  Cross-lingual transfer learning has proven useful in a variety of Natural
Language Processing (NLP) tasks, but it is understudied in the context of legal
NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer
learning techniques on LJP using the trilingual Swiss-Judgment-Prediction
dataset, including cases written in three languages. We find that cross-lingual
transfer improves the overall results across languages, especially when we use
adapter-based fine-tuning. Finally, we further improve the model's performance
by augmenting the training dataset with machine-translated versions of the
original documents, using a 3x larger training corpus. Further on, we perform
an analysis exploring the effect of cross-domain and cross-regional transfer,
i.e., train a model across domains (legal areas), or regions. We find that in
both settings (legal areas, origin regions), models trained across all groups
perform overall better, while they also have improved results in the worst-case
scenarios. Finally, we report improved results when we ambitiously apply
cross-jurisdiction transfer, where we further augment our dataset with Indian
legal cases.
