In the translation industry, improving machine translation (MT) output by post-editing is commonly used to increase productivity and consistency. However, some research showed that post-editing leads to homogenization and normalization. And, although the application of MT to literary translation has been limited, with the increased MT quality due to neural MT, literary translators could also benefit from this technology. Current translation environments allow users to personalise MT systems further by training them on their previously made translations or by adapting to changes a translator makes
during translation.
Considering that literary translators are under a lot of time pressure (sometimes to the extent that more than one translator has to work on the same text) and that the translator who started the translation is not always the person to complete the work (e.g., illness, death), MT could help by increasing speed and preserving the original translator's style.
This research proposal uses techniques from stylometry (i.e., identifying textual features to classify texts) to examine (i) whether a personalised MT system can preserve a translator's style better than a generic MT system, and this (ii) even to the extent that it can be
used to ensure uniformity in translation projects with more than one translator, and (iii) whether an adaptive MT system adapts to a translator's style or whether it is the translator adapting to the MT system's output.