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        <title>RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation</title>
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    <abstract>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 style-agnostic 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 task-specific training. This turns normalization from an inference-time patch into a data-generation tool. Roundtrip-translating a monolingual in-style 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-of-the-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.</abstract>
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%0 Conference Proceedings
%T RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation
%A Liu, Ruoxi
%A Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F liu-koehn-2026-rt
%X 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 style-agnostic 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 task-specific training. This turns normalization from an inference-time patch into a data-generation tool. Roundtrip-translating a monolingual in-style 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-of-the-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.
%U https://aclanthology.org/2026.wmt-main.23/
%P 398-411

Markdown (Informal)

[RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation](https://aclanthology.org/2026.wmt-main.23/) (Liu & Koehn, WMT 2026)

• RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation (Liu & Koehn, WMT 2026)

ACL

• Ruoxi Liu and Philipp Koehn. 2026. RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 398–411, Budapest, Hungary. Association for Computational Linguistics.