Enhancing Multilingual Text Understanding viaTransformer-Based Meta-Learning

In the evolving landscape of computational sciences, the integration of structured linguistic knowledge into deep learningmodels has become increasingly vital for advancing multilingual text understanding. Traditional transformer-based architectures,while powerful, often lack the capacity to inherently capture the syntactic and semantic nuances present across diverselanguages, leading to limitations in generalization and interpretability. To address these challenges, we introduce a novelframework that synergizes symbolic linguistic formalism with neural representation learning. Our approach employsa structure-aware neural language model that incorporates grammatical constraints directly into the learning process,enhancing the model’s ability to comprehend and process multilingual data effectively. Central to our methodology is theGrammar-Infused Representation Alignment (GIRA) strategy, which aligns neural attention mechanisms with syntacticdependencies, ensuring that the model’s internal representations adhere to linguistic structures. Experimental evaluationsdemonstrate that our model achieves superior performance in multilingual understanding tasks, exhibiting enhanced robustnessinterpretability. This work contributes to the field by offering a principled method that bridges the gap between statistical learningand linguistic theory, aligning with the journal’s commitment to advancing both fundamental and applied computational sciences.