Vol. 1, Issue 1, Part A (2024)

This study investigates the effectiveness of transformer-based cross-lingual transfer learning for low-resource Natural Language Processing (NLP) using modular architectures such as MAD-X. Despite major advances in multilingual pretrained models like BERT and XLM-R, significant performance disparities persist between high-resource and low-resource languages due to limited annotated data, lexical diversity, and typological variations. To address these challenges, this research evaluates the impact of adapter-based fine-tuning strategies that selectively share model parameters across languages while isolating language-specific representations. The experimental framework employed multilingual corpora from benchmarks including XTREME and MasakhaNER, assessing tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging under zero-shot and few-shot settings. Statistical analyses using permutation tests and effect size estimation confirmed that the proposed MAD-X framework consistently outperformed baseline models in both accuracy and stability. The macro-average F1 and accuracy improvements demonstrated the efficacy of modular adaptation in mitigating negative transfer and enhancing generalization across typologically diverse languages. Furthermore, the study identified that few-shot fine-tuning with adapter layers significantly improves model robustness without compromising computational efficiency. These findings underscore the critical role of parameter-efficient adaptation methods in advancing equitable multilingual Natural Language Processing (NLP) systems. The research concludes that cross-lingual transfer in low-resource environments can be substantially optimized by integrating modular transformer architectures with targeted fine-tuning, paving the way for scalable, inclusive, and linguistically adaptive language technologies. Practical recommendations are also proposed to guide future development, including the creation of open adapter repositories, sustainable data ecosystems, and efficient multilingual deployment strategies tailored to low-resource communities.