  Translated texts exhibit systematic linguistic differences compared to
original texts in the same language, and these differences are referred to as
translationese. Translationese has effects on various cross-lingual natural
language processing tasks, potentially leading to biased results. In this
paper, we explore a novel approach to reduce translationese in translated
texts: translation-based style transfer. As there are no parallel
human-translated and original data in the same language, we use a
self-supervised approach that can learn from comparable (rather than parallel)
mono-lingual original and translated data. However, even this self-supervised
approach requires some parallel data for validation. We show how we can
eliminate the need for parallel validation data by combining the
self-supervised loss with an unsupervised loss. This unsupervised loss
leverages the original language model loss over the style-transferred output
and a semantic similarity loss between the input and style-transferred output.
We evaluate our approach in terms of original vs. translationese binary
classification in addition to measuring content preservation and target-style
fluency. The results show that our approach is able to reduce translationese
classifier accuracy to a level of a random classifier after style transfer
while adequately preserving the content and fluency in the target original
style.
