@inproceedings{soun-etal-2024-rise-robust,
title = "{RISE}: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation",
author = "Soun, Ritesh Singh and
Neerkaje, Atula Tejaswi and
Sawhney, Ramit and
Aletras, Nikolaos and
Nakov, Preslav",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1232",
pages = "14134--14145",
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.",
}
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%0 Conference Proceedings
%T RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation
%A Soun, Ritesh Singh
%A Neerkaje, Atula Tejaswi
%A Sawhney, Ramit
%A Aletras, Nikolaos
%A Nakov, Preslav
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F soun-etal-2024-rise-robust
%X 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.
%U https://aclanthology.org/2024.lrec-main.1232
%P 14134-14145
Markdown (Informal)
[RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation](https://aclanthology.org/2024.lrec-main.1232) (Soun et al., LREC-COLING 2024)
ACL