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The role of interpreters and translators has long been a subject of interest within the field of Translation Studies. This study delves into a relatively underexplored dimension of this domain, focusing on the portrayal of interpreters and translators within the realm of fiction, specifically in serialized drama shows and feature films. The central objective of this study is to investigate the accuracy and specificity with which interpreters and translators are represented in cinematic narratives. Through the analysis of five notable films and serialized shows such as 〈The Interpreter〉, 〈The Terminal〉, 〈The Point Man〉, 〈Herstory〉, and 〈Mr. Sunshine〉, this study identifies and evaluates the nuances of interpreter and translator characterizations, drawing comparisons to established criteria extrapolated from prior research on interpreters in conflict zones and war settings. The findings suggest that while fictional narratives may take creative liberties, the portrayal of interpreters and translators often aligns with realities in history and theory. This alignment between fictional portrayal and actual practice holds significant potential for educational purposes. This research contributes to the growing body of literature within Translation Studies by exploring the fictional turn and its implications for the portrayal of interpreters and translators in movies and dramas.

This is a comparative study of the Chinese original text of The True Origin of All Things (萬物眞原) and its Russian translation Истинный источник всех тварей или Книга о истинном источнике. Penned by Italian Jesuit missionary Aleni in 1628, this doctrinal treatise employs Western medieval theology, Aristotle's natural science, and Western scientific philosophy to demonstrate the formation of heaven and earth and the existence of God. Widely influential from the 16th to the 18th centuries, the book underwent translations into various languages during this period, including Manchurian, Mongolian, and Korean. Notably, the Russian translation in 1791 by Agafonov, a student at the Russian Spiritual Mission in Beijing, remains the sole Western language edition in existence. Delving into such a rare historical text, this study analyzes concepts expressed in Eastern languages by Westerners and their subsequent translations. With reference to Korean-Russian translations, this study investigates aspects of Chinese translations into Russian, across themes of history, concepts, and religion, with the aim of pinpointing instances of paraphrasing and mistranslation, and generating data for cross-linguistic research. This multifaceted study is the first of its kind in Korea, and will serve as a valuable resource for terminology used in Chinese-Russian translation.

This study aims to articulate the contextual awareness and implementation of machine translation in translation of literary text through the analysis of short stories. Utilization of machine translation in literary translation poses difficulties, especially in genres that pose challenges for machine translation, involves difficulties such as distinguishing the speaker‘s voice, recognizing dialogue situations, and translating parts omitted in discourse. For this study, three short stories were selected on the criteria of rapid plot development, frequent changes in narrative voice, and containing instances of omission or ellipsis. This study examines the contextual awareness manifested in the changes in the translated text by comparing the short stories’ source texts, human translations and machine translations. The comparison focuses on how such contextual awareness presents in syntactic variations in translating between Korean-Spanish and Korean-Japanese language pairs. This study serves as an attempt to broaden the scope of machine translation research by delving into the implementation of contextual awareness in machine translation across syntactically diverse yet similar in terms of discourse grammar, language pairs.

The purpose of this paper is to explore the discourse structures of translators' epilogues written by three business book translators. This study arrived at two findings based on the triangulation approach combining semi-structured one-on-one interviews and move analyses on 53 epilogues. First, this study found that publishers prefer source-oriented epilogues that they think could promote the commercial success of translated books. Second, this study found statistically significant differences between the three translators’ epilogues in terms of the proportion of TT-related moves. These two findings strongly support the notion that epilogues are shaped by complex and diverse interactions between the norm in the industry and translators with distinct career backgrounds and capabilities.

A previous study evaluated the quality and productivity of machine translation post-editing (MTPE) for published science-technology articles classified under Reiss’ informative text type. MTPE accuracy was compared against human translation (HT) accuracy in that study. This study succeeds that study with a broader scope of analysis, including a weekly newspaper article presented in a magazine format as another variant of informational text. This broadened effort seeks to identify differences in machine translation (MT) error types across different text types while also providing further supporting evidence for the major findings of the previous study. Based on those identified differences, this study explores how post-editors respond to these errors. Similar to the results of the previous study, the HT group in this study spent approximately 50% more time than the MTPE group, and the average post-editing accuracy of the MTPE group was significantly lower (36.7%) compared to the HT group (84.6%). However, in contrast to the findings of the previous research, where all corrections attempted by post-editors were considered appropriate, some corrections made by post-editors in this study were deemed inappropriate and thus classified as accuracy errors.

This article aims to redefine the role of corpus as that of an intersection between human translation (HT) and machine translation (MT). Contemporary translation studies recognize MT's rapid technological progress, but also its many shortfalls, particularly on the front that it is not translation-actual but mimicry. In this article, we first examine machine translation's history, then the creative differences between human translation, generative AI, and MT. Machine learning for generative AI is based on a corpus, that is a collection of text written by humans. This corpus can also serve as the repository of creative writing for MT to learn from, by means beyond the traditional concept of archival language text collection and machine-imitation. In this way, corpus serves as the intersection between HT and MT, and also as the means to improve the creative capacity of MT.

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.