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

  • Riheng Yao Chinese Academy of Sciences
  • Shuangyong Song Alibaba Group
  • Qiudan Li Chinese Academy of Sciences
  • Chao Wang Alibaba Group
  • Huan Chen Alibaba Group
  • Haiqing Chen Alibaba Group
  • Daniel Dajun Zeng Chinese Academy of Sciences

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.

Published
2020-04-03
Section
Student Abstract Track