EmoShiftNet:一个变化意识的多任务学习框架,在多方对话中用于情绪识别的融合策略
Hinduja Nirujan1, Y H P P Priyadarshana2
1Informatics Institute of Technology, Colombo, Sri Lanka.
Frontiers in artificial intelligence
|September 19, 2025
概括
这项研究介绍了EmoShiftNet,这是一种用于在对话中识别情绪的新框架,可以有效地模拟微妙的情绪转变. 这种方法提高了准确性,特别是在多党对话中的少数派情绪.
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.
- 机器学习 机器学习
背景情况:
- 对人工智能应用来说,对话中的情绪识别 (ERC) 是至关重要的,但在多方对话 (MPC) 中,它难以检测情绪转移.
- 现有的模型往往忽视了微妙的,上下文依赖的情感转变,这种转变被像MELD这样的不平衡数据集所加剧.
- 检测这些转变对于微妙的对话AI至关重要.
研究的目的:
- 提出EmoShiftNet,这是一个新的转移意识多任务学习 (MTL) 框架,用于改善MPC中的ERC.
- 共同解决情绪分类和情绪转移检测问题.
- 通过明确建模情绪转换来增强ERC模型的稳定性.
主要方法:
- EmoShiftNet集成了多式联络功能:BERT文本嵌入,声学功能 (MFCC,音调,声音强度) 和时间线索 (暂停时间,扬声器重叠,发言长度).
- 一个移动感知MTL框架执行联合情绪分类和移动检测.
- 一个复合损失函数 (焦点损失,二进制交叉,三倍边缘损失) 将情绪转移检测作为辅助任务.
主要成果:
- 在MELD数据集上,EmoShiftNet的表现优于传统和基于图形的ERC模型,获得更高的F1分数.
- 该框架在不平衡的数据条件下更好地识别了少数群体的情绪.
- 多式联接和明确的班次监督被证明是提高ERC绩效的有效手段.
结论:
- 模拟情绪过渡对于在多方对话中推进ERC至关重要.
- 带有明确转移检测的EmoShiftNet的MTL方法增强了上下文意识和稳定性.
- 这一框架为复杂对话中识别情绪提供了更全面的解决方案.
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