Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer. (arXiv:2205.02309v1 [cs.CL])

Web: http://arxiv.org/abs/2205.02309

Recent studies show that auto-encoder based approaches successfully perform
language generation, smooth sentence interpolation, and style transfer over
unseen attributes using unlabelled datasets in a zero-shot manner. The latent
space geometry of such models is organised well enough to perform on datasets
where the style is "coarse-grained" i.e. a small fraction of words alone in a
sentence are enough to determine the overall style label. A recent study uses a
discrete token-based perturbation approach to map "similar" sentences
("similar" defined …

arxiv style transfer text text style transfer transfer unsupervised