Enhancing NLP for Low-Resource Languages using Cross-Lingual Transfer and Few-Shot Learning

Much progress in NLP has happened in recent years and most of that progress is driven by data-rich languages. The absence of sufficient data in many low-resource languages causes many issues in the development of NLP tools. The framework introduced in this study helps overcome this limit, as it merges methods for language learning and minimal data training for supporting NLP in low-resource languages. Relying on the XLM-R multilingual language model, we help information transfer from big languages to similar smaller counterparts. When we arrange our tasks with premise-hypothesis design, we can more easily use language understanding systems and relate the different tasks we learn. To help further, few-shot learning is applied which allows the model to work with very little target language data. Our results indicate that learned models outperform others when there are few examples, since they work well in unseen circumstances. As a result, this project closes the digital divide among languages by creating a way to help NLP support underrepresented languages more effectively and economically. The framework supports and shapes the study of AI that is inclusive and can handle several languages.