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Style transfer aims to rewrite a source text in a different target stylewhile preserving its content. We propose a novel approach to this task thatleverages generic resources, and without using any task-specific parallel(source-target) data outperforms existing unsupervised approaches on the twomost popular style transfer tasks: formality transfer and polarity swap. Inpractice, we adopt a multi-step procedure which builds on a generic pre-trainedsequence-to-sequence model (BART). First, we strengthen the model's ability torewrite by further pre-training BART on both an existing collection of genericparaphrases, as well as on synthetic pairs created using a general-purposelexical resource. Second, through an iterative back-translation approach, wetrain two models, each in a transfer direction, so that they can provide eachother with synthetically generated pairs, dynamically in the training process.Lastly, we let our best reresulting model generate static synthetic pairs to beused in a supervised training regime. Besides methodology and state-of-the-artresults, a core contribution of this work is a reflection on the nature of thetwo tasks we address, and how their differences are highlighted by theirresponse to our approach.

Generic resources are what you need: Style transfer tasks without task-specific parallel training data