The paper "Optimization Strategies for Deep Learning Models in Natural Language Processing" by Jerry Yao and Bin Yuan addresses the challenges faced by deep learning models in natural language processing (NLP), such as data heterogeneity, model interpretability, and transferability across multilingual and cross-domain scenarios. The authors propose four main optimization strategies: model structure, loss functions, regularization methods, and optimization algorithms. Extensive experiments on text classification, named entity recognition, and reading comprehension tasks demonstrate the effectiveness of these strategies. Key findings include the significant improvement in model performance through techniques like Multi-Head Attention, Focal Loss, LayerNorm, and AdamW. The paper also explores model compression techniques, providing valuable insights for deploying deep models in resource-constrained environments. The results highlight the importance of optimizing deep learning models to enhance their performance and generalization capabilities in NLP tasks.The paper "Optimization Strategies for Deep Learning Models in Natural Language Processing" by Jerry Yao and Bin Yuan addresses the challenges faced by deep learning models in natural language processing (NLP), such as data heterogeneity, model interpretability, and transferability across multilingual and cross-domain scenarios. The authors propose four main optimization strategies: model structure, loss functions, regularization methods, and optimization algorithms. Extensive experiments on text classification, named entity recognition, and reading comprehension tasks demonstrate the effectiveness of these strategies. Key findings include the significant improvement in model performance through techniques like Multi-Head Attention, Focal Loss, LayerNorm, and AdamW. The paper also explores model compression techniques, providing valuable insights for deploying deep models in resource-constrained environments. The results highlight the importance of optimizing deep learning models to enhance their performance and generalization capabilities in NLP tasks.