Rethinking Depression Prediction from a Fine-Grained Subscore Modeling Perspective via Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability. |
| Approach: | They propose a fine-grained model for subscore prediction via multi-task learning that can be used to predict depression severity using multiple tasks. |
| Outcome: | The proposed model outperforms baselines and Qwen3-14B direct scoring on the public E-DAIC dataset and to a large-scale private clinical dataset. |
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Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Sricharan Mukku, Manan Soni, Rashmi Patange
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
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| Challenge: | Mental disorders affect millions of people around the world and depression is among the most common. |
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
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