Abstract Syntactic complexity poses significant challenges in simultaneous interpreting (SI), affecting both the process and product and requiring interpreters to cope using various tactics. The role of subject length in English-Arabic SI has received little attention. This study investigates the correlation between SL subject length and structural choices in Arabic SI. A bilingual corpus of authentic English conference speeches and their professional Arabic SIs was analyzed. The results revealed a tendency to mirror English subject–verb–object (SVO) structures by opting for the Arabic marked SVO pattern instead of the more typical verb–subject–object (VSO) order. Subject length was found to be a reliable predictor of structural choice, with a weak but statistically significant correlation indicating that longer subjects slightly increase the likelihood of selecting SVO. This pattern suggests that structural alignment is influenced by automatic priming tendencies interacting with strategic load management, with clear implications for interpreter training and professional practice.
This case study uses microanalysis to explore how cognitive overload regarding numbers causes errors in a professional interpreter’s authentic simultaneous interpretations. The interpreter rendered impromptu speeches — interviewees’ long answers to interviewers’ questions — between English (B) and Mandarin (A) in the simultaneous mode in conference settings. The microanalysis involves local analysis of adjacent simultaneous interpretations of source speech segments featuring numbers. The results revealed that factors such as increased total processing capacity requirements associated with numbers, features of spontaneous speeches, interpreters’ inappropriate allocation of available processing capacity, syntactic restructuring, and excessively long processing time jointly caused cognitive overload in simultaneous interpreting (SI), which in turn led to inaccurate renditions of numbers and their neighbouring elements. Additionally, the interpreter employed strategies to cope with number-related cognitive overload such as making conscious strategic omissions and using short processing time for numbers. Microanalysis has proven useful for investigating cognitive load in SI.
Artificial intelligence (AI) has significantly improved the efficiency of translation and interpreting (T&I), yet its cognitive and psychological impacts remain underexplored. To address this, 299 Chinese translators and interpreters were surveyed, and interviews were conducted with 12 respondents, focusing on mental workload, anxiety, adaptation, and professional well-being. The results show that AI adoption reduces mental workload and improves professional well-being, but also increases the anxiety induced by AI use. However, this anxiety does not directly affect professional well-being. Effective adaptation strategies and continual training are essential for optimizing AI’s benefits. While AI streamlines initial T&I tasks, it increases cognitive demands during post-editing. These findings emphasize the need for a human-centered approach that balances AI’s efficiency gains with its negative cognitive effects, so as to foster a collaborative relationship between T&I professionals and AI.
We investigated how translation expertise and untrained bilingualism modulate the retrieval of translation equivalents of idiomatic units across languages. We employed a dual task paradigm to assess the cognitive resources that untrained bilinguals and professional translators engaged to process idioms for later translation. Our hypothesis was that translators would be able to map idioms cross-linguistically in a relatively more automatic way than untrained bilinguals, based on the idea that comprehension and retrieval processes become faster and more automatic because of training in translation tasks. This automaticity presumably plays a key role in the allocation of task-relevant cognitive resources. Results did not match our predictions and showed that translators exerted higher levels of cognitive control over the task, possibly to guarantee the high-quality standards required by professional practice. Findings are discussed considering theoretical models of bilingual idiomatic processing, implications for the translation process and translation pedagogy.
This study explores coaching as a pedagogical framework to enhance translation learners’ competence development through a problem-solving lens. Grounded in three core assumptions — translation essentially as a complex, iterative process of resolving ill-structured problems; translation competence as an ill-structured problem-solving competence with translation thinking abilities as its core; and the necessity of cultivating learners’ translation thinking and facilitating their translation learning processes to promote translation competence development and acquisition — the research proposes an interconnection model integrating constructivism, complexity theory, and metacognitive self-regulation. To operationalize this framework, two sub-models are introduced: (1) the Translation Problem-Solving Coaching (TPSC) model, which guides learners through structured cycles to develop adaptive thinking; and (2) the Student-Centred Learning Process Coaching (SCLPC) model, which supports learners in aligning their learning processes with goals. Case-based validation illustrates how coaching scaffolds cognitive and metacognitive growth. Implications for translation pedagogy are discussed.
