Multitask Learning for Emotion and Personality Detection

• URL: http://arxiv.org/abs/2101.02346v1
• Date: Thu, 7 Jan 2021 03:09:55 GMT
• Title: Multitask Learning for Emotion and Personality Detection
• Authors: Yang Li, Amirmohammad Kazameini, Yash Mehta, Erik Cambria
• Abstract summary: We build on the known correlation between personality traits and emotional behaviors, and propose a novel multitask learning framework, SoGMTL. Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets.
• Score: 17.029426018676997
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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