@inproceedings{cao-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models",
    author = "Cao, Pengfei  and
      Yang, Mingxuan  and
      Chen, Yubo  and
      Zhang, Chenlong  and
      Liu, Mingxuan  and
      Liu, Kang  and
      Zhao, Jun",
    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-1.446/",
    pages = "3659--3672",
    ISBN = "979-8-89176-414-9",
    abstract = "Understanding why real-world events occur is important for both natural language processing and practical decision-making, yet direct-cause inference remains underexplored in evidence-rich settings. To address this gap, we organized SemEval-2026 Task 12: Abductive Event Reasoning (AER). The task asks systems to identify the most plausible direct cause of a target event from supporting evidence. We formulate AER as an evidence-grounded multiple choice benchmark that captures key challenges of real-world causal reasoning, including distributed evidence, indirect background factors, and semantically related but non-causal distractors. The shared task attracted 122 participants and received 518 submissions. This paper presents the task formulation, dataset construction pipeline, evaluation setup, and system results. AER provides a focused benchmark for abductive reasoning over real-world events and highlights challenges for future work on causal reasoning and multi-document understanding."
}