Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
This addresses the problem of generating style-transferred text without parallel data for natural language processing applications, representing a novel method for a known bottleneck.
The paper tackles unsupervised text style transfer by proposing Cycle-consistent Adversarial Autoencoders (CAE), which combine autoencoders, adversarial networks, and cycle-consistency constraints to achieve improved performance in style transfer and content preservation, as validated by experiments on public datasets.
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.