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We propose a new method to detect when users express the intent to leave aservice, also known as churn. While previous work focuses solely on socialmedia, we show that this intent can be detected in chatbot conversations. Ascompanies increasingly rely on chatbots they need an overview of potentiallychurny users. To this end, we crowdsource and publish a dataset of churn intentexpressions in chatbot interactions in German and English. We show thatclassifiers trained on social media data can detect the same intent in thecontext of chatbots.  We introduce a classification architecture that outperforms existing work onchurn intent detection in social media. Moreover, we show that, using bilingualword embeddings, a system trained on combined English and German dataoutperforms monolingual approaches. As the only existing dataset is in English,we crowdsource and publish a novel dataset of German tweets. We thus underlinethe universal aspect of the problem, as examples of churn intent in Englishhelp us identify churn in German tweets and chatbot conversations.

Churn Intent Detection in Multilingual Chatbot Conversations and Social Media