2016.
SUN M S，CHEN X X，ZHANG K X et al.THULAC：an efficient Chinese lexical analysis toolkit[R].2016.
[31] KIM Y.Convolutional neural networks for sentence classification[J].arXiv：1408.5882，2014.
[32] YANG Z，YANG D，DYER C，et al.Hierarchical attention networks for document classification[C]//North American chapter of the Association for Computational Linguistics，2016：1480-1489.