@inproceedings{zhang-yu-2026-cicl26,
    title = "{CICL}26 at {S}em{E}val-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning",
    author = "Zhang, Wanzhao  and
      Yu, Yue",
    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-1.310/",
    pages = "2456--2460",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission to SemEval-2026 Task 4 (Track A) on narrative similarity.The task requires systems to determine which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) demonstrate strong semantic reasoning abilities, their predictions in comparative settings can be sensitive to stochastic decoding and input order.We propose a lightweight inference-time cascade strategy that improves robustness without modifying the underlying model. Our approach combines self-consistency voting to reduce sampling variance,a swap-based symmetry test to mitigate positional bias, and a margin-based deterministic decision rule to resolve disagreements. This design explicitly leverages model uncertainty while maintaining reproducibility and simplicity."
}