Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2205.10850
