Translation Quality of Chinese Diplomatic Discourse by NMT and LLMs Based on Multidimensional Quality Metrics
Keywords:
Neural Machine Translation (NMT), Large Language Models (LLMs), Translation of Chinese Diplomatic Discourse, Translation Quality AssessmentAbstract
This research studies the translation of Chinese diplomatic discourse. In particular, it studies the statement of Lin Jian, the spokesman of the Chinese Foreign Affairs Ministry, on the incident of tariffs in 2025. For this analysis, the reference translations are on the official English website of China Daily what are published as their translation. According to a research study, the paper studies the translation performance of four NMT systems, with NiuTrans, Youdao Translate, Google Translate and DeepL. At the same time, the translation performance of four LLMs, Deepseek, ERNIE 4.5 Turbo, GPT-4.0, and Gemini is also studied. These translation performances are tested for a commercial machine translation system, which is usually used to translate Chinese diplomatic discourse. The results show that Large Language Models (LLMs) appear to have a stronger potential than Neural Machine Translation (NMT) systems, especially regarding cultural metaphors and abstract concepts. Despite their strengths, NMT systems and LLMs have notable shortcomings. This refers to misunderstanding concepts, not including information and generating low-quality language. In the future, more studies could explore translation strategies to promote more accurate translations. Thus, helping improve communication between parties.
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