Decoding Multimodal Semantic Drift in Neuro-Linguistic Frameworks for Enhanced Computational Translatability

Abstract

This study addresses the pervasive challenge of semantic drift in multimodal linguistic frameworks, which hampers effective computational translation. By leveraging neuro-linguistic processing models, we evaluate the disruptions in semantic coherence across convergent and divergent linguistic modalities. Utilizing a mixed-methods approach, we employ advanced neuroimaging techniques alongside computational linguistic analyses to quantify the impact of semantic drift on translation accuracy. Our findings underscore the necessity for adaptive models that can dynamically recalibrate semantic alignments in real-time, thereby enhancing translatability in multilingual contexts. This research contributes a novel perspective on mitigating semantic discrepancies in complex language systems.