@inproceedings{khaidukova-etal-2026-narrative,
    title = "Narrative Team at {S}em{E}val-2026 Task 4: Two-Stage Contrastive Learning for Narrative Similarity Assessment",
    author = "Khaidukova, Tatiana  and
      Ciobanu, Ana  and
      Gifu, Daniela  and
      Trandabat, Diana",
    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-1.22/",
    pages = "146--152",
    ISBN = "979-8-89176-414-9",
    abstract = "For SemEval-2026 Task 4, we introduce a unified two-stage framework based on a RoBERTa-large encoder. Stage 1 performs contrastive pre-training on synthetic triplets to learn general narrative similarity patterns. Stage 2 fine-tunes the model with a ranking-based objective tailored to Track A.The resulting encoder supports both binary similarity classification (Track A) and narrative embedding generation (Track B) without architectural changes. Our system achieves an accuracy of 0.64 on Track A and 0.69 on Track B, outperforming single-stage baselines and demonstrating that combining synthetic contrastive supervision with task-specific ranking yields stable and reusable narrative representations."
}