Legal Judgment Prediction via Topological Learning

By Haoxiang Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, Maosong Sun. Conference on Empirical Methods in Natural Language Processing, 2018

359 citations recorded by the source corpus.

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from https://github.com/thunlp/TopJudge.

Subfields: Artificial Intelligence, Law, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/c39e54d62140121395230f6f872f56400ea0929f

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Citations in the corpus

Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? (2020, background): “g court documents for NLP research. Having said this, we acknowledge that similar datasets have been constructed and used by others (esp. Xiao et al. (2018)), including in major NLP conferences (e.g. Zhong et al. (2018), Hu et al. (2018)). However, this should never be taken as a waiver for data eth”
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents (2021, positive): “Thus, LegalAI has received great attention from both natural language processing (NLP) researchers and legal professionals (Zhong et al., 2018, 2020b; Wu et al., 2020; Chalkidis et al., 2020; Hendrycks et al., 2021).”
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.”
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).”
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain (2019, background): “CAIL was ﬁrst held in 2018, and the main task of CAIL2018 (Xiao et al., 2018; Zhong et al., 2018b) is predicting the judgment results from the fact description.”
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, methodology): “Legal Judgment Prediction (LJP) is one of the most critical tasks in LegalAI, especially in the Civil Law system.”
Legal requirements on explainability in machine learning (2020, methodology): “Following the same idea, Zhong et al. (2018) define the sub-tasks as learning (i) the applicable legal articles, (ii) the charges and (iii) the terms of penalty of a legal judgment, based on a textual fact description.”
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).”
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2021, background): “, 2021), from judgment prediction (Aletras et al., 2016; Sim et al., 2016; Katz et al., 2017; Zhong et al., 2018; Chalkidis et al., 2019a; Malik et al., 2021), information extraction from legal documents (Chalkidis et al.”