Abstract
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.
Highlights
Intelligent, situation-aware applications must produce naturalistic outputs, lexicalizing the same meaning differently, depending upon the environment
The style transfer accuracy is performed using classifiers trained on held-out train data that were not used in training the style transfer models
We first present the quality of our neural machine translation systems, we present the evaluation setups, and present the results of our experiments
Summary
Intelligent, situation-aware applications must produce naturalistic outputs, lexicalizing the same meaning differently, depending upon the environment. These goals have motivated a considerable amount of recent research efforts focused at “controlled” language generation—aiming at separating the semantic content of what is said from the stylistic dimensions of how it is said These include approaches relying on heuristic substitutions, deletions, and insertions to modulate demographic properties of a writer (Reddy and Knight, 2016), integrating stylistic and demographic speaker traits in statistical machine translation (Rabinovich et al, 2016; Niu et al, 2017), and deep generative models controlling for a particular stylistic aspect, e.g., politeness (Sennrich et al, 2016), sentiment, or tense (Hu et al, 2017; Shen et al, 2017). The latter approaches to style transfer, while more powerful and flexible than heuristic methods, have yet to show that in addition to transferring style they effectively preserve meaning of input sentences
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