Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer

2 Oct 2020 Yufang Huang Wentao Zhu Deyi Xiong Yiye Zhang Changjian Hu Feiyu Xu

Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach to unsupervised text style transfer, which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data... (read more)

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