Quantification of CITTA Chinese Translation Interpretation Differentiation based on BEiT-RoBERTa Model

Abstract

This study analyzes varied Chinese renderings of the Buddhist core concept CITTA via quantitative modeling to expose cross-cultural translation discrepancies. It constructs a BEiT-RoBERTa fusion model combining BEiT visual feature extraction and RoBERTa linguistic encoding. Based on a self-built CITTA Chinese translation text dataset containing ancient and modern translations, experiments were conducted from four dimensions: vocabulary recognition, model comparison, semantic coherence, and multi-context adaptation. Performance tests were conducted using indicators such as character error rate (CER), syllable recognition accuracy (SRA), line recognition accuracy (LRA), and manual evaluation. Results show the syllable-based model performs best: CER=0.167, SRA=0.850, LRA=0.791, surpassing CRNN and SRN. The overall accuracy of sentence semantic coherence judgment is 92.68%, and the translation effect in different contexts is between professional translators and non professional enthusiasts, with stable accuracy and fluency. Research has confirmed that the BEiT-RoBERTa model quantifies CITTA translation variations but poorly captures philosophical depth and cultural subtleties.