Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection

TL;DR

This paper introduces Semi-SMDNet, a semi-supervised ensemble framework that improves multilingual depression detection from social media text by leveraging uncertainty-based pseudo-label filtering and data augmentation.

Contribution

It presents a novel semi-supervised ensemble approach combining teacher-student models, uncertainty filtering, and data augmentation for multilingual depression detection.

Findings

Outperforms strong baselines across multiple languages.

Reduces resource gap in low-resource settings.

Effective in scalable mental health monitoring.

Abstract

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.