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Neural Legal Judgment Prediction in English
Open accessAbstract
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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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
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- 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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