An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen
Abstract
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. According to the survey of user studies and the real online testing, emotional comfort of customers’ negative emotions, which make up more than 5% of whole number of customer visits on AliMe, is a key point for providing considerate service. In this paper, we propose a framework to obtain proper answer to customers’ emotional questions. The framework takes emotion classification model as a core, and final answer selection is based on topic classification and text matching. Our experiments on real online systems show that the framework is very promising.
BibTeX
@inproceedings{song-etal-2021-emotional,
title = "An Emotional Comfort Framework for Improving User Satisfaction in {E}-Commerce Customer Service Chatbots",
author = "Song, Shuangyong and
Wang, Chao and
Chen, Haiqing and
Chen, Huan",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.17/",
doi = "10.18653/v1/2021.naacl-industry.17",
pages = "130--137"
}