C3KG: A Chinese Commonsense Conversation Knowledge Graph

Findings of the Association for Computational Linguistics: ACL 2022, pages 1369 - 1383 May 22-27, 2022 c⃝2022 Association for Computational Linguistics C3KG: A Chinese Commonsense Conversation Knowledge Graph Dawei Li, Yanran Li∗, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, Bin Wang Xiaomi AI Lab lidawei,liyanran,zhangjiayi3,like16, weichen,cuijianwei,wangbin11@xiaomi.com Abstract Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational mod- els to plan the next steps. To fill the gap, we cu- rate a large-scale multi-turn human-written con- versation 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. 1 Introduction Commonsense knowledge describes facts and re- lated judgments in our everyday world, which is es- sential for machine when interacting with humans. These years have witnessed a growing number of literature incorporating commonsense knowledge into various downstream tasks (Bauer et al., 2018; Chen et al., 2019; Lin et al., 2019; Guan et al., 2019; Ji et al., 2020). Recently, Sap et al. (2019) curate ATOMIC, a large-scale commonsense knowledge base, which covers event-centered social aspects of inferential knowledge tuples. For example, there exist tu- ples like {PersonX adopts a cat, xEffect, feels happy} and {PersonX adopts a cat, xWant, com- pany}. Here, xEffect and xWant are two of nine relations defined in ATOMIC to infer peo- ple’s mental states for a given event, e.g., PersonX adopts a cat. As such, it is promising to detect ATOMIC events mentioned in conversations, and utilize the inferred knowledge when developing social chatbots. In spite of the potential, it has two major dif- ficulties. For instance, when a friend in distress tells us that he recently adopted a cat, we humans ∗Corresponding author: yanranli.summer@gmail.com Figure 1: A tiny subset of C3KG, with four unique types of dialog flow relations. will easily suspect that he might has allergies to the cat. However, such reasoning is difficult for chatbots. Given the event-relation pair {PersonX adopts a cat, xEffect, ___}, ATOMIC contains multiple tails like {finds out he has allergies} and the tail {becomes less lonely}. To this end, the first difficulty comes from the existence of mul- tiple tails, which will confuse the chatbots when inferring the cause behind the negative emotion. Secondly, the knowledge tuples in ATOMIC are isolated. It is thus more difficult for the chatbots to reason which tail(s) of knowledge should be used to produce coherent responses. For example, if the tuple {PersonX adopts a cat, isAfter, finds a cat at the animal shelter} is detected from the dialogue history, then the tuple {PersonX adopts a cat, xNeed, go to an animal rescue center} should not be considered anymore for future conversations. We argue that these issues hamper the application of ATOMIC to multi-turn dialogue modeling where the conversational agents need not only know the current state …

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