  Style is an integral component of a sentence indicated by the choice of words
a person makes. Different people have different ways of expressing themselves,
however, they adjust their speaking and writing style to a social context, an
audience, an interlocutor or the formality of an occasion. Text style transfer
is defined as a task of adapting and/or changing the stylistic manner in which
a sentence is written, while preserving the meaning of the original sentence.
  A systematic review of text style transfer methodologies using deep learning
is presented in this paper. We point out the technological advances in deep
neural networks that have been the driving force behind current successes in
the fields of natural language understanding and generation. The review is
structured around two key stages in the text style transfer process, namely,
representation learning and sentence generation in a new style. The discussion
highlights the commonalities and differences between proposed solutions as well
as challenges and opportunities that are expected to direct and foster further
research in the field.
