Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification

Abstract

We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual transfer, we develop a new version of the dataset without parallel documents. We use it to show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models, the best previous zero-shot transfer method for Multi-EURLEX. We also develop a bilingual teacher-student zero-shot transfer approach, which exploits additional unlabeled documents of the target language and performs better than a model fine-tuned directly on labeled target language documents.