  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.
