Abstract
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.