@inproceedings{soun-etal-2024-rise,
    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.",
}

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.
Soun, Ritesh Singh Neerkaje, Atula Tejaswi Sawhney, Ramit Aletras, Nikolaos Nakov, Preslav
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation