Predicting Cognate Translation

Abstract

Empirically-based translation research has so far been developed within two major self-standing approaches: corpus-based work on properties of translated texts or translation universals (product) and experimental studies of translators’ expert performance (process). Recently, advances in corpus architecture and multi-level corpus querying are combined with methods from psycholinguistics and cognitive science in order to determine predictors for translation candidate probabilities, which in turn may range from free to literal translation solutions. In the corpus-based realm, free translations lead to normalization effects, whereas literal ones trigger shining-through. Speaking from a cognitive point of view, shining-through can be related to the literal translation hypothesis, while normalization may occur due to monitoring processes. This paper investigates the conditions under which cognates are translated into more literal or free translation candidates. Some of the influential factors are text internal (e.g. context) or external (e.g. language status); others are translation inherent, such as the expertise of the translator and the translation mode. The former are discussed from a product-based perspective, the latter are analyzed in a more process-oriented manner. Multi-method approaches including translation corpora and experimental data are used for predicting the probability of cognate variation in translation. As a consequence, the predictors are discussed against the background of the monitor model.