| 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. |
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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 . |
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization (2020.findings-emnlp)
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| Challenge: | Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences. |
| Approach: | They propose a semi-supervised formality style transfer model that utilizes a language model-based discriminator to maximize the likelihood of the output sentence being formal. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automated metrics and human judgement. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
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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. |
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. |
Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)
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| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |
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. |
| 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. |
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. |
Text Style Transfer via Optimal Transport (2022.naacl-main)
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| Challenge: | Text style transfer (TST) is a task that aims to change the style of a text from source to target while preserving its content. |
| Approach: | They propose a method to incorporate syntactic and semantic information into similarity computation between the source and the converted text. |
| Outcome: | The proposed method is superior in both supervised and unsupervised settings. |
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)
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| Challenge: | Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning. |
| Approach: | They propose a modular approach for multilingual formality transfer using machine translated data and gold aligned English sentences. |
| Outcome: | The proposed approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages. |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |