Comparison and Transformer Model and its Variant Algorithms in Natural Language Processing
Yuwei Ma · 2024
The application of transformer model and its variant algorithm in natural language processing greatly improves the ability and accuracy of language understanding. Through comparative experiments, the application effects of Transformer, BERT (Transformers Bidirectional Coding Representation), GPT (Generative Pre-trained Transformer) and XLNET (Generalized Language Model Pre-Training) models in natural language processing tasks are analyzed. This paper describes the structure and working principle of Transformer model, including self-attention mechanism, multi-head attention, position coding, residual connection and layer normalization. It introduces different models, such as Bert, GPT and XLNet, and their optimization and improvement in specific tasks. Experiments show that the maximum confusion level of XLNet is only 30, and the minimum confusion level is only 11, which shows the highest BLEU score in machine translation tasks, while the Transformer model faces challenges in computational efficiency.