An Empirical Study on Cross-X Transfer for Legal Judgment Prediction

Understand

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.