Papers with Unsupervised text style transfer
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer (2020.coling-main)
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| Challenge: | Existing methods for unsupervised text style transfer lack parallel data and difficulties in content preservation. |
| Approach: | They propose a neural approach to unsupervised text style transfer using non-parallel data. |
| Outcome: | The proposed approach can be trained end-to-end on two widely-used public datasets. |
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer (P19-1)
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| Challenge: | Existing text style transfer methods face three challenges: 1) the transfer is weakly interpretable; 2) generated outputs struggle in content preservation; 3) the trade-off between content and style is intractable. |
| Approach: | They propose a hierarchical reinforced sequence operation method that proposes operation positions and alters the sentence. |
| Outcome: | The proposed method significantly outperforms existing methods on two text style transfer datasets. |
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for unsupervised text style transfer struggle to achieve high style conversion rate and low content loss. |
| Approach: | They propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders. |
| Outcome: | The proposed framework achieves strong empirical results on style compatibility and content preservation. |
Prefix-Tuning Based Unsupervised Text Style Transfer (2023.findings-emnlp)
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| 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. |
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)
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| 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. |