Jul 2026· The 2026 International Conference on Optical Communication and Intelligent Algorithms (OCIA 2026)· Vol 14301, pp. 1430125 - 1430125-6· 0 citations· 10 references
Engineering
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.
A GAT-BiLSTM fusion model that integrates dependency syntactic analysis with graph attention mechanisms and bidirectional long short-term memory networks improves translation fidelity and semantic consistency while exhibiting strong robustness for structurally complex inputs.
J. Leng, X. Lin· Advanced Electromagnetics· 0 citations
The proposed framework provides an efficient solution for multilingual engineering knowledge organization, technical document retrieval, and intelligent information processing, offering valuable support for cross-language electromagnetic engineering documentation, antenna technology resources, and multilingual communic...
Experiments show that MCRA-Net outperforms baseline models on BLEU, METEOR, Culture-BLEU, and TERM-F1, especially in translating culture-specific and technical terms, especially in translating culture-specific and technical terms.
Xia Wu· International Conference on...· 0 citations
As a representative Chinese classical text, the Analects has been translated into English in many versions over a long historical period, and different translators show substantial variation in concept rendering and language style. To address the lack of a quantifiable and reproducible framework for comparing semantic...
Tang He, Peng Dai, Rong Wang et al.· PLoS ONE· 0 citations
DSF–MarianMT, a semantic fusion enhanced neural machine translation framework built upon MarianMT, is proposed, demonstrating that the proposed framework effectively enhances semantic representation learning and translation fidelity for Chinese–English neural machine translation.
The results of this research demonstrate that multimodal fusion and cross-language alignment mechanisms offer effective approaches to improve the accuracy and reliability of multimodal translation systems.
