Deep Learning Approaches for Natural Language Processing in Low-Resource Languages
Keywords: deep learning NLP low-resource languages transformer machine translation
Abstract
This study presents novel deep learning architectures specifically designed for natural language processing tasks in low-resource languages. We introduce a multilingual transformer model that achieves state-of-the-art performance on 15 underrepresented languages, demonstrating significant improvements in machine translation, sentiment analysis, and named entity recognition. Our approach leverages cross-lingual transfer learning and synthetic data augmentation techniques.