  In this paper, we propose a new framework for fine-grained emotion prediction
in the text through emotion definition modeling. Our approach involves a
multi-task learning framework that models definitions of emotions as an
auxiliary task while being trained on the primary task of emotion prediction.
We model definitions using masked language modeling and class definition
prediction tasks. Our models outperform existing state-of-the-art for
fine-grained emotion dataset GoEmotions. We further show that this trained
model can be used for transfer learning on other benchmark datasets in emotion
prediction with varying emotion label sets, domains, and sizes. The proposed
models outperform the baselines on transfer learning experiments demonstrating
the generalization capability of the models.
