Effective writing style imitation via combinatorial paraphrasing

Stylometry can be used to profile authors based on their written text. Transforming text to imitate someone else's writing style while retaining meaning constitutes a defence. A variety of deep learning methods for style imitation have been proposed in recent research literature. Via empirical evaluation of three state-of-the-art models on four datasets, we illustrate that none succeed in semantic retainment, often drastically changing the original meaning or removing important parts of the text. To mitigate this problem we present ParChoice: an alternative approach based on the combinatorial application of multiple paraphrasing techniques. ParChoice first produces a large number of possible candidate paraphrases, from which it then chooses the candidate that maximizes proximity to a target corpus. Through systematic automated and manual evaluation as well as a user study, we demonstrate that ParChoice significantly outperforms prior methods in its ability to retain semantic content. Using state-of-the art deep learning author profiling tools, we additionally show that ParChoice accomplishes better imitation success than A$^4$NT, the state-of-the-art style imitation technique with the best semantic retainment.