论文标题
检测会话领域中抑郁症的早期迹象：在低资源场景中转移学习的作用
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios
论文作者
论文摘要
社会上抑郁症的高度流行促使人们需要新的数字工具来协助其早期发现。为此，现有的研究主要集中在发现有足够数据的社交媒体领域中的抑郁症。但是，随着Siri或Alexa等会话代理的兴起，对话领域变得越来越关键。不幸的是，对话域中缺乏数据。我们进行了一项研究，重点是从社交媒体到对话领域的领域适应。我们的方法主要利用文本矢量表示中保存的语言信息。我们描述了转移学习技术，以对患有早期抑郁症状的用户进行分类，并召回了很高的回忆。我们在常用的对话数据集上实现了最新的结果，我们强调了如何在对话剂中轻松使用该方法。我们公开发布所有源代码。
The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on detecting depression in the domain of social media, where there is a sufficient amount of data. However, with the rise of conversational agents like Siri or Alexa, the conversational domain is becoming more critical. Unfortunately, there is a lack of data in the conversational domain. We perform a study focusing on domain adaptation from social media to the conversational domain. Our approach mainly exploits the linguistic information preserved in the vector representation of text. We describe transfer learning techniques to classify users who suffer from early signs of depression with high recall. We achieve state-of-the-art results on a commonly used conversational dataset, and we highlight how the method can easily be used in conversational agents. We publicly release all source code.
