text: 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.
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  @cite_37: Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.
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  @cite_42: Abstract Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although a few previous studies focused on assisting professionals in the retrieval of related legal documents, to our knowledge, no previous studies could provide relevant statutes to the general public using problem statements. In this work, we design a text mining based method, the three-phase prediction (TPP) algorithm, which allows the general public to use everyday vocabulary to describe their problems and find pertinent statutes for their cases. The experimental results indicate that our approach can help the general public, who are not familiar with professional legal terms, to acquire relevant statutes more accurately and effectively.
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  @cite_27: The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.
  @cite_44: We describe a legal question answering system which combines legal information retrieval and textual entailment. We have evaluated our system using the data from the first competition on legal information extraction entailment (COLIEE) 2014. The competition focuses on two aspects of legal information processing related to answering yes no questions from Japanese legal bar exams. The shared task consists of two phases: legal ad hoc information retrieval and textual entailment. The first phase requires the identification of Japan civil law articles relevant to a legal bar exam query. We have implemented two unsupervised baseline models (tf-idf and Latent Dirichlet Allocation (LDA)-based Information Retrieval (IR)), and a supervised model, Ranking SVM, for the task. The features of the model are a set of words, and scores of an article based on the corresponding baseline models. The results show that the Ranking SVM model nearly doubles the Mean Average Precision compared with both baseline models. The second phase is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. The features used for phase two are syntactic semantic similarities and identification of negation antonym relations. The results show that our method, combined with rule-based model and the unsupervised model, outperforms the SVM-based supervised model.
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  @cite_17: Along with the increasing accessibility of the Web, services available on the Web have expanded into several aspects of our lives. As a relatively new comer, Web-based legal services have become another front in the arena. For instance, through such services, people obtain rough pictures of the lawsuits of interest before they decide whether and how to approach their counselors. As a step toward offering online legal services, we propose algorithms for classifying criminal charges in Chinese based on the alleged criminal violations that the district attorney used to prosecute the defendants. The classification is not trivial because some prosecutions involve violations of multiple articles in the criminal law, and we wish to classify lawsuits as precisely as possible. We employ techniques of instance-based classification and introspective learning for the classification task, and achieve satisfactory initial results for classifying charges of larceny and gambling.
summary: Our work is firstly related to previous studies on legal assistant systems. Previous work considers the task of charge prediction as a text classification problem @cite_27 @cite_29 @cite_43 @cite_24 . Recently, investigate deep learning methods for this task. Besides, there are also works on identifying applicable articles for a given case @cite_17 @cite_43 @cite_22 , answering legal questions as a consulting system @cite_44 @cite_19 and searching relevant cases for a given query @cite_42 @cite_32 . As a legal assistant system, @math can benefit automatic legal document generation by generating court views from fact descriptions obtained from the last phase, through legal professionals or other technics like information extraction @cite_30 from raw documents in a case, if we generate legal documents step by step. Our work is also related to recent studies on model interpretation @cite_37 @cite_10 @cite_6 . Recently, much work has paid attention to giving textual explanations for classifications. generate visual explanations for image classification. propose to learn to select most supportive snippets from raw texts for text classification. @math @math @math can improve the interpretability of charge prediction systems by generating textual court views when predict the charges.