@inproceedings{li-etal-2022-multi,
title = "Multi-Task Learning for Depression Detection in Dialogs",
author = "Li, Chuyuan and
Braud, Chlo{\'e} and
Amblard, Maxime",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.7",
doi = "10.18653/v1/2022.sigdial-1.7",
pages = "68--75",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Depression Detection in Dialogs
%A Li, Chuyuan
%A Braud, Chloé
%A Amblard, Maxime
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F li-etal-2022-multi
%X 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.
%R 10.18653/v1/2022.sigdial-1.7
%U https://aclanthology.org/2022.sigdial-1.7
%U https://doi.org/10.18653/v1/2022.sigdial-1.7
%P 68-75
Markdown (Informal)
[Multi-Task Learning for Depression Detection in Dialogs](https://aclanthology.org/2022.sigdial-1.7) (Li et al., SIGDIAL 2022)
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.