  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.
