MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction

Li, Shang; Zhang, Hongli; Lin, Yuh-Lang; Guo, Xiaoding; Fang, Binxing

doi:10.1109/access.2019.2945771

Cited by 43 publications

(18 citation statements)

References 18 publications

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“…The resultant of the proposed work had achieved significant improvements in terms of prediction accuracy. Shang Li et al [12] discussed a Multichannel Attentive Neural Network(MANN) framework which predicts applicable charges, punishment terms and articles for Chinese court cases based on case facts for single defendant person using two-tier hierarchical architecture. K. www.ijacsa.thesai.org Zhu et.al [22] proposed Sequential Generation Network using a nested hierarchical attention mechanism for multi-charge prediction with single case defendants.…”

Section: A Empirical Literature On Legal Judgment Prediction Methodsmentioning

confidence: 99%

“…At the initial stages, Machine Learning methods such as optimized Lasso Regression [5] [6] for Chinese cases and then deep learning models [7] [8] for automated judgment predictions were used. Providing fair and timely justice by the courts is not only the most important obligation of the country but is an important characteristic of democracy [9] [10] [11] [12] [13] [14]. India being the world"s largest democracy is still in need of an intelligent judicial system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Sukanya¹,

J.Priyadarshini²

2021

IJACSA

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Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on.

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Section: A Empirical Literature On Legal Judgment Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Sukanya¹,

J.Priyadarshini²

2021

IJACSA

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“…With the development of the neural network, many researchers begin to explore LJP using deep learning technology Li et al, 2019b;Li et al, 2019a;Kang et al, 2019). These works can be divided into two primary directions.…”

Section: Relevant Articlesmentioning

confidence: 99%

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

Zhong¹,

Xiao²,

Tu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

176

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Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rulebased and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we describe the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an indepth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github. com/thunlp/CLAIM.

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“…117 Li et al have also challenged the commonly used technical approaches in legal judgment prediction in China by highlighting that AI and natural language processing may fail to consider and learn sufficient semantic representations from different parts of case description. 118 As a result, there is a risk that the independence of 'judges' could be undermined by the combined intentions of programmers, software engineers, information technology companies and other entities so long as they participate in the design of the automated judicial decision making process. 119 The full scale involvement of legal technology companies in justice system in China is a particular concern in the context of the smart court, which highlights the risks in commercialising and outsourcing judicial services.…”

Section: Concerns About Technology Use In the Smart Courtmentioning

confidence: 99%

The Smart Court – A New Pathway to Justice in China?

Shi

Sourdin²,

Бин³

2021

International Journal for Court Administration

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As with other jurisdictions, China has introduced a range of newer technologies into its justice sector to promote greater access to justice, improve judicial transparency and to promote just outcomes for disputing parties with legal issues. Chinese courts' practice of embracing technology is unique as an overarching approach has been adopted by the central government to build the 'smart court' system across the entirety of its courts. Arguably, the smart court system, which relies on computer technologies that enable big data use, blockchain formation and advisory and determinative forms of artificial legal intelligence, has, to a certain degree, promoted easier access to justice, enabled faster dispute resolution, saved costs by moving judicial process online and ensured that judgments can be enforced. On the other hand, however, there are concerns relating to the use of some technologies that include the use of automated judgments, digital divide issues, judicial independence, as well as issues linked to privacy and data protection. This article concludes that some caution should be exercised in developing the 'smart court' system, primarily in relation to the oversight and introduction of more disruptive technologies to ensure that cheap and quick dispute resolution can be achieved without detrimental impacts on justice.

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MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction

Cited by 43 publications

References 18 publications

A Meta Analysis of Attention Models on Legal Judgment Prediction System

A Meta Analysis of Attention Models on Legal Judgment Prediction System

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

The Smart Court – A New Pathway to Justice in China?

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