Quantitative Evaluation of Translation Quality and Computational Efficiency in Semantic vs. Phonetic Strategies for Chinese Scientific Terms

This study presents a quantitative evaluation of translation quality and computational efficiency between semantic and phonetic translation strategies for Chinese scientific terms, using Large Language Models (LLMs). Focusing on two representative domains—the Chinese periodic table and heterocyclic compound nomenclature—we propose a back-translation framework (LLM-BT) specifically designed for evaluating scientific terminology translation, conducting Chinese→ English→ Chinese cycles with Grok, DeepSeek, and GPT-4 models. BLEU scores are used to assess translation accuracy. Results show that semantic translation of element names achieves near-perfect performance (BLEU ≈1.00), leveraging the compositional properties of Chinese characters. In contrast, phonetic translation of heterocyclic compounds yields lower BLEU scores (0.55-0.65), reflecting the inherent challenges of transliteration. Multilingual comparisons using Google Translate (Chinese, Japanese, Korean, English) further validate the superiority of semantic translation strategies (BLEU 0.94-0.99) over phonetic approaches (0.16-0.36). Also, byte-level analysis reveals that Chinese semantic and pictophonetic names are more compact (3 bytes per element) compared to Japanese (13.73 bytes) and Korean (8.97 bytes) transliterations. These findings highlight the dual advantages of semantic translation for both accuracy and computational efficiency. We advocate prioritizing semantic strategies in scientific terminology standardization and cross-linguistic knowledge transfer.