RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation

Text style transfer (TST) is naturally a supervised task -rewrite a sentence in a target style while preserving its meaning -yet the parallel corpora that supervision requires exist for only a handful of style domains.A common workaround is to normalize an input into a styleagnostic intermediate and then stylize it into the target style, but the normalizer is typically a lightweight, task-specific paraphraser applied only at test time, feeding a correspondingly small stylizer.We observe that a style-stripping normalizer already exists at scale: neural MT systems trained on hundreds of millions of general-domain sentence pairs preserve content while regressing toward generic phrasing, so roundtrip translation through a pivot language strips stylistic signal without any taskspecific training.This turns normalization from an inference-time patch into a data-generation tool.Roundtrip-translating a monolingual instyle corpus yields a pseudo-parallel corpus on which we LoRA-finetune an instruction-tuned LLM as the stylizer (RT-SFT); applying the same normalizer to test queries keeps that stylizer in-distribution.We show that across four style domains, RT-SFT outperforms state-ofthe-art methods, such as few-shot in-context learning, by considerable margins.We also report on effective retrieval augmentation methods for expert style domains with strict terminology and naming conventions.