Measuring ’Registerness’ in Human and Machine Translation: A Text Classification Approach
Ekaterina Lapshinova‐Koltunski, Mihaela Vela · 2015
In this paper, we apply text classification techniques to prove how well translated texts obey linguistic conventions of the target language measured in terms of registers, which are characterised by particular distributions of lexico-grammatical features according to a given contextual configuration.The classifiers are trained on German original data and tested on comparable English-to-German translations.Our main goal is to see if both human and machine translations comply with the nontranslated target originals.The results of the present analysis provide evidence for our assumption that the usage of parallel corpora in machine translation should be treated with caution, as human translations might be prone to errors.