@inproceedings{mao-etal-2024-metapro,
    title = "{M}eta{P}ro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling",
    author = "Mao, Rui  and
      He, Kai  and
      Ong, Claudia  and
      Liu, Qian  and
      Cambria, Erik",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-acl.590/",
    doi = "10.18653/v1/2024.findings-acl.590",
    pages = "9891--9908",
    abstract = "Metaphor interpretation is a difficult task in natural language understanding. The development of relevant techniques in this domain is slow, mostly because of the lack of large annotated datasets and effective pre-trained language models (PLMs) for metaphor learning. Thus, we propose a large annotated dataset and a PLM for the metaphor interpretation task. Our foundation model is based on a novel anomalous language modeling (ALM) method, which we benchmark with comparable PLM baselines on the new dataset, finding that it largely improves model performance on metaphor identification and interpretation."
}