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Neural Legal Judgment Prediction in English

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arxiv 1906.02059 v1 pith:KNBNLLSY submitted 2019-06-05 cs.CL

Ilias Chalkidis , Ion Androutsopoulos , Nikolaos Aletras This is my paper · ORCID
classification cs.CL
keywords modelspredictioncaseenglishjudgmentlegalneuralbert
verification ladder T0 review T1 audit T2 compute T3 formal
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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 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.

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Cited by 6 Pith papers

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