Legal Article-Aware End-To-End Memory Network for Charge Prediction

Shen, Yatian; Sun, Jun; Li, Xiaopeng; Zhang, Lei; Li, Yan; Shen, Xiajiong

doi:10.1145/3207677.3278068

“…Owing to the massive quantity of high-quality textual data in the legal domain, employing NLP techniques to solve legal intelligence problems has been more and more popular in recent years, e.g., generating court views to interpret charge results (Ye et al 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;Shen et al 2018).…”

Section: Legal Intelligencementioning

confidence: 99%

JEC-QA: A Legal-Domain Question Answering Dataset

Zhong¹,

Xiao²,

Tu³

et al. 2020

AAAI

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We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluation of professional skills for legal practitioners. College students are required to pass the examination to be certified as a lawyer or a judge. The dataset is challenging for existing question answering methods, because both retrieving relevant materials and answering questions require the ability of logic reasoning. Due to the high demand of multiple reasoning abilities to answer legal questions, the state-of-the-art models can only achieve about 28% accuracy on JEC-QA, while skilled humans and unskilled humans can reach 81% and 64% accuracy respectively, which indicates a huge gap between humans and machines on this task. We will release JEC-QA and our baselines to help improve the reasoning ability of machine comprehension models. You can access the dataset from http://jecqa.thunlp.org/.

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“…anks to the development of machine learning and text mining techniques, more researchers formalize this task under text classification frameworks. Most of these studies attempt to extract textual features [11][12][13] or introduce some external knowledge [4,14]. However, these methods can only utilize shallow features and manually designed factors; usually the effect of these methods becomes worse when applied to other scenarios.…”

Section: Legal Judgment Prediction With the Development Ofmentioning

confidence: 99%

Legal Judgment Prediction Based on Multiclass Information Fusion

Zhu

¹

,

Guo

²

,

Hu

³

et al. 2020

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Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

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“…Owing to the massive quantity of high-quality textual data in the legal domain, employing NLP techniques to solve legal intelligence problems has been more and more popular in recent years, e.g., generating court views to interpret charge results (Ye et al 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;He et al 2018a;Shen et al 2018).…”

Section: Legal Intelligencementioning

confidence: 99%

JEC-QA: A Legal-Domain Question Answering Dataset

Zhong¹,

Xiao²,

Tu³

et al. 2019

Preprint

View full text Add to dashboard Cite

We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluation of professional skills for legal practitioners. College students are required to pass the examination to be certified as a lawyer or a judge. The dataset is challenging for existing question answering methods, because both retrieving relevant materials and answering questions require the ability of logic reasoning. Due to the high demand of multiple reasoning abilities to answer legal questions, the state-of-the-art models can only achieve about 28% accuracy on JEC-QA, while skilled humans and unskilled humans can reach 81% and 64% accuracy respectively, which indicates a huge gap between humans and machines on this task. We will release JEC-QA and our baselines to help improve the reasoning ability of machine comprehension models. You can access the dataset from http://jecqa.thunlp.org/.

show abstract

Legal Article-Aware End-To-End Memory Network for Charge Prediction

Cited by 9 publications

References 8 publications

JEC-QA: A Legal-Domain Question Answering Dataset

JEC-QA: A Legal-Domain Question Answering Dataset

Legal Judgment Prediction Based on Multiclass Information Fusion

JEC-QA: A Legal-Domain Question Answering Dataset

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