@inproceedings{shu-etal-2026-comhis,
    title = "Comhis at {S}em{E}val-2026 Task 4: Embedding-Space Adaptation and {LLM}-Assisted Inference for Narrative Similarity",
    author = {Shu, Ke  and
      M{\"a}kel{\"a}, Eetu  and
      Tolonen, Mikko},
    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-1.118/",
    pages = "862--868",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a two-stage system for the SemEval Narrative Similarity task that separates representation learning from comparative decision making. In Track B, we adapt a frozen large-scale embedding model using a lightweight projection layer trained with a triplet objective and hard example mining, producing a task-specific similarity space. In Track A, similarity scores derived from the adapted embedding space are incorporated into a large language model, which performs the final binary decision. On the official test set, our system achieves 0.68 accuracy on Track A and 0.66 on Track B, clearly outperforming the provided baselines and ranking 20th out of 44 teams on Track A and 10th out of 27 teams on Track B. These results demonstrate that efficient embedding adaptation combined with embedding-informed LLM reasoning is effective for modeling high-level narrative similarity."
}