ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

Malik, Vijit; Sanjay, R; Nigam, Shubham Kumar; Ghosh, Kripa; Guha, Shouvik Kumar; Bhattacharya, Arnab; Modi, Ashutosh

doi:10.48550/arxiv.2105.13562

Cited by 4 publications

(15 citation statements)

References 18 publications

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“…Language Input Output Download Rationale Decision SwissJudgmentPrediction [26] German Pleas r Facts r -Plea judgments r Link Italian French Sulea et al [14] Facts r Others r -TSCC [40] Thai Facts a Articles a -Link JUSTICE [27] English Pleas r Facts r -Plea judgments r -BStricks_LDC [41] Facts r Others r Link ILDC [42] Fact snippets r Link ACI [43] Facts a,r Charges a Link echr [13] Facts r…”

Section: Datasetmentioning

confidence: 99%

“…(3) Case material except for the plea judgments. To predict interpretable plea judgments based on case material masked with the plea judgments has been present in [42,41,14].…”

Section: Charges Rmentioning

confidence: 99%

“…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]). Specially, we categorize public LJP datasets into two categories: task-specific datasets and multi-task datasets.…”

Section: Datasetsmentioning

confidence: 99%

“…Furthermore, Sulea et al [14] proposed a dataset comprising 126,865 unique court rulings firstly used to predict court rulings in the French Supreme Court cases. Indian Legal Documents Corpus (ILDC [42]) with 35k cases is introduced for court judgment prediction and explanation. Moreover, a labeled data set of 4,959 UK court cases [41] is also created for legal judgment prediction.…”

Section: Plea Judgment Prediction Datasetsmentioning

confidence: 99%

“…Sulea et al [14] BStricks_LDC [41] CAIL2018 [24] Articles Charges Prison terms TOPJUDGE-CJO [29] TOPJUDGE-PKU [29] TOPJUDGE-CAIL [29] Articles Charges Prison terms Criminal-S,Criminal-M,Criminal-L [51] Charges QAjudge-CJO, QAjudge-PKU, QAjudge-CAIL [52] Articles Charges Auto-Judge [53] Plea judgments FLA [15] Charges MaF/MiF MaP/Mip MaR/MiR LJP-MSJudge [54] Plea judgments MaP MaR ILDC [42] Acc MaF Fact snippets JS ROUGE BLEU Meteor Court-View-Gen [33] Court View ROUGE BLEU AC-NLG [34] BERT SCORE ROUGE CPTP [50] Prison terms S EM Acc@p…”

Section: Maf Map Marmentioning

confidence: 99%

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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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Section: Datasetmentioning

confidence: 99%

“…(3) Case material except for the plea judgments. To predict interpretable plea judgments based on case material masked with the plea judgments has been present in [42,41,14].…”

Section: Charges Rmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Plea Judgment Prediction Datasetsmentioning

confidence: 99%

Section: Maf Map Marmentioning

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

View full text Add to dashboard Cite

show abstract

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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Identification, Categorisation and Forecasting of Court Decisions

Medvedeva¹

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I will not pretend that getting this dissertation to where it is today was an easy feat, it definitely was not. Diving into a completely new field, learning to navigate between the two disciplines, figuring out the academic world, decrypting legal Dutch, working from home, it was not without challenge. As overwhelming as it may have been at times, it was an exciting and rewarding endeavour, learning, figuring out new things, publishing, presenting and travelling.Throughout the process, I've been guided and supported by Martijn Wieling and Michel Vols. I couldn't wish for better supervisors. I am not able to express how much of an impact they both had on my academic and personal life. Thank you for caring, and for being so involved. Working on every project, paper, and finally the dissertation, I've always felt like a part of the team. Though this dissertation has my name on the cover, it has been a true collaborative effort.I would like to thank my reading committee members Kevin Ashley, Bart Verhij and Janneke Gerards for reading my dissertation and providing such valuable feedback.Being part of two departments, I've had many colleagues over the years who brought endless insights to my work and life. While I will not be able to mention them all here, in no particular order I'd like to thank

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ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

Cited by 4 publications

References 18 publications

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

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

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

Identification, Categorisation and Forecasting of Court Decisions

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