HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks

Abstract:Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target author using only a few reference examples while preserving the original meaning. Existing methods often struggle to achieve both high style fidelity and semantic preservation because they compress diverse references into a single static author embedding, which averages out context-dependent stylistic variation, and rely on hidden representations for style control, which entangle style with content. We propose HyperStyler, a novel architecture that decouples LAST into style selection and style realization. Stylo-navigator predicts style coordinates by jointly modeling the source context and target-author references, and Stylo-hypernet realizes them via dynamic parameter modulation instead of hidden-state injection. Our experiments on Reddit, Blog, and News datasets demonstrate that HyperStyler consistently outperforms prior methods including LLM-based approaches and generalizes robustly across domains. Notably, HyperStyler achieves superior performance with as few as 2.4% additional parameters over T5-large, while being over 1.8x faster than LLMs at inference.

Computation and Language (cs.CL)

arXiv:2609.02772 [cs.CL]

(or arXiv:2609.02772v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.02772 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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