Name
Abstract | ||
|---|---|---|
Depression is considered a serious medical condition and a large number of people around the world are suffering from it. Within this context, a lot of studies have been proposed to estimate the degree of depression based on different features and modalities, specific to depression. Supported by medical studies that show how depression is a disorder of impaired emotion regulation, we propose a different approach, which relies on the rationale that the estimation of depression level can benefit from the concurrent learning of emotion intensity. To test this hypothesis, we design different attention-based multi-task architectures that concurrently regress/classify both depression level and emotion intensity using text data. Experiments based on two benchmark datasets, namely, the Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ), and the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) show that substantial performance improvements can be achieved when compared to emotion-unaware single-task and multitask approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MCI.2020.2998234 | IEEE Computational Intelligence Magazine |
Keywords | DocType | Volume |
emotion-unaware single-task,emotion intensity,serious medical condition,medical studies,impaired emotion regulation,concurrent learning,concurrently regress,depression level estimation,attention-based multitask architectures | Journal | 15 |
Issue | ISSN | Citations |
3 | 1556-603X | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Syed Arbaaz Qureshi | 1 | 0 | 0.34 |
| Gaël Dias | 2 | 354 | 41.95 |
| Gaël Dias | 3 | 354 | 41.95 |
| Sriparna Saha | 4 | 1064 | 106.07 |