自适应的注意力融合,用于基于多式联络方面的情绪分析
Ziyue Wang1,2, Junjun Guo1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究引入了一个新的架构,用于联合多式联网基于方面情绪分析 (JMABSA). 拟议的模型有效地弥合了多式联络情绪分析中的语义差距,优于现有的方法.
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:
- 多式联络方面术语提取 (MATE) 和多式联络方面情绪分类 (MASC) 是多式联络情绪分析的关键组成部分.
- 预训练的生成模型对基于方面的情绪分析 (ABSA) 有希望,但由于语义差距,在跨模式转移方面面临挑战.
- 现有的方法很难有效地整合文本和视觉信息来完成情绪分析任务.
研究的目的:
- 提出一种自适应的跨模态注意力融合架构,用于联合多模态基于方面情绪分析 (JMABSA).
- 在生成模型中弥合文本和视觉模式之间的语义差距.
- 适应基于文本的预训练模型,用于多式联络情绪分析.
主要方法:
- 开发了一种使用图像-文本选择性融合机制的生成模型.
- 实现了一个自我适应的跨模态注意力融合架构.
- 使用预训练的生成模型,适应多式联网输入.
主要成果:
- 拟议的JMABSA模型在两个基准数据集上显著优于最先进的方法.
- 证明有效地弥合了文本和图像表示之间的语义差距.
- 展示了成功地适应文本预训练用于多式联络情绪分析.
结论:
- 这种新的架构在联合多式联络方面基于情绪分析方面取得了重大进展.
- 选择性融合机制有效地解决了跨模式的语义差异.
- 该模型为图像文本数据中的情感分析提供了强大的解决方案.
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