Semantic Translation Model for Low-Resource Scenarios Based on Semantic Context Data Enhancement

In cross-language and long-text translation tasks involving low-resource language pairs, traditional neural machine translation models often face weak semantic modeling and low accuracy. To address this, a semantic context-enhanced neural machine translation model is proposed to improve accuracy and robustness. A conditional generative adversarial network builds a back-translation model to generate coherent pseudo-parallel corpora, while a multilingual pre-trained model combined with RoBERTa's dynamic context modeling achieves deep semantic alignment between source and target languages. Experiments show the enhanced model's back-translation error rate stays below 0.42, significantly outperforming others. On low-resource corpora, it achieved a BLEU score of 35.2, with 0.99 context-specific and 0.95 overall translation accuracy. The model demonstrates strong semantic coherence and robustness, supporting efficient multilingual translation for knowledge sharing, cultural exchange, and cross-border information services.