  Both grammatical error correction and text style transfer can be viewed as
monolingual sequence-to-sequence transformation tasks, but the scarcity of
directly annotated data for either task makes them unfeasible for most
languages. We present an approach that does both tasks within the same trained
model, and only uses regular language parallel data, without requiring
error-corrected or style-adapted texts. We apply our model to three languages
and present a thorough evaluation on both tasks, showing that the model is
reliable for a number of error types and style transfer aspects.
