Intelligent Human-Computer Interaction Dialog Model Based on End to End Neural Networks and Emotion Information

Abstract

The current dialog model suffers from the lack of grammatical accuracy and content relevance. To provide interactive content with high quality, the study constructs an intelligent human-computer interaction dialog model using end to end neural network and emotion information. The study utilizes the transformer-based bidirectional coding model and the end to end neural network model to realize the interactive dialogue, and embeds emotional information in the model to realize the intelligent human-computer interaction dialog. The outcomes demonstrated that the classification accuracy of the research-designed sentiment classification model on the three datasets was 89.97%, 90.15%, and 91.44%, respectively. The research-designed model consistently outperformed the other models in terms of emotion accuracy as well as Distinct-1 and Distinct-2 score values, which were 0.068, 0.204, and 80.425%, respectively. Meanwhile, the average accuracy of the research-designed model was higher than 90% on all three datasets. The emotional quality and content quality of the model during the dialog process were higher than several more popular emotional interaction dialog models. The comprehensive analysis outcomes reveal that the research-designed model is able to generate high-quality emotion-based responses in the conversation, providing users with a more intelligent human-computer interaction experience.

Keywords

• End to end neural network
• emotional information
• human-computer interaction dialogue
• intelligence
• emotion classification