Learning to Predict Charges for Criminal Cases with Legal Basis

By Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao. 2017

309 citations recorded by the source corpus.

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.

Subfields: Information Systems, Law, Political Science and International Relations

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Citations in the corpus

CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain (2019, background): “With the development of deep learning, more researchers pay much efforts on predicting the judgment result of legal cases (Luo et al., 2017; Hu et al., 2018; Zhong et al., 2018a; Chalkidis et al., 2019; Jiang et al., 2018; Yang et al., 2019).”
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, methodology): “Although embedding-based methods can achieve promising performance, we still need to consider combining symbol-based with embedding-based methods in LJP.”
JEC-QA: A Legal-Domain Question Answering Dataset (2019, background): “2018), retrieving relevant or similar cases (Chen, Liu, and Ho 2013; Raghav, Reddy, and Reddy 2016), predicting charges or identifying applicable articles (Luo et al. 2017; Hu et al. 2018; He et al. 2018a; Zhong et al. 2018; Xiao et al. 2018; Shen et al. 2018).”
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents (2021, unknown): “With the development of deep learning, many efforts have been devoted to solving various legal tasks, such as legal charge prediction (Luo et al., 2017; Zhong et al., 2018; Xiao et al., 2018; Chalkidis et al., 2019; Yang et al., 2019), relevant law article retrieval (Chen et al., 2013; Raghav et…”
Legal Judgment Prediction via Topological Learning (2018, methodology): “For example, Luo et al. (2017) present an attention-based neural network that jointly models charge prediction and relevant article extraction.”
Few-Shot Charge Prediction with Discriminative Legal Attributes (2018, methodology): “Besides, in order to examine the performance of our method on few-shot charges, we keep 149 distinct charges (near 3 times as compared with (Luo et al., 2017)) with at least 10 cases.”
Neural Legal Judgment Prediction in English (2019, methodology): “Here, we focus on legal judgment prediction , where given a text describing the facts of a legal case, the goal is to predict the court’s outcome (Aletras et al., 2016; S¸ulea et al., 2017; Luo et al., 2017; Zhong et al., 2018; Hu et al., 2018).”
Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases (2021, background): “, 2019) and for Chinese criminal court cases (Luo et al., 2017; Hu et al., 2018; Zhong et al., 2018), but there is no precedent of work investigating the justification of the models’ decisions.”
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction (2018, methodology): “Inspired by the success of deep learning techniques on natural language processing tasks, researchers attempt to employ neural models to handle judgment prediction task under the text clas-siﬁcation framework (Luo et al., 2017; Hu et al., 2018).”
Legal requirements on explainability in machine learning (2020, unknown): “Luo et al. (2017) propose to use a neural network (with a mechanism called attention ) to predict the charges in criminal cases while also providing legal articles supporting the decision.”