Churn Intent Detection in Multilingual Chatbot Conversations and Social Media

Christian Abbet, Meryem M’hamdi, Athanasios Giannakopoulos, Robert West, Andreea Hossmann, Michael Baeriswyl, Claudiu Musat


Abstract
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.
Anthology ID:
K18-1016
Volume:
Proceedings of the 22nd Conference on Computational Natural Language Learning
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
161–170
Language:
URL:
https://www.aclweb.org/anthology/K18-1016
DOI:
10.18653/v1/K18-1016
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PDF:
http://aclanthology.lst.uni-saarland.de/K18-1016.pdf