  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. CAE consists of three essential components: (1) LSTM
autoencoders that encode a text in one style into its latent representation and
decode an encoded representation into its original text or a transferred
representation into a style-transferred text, (2) adversarial style transfer
networks that use an adversarially trained generator to transform a latent
representation in one style into a representation in another style, and (3) a
cycle-consistent constraint that enhances the capacity of the adversarial style
transfer networks in content preservation. The entire CAE with these three
components can be trained end-to-end. Extensive experiments and in-depth
analyses on two widely-used public datasets consistently validate the
effectiveness of proposed CAE in both style transfer and content preservation
against several strong baselines in terms of four automatic evaluation metrics
and human evaluation.
