Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching
This addresses the challenge of limited data for low-resource languages in NLP, making advanced language models more accessible, though it is incremental as it builds on existing BERT transfer methods.
The paper tackles the problem of training BERT models for low-resource languages by transferring capabilities from high-resource languages using vocabulary matching, demonstrating effectiveness on Silesian and Kashubian languages with improved performance despite minimal training data.
Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap by transferring BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data. Our results highlight the potential of the proposed technique to effectively train BERT models for low-resource languages, thus democratizing access to advanced language understanding models.