AFEC: A Knowledge Graph Capturing Social Intelligence in Casual Conversations

TL;DR

This paper presents AFEC, a knowledge graph derived from casual conversations that enhances social understanding in chatbots, demonstrating improved response diversity and quality in empathetic dialogue generation.

Contribution

The paper introduces a large-scale, automatically curated knowledge graph from casual conversations and applies it to improve empathetic response generation in chatbots.

Findings

Chatbot with AFEC generates 15% more diverse responses.

Our model outperforms two baselines in response quality.

AFEC captures social and empathetic conversational knowledge.

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.