Construction of Chinese-Japanese-English Translation Database Based on Cross-Lingual Transformer-Based Models

Abstract

Accurate semantic alignment and comprehensive multilingual coverage remain major challenges in constructing Chinese–Japanese–English translation databases for technical knowledge sharing and engineering information exchange. This study proposes a cross-lingual translation database construction framework based on Transformerbased neural networks and hierarchical contrastive representation learning. Using InfoXLM as the shared semantic encoder, dependency-syntax perturbation and cross-lingual lexical substitution are employed to generate codeswitching hard negative samples, while a three-way triplet contrastive optimization strategy jointly constrains Chinese, Japanese, and English semantic spaces to improve representation consistency. The fine-tuned model is further integrated with semantic similarity filtering and bidirectional consistency verification to construct a large-scale trilingual translation database from multilingual candidate corpora. Experimental results demonstrate an alignment precision of 94.7%, a recall of 92.3%, an F1-score of 93.5%, and a translation coverage of 89.8%, significantly outperforming conventional multilingual alignment approaches. 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 communication systems requiring robust semantic alignment and signal-aware information management.