Emotion-Infused Models for Explainable Psychological Stress Detection
Elsbeth Turcan, Smaranda Muresan, Kathleen McKeown
Abstract
The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.
BibTeX
@inproceedings{turcan-etal-2021-emotion,
title = "Emotion-Infused Models for Explainable Psychological Stress Detection",
author = "Turcan, Elsbeth and
Muresan, Smaranda and
McKeown, Kathleen",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.230/",
doi = "10.18653/v1/2021.naacl-main.230",
pages = "2895--2909"
}