C3KG: A Chinese Commonsense Conversation Knowledge Graph
This addresses the problem of improving commonsense conversational AI for Chinese language users, though it is incremental as it builds on existing knowledge base concepts.
The authors tackled the deficiency of isolated commonsense knowledge for conversational models by creating C3KG, a large-scale Chinese commonsense conversation knowledge graph that integrates social commonsense and dialog flow, and demonstrated its potential through a graph-conversation matching approach and benchmarks on two graph-grounded tasks.
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps. To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph which incorporates both social commonsense knowledge and dialog flow information. To show the potential of our graph, we develop a graph-conversation matching approach, and benchmark two graph-grounded conversational tasks.