  We propose a new method to detect when users express the intent to leave a
service, also known as churn. While previous work focuses solely on social
media, we show that this intent can be detected in chatbot conversations. As
companies increasingly rely on chatbots they need an overview of potentially
churny users. To this end, we crowdsource and publish a dataset of churn intent
expressions in chatbot interactions in German and English. We show that
classifiers trained on social media data can detect the same intent in the
context of chatbots.
  We introduce a classification architecture that outperforms existing work on
churn intent detection in social media. Moreover, we show that, using bilingual
word embeddings, a system trained on combined English and German data
outperforms monolingual approaches. As the only existing dataset is in English,
we crowdsource and publish a novel dataset of German tweets. We thus underline
the universal aspect of the problem, as examples of churn intent in English
help us identify churn in German tweets and chatbot conversations.

Computation and Language