Enhancing Contextual Semantic Reconstruction in College English Translation Using the BART Auto-Encoding Generation Framework

To address the decline in coherence and accuracy of college English translation caused by insufficient contextual semantic modeling, this paper proposes a Context-Aware Semantic Reconstruction Fine-tuning (CASR-F) method based on BART (Bidirectional and Auto-Regressive Transformers). The method is particularly useful for translating specialized terminology and complex technical descriptions in engineering fields such as electromagnetic wave analysis, antenna systems, and propagation scenario documentation. CASR-F introduces a dependency syntax-guided dynamic noise masking mechanism, which improves the encoder’s ability to identify core predicates and argument nodes through controlled noise injection. A gated semantic anchor attention module uses the top-level hidden state of the encoder as the semantic hub and dynamically adjusts cross-sentence information transmission weights through a gating function, thereby improving text-level semantic relation modeling during decoding. The model further enhances semantic recovery by jointly optimizing sequence reconstruction loss and cross-sentence semantic consistency constraints. Experiments show that CASR-F achieves a BLEU-4 score of 36.72 and a METEOR score of 31.45 in the College English Translation task, with a referential resolution accuracy of 78.6%. The F1 score for semantic role labeling on core argument A0 is improved to 83.9%. These results demonstrate the effectiveness of the proposed method in improving translation quality, reconstructing semantic structures, and enhancing semantic restoration, providing practical support for the accurate translation of electromagnetic engineering documents, antenna design descriptions, and wave propagation technical materials.