Generic resources are what you need: Style transfer tasks without task-specific parallel training data (2021.emnlp-main)
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| Challenge: | Text style transfer is a task aimed at converting a text of one style into another while preserving its content. |
| Approach: | They propose a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model and an iterative back-translation approach to train two models in a transfer direction. |
| Outcome: | The proposed method outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
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Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
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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. |
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Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer (2021.acl-short)
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| Challenge: | Formality style transfer models have limited success in preserving content due to the scarcity of parallel data. |
| Approach: | They propose to fine-tune pre-trained language and sequence-to-sequence models with rewards that target style and content to enhance content preservation. |
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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. |
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| Challenge: | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. |
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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. |
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Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
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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 . |
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Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)
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| Challenge: | Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. |
| Approach: | They propose a method for fine-tuning pretrained language models on automatically generated paraphrase data to improve the efficiency of style transfer. |
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Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods to increase training data in low-resource domains may not be effective due to data scarcity. |
| Approach: | They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training. |
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