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 https://github.com/HKUST-KnowComp/CAT.

翻译：常识推理旨在赋予机器类似人类的情境假设能力，其泛化极具挑战性。对于一位几乎不了解"冥想"但熟悉"唱歌"的人而言，他仍可通过首先将"唱歌"概念化为"放松事件"，再将该事件实例化为"冥想"，从而从现有知识"唱歌使人放松"推断出"冥想使人放松"。这一被称为概念归纳与演绎的过程是常识推理的基础，但当前既缺乏标注数据，也缺少增强常识建模的方法。为填补这一研究空白，我们提出CAT（情境化概念化与实例化框架），这是一个半监督学习框架，通过整合事件概念化与实例化，实现大规模常识知识库的概念化。大量实验表明，我们的框架在两个概念化任务上达到最先进性能，并且所获取的抽象常识知识能够显著提升常识推理建模。我们的代码、数据和微调模型已公开于https://github.com/HKUST-KnowComp/CAT。