  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.
