Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211100019-6.doi: 10.11896/jsjkx.211100019

• Artificial Intelligence • Previous Articles     Next Articles

Personalized Dialogue Generation Integrating Sentimental Information

XU Hui, WANG Zhong-qing, LI Shou-shan, ZHANG Min   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:XU Hui,born in 1996,postgraduate.His main research interests include natural language processing and so on.
    WANG Zhong-qing,born in 1987,Ph.D,associate professor.His main research interests include natural language processing,sentiment analysis and dialog generation.

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Abstract: Nowadays,more and more attention has been paid to the man-machine dialogue system.However,the current mainstream man-machine dialogue system rarely considers the personalized characteristics of the speaker.An important aspect of the dialogue system is to improve the response quality of dialogue according to the personality of interactive personnel.Personalization is the key to create intelligent dialogue system,which can be well adapted to human life.Emotion is a very important factor in the generation of personalized dialogue.Therefore,a personalized dialogue generation model integrating attribute level emotion is proposed in this paper.The BERT-MRC model is used to extract the emotional and attribute information of character personality and historical dialogue.The improved UNILM neural network model is used to encode character personality and historical dialogue.At the same time,the emotional word information and attribute word information are combined in the coding representation to finally generate a dialogue in line with character personality.Experiments show that the proposed method can effectively improve the quality of personalized dialogue generation and increase the diversity of generated responses.

Key words: Natural language processing, Dialogue generation, Personality, Neural network, Emotion, Attribute

CLC Number: 

  • TP183
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