Style Transfer in Text: Exploration and Evaluation

Understand

Style transfer is an important problem in natural language processing (NLP).

• However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics.
• In this paper, we propose to learn style transfer with non-parallel data.
• We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks.