2021/01/05 by Akshay Agarwal, Agarwal, Akshay, Shashank V. Maiya +4 · 8 citations
Computer Science · Psychology · #AI in Service Interactions #Artificial intelligence #Business #Chatbot #Computation and Language (cs.CL) #Computer science #Customer advocacy #Customer knowledge #Customer service #Dialog box #Dialog system #Domain (mathematical analysis) #Empathy #FOS: Computer and information sciences #Knowledge management #Leverage (statistics) #Marketing #Open domain #Psychology #Question answering #Sentiment Analysis and Opinion Mining #Service (business) #Service quality #Social psychology #Topic Modeling #World Wide Web #cs.CL
paper · pdf · doi:10.48550/arxiv.2101.01334
published in arXiv (Cornell University) (Cornell University) · 8 pages, 7 figures
arxiv created 2021/01/05 · openalex publication_date 2021/01/05 · arxiv updated 2021/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Customer service is a setting that calls for empathy in live human agent responses. Recent advances have demonstrated how open-domain chatbots can be trained to demonstrate empathy when responding to live human utterances. We show that a blended skills chatbot model that responds to customer queries is more likely to resemble actual human agent response if it is trained to recognize emotion and exhibit appropriate empathy, than a model without such training. For our analysis, we leverage a Twitter customer service dataset containing several million customeragent dialog examples in customer service contexts from 20 well-known brands.