An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
This work addresses the problem of improving legal judgment prediction for multilingual and cross-jurisdictional legal systems, but it is incremental as it applies existing transfer learning methods to a new domain.
The study tackled the problem of understudied cross-lingual transfer learning in Legal Judgment Prediction by exploring techniques on a trilingual dataset, finding that cross-lingual transfer improved results, especially with adapter-based fine-tuning, and further enhancements came from data augmentation and cross-domain/regional/jurisdiction transfers, with models trained across all groups performing better overall and in worst-case scenarios.
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.