双语对话数据集与人格和情绪注释,用于教育中的人格识别
Zhi Liu1,2, Yao Xiao1, Zhu Su3,4
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China.
Scientific data
|March 28, 2025
概括
这项研究引入了具有个性和情感注释的新型双语对话数据集,增强了自然语言处理 (NLP) 模型,以进行更类似人类的对话.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 情感计算是一种情感计算.
背景情况:
- 现有的对话数据集缺乏关键的个性和情感注释,阻碍了个性化和类似人类的对话AI的发展.
- 这种限制影响了用户体验和自然语言处理 (NLP) 模型捕捉微妙的人际交互的能力.
研究的目的:
- 构建全面的双语 (中英) 对话数据集,丰富五大个性特征和情感注释.
- 解决现有的对话资源中多元人格代表的稀缺问题.
主要方法:
- 在多代理框架内使用AutoGen工具来生成基于寓言的多轮问答对话数据集.
- 创建了具有多样性个性的人格代理,以确保异质性,并克服人格多样性的局限性.
- 整合了每个发言的情感注释.
主要成果:
- 成功构建了双语对话数据集,集成了五大性格特征和情绪注释.
- 验证了发言的质量,并调查了对话内容与发言者的个性特征之间的一致性.
- 通过创建个人代理来证明增强人格异质性.
结论:
- 开发的数据集是推进情感智能对话系统和人格和情感计算研究的宝贵资源.
- 该数据集有助于开发能够理解和产生反映个性和情绪的对话的NLP模型.
- 能够创建情感感知系统,用于自动检测人格特征.
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