Despite the success of style transfer in image processing, it has seen
limited progress in natural language generation. Part of the problem is that
content is not as easily decoupled from style in the text domain. Curiously, in
the field of stylometry, content does not figure prominently in practical
methods of discriminating stylistic elements, such as authorship and genre.
Rather, syntax and function words are the most salient features. Drawing on
this work, we model style as a suite of low-level linguistic controls, such as
frequency of pronouns, prepositions, and subordinate clause constructions. We
train a neural encoder-decoder model to reconstruct reference sentences given
only content words and the setting of the controls. We perform style transfer
by keeping the content words fixed while adjusting the controls to be
indicative of another style. In experiments, we show that the model reliably
responds to the linguistic controls and perform both automatic and manual
evaluations on style transfer. We find we can fool a style classifier 84% of
the time, and that our model produces highly diverse and stylistically
distinctive outputs. This work introduces a formal, extendable model of style
that can add control to any neural text generation system.

利用低层级的语言控制来生成具备样式特点的文本，有效性较高，进一步为神经文本生成系统添加语言控制。