适应式图形学习与多模式融合用于在对话中识别情绪
Jian Liu1, Jian Li2, Jiawei Dong1
1Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China.
Biomimetics (Basel, Switzerland)
|July 25, 2025
概括
这项研究引入了GASMER,这是一种用于对话情绪识别的新方法. GASMER有效地模拟复杂的对话动态,显著提高了多式联络情绪识别任务的准确性.
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:
- 对话式情绪识别对于自然的人机交互至关重要.
- 现有的方法与全球话题流和本地演讲者互动的双重影响作斗争.
- 强大的情感识别需要理解复杂的对话依赖关系.
研究的目的:
- 引入GASMER (多模态情感识别的图形适应结构),用于对话情感识别的统一架构.
- 为应对全球话题流和本地扬声器对扬声器的依赖所带来的挑战.
- 为了提高对话中的多式联络情绪识别的准确性和稳定性.
主要方法:
- 开发了GASMER,这是一种利用图形神经网络 (GNN) 来建模对话依赖性的新型架构.
- 在GNN框架内实施了自适应图形学习机制.
- 采用了细粒度多式联通融合技术.
主要成果:
- 在对话情绪识别方面,GASMER 优于现有的基于图形的方法.
- 与最近的多式联接融合模型相比,该模型实现了竞争性性能.
- 在IEMOCAP数据集上,GASMER提高了2.7%的准确性,加权F1得分提高了3.6%.
- 在MOSEI数据集上,GASMER在二进制分类准确度 (ACC-2) 中获得了1.2%的收益.
结论:
- 将细粒度的多式联络融合与自适应图形学习相结合,对于有效的对话情绪识别至关重要.
- 加斯默 (GASMER) 证明了自适应图形学习在模拟复杂的对话动态方面的有效性.
- 拟议的架构在情感识别领域取得了重大进展.
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