情绪交互式机器人的情绪语义意识模型
Tiehua Zhou1, Zihan Yu1, Ling Wang1
1Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了Icc_dialogue模型,用于改进情感机器人.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 自然语言处理自然语言处理.
背景情况:
- 自然语言处理和计算能力的进步推动了人机对话系统的采用.
- 现有的系统往往表现出化的情绪表达和过于有目的的反应.
- 会话机器人在回答用户查询后难以继续主题,导致主题过早中断.
研究的目的:
- 提出ICC_dialogue模型,用于增强情绪交互式情感机器人的语义意识.
- 通过根据用户兴趣动态管理对话话题过渡来提高用户参与度.
- 为了在对话AI中实现无的主题延续.
主要方法:
- 基于模型响应的情绪计算的语音交互模块.
- 制定计算用户利益水平的规则.
- 开发文本相似度矩阵用于对话数据集分析,以确定主题的延续.
主要成果:
- Icc_dialogue模型成功地提高了机器人对用户情绪的语义意识.
- 该系统有效地确定主题转换,以保持用户的兴趣.
- 解决了过于有目的的回应,硬的情绪表达和查询解决后的主题中断的问题.
结论:
- Icc_dialogue模型显著提高了情感交互式机器人的对话质量和用户体验.
- 提出的方法提供了一个强大的解决方案,以保持有吸引力和连贯的对话.
- 通过自动和手动评估验证,证明了显著的性能提升.
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