Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages
BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands. Translating non-English data into English and fine-tuning existing English BERT models offers a resource-efficient alternative, yet few studies have structurally compared translation-based fine-tuning with native-language BERT performance across tasks and languages. This study provides such a comparison,
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- LinkedLinked via arxiv author · 85%Hielke Muizelaar →
“Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages”
- LinkedLinked via arxiv author · 85%Giulia Rivetti →
“Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages”
- LinkedLinked via arxiv author · 85%Marco Spruit →
“Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages”
- LinkedLinked via arxiv author · 85%Marcel Haas →
“Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages”