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This study compares the English and Japanese translations of The Vegetarian (Part I) by Han Kang, focusing on how translation strategies differ according to high-context and low-context cultural frameworks. The analysis centers on three scenes—Yeong-hye’s physical description, her husband’s self-characterization, and the “Dreams of murder” passage—to identify how each translation negotiates cultural and interpretive shifts. In the English version, the translator employs a range of explicitation strategies to accommodate low-context readers, manifested in four main types: explicitation with deletion, judgmental insertion, mistranslation, and addition. These strategies often restructure the original’s implicit tone, emotional ambiguity, and narrative restraint into clarified and interpretively guided expressions, leading to semantic and stylistic shifts. In contrast, the Japanese translation demonstrates high fidelity, preserving the original’s indirectness, lexical nuance, and affective texture in line with high-context communication norms. This comparative analysis shows that literary translation is not merely linguistic transfer but a culturally embedded act of interpretive reconstruction. The study concludes by emphasizing the pedagogical implications of training translators to recognize contextual asymmetries, navigate the ethical boundaries of interpretation, and maintain the tonal integrity of high-context narratives.

This study examines whether, when using the generative AI ChatGPT for literary translation, training it in specific habitus and strategies to construct a translator persona enables the model to move beyond its tendency toward literal translation and instead produce creative renderings aligned with that persona. To explore this, the paper focuses on Deborah Smith’s controversial translation of The Vegetarian, which sparked debate for employing a distinct habitus and feminist translation strategies that produced a work differing noticeably fromthe original. Before the habitus and strategy training, ChatGPT adhered closely to the source text in its translations; after the training, however, it generated outputs that diverged fromthe original according to the specific strategies, and these results were found not to differ significantly fromSmith’s translation. In addition, by investigating and applying prompt-design techniques, this study explores ways to employ AI as an IA tool for literary translation.

This study explores the stylistic characteristics of literary translation using distant reading methods, focusing on two Korean translations of Ernest Hemingway’s The Old Man and the Sea. By dividing the translations into ten segments and applying POS tagging, the study analyzes lexico-syntactic features such as lexical distribution, lexical diversity, and average sentence length. As a more distant reading, statistical tools such as correspondence analysis (CA) and document similarity are applied to examine stylistic consistency and divergence between translators. The findings demonstrate that each translator maintains a distinct stylistic cluster, with varying degrees of lexical density and cohesion, particularly during climactic segments of the narrative. These results correspond to the translators’ stated intentions and paratextual commentary, suggesting that stylistic choices in translation are not only reflective of individual voice but also strategically aligned with the narrative arc. This study illustrates the potential of distant reading as a robust method for translation stylistics and suggests new directions for empirical translation criticism.

This study aims to analyze police interpreters’ note-taking patterns, methods, and their correlation with interpreting accuracy. Data were collected from 12 Russian-speaking police interpreters, including interpreting outputs and corresponding note-taking samples. The analysis yielded the following findings. First, the amount of note-taking varied by source language. Interpreters tended to take more notes when working from their native language, though individual approaches varied greatly. Some produced structured, consistent notes, while others recorded selectively based on personal judgment. Second, interpreters’ choice of note-taking language depended on the source language. When working from Korean, interpreters preferred using Korean in their notes, reflecting efficiency in notation. When interpreting from Russian, they used both Korean and Russian, suggesting an effort to balance efficiency with cognitive processing. Third, a negative correlation was found between note-taking rate and omission rate across both language directions. Although not statistically significant, this suggests that more active note-taking may contribute to improved accuracy.

This study is a comparative analysis of translated-text error-rates in song lyrics by K-Pop group New Jeans. Seven non-official machine translations (MTs) of ten songs were analyzed against official human-translated lyrics. The ten songs were consisted of a total 235 segments and the seven MTs were categorized under neural-network types (DeepL, Papago, Google Translate) and generative-AI types (ChatGPT, Bard, ClovaX, MS Bing Translate). Analysis discovered three salient points. First, neural-network types presented significantly higher error rates than generative-AI types. DeepL (66%), Papago (64%), Google Translate (59%). The most common errors were semantic and grammatical. A common feature of the errors was the poor contextual understanding and consistency between consecutive segments. This suggests that neural network MTs may have limited application for translating K-pop lyrics, which are expressive text. Second, neural network MTs were twice as erroneous as generative AI translations, with the official human translation as the baseline. T his suggests that AI translation may be more useful for translating K -pop lyrics than neural network MT in terms of semantic accuracy an d structural form. Third, generative AI translation quality improved in general when additional parameters and descriptions were provided via the services’ chat functions.

In this study, we carried out a series of experiments to explore how ChatGPT (version 4o) evaluated Korean-English translations. Using two datasets of human translations (n=57) and two datasets of post-edited translations (n=56), all drawn from Lee and Lee (2021), we adopted two evaluation approaches with strict prompt control. In Experiment A, ChatGPT rated the four datasets freely on a five-point scale without specific criteria. In Experiment B, which was conducted concurrently with Experiment A, ChatGPT rated the same datasets using a prescribed, criterion-referenced five-point scale. To assess intra-rater reliability, we repeated both experiments one month later. This study yielded both quantitative and qualitative findings, including the following: (1) ChatGPT’s average scores differed significantly from those of human raters; (2) correlations between human and ChatGPT scores ranged from ‘moderate’ to ‘strong’; (3) the use of the prescribed rating scale improved ChatGPT’s reliability as a rater; (4) ChatGPT exhibited very low intra-rater reliability; and (5) ChatGPT’s self-justifications for its ratings varied in quality, often failing to identify obvious errors.

This study compares how Papago, Google Translate, and ChatGPT perform when translating police dialogues, using BLEU scores, manual assessment, and error analysis. The analysis results are as follows. All three tools showed low BLEU scores compared to reference translations. In manual evaluation, ChatGPT significantly outperformed others, scoring 8.6 out of 10, compared to Google Translate's 6.5 and Papago's 5.6. Error analysis confirmed ChatGPT's superiority, with only 41 errors, while Google Translate and Papago produced 123 and 152 errors respectively. Across all tools, substitution was the most common accuracy error, followed by omission and addition. These results suggest that ChatGPT is the most reliable tool for police communication with foreign nationals.

This study aims to evaluate the quality of AI translation in comparison to human translation, identify challenges in applying metaphor translation methods to AI-generated texts, and examine the relationship between translation quality and the strategies employed. The analysis delves into the Korean translation of metaphorical expressions from the Chinese literary work Dawn Blossoms Plucked at Dusk, utilizing outputs from Google Translate and ChatGPT. The initial step of this study involved the categorization and statistical analysis of translation errors generated by AI. The findings show that literal translations and omissions were relatively infrequent, whereas distortions of meaning and stylistic issues were more prevalent. Furthermore, this study revealed that the types and frequency of errors were reduced when AI translation employed the same methods as human translation. It is anticipated that the findings of this study will serve as foundational data for future research. However, the study is constrained by limited data and a lack of diversity, which calls for further investigation to overcome these limitations.

This study explores gender neutrality in AI-based machine translation (MT), focusing on the translation of the Korean third-person singular gender-neutral expression “그 애” (that person) into English by three widely-used AI-based translation models: GPT-4o, Gemini 2.5 Flash, and DeepL. Using a parallel corpus extracted from the Korean novel Concerning My Daughter (Kim Hye-jin, 2017), the analysis compares AI-generated translations with human translations under conditions lacking contextual gender cues. Results indicate significant differences in gender marking strategies among models. GPT-4o and DeepL frequently added gender-specific pronouns (e.g., “she,” “he”) absent from the original text, while Gemini preserved gender neutrality but utilized overly formal and unnatural expressions (e.g., “that person”) for literary contexts. Additionally, human translators maintained neutrality through diverse strategies, including proper nouns. This research highlights critical ethical issues in AI-based MT regarding gender bias and suggests future research to enhance translation quality and gender neutrality in creative texts.

This study analyzes the evolution of AI translation technology from the perspective of the philosophy of language. AI translation has advanced to a stage where it infers context and meaning through NMT. This evolution has triggered a philosophical debate and fear regarding whether AI can encroach on the domain of pragmatics, including human intention and emotion. This debate is broadly divided into two main viewpoints: anti-AI and pro-AI. Meanwhile, Quine and Wittgenstein view meaning not as a fixed entity, but as something determined by usage, context, and communal consensus. This perspective is analogous to AI, however, and AI lacks deep consensus and social practice of a human community, thus showing limitations in translating pragmatic discourse that goes beyond the straightforward narrative level. Beyond these technical limitations, a critical challenge is that AI translation faces major hurdles: algorithmic bias, which amplifies social prejudices inherent in the training data, and linguistic homogenization, which threatens the diversity of minority languages. As a methodical solution, this article proposes defining AI translation as a distinct 'sociolect' (or 'social language') with its own unique rules and characteristics.