Comparative analysis and prospect of RNN and Transformer

Deep learning techniques have emerged as a key catalyst for innovation in the rapid advancement of artificial intelligence and machine learning. Deep learning has significantly transformed the field of natural language processing (NLP) and voice processing. It has revolutionized the processing and comprehension of linguistic data, leading to advancements in various applications, ranging from simple text categorization to intricate speech recognition. The progress made in this area would not have been feasible without the utilization of two crucial neural network models: the Recurrent Neural Network (RNN) and the Transformer. These models, with their distinctive processing skills, have attained significant accomplishments in the domains of NLP, computer vision, and several other sectors. Nevertheless, although they demonstrate exceptional performance in their specific application scenarios, there are notable distinctions in processing techniques, performance enhancement, and application range. This study aims to thoroughly investigate and compare the efficacy of two models, namely RNN and Transformer, in processing natural language and speech data. This will be achieved using the research methods of literature analysis and literature review. Through the analysis of their structures, strengths, flaws, and performance in practical applications, it offers a more comprehensive viewpoint and benchmark for future study and applications. As technology advances, we anticipate the emergence of more novel models and methodologies in the field of natural language processing and voice processing. This will further facilitate the development of these technologies.