Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.

[1]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[2]  Xinya Du,et al.  Learning to Ask: Neural Question Generation for Reading Comprehension , 2017, ACL.

[3]  Mirella Lapata,et al.  Paraphrasing Revisited with Neural Machine Translation , 2017, EACL.

[4]  Mirella Lapata,et al.  Learning to Generate Product Reviews from Attributes , 2017, EACL.

[5]  Xiaojun Wan,et al.  Recent advances in document summarization , 2017, Knowledge and Information Systems.

[6]  Chao-Lin Liu,et al.  Classifying Criminal Charges in Chinese for Web-Based Legal Services , 2005, APWeb.

[7]  Minh-Tien Nguyen,et al.  Lexical-Morphological Modeling for Legal Text Analysis , 2015, JSAI-isAI Workshops.

[8]  Chao-Lin Liu,et al.  Case Instance Generation and Refinement for Case-Based Criminal Summary Judgments in Chinese , 2004, J. Inf. Sci. Eng..

[9]  Yi-Hung Liu,et al.  Predicting associated statutes for legal problems , 2015, Inf. Process. Manag..

[10]  Wang Ling,et al.  Latent Predictor Networks for Code Generation , 2016, ACL.

[11]  Stephen E. Robertson,et al.  Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval , 1994, SIGIR '94.

[12]  Regina Barzilay,et al.  Rationalizing Neural Predictions , 2016, EMNLP.

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  Dongyan Zhao,et al.  Learning to Predict Charges for Criminal Cases with Legal Basis , 2017, EMNLP.

[15]  Chao-Lin Liu,et al.  Exploring Phrase-Based Classification of Judicial Documents for Criminal Charges in Chinese , 2006, ISMIS.

[16]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[17]  Yi-Hung Liu,et al.  A text mining approach to assist the general public in the retrieval of legal documents , 2013, J. Assoc. Inf. Sci. Technol..

[18]  Mirella Lapata,et al.  Neural Summarization by Extracting Sentences and Words , 2016, ACL.

[19]  Mirella Lapata,et al.  Language to Logical Form with Neural Attention , 2016, ACL.

[20]  Mi-Young Kim,et al.  Legal Question Answering Using Ranking SVM and Syntactic/Semantic Similarity , 2014, JSAI-isAI Workshops.

[21]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[22]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[23]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[24]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[25]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[26]  Bowen Zhou,et al.  Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.

[27]  Wang Ling,et al.  Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems , 2017, ACL.

[28]  Joelle Pineau,et al.  How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation , 2016, EMNLP.

[29]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[30]  Shou-De Lin,et al.  Exploiting Machine Learning Models for Chinese Legal Documents Labeling, Case Classification, and Sentencing Prediction , 2012, Int. J. Comput. Linguistics Chin. Lang. Process..

[31]  Xiaojun Wan,et al.  Abstractive Document Summarization with a Graph-Based Attentional Neural Model , 2017, ACL.

[32]  Trevor Darrell,et al.  Generating Visual Explanations , 2016, ECCV.

[33]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[34]  Philipp Koehn,et al.  Scalable Modified Kneser-Ney Language Model Estimation , 2013, ACL.

[35]  Maria T. Pazienza,et al.  Information Extraction , 2002, Lecture Notes in Computer Science.

[36]  Emiel Krahmer,et al.  Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation , 2017, J. Artif. Intell. Res..

[37]  V. Balakista Reddy,et al.  Analyzing the Extraction of Relevant Legal Judgments using Paragraph-level and Citation Information , 2016 .

[38]  Alexander M. Rush,et al.  Abstractive Sentence Summarization with Attentive Recurrent Neural Networks , 2016, NAACL.

[39]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.