Translation revision (TR) is an important step in the translation workflow. However, translation revision competence (TRC) remains an ill-defined concept. This article addresses that gap by operationalizing TR and by presenting a theoretical TRC model. Subsequently, the article analyses and interprets the results of an empirical pilot study designed to test the presence of two TR subcompetences hypothesized by the TRC model, in an experimental group and a control group of 21 MA language students. The experimental group was given TR training whereas the control group was not. The two subcompetences that were tested using a pretest—posttest experimental design were declarative-procedural knowledge about TR and the procedural strategic revision subcompetence. Both groups of participants replied to questionnaires and performed controlled revision tasks, which were subjected to quantitative statistical analyses. This article provides a detailed analysis of the results and the causes of the limited progress. In addition, it discusses the lessons learnt for both TR training and further research.
We investigate the cost-effectiveness of special-purpose crawled corpora versus more focused corpora for automatic terminology extraction (ATE). Our focus is on medical terminology on heart failure for two languages, viz. English for which we have more web and specialized resources at our disposal and the less resourced Dutch. We show that, although term density in the dedicated corpora is larger for both languages, the potential for term extraction is higher in the crawled corpora than in the dedicated corpora. Furthermore, in a set of experiments in which we evaluate both types of corpora, while keeping size constant, we observe that more Gold Standard (GS) terms are covered by the “noisy” crawled corpus than with a dedicated corpus of the same size.