Balancing the Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer (2023.findings-acl)
Copied to clipboard
| Challenge: | Text style transfer requires a high-quality paired dataset and quality training data. |
| Approach: | They propose to use a pseudo-parallel dataset to adjust the style distribution in training data to balance the style transfer model. |
| Outcome: | The proposed model produces more effective control effects over multiple styles than an imbalanced or skewed one. |
Similar Papers
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. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
Copied to clipboard
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. |
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. |
Text Style Transfer for Bias Mitigation using Masked Language Modeling (2022.naacl-srw)
Copied to clipboard
| Challenge: | Various research findings have concluded that biased textual data has significant effects on target demographic groups. |
| Approach: | They propose a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data. |
| Outcome: | The proposed model improves on limitations of existing methods while maintaining good style transfer accuracy. |
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. |
Understanding the effects of language-specific class imbalance in multilingual fine-tuning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to fine-tune large language models have been developed to reduce the amount of resources needed to perform classification tasks. |
| Approach: | They modify traditional class weighing approach to reduce imbalance by calculating class weights separately for each language. |
| Outcome: | The proposed model improves performance and reduces the promotion of uninformative features. |
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. |
Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding (2021.acl-long)
Copied to clipboard
| Challenge: | a benchmark corpus of text in 15 different styles is used to study stylistic language . a similar benchmark is used for cross-style language understanding . |
| Approach: | They propose a benchmark corpus that combines existing datasets and collects a new one for cross-style language understanding. |
| Outcome: | The proposed benchmark corpus contains 15 different styles under four theoretical groupings: figurative, personal, affective, and interpersonal groups. |
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. |
Towards Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator . |
| Approach: | They propose to augment a neural machine translation model with translator information . they use TED talk datasets to model and control translator-related stylistic variations . |
| Outcome: | The proposed models capture the style variations of translators and generate translations with different styles on new data. |