Charge-Based Prison Term Prediction with Deep Gating Network

Chen, Huajie; Cai, Deng; Dai, Wei; Dai, Zehui; Ding, Yadong

doi:10.48550/arxiv.1908.11521

Cited by 4 publications

(18 citation statements)

References 21 publications

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“…Link CPTP [50] Charges r -Prison terms r Link Criminal-S [51] Charge attributes a Attributes prediction r…”

Section: Pleas Rmentioning

confidence: 99%

“…5, the inadequacy of Prison term prediction datasets should be modified. CPTP [50], TOPJUDGE-CJO [29], TOPJUDGE-PKU [29] and TOPJUDGE-CAIL [29] are datasets built to predict the prison terms according to the relevant charges.…”

Section: Charges Rmentioning

confidence: 99%

“…(2) Facts from court debate. LJP-MSJudge [54] predicts the verdicts of the plaintiffs' pleas 60,000 321 50 -RACP [48] 100,000 -51 MAMD [49] 164,997 -CPTP [50] 238,749 157 226 Court-View-Gen [33] 171,981 51 -Criminal-S [51] 77,046 149 Criminal-M [51] 191,960 149 Criminal-L [51] 383,697 149 QAjudge-CJO [52] 15,120 19 20 QAjudge-PKU [52] 14,000 19 20 QAjudge-CAIL [52] 13,423 [34] 66,904 Auto-Judge [53] 100,000 62 echr [13] ECtHR Human rights 584 3 ECHR-CASES [25] 11,478 66 ECtHR_crystal_ball [44] 11,532 14 SwissJudgmentPrediction [26] FSCS Generic 85,268 -Sulea et al [14] FSC 126,865…”

Section: Charges Rmentioning

confidence: 99%

“…In constructing datasets, there have been at least two lines of work, as seen in Table 2. The first one employs a machine to extract the necessary metadata, such as using Regular Expression to identify and mask judgment results ( [26,14,41,42]) or to extract the input-output data samples( [13,25,44,45,24,29,47,15,49,33,50,53]) from judgment documents of given court and applying the deep neural model to extract facts from Court judgment documents as [43] for evaluating the LJP model. The second line of work recruits legal experts to annotate rationale sentences [42,48] as gold sentences or to annotate legal domain knowledge (which are not included in the judgment documents, such as [46,51,52]).…”

Section: Datasetsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Cui¹,

Shen²,

Nie³

et al. 2022

Preprint

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Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. Recently, large-scale public datasets and advances in NLP research have led to increasing interest in LJP. Despite a clear gap between machine and human performance, impressive results have been achieved in various benchmark datasets. In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes;(2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges. This paper can provide up-to-date and comprehensive reviews to help readers understand the status of LJP. We hope to facilitate both NLP researchers and legal professionals for further joint efforts in this problem.

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“…Link CPTP [50] Charges r -Prison terms r Link Criminal-S [51] Charge attributes a Attributes prediction r…”

Section: Pleas Rmentioning

confidence: 99%

Section: Charges Rmentioning

confidence: 99%

Section: Charges Rmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Cui¹,

Shen²,

Nie³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thanks to the accessibility of the large amount of legal judgment data 2 , a rising number of work has been dedicated to this research topic. However, they simplified the task to predict the court's outcome given a text describing the facts of a legal case manually summarized by the judge based on the case materials [2,3,7,8,12,13,26,27,[29][30][31]. Xiao et al [23] proposed to take the fact description as input and predict 1 compares the state-of-the-art works related to judgment prediction over different dimensions, including the task to be solved, case type, methodology (highlight) as well as the phases of dataset used in the study.…”

Section: Related Work 21 Legal Judgment Predictionmentioning

confidence: 99%

Legal Judgment Prediction with Multi-Stage Case Representation Learning in the Real Court Setting

Zhang

Wang

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as the input to predict the judgment, neglecting critical case life-cycle information in real court setting could threaten the case logic representation quality and prediction correctness. In this paper, we introduce a novel challenging dataset 1 from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case -plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction, achieving significant improvements over strong state-of-the-art baselines. Moreover, the user study conducted with real judges and law school students shows the neural predictions can also be interpretable and easily observed, and thus enhancing the trial efficiency and judgment quality. CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics; Multi-task learning; • Applied computing → Law.

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A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Cui,

Shen,

Wen

2023

IEEE Access

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Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. The present work addresses the growing interest in the application of NLP techniques to the task of LJP. Despite the current performance gap between machines and humans, promising results have been achieved in a variety of benchmark datasets, owing to recent advances in NLP research and the availability of large-scale public datasets. To provide a comprehensive survey of existing LJP tasks, datasets, models, and evaluations, this study presents the following contributions: (1) an analysis of 43 LJP datasets constructed in 9 different languages, together with a classification method of LJP based on three different attributes; (2) a summary of 16 evaluation metrics categorized into 4 different types to evaluate the performance of LJP models for different outputs;(3) a review of 8 legal-domain pretrained models in 4 languages, highlighting four major research directions for LJP; (4) state-of-the-art results for 11 representative datasets from different court cases and an in-depth discussion of the open challenges in this area. This study aims to provide a comprehensive review for NLP researchers and legal professionals to understand the advances in LJP over the past years, and to facilitate further joint efforts towards improving the performance of LJP models.

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Charge-Based Prison Term Prediction with Deep Gating Network

Cited by 4 publications

References 21 publications

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Legal Judgment Prediction with Multi-Stage Case Representation Learning in the Real Court Setting

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

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