RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation

Document Type

Conference Proceeding

Publication Title

2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings

Abstract

Suicide is a serious public health issue, but it is preventable with timely intervention. Emerging studies have suggested there is a noticeable increase in the number of individuals sharing suicidal thoughts online. As a result, utilising advance Natural Language Processing techniques to build automated systems for risk assessment is a viable alternative. However, existing systems are prone to incorrectly predicting risk severity and have no early detection mechanisms. Therefore, we propose RISE, a novel robust mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early-exit inference capabilities. Through quantitative, qualitative and ablative experiments, we demonstrate RISE as an efficient and robust human-in-the-loop approach for risk assessment over the Columbia Suicide Severity Risk Scale (C-SSRS) and CLPsych 2022 datasets. It is able to successfully abstain from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier.

First Page

14134

Last Page

14145

Publication Date

1-1-2024

Keywords

Hyperbolic Learning, Internal Classifiers, Suicide Risk Evaluation

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