@inproceedings{grecu-etal-2026-narsil,
    title = "{N}ar{S}i{L} at {S}em{E}val-2026 Task 4: A Multi-Expert, Multi-Pathway System for Narrative Story Similarity",
    author = "Grecu, Bogdan Octavian  and
      Chiru, Costin  and
      Cocarascu, Oana",
    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-1.381/",
    pages = "3035--3044",
    ISBN = "979-8-89176-414-9",
    abstract = "We present NarSiL (Narrative Similarity Learners), our system for SemEval-2026 Task 4 Track A on Narrative Story Similarity. NarSiL employs a two-stage architecture: a Mixture-of-Experts (MoE) initial classifier that also leverages supermajority voting across three large language models (Gemma-3-12B, GPT-3.5-turbo-instruct, and Gemini-2.5-Flash) over multiple runs, followed by a structured three-pathway fallback for ambiguous cases. The three pathways correspond directly to the task{'}s three core similarity components, abstract theme, narrative outcome, and course of action. Each path yields a similarity score corresponding to its respective component, and the scores are then combined through a weighted aggregation step. NarSiL achieves 64.25{\%} accuracy on the official test set. An improved score of 70.25{\%} is obtained by considering only the supermajority voting of GPT, followed by the previously described fallback."
}