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

arXiv: https://arxiv.org/abs/2105.13562 · HTML: https://mlsanity.com/paper/2105.13562/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 30 of 30)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [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)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [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)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (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)
- [Predicting the Law Area and Decisions of French Supreme Court Cases](https://mlsanity.com/paper/1708.01681/) (2017)
- [Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model](https://mlsanity.com/paper/2009.14083/) (2020)
- [Automatic Judgment Prediction via Legal Reading Comprehension](https://mlsanity.com/paper/1809.06537/) (2018)
- [Topic Spotting using Hierarchical Networks with Self Attention](https://mlsanity.com/paper/1904.02815/) (2019)

## Built on by (most cited first, 30 of 78)

- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://mlsanity.com/paper/2212.02199/) (2022)
- [LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://mlsanity.com/paper/2305.07507/) (2023)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law](https://mlsanity.com/paper/2209.06049/) (2022)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer](https://mlsanity.com/paper/2211.00974/) (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)
- [An Empirical Study on Cross-X Transfer for Legal Judgment Prediction](https://mlsanity.com/paper/2209.12325/) (2022)
- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding](https://mlsanity.com/paper/2112.01047/) (2021)
- [Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts](https://mlsanity.com/paper/2406.04136/) (2024)
- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification](https://mlsanity.com/paper/2310.11878/) (2023)
- [Multimodal Multi-Agent Empowered Legal Judgment Prediction](https://mlsanity.com/paper/2601.12815/) (2026)
- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00768/) (2023)
- [HLDC: Hindi Legal Documents Corpus](https://mlsanity.com/paper/2204.00806/) (2022)
- [ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks](https://mlsanity.com/paper/2405.14211/) (2024)
- [Fact-based Court Judgment Prediction](https://mlsanity.com/paper/2311.13350/) (2023)
- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation](https://mlsanity.com/paper/2310.11049/) (2023)
- [On Evaluating Explanation Utility for Human-AI Decision Making in NLP](https://mlsanity.com/paper/2407.03545/) (2024)
- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [SemEval 2023 Task 6: LegalEval - Understanding Legal Texts](https://mlsanity.com/paper/2304.09548/) (2023)
- [Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset](https://mlsanity.com/paper/2402.17013/) (2024)

## Abstract (arXiv)

An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanation (CJPE). The task requires an automated system to predict an explainable outcome of a case. We experiment with a battery of baseline models for case predictions and propose a hierarchical occlusion based model for explainability. Our best prediction model has an accuracy of 78% versus 94% for human legal experts, pointing towards the complexity of the prediction task. The analysis of explanations by the proposed algorithm reveals a significant difference in the point of view of the algorithm and legal experts for explaining the judgments, pointing towards scope for future research.