Neural Legal Judgment Prediction in English
This work addresses the lack of neural models for legal judgment prediction in English, which is incremental as it extends existing methods to a new language and dataset.
The authors tackled the problem of legal judgment prediction in English by releasing a new dataset from the European Court of Human Rights and evaluating neural models, achieving strong baselines that surpass previous feature-based models in tasks like binary violation classification and case importance prediction.
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.