TransAlignNet: Semantic Consistency Learning With Multi-Modal Attention for English Translation Inference

This study addresses the critical challenge of semantic fidelity in English Translation Inference (ETI) within linguistically diverse and semantically complex domains, aligning with the thematic focus on cross-modal and semantic understanding in intelligent systems. Traditional ETI models, despite achieving fluent generation, often exhibit semantic drift and structural misalignment due to insufficient handling of latent semantic representations and cross-lingual projection fidelity. Prevailing methods predominantly depend on shallow attention mechanisms and heuristic decoding, which constrain interpretability and fail to maintain deep semantic equivalence, particularly in ambiguous or low-resource contexts. To address these challenges, we introduce the CrossLingual Trace Inference Network (CL-TIN) augmented by the Semantic Drift Suppression Strategy (SDSS), a unified architecture that integrates structural and semantic cues. CL-TIN employs dual-path contextual encoding, a latent alignment trace generator, and a feedback-guided decoder to achieve fine-grained alignment and semantically anchored generation. SDSS incorporates projection-based masking, semantic topology, and entropy-aware regularization, transforming decoding into a geometry-constrained, context-aware inference process. Experimental results demonstrate that the proposed method significantly enhances translation accuracy, alignment interpretability, and semantic robustness across diverse datasets. This work advances cross-lingual semantic modeling with a focus on interpretability and alignment-centric optimization, contributing to the broader objectives of semantic-centric AI research in intelligent multi-modal systems.