Customer Service Hot event Discovery Based on Dynamic Dialogue Embedding

Abstract

Frequent customer service conversations focus on hot topics of communication users, and automatic hot topic discovery is critical to improving user experience. Traditionally, Customer service relies on operator to write traffic summaries. It leads to the source of the conversation difficult to analyze, which makes difficult to spot aggregated hotspot events. In this paper, we propose a Customer Service hot event Discovery based on dynamic dialogue embedding (CShe-D). This model includes dynamic semantic representation of customer service dialogue, clustering-based customer service hot event discovery and new hot event prediction. In the dialogue semantic embedding module, we obtain the dynamic embedding of each dialogue with combining word importance and word length based on the pre-trained language model to capture richer semantic information in different contexts. We further apply a clustering iterative algorithm with dynamic dialogue embedding to discover customer service hotspots. It can monitor the change trend of events in real time, optimize the accuracy of hot event discovery in operator customer service. Finally, the effectiveness of our CShe-D model is verified by experiments on real dialogue data in the field of customer service.