Fine-grained emotion classification (FEC) is a challenging task.
Specifically, FEC needs to handle subtle nuance between labels, which can be
complex and confusing. Most existing models only address text classification
problem in the euclidean space, which we believe may not be the optimal
solution as labels of close semantic (e.g., afraid and terrified) may not be
differentiated in such space, which harms the performance. In this paper, we
propose HypEmo, a novel framework that can integrate hyperbolic embeddings to
improve the FEC task. First, we learn label embeddings in the hyperbolic space
to better capture their hierarchical structure, and then our model projects
contextualized representations to the hyperbolic space to compute the distance
between samples and labels. Experimental results show that incorporating such
distance to weight cross entropy loss substantially improves the performance
with significantly higher efficiency. We evaluate our proposed model on two
benchmark datasets and found 4.8% relative improvement compared to the previous
state of the art with 43.2% fewer parameters and 76.9% less training time. Code
is available at https: //github.com/dinobby/HypEmo.

本论文提出了 HypEmo，一种能够融合双曲嵌入以提高情感分类任务性能的新型框架。我们使用超平面空间中的标签嵌入来更好地捕捉其层次结构，然后将上下文表示投射到该空间中来计算样本和标签之间的距离，并发现相对于现有技术，使用此距离加权交叉熵损失能够大大提高性能并减少参数和训练时间。