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Multitask Learning for Emotion and Personality Detection

2021-01-07 03:09:55
Yang Li, Amirmohammad Kazameini, Yash Mehta, Erik Cambria

Abstract

tract: 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.

Abstract (translated)

URL

https://arxiv.org/abs/2101.02346

PDF

https://arxiv.org/pdf/2101.02346


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