Quantification of CITTA Chinese Translation Interpretation Differentiation based on BEiT-RoBERTa Model
TL;DR
Research has confirmed that the BEiT-RoBERTa model quantifies CITTA translation variations but poorly captures philosophical depth and cultural subtleties.
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.