Text-based emotion prediction is an important task in the field of affective computing. Most prior work has been restricted to predicting emotions corresponding to a few high-level emotion classes. This paper explores and experiments with various techniques for fine-grained (27 classes) emotion prediction†^\dagger. In particular, 1) we present a method to incorporate multiple annotations from different raters, 2) we analyze the model's performance on fused emotion classes and with sub-sampled training data, 3) we present a method to leverage the correlations among the emotion categories, and 4) we propose a new framework for text-based fine-grained emotion prediction through emotion definition modeling. The emotion definition-based model outperforms the existing state-of-the-art for fine-grained emotion dataset GoEmotions. The 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. We 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 model's generalization capability.

中文翻译：

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基于文本的细粒度情绪预测


基于文本的情感预测是情感计算领域的一项重要任务。大多数先前的工作仅限于预测与一些高级情绪类别相对应的情绪。本文探索并实验了细粒度（27 类）情绪预测的各种技术†^\dagger。特别是，1）我们提出了一种合并来自不同评分者的多个注释的方法，2）我们分析了模型在融合情感类别和子采样训练数据上的性能，3）我们提出了一种利用情感类别之间的相关性的方法，4）我们提出了一个通过情感定义建模进行基于文本的细粒度情感预测的新框架。基于情感定义的模型优于现有最先进的细粒度情感数据集 GoEmotions。该方法涉及一个多任务学习框架，该框架将情绪定义建模为辅助任务，同时接受情绪预测主要任务的训练。我们使用掩码语言建模和类定义预测任务来对定义进行建模。我们表明，这种经过训练的模型可用于在具有不同情感标签集、域和大小的情感预测中的其他基准数据集上进行迁移学习。所提出的模型优于迁移学习实验的基线，证明了该模型的泛化能力。