In this paper, we present a method for adversarial decomposition of text
representation. This method can be used to decompose a representation of an
input sentence into several independent vectors, each of them responsible for a
specific aspect of the input sentence. We evaluate the proposed method on two
case studies: the conversion between different social registers and diachronic
language change. We show that the proposed method is capable of fine-grained
controlled change of these aspects of the input sentence. It is also learning a
continuous (rather than categorical) representation of the style of the
sentence, which is more linguistically realistic. The model uses
adversarial-motivational training and includes a special motivational loss,
which acts opposite to the discriminator and encourages a better decomposition.
Furthermore, we evaluate the obtained meaning embeddings on a downstream task
of paraphrase detection and show that they significantly outperform the
embeddings of a regular autoencoder.

本文提出了一种文本表示的对抗性分解方法，并通过两个案例展示其能够进行精细控制元素变化，并学习连续的风格表示。该模型使用对抗性训练和特定的动机损失，并在下游任务中表现优异。