Source language difficulties in learner translation: Evidence from an error-annotated corpus
MariaKunilovskaya,TatyanaIlyushchenya,NataliaMorgoun and RuslanMitkov
University of Wolverhampton | University of Tyumen | Lomonosov Moscow State University
Abstract
This study uses an error-annotated, mass-media subset of a sentence-aligned, multi-parallel learner translator corpus to reveal source-language items that are challenging in English–Russian translation. Our data includes multiple translations of the most challenging source sentences, drawn from a large collection of student translations on the basis of error statistics. This sample was subjected to manual contrastive-comparative analysis, which resulted in a list of English items that were difficult for students. The outcome of the analysis was compared to the topics discussed in translation textbooks that are recommended for BA and specialist-degree students in Russia. We discuss items that deserve more prominence in training as well as items that call for improvements to traditional learning activities. This study presents evidence that a more empirically motivated design of the practical translation syllabus as part of translator education is required.
Translation students are typically exposed to a set of source-language (SL) items that are likely to cause difficulties in translation and prompt various errors. These difficult-to-translate items are used to develop a professional approach to translation, in which a learner is able to recognise a new translation problem by analogy and come up with a solution coherent with a selected translation strategy. However, to make learning more effective it makes sense to choose didactic items carefully and target those that most often lead to lower-quality translations. This study aims to identify the SL items that cause the most problems in student translations based on a significant amount of corpus data. We regard this as a step towards a more empirically motivated educational curriculum that recognises the “areas of the learning curriculum where teaching is most needed” (Castagnoli et al. 2011, 234). We believe that corpus approaches help to overcome limitations of individual studies based on translation errors in a specific translation task, which may or may not be characteristic of a specific student population. While we accept that teachers’ professional intuition and experience can be a powerful tool in detecting common problems, introspective approaches that are not supported by sufficient evidence are prone to biases and could lack justification.
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