  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 customer<->agent dialog examples in customer service
contexts from 20 well-known brands.
