Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

Authors

• Riheng Yao
• Shuangyong Song China Telecom Corporation Ltd. Data&AI Technology Company
• Qiudan Li The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences
• Chao Wang
• Huan Chen
• Haiqing Chen
• Daniel Dajun Zeng Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences

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