Sangwon Youn  Youngjoong Ko

10.5626/JOK.2025.52.10.869

Natural Language Processing persona extraction quadruple dialogue generation

In developing AI systems that generate personalized conversations, understanding persona is crucial for reflecting a user’s characteristics. In this study, we introduce a quadruple structure- comprising Core, Expression, Sentiment, and Category-as a new way to accurately extract persona information from utterances and leverage it for response generation. We construct a quadruple dataset using LLM-based automatic annotation and implement various extraction models for comparison. After training the model, we enhance data quality through additional verification before retraining to obtain the final model. By supplying the extracted quadruple information to a response generation model, we evaluate performance differences across various persona representation methods and observe improved outcomes. This work advances persona extraction by structuring dynamic persona information to encompass sentiment and category levels, resulting in a more fine-grained and effective extraction system.