@inproceedings{wang-etal-2023-cat,
    title = "{CAT}: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning",
    author = "Wang, Weiqi  and
      Fang, Tianqing  and
      Xu, Baixuan  and
      Bo, Chun Yi Louis  and
      Song, Yangqiu  and
      Chen, Lei",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.733",
    doi = "10.18653/v1/2023.acl-long.733",
    pages = "13111--13140",
    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. 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 [\url{https://github.com/HKUST-KnowComp/CAT}](\url{https://github.com/HKUST-KnowComp/CAT}).",
}

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 [\url{https://github.com/HKUST-KnowComp/CAT}](\url{https://github.com/HKUST-KnowComp/CAT}).
Wang, Weiqi Fang, Tianqing Xu, Baixuan Bo, Chun Yi Louis Song, Yangqiu Chen, Lei
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning