| 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. |
Similar Papers
Low Resource Style Transfer via Domain Adaptive Meta Learning (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing unsupervised text style transfer methods suffer from performance degradation when fine-tuning the model in new domains. |
| Approach: | They propose a domain adaptive meta-learning approach with an adversarial style training approach for better content preservation and style transfer. |
| Outcome: | The proposed approach generalizes well on unseen low-resource domains against ten strong baselines. |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)
Copied to clipboard
| Challenge: | Text style transfer task has long suffered from the shortage of parallel data . |
| Approach: | They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it. |
| Outcome: | The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning. |
Contextual Text Style Transfer (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text style transfer are limited by the lack of parallel data. |
| Approach: | They propose a task to translate a sentence into a desired style with its surrounding context taken into account. |
| Outcome: | The proposed model outperforms state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics. |
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to increase training data in low-resource domains may not be effective due to data scarcity. |
| Approach: | They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training. |
| Outcome: | The proposed method can significantly improve results on five domain pairs under different data regimes. |
Style versus Content: A distinction without a (learnable) difference? (2020.coling-main)
Copied to clipboard
| Challenge: | Textual style transfer assumes that it is possible to separate style from content . however, style transfer can provide insight into language more generally . |
| Approach: | They propose to use sentiment transfer to examine whether style transfer is possible . they employ adversarial encoder-decoder networks to analyze style-related features . |
| Outcome: | The proposed method combines style transfer with content preservation and fluency to show that style cannot be usefully separated from content within style transfer systems. |
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)
Copied to clipboard
| Challenge: | Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning. |
| Approach: | They propose a modular approach for multilingual formality transfer using machine translated data and gold aligned English sentences. |
| Outcome: | The proposed approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages. |
STEER: Unified Style Transfer with Expert Reinforcement (2023.findings-emnlp)
Copied to clipboard
| 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 . |
Disentangled Representation Learning for Non-Parallel Text Style Transfer (P19-1)
Copied to clipboard
| Challenge: | a paper aims to disentangle latent representations of style and content in language models . auxiliary multi-task and adversarial objectives are used to disentangle the latent space . |
| Approach: | They propose a simple yet effective approach to disentangling latent representations . they propose auxiliary multi-task and adversarial objectives to disentangle style and content . |
| Outcome: | The proposed approach achieves high performance in terms of transfer accuracy, content preservation, and language fluency compared to previous approaches . |
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. |
How Positive Are You: Text Style Transfer using Adaptive Style Embedding (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches for unsupervised text style transfer are disentanglement between content and style. |
| Approach: | They propose to separate a model with a sentence reconstruction module and a style module to improve model architecture. |
| Outcome: | The proposed method improves style transfer performance and content preservation . the proposed method can be used to modify a sentence with a specified style attribute . |