Abstract
This study focuses on evaluating the performance of machine translations (MTs) compared to human translations (HTs) in English-to-Chinese children's literature translation (CLT) from a stylometric perspective. The research constructs a Peter Pan corpus, comprising 21 translations: 7 human translations (HTs), 7 large language model translations (LLMs), and 7 neural machine translation outputs (NMTs). The analysis employs a generic feature set (including lexical, syntactic, readability, and n-gram features) and a creative text translation (CTT-specific) feature set, which captures repetition, rhythm, translatability, and miscellaneous levels, yielding 447 linguistic features in total. Using classification and clustering techniques in machine learning, we conduct a stylometric analysis of these translations. Results reveal that in generic features, HTs and MTs exhibit significant differences in conjunction word distributions and the ratio of 1-word-gram-YiYang, while NMTs and LLMs show significant variation in descriptive words usage and adverb ratios. Regarding CTT-specific features, LLMs outperform NMTs in distribution, aligning more closely with HTs in stylistic characteristics, demonstrating the potential of LLMs in CLT.
Abstract (translated)
这项研究重点评估机器翻译（MT）与人工翻译（HT）在从英语到中文的儿童文学翻译（CLT）中的表现，采用的是文体测量学的方法。研究构建了一个《彼得潘》语料库，包含21个译本：7个人工译本（HTs），7个大型语言模型译本（LLMs），以及7个神经机器翻译输出（NMTs）。分析采用了通用特征集（包括词汇、句法、可读性和n-gram特征）和创意文本翻译（CTT特定）的特征集，后者捕捉重复性、节奏感、可译性和杂项水平，总共产生了447种语言特征。通过机器学习中的分类和聚类技术，我们对这些翻译进行了文体分析。 结果显示，在通用特征方面，HTs与MTs在连词分布和1-gram词汇比例上存在显著差异；而在NMTs与LLMs之间，则是在描述性词语的使用和副词比率上的显著变化。就CTT特定特征而言，LLMs的表现优于NMTs，并且其风格特性更接近于HTs，这表明LLMs在CLT中具有潜力。
URL
https://arxiv.org/abs/2506.22038
