Editorial: Integrating trans-disciplinary methods between physics and linguistics

Kazuya Hayata^1*Andrij Rovenchak²

• ¹Sapporo Gakuin University, Ebetsu, Japan
• ²Nacional'nij universitet L'vivs'ka politehnika, Lviv, Ukraine

examining forward versus reverse reading to detect asymmetries. The methodology is carefully validated using control cases (e.g. ranking of Japanese boy names, passages from a Japanese novel) before being deployed on sixteen human translations of American writer Edgar Allan Poe (1809-1849) and five backtranslations of works by Japanese Nobel laureate Kenzaburō Ōe (大江 健三郎, Ōe Kenzaburō; 1935-2023). The results show that human translations consistently reflect stronger long-tail dominance (see Fig. 1 cited from Supplementary material) and more significant TRA than older machine outputs. But in a diachronic analysis, newer AI translations (as of October 2024) begin approaching human translators on these metrics. The perturbation experiment (injecting repetitive conjunctives) further demonstrates that disrupting long-range correlations degrades these statistical markers and presumably translation "quality."Overall, the paper makes a compelling contribution to the interdisciplinary space between physics/statistics and translation studies. Its strengths lie in methodological rigor, clear motivation, and illustrative case studies. One limitation is that the approach is tested on a narrow pair of languages (English ↔ Japanese) and with one syllabic coding scheme; further generalization would strengthen its applicability. With recent rapid advances in neural machine-translation and newer evaluation metrics (e.g., COMET) [2, 3], Hayata's approach offers an alternative perspective on benchmarking and encourages a fruitful cross-fertilization between physics and linguistics by suggesting a new way to evaluate translation fidelity and style. Lee and Choi investigate the roles of manual and nonmanual features in Korean Sign Language (KSL) prosody, focusing on how they mark prominence and prosodic boundaries. In addition to the conventional speech and written languages, visual aspects of language have become an interesting topic in connection with gesture studies, multimodality, and iconicity [4]. Using Bayesian mixed-effects modeling, Lee and Choi analyzed both articulations across prominence conditions and boundary positions. To date the latter features such as raised eyebrows, widened eyes, head movements, and directed gaze have been identified as key markers of informational focus in many sign languages. The authors found that in contrast to previous findings in Israeli Sign Language [5], where eyebrow movements primarily marked boundaries, KSL signers predominantly used both eyebrow and eye movements as prominence markers. Their finding suggests that prosodic cues are subject to the cross-linguistic as well as crossmodal variation.In addition to the abovementioned two languages, there are many articles relevant to American, British, Dutch, Italian, and Japanese sign languages. In the future, along with an effort to develop powerful physical modellings, comparative studies among the different sign languages will be needed to reveal an unknown feature in the languages.In the context of the physics-informed neural networks, Wu, Zhang, Wu, Wang, Zhou, and Yin developed a method of embedded physical neural networks to construct a framework suitable for modeling the dynamics of public opinion propagation that is based on a set of parabolic-type nonlinear partial differential equations. The prototype of the equations had been presented for explaining propagating plagues [6] As the authors mention explicitly, at present their method includes a few limitations.That is, in the design of the loss function of the neural network, the weights need to be determined empirically by humans. Furthermore, certain noise results in the so-called overfitting problem. There is further work ahead.Zhou & Cheng's article introduces a computational stylometric framework to study the style of translations. Specifically, they compare English renderings of Chinese science fiction by two sources: the acclaimed human translator Ken (Kenneth) Liu (刘宇昆, Liú Yǔkūn; born 1976), a Chinese-American writer, and ChatGPT-4o. Their method relies on Multi-Dimensional (MD) analysis. The central idea is straightforward: a translator's style can be quantified across linguistic scales, such as involvement and density. The authors hypothesize that human translators may exhibit greater stylistic flexibility compared to large language model (LLM) outputs. This work revives classic corpus stylistics traditions [7,8] and applies them directly to the challenge of evaluating AIbased translation, moving beyond standard quality metrics like BLEU [1] or COMET [2].To conduct the research, Zhou and Cheng built a parallel corpus from thirteen sciencefiction works translated by Ken Liu and then retranslated by GPT in five runs each to test the model's consistency. Using the MAT (Multidimensional Analysis Tagger tool [9], they segmented the texts, aligned the segments, and calculated normalized frequencies for 67 linguistic features to generate MD scores. Statistical tests (e.g. Kruskal-Wallis, χ²) then compare within-translator variation (across works) and between-translator variation (Ken Liu versus GPT). The findings indicate that Ken Liu shows substantially more stylistic adaptation across works, whereas GPT maintains a more uniform style; this mainly concerns Dimension 1 (involvement) and less so on Dimension 2 (information density). Functional analysis suggests Liu's minimalist style enhances resonance in science-fiction themes, while GPT's more narrative clarity may smooth over thematic depth.The study contributes to comparative stylistics in the age of AI by offering a method to isolate translator style and contrast it with algorithmic consistency. Naturally, the work has limitations: it focuses exclusively on one narrow genre (science fiction), the MD analysis might not capture highly sophisticated rhetorical or semantic artistry, and there is currently no reader reception data to link these stylistic differences to actual reader impact. As translation AI and its evaluation advance, the authors suggest integrating semantic, pragmatic, and reception dimensions into future research.