Earticle

This study investigates the qualifications, competencies, and roles required of translation project managers (PMs) based on the analysis of 67 job advertisements. The results show a strong demand for PMs in the localization of cultural content, with Language Service Providers (LSPs) as the primary employers. While many content PM positions did not specify academic requirements, LSP positions more often required at least a bachelor’s degree. Career experience was commonly expected for content PMs, whereas LSP positions were frequently accessible to applicants without prior professional backgrounds. The majority of advertised positions were permanent, with remote work options widely offered. Among the competencies, foreign language proficiency was prioritized, followed by subject-matter expertise, communication and psychophysiological skills, instrumental competence, translation project management, translation and revision skills, and problem-solving ability. The role most often mentioned was project management, followed by internal communication, customer feedback, quality control, translation and revision, documentation, and marketing. These findings suggest that project management offers promising opportunities for translation students and highlight the need for further research to guide the development of relevant educational programs.

This study explores the potential impact of translation and interpreting (T&I) courses on university students’ metacognitive development, with a particular focus on how such courses influence learners’ awareness and regulation of their learning processes. To examine this, two surveys were administered: the Metacognitive Awareness Inventory (MAI) was used to measure students’ general metacognitive awareness at the beginning and end of the semester, while a separate perception-based survey developed by the author was conducted to investigate how students perceived the T&I course’s influence on their English learning. The MAI results showed no statistically significant change in overall scores but a strong upward trend in students’ metacognitive awareness was found, particularly in the Regulation of Cognition category and a statistically significant improvement in the ‘Information Management Strategies’ subcomponent. The perception-based responses further revealed that students developed heightened awareness of their individual weaknesses and a growing tendency to evaluate and adjust their learning behaviors accordingly. This suggests that T&I education helps develop higher-order cognitive abilities and self-regulated learning attitudes. These findings demonstrate the pedagogical potential of T&I training to promote metacognitive growth in language learners, and to support their cognitive engagement and learner autonomy.

This study examines the quality of machine translation post-editing (MTPE) for Korean-Chinese legislative texts and proposes an evaluation approach appropriate for legal translation. A total of 109 sentences from the Enforcement Decree of the Framework Act on Administrative Regulation were analyzed by comparing GPT-4-generated translations with MTPE outputs produced by Chinese-native doctoral students in translation studies. The study investigates the correlations between expert-based human evaluation and automatic metrics such as BLEU, METEOR, BERTScore, COMET, and HTER to assess whether these metrics adequately reflect MTPE quality improvement. The results show that automatic metrics correlate only moderately with human evaluation, with METEOR exhibiting the highest correlation. BLEU revealed structural limitations in capturing semantic accuracy in legislative texts. Additionally, HTER showed negative correlations with both human and automatic evaluation, indicating that greater editing effort does not necessarily improve translation quality. These findings suggest that the structural characteristics of legislative texts and the limited legal expertise of MTPE performers influence evaluation outcomes. The study concludes that a multifaceted evaluation framework combining automatic and human assessments is necessary to ensure reliable MTPE quality evaluation for Korean-Chinese legislative translation. The results provide empirical evidence for refining assessment models tailored to the genre-specific features of legislative texts.

This study examines three emerging concepts—‘hypertraduction’, ‘超訳 (chouyaku)’, and ‘초월번역 (chowolbeonyeok)’—which collectively reflect the notion of “beyond translation” and remain absent from official lexicons. Positioned within the context of a post-translation era, these neologisms challenge conventional definitions of translation and reveal a significant gap between academic conceptualizations of translation and its practical and popular interpretations. Through a comprehensive review of relevant literature, the research traces the origins and semantic dimensions of these terms and situates them in relation to similar concepts. Furthermore, the study explores the broader social, educational, and scholarly implications of these concepts, and proposes strategic approaches to address the dilemmas faced by translators in the age of AI.

Interpreter training programs in Korea are usually taught by practitioners with extensive field experience, who often lack pedagogical training and theoretical knowledge in interpreting studies. As a result, they may experience difficulties in their instructional activities. Therefore, there is a growing need for systematic training programs for interpreter trainers. This study explored interpreter trainers’ views on essential competencies of interpreter trainers and investigated their needs for trainer training. Five early-career English-Korean interpreting instructors at a graduate school of interpretation and translation in Korea participated in the study. Data were collected through a questionnaire and a focus group interview. The analysis showed that participants recognized the importance of interpreting competence, course organizational skills, and instructional skills. However, their awareness of the value of theoretical knowledge in interpreting studies, contexts of interpreter training, and educational tools and resources was relatively low. The participants expressed desire to improve their skills in providing effective feedback and conducting fair and objective assessment. They also hoped to share experiences and challenges with fellow trainers and to discuss the direction of interpreter training.

This study explores the challenges associated with light post-editing (LPE) in translation practice and training. It first reviews existing definitions and guidelines for both full and light post-editing, alongside previous research on the difficulties posed by LPE. Building on this foundation, three rounds of small-scale experiments were conducted within an action research framework to generate empirical insights and inform pedagogical recommendations. The findings highlight fundamental differences between full and light post-editing, revealing that transitioning to LPE remains highly challenging and that vague guidelines hinder students from consistently meeting its requirements. The study highlights the need for targeted interventions, such as clearer guidelines and gradual transition planning, when opting for light post-editing training. It also argues that, given LPE’s reliance on outdated assumptions, the rigid ‘light versus full’ dichotomy should be replaced with a more flexible, context-driven approach.

The rapid advancement of AI and its expanding application in translation are bringing profound changes to translation education. However, the perspectives of instructors and learners, particularly those engaged in the professional translation training, remain underexplored in this shifting landscape. This qualitative study investigates their experiences in the evolving translation environment and their perceptions of translation education in the AI era through semi-structured interviews. The findings reveal that both groups acknowledge AI as a major force reshaping the field, while differing in their views on its implications for human translators and translator training. Learners tended to feel more intimidated by AI and often underestimated the human advantage in translation. Both groups, however, agreed that the ability to translate manually without AI support remains a fundamental prerequisite for translator training. Yet, they diverged in their views on pedagogy: Instructors emphasized text-based analysis supported by AI tools, whereas learners preferred more practical, hands-on training. This gap highlights the need for pedagogical models that integrate both perspectives, ensuring that translation education remains grounded in core translation skills while also reflecting learners’ demand for AI-supported practical training.

This study revisits George Steiner’s After Babel(1975) to evaluate translation in the age of artificial intelligence(AI). Using Steiner’s hermeneutic motion—trust, invasion, incorporation, and reciprocity—the research examines how understanding is reshaped when translation is produced by AI systems. Paul Ricoeur’s linguistic hospitality and Antoine Berman’s ethics of alterity provide an ethical lens for assessing AI translation. Three passages from Floridi’s The Logic of Information(2019) were analyzed through a qualitative comparison of human, AI(DeepL and ChatGPT), and post-edited translations. Results show that AI can mimic the structural stages of hermeneutic motion but cannot realize reciprocity, which requires intentional and ethical engagement. Human translation maintains this reciprocity, and post-editing partially restores it. The study concludes that AI’s limitations stem from systemic constraints rather than linguistic factors. Translation in the AI era must therefore be redefined as an ethical practice of meaning restoration.

This study quantitatively examines stylistic differences between human and machine translation in literary texts. A parallel corpus was constructed from five source languages-German, Arabic, Indonesian, Japanese, and Chinese-each translated into Korean by professional human translators and two machine translation systems, Google Translate and Gemini. Using five machine learning algorithms (Decision Tree, Random Forest, XG-Boost, t-SNE, and PCA), we analyzed a range of stylometric features to determine the separability of human and machine outputs. Our results show that human and machine translations are highly distinguishable, with classification models achieving over 90%accuracy. Gemini’s stylistic profile aligned more closely with that of Google Translate than with human translators. The analysis revealed comma ratio as the strongest discriminator, followed by sentence count and pronoun ratio. These findings provide empirical evidence of a persistent stylistic gap between human and machine translation in the literary domain and identify specific linguistic features that mark this divide.

The rapid advancement of digital technologies and the transformation of global markets have reshaped the competences expected of translators. In China, one of the world’s largest translation markets, these shifts are reflected in the competence requirements articulated by employers. This study investigates market expectations by examining 3,380 job advertisements collected from two leading Chinese recruitment platforms using a web crawler. The data were analyzed through content analysis, frequency counts, keyword extraction, and word cloud visualization. The findings show that employers place the strongest emphasis on three areas: bilingual competence, interpersonal competence, and instrumental competence. Commonly used tools include office software, CAT tools, knowledge management platforms, and search engines, which are primarily employed to improve translation efficiency and to facilitate the systematic collection and management of terminology and information. This highlights the growing expectation that translators combine linguistic expertise with technological literacy and soft skills. By providing empirical evidence of evolving professional demands, this study contributes to a more nuanced understanding of the translation profession in China. It also carries pedagogical implications, underscoring the need for translation education to align more closely with market realities and to prepare students as adaptable and competitive professionals in a changing global environment.