@inproceedings{azim-etal-2022-detecting,
title = "Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning",
author = "Azim, Tayyaba and
Gyanendro Singh, Loitongbam and
Middleton, Stuart E.",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.19",
doi = "10.18653/v1/2022.clpsych-1.19",
pages = "213--218",
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.",
}
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%0 Conference Proceedings
%T Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning
%A Azim, Tayyaba
%A Gyanendro Singh, Loitongbam
%A Middleton, Stuart E.
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F azim-etal-2022-detecting
%X 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.
%R 10.18653/v1/2022.clpsych-1.19
%U https://aclanthology.org/2022.clpsych-1.19
%U https://doi.org/10.18653/v1/2022.clpsych-1.19
%P 213-218
Markdown (Informal)
[Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning](https://aclanthology.org/2022.clpsych-1.19) (Azim et al., CLPsych 2022)
ACL