RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation
Ritesh Singh Soun, Atula Tejaswi Neerkaje, Ramit Sawhney, Nikolaos Aletras, Preslav Nakov
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.- Anthology ID:
- 2024.lrec-main.1232
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 14134–14145
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1232
- DOI:
- Cite (ACL):
- Ritesh Singh Soun, Atula Tejaswi Neerkaje, Ramit Sawhney, Nikolaos Aletras, and Preslav Nakov. 2024. RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14134–14145, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (Soun et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1232.pdf