Abstract
This study explores Machine Translationese (MTese) -- the linguistic peculiarities of machine translation outputs -- focusing on the under-researched English-to-Chinese language pair in news texts. We construct a large dataset consisting of 4 sub-corpora and employ a comprehensive five-layer feature set. Then, a chi-square ranking algorithm is applied for feature selection in both classification and clustering tasks. Our findings confirm the presence of MTese in both Neural Machine Translation systems (NMTs) and Large Language Models (LLMs). Original Chinese texts are nearly perfectly distinguishable from both LLM and NMT outputs. Notable linguistic patterns in MT outputs are shorter sentence lengths and increased use of adversative conjunctions. Comparing LLMs and NMTs, we achieve approximately 70% classification accuracy, with LLMs exhibiting greater lexical diversity and NMTs using more brackets. Additionally, translation-specific LLMs show lower lexical diversity but higher usage of causal conjunctions compared to generic LLMs. Lastly, we find no significant differences between LLMs developed by Chinese firms and their foreign counterparts.
Abstract (translated)
这项研究探讨了机器翻译语（Machine Translationese，简称MTese）——即机器翻译输出中的语言特征。重点是较少被研究的英语到中文的语言对，在新闻文本中进行分析。我们构建了一个包含4个子语料库的大数据集，并使用全面的五层特征集合。然后，在分类和聚类任务中应用卡方排名算法来进行特征选择。 我们的发现证实了在神经机器翻译系统（NMTs）和大型语言模型（LLMs）中都存在MTese现象。原始中文文本几乎可以被完全区分为来自这两种系统的输出。值得注意的是，机器翻译的输出具有较短的句子长度以及更频繁地使用对立连词。 将LLMs与NMTs进行比较时，我们实现了大约70%的分类准确性。LLMs表现出更大的词汇多样性，而NMT则更多地使用括号。此外，专门用于翻译任务的LLM显示较低的词汇多样性但因果连词使用的频率更高，相比通用型LLM而言。 最后，我们没有发现中国公司开发的大型语言模型与其外国同行之间存在显著差异。
URL
https://arxiv.org/abs/2506.22050
