2022/09/25 by Joel Niklaus, Niklaus, Joel, Matthias Stürmer +3 · 2 citations
Computer Science · Social Sciences · #68T50 #Artificial Intelligence (cs.AI) #Artificial Intelligence Applications #Artificial Intelligence in Law #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2 #Legal Education and Practice Innovations #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2209.12325
openalex publication_date 2022/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.