Papers with text style transfer
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation (D19-3)
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| Challenge: | Generative modeling of editing text with respect to control attributes has seen increasing progress over the past few years. |
| Approach: | They propose an auxiliary text rewriting tool that facilitates the rewrite process for natural language generation tasks. |
| Outcome: | The proposed tool facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewrite, and text style transfer. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
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Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
| Challenge: | a limited amount of style data is needed for text style transfer, but there are no convincing methods for evaluating them. |
| Approach: | They propose an efficient method for neutral-to-style transformation using the transformer framework. |
| Outcome: | The proposed method can train neutral-to-style transformation models using large paraphrases and a small style transfer corpus. |
Style Transfer with Multi-iteration Preference Optimization (2025.naacl-long)
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| Challenge: | Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. |
| Approach: | They propose to use a pseudo-parallel data generation method and a dynamic weighted reward aggregation method to improve upon established preference optimization techniques. |
| Outcome: | The proposed model outperforms existing models on two commonly used text style transfer datasets and is compared with state-of-the-art models. |
Audience-Centric Natural Language Generation via Style Infusion (2022.findings-emnlp)
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| Challenge: | Existing approaches to text style transfer (TST) with large volumes of parallel or non-parallel data are limiting for two reasons: it is difficult to collect large volumes and some stylistic objectives are hard to define without audience feedback. |
| Approach: | They propose a task of style infusion - infusing stylistic preferences of audiences into pretrained language generation models by leveraging pairwise human judgments to bootstrap a style analysis model and augment a seed set of judgments. |
| Outcome: | The proposed method generates compelling stylized examples with generic text prompts while balancing fluency and style adoption. |
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
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| Challenge: | Disentangled representations of texts encode information pertaining to different aspects of the text in separate vector embeddings. |
| Approach: | They propose to use a highly-structured natural language dataset to evaluate disentangled representations for texts. |
| Outcome: | The proposed models are well-suited for learning disentangled representations of texts on a synthetic natural language dataset. |
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer (2021.naacl-main)
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Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods for text style transfer focus on individual high-level semantic changes but do not offer fine-grained control of sentence structure, emphasis, and content. |
| Approach: | They propose a large-scale text style transfer benchmark with 21 fine-grained stylistic changes across atomic lexical, syntactic, semantic, and thematic transfers. |
| Outcome: | The proposed method allows modeling fine-grained changes as building blocks for more complex, high-level transfers. |
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer (2022.findings-naacl)
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| Challenge: | Text style transfer is an important task in controllable language generation due to the scarcity of large-scale parallel data. |
| Approach: | They propose a semi-supervised framework for text style transfer that bootstraps with supervision guided by automatically constructed pseudo-parallel pairs and improves the sequence-to-sequence policy gradient via reinforcement rewards. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple datasets and produces effective generation with as minimal as 10% of training data. |
Learning to Model Editing Processes (2022.findings-emnlp)
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| Challenge: | Existing sequence generation models produce outputs in one pass, usually left-to-right . current models model only a single edit step, and do not fully model editing . |
| Approach: | They propose to model editing processes, modeling the whole process of iteratively generating sequences. |
| Outcome: | The proposed model improves performance on a variety of axes compared to previous models . iterative refinement and editing are central parts of human creative workflow . |
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling (2021.acl-long)
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| Challenge: | Existing methods for text style transfer require style-labeled training data, but use only labeled data at inference time. |
| Approach: | They propose a method that uses readily-available unlabeled text to train style transfer . they use a style vector to condition a decoder to perform style transfer using unlabelled text . |
| Outcome: | The proposed method is competitive on sentiment transfer, even compared to models trained fully on labeled data. |
Diff4TST: Masked Diffusion Language Model for Text Style Transfer (2026.acl-long)
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| Challenge: | Existing methods for text style transfer rely on task-specific training and expensive training stages. |
| Approach: | They propose a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process. |
| Outcome: | The proposed model improves style accuracy and controllability while maintaining strong content preservation and fluency. |
Domain Adaptive Text Style Transfer (D19-1)
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| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer (2021.findings-acl)
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| Challenge: | Recent work on text style transfer proposes single-span editing as an alternative to generating the target text from scratch. |
| Approach: | They propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). |
| Outcome: | The proposed method outperforms existing methods on sentiment and politeness transfer and improves model performance. |
HarfoSokhan: A Comprehensive Parallel Dataset for Transitions between Persian Colloquial and Formal Variations (2026.eacl-long)
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Hamid Jahad Sarvestani, Vida Ramezanian, Saee Saadat, Neda Taghizadeh Serajeh, Maryam Sadat Razavi Taheri, Shohreh Kasaei, MohammadAmin Fazli, Ehsaneddin Asgari
| Challenge: | A wide array of NLP/NLU models have been developed for the Persian language but performance drops when applied to the colloquial form of Persian. |
| Approach: | They propose to use a large-scale colloquial to formal Persian parallel dataset to train a GPT2 model that exhibited remarkable proficiency in colloqual to informal text style transfer. |
| Outcome: | The proposed dataset outperforms OpenAI’s GPT-3.5-turbo model and a leading rule-based system in colloquial to formal Persian conversion. |
Learning Implicit Text Generation via Feature Matching (2020.acl-main)
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Inkit Padhi, Pierre Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
| Challenge: | Generative feature matching networks are an approach for training implicit generative models for images . a novel formulation of GFMN for unconditional sequence generation is proposed . |
| Approach: | They propose a new GFMN formulation that performs token level feature matching on pre-trained neural networks. |
| Outcome: | The proposed method outperforms adversarial approaches for text generation and style transfer. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer (2021.emnlp-main)
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| Challenge: | Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer. |
| Approach: | They propose to conduct adversarial and backdoor attacks based on text style transfer . the authors propose to use text style to alter the style of a sentence . |
| Outcome: | The proposed methods show that popular models are vulnerable to both attacks based on text style transfer . the results show that the proposed methods perform better than baselines in many aspects . |
Prompt-Based Editing for Text Style Transfer (2023.findings-emnlp)
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| Challenge: | Text style transfer is a type of textual prompt that generates style-transferred texts word by word . early prediction errors may affect future word predictions. |
| Approach: | They propose a prompt-based editing approach to text style transfer using a pretrained language model. |
| Outcome: | The proposed approach outperforms existing systems with 20 times more parameters on three style-transfer benchmark datasets. |
Neuron Activation Modulation for Text Style Transfer: Guiding Large Language Models (2025.findings-acl)
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| Challenge: | Text style transfer (TST) aims to flexibly adjust the style of text while preserving its core content. |
| Approach: | They propose a method that aligns activation values of style-related neurons with those of the target style to guide the model in performing the transfer. |
| Outcome: | The proposed method significantly improves style transfer quality while preserving core content. |
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer (2024.findings-emnlp)
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| Challenge: | Existing methods to transfer text style focus on sentence-level data, limiting performance . current LLMs struggle to generate public speaking texts that align with human preferences . |
| Approach: | They propose a task to transform official texts into public-speaking styles by analyzing real-world data. |
| Outcome: | The proposed task aims to transform public speaking texts into public-speaking styles . the proposed framework analyzes characteristics and identifies problems of stylized texts . |
STEER: Unified Style Transfer with Expert Reinforcement (2023.findings-emnlp)
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| Challenge: | Experimental results show unified style transfer models outperform the 175B instruction-tuned GPT-3 on overall style transfer quality. |
| Approach: | They propose a unified style transfer framework that can transfer to multiple target styles from an arbitrary source style. |
| Outcome: | The proposed method outperforms the 175B instruction-tuned GPT-3 on overall style transfer quality despite being 226 times smaller in size . |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
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| Challenge: | Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency. |
| Approach: | They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges. |
| Outcome: | The proposed model performs well on two benchmark text style transfer data sets. |
Multilingual and Explainable Text Detoxification with Parallel Corpora (2025.coling-main)
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Daryna Dementieva, Nikolay Babakov, Amit Ronen, Abinew Ali Ayele, Naquee Rizwan, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Daniil Alekhseevich Moskovskiy, Elisei Stakovskii, Eran Kaufman, Ashraf Elnagar, Animesh Mukherjee, Alexander Panchenko
| Challenge: | Existing approaches to manage toxic speech on social platforms are limited . however, there is a need for more proactive moderation of abusive speech. |
| Approach: | They extend parallel text detoxification corpus to new languages to test the approach . they propose a method that combines toxic and non-toxic sentences into a more neutral form . |
| Outcome: | The proposed method integrates the descriptive features of toxic and non-toxic sentences into a more neutral or non- toxic form. |
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation (2022.emnlp-main)
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| Challenge: | Unavailability of parallel corpora for training text style transfer models is a challenge but common . a large corpus of parallel data is not available for text style transfers . |
| Approach: | They propose to use AMR as an intermediate style agnostic representation to train TST models. |
| Outcome: | The proposed model outperforms state-of-the-art models in the style agnostic task. |
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)
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| Challenge: | Existing models for task-specific natural language generation do not contain any labeled examples. |
| Approach: | They propose a variational autoencoder with disentanglement priors for task-specific natural language generation with none or a handful of task-related labeled examples. |
| Outcome: | The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting. |
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)
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| Challenge: | Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models. |
| Approach: | They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval. |
| Outcome: | The proposed framework outperforms existing methods across MT and TST tasks. |
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings (2024.findings-emnlp)
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| Challenge: | Existing methods for text style transfer rely on few-shot capabilities of large language models or complex controllable text generation approaches that are inefficient and underperform on fluency metrics. |
| Approach: | They propose a lightweight but effective approach which leverages a small language model and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. |
| Outcome: | The proposed method outperforms strong approaches such as GPT-4 and performs form attribute style transfer with automatic and human evaluations. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |