Transfer Text from one Style to Another
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We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
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.
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
We evaluate our approach on two review datasets, Yelp and Amazon.
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.
Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.
Text style transfer aims to modify the style of a sentence while keeping its content unchanged.
Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information.
Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.