Papers with style classifier
Style Obfuscation by Invariance (C18-1)
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| Challenge: | obfuscation-by-transfer is a method of obliging writing style using sequence models . a side effect of this approach is the frequent major alterations to the semantic content of the input . |
| Approach: | They propose obfuscation-by-invariance and investigate to what extent models trained to be explicitly style-independent preserve semantics. |
| Outcome: | The proposed model performs better than models trained to be explicitly style-invariant, while human evaluation shows a trade-off between the level of obfuscation and the quality of the output. |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
| Approach: | They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods . |
| Outcome: | The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets. |
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. |