Multitask Learning for Mental Health Conditions with Limited Social Media Data

Adrian Benton, Margaret Mitchell, Dirk Hovy


Abstract
Language contains information about the author’s demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.
Anthology ID:
E17-1015
Original:
E17-1015v1
Version 2:
E17-1015v2
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
152–162
Language:
URL:
https://www.aclweb.org/anthology/E17-1015
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1015.pdf