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

• URL: http://arxiv.org/abs/2206.03785v1
• Date: Wed, 8 Jun 2022 10:02:11 GMT
• Title: Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification
• Authors: Stratos Xenouleas, Alexia Tsoukara, Giannis Panagiotakis, Ilias Chalkidis, Ion Androutsopoulos
• Abstract summary: We consider zero-shot cross-lingual transfer in legal topic classification using the recent MultiEURLEX 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 show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models.
• Score: 21.44895570621707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider zero-shot cross-lingual transfer in legal topic classification using the recent MultiEURLEX 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 MultiEURLEX. 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.

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