Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
This work improves text style transfer for natural language processing applications, though it appears incremental as it builds on existing Transformer architectures.
The paper tackles the problem of unpaired text style transfer by addressing issues in disentangling style from content and handling long-term dependencies, resulting in a model that achieves better style transfer and content preservation.
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.