  In this paper, we cast Legal Judgment Prediction on European Court of Human
Rights cases into an article-aware classification task, where the case outcome
is classified from a combined input of case facts and convention articles. This
configuration facilitates the model learning some legal reasoning ability in
mapping article text to specific case fact text. It also provides an
opportunity to evaluate the model's ability to generalize to zero-shot settings
when asked to classify the case outcome with respect to articles not seen
during training. We devise zero-shot experiments and apply domain adaptation
methods based on domain discrimination and Wasserstein distance. Our results
demonstrate that the article-aware architecture outperforms straightforward
fact classification. We also find that domain adaptation methods improve
zero-shot transfer performance, with article relatedness and encoder
pre-training influencing the effect.
