Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection
This work addresses scalable cross-language mental health monitoring where labeled data is limited, though it appears to be an incremental improvement combining existing techniques.
The authors tackled multilingual depression detection from social media text by proposing Semi-SMDNet, a semi-supervised ensemble teacher framework that uses uncertainty-aware pseudo-labeling and confidence-weighted training, achieving consistent performance improvements across Arabic, Bangla, English, and Spanish datasets and significantly reducing the performance gap between high-resource and low-resource settings.
Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Network. It combines teacher-student pseudo-labelling, ensemble learning, and augmentation of data. Our framework uses a group of teacher models. Their predictions come together through soft voting. An uncertainty-based threshold filters out low-confidence pseudo-labels to reduce noise and improve learning stability. We also use a confidence-weighted training method that focuses on reliable pseudo-labelled samples. This greatly boosts robustness across languages. Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines. It significantly reduces the performance gap between settings that have plenty of resources and those that do not. Detailed experiments and studies confirm that our framework is effective and can be used in various situations. This shows that it is suitable for scalable, cross-language mental health monitoring where labelled resources are limited.