Abstract: Despite the emergence of large-scale multilingual pre-trained models like mBERT, XLM-RoBERTa, and mT5, natural language processing (NLP) still struggles in low-resource languages due to limited annotated data. This paper explores the use of transfer learning to adapt pre-trained multilingual models to low-resource tasks such as Named Entity Recognition (NER), sentiment analysis, and machine translation for languages like Amharic, Hausa, and Sinhala. By leveraging zero-shot and few-shot learning paradigms and evaluating cross-lingual embeddings and token overlap, we demonstrate signif Research goal: Can the incorporation of pre-trained multilingual language models (e.g., mBERT, XLM-R) further enhance the effectiveness of multimodal teacher-student learning for cross-lingual NER, as evaluated using standard NER benchmarks in low-resource settings? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.9/10.