Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning

Tayyaba Azim, Loitongbam Gyanendro Singh, Stuart E. Middleton


Abstract
This work describes the classification system proposed for the Computational Linguistics and Clinical Psychology (CLPsych) Shared Task 2022. We propose the use of multitask learning approach with bidirectional long-short term memory (Bi-LSTM) model for predicting changes in user’s mood and their suicidal risk level. The two classification tasks have been solved independently or in an augmented way previously, where the output of one task is leveraged for learning another task, however this work proposes an ‘all-in-one’ framework that jointly learns the related mental health tasks. The experimental results suggest that the proposed multi-task framework outperforms the remaining single-task frameworks submitted to the challenge and evaluated via timeline based and coverage based performance metrics shared by the organisers. We also assess the potential of using various types of feature embedding schemes that could prove useful in initialising the Bi-LSTM model for better multitask learning in the mental health domain.
Anthology ID:
2022.clpsych-1.19
Volume:
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Month:
July
Year:
2022
Address:
Seattle, USA
Editors:
Ayah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
213–218
Language:
URL:
https://aclanthology.org/2022.clpsych-1.19
DOI:
10.18653/v1/2022.clpsych-1.19
Bibkey:
Cite (ACL):
Tayyaba Azim, Loitongbam Gyanendro Singh, and Stuart E. Middleton. 2022. Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 213–218, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning (Azim et al., CLPsych 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.clpsych-1.19.pdf
Code
 stuartemiddleton/uos_clpsych
Data
TweetEval