# Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting

arXiv: https://arxiv.org/abs/2107.05192 · HTML: https://mlsanity.com/paper/2107.05192/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Learning to Predict Charges for Criminal Cases with Legal Basis](https://mlsanity.com/paper/1707.09168/) (2017)
- [Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network](https://mlsanity.com/paper/1905.03969/) (2019)
- [Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction](https://mlsanity.com/paper/1905.08511/) (2019)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation](https://mlsanity.com/paper/1805.11004/) (2018)
- [Automatic Judgment Prediction via Legal Reading Comprehension](https://mlsanity.com/paper/1809.06537/) (2018)
- [Improving Multi-Document Summarization via Text Classification](https://mlsanity.com/paper/1611.09238/) (2016)
- [On Measuring Bias in Online Information](https://mlsanity.com/paper/1704.05730/) (2017)

## Built on by (most cited first, 24 of 24)

- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction](https://mlsanity.com/paper/2206.05224/) (2022)
- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts](https://mlsanity.com/paper/2210.13836/) (2022)
- [Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning](https://mlsanity.com/paper/2312.05762/) (2023)
- [ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US](https://mlsanity.com/paper/2211.00582/) (2022)
- [CitaLaw: Enhancing LLM with Citations in Legal Domain](https://mlsanity.com/paper/2412.14556/) (2024)
- [LAiW: A Chinese Legal Large Language Models Benchmark](https://mlsanity.com/paper/2310.05620/) (2023)
- [Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction](https://mlsanity.com/paper/2409.07055/) (2024)
- [The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal](https://mlsanity.com/paper/2409.08098/) (2024)
- [General Information Metrics for Improving AI Model Training Efficiency](https://mlsanity.com/paper/2501.02004/) (2025)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgment Prediction with Legal Clue Tracing](https://mlsanity.com/paper/2408.09717/) (2024)
- [Generative Modeling of Individual Behavior at Scale](https://mlsanity.com/paper/2502.14998/) (2025)
- [Exploring Graph Neural Networks for Indian Legal Judgment Prediction](https://mlsanity.com/paper/2310.12800/) (2023)
- [JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning](https://mlsanity.com/paper/2504.17264/) (2025)
- [ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation](https://mlsanity.com/paper/2508.17234/) (2025)
- [Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction](https://mlsanity.com/paper/2204.07046/) (2022)
- [FAIR: A Causal Framework for Accurately Inferring Judgments Reversals](https://mlsanity.com/paper/2306.11585/) (2023)
- [A Korean Legal Judgment Prediction Dataset for Insurance Disputes](https://mlsanity.com/paper/2401.14654/) (2024)
- [Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models](https://mlsanity.com/paper/2403.12936/) (2024)
- [BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction](https://mlsanity.com/paper/2404.10481/) (2024)
- [Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?](https://mlsanity.com/paper/2406.10974/) (2024)

## Abstract (arXiv)

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 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.