Cross-Lingual Knowledge Distillation: Enhancing Bilingual Models with Multilingual Pretraining

Abstract

In this paper, we explore the concept of Cross-Lingual Knowledge Distillation (CLKD) and its effectiveness in enhancing bilingual models through multilingual pretraining. We propose a novel framework for CLKD that leverages pre-trained multilingual models to distill knowledge into bilingual models, improving their performance on cross-lingual tasks. Our approach addresses the challenges of limited bilingual training data and demonstrates significant improvements in various natural language processing (NLP) tasks.