削弱文本的主导作用:CMOSI数据集和多模式语义增强网络
概括
本研究引入了一个新的数据集和模型,以解决多式联络情绪分析 (MSA) 中的文本主导地位问题. 提出的方法通过减少对文本的依赖,有效地分析情绪,在多个数据集中显示出强的性能.
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.
- 计算机视觉 计算机视觉
背景情况:
- 多式联络情绪分析 (MSA) 对于理解意见至关重要,但经常受到文本主导的阻碍.
- 现有的方法难以平衡来自不同模式的贡献,导致结果偏差.
研究的目的:
- 通过提出新的数据集和新的网络架构来解决MSA中的文本主导地位.
- 开发一种有效利用非文本信息进行情感分析的方法.
主要方法:
- 引入了中国多式联运论水平情绪强度 (CMOSI) 数据集的三个版本,包括文本减弱的变体.
- 提出了一种使用多头注意力机制的多式语义增强网络 (MSEN).
- 进行了CMOSI实验和MOSI,MOSEI和CH-SIMS的交叉数据集概括测试.
主要成果:
- 该MSEN模型表现出强大的性能,即使在CMOSI数据集的文本减弱版本上也是如此.
- 该网络有效地捕获了非文本形式的潜在语义.
- 在一般化实验中,MSEN显示出具有竞争力的结果和良好的跨语言稳定性.
结论:
- 削弱文本主导地位对于有效的多式联络情绪分析至关重要.
- 拟议的CMOSI数据集和MSEN模型为平衡的多式联运情绪理解提供了一个有希望的方法.
- MSEN网络在不同的数据集和语言中表现出强大的概括能力.
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