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 . |
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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 . |
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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. |
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| Challenge: | Existing methods to separate content from style but some words contain both content and style information. |
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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. |
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| Challenge: | Existing methods for text style transfer are limited by the lack of parallel data. |
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| Challenge: | Unsupervised text style transfer is an important task in computer vision and natural language processing. |
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Transductive Learning for Unsupervised Text Style Transfer (2021.emnlp-main)
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| 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. |
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Style Transfer Through Back-Translation (P18-1)
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| Challenge: | a new method for automatic style transfer is proposed to preserve the meaning of the text while reducing stylistic properties. |
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StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples (2025.naacl-long)
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Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch
| Challenge: | Existing methods for embedding text are limited by the imperfect nature of data acquired under such assumptions. |
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Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models (2025.coling-main)
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| Challenge: | Using a literary corpus that alternates between topics and styles, we compare language models across French and English. |
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