  This paper introduces AFEC, an automatically curated knowledge graph based on
people's day-to-day casual conversations. The knowledge captured in this graph
bears potential for conversational systems to understand how people offer
acknowledgement, consoling, and a wide range of empathetic responses in social
conversations. For this body of knowledge to be comprehensive and meaningful,
we curated a large-scale corpus from the r/CasualConversation SubReddit. After
taking the first two turns of all conversations, we obtained 134K speaker nodes
and 666K listener nodes. To demonstrate how a chatbot can converse in social
settings, we built a retrieval-based chatbot and compared it with existing
empathetic dialog models. Experiments show that our model is capable of
generating much more diverse responses (at least 15% higher diversity scores in
human evaluation), while still outperforming two out of the four baselines in
terms of response quality.
