CGRA-DeBERTa 概念引导残差增强变换器在伊斯兰神学理解中的应用

Accurate question answering (QA) over classical Islamic texts remains challenging due to domain-specific semantics, long context dependencies, and concept-sensitive reasoning. A new framework, CGRA DeBERTa, is proposed to enhance theological QA over Hadith corpora. CGRA DeBERTa builds on a customized DeBERTa transformer backbone with lightweight LoRA-based adaptations and a residual concept-aware gating mechanism. The customized DeBERTa embedding block learns global and positional context, while Concept Guided Residual Blocks incorporate theological priors from a curated Islamic Concept Dictionary of 12 core terms. Furthermore, the Concept Gating Mechanism selectively amplifies semantically relevant features, thereby improving the accuracy and effectiveness of the QA process.