通过选择性对抗性训练实现多样化情感对话生成
Bo Li1, Huan Zhao1, Zixing Zhang1
1College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种新的方法,通过使用选择性扰动在对话AI中产生多样化的情绪反应. 该方法增强了情绪表达和响应的多样性,解决了当前类似人类的人工智能系统的局限性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 像人类一样的交谈AI需要复杂的情感感知和表达.
- 目前用于对话生成的深度神经方法在控制情绪反应方面存在局限性,并且经常产生通用,高频输出.
研究的目的:
- 开发一种方法,在对话式人工智能中产生多样化和情感上适当的反应.
- 解决AI产生的对话中通用响应的挑战.
主要方法:
- 一个新的模型,采用选择性的词扰动来增强响应多样性.
- 全球情绪控制模块,以保持响应的一致性,防止情绪/语义漂移.
主要成果:
- 拟议的模型显著改善了AI产生的反应中的情绪表达.
- 实验结果表明,与现有的基线方法相比,反应多样性得到了增强.
结论:
- 选择性干扰与全球情绪控制相结合,可以有效地产生多样化和情绪细微的AI反应.
- 这种方法推进了更类似人类和引人入胜的对话性AI系统的开发.
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