  Unsupervised text attribute transfer automatically transforms a text to alter
a specific attribute (e.g. sentiment) without using any parallel data, while
simultaneously preserving its attribute-independent content. The dominant
approaches are trying to model the content-independent attribute separately,
e.g., learning different attributes' representations or using multiple
attribute-specific decoders. However, it may lead to inflexibility from the
perspective of controlling the degree of transfer or transferring over multiple
aspects at the same time. To address the above problems, we propose a more
flexible unsupervised text attribute transfer framework which replaces the
process of modeling attribute with minimal editing of latent representations
based on an attribute classifier. Specifically, we first propose a
Transformer-based autoencoder to learn an entangled latent representation for a
discrete text, then we transform the attribute transfer task to an optimization
problem and propose the Fast-Gradient-Iterative-Modification algorithm to edit
the latent representation until conforming to the target attribute. Extensive
experimental results demonstrate that our model achieves very competitive
performance on three public data sets. Furthermore, we also show that our model
can not only control the degree of transfer freely but also allow to transfer
over multiple aspects at the same time.
