CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

By Chaojun Xiao, Haoxiang Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang and 1 more. 2018

370 citations recorded by the source corpus.

In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \dataset contains more than $2.6$ million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction, both \dataset and baselines will be released after the CAIL competition\footnote{this http URL}.

Subfields: Economics and Econometrics, Law, Political Science and International Relations, Evaluation & Benchmarks

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “mately 4M judicial decisions in the United States (serving as the corpora), 12 base cases (serving as the topics), and 2572 relevance assessments made by legal professionals. A similar dataset is CM [Xiao et al., 2018], which contains 8,964 triples (A, B, C) consisting of legal documents, where the ”
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021, neutral): “• Legal Documents Analysis: CAIL2018-Task1 [71], CAIL2018-Task2 [71].”
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.”
A Comprehensive Overview of Large Language Models (2023, neutral): “0 controllable dataset NLU: NLPCC2014-SC, SE-ABSA16_PHNS, SE-ABSA16_CAME, BDCI2019, EPRSTMT [293], COTE-BD [268], COTE-MFW [268], OCNLI [269], CMNLI [269], OCNLI-FC [293], CLUEWSC [269] CLUEWSC-FC [293], FinRE [270], SanWen [271], AFQMC [269], LCQMC [242], PAWS-X [231], BQ Corpus [272], CSL [269] CS”
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents (2021, neutral): “…deep learning, many efforts have been devoted to solving various legal tasks, such as legal charge prediction (Luo et al., 2017; Zhong et al., 2018; Xiao et al., 2018; Chalkidis et al., 2019; Yang et al., 2019), relevant law article retrieval (Chen et al., 2013; Raghav et al., 2016), court view…”
Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? (2020, background): “in Section 2.2, in addition to discussing the legality of using 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))”
JEC-QA: A Legal-Domain Question Answering Dataset (2019, methodology): “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).”
The Emerging Trends of Multi-Label Learning (2020, neutral): “XMLC [192] and DL MLC [193] are proposed to”
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021, neutral): “• Legal Documents Analysis (LDA) : CAIL2018-Task1 [91], CAIL2018-Task2 [91].”
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, background): “Secondly, we implement several models which are specially designed for LJP, including FactLaw (Luo et al., 2017), TopJudge (Zhong et al., 2018), and Gating Network (Chen et al., 2019).”