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

AFEC: A Knowledge Graph Capturing Social Intelligence in Casual Conversations