| 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 . |
Similar Papers
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer (2022.findings-naacl)
Copied to clipboard
| 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. |
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (D19-1)
Copied to clipboard
| Challenge: | Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. |
| Approach: | They propose a new approach to rewriting sentences to a target style in the absence of parallel style corpora by exploiting the Transformer. |
| Outcome: | The proposed method outperforms state-of-the-art systems across 5 datasets on sentiment, gender and political slant transfer. |
Style Transfer with Multi-iteration Preference Optimization (2025.naacl-long)
Copied to clipboard
| 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. |
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora (D19-55)
Copied to clipboard
| Challenge: | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. |
| Approach: | They propose a technique to derive a dataset of aligned pairs from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. |
| Outcome: | The proposed method significantly outperforms OpenNMT’s Seq2Seq model trained on the Yahoo Formality Dataset and 6 novel datasets. |
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling (2021.acl-long)
Copied to clipboard
| 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. |
Prefix-Tuning Based Unsupervised Text Style Transfer (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Unsupervised text style transfer is an important task in computer vision and natural language processing. |
| Approach: | They propose a method that uses pre-trained large language models to train a generative model that can alter the style of the input sentence without using any parallel data. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on well-known datasets. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
Copied to clipboard
| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
Style Vectors for Steering Generative Large Language Models (2024.findings-eacl)
Copied to clipboard
Kai Konen, Sophie Jentzsch, Diaoulé Diallo, Peer Schütt, Oliver Bensch, Roxanne El Baff, Dominik Opitz, Tobias Hecking
| Challenge: | Large language models (LLMs) can be trained on vast corpora and can generate text in a nuanced and parameterisable way. |
| Approach: | They propose to add style vectors to the activations of hidden layers during text generation to steer output towards specific styles. |
| Outcome: | The proposed approach differs from prompt engineering in that it can be nuanced and parameterisable. |
Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. |
| Approach: | They propose a method for fine-tuning pretrained language models on automatically generated paraphrase data to improve the efficiency of style transfer. |
| Outcome: | The proposed method outperforms state-of-the-art style transfer systems on human and automatic evaluations and proposes fixed variants. |
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen (2020.acl-main)
Copied to clipboard
| Challenge: | a curse of knowledge can impede communication between experts and laymen. |
| Approach: | They propose a task of expertise style transfer to automate the communication between experts and laymen using an annotated dataset. |
| Outcome: | The proposed task simplifies professional language and improves accuracy and expertise level of laymen descriptions using simple words. |