Multitask learning for emotion and personality traits detection

In recent years, deep learning-based automated personality traits detection has received a lot of attention, especially now, due to the massive digital footprints of an individual. Moreover, many researchers have demonstrated that there is a strong link between personality traits and emotions. In this paper, we build on the known correlation between personality traits and emotional behaviors and propose a novel transferring based multitask learning framework that simultaneously predicts both of them. We also empirically evaluate and discuss different information-sharing mechanisms between the two tasks. To ensure the high quality of the learning process, we adopt a model-agnostic meta-learning-like framework for model optimization. Our computationally efficient multitask learning model achieves the state-of-the-art performance across multiple famous personality and emotion datasets, even outperforming language model-based models.