Abstract
In this article we present a corpus-based statistical approach to measuring translation quality, more particularly translation acceptability, by comparing the features of translated and original texts. We discuss initial findings that aim to support and objectify formative quality assessment. To that end, we extract a multitude of linguistic and textual features from both student and professional translation corpora that consist of many different translations by several translators in two different genres (fiction, news) and in two translation directions (English to French and French to Dutch). The numerical information gathered from these corpora is exploratively analysed with Principal Component Analysis, which enables us to identify stable, language-independent linguistic and textual indicators of student translations compared to translations produced by professionals. The differences between these types of translation are subsequently tested by means of ANOVA. The results clearly indicate that the proposed methodology is indeed capable of distinguishing between student and professional translations. It is claimed that this deviant behaviour indicates an overall lower translation quality in student translations: student translations tend to score lower at the acceptability level, that is, they deviate significantly from target-language norms and conventions. In addition, the proposed methodology is capable of assessing the acceptability of an individual student’s translation – a smaller linguistic distance between a given student translation and the norm set by the professional translations correlates with higher quality. The methodology is also able to provide objective and concrete feedback about the divergent linguistic dimensions in their text.
Highlights
Empirical Translation Studies has undergone major descriptive and theoretical advances in the past few years which have clearly been brought about by what one could call a “methodological shift” from monodimensional comparable corpus analyses to multidimensional empirical analyses (e.g., Evert & Neumann, 2017)
We argued for a corpus-based, statistical approach to translation evaluation, more translation acceptability and feedback based on the distribution of a large set of “basic” linguistic features
By investigating the distribution of these features in a corpus of student translations compared to their distribution in a corpus of professional writers and translators, we have shown that such an approach is feasible
Summary
Empirical Translation Studies has undergone major descriptive and theoretical advances in the past few years which have clearly been brought about by what one could call a “methodological shift” from monodimensional comparable corpus analyses (in which the frequency of a given linguistic feature in a corpus of translated texts is compared to its frequency in a corpus of non-translated, original texts; e.g., Olohan & Baker, 2000) to multidimensional empirical analyses (e.g., Evert & Neumann, 2017) This shift includes stricter data control, analysis of both comparable and parallel data, the use of more advanced statistical techniques and the integration of different methodological designs in order to arrive at so-called “converging evidence” (see, for example, De Sutter, Delaere, & Lefer, 2017). The use of corpora in translator training is widespread, and translator aids are being updated on the basis of carefully designed analyses, but it is still fair to say that the full potential of the methodological and analytical resources which are increasingly being used in Empirical Translation Studies still have to find their way into Applied Translation Studies (see, Daems, Vandepitte, Hartsuiker, & Macken, in press)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
