Multi-Task Learning for Jointly Detecting Depression and Emotion
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A4 Artikkeli konferenssijulkaisussa
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2021
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en
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8
3142-3149
3142-3149
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Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Abstract
Depression is a typical mood disease that makes people a persistent feeling of sadness and loss of interest and pleasure. Emotion thus comes into sight and is tightly entangled with depression in that one helps the understanding of the other. Depression and emotion detection has been a new research task. The central challenges in this task are multi-modal interaction and multi-task correlation. The existing approaches treat them as two separate tasks, and fail to model the relationships between them. In this paper, we propose an attentive multi-modal multitask learning framework, called AMM, to generically address such issues. The core modules are two attention mechanisms, viz. inter-modal (I {mathrm{e}}) and inter-task (I {t}) attentions. The main motivation of I {mathrm{e}} attention is to learn multi-modal fused representation. In contrast, It attention is proposed to learn the relationship between depression detection and emotion recognition. Extensive experiments are conducted on two large scale datasets, i.e., DAIC and multi-modal Getty Image depression (MGID). The results show the effectiveness of the proposed AMM framework, and also shows that AMM obtains better performance for the main task, i.e., depression detection with the help of the secondary emotion recognition task.Description
Funding Information: This work is supported by National Science Foundation of China under grant No. 62006212, the fund of State Key Lab. for Novel Software Technology in Nanjing University under grant No.KFKT2021B41. Publisher Copyright: © 2021 IEEE.
Keywords
artificial intelligence, deep learning, emotion recognition, multi-modal depression detection, multi-task learning
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Citation
Zhang, Y, Li, X, Rong, L & Tiwari, P 2021, Multi-Task Learning for Jointly Detecting Depression and Emotion . in Y Huang, L Kurgan, F Luo, X T Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 . IEEE, pp. 3142-3149, IEEE International Conference on Bioinformatics and Biomedicine, Virtual, Online, United States, 09/12/2021 . https://doi.org/10.1109/BIBM52615.2021.9669546