Text style transfer, a challenging task in natural language processing, seeks to transform the stylistic attributes of a given text while maintaining its original meaning. Stylistic attributes are defined by a user. Most frequently used styles are sentiment, formality, politeness, etc. This task can be solved by two methodologies: supervised learning and unsupervised learning. The supervised approach, while effective, faces the challenge of constructing parallel datasets, which are pairs of source and target texts. This limitation has encouraged the investigation of unsupervised text style transfer, a possible approach that eliminates the need for parallel dataset. One method for achieving unsupervised text style transfer is controllable style transfer, which enables the regulation of the degree of style modification. However, a challenge with controllable style transfer is that the fluency of the translated text deteriorates as the degree of style modification increases. To address this issue, we introduce a novel methodology that integrates syntactic parsing information into the style transfer process. By leveraging syntactic information, our model is guided to generate natural sentences that effectively reflect the desired style while maintaining fluency. Extensive experimental results have shown that incorporating syntactic parsing information into the controllable style transfer process leads to significant improvements in both the overall performance and the fluency of the generated text compared to existing controllable style transfer methods. This enhancement stems from the ability of syntactic information to provide guidance for the model, enabling it to generate natural-sounding sentences that accurately reflect the desired style while maintaining coherence.
