Research Paper#Natural Language Processing, Mental Health, Semi-Supervised Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:42
Uncertainty-aware Semi-supervised Ensemble for Multilingual Depression Detection
Published:Dec 31, 2025 10:35
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of multilingual depression detection, particularly in resource-scarce scenarios. The proposed Semi-SMDNet framework leverages semi-supervised learning, ensemble methods, and uncertainty-aware pseudo-labeling to improve performance across multiple languages. The focus on handling noisy data and improving robustness is crucial for real-world applications. The use of ensemble learning and uncertainty-based filtering are key contributions.
Key Takeaways
- •Proposes a semi-supervised learning framework for multilingual depression detection.
- •Employs ensemble learning and uncertainty-aware pseudo-labeling to improve performance.
- •Addresses the challenge of limited labeled data in various languages.
- •Demonstrates improved performance compared to strong baselines across multiple languages.
Reference
“Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines.”