Commonsense reasoning, aiming at endowing machines with a human-like ability
to make situational presumptions, is extremely challenging to generalize. For
someone who barely knows about "meditation," while is knowledgeable about
"singing," he can still infer that "meditation makes people relaxed" from the
existing knowledge that "singing makes people relaxed" by first conceptualizing
"singing" as a "relaxing event" and then instantiating that event to
"meditation." This process, known as conceptual induction and deduction, is
fundamental to commonsense reasoning while lacking both labeled data and
methodologies to enhance commonsense modeling. To fill such a research gap, we
propose CAT (Contextualized ConceptuAlization and InsTantiation), a
semi-supervised learning framework that integrates event conceptualization and
instantiation to conceptualize commonsense knowledge bases at scale. Extensive
experiments show that our framework achieves state-of-the-art performances on
two conceptualization tasks, and the acquired abstract commonsense knowledge
can significantly improve commonsense inference modeling. Our code, data, and
fine-tuned models are publicly available at
this https URL

CAT 是一种语境化的概念归纳和实例化的半监督学习框架，可大规模概念化常识知识库，并通过实验证明，其获得的抽象常识知识可以显著提高常识推理建模的准确性。