Jiang Zeyuan
School of Foreign Languages, Qingdao University, Qingdao, Shandong, China.
PLoS One. 2025 Jun 2;20(6):e0324830. doi: 10.1371/journal.pone.0324830. eCollection 2025.
This study examines the presence of simplification, a translation universal (TU), in English-to-Chinese translation by comparing the Mean Dependency Distance (MDD) and Mean Hierarchical Distance (MHD) of Crowdsourcing human translations, Large Language Model (LLM) translations, and original Chinese texts across fifteen genres. Through analysis of three balanced comparable corpora, the research found that: (i) Compared to original Chinese texts, both human-translated and LLM-translated Chinese texts demonstrated significant syntactic simplification across all genres. (ii) Human translations exhibited a more pronounced tendency toward syntactic simplification than LLM translations across all genres. These findings not only validate the simplification hypothesis at the syntactic level but also highlight the different cognitive and processing mechanisms underlying human and LLM translation processes. The research indicates that human translators possess an active ability to optimize complex syntax that current LLMs lack, providing valuable reference for future development of LLMs and methods for LLM-assisted translation. Additionally, by adopting MDD and MHD as holistic measures of syntactic complexity, this study offers new perspectives for TU research and provides empirical insights into the linguistic nature of crowdsourcing translations from an English-to-Chinese perspective.
本研究通过比较众包人工翻译、大语言模型(LLM)翻译以及十五种体裁的中文原文的平均依存距离(MDD)和平均层级距离(MHD),考察了翻译共性(TU)中的简化现象在英译汉翻译中的存在情况。通过对三个平衡可比语料库的分析,研究发现:(i)与中文原文相比,人工翻译和LLM翻译的中文文本在所有体裁中均表现出显著的句法简化。(ii)在所有体裁中,人工翻译比LLM翻译表现出更明显的句法简化倾向。这些发现不仅在句法层面验证了简化假设,还突出了人工翻译和LLM翻译过程背后不同的认知和处理机制。研究表明,人工译者拥有优化复杂句法的主动能力,而目前的大语言模型缺乏这种能力,这为大语言模型的未来发展和LLM辅助翻译方法提供了有价值的参考。此外,通过采用MDD和MHD作为句法复杂性的整体度量,本研究为翻译共性研究提供了新的视角,并从英译汉的角度为众包翻译的语言性质提供了实证见解。