Abstract
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. Our code and data could be found in https://github.com/XiaoMi/C3KG.
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CITATION STYLE
Li, D., Li, Y., Zhang, J., Li, K., Wei, C., Cui, J., & Wang, B. (2022). C3KG: A Chinese Commonsense Conversation Knowledge Graph. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 1369–1383). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.findings-acl.107
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