Multi-Task Learning for Depression Detection in Dialogs

Chuyuan Li, Chloé Braud, Maxime Amblard


Abstract
Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC and DailyDialog corpora – both contain dialogs in English – and show important improvements over state-of-the-art on depression detection (at best 70.6% F1), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.
Anthology ID:
2022.sigdial-1.7
Volume:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2022
Address:
Edinburgh, UK
Editors:
Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–75
Language:
URL:
https://aclanthology.org/2022.sigdial-1.7
DOI:
10.18653/v1/2022.sigdial-1.7
Bibkey:
Cite (ACL):
Chuyuan Li, Chloé Braud, and Maxime Amblard. 2022. Multi-Task Learning for Depression Detection in Dialogs. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 68–75, Edinburgh, UK. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Learning for Depression Detection in Dialogs (Li et al., SIGDIAL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.sigdial-1.7.pdf
Code
 chuyuanli/mtl4depr
Data
DailyDialog