论文标题
语言模型作为文本对话的情感分类器
Language Models as Emotional Classifiers for Textual Conversations
论文作者
论文摘要
通过改变我们对环境的感知，处理和反应方式，情感在我们的日常生活中起着关键作用。情感计算旨在在计算机中灌输检测和对人类参与者情绪的能力。任何情感计算系统的核心方面是对用户情绪的分类。在这项研究中，我们提出了一种在对话中对情感进行分类的新方法。在我们提出的方法论的骨干上是预训练的语言模型（LM），该模型由图形卷积网络（GCN）补充，该网络（GCN）通过在话语中识别的谓词芳香记录结构传播信息。我们将提出的方法应用于IEMocap和朋友的数据集，在前者上实现最先进的性能，并在后者的某些情感标签上具有更高的准确性。此外，我们通过更改模型在进行分类时可以访问的前面对话中的多少来研究上下文在我们的方法中的作用。
Emotions play a critical role in our everyday lives by altering how we perceive, process and respond to our environment. Affective computing aims to instill in computers the ability to detect and act on the emotions of human actors. A core aspect of any affective computing system is the classification of a user's emotion. In this study we present a novel methodology for classifying emotion in a conversation. At the backbone of our proposed methodology is a pre-trained Language Model (LM), which is supplemented by a Graph Convolutional Network (GCN) that propagates information over the predicate-argument structure identified in an utterance. We apply our proposed methodology on the IEMOCAP and Friends data sets, achieving state-of-the-art performance on the former and a higher accuracy on certain emotional labels on the latter. Furthermore, we examine the role context plays in our methodology by altering how much of the preceding conversation the model has access to when making a classification.
