@inproceedings{m-etal-2026-cryptix,
    title = "Cryptix at {S}em{E}val-2026 Task 4: Zero-Shot Bi-Encoder Modeling for Narrative Story Similarity - A Sentence Transformer Approach",
    author = "M, Sushmitha  and
      P, Sarath Kumar  and
      S, Thanalaxmi  and
      A, Beulah",
    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-1.168/",
    pages = "1282--1287",
    ISBN = "979-8-89176-414-9",
    abstract = "This submission presents a zero-shot embedding-based approach for SemEval-2026 Task 4 on Narrative Story Similarity. The system employs the pretrained sentence-transformers/all-mpnet-base-v2 model within a bi-encoder architecture to generate 768-dimensional story embeddings. Narrative similarity is modeled using cosine similarity in embedding space for comparative prediction in Track A and representation generation in Track B. The approach does not involve task-specific fine-tuning and treats narrative comparison as a geometric proximity problem. Experimental results and error analysis highlight the strengths of pretrained semantic encoders in capturing thematic similarity, while revealing limitations in modeling deeper narrative structure and causal progression."
}