Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
Table 5: Performance comparison of our model on English against the current state-of-the-art Mourad Gridach (2017). EN and DE are scores for language dependent models using monolingual embeddings, whereas EN+DE is for system trained on both languages at the same time using multilingual word embeddings. The indices T stands for Twitter.
['[BOLD] Twitter Data  [BOLD] Model', '[BOLD] Twitter Data  [BOLD] Train', '[BOLD] Twitter Data  [BOLD] Test', '[BOLD] Twitter Data  [BOLD] F1-Score (%)', '[BOLD] Twitter Data  [BOLD] Precision (%)', '[BOLD] Twitter Data  [BOLD] Recall (%)']
The first row shows the results for training and testing on ENT data which allows us to compare our score to state-of-the-art results. However, an increase of 2.03% of the mean still represents an important improvement over the state-of-the-art. As a result, we prove that our novel architecture provides an efficient way to detect churn in social media.