Papers with human-generated transfer references
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. |