Depression is a critical problem in modern society that affects an estimated 350 million people worldwide, causing feelings of sadness and a lack of interest and pleasure. Emotional disorders are gaining interest and are closely entwined with depression, because one contributes to an understanding of the other. Despite the achievements in the two separate tasks of emotion recognition and depression detection, there has not been much prior effort to build a unified model that can connect these two tasks with different modalities, including multimedia (text, audio, and video) and unobtrusive physiological signals (e.g., electroencephalography). We propose a novel temporal convolutional transformer with knowledge embedding to address the joint task of depression detection and emotion recognition. This approach not only learns multimodal embeddings across domains via the temporal convolutional transformer but also exploits special-domain knowledge from medical knowledge graphs to improve the performance of detection and recognition. It is essential that the features learned by our method can be perceived as a priori and are suitable for increasing the performance of other related tasks. Our method illustrates the case of “two birds with one stone” in the sense that two or more tasks can be efficiently handled with our unique model, which captures effective features. Experimental results on ten real-world datasets show that the proposed approach significantly outperforms other state-of-the-art approaches. On the other hand, experiments in which our methodology is applied to other reasoning tasks show that our approach effectively supports model reasoning related to emotion and improves its performance.

中文翻译：

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一石二鸟：用于抑郁检测和情绪识别的知识嵌入式时间卷积变压器


抑郁症是现代社会的一个严重问题，影响着全球约 3.5 亿人，导致悲伤感以及缺乏兴趣和快乐。情绪障碍越来越受到人们的关注，并且与抑郁症密切相关，因为一种情绪障碍有助于理解另一种情绪障碍。尽管在情绪识别和抑郁检测这两个独立的任务中取得了成就，但之前并没有做出太多努力来建立一个统一的模型，可以将这两个任务与不同的模式连接起来，包括多媒体（文本、音频和视频）和不引人注目的生理学信号（例如脑电图）。我们提出了一种具有知识嵌入的新型时间卷积变换器来解决抑郁症检测和情感识别的联合任务。这种方法不仅通过时间卷积变换器学习跨域的多模态嵌入，而且还利用医学知识图谱中的特殊域知识来提高检测和识别的性能。至关重要的是，我们的方法学到的特征可以被视为先验的，并且适合于提高其他相关任务的性能。我们的方法说明了“一石二鸟”的情况，即可以使用我们独特的模型有效地处理两个或多个任务，该模型捕获了有效的特征。十个真实世界数据集的实验结果表明，所提出的方法明显优于其他最先进的方法。另一方面，将我们的方法应用于其他推理任务的实验表明，我们的方法有效支持与情感相关的模型推理并提高了其性能。