@inproceedings{wang-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 12: Knowledge Graph with hyperbolic embedding in Abductive Event Reasoning",
    author = "Wang, Mingkai  and
      Ojha, Varun  and
      Liang, Huizhi",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.114/",
    pages = "821--825",
    ISBN = "979-8-89176-414-9",
    abstract = "This task introduces Abductive Event Reasoning (AER), a novel shared task, to investigate the ability of Large Language Models(LLMs) to reason about the causality of real-world events. More specifically, a data set consisting of different topics and choices is introduced, and we need to enable the model to select the best options for the given event. Three methods are separately introduced to explore thequestion, including the traditional natural language processing(NLP) method (DeBERTa), theenhanced knowledge graph(KG), and the KG embedded in hyperbolic space."
}