论文标题
图表：使用图网络捕获情绪相关性
EmoGraph: Capturing Emotion Correlations using Graph Networks
论文作者
论文摘要
大多数情感识别方法通过独立考虑个人情绪而忽略其模糊性质和之间的互连来解决情绪理解任务。在本文中，我们探讨了如何捕获情感相关性并帮助不同的分类任务。我们提出了通过图网络捕获不同情绪之间依赖关系的杂音。这些图是通过利用不同情绪类别之间的共发生统计数据来构建的。两个多标签分类数据集的经验结果表明，Emograph的表现优于强基础，尤其是对于宏F1。另一个实验说明了捕获的情绪相关性也可以使单标签分类任务受益。
Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnections among them. In this paper, we explore how emotion correlations can be captured and help different classification tasks. We propose EmoGraph that captures the dependencies among different emotions through graph networks. These graphs are constructed by leveraging the co-occurrence statistics among different emotion categories. Empirical results on two multi-label classification datasets demonstrate that EmoGraph outperforms strong baselines, especially for macro-F1. An additional experiment illustrates the captured emotion correlations can also benefit a single-label classification task.
