  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.
