Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language
This addresses suicide prevention in mental health services for low-resource language speakers, but it is incremental as it builds on existing methods with domain-specific adaptations.
The paper tackled the problem of detecting suicide risk in online counseling services for low-resource languages by proposing a model combining pre-trained language models with clinically approved suicidal cues and two-stage fine-tuning, achieving 0.91 ROC-AUC and 0.55 F2-score while outperforming baselines early in conversations.
With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.