Optimising non-native-speaker source texts : a qualitative Relevance Theory-based account of interpreters’ renditions

This chapter is a follow-up paper to Albl-Mikasa (2023), which for the first time applied Relevance Theory to English as a lingua franca, or ELF, and ELF interpreting. It provided a quantitative Relevance Theory-based analysis of 56 renditions by 28 professional interpreters of two different texts – one more general and one more technical –, each available in an original version produced by a non-native speaker of English and a version edited to conform to native speaker conventions. Interpreters’ enrichments were counted for the Relevance Theory-specific categories of reference assignment, disambiguation, neutralization, general enrichment, filling in elliptic gaps, specification and supplementing cohesion. The results showed that the interpreters engaged in a considerable amount of enrichment activity in their effort to support target speech listeners in their recovery of the source speech propositions and that the number of such enrichments was significantly higher in the renditions of the ELF as opposed to the edited version of each text. In this follow-up paper, that same data is analysed qualitatively with a view to illustrating interpreters’ optimizations efforts (optimization being operationalized as enrichments supplied in the renditions with a potential of maximizing relevance for the listeners) as well as possible differences between working from ELF as opposed to non-ELF source texts.