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A Mood Semantic Awareness Model for Emotional Interactive Robots.

Tiehua Zhou1, Zihan Yu1, Ling Wang1

  • 1Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.

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Summary
This summary is machine-generated.

This study introduces the Icc_dialogue model to improve emotional robots

Keywords:
continuation of the topicdialogue modelingemotional interactive robotssemantic interaction service

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Advancements in natural language processing and computing power have driven the adoption of human-machine dialogue systems.
  • Existing systems often exhibit rigid emotional expressions and overly purposeful responses.
  • Conversational bots struggle with topic continuation after answering user queries, leading to premature topic interruption.

Purpose of the Study:

  • To propose the Icc_dialogue model for enhancing mood semantic awareness in emotional interactive robots.
  • To improve user engagement by dynamically managing dialogue topic transitions based on user interest.
  • To enable seamless topic continuation in conversational AI.

Main Methods:

  • Voice interaction module for emotion calculation based on model responses.
  • Formulation of rules to calculate user interest levels.
  • Development of a text similarity matrix for dialogue dataset analysis to determine topic continuation.

Main Results:

  • The Icc_dialogue model successfully enhances robots' semantic awareness of user moods.
  • The system effectively determines topic transitions to maintain user interest.
  • Resolved issues of overly purposeful responses, rigid emotional expressions, and topic interruption after query resolution.

Conclusions:

  • The Icc_dialogue model significantly improves the dialogue quality and user experience of emotional interactive robots.
  • The proposed methods provide a robust solution for maintaining engaging and coherent conversations.
  • Validated through both automatic and manual evaluations, demonstrating substantial performance gains.