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Neural Legal Judgment Prediction in English
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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 featurebased 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.
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“Training Transformers with longer sequences with FlashAttention improves performance on the MIMIC-III [47] and ECtHR [6, 7] datasets.”
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
(2010) · Cited by 14,134
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Hierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 5,022
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121
Jain, S., & Wallace, B. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1357 · Cited by 1,133
Research works printed but not linked: 3
- Daniel Martin Katz. 2012. Quantitative legal prediction-or-how I learned to stop worrying and start preparing for the data-driven future of the le- gal services industry. Emory Law Journal, 62:909.
- Reed C Lawlor. 1963. What computers can do: Anal- ysis and prediction of judicial decisions. American Bar Association Journal, pages 337-344.
- Ramesh Nallapati and Christopher D. Manning. 2008. Legal docket classification: Where machine learn- ing stumbles. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP)), pages 438-446.
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Gray, M., Šavelka, J., Oliver, W. M., & Ashley, K. D. (2024) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2023.0155 · Cited by 15
Boosting court judgment prediction and explanation using legal entities
Benedetto, I., Koudounas, A., Vaiani, L., Pastor, E., Cagliero, L., Tarasconi, F., & Baralis, E. (2024) · Artificial Intelligence and Law · doi:10.1007/s10506-024-09397-8 · Cited by 17
An LLMs-based neuro-symbolic legal judgment prediction framework for civil cases
Wei, B., Yu, Y., Gan, L., & Wu, F. Q. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09433-1 · Cited by 15
Extracting Outcomes from Appellate Decisions in US State Courts
Petrova, A., Armour, J. H., & Lukasiewicz, T. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200857 · Cited by 15
T.y.s.s, S., Xu, S., Ichim, O., & Grabmair, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.74 · Cited by 18
Dina, N. Z., Ravana, S. D., & Idris, N. B. (2025) · SAGE Open · doi:10.1177/21582440251329663 · Cited by 24
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
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An Empirical Study on Explanations in Out-of-Domain Settings
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He, C., Tan, T.-P., Xue, S., & Tan, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125462 · Cited by 15
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Investigating the State-of-the-Art Performance and Explainability of Legal Judgment Prediction
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When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD DatasetPreprint
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Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models
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Metadata
- Authors
- Chalkidis, I., Androutsopoulos, I., & Aletras, N.
- Affiliations
- Athens University of Economics and Business (GR)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4317-4323
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2948586190
- Cited by
- 352
- References
- 26
- Citations by year
- 2026: 53 · 2025: 62 · 2024: 53 · 2023: 69 · 2022: 46 · 2021: 46 · 2020: 22 · 2019: 1
- FWCI
- 198.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Athens University of Economics and Business
- Topics
- Social Sciences › Law › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- legal judgment prediction, European Court of Human Rights, English legal texts, multi-label classification, demographic bias, data anonymization, neural language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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