A Review of Text Style Transfer using Deep Learning
Date Issued
2021-09-28
Author(s)
Toshevska, Martina
Abstract
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.
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.
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