随着人工智能和大数据处理技术的发展,人工智能技术在辅助法官办案、辅助诉讼、辅助司法管理等诸多方面起着重大作用,推进了智慧法院的发展,并受到学术界及工业界的广泛关注。该文在针对人工智能技术在辅助司法办案相关模型分析的基础上,归纳并提出了目前司法判决预测领域存在的多特征的罪名分类预测、多标签的罪名分类预测、司法判决预测中多个子任务处理、司法判决预测中的不平衡数据处理、判决预测结果的可解释性以及将已有的刑事案件预测算法迁移学习推广到不同类别案件等6项关键性问题与挑战。同时,该文针对这些关键问题与技术挑战进行了理论探讨、技术分析以及当前工作进展与趋势分析,总结了司法判决预测领域目前使用到的一些数据集及其对应的评价指标,为深入研究司法判决预测提供新的研究线索与方向。
Abstract
Artificial intelligence is increasingly emphasized in judicial practices in recent years. Based on the literature on intelligent models for assisting judicial cases, this paper suggests the following six challenges in legal judgement decision prediction: multi-feature crime prediction, multi-label crime prediction, multiple sub-task processing, unbalanced data issue, the interpretability of decision prediction and the adaption of existing algorithms to different types of cases. Meanwhile, the paper provides theoretical discussion, technical analysis, technical challenges as well as trend analysis for these problems. The datasets used in this field and the corresponding evaluation metrics are also summarized.
关键词
自动判决 /
司法判决预测 /
人工智能 /
司法
{{custom_keyword}} /
Key words
auto judgment /
legal judgment prediction /
artifactial intelligence /
judicature
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 夏凯. 智慧法院建设问题与对策研究[J]. 法制博览, 2019, 000(034):161-162.
[2] Kittler J, Hatef M, Duin P, et al. On combining classifiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3): 226-239.
[3] Zhong H, Guo Z, C Tu, et al. Legal judgment prediction via topological learning[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 3540-3549.
[4] Liu C, Chang C, Ho J. Case instance generation and refinement for case-based criminal summary judgments in Chinese [J]. Journal of Information Science and Engineering, 2004, 20(4): 783-800.
[5] Liu C, Hsieh C. Exploring phrase-based classification of judicial documents for criminal charges in Chinese[C]//Proceedings of the International Symposium on Methodologies for Intelligent Systems, 2006: 681-690.
[6] Luo B, Feng Y, Xu J, et al. Learning to predict charges for criminal cases with legal basis[J]. arXiv preprint arXiv:1707.09168, 2017.
[7] Shen Y,Sun J, Li X, et al. Legal article-aware end-to-end memory network for charge prediction[C]//Proceedings of the 2nd International Conference on Computer Science and Application Engineering, 2018: 1-5.
[8] Sukhbaatar S, Weston J, Fergus R. End-to-end memory networks[C]//Proceedings of the Advances in Neural Information Processing Systems, 2017: 2440-2448.
[9] Long S, Tu C, Liu Z, et al. Automatic judgment prediction via legal reading comprehension[C]//Proceedings of the China National Conference on Chinese Computational Linguistics, 2018: 558-572.
[10] Yang Z, Wang P, Zhang L, et al. A Recurrent attention network for judgment prediction[C]//Proceedings of the International Conference on Artificial Neural Networks, 2019: 253-266.
[11] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[12] Xiao C, Zhong H, Guo Z, et al. Cail2018: A large-scale legal dataset for judgment prediction[J]. arXiv preprint arXiv:1807.02478, 2018.
[13] Kang L, Liu J, Liu L, et al. Creating auxiliary representations from charge definitions for criminal charge prediction[J]. arXiv preprint arXiv:1911.05202, 2019.
[14] Bao Q, Zan H, Gong P, et al. Charge prediction with legal attention[C]//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing, 2019: 447-458.
[15] 刘宗林, 张梅山, 甄冉冉,等. 融入罪名关键词的法律判决预测多任务学习模型[J]. 清华大学学报:自然科学版, 2019, 59(07): 497-504.
[16] Chen S, Wang P, Fang W, et al. Learning to predict charges for judgment with legal graph[C]//Proceedings of the International Conference on Artificial Neural Networks, 2019: 240-252.
[17] Wei D, Lin L. An external knowledge enhanced multi-label charge prediction approach with label number learning[J]. arXiv preprint arXiv:1907.02205, 2019.
[18] Yang W, Jia W, Zhou X, et al. Legal judgment prediction via multi-perspective bi-feedback network[J]. arXiv preprint arXiv:1905.03969, 2019.
[19] Kim Y. Convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1408.5882, 2014.
[20] Li S, Zhang H, Ye L, et al. MANN: A multichannel attentive neural network for legal judgment prediction[J]. IEEE Access, 2019, 7(1):151144-151155.
[21] Cho K, Merrienboer B V, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.
[22] Hu Z, Li X, Tu C, et al. Few-shot charge prediction with discriminative legal attributes[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018: 487-498.
[23] He C, Peng L, LEe Y, et al. SECaps: A sequence enhanced capsule model for charge prediction[C]//Proceedings of the International Conference on Artificial Neural Networks, 2019: 227-239.
[24] Doshi-velez F, Kim B. Towards a rigorous science of interpretable machine learning[J]. arXiv preprint arXiv:1702.08608, 2017.
[25] Molnar C. Interpretable Machine Learning[M]. USA: Lulu Press, 2019: 56-58.
[26] Leila A, Franziska H, M Grégoire, et al. "What is relevant in a text document?": An interpretable machine learning approach[J]. Plos One, 2017, 12(8): e0181142-e0181147.
[27] Jiang X, Ye H, Luo Z, et al. Interpretable rationale augmented charge prediction system[C]//Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, 2018: 146-151.
[28] Ye H, Jiang X, Luo Z, et al. Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions[J]. arXiv preprint arXiv:1802.08504, 2018.
[29] Li J, Zhang G, Yu L, et al. Research and design on cognitive computing framework for predicting judicial decisions[J]. Journal of Signal Processing Systems, 2019, 91(10): 1159-1167.
[30] Li J, Zhang G, Yan H, et al. A Markov logic networks based method to predict judicial decisions of divorce cases[C]//Proceedings of the 2018 IEEE International Conference on Smart Cloud (SmartCloud), 2018: 129-132.
[31] Liu Z, Tu C, Sun M. Legal cause prediction with inner descriptions and outer hierarchies[C]//Proceedings of the China National Conference on Chinese Computational Linguistics, 2019: 573-586.
[32] 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.
[33] Wang P, Yang Z, Niu S, et al. Modeling dynamic pairwise attention for crime classification over legal articles[C]//Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018: 485-494.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
科技部重点研发计划(2019YFB2102300);深圳市科技创新项目(JCYJ20180306170836595);四维图新-西安市智能时空数据分析工程实验室联合项目(C2020103);教育部社科重大项目(18JZD022);中央高校建设世界一流大学(学科)和特色发展引导专项资金(PY3A022);西安市碑林区科技项目(GX1803);教育部“云数融合”基金(2017B00030);中央高校基本科研业务费西安交通大学重点项目(zdyf2017006)
{{custom_fund}}
PDF(7909 KB)
