C ^3 KG : A C hinese Commonsense Conversation Knowledge Graph (2022)

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. All the resources in this work will be released to foster future research.

## introduction commonsense knowledge describes facts and related judgments in our everyday world, which is essential for machine when interacting with humans. these years have witnessed a growing number of literature incorporating commonsense knowledge into various downstream tasks @xcite @xcite @xcite . recently, @xcite curate atomic, a large-scale commonsense knowledge base, which covers event-centered social aspects of inferential knowledge tuples. for example, there exist tuples 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 people'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 difficulties. for instance, when a friend in distress tells us that he recently adopted a cat, we humans * corresponding author: yanranli.summer@gmail.com 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 multiple 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 but also plan the future dialog flow. to remedy these issues, we define 4 novel dialog flow relations, i.e., event flow, concept flow, emotion-cause flow, emotion-intent flow, as depicted in figure 1 . to build up the relations, we collect a large-scale multi-turn conversations in everyday scenarios, and manually annotate the conversations with emotional information. based on the annotations, we are able to extract conversationrelated events in atomic and connect them using different dialog flows. in this way, we augment atomic with conversation-specific knowledge, which facilitates chatbots to pick out useful commmonsense knowledge, and relieves their confusion on noisy knowledge that are incoherent with dialog flows. we believe our graph is favorable for commonsense conversation modeling. to highlight: (1) we curate a new chinese corpus, containing multi-turn human-written conversations on dailylife topics and rich, high-quality annotations on the level of sub-utterance; (2) we create and will release the first large-scale chinese commonsense conversation knowledge graph, c 3 kg, which contain 4 types of unique dialog-flow edges to store the distilled conversation knowledge from the multi-turn conversation corpus; (3) we devise a graph-conversation matching approach, and benchmark 2 typical tasks grounded on commonsense conversation graph. ## a scenario-based multi-turn conversation corpus our aim is to extract common dialog flow information from real conversations. in this way, it is crucial to ensure the quality of the conversation corpus and the reliability of the extraction method. in the following, we firstly introduce the conversation corpus cconv we depend on. instead of using the noisy internet data, we collect a multi-turn human-written chinese conversation corpus based on crowdsourcing. initially, 100 workers are hired, and they are randomly paired to talk in text under a given scenario. each scenario is one sentence describing the suggested conversation context which often involves certain everyday events. besides, the workers are also required to follow certain rules like "each utterance should longer than 6 chinese characters", which are critical to help ensure the quality of the collected conversation. at the beginning of the crowdsourcing, we check each collected conversation and re-train the workers. to ensure the quality, we keep only 62 well-trained workers and let them finish our task. note that the workers are paid with 1 cny per utterance (nearly 0.2 dollar per utterance). finally, we obtain 32k sessions of high-quality two-party conversations (650k utterances in total) on 200 scenarios of 15 daily topics. to facilitate future research, we then hire another 3 well-trained assistants to manually annotate the conversations with fine-grained emotional labels including speaker's emotion type, emotion cause, and response intention type. following rashkin et al. (2019) , we define emotion type with 5 general classes {joy, angry, sad, surprising, other}. emotion cause span is a continuous text spans implying the reason of certain emotion @xcite . response intention type is essential for building empathetic chatbots, and we define 6 commonlyadopted intent classes of {ask, advise, describe, opinion, console, other} following @xcite . a snippet of a conversation example is given in figure 2 . in appendix, we present more information of the constructed corpus. by utilizing the annotations, we are able to distill dialogue knowledge to enhance the conversation graph and graph-grounded conversation modeling. ## overview and processing of atomic because our conversation corpus is chinese, we want to build a chinese conversation knowledge graph. it is well known that to build a knowledge graph from scratch is laborious and timeconsuming. instead, we base on atomic and design a pipeline method to translate it into chinese, meanwhile ensuring the resulted knowledge graph is reliable and suitable for conversation grounding. ## proposed tasks to show the potential, we propose two graphgrounded conversational tasks, i.e., emotion classification and intent prediction, and train benchmark models using our labeled corpus cconv. task 1: emotion classification requires to produce an emotion label conditions on the conversations. following common practice, we choose the bert model, and sample the xattr, xreact tails from our matching head as extra input. task 2: intent prediction requires to predict a proper type of response intent for the conversations. we choose bert model, and sample the oreact, oeffect tails from our matching heads. as simple baselines, we introduce history and graph knowledge through concatenation with an input format as both of the above sampling steps use a threshold of 0.7 between processed sub-utterances and matched heads, to reduce noise introducing of our sampled knowledge. the accuracies of baseline methods are reported in ## discussions of future work in this work, we provide a systematic approach from event mention detection, event linking to conversation graph construction which consists of 4 distinguished types of dialog flows. for each step, there exist possible refinements. for example, we plan to include other event-based resources to improve graph-conversation matching accuracy as well as the graph knowledge coverage. we also plan to continue the annotations to supply more dialog flow information especially those empathy ones, and evaluate more dialog flow relations on other datasets. we would like to thank the anonymous reviewers for their constructive comments. this work was approved by xiaoai team. all personally identifiable information in our dataset was removed. at last, we discuss the potential ethic impacts of this work. (1) transparency: we will release the newly introduced corpus and the built conversation knowledge graph, as well as the benchmark approaches to facilitate future research. similar datasets and knowledge bases include empathetic-dialogues @xcite and atomic @xcite , which are often public available and have been used extensively. (2) privacy: the corpus is crowdsourced under a set of specific rules to forbid the workers disclosure sensitive and personal identifiable information. (3) politeness: because our conversations are human-written and are related to healthy dailylife scenarios, they are expected to be clean, legal, and polite. the crowdsourcing rules are designed to avoid emotionally triggering words as much as possible.