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

2026-09-02

AI Summary

本文提出HyperStyler，通过上下文感知风格导航和超网络解决低资源作者风格迁移问题，提高风格保真度和语义保留。

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.

Problem

• Low-resource authorship style transfer
• Style fidelity
• Semantic preservation

Innovation

• HyperStyler
• context-aware style navigation
• hypernetworks
• dynamic parameter modulation
• low-resource authorship style transfer