Aug 2026· Advanced Electromagnetics· Vol 15, pp. 6524-6528· 0 citations
TL;DR
This study proposes a systematic path for corpus construction and translation software optimization tailored to French literature, providing corpus-based technical support for improving translation accuracy, cultural adaptability, and stylistic fidelity in French literary translation.
Abstract
Amid deepening global cultural exchange and rapid advances in translation technology, French literary translation still faces challenges such as metaphor distortion, insufficient literary expression, and inconsistent rendering of culturally loaded terms. Current translation software often fails to balance semantic accuracy and literary quality because of insufficient targeted corpus support and weak adaptation algorithms for literary translation. This study proposes a systematic path for corpus construction and translation software optimization tailored to French literature. A dedicated bilingual parallel corpus is constructed by integrating classic literary works, authoritative translations, and cultural annotations. The corpus contains one million original French words and 800,000 parallel words, covering major literary genres and schools including Classicism, Romanticism, Realism, Modernism, and Existentialism. Based on corpus data, software optimization algorithms are designed across four dimensions: vocabulary alignment, syntactic structure parsing, cultural image mapping, and literary style adaptation. The study provides corpus-based technical support for improving translation accuracy, cultural adaptability, and stylistic fidelity in French literary translation.
It is argued that NLP becomes most valuable for the Iraqi humanities when computational methods are combined with cultural knowledge, philological care, translation ethics, and close literary interpretation.
Tetyana Panyok· Iraqi Literary and Cultural...· 0 citations
Accurate translation of domain-specific terminology is essential for preserving technical semantics and enabling reliable international knowledge exchange in engineering disciplines, including electromagnetic waves, antennas, and propagation, where standardized descriptions and interdisciplinary communication are increasingly important. To address semantic collapse and cultural information loss in the English translation of Chinese silk cultural terms, this study proposes a prompt tuning framework incorporating a cultural parameter decoupling mechanism. A gated attention strategy is introduced to separate cultural features from underlying semantic representations, while a three-dimensional dynamic prompt template and a hierarchical knowledge correction mechanism are developed to preserve historical symbolism, craft characteristics, and customary functions during translation. Experiments conducted on 12,800 Chinese–English parallel corpora demonstrate that the proposed method reduces the cultural mistranslation rate to 6.1% while achieving high robustness against spelling errors, dialect interference, abbreviation ambiguity, and symbol omission. Compared with conventional neural machine translation models, the framework exhibits superior terminology accuracy and cultural semantic fidelity through coordinated optimization of parameter decoupling and knowledge-guided correction. Beyond silk cultural translation, the proposed methodology provides a transferable strategy for preserving specialized semantic information in technical documentation and multilingual knowledge systems, offering potential reference for cross-domain terminology management and intelligent engineering information processing.
Y. M. Zeng· Advanced Electromagnetics· 0 citations
Backround - The rapid advancement of Artificial Intelligence (AI) in translation studies has transformed how literary texts are processed, shifting from literal word-for-word transfer to more contextually nuanced approaches. Literary fables demand particular sensitivity to personification, onomatopoeia, and moral messaging.
Urgency of Research - Despite the proliferation of AI translation tools, previous studies have predominantly focused on macro-level quality evaluation (e.g., fluency and accuracy) rather than examining the micro-linguistic strategies AI employs. There remains a significant gap in understanding how different AI models identify and apply established translation techniques within Molina and Albir's comprehensive framework of 18 translation techniques.
Research Objectives - This study aims to evaluate and compare the performance of four AI tools—ChatGPT, Gemini, Claude, and DeepL—in identifying and applying translation techniques in the literary fable "The Clever Rabbit," specifically examining how each model utilizes Molina and Albir's 18 translation techniques to achieve dynamic equivalence.
Research Method - Adopting a qualitative descriptive approach, this study employs purposive sampling to select translation units demonstrating specific techniques. Data were collected through comparative textual analysis of one English source text and four Indonesian target texts, validated through theoretical triangulation and source triangulation.
Research Findings - The findings reveal a clear strategic polarization: generative AI models (ChatGPT, Gemini, Claude) demonstrate dominance in complex transformation techniques such as Modulation (20-23%), Equivalence (12-13%), and Explicitation, reflecting deeper contextual understanding. In contrast, DeepL shows extreme reliance on Literal Translation (>65%) with minimal cultural or stylistic adaptation. ChatGPT excels in local adaptation through generalization and particularization; Gemini stands out in narrative vitality through expressive lexical variation; and Claude offers structural efficiency through precise grammatical reduction.
Research Conclusion & Novelty - This study concludes that while all AI tools can transfer denotative meaning, generative models (LLMs) are superior in applying high-level translation techniques necessary for maintaining emotional nuance, discourse cohesion, and literary appeal. The novelty lies in its micro-linguistic analysis using Molina and Albir's comprehensive taxonomy across four distinct AI platforms, providing unprecedented insight into the "black box" of AI translation strategies. The findings offer practical guidance for educators, researchers, and translators in selecting appropriate AI tools, emphasizing that critical human post-editing remains indispensable for achieving true literary equivalence.
This paper applied the Transformer model combined with the back-translation strategy module to the machine translation of English literary works. Simulation experiments compared the improved model traditional long short-term memory (LSTM) and Transformer models. The results showed that the improved model had a Bilingual Evaluation Understudy (BLEU) score of 49.9%, a Metric for Evaluation of Translation with Explicit ORdering (METEOR) score of 61.3%, a fluency score of 3.9, a translation accuracy score of 4.2, and a literary score of 4.6, respectively. These findings indicate that the proposed model translates English literary texts more accurately and aligns the translation more closely with Chinese literary style.
Lin Cao· Discover Artificial Intellig...· 0 citations
This study evaluates how web-based machine translation (MT) systems and large language model (LLM) translation agents perform in the English translation of Chinese city publicity texts. City publicity translation is a high-stakes form of institutional intercultural communication: it must be factually accurate, culturally legible, pragmatically appropriate, accessible to international readers, and capable of representing a city image without exaggeration or distortion. A corpus of 90 official Chinese source segments from Qingdao, Xi'an, and Hangzhou was translated under four conditions: DeepL web MT, Google Translate web MT, a GPT-5.5 translation agent, and a DeepSeek V4 Pro translation agent, yielding 360 English translations. Two trained coders independently evaluated all translations on five 1-5 dimensions: accuracy, cultural adequacy, pragmatic appropriateness, audience accessibility, and city image representation. Formal coding showed acceptable to strong reliability: Cohen's kappa for primary issue coding was 0.777, and quadratic weighted kappa values for the five rating dimensions ranged from 0.786 to 0.847. The strongest composite score was observed for DeepL web MT (M=4.683), followed by GPT-5.5 (M=4.599), DeepSeek V4 Pro (M=4.553), and Google Translate (M=4.261). Paired permutation tests showed that DeepL, GPT-5.5, and DeepSeek V4 Pro all significantly outperformed Google Translate on composite quality, while the differences between DeepL and the two LLM agents were not statistically significant. The findings therefore do not support a simple claim that LLM agents uniformly surpass MT. Instead, they suggest that LLM agents can reach a strong MT baseline and may offer pragmatic and audience-oriented affordances, while their value depends on the benchmark system, the target discourse function, and the evaluation dimension.
This study examines word choice problems in literary translations from German into Azerbaijani Turkish in terms of translation theory, cultural transfer, and stylistic structure. A qualitative research method was employed in the study; historical sources, translation theories, and literary works translated from German into Azerbaijani Turkish were analyzed through a comparative textual analysis method. Within the scope of the research, the semantic, aesthetic, and stylistic problems caused by synonyms, antonyms, homonyms, and polysemous words during the translation process were discussed, and the necessity for translators to develop context-appropriate equivalence strategies was demonstrated through examples from the works of writers such as Goethe, Remarque, and Zweig. The findings reveal that success in literary translation depends not only on transferring the literal meanings of words, but also on reconstructing the emotional atmosphere, cultural connotations, and aesthetic structure of the text in the target language. The study emphasizes the importance of contextual and cultural analysis in literary translations between German and Azerbaijani Turkish.
N. I. Huseynova· Baltic Journal of Legal and...· 0 citations