Analysis on the Method of Improving the Performance of Natural Language Processing Model Driven by Artificial Intelligence

Abstract

Natural language processing (NLP), as the core field of artificial intelligence (AI), has made remarkable progress with the help of Transformer to pre-train large models, but it is still restricted by bottlenecks such as strong data dependence, high computational cost, and insufficient generalization and robustness. Aiming at the problem that the existing research is mostly limited to one-dimensional optimization, this study proposes a hierarchical optimization framework (HOF) covering data, model, training and deployment, and improves the performance of NLP model through full link collaborative design. In the data layer, an antagonistic knowledge injection (AKI) method is proposed, which uses external knowledge maps to guide text generation and verification, thus alleviating the problem of data shortage in low-resource scenes. In the model layer, Dynamic Sparse Gating Transformer (DSGT) is designed to balance accuracy and reasoning efficiency through dynamic sparse gating mechanism. In the training layer, the Meta-Adaptive Multitask Learning (MAMTL) method driven by meta-learning is adopted to enhance the cross-language and cross-domain generalization ability. In the deployment layer, an Optimal Transport Alignment (OTA) method based on optimal transport is proposed to achieve efficient multimodal semantic fusion. The experimental results show that the BLEU of HOF is 47.5 on FLORES-200 data set, the macro F1 is 84.7 on X-Cross data set, the reasoning delay of edge devices is reduced to 62ms, and the gender and race prediction bias is significantly reduced. This research fills the gap of NLP full link collaborative design, and provides a new paradigm for promoting efficient and robust NLP technology and building a trusted AI system.