Dynamic semantic fusion based transformer modelling for automatic translation of Chinese–English languages using deep embeddings
Chinese English neural machine translation remains challenging due to substantial syntactic divergence, word-order variation, lexical ambiguity, and cross-lingual semantic mismatch. These challenges often lead to semantic omissions, over-translation, and weak source–target semantic alignment in transformer-based translation systems. Although transformer architecture has achieved remarkable progress, they frequently exhibit limitations in capturing complementary lexical and sentence-level semantic information while offering limited interpretability of the translation process. To address these challenges, this paper proposes DSF–MarianMT, a semantic fusion enhanced neural machine translation framework built upon MarianMT. The proposed framework integrates word-level and sentence-level semantic representations through a dynamic semantic fusion mechanism and employs a contrastive semantic learning objective to improve source–target semantic consistency during training. Experimental evaluation on a Chinese English translation dataset demonstrates the effectiveness of the proposed approach, achieving 36.5 BLEU, 60.2 chrF, 39.8 TER, and a COMET score of 0.78, outperforming the standard MarianMT baseline across all evaluation metrics. In addition to improved translation quality, interpretability analyses reveal reduced attention entropy, lower redundancies among attention heads, and stable token-level semantic learning. Furthermore, error analysis indicates fewer semantic omissions and over-translation errors, while consistent performance is maintained across sentences of varying lengths. These findings demonstrate that the proposed framework effectively enhances semantic representation learning and translation fidelity for Chinese–English neural machine translation.