CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning
Weiqi Wang, Tianqing Fang, Baixuan Xu, Chun Yi Louis Bo, Yangqiu Song,, Lei Chen

TL;DR
The paper introduces CAT, a semi-supervised framework that enhances commonsense reasoning by conceptualizing and instantiating knowledge, leading to improved inference capabilities.
Contribution
It presents a novel semi-supervised learning framework for large-scale conceptualization and instantiation in commonsense knowledge bases, advancing reasoning models.
Findings
Achieves state-of-the-art results on conceptualization tasks
Improves commonsense inference modeling significantly
Provides publicly available code and data for further research
Abstract
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.…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Natural Language Processing Techniques
