Year of Graduation
Neural Style Transfer for Texts
Style transfer is a task that requires to create a mapping between source and target styles without changing the intent of the original sample (usually we choose an image or a sentence as a sample). In this work, we present the methodology for style transfer in NLP set up. We show that models which use techniques of adversarial examples generation outperform models proposed in recent works. Moreover, experiments prove that conditioning decoder with proper latent representation in variational autoencoder for text modeling could change the style of the sentence preserving its meaning. Compared to the previous state-of-the-art works model proposed in this research shows better results in the same experimental setup.