Abstract This article presents an exploratory study based on a Master’s thesis ( Cooper 2024 ) that juxtaposes a small sample of 16 renditions produced by eight translators (four students and four professionals) and eight interpreters (four students and four professionals) using Relevance Theory (RT). Based on the data collected as part of the CLINT (Cognitive Load in Interpreting and Translation) project, the first-time application of Relevance Theory ( Sperber and Wilson 1986/1995 ) to interpreter data in Albl-Mikasa ( 2023 , 2025 ) is continued here by extending the specific RT-based enrichment analysis to translations. Against the backdrop of the spread of English as a lingua franca (ELF) and the consequently growing number of speakers whose first language (L1) is not English, this RT-based enrichment analysis is a newly introduced method used to gauge whether and to what extent translators and interpreters feel a greater need for interventions in the renditions of the (non-L1) ELF original as opposed to the edited (non-ELF) version of that input and thus a need to optimize the ELF source text. Optimization is operationalized as number and quality of enrichments produced by translators and interpreters in their target renditions. The analysis shows a substantially higher enrichment frequency in all ELF as opposed to non-ELF renditions, suggesting an increased effort to optimize non-L1 English input. The results testify to the added value of employing the Relevance Theory framework for a detailed analysis of both translator and interpreter renditions in general as well as their renditions from either ELF or non-ELF source input in particular, and possibly also for machine translation output.
Albl‐Mikasa et al. (Thu,) studied this question.