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This research aims to address the issues of semantic coherence in neural machine translation for multi-sentence texts.

Deep Learning Algorithms Integrating Semantic Understanding to Enhance Translation Semantic Coherence

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This research aims to address the issues of semantic coherence in neural machine translation for multi-sentence texts.
Developed a deep learning translation framework integrating multi-layered semantic representations and cross-sentence modeling.
Employed semantic coherence constraints and context-sensitive attention during the encoding and decoding phases.
Conducted experiments on standard parallel corpora to evaluate the framework's performance.
Increased translation semantic coherence index Coh from 0.796 to 0.834 (p<0.01).
Improved referential consistency rate from 83.4% to 87.9% (p<0.01).
Enhanced BLEU score from 28.91 to 29.47 (p<0.01).

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Randomized trial demonstrates improved translation coherence in multilingual texts, suggesting enhanced AI capabilities.

Diao et al. (2026) studied this question.

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