This paper sets out to investigate the coexistence of two different versions of explicitation in translation studies, which, with reference to Chesterman’s distinction between S-universals and T-universals, are called S-explicitation and T-explicitation in the first part of this article. Following a brief survey of the major strands in explicitation research, the specific characteristics of S-explicitation and T-explicitation are discussed. By tracing the development of the explicitation concept from its origins in Vinay and Darbelnet’s comparative stylistics to its widespread application in corpus-based translation studies, the circumstances leading to the emergence of T-explicitation are identified and it is shown that T-explicitation has developed in the wake of the more general paradigm shift from source-text orientation to target-text orientation. Looking at the issue from the conceptual side, several arguments for a profound conceptual difference between S-explicitation and T-explicitation are then laid out. The terminological implications of subsuming the two concepts under a common designation are discussed and it is argued that, after all, T-explicitation is not a form of explicitation proper but rather a form of comparative explicitness, since it lacks the necessary criterion of translational intertextuality and thus falls outside the cognitive reality and the translational action of the translator.
This paper is concerned with the following question: to what extent does neural machine translation (NMT) – a relatively new approach to machine translation (MT), which can draw on richer contextual information than previous MT architectures – perform explicitation shifts in translation and how are these shifts realised in linguistic terms? In order to answer this question, the paper attempts to identify instances of explicitation in the machine-translated version of a research report on carbon dioxide capture and storage. The machine-translated text was created using the publicly available generic NMT system DeepL. The human translation of the research report was analysed in a prior research project for instances of explicitation and implicitation (Krüger 2015). After a brief quantitative di scussion of the frequency and distribution of explicitation shifts identiﬁed in the DeepL output as compared to the shifts identiﬁed in the human translation of the research report, the paper analyses in detail several examples in which DeepL performed explicitation shifts of various kinds. The quantitative and qualitative analyses are intended to yield a tentative picture of the capacity of state-of-the art neural machine translation systems to perform explicitation shifts in translation. As explicitation is understood in this article as an indicator of translational text–context interaction, the explicitation performance of NMT can – to some extent – be taken to be indicative of the “contextual awareness” of this new MT architecture.