论文标题
对话中的抑郁症检测多任务学习
Multi-Task Learning for Depression Detection in Dialogs
论文作者
论文摘要
抑郁症是一种严重的精神疾病，会影响人们交流的方式，尤其是通过情绪，据称是他们与他人互动的方式。这项工作检查了对话中的抑郁信号，这是一个较少的研究环境，遭受数据稀疏性的影响。我们假设抑郁和情感可以互相告知，我们建议通过主题和对话行为预测来探索对话结构的影响。我们研究了多任务学习（MTL）方法，其中上面提到的所有任务都是通过对话量的层次结构建模共同学习的。我们在DAIC和DailyDialog Corpor上进行了试验，其中包含英语中的对话，并显示了对抑郁症检测的最先进的重要改进（最多为70.6％F 1），这表明抑郁症与情绪和对话组织的相关性以及MTL的力量与MTL的力量相关，以利用来自不同来源的信息。
Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC and DailyDialog corpora-both contain dialogs in English-and show important improvements over state-ofthe-art on depression detection (at best 70.6% F 1), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.
