We present a novel deep learning-based framework to generate embedding
representations of fine-grained emotions that can be used to computationally
describe psychological models of emotions. Our framework integrates a
contextualized embedding encoder with a multi-head probing model that enables
to interpret dynamically learned representations optimized for an emotion
classification task. Our model is evaluated on the Empathetic Dialogue dataset
and shows the state-of-the-art result for classifying 32 emotions. Our layer
analysis can derive an emotion graph to depict hierarchical relations among the
emotions. Our emotion representations can be used to generate an emotion wheel
directly comparable to the one from Plutchik's\LN model, and also augment the
values of missing emotions in the PAD emotional state model.

本研究提出了一种新型深度学习框架，用于生成细粒度情感的嵌入式表示，可用于计算描述情感心理模型。通过与多头探测模型集成的上下文嵌入编码器，可以动态地学习情感表示并优化情感分类任务，评估结果表明可以达到最先进的 32 种情感分类。最后结合情感图生成情感轮并增强丢失的情感状态模型价值。