Moving Beyond the Style-Guide: Enterprise-Scale Style Transfer

Oshry Ben-Harush, Joseph Standerfer

Keywords: style transfer, natural language processing, enterprise systems, document processing, technical documentation, neural text generation

TL;DR: Onoma transforms enterprise documentation to maintain consistent brand voice while preserving document structure, achieving 83% style accuracy through fine-tuned language models and structure-aware techniques.

Abstract: Text style transfer in enterprise environments presents unique challenges that extend beyond traditional style transfer approaches, particularly when dealing with complex technical documentation and strict organizational guidelines. This paper introduces Onoma, a novel enterprise-scale style transfer system that addresses the fundamental challenges of maintaining consistent brand voice while preserving document structure and semantic meaning. We present a hybrid architecture that combines fine-tuned large language models with structure-aware generation techniques, capable of handling technical documentation, marketing content, and complex formatting requirements. Our system demonstrates significant improvements over baseline approaches, achieving up to 83\% style transfer accuracy while maintaining 87\% content preservation across diverse document types. Through comprehensive empirical evaluation, we show that Onoma effectively bridges the gap between theoretical style transfer capabilities and practical enterprise requirements. Our approach introduces new methodologies for handling document structure preservation and style consistency at scale, contributing both to the theoretical understanding of enterprise-scale style transfer and providing practical solutions for large-scale content management systems. The results demonstrate that Onoma successfully addresses key limitations in existing approaches, particularly in generating parallel datasets and handling complex technical documentation while maintaining formatting integrity and semantic coherence.

Submission Number: 7