Papers with real-world interview-based datasets
Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding (2025.findings-emnlp)
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| Challenge: | Clinical interviews are a standard method for assessing depression . however, these methods neglect the broader conversational context . |
| Approach: | They develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview . they also build an adversarial classifier with a gradient reversal layer to learn shared representations . |
| Outcome: | The proposed model captures the dynamics of dialogue within each interview using positional embedding and question context vectors. |