Improving depression level estimation by concurrently learning emotion intensity

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.