Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning. (arXiv:2202.00469v1 [cs.CL])

Text style transfer is a challenging text generation problem, which aims at
altering the style of a given sentence to a target one while keeping its
content unchanged. Since there is a natural scarcity of parallel datasets,
recent works mainly focus on solving the problem in an unsupervised manner.
However, previous gradient-based works generally suffer from the deficiencies
as follows, namely: (1) Content migration. Previous approaches lack explicit
modeling of content invariance and are thus susceptible to content shift
between …

arxiv gradient learning style transfer text text style transfer unsupervised