  In recent years, deep learning-based automated personality trait 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 multitask learning framework, SoGMTL 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 MAML-like framework for model
optimization. Our more computationally efficient CNN-based multitask model
achieves the state-of-the-art performance across multiple famous personality
and emotion datasets, even outperforming Language Model based models.
