| Challenge: | Existing methods for style transfer are based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. |
| Approach: | They propose a retrieval-based context-aware style representation that involves top-K relevant sentences in the target style in the transfer process. |
| Outcome: | The proposed method outperforms several strong baselines and is general and effective to the task of unsupervised style transfer. |
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Contextual Text Style Transfer (2020.findings-emnlp)
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| 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. |
How Positive Are You: Text Style Transfer using Adaptive Style Embedding (2020.coling-main)
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| 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 . |
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer (2020.acl-main)
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| Challenge: | Existing methods to unsupervised style transfer lack fine-grained control of the influence from the target style. |
| Approach: | They propose a model that exploits the relevance of each output word to the target style . they pretrain a style classifier and train an attentional Seq2seq model to reconstruct input sentences . |
| Outcome: | The proposed model achieves state-of-the-art performance in terms of transfer accuracy and content preservation. |
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. |
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (P19-1)
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| 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. |
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. |
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling (2021.acl-long)
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| 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. |
Unsupervised Learning of Style-sensitive Word Vectors (P18-2)
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| Challenge: | Existing studies on what is said and how it is said focus on stylistic variations . lack of objective definitions is a major difficulty in studying style . |
| Approach: | They propose to extend the continuous bag of words embedding model to learn style-sensitive word vectors using a wider context window. |
| Outcome: | The proposed extensions contribute to the acquisition of style-sensitive word embeddings. |
Disentangled Representation Learning for Non-Parallel Text Style Transfer (P19-1)
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| 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 . |
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer (2022.naacl-main)
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| Challenge: | Recent studies show that auto-encoders perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. |
| Approach: | They propose a discrete token-based perturbation approach to map "similar" sentences close by in latent space. |
| Outcome: | The proposed model can generate and perform language generation, style transfer and sentence interpolation tasks on unlabelled datasets in a zero-shot manner. |