# SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer

arXiv: https://arxiv.org/abs/2406.04578 · HTML: https://mlsanity.com/paper/2406.04578/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://mlsanity.com/paper/1808.04339/) (2018)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://mlsanity.com/paper/1905.05621/) (2019)
- [Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models](https://mlsanity.com/paper/2205.11503/) (2022)
- ["Mask and Infill" : Applying Masked Language Model to Sentiment Transfer](https://mlsanity.com/paper/1908.08039/) (2019)
- [d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding](https://mlsanity.com/paper/1905.12775/) (2019)
- [TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling](https://mlsanity.com/paper/2010.03802/) (2020)
- [IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation](https://mlsanity.com/paper/1901.11333/) (2019)
- [Text Style Transfer for Bias Mitigation using Masked Language Modeling](https://mlsanity.com/paper/2201.08643/) (2022)
- [Structuring Latent Spaces for Stylized Response Generation](https://mlsanity.com/paper/1909.05361/) (2019)
- [SLM: Learning a Discourse Language Representation with Sentence Unshuffling](https://mlsanity.com/paper/2010.16249/) (2020)
- [LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation](https://mlsanity.com/paper/2108.12960/) (2021)
- [Adapting Language Models for Non-Parallel Author-Stylized Rewriting](https://mlsanity.com/paper/1909.09962/) (2019)
- [Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization](https://mlsanity.com/paper/2108.00449/) (2021)
- [Contextual Text Style Transfer](https://mlsanity.com/paper/2005.00136/) (2020)
- [Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer](https://mlsanity.com/paper/2005.02049/) (2020)
- [Non-Autoregressive Neural Dialogue Generation](https://mlsanity.com/paper/2002.04250/) (2020)
- [StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing](https://mlsanity.com/paper/2208.13423/) (2022)
- [NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2106.02210/) (2021)
- [So Different Yet So Alike! Constrained Unsupervised Text Style Transfer](https://mlsanity.com/paper/2205.04093/) (2022)
- [Supervised Domain Adaptation using Graph Embedding](https://mlsanity.com/paper/2003.04063/) (2020)
- [Few-Shot Domain Adaptation for Charge Prediction on Unprofessional Descriptions](https://mlsanity.com/paper/2309.17313/) (2023)

## Built on by (most cited first, 4 of 4)

- [Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model](https://mlsanity.com/paper/2502.01090/) (2025)
- [TIP and Polish: Text-Image-Prototype Guided Multi-Modal Generation via Commonality-Discrepancy Modeling and Refinement](https://mlsanity.com/paper/2511.21698/) (2025)
- [Are LLMs More Skeptical of Entertainment News?](https://mlsanity.com/paper/2605.01727/) (2026)
- [HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks](https://mlsanity.com/paper/2609.02772/) (2026)

## Abstract (arXiv)

Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content. Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications. The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences. In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues. The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation. The multiple JSCW layers further progressively refine content representations. We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity. Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training. We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines. Our code: https://github.com/jiezhao6/SC2.