Style Transfer Through Back-Translation

Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.

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Datasets


Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Unsupervised Text Style Transfer GYAFC BackTrans [[Tsvetkov et al.2018]] BLEU 0.9 # 10
Unsupervised Text Style Transfer Yelp BackTrans [[Tsvetkov et al.2018]] BLEU 5.0 # 9

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