Papers with attribute transfer
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. |
| Outcome: | The proposed method outperforms state-of-the-art style transfer systems on human and automatic evaluations and proposes fixed variants. |
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer (N18-1)
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| Challenge: | Previous work using adversarial methods has struggled to produce high-quality outputs. |
| Approach: | They propose a method that transforms a sentence to alter a specific attribute while preserving its attribute-independent content. |
| Outcome: | The proposed method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets. |
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (D19-1)
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| Challenge: | Existing approaches to rewrite sentences with certain attributes are difficult and often result in poor content-preservation and ungrammaticality. |
| Approach: | They propose a method that uses a sequence-to-sequence model to learn attribute transfer . existing approaches try to explicitly disentangle content and attribute information . |
| Outcome: | The proposed method outperforms complex state-of-the-art systems by a large margin in sentiment modification and formality transfer tasks. |