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2017

Improved Neural Text Attribute Transfer with Non-parallel Data

Melnyk, Igor, Santos, Cicero Nogueira dos, Wadhawan, Kahini et al.

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Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.

In this work, we propose multiple improvements over the existing approaches that enable the encoder-decoder framework to cope with the text attribute transfer from non-parallel data.
We perform experiments on the sentiment transfer task using two datasets.
For both datasets, our proposed method outperforms a strong baseline in two of the three employed evaluation metrics.

Improved Neural Text Attribute Transfer with Non-parallel Data · Around

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