Hayata Kazuya
Sapporo Gakuin University, Ebetsu 069-8555, Japan.
Entropy (Basel). 2025 Sep 20;27(9):984. doi: 10.3390/e27090984.
To date, the ordinal pattern-based method has been applied to problems in natural and social sciences. We report, for the first time to our knowledge, an attempt to apply this methodology to a topic in the humanities. Specifically, in an effort to investigate the applicability of the methodology in analyzing the quality of texts that are translated into a language preserving the so-called vowel harmony, computed results are presented for the metrics of divergence between the back-translated and the original texts. As a specific language we focus on Japanese, and as metrics the Hellinger distance as well as the chi-square statistic are employed. Here, the former is a typical information-theoretical measure that can be quantified in natural unit, nat for short, while the latter is useful for performing a non-parametric testing of a null hypothesis with a significance level. The methods are applied to three cases: a Japanese novel along with a translated version available, the Preamble to the Constitution of Japan, and seventeen translations of an opening paragraph of a famous American detective story, which include thirteen human and four machine translations using DeepL and Google Translate. Numerical results aptly show unexpectedly high scores of the machine translations, but it still might be too soon to speculate on their unconditional potentialities. Both our attempt and results are not only novel but are also expected to make a contribution toward an interdisciplinary study between physics and linguistics.
迄今为止,基于序数模式的方法已应用于自然科学和社会科学领域的问题。据我们所知,我们首次尝试将这种方法应用于人文学科的一个主题。具体而言,为了研究该方法在分析翻译成保留所谓元音和谐语言的文本质量方面的适用性,给出了回译文本与原文之间差异度量的计算结果。作为一种特定语言,我们聚焦于日语,并采用海林格距离以及卡方统计量作为度量。在此,前者是一种典型的信息理论度量,可自然单位(简称为奈特)进行量化,而后者对于在显著水平下对原假设进行非参数检验很有用。这些方法应用于三个案例:一部有译本的日本小说、日本宪法序言以及一篇著名美国侦探小说开篇段落的十七个译本,其中包括十三个人工翻译以及使用DeepL和谷歌翻译的四个机器翻译。数值结果恰当地显示出机器翻译的得分出奇地高,但推测它们的无条件潜力可能还为时过早。我们的尝试和结果不仅新颖,而且有望为物理学和语言学之间的跨学科研究做出贡献。