  Text style transfer is the task that generates a sentence by preserving the
content of the input sentence and transferring the style. Most existing studies
are progressing on non-parallel datasets because parallel datasets are limited
and hard to construct. In this work, we introduce a method that follows two
stages in non-parallel datasets. The first stage is to delete attribute markers
of a sentence directly through a classifier. The second stage is to generate a
transferred sentence by combining the content tokens and the target style. We
experiment on two benchmark datasets and evaluate context, style, fluency, and
semantic. It is difficult to select the best system using only these automatic
metrics, but it is possible to select stable systems. We consider only robust
systems in all automatic evaluation metrics to be the minimum conditions that
can be used in real applications. Many previous systems are difficult to use in
certain situations because performance is significantly lower in several
evaluation metrics. However, our system is stable in all automatic evaluation
metrics and has results comparable to other models. Also, we compare the
performance results of our system and the unstable system through human
evaluation. Our code and data are available at the link
(https://github.com/rungjoo/Stable-Style-Transformer).
