Scholarlink

<p>This study investigates Tibetan–Chinese poetry translation through a mixed-methods, cross-system comparison of dedicated neural machine translation (NMT) engines and a prompted large language model (LLM). Utilizing a curated corpus of 35 contemporary Tibetan poems paired with published Chinese reference translations, we evaluated four translation conditions: Google Translate, Bing Translator, and ChatGPT-4o subjected to two distinct prompt designs. Quantitative evaluation employing BERTScore, COMET-22, and LEPOR captured complementary dimensions of reference-based similarity and linguistic adequacy. The results revealed clear, metric-dependent hierarchies. Bing Translator achieved the highest BERTScore, Google Translate obtained the strongest performance under LEPOR, and ChatGPT-4o with Prompt 1 yielded the best COMET-22 scores. Within the ChatGPT-4o framework, prompt design produced measurable variation; notably, the more stylistically elaborative prompt exhibited reduced reference-based alignment, suggesting an inherent trade-off between rhetorical elaboration and semantic proximity to the reference. Complementing the quantitative metric findings, a qualitative error analysis grounded in Nord’s functionalist typology identified cross-system failure modes. These included numerical misrecognition, loss of symbolic and relational nuance, affective shifts, and disruptions to the text’s metaphorical architecture. Ultimately, these findings highlight both the potential and the prevailing limitations of automated systems for low-resource literary translation, underscoring the critical value of integrating automated metrics with theory-informed qualitative analysis for poetry evaluation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quality assessment of Tibetan–Chinese poetry translation: integrating automated metrics and qualitative insights through a cross-system comparison of dedicated NMT engines and a prompted LLM

  • 期刊论文

  • 发表日期：

    2026年4月29日

查看全文
  • Qiufen Wang,
  • Ying Wang,
  • Mansour Amini,
  • Ming Xian,
  • Ziqiang Fu

摘要

This study investigates Tibetan–Chinese poetry translation through a mixed-methods, cross-system comparison of dedicated neural machine translation (NMT) engines and a prompted large language model (LLM). Utilizing a curated corpus of 35 contemporary Tibetan poems paired with published Chinese reference translations, we evaluated four translation conditions: Google Translate, Bing Translator, and ChatGPT-4o subjected to two distinct prompt designs. Quantitative evaluation employing BERTScore, COMET-22, and LEPOR captured complementary dimensions of reference-based similarity and linguistic adequacy. The results revealed clear, metric-dependent hierarchies. Bing Translator achieved the highest BERTScore, Google Translate obtained the strongest performance under LEPOR, and ChatGPT-4o with Prompt 1 yielded the best COMET-22 scores. Within the ChatGPT-4o framework, prompt design produced measurable variation; notably, the more stylistically elaborative prompt exhibited reduced reference-based alignment, suggesting an inherent trade-off between rhetorical elaboration and semantic proximity to the reference. Complementing the quantitative metric findings, a qualitative error analysis grounded in Nord’s functionalist typology identified cross-system failure modes. These included numerical misrecognition, loss of symbolic and relational nuance, affective shifts, and disruptions to the text’s metaphorical architecture. Ultimately, these findings highlight both the potential and the prevailing limitations of automated systems for low-resource literary translation, underscoring the critical value of integrating automated metrics with theory-informed qualitative analysis for poetry evaluation.