AI in Natural Language Processing: Techniques, Challenges, and Applications in Text and Speech Analysis
Harshit Singh, Kadir Ali · 2024
The advent of transformer models and the integration of pre-trained models with transfer learning have revolutionized Natural Language Processing, setting new standards in model efficiency and performance. This chapter provides a comprehensive analysis of these advancements, focusing on their impact on NLP benchmarks and their transformative effects on model development. The discussion begins with a detailed exploration of how transformer architectures have set new performance benchmarks across various NLP tasks, including text classification, named entity recognition, and language generation. The chapter further delves into the influence of pre-trained models and transfer learning on computational efficiency, performance enhancement, and data requirements. By leveraging pre-trained models, NLP systems now achieve higher accuracy with reduced computational resources and development time, thus accelerating the deployment of advanced language processing applications. Additionally, the chapter addresses the future directions of these technologies, including ongoing innovations and their implications for the broader field of NLP. Key topics covered include transformer models, pre-trained models, transfer learning, model efficiency, NLP benchmarks, and performance optimization. This chapter offers valuable insights for researchers and practitioners aiming to understand and leverage the latest advancements in NLP for practical and theoretical applications.