Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (3): 227-232.doi: 10.11896/jsjkx.200700056

• Artificial Intelligence • Previous Articles     Next Articles

Process Supervision Based Sequence Multi-task Method for Legal Judgement Prediction

ZHANG Chun-yun1, QU Hao2, CUI Chao-ran1, SUN Hao-liang2, YIN Yi-long2   

  1. 1 School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China
    2 School of Software,Shandong University,Jinan 250101,China
  • Received:2020-07-09 Revised:2020-09-18 Online:2021-03-15 Published:2021-03-05
  • About author:ZHANG Chun-yun,born in 1986,Ph.D,assistant professor,is a member of China Computer Federation.Her main research interests include machine lear-ning,natural language processing and information extraction.
    YIN Yi-long,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include machine learning,data mining and biometrics.
  • Supported by:
    National Natural Science Foundation of China (61703234) and National Key R & D Plan Project (2018YFC0830102).

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Abstract: Legal judgment prediction is an application of artificial intelligence technology in legal field.Hence,the research on the legal judgment prediction method has important theoretical value and practical significance for the realization of intelligent justice.Traditional legal judgment prediction methods only make single task prediction or just use multi-task prediction based on parameter sharing,without considering the sequence dependence among subtasks,so the prediction performance is difficult to be further improved.This paper proposes a process supervision based sequence multi-task framework (PS-SMTL) by encoding sequence dependency of subtasks in legal judgement.It is an end to end legal judgement prediction method without any external features.By introducing process supervision,the proposed model ensures the accuracy of the obtained dependent prior information from advance tasks.The proposed model is applied to CAIL2018 dataset and a good classification result is achieved.The average classification accuracy is 2% higher than that of the existing state-of-the-art method.

Key words: Deep learning, Legal judgement prediction, Multi-task learning, Process supervision

CLC Number: 

  • TP391
[1]FRED K.Predicting Supreme Court decisions mathematically:Aquantitative analysis of the “right to counsel” cases [R].American Political Science Review,1957.
[2]STUART S.Applying correlation analysis to case prediction[R].Texas Law Review,1963.
[3]KEOWN R.Mathematical Models For Legal Prediction[J].Computer Journal,1980(1):829-831.
[4]JEFFREY A.Predicting Supreme Court cases probabilistically:The search and seizure cases(1962-1981)[R].American Political Science Review,1984,78(4):891-900.
[5]BENJAMIN E L,TOM S C.The Supreme Court’s many median justices [J].American Political Science Review,2012,106(4):847-866.
[6]CHEN H J,CAI D,DAI W,et al.Charge-Based Prison TermPrediction with Deep Gating Network[C]//Proceedings of EMNLP-IJCNLP.2019:6362-6367.
[7]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 NAACL.2018:1854-1864.
[8]JIANG X,YE H,LUO Z C,et al.Interpretable Rationale Augmented Charge Prediction System[C]//Proceedings of COLING.2018:146-151.
[9]LIU C L,CHENG T C,JIM H H.Case instance generation and refinement for case-based criminal summary judgments in Chinese [J].Journal of Information Science & Engineering,2004,20(4):783-800.
[10]LIU C L,CHWEN D H.Exploring phrase-based classification of judicial documents for criminal charges in Chinese[C]//Proceedings of ISMIS.2006,681-690.
[11]DANNIEL M K,MICHAEL J B II,JOSH B.A general ap-proach for predicting the behavior of the supreme court of the united states [J].Plos One,2017,12(4).
[12]LUO B F,FENG Y S,XU J B,et al.Learning to predict charges for criminal cases with legal basis[C]//Proceedings of EMNLP.2017:1-10.
[13]ZHONG H X,GUO Z P,TU C C,et al.Legal judgment prediction via topological learning[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing.2018:3540-3549.
[14]BAHARUM B,LAM H L,KHAIRULLAH K.A review of machine learning algorithms for text-documents classification [J].Journal of Advances in Information Technology,2010,1(1):4-20.
[15]YOON K.Convolutional neural networks for sentence classification[C]//Proceedings of EMNLP.2014.
[16]TANG D Y,QIN B,LIU T.Document modeling with gated recurrent neural network for sentiment classification [C]//Proceedings of EMNLP.2015:1422-1432.
[17]LIN W C,TSUNG T K,TUNG J C.Exploiting machine lear-ning models for chinese legal documents labeling,case classification,and sentencing prediction[C]//Proceedings of ROCLING.2012:140.
[18]YANG W,JIA W,ZHOU X,et al.Legal Judgment Predictionvia Multi-Perspective Bi-Feedback Network[C]//Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19).2019.
[19]NIKOLAOS A,DIMITRIOS T,DANIEL P,et al.Predicting judicial decisions of the European court of human rights:A natural language processing perspective [J].PeerJ Computer Science,2016(2):e93.
[20]OCTAVIA M S,MARCOS Z,MIHAELA V,et al.Exploring the use of text classification in the legal domain[C]//Procee-dings of ASAIL Workshop.2017.
[21]HU Z K,LI X,TU C C,et al.Few-shot charge prediction with discriminative legal attributes[C]//Proceedings of the 27th International Conference on Computational Linguistics.2018:487-498.
[22]RONAN C,JASON W.A unified architecture for natural language processing:Deep neural networks with multitask learning[C]//Proceedings of ICML.2008:160-167.
[23]LONG S,TU C,LIU Z,et al.Automatic Judgment Prediction via Legal Reading Comprehension [J].arXiv:1809.06537v1,2018.
[24]MAX R S M,TOMMASO B,MAXIME R,et al.Machine lear-ning for explaining and ranking the most influential matters of law[C]//Proceeding of ICAIL.2019:239-243.
[25]SANDEEP S,ADAM T,YOSHUA B,et al.Learning GeneralPurpose Distributed Sentence Representations via Large Scale Multi-task Learning[C]//Proceeding of ICLR.2018.
[26]WANG A,AMANPREET S,JULIAN M,et al.GLUE:AMulti-Task Benchmark and Analysis Platform for Natural Language Understanding[C]//Proceeding of ICLR.2019.
[27]WU Z,VALENTINI B C,WATTS O,et al.Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis[C]//IEEE International Confe-rence on Acoustics.2015:4460-4464.
[28]RAVANELLI M,ZHONG J,PASCUAL S,et al.Multi-TaskSelf-Supervised Learning for Robust Speech Recognition[C]//Proceeding of ICASSP.2020.
[29]TREVOR S,AMIR R Z,DAWN C,et al.Which Tasks Should Be Learned Together in Multi-task Learning?[C]//Proceeding of ICCV.2019.
[30]LIU S K,EDWARD J,ANDREW J D.End-to-End Multi-TaskLearning with Attention[C]//Proceeding of CVPR.2019.
[31]RONAN C,JASON W.A unified architecture for natural lan-guage processing:Deep neural networks with multitask learning[C]//Proceedings of ICML.2008:160-167.
[32]DONG D X,WU H,HE W,et al.Multi-task learning for multiple language translation[C]//Proceedings of ACL.2015:1723-1732.
[33]MINH T L,QUOC V L,ILYA S,et al.Multi-task sequence to sequence learning[C]//Proceedings of ICLR.2016.
[34]ORHAN F,KYUNGHYUN C,YOSHUA B.Multi-way,multilingual neural machine translation with a shared attention mecha-nism[C]//Proceedings of NAACL.2016:866-875.
[35]LONG D,TREVOR C,STEVEN B,et al.Low resource dependency parsing:Cross-lingual parameter sharing in a neural network parser[C]//Proceedings of ACL.2015:845-850.
[36]YANG Y X,TIMOTHY H.Deep multi-task representationlearning:A tensor factorization approach[C]//Proceedings of ICLR.2017.
[37]LIU P F,QIU X P,HUANG X J.Recurrent neural network for text classification with multi-task learning[C]//Proceedings of IJCAI.2016.
[38]SEBASTIAN R,JOACHIM B,ISABELLE,et al.Learning what to share between loosely related tasks [J].arXiv:1705.08142,2017.
[39]POORYA Z,GHOLAMREZA H.Neural Machine Translation for Bilingually Scarce Scenarios:A Deep Multi-task Learning Approach[C]//Proceeding of NAACL.2018.
[40]YANG Z C,YANG D Y,DYER C,et al,Hierarchical attention networks for document classification[C]//Proceedings of NAACL-HLT.2016:1480-1489.
[41]ZHANG S,ZHENG D Q,HU X C,et al.Bidirectional longshort-term memory networks for relation classification[C]//Proceeding of PACLIC.2015:1480-1489.
[42]DIEDERIK K,JIMMY B.Adam:A method for stochastic optimization[C]//Proceedings of ICLR.2015.
[43]NITISH S,GEOFFREY E H,ALEX K,et al.Dropout:a simple way to prevent neural networks from overfitting [J].Journal of Machine Learning Research (JMLR),2014,15(1):1929-1958.
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