Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (2019-05-14T00:00:00.000000Z)

TL;DR

The Style Transformer is proposed, 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.

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.

Authors

Xipeng Qiu

18 papers

Xuanjing Huang

13 papers

Ning Dai

1 papers

TL;DR

Abstract

Authors

References34 items

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