Text Style Transfer: A Comprehensive Study on Methodologies and Evaluation

Text Style Transfer (TST) rewords a sentence from one style (e.g., polite) to another (e.g., impolite) while conserving the meaning and content. This domain has attracted the attention of many researchers as it makes natural language generation (NLG) tasks more user-oriented. TST finds its applications widely in industry such as conversational bots and writing assistance tools. With the success of deep learning, a plethora of research works on style transfer based on machine learning have been proposed, developed, and tested. This systematic review presents the past work on TST clustered into categories based on machine learning and deep learning algorithms. It briefly explains the various subtasks within TST and assembles its publicly available datasets. It also summarizes the automatic and manual evaluation practices used for style transfer tasks and finally, sheds some light on current challenges and points towards promising future directions for research in the TST domain.