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Detecting moments of shange and suicidal risks in longitudinal user texts using multi-task learning

Detecting moments of shange and suicidal risks in longitudinal user texts using multi-task learning
Detecting moments of shange and suicidal risks in longitudinal user texts using multi-task learning
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 a bidirectional long-short term memory (Bi-LSTM) model for predicting changes in user’s mood (Task A) and their suicidal risk level (Task B). 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-inone’ framework that jointly learns the related mental health tasks. Our experimental results (ranked top for task A) suggest that the proposed multi-task framework outperforms the alternative single-task frameworks submitted to the challenge and evaluated via the 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.
Natural Language Processing, NLP, mental health and social care, Suicide
Azim, Tayyaba
4e6decad-1e15-4e87-822c-70ca0ac8654a
Loitongbam, Gyanendro Singh
c1d8ea4f-7a54-4c78-8830-3c3064e26ae6
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Azim, Tayyaba
4e6decad-1e15-4e87-822c-70ca0ac8654a
Loitongbam, Gyanendro Singh
c1d8ea4f-7a54-4c78-8830-3c3064e26ae6
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f

Azim, Tayyaba, Loitongbam, Gyanendro Singh and Middleton, Stuart (2022) Detecting moments of shange and suicidal risks in longitudinal user texts using multi-task learning. Workshop on Computational Linguistics and Clinical Psychology: North American Chapter of the Association for Computational Linguistics 2022 (NAACL-2022), , Seattle, United States. 15 Jul 2022. (In Press)

Record type: Conference or Workshop Item (Paper)

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 a bidirectional long-short term memory (Bi-LSTM) model for predicting changes in user’s mood (Task A) and their suicidal risk level (Task B). 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-inone’ framework that jointly learns the related mental health tasks. Our experimental results (ranked top for task A) suggest that the proposed multi-task framework outperforms the alternative single-task frameworks submitted to the challenge and evaluated via the 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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CLPsych_Shared_Task_Manuscript camera ready 30-05-2022 - Accepted Manuscript
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More information

Accepted/In Press date: 29 May 2022
Venue - Dates: Workshop on Computational Linguistics and Clinical Psychology: North American Chapter of the Association for Computational Linguistics 2022 (NAACL-2022), , Seattle, United States, 2022-07-15 - 2022-07-15
Keywords: Natural Language Processing, NLP, mental health and social care, Suicide

Identifiers

Local EPrints ID: 457919
URI: http://eprints.soton.ac.uk/id/eprint/457919
PURE UUID: 262c60ed-b9e3-4b77-a649-5aa13bb35aa8
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176

Catalogue record

Date deposited: 22 Jun 2022 16:41
Last modified: 23 Jun 2022 01:38

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Contributors

Author: Tayyaba Azim
Author: Gyanendro Singh Loitongbam

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