Design and performance comparison of cross-language semantic modeling algorithms for language transfer learning

Abstract

This paper proposes a cross-lingual semantic modeling algorithm design for language transfer learning, aiming to improve the quality and efficiency of multilingual semantic representation by using better semantic alignment and semantic fusion strategies. This method employs a deep neural network semantic representation model and combines statistical alignment methods for cross-lingual semantic alignment, thereby expanding the common semantic domains between languages. To address the issue of low-resource languages, a transfer learning strategy of task adaptation and feature adaptation is used, which significantly enhances the cross-lingual learning effect. Additionally, the integration of multimodal information is utilized for improved semantic restoration, thereby enhancing the stability of semantic expression. Experimental results show that this method outperforms traditional methods in terms of sentence meaning accuracy, convergence speed, and computational efficiency. The system can be applied in cross-lingual language environments and has good portability.