Balancing the Style-Content Trade-Off in Sentiment Transfer Using
  Polarity-Aware Denoising
Sourabrata Mukherjee Zdeněk Kasner Ondřej Dušek
Text sentiment transfer aims to flip the sentiment polarity of a sentence
(positive to negative or vice versa) while preserving its sentiment-independent
content. Although current models show good results at changing the sentiment,
content preservation in transferred sentences is insufficient. In this paper,
we present a sentiment transfer model based on polarity-aware denoising, which
accurately controls the sentiment attributes in generated text, preserving the
content to a great extent and helping to balance the style-content trade-off.
Our proposed model is structured around two key stages in the sentiment
transfer process: better representation learning using a shared encoder and
sentiment-controlled generation using separate sentiment-specific decoders.
Empirical results show that our methods outperforms state-of-the-art baselines
in terms of content preservation while staying competitive in terms of style
transfer accuracy and fluency.