  Open-domain dialog systems (also known as chatbots) have increasingly drawn
attention in natural language processing. Some of the recent work aims at
incorporating affect information into sequence-to-sequence neural dialog
modeling, making the response emotionally richer, while others use hand-crafted
rules to determine the desired emotion response. However, they do not
explicitly learn the subtle emotional interactions captured in human dialogs.
In this paper, we propose a multi-turn dialog system aimed at learning and
generating emotional responses that so far only humans know how to do. Compared
with two baseline models, offline experiments show that our method performs the
best in perplexity scores. Further human evaluations confirm that our chatbot
can keep track of the conversation context and generate emotionally more
appropriate responses while performing equally well on grammar.
