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Automatic Generation of Custom Advertisement Messages based on Literacy Styles of Classified Personality Types
Jimin Seong, Yunjong Choi, Doyeon Kwak, Hansaem Kim
http://doi.org/10.5626/JOK.2024.51.1.23
This study introduces a novel framework that defines marketing styles based on the MBTI personality types, and presents a machine learning technique to generate customized advertising messages aligned to these types. We use the BART algorithm to synthesize customized advertising content by training on the advertisement texts incorporating personality type prefixes. Our experiments confirm the model’s efficacy in transforming generic advertising copy into custom messages that embody the distinct style characteristics of each personality type, via prefix manipulation. Theoretically, our research establishes the relationship between style characteristics and personality types; practically, it provides the technique to fine-tune a language model to generate advertising messages that align with specific personality types. Moreover, this research serves as a foundational work for systematizing and replicating stylistic differences across various languages and regions.
Style Transfer for Chat Language using Unsupervised Machine Translation
Youngjun Jung, Changki Lee, Jeongin Hwang, Hyungjong Noh
http://doi.org/10.5626/JOK.2023.50.1.19
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.
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