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. |
Similar Papers
Style Transfer Through Back-Translation (P18-1)
Copied to clipboard
| Challenge: | a new method for automatic style transfer is proposed to preserve the meaning of the text while reducing stylistic properties. |
| Approach: | They propose a method for automatic style transfer that uses latent representations of the input sentence to preserve meaning while reducing stylistic properties. |
| Outcome: | The proposed method improves on sentiment, gender and political slant styles on three different styles. |
Evaluating Style Transfer for Text (N19-1)
Copied to clipboard
| Challenge: | Existing studies on style transfer for text are lacking a standard set of evaluation practices. |
| Approach: | They propose a set of metrics for automated evaluation that are more strongly correlated with human judgment and show tradeoffs between aspects of interest. |
| Outcome: | The proposed models exhibit tradeoffs between aspects of interest and human judgment, demonstrating the importance of evaluating them at specific points of their tradeoff plots. |
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 . |
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 Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (P19-1)
Copied to clipboard
| Challenge: | Disentangling the content and style in the latent space is prevalent in text style transfer . recurrent neural networks (RNN) based encoder and decoder cannot deal with the long-term dependency . |
| Approach: | They propose a style transformer which disentangles style information in latent space . they propose encoding and decoding methods that disentangle style information . |
| Outcome: | The proposed method can achieve better style transfer and better content preservation. |
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)
Copied to clipboard
| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
| Approach: | They propose ways to aggregate the three metrics into one evaluator. |
| Outcome: | The proposed method could be used to aggregate the three metrics into one evaluator. |
Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) make it easy to rewrite a text in any style, but they are not straightforward when evaluating content preservation. |
| Approach: | They propose a large meta-evaluation of metrics for evaluating style and attribute transfer, focusing on content preservation. |
| Outcome: | The proposed method achieves higher alignment with human judgements than prompting a model of a similar size as an autorater. |
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization (2021.acl-long)
Copied to clipboard
| Challenge: | Text style transfer aims to alter the style of a sentence while preserving its content. |
| Approach: | They propose to remove style information at token level and fuse it to style representations using conditional layer normalization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in terms of content preservation and fluency. |
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to separate content from style but some words contain both content and style information. |
| Approach: | They propose a method which uses a reversible encoder to improve content disentanglement. |
| Outcome: | The proposed method outperforms baselines on sentiment transfer and formality transfer tasks. |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
Copied to clipboard
| 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. |