Neural Legal Judgment Prediction in English
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Abstract (arXiv)
Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT's length limitation.
Key facts. Cited by 208 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2019.
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Citing papers per year: 2021: 24; 2022: 32; 2023: 36; 2024: 55; 2025: 33; 2026: 28.
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- 2021When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Datasetcited by 199
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- 2022Calibrated Learning to Defer with One-vs-All Classifierscited by 68
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 61
- 2021Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmarkcited by 60
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- 2026ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts4 Aug 2026
- 2026Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion11 Jul 2026
- 2026Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning25 Jun 2026
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