Style Transformer: Unpaired Text Style Transfer without
Disentangled Latent Representation
Abstract
Disentangling the content and style in the latent space is prevalent in unpaired text style
transfer. However, two major issues exist in
most of the current neural models. 1) It is
difficult to completely strip the style information from the semantics for a sentence. 2)
The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of
the long-term dependency, resulting in poor
preservation of non-stylistic semantic content.
In this paper, we propose the Style Transformer, which makes no assumption about the
latent representation of source sentence and
equips the power of attention mechanism in
Transformer to achieve better style transfer
and better content preservation. Source code
will be available on Github