Advanced Transformer-Based Deep Learning Techniques For Enhancing Contextual Understanding In Natural Language Processing

Advanced modifications to transformer-based models, aim to enhance contextual understanding in natural language processing (NLP) tasks. In this study architectural enhancements, including enhanced attention mechanisms, external knowledge integration, and layer optimisation, validated across diverse benchmarks such as text classification, summarisation, question answering, and sentiment analysis, were performed. Results demonstrate that the proposed models outperform baseline architectures like BERT and GPT, showcasing improved accuracy, efficiency, and adaptability to low-resource languages.