Optimization Strategies for Deep Learning Models in Natural Language Processing

Deep learning models have achieved remarkable performance in the field of natural language processing (NLP), but they still face many challenges in practical applications, such as data heterogeneity and complexity, the black-box nature of models, and difficulties in transfer learning across multilingual and cross-domain scenarios. In this paper, corresponding improvement measures are proposed from four perspectives: model structure, loss functions, regularization methods, and optimization strategies, to address these issues. Extensive experiments on three tasks including text classification, named entity recognition, and reading comprehension confirm the feasibility and effectiveness of the proposed optimization solutions. The experimental results demonstrate that introducing innovative mechanisms like Multi-Head Attention and Focal Loss, and judiciously applying techniques such as LayerNorm and AdamW, can significantly improve model performance. Finally, this paper also explores model compression techniques, providing new insights for deploying deep models in resource-constrained scenarios.