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.

中文翻译：

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通过同时学习情绪强度来改进抑郁水平估计


抑郁症被认为是一种严重的疾病，世界各地有很多人患有抑郁症。在此背景下，人们提出了许多研究来根据抑郁症的不同特征和模式来估计抑郁症的程度。医学研究表明抑郁症是一种情绪调节受损的疾病，在这些研究的支持下，我们提出了一种不同的方法，该方法依赖于抑郁水平的估计可以从情绪强度的同时学习中受益的基本原理。为了检验这一假设，我们设计了不同的基于注意力的多任务架构，该架构使用文本数据同时对抑郁水平和情绪强度进行回归/分类。基于两个基准数据集（即 Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ) 和 CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)）的实验表明，与无情感意识的单任务和多任务方法。