  Recent research on style transfer takes inspiration from unsupervised neural
machine translation (UNMT), learning from large amounts of non-parallel data by
exploiting cycle consistency loss, back-translation, and denoising
autoencoders. By contrast, the use of self-supervised NMT (SSNMT), which
leverages (near) parallel instances hidden in non-parallel data more
efficiently than UNMT, has not yet been explored for style transfer. In this
paper we present a novel Self-Supervised Style Transfer (3ST) model, which
augments SSNMT with UNMT methods in order to identify and efficiently exploit
supervisory signals in non-parallel social media posts. We compare 3ST with
state-of-the-art (SOTA) style transfer models across civil rephrasing,
formality and polarity tasks. We show that 3ST is able to balance the three
major objectives (fluency, content preservation, attribute transfer accuracy)
the best, outperforming SOTA models on averaged performance across their tested
tasks in automatic and human evaluation.
