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

Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer