Youngjun Jung  Changki Lee  Jeongin Hwang  Hyungjong Noh

10.5626/JOK.2023.50.1.19

Style Transfer Unsupervised learning machine translation chat language

Style transfer is the task of generating text of a target style while maintaining content of given text written in a source style. In general, it is assumed that the content is an invariant and the style is variable when the style of the text is transferred. However, in the case of chat language, there is a problem in that it is not well trained by existing style transfer model. In this paper, we proposed a method of transfer chat language into written language using a style transfer model with unsupervised machine translation. This study shows that it is possible to construct a word transfer dictionary between styles that can be used for style transfer by utilizing transferred results. Additionally, it shows that transferred results can be improved by applying a filtering method to transferred result pair so that only well transferred results can be used and by training the style transfer model using a supervised learning method with filtered results.