Natural Language Processing Using Transformer-Based Deep Learning Models

Abstract

The advent of deep learning models based on transformers has revolutionized natural language processing by changing the way robots comprehend and produce human language. To express long-range dependencies in textual material effectively and enable parallel processing, transformers rely on self-attention mechanisms, unlike typical sequence models. Numerous natural language processing (NLP) tasks have benefited greatly from this architecture change. the function of transformer-based models in NLP, with an emphasis on designs like BERT, GPT, and associated variations. In this study, we look at how attention mechanisms improve scalability, language representation, contextual comprehension, and text production, machine translation, question answering, and categorization. It takes into consideration issues with computational cost, data needs, and the interpretability of the models. When compared to previous neural techniques, transformer-based models are far more accurate and flexible. The study wraps up by highlighting areas for future research that could help in making transformer-based NLP systems more efficient, easier to understand, and more ethical to use in the real world.