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Style Transfer in Text: Exploration and Evaluation
[article]
2017
arXiv
pre-print
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
arXiv:1711.06861v2
fatcat:hna3yquv7rhsdlogsxbkum773y