Fine-Grained Emotion Prediction by Modeling Emotion Definitions
This work addresses emotion analysis in NLP, offering improved accuracy for applications like sentiment analysis, but it is incremental as it builds on existing multi-task learning methods.
The authors tackled fine-grained emotion prediction in text by modeling emotion definitions as an auxiliary task, achieving state-of-the-art results on the GoEmotions dataset and demonstrating strong generalization in transfer learning experiments.
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.